Sample records for climate modeling grid
-
High performance computing (HPC) requirements for the new generation variable grid resolution (VGR) global climate models differ from that of traditional global models. A VGR global model with 15 km grids over the CONUS stretching to 60 km grids elsewhere will have about ~2.5 tim...
-
Identification of reliable gridded reference data for statistical downscaling methods in Alberta
NASA Astrophysics Data System (ADS)
Eum, H. I.; Gupta, A.
2017-12-01
Climate models provide essential information to assess impacts of climate change at regional and global scales. However, statistical downscaling methods have been applied to prepare climate model data for various applications such as hydrologic and ecologic modelling at a watershed scale. As the reliability and (spatial and temporal) resolution of statistically downscaled climate data mainly depend on a reference data, identifying the most reliable reference data is crucial for statistical downscaling. A growing number of gridded climate products are available for key climate variables which are main input data to regional modelling systems. However, inconsistencies in these climate products, for example, different combinations of climate variables, varying data domains and data lengths and data accuracy varying with physiographic characteristics of the landscape, have caused significant challenges in selecting the most suitable reference climate data for various environmental studies and modelling. Employing various observation-based daily gridded climate products available in public domain, i.e. thin plate spline regression products (ANUSPLIN and TPS), inverse distance method (Alberta Townships), and numerical climate model (North American Regional Reanalysis) and an optimum interpolation technique (Canadian Precipitation Analysis), this study evaluates the accuracy of the climate products at each grid point by comparing with the Adjusted and Homogenized Canadian Climate Data (AHCCD) observations for precipitation, minimum and maximum temperature over the province of Alberta. Based on the performance of climate products at AHCCD stations, we ranked the reliability of these publically available climate products corresponding to the elevations of stations discretized into several classes. According to the rank of climate products for each elevation class, we identified the most reliable climate products based on the elevation of target points. A web-based system was developed to allow users to easily select the most reliable reference climate data at each target point based on the elevation of grid cell. By constructing the best combination of reference data for the study domain, the accurate and reliable statistically downscaled climate projections could be significantly improved.
-
Kittel, T.G.F.; Rosenbloom, N.A.; Royle, J. Andrew; Daly, Christopher; Gibson, W.P.; Fisher, H.H.; Thornton, P.; Yates, D.N.; Aulenbach, S.; Kaufman, C.; McKeown, R.; Bachelet, D.; Schimel, D.S.; Neilson, R.; Lenihan, J.; Drapek, R.; Ojima, D.S.; Parton, W.J.; Melillo, J.M.; Kicklighter, D.W.; Tian, H.; McGuire, A.D.; Sykes, M.T.; Smith, B.; Cowling, S.; Hickler, T.; Prentice, I.C.; Running, S.; Hibbard, K.A.; Post, W.M.; King, A.W.; Smith, T.; Rizzo, B.; Woodward, F.I.
2004-01-01
Analysis and simulation of biospheric responses to historical forcing require surface climate data that capture those aspects of climate that control ecological processes, including key spatial gradients and modes of temporal variability. We developed a multivariate, gridded historical climate dataset for the conterminous USA as a common input database for the Vegetation/Ecosystem Modeling and Analysis Project (VEMAP), a biogeochemical and dynamic vegetation model intercomparison. The dataset covers the period 1895-1993 on a 0.5?? latitude/longitude grid. Climate is represented at both monthly and daily timesteps. Variables are: precipitation, mininimum and maximum temperature, total incident solar radiation, daylight-period irradiance, vapor pressure, and daylight-period relative humidity. The dataset was derived from US Historical Climate Network (HCN), cooperative network, and snowpack telemetry (SNOTEL) monthly precipitation and mean minimum and maximum temperature station data. We employed techniques that rely on geostatistical and physical relationships to create the temporally and spatially complete dataset. We developed a local kriging prediction model to infill discontinuous and limited-length station records based on spatial autocorrelation structure of climate anomalies. A spatial interpolation model (PRISM) that accounts for physiographic controls was used to grid the infilled monthly station data. We implemented a stochastic weather generator (modified WGEN) to disaggregate the gridded monthly series to dailies. Radiation and humidity variables were estimated from the dailies using a physically-based empirical surface climate model (MTCLIM3). Derived datasets include a 100 yr model spin-up climate and a historical Palmer Drought Severity Index (PDSI) dataset. The VEMAP dataset exhibits statistically significant trends in temperature, precipitation, solar radiation, vapor pressure, and PDSI for US National Assessment regions. The historical climate and companion datasets are available online at data archive centers. ?? Inter-Research 2004.
-
NASA Astrophysics Data System (ADS)
Meng, M.; Macknick, J.; Tidwell, V. C.; Zagona, E. A.; Magee, T. M.; Bennett, K.; Middleton, R. S.
2017-12-01
The U.S. electricity sector depends on large amounts of water for hydropower generation and cooling thermoelectric power plants. Variability in water quantity and temperature due to climate change could reduce the performance and reliability of individual power plants and of the electric grid as a system. While studies have modeled water usage in power systems planning, few have linked grid operations with physical water constraints or with climate-induced changes in water resources to capture the role of the energy-water nexus in power systems flexibility and adequacy. In addition, many hydrologic and hydropower models have a limited representation of power sector water demands and grid interaction opportunities of demand response and ancillary services. A multi-model framework was developed to integrate and harmonize electricity, water, and climate models, allowing for high-resolution simulation of the spatial, temporal, and physical dynamics of these interacting systems. The San Juan River basin in the Southwestern U.S., which contains thermoelectric power plants, hydropower facilities, and multiple non-energy water demands, was chosen as a case study. Downscaled data from three global climate models and predicted regional water demand changes were implemented in the simulations. The Variable Infiltration Capacity hydrologic model was used to project inflows, ambient air temperature, and humidity in the San Juan River Basin. Resulting river operations, water deliveries, water shortage sharing agreements, new water demands, and hydroelectricity generation at the basin-scale were estimated with RiverWare. The impacts of water availability and temperature on electric grid dispatch, curtailment, cooling water usage, and electricity generation cost were modeled in PLEXOS. Lack of water availability resulting from climate, new water demands, and shortage sharing agreements will require thermoelectric generators to drastically decrease power production, as much as 50% during intensifying drought scenarios, which can have broader electricity sector system implications. Results relevant to stakeholder and power provider interests highlight the vulnerabilities in grid operations driven by water shortage agreements and changes in the climate.
-
Toward a Unified Representation of Atmospheric Convection in Variable-Resolution Climate Models
DOE Office of Scientific and Technical Information (OSTI.GOV)
Walko, Robert
2016-11-07
The purpose of this project was to improve the representation of convection in atmospheric weather and climate models that employ computational grids with spatially-variable resolution. Specifically, our work targeted models whose grids are fine enough over selected regions that convection is resolved explicitly, while over other regions the grid is coarser and convection is represented as a subgrid-scale process. The working criterion for a successful scheme for representing convection over this range of grid resolution was that identical convective environments must produce very similar convective responses (i.e., the same precipitation amount, rate, and timing, and the same modification of themore » atmospheric profile) regardless of grid scale. The need for such a convective scheme has increased in recent years as more global weather and climate models have adopted variable resolution meshes that are often extended into the range of resolving convection in selected locations.« less
-
DOE Office of Scientific and Technical Information (OSTI.GOV)
Gustafson, William I.; Qian, Yun; Fast, Jerome D.
2011-07-13
Recent improvements to many global climate models include detailed, prognostic aerosol calculations intended to better reproduce the observed climate. However, the trace gas and aerosol fields are treated at the grid-cell scale with no attempt to account for sub-grid impacts on the aerosol fields. This paper begins to quantify the error introduced by the neglected sub-grid variability for the shortwave aerosol radiative forcing for a representative climate model grid spacing of 75 km. An analysis of the value added in downscaling aerosol fields is also presented to give context to the WRF-Chem simulations used for the sub-grid analysis. We foundmore » that 1) the impact of neglected sub-grid variability on the aerosol radiative forcing is strongest in regions of complex topography and complicated flow patterns, and 2) scale-induced differences in emissions contribute strongly to the impact of neglected sub-grid processes on the aerosol radiative forcing. The two of these effects together, when simulated at 75 km vs. 3 km in WRF-Chem, result in an average daytime mean bias of over 30% error in top-of-atmosphere shortwave aerosol radiative forcing for a large percentage of central Mexico during the MILAGRO field campaign.« less
-
A Variable Resolution Stretched Grid General Circulation Model: Regional Climate Simulation
NASA Technical Reports Server (NTRS)
Fox-Rabinovitz, Michael S.; Takacs, Lawrence L.; Govindaraju, Ravi C.; Suarez, Max J.
2000-01-01
The development of and results obtained with a variable resolution stretched-grid GCM for the regional climate simulation mode, are presented. A global variable resolution stretched- grid used in the study has enhanced horizontal resolution over the U.S. as the area of interest The stretched-grid approach is an ideal tool for representing regional to global scale interaction& It is an alternative to the widely used nested grid approach introduced over a decade ago as a pioneering step in regional climate modeling. The major results of the study are presented for the successful stretched-grid GCM simulation of the anomalous climate event of the 1988 U.S. summer drought- The straightforward (with no updates) two month simulation is performed with 60 km regional resolution- The major drought fields, patterns and characteristics such as the time averaged 500 hPa heights precipitation and the low level jet over the drought area. appear to be close to the verifying analyses for the stretched-grid simulation- In other words, the stretched-grid GCM provides an efficient down-scaling over the area of interest with enhanced horizontal resolution. It is also shown that the GCM skill is sustained throughout the simulation extended to one year. The developed and tested in a simulation mode stretched-grid GCM is a viable tool for regional and subregional climate studies and applications.
-
NASA Astrophysics Data System (ADS)
Im, Eun-Soon; Coppola, Erika; Giorgi, Filippo
2010-05-01
Since anthropogenic climate change is a rather important factor for the future human life all over the planet and its effects are not globally uniform, climate information at regional or local scales become more and more important for an accurate assessment of the potential impact of climate change on societies and ecosystems. High resolution information with suitably fine-scale for resolving complex geographical features could be a critical factor for successful linkage between climate models and impact assessment studies. However, scale mismatch between them still remains major problem. One method for overcoming the resolution limitations of global climate models and for adding regional details to coarse-grid global projections is to use dynamical downscaling by means of a regional climate model. In this study, the ECHAM5/MPI-OM (1.875 degree) A1B scenario simulation has been dynamically downscaled by using two different approaches within the framework of RegCM3 modeling system. First, a mosaic-type parameterization of subgrid-scale topography and land use (Sub-BATS) is applied over the European Alpine region. The Sub-BATS system is composed of 15 km coarse-grid cell and 3 km sub-grid cell. Second, we developed the RegCM3 one-way double-nested system, with the mother domain encompassing the eastern regions of Asia at 60 km grid spacing and the nested domain covering the Korean Peninsula at 20 km grid spacing. By comparing the regional climate model output and the driving global model ECHAM5/MPI-OM output, it is possible to estimate the added value of physically-based dynamical downscaling when for example impact studies at hydrological scale are performed.
-
Prein, Andreas F; Gobiet, Andreas
2017-01-01
Gridded precipitation data sets are frequently used to evaluate climate models or to remove model output biases. Although precipitation data are error prone due to the high spatio-temporal variability of precipitation and due to considerable measurement errors, relatively few attempts have been made to account for observational uncertainty in model evaluation or in bias correction studies. In this study, we compare three types of European daily data sets featuring two Pan-European data sets and a set that combines eight very high-resolution station-based regional data sets. Furthermore, we investigate seven widely used, larger scale global data sets. Our results demonstrate that the differences between these data sets have the same magnitude as precipitation errors found in regional climate models. Therefore, including observational uncertainties is essential for climate studies, climate model evaluation, and statistical post-processing. Following our results, we suggest the following guidelines for regional precipitation assessments. (1) Include multiple observational data sets from different sources (e.g. station, satellite, reanalysis based) to estimate observational uncertainties. (2) Use data sets with high station densities to minimize the effect of precipitation undersampling (may induce about 60% error in data sparse regions). The information content of a gridded data set is mainly related to its underlying station density and not to its grid spacing. (3) Consider undercatch errors of up to 80% in high latitudes and mountainous regions. (4) Analyses of small-scale features and extremes are especially uncertain in gridded data sets. For higher confidence, use climate-mean and larger scale statistics. In conclusion, neglecting observational uncertainties potentially misguides climate model development and can severely affect the results of climate change impact assessments.
-
Integrated assessment of water-power grid systems under changing climate
NASA Astrophysics Data System (ADS)
Yan, E.; Zhou, Z.; Betrie, G.
2017-12-01
Energy and water systems are intrinsically interconnected. Due to an increase in climate variability and extreme weather events, interdependency between these two systems has been recently intensified resulting significant impacts on both systems and energy output. To address this challenge, an Integrated Water-Energy Systems Assessment Framework (IWESAF) is being developed to integrate multiple existing or developed models from various sectors. In this presentation, we are focusing on recent improvement in model development of thermoelectric power plant water use simulator, power grid operation and cost optimization model, and model integration that facilitate interaction among water and electricity generation under extreme climate events. A process based thermoelectric power water use simulator includes heat-balance, climate, and cooling system modules that account for power plant characteristics, fuel types, and cooling technology. The model is validated with more than 800 power plants of fossil-fired, nuclear and gas-turbine power plants with different cooling systems. The power grid operation and cost optimization model was implemented for a selected regional in the Midwest. The case study will be demonstrated to evaluate the sensitivity and resilience of thermoelectricity generation and power grid under various climate and hydrologic extremes and potential economic consequences.
-
Hydrologic extremes - an intercomparison of multiple gridded statistical downscaling methods
NASA Astrophysics Data System (ADS)
Werner, A. T.; Cannon, A. J.
2015-06-01
Gridded statistical downscaling methods are the main means of preparing climate model data to drive distributed hydrological models. Past work on the validation of climate downscaling methods has focused on temperature and precipitation, with less attention paid to the ultimate outputs from hydrological models. Also, as attention shifts towards projections of extreme events, downscaling comparisons now commonly assess methods in terms of climate extremes, but hydrologic extremes are less well explored. Here, we test the ability of gridded downscaling models to replicate historical properties of climate and hydrologic extremes, as measured in terms of temporal sequencing (i.e., correlation tests) and distributional properties (i.e., tests for equality of probability distributions). Outputs from seven downscaling methods - bias correction constructed analogues (BCCA), double BCCA (DBCCA), BCCA with quantile mapping reordering (BCCAQ), bias correction spatial disaggregation (BCSD), BCSD using minimum/maximum temperature (BCSDX), climate imprint delta method (CI), and bias corrected CI (BCCI) - are used to drive the Variable Infiltration Capacity (VIC) model over the snow-dominated Peace River basin, British Columbia. Outputs are tested using split-sample validation on 26 climate extremes indices (ClimDEX) and two hydrologic extremes indices (3 day peak flow and 7 day peak flow). To characterize observational uncertainty, four atmospheric reanalyses are used as climate model surrogates and two gridded observational datasets are used as downscaling target data. The skill of the downscaling methods generally depended on reanalysis and gridded observational dataset. However, CI failed to reproduce the distribution and BCSD and BCSDX the timing of winter 7 day low flow events, regardless of reanalysis or observational dataset. Overall, DBCCA passed the greatest number of tests for the ClimDEX indices, while BCCAQ, which is designed to more accurately resolve event-scale spatial gradients, passed the greatest number of tests for hydrologic extremes. Non-stationarity in the observational/reanalysis datasets complicated the evaluation of downscaling performance. Comparing temporal homogeneity and trends in climate indices and hydrological model outputs calculated from downscaled reanalyses and gridded observations was useful for diagnosing the reliability of the various historical datasets. We recommend that such analyses be conducted before such data are used to construct future hydro-climatic change scenarios.
-
Hydrologic extremes - an intercomparison of multiple gridded statistical downscaling methods
NASA Astrophysics Data System (ADS)
Werner, Arelia T.; Cannon, Alex J.
2016-04-01
Gridded statistical downscaling methods are the main means of preparing climate model data to drive distributed hydrological models. Past work on the validation of climate downscaling methods has focused on temperature and precipitation, with less attention paid to the ultimate outputs from hydrological models. Also, as attention shifts towards projections of extreme events, downscaling comparisons now commonly assess methods in terms of climate extremes, but hydrologic extremes are less well explored. Here, we test the ability of gridded downscaling models to replicate historical properties of climate and hydrologic extremes, as measured in terms of temporal sequencing (i.e. correlation tests) and distributional properties (i.e. tests for equality of probability distributions). Outputs from seven downscaling methods - bias correction constructed analogues (BCCA), double BCCA (DBCCA), BCCA with quantile mapping reordering (BCCAQ), bias correction spatial disaggregation (BCSD), BCSD using minimum/maximum temperature (BCSDX), the climate imprint delta method (CI), and bias corrected CI (BCCI) - are used to drive the Variable Infiltration Capacity (VIC) model over the snow-dominated Peace River basin, British Columbia. Outputs are tested using split-sample validation on 26 climate extremes indices (ClimDEX) and two hydrologic extremes indices (3-day peak flow and 7-day peak flow). To characterize observational uncertainty, four atmospheric reanalyses are used as climate model surrogates and two gridded observational data sets are used as downscaling target data. The skill of the downscaling methods generally depended on reanalysis and gridded observational data set. However, CI failed to reproduce the distribution and BCSD and BCSDX the timing of winter 7-day low-flow events, regardless of reanalysis or observational data set. Overall, DBCCA passed the greatest number of tests for the ClimDEX indices, while BCCAQ, which is designed to more accurately resolve event-scale spatial gradients, passed the greatest number of tests for hydrologic extremes. Non-stationarity in the observational/reanalysis data sets complicated the evaluation of downscaling performance. Comparing temporal homogeneity and trends in climate indices and hydrological model outputs calculated from downscaled reanalyses and gridded observations was useful for diagnosing the reliability of the various historical data sets. We recommend that such analyses be conducted before such data are used to construct future hydro-climatic change scenarios.
-
NASA Astrophysics Data System (ADS)
Macknick, J.; Miara, A.; O'Connell, M.; Vorosmarty, C. J.; Newmark, R. L.
2017-12-01
The US power sector is highly dependent upon water resources for reliable operations, primarily for thermoelectric cooling and hydropower technologies. Changes in the availability and temperature of water resources can limit electricity generation and cause outages at power plants, which substantially affect grid-level operational decisions. While the effects of water variability and climate changes on individual power plants are well documented, prior studies have not identified the significance of these impacts at the regional systems-level at which the grid operates, including whether there are risks for large-scale blackouts, brownouts, or increases in production costs. Adequately assessing electric grid system-level impacts requires detailed power sector modeling tools that can incorporate electric transmission infrastructure, capacity reserves, and other grid characteristics. Here, we present for the first time, a study of how climate and water variability affect operations of the power sector, considering different electricity sector configurations (low vs. high renewable) and environmental regulations. We use a case study of the US Eastern Interconnection, building off the Eastern Renewable Generation Integration Study (ERGIS) that explored operational challenges of high penetrations of renewable energy on the grid. We evaluate climate-water constraints on individual power plants, using the Thermoelectric Power and Thermal Pollution (TP2M) model coupled with the PLEXOS electricity production cost model, in the context of broader electricity grid operations. Using a five minute time step for future years, we analyze scenarios of 10% to 30% renewable energy penetration along with considerations of river temperature regulations to compare the cost, performance, and reliability tradeoffs of water-dependent thermoelectric generation and variable renewable energy technologies under climate stresses. This work provides novel insights into the resilience and reliability of different configurations of the US electric grid subject to changing climate conditions.
-
Geospatial modeling of plant stable isotope ratios - the development of isoscapes
NASA Astrophysics Data System (ADS)
West, J. B.; Ehleringer, J. R.; Hurley, J. M.; Cerling, T. E.
2007-12-01
Large-scale spatial variation in stable isotope ratios can yield critical insights into the spatio-temporal dynamics of biogeochemical cycles, animal movements, and shifts in climate, as well as anthropogenic activities such as commerce, resource utilization, and forensic investigation. Interpreting these signals requires that we understand and model the variation. We report progress in our development of plant stable isotope ratio landscapes (isoscapes). Our approach utilizes a GIS, gridded datasets, a range of modeling approaches, and spatially distributed observations. We synthesize findings from four studies to illustrate the general utility of the approach, its ability to represent observed spatio-temporal variability in plant stable isotope ratios, and also outline some specific areas of uncertainty. We also address two basic, but critical questions central to our ability to model plant stable isotope ratios using this approach: 1. Do the continuous precipitation isotope ratio grids represent reasonable proxies for plant source water?, and 2. Do continuous climate grids (as is or modified) represent a reasonable proxy for the climate experienced by plants? Plant components modeled include leaf water, grape water (extracted from wine), bulk leaf material ( Cannabis sativa; marijuana), and seed oil ( Ricinus communis; castor bean). Our approaches to modeling the isotope ratios of these components varied from highly sophisticated process models to simple one-step fractionation models to regression approaches. The leaf water isosocapes were produced using steady-state models of enrichment and continuous grids of annual average precipitation isotope ratios and climate. These were compared to other modeling efforts, as well as a relatively sparse, but geographically distributed dataset from the literature. The latitudinal distributions and global averages compared favorably to other modeling efforts and the observational data compared well to model predictions. These results yield confidence in the precipitation isoscapes used to represent plant source water, the modified climate grids used to represent leaf climate, and the efficacy of this approach to modeling. Further work confirmed these observations. The seed oil isoscape was produced using a simple model of lipid fractionation driven with the precipitation grid, and compared well to widely distributed observations of castor bean oil, again suggesting that the precipitation grids were reasonable proxies for plant source water. The marijuana leaf δ2H observations distributed across the continental United States were regressed against the precipitation δ2H grids and yielded a strong relationship between them, again suggesting that plant source water was reasonably well represented by the precipitation grid. Finally, the wine water δ18O isoscape was developed from regressions that related precipitation isotope ratios and climate to observations from a single vintage. Favorable comparisons between year-specific wine water isoscapes and inter-annual variations in previous vintages yielded confidence in the climate grids. Clearly significant residual variability remains to be explained in all of these cases and uncertainties vary depending on the component modeled, but we conclude from this synthesis that isoscapes are capable of representing real spatial and temporal variability in plant stable isotope ratios.
-
NASA Astrophysics Data System (ADS)
Benedict, James J.; Medeiros, Brian; Clement, Amy C.; Pendergrass, Angeline G.
2017-06-01
Precipitation distributions and extremes play a fundamental role in shaping Earth's climate and yet are poorly represented in many global climate models. Here, a suite of idealized Community Atmosphere Model (CAM) aquaplanet simulations is examined to assess the aquaplanet's ability to reproduce hydroclimate statistics of real-Earth configurations and to investigate sensitivities of precipitation distributions and extremes to model physics, horizontal grid resolution, and ocean type. Little difference in precipitation statistics is found between aquaplanets using time-constant sea-surface temperatures and those implementing a slab ocean model with a 50 m mixed-layer depth. In contrast, CAM version 5.3 (CAM5.3) produces more time mean, zonally averaged precipitation than CAM version 4 (CAM4), while CAM4 generates significantly larger precipitation variance and frequencies of extremely intense precipitation events. The largest model configuration-based precipitation sensitivities relate to choice of horizontal grid resolution in the selected range 1-2°. Refining grid resolution has significant physics-dependent effects on tropical precipitation: for CAM4, time mean zonal mean precipitation increases along the Equator and the intertropical convergence zone (ITCZ) narrows, while for CAM5.3 precipitation decreases along the Equator and the twin branches of the ITCZ shift poleward. Increased grid resolution also reduces light precipitation frequencies and enhances extreme precipitation for both CAM4 and CAM5.3 resulting in better alignment with observational estimates. A discussion of the potential implications these hydrologic cycle sensitivities have on the interpretation of precipitation statistics in future climate projections is also presented.
Plain Language SummaryPrecipitation plays a fundamental role in shaping Earth's climate. Global climate models predict the average precipitation reasonably well but often struggle to accurately represent how often it precipitates and at what intensity. Model precipitation errors are closely tied to imperfect representations of physical processes too small to be resolved on the model grid. The problem is compounded by the complexity of contemporary climate models and the many model configuration options available. In this study, we use an aquaplanet, a simplified global climate model entirely devoid of land masses, to explore the response of precipitation to several aspects of model configuration in a present-day climate state. Our results suggest that critical precipitation patterns, including extreme precipitation events that have large socio-economic impacts, are strongly sensitive to horizontal grid resolution and the representation of unresolved physical processes. Identification and understanding of such model configuration-related precipitation responses in the present-day climate will provide a more accurate estimate of model uncertainty necessary for an improved interpretation of precipitation changes in global warming projections.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5214405','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5214405"><span>Impacts of uncertainties in European gridded precipitation observations on regional climate analysis</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Gobiet, Andreas</p> <p>2016-01-01</p> <p>ABSTRACT Gridded precipitation data sets are frequently used to evaluate climate models or to remove model output biases. Although precipitation data are error prone due to the high spatio‐temporal variability of precipitation and due to considerable measurement errors, relatively few attempts have been made to account for observational uncertainty in model evaluation or in bias correction studies. In this study, we compare three types of European daily data sets featuring two Pan‐European data sets and a set that combines eight very high‐resolution station‐based regional data sets. Furthermore, we investigate seven widely used, larger scale global data sets. Our results demonstrate that the differences between these data sets have the same magnitude as precipitation errors found in regional climate models. Therefore, including observational uncertainties is essential for climate studies, climate model evaluation, and statistical post‐processing. Following our results, we suggest the following guidelines for regional precipitation assessments. (1) Include multiple observational data sets from different sources (e.g. station, satellite, reanalysis based) to estimate observational uncertainties. (2) Use data sets with high station densities to minimize the effect of precipitation undersampling (may induce about 60% error in data sparse regions). The information content of a gridded data set is mainly related to its underlying station density and not to its grid spacing. (3) Consider undercatch errors of up to 80% in high latitudes and mountainous regions. (4) Analyses of small‐scale features and extremes are especially uncertain in gridded data sets. For higher confidence, use climate‐mean and larger scale statistics. In conclusion, neglecting observational uncertainties potentially misguides climate model development and can severely affect the results of climate change impact assessments. PMID:28111497</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/biblio/1336008-vulnerability-us-western-electric-grid-hydro-climatological-conditions-how-bad-can-get','SCIGOV-STC'); return false;" href="https://www.osti.gov/biblio/1336008-vulnerability-us-western-electric-grid-hydro-climatological-conditions-how-bad-can-get"><span>Vulnerability of the US western electric grid to hydro-climatological conditions: How bad can it get?</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Voisin, N.; Kintner-Meyer, M.; Skaggs, R.</p> <p></p> <p>Recent studies have highlighted the potential impact of climate change on US electricity generation capacity by exploring the effect of changes in stream temperatures on available capacity of thermo-electric plants that rely on fresh-water cooling. However, little is known about the electric system impacts under extreme climate event such as drought. Vulnerability assessments are usually performed for a baseline water year or a specific drought, which do not provide insights into the full grid stress distribution across the diversity of climate events. In this paper we estimate the impacts of the water availability on the electricity generation and transmission inmore » the Western US grid for a range of historical water availability combinations. We softly couple an integrated water model, which includes climate, hydrology, routing, water resources management and socio-economic water demand models, into a grid model (production cost model) and simulate 30 years of historical hourly power flow conditions in the Western US grid. The experiment allows estimating the grid stress distribution as a function of inter-annual variability in regional water availability. Results indicate a clear correlation between grid vulnerability (as quantified in unmet energy demand and increased production cost) for the summer month of August and annual water availability. There is a 3% chance that at least 6% of the electricity demand cannot be met in August, and 21% chance of not meeting 0.5% of the load in the Western US grid. There is a 3% chance that at least 6% of the electricity demand cannot be met in August, and 21% chance of not meeting 0.1% or more of the load in the Western US grid. The regional variability in water availability contributes significantly to the reliability of the grid and could provide trade off opportunities in times of stress. This paper is the first to explore operational grid impacts imposed by droughts in the Western U.S. grid.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.fs.usda.gov/treesearch/pubs/21379','TREESEARCH'); return false;" href="https://www.fs.usda.gov/treesearch/pubs/21379"><span>VEMAP phase 2 bioclimatic database. I. Gridded historical (20th century) climate for modeling ecosystem dynamics across the conterminous USA</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.fs.usda.gov/treesearch/">Treesearch</a></p> <p>Timothy G.F. Kittel; Nan. A. Rosenbloom; J.A. Royle; C. Daly; W.P. Gibson; H.H. Fisher; P. Thornton; D.N. Yates; S. Aulenbach; C. Kaufman; R. McKeown; Dominque Bachelet; David S. Schimel</p> <p>2004-01-01</p> <p>Analysis and simulation of biospheric responses to historical forcing require surface climate data that capture those aspects of climate that control ecological processes, including key spatial gradients and modes of temporal variability. We developed a multivariate, gridded historical climate dataset for the conterminous USA as a common input database for the...</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010AGUFM.A21G0182F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010AGUFM.A21G0182F"><span>Comparison of Grid Nudging and Spectral Nudging Techniques for Dynamical Climate Downscaling within the WRF Model</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Fan, X.; Chen, L.; Ma, Z.</p> <p>2010-12-01</p> <p>Climate downscaling has been an active research and application area in the past several decades focusing on regional climate studies. Dynamical downscaling, in addition to statistical methods, has been widely used in downscaling as the advanced modern numerical weather and regional climate models emerge. The utilization of numerical models enables that a full set of climate variables are generated in the process of downscaling, which are dynamically consistent due to the constraints of physical laws. While we are generating high resolution regional climate, the large scale climate patterns should be retained. To serve this purpose, nudging techniques, including grid analysis nudging and spectral nudging, have been used in different models. There are studies demonstrating the benefit and advantages of each nudging technique; however, the results are sensitive to many factors such as nudging coefficients and the amount of information to nudge to, and thus the conclusions are controversy. While in a companion work of developing approaches for quantitative assessment of the downscaled climate, in this study, the two nudging techniques are under extensive experiments in the Weather Research and Forecasting (WRF) model. Using the same model provides fair comparability. Applying the quantitative assessments provides objectiveness of comparison. Three types of downscaling experiments were performed for one month of choice. The first type is serving as a base whereas the large scale information is communicated through lateral boundary conditions only; the second is using the grid analysis nudging; and the third is using spectral nudging. Emphases are given to the experiments of different nudging coefficients and nudging to different variables in the grid analysis nudging; while in spectral nudging, we focus on testing the nudging coefficients, different wave numbers on different model levels to nudge.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011AGUFMIN41A1395E','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011AGUFMIN41A1395E"><span>OpenClimateGIS - A Web Service Providing Climate Model Data in Commonly Used Geospatial Formats</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Erickson, T. A.; Koziol, B. W.; Rood, R. B.</p> <p>2011-12-01</p> <p>The goal of the OpenClimateGIS project is to make climate model datasets readily available in commonly used, modern geospatial formats used by GIS software, browser-based mapping tools, and virtual globes.The climate modeling community typically stores climate data in multidimensional gridded formats capable of efficiently storing large volumes of data (such as netCDF, grib) while the geospatial community typically uses flexible vector and raster formats that are capable of storing small volumes of data (relative to the multidimensional gridded formats). OpenClimateGIS seeks to address this difference in data formats by clipping climate data to user-specified vector geometries (i.e. areas of interest) and translating the gridded data on-the-fly into multiple vector formats. The OpenClimateGIS system does not store climate data archives locally, but rather works in conjunction with external climate archives that expose climate data via the OPeNDAP protocol. OpenClimateGIS provides a RESTful API web service for accessing climate data resources via HTTP, allowing a wide range of applications to access the climate data.The OpenClimateGIS system has been developed using open source development practices and the source code is publicly available. The project integrates libraries from several other open source projects (including Django, PostGIS, numpy, Shapely, and netcdf4-python).OpenClimateGIS development is supported by a grant from NOAA's Climate Program Office.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li class="active"><span>1</span></li> <li><a href="#" onclick='return showDiv("page_2");'>2</a></li> <li><a href="#" onclick='return showDiv("page_3");'>3</a></li> <li><a href="#" onclick='return showDiv("page_4");'>4</a></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_1 --> <div id="page_2" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_1");'>1</a></li> <li class="active"><span>2</span></li> <li><a href="#" onclick='return showDiv("page_3");'>3</a></li> <li><a href="#" onclick='return showDiv("page_4");'>4</a></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="21"> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018WRR....54.1999L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018WRR....54.1999L"><span>Testing the Hydrological Coherence of High-Resolution Gridded Precipitation and Temperature Data Sets</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Laiti, L.; Mallucci, S.; Piccolroaz, S.; Bellin, A.; Zardi, D.; Fiori, A.; Nikulin, G.; Majone, B.</p> <p>2018-03-01</p> <p>Assessing the accuracy of gridded climate data sets is highly relevant to climate change impact studies, since evaluation, bias correction, and statistical downscaling of climate models commonly use these products as reference. Among all impact studies those addressing hydrological fluxes are the most affected by errors and biases plaguing these data. This paper introduces a framework, coined Hydrological Coherence Test (HyCoT), for assessing the hydrological coherence of gridded data sets with hydrological observations. HyCoT provides a framework for excluding meteorological forcing data sets not complying with observations, as function of the particular goal at hand. The proposed methodology allows falsifying the hypothesis that a given data set is coherent with hydrological observations on the basis of the performance of hydrological modeling measured by a metric selected by the modeler. HyCoT is demonstrated in the Adige catchment (southeastern Alps, Italy) for streamflow analysis, using a distributed hydrological model. The comparison covers the period 1989-2008 and includes five gridded daily meteorological data sets: E-OBS, MSWEP, MESAN, APGD, and ADIGE. The analysis highlights that APGD and ADIGE, the data sets with highest effective resolution, display similar spatiotemporal precipitation patterns and produce the largest hydrological efficiency indices. Lower performances are observed for E-OBS, MESAN, and MSWEP, especially in small catchments. HyCoT reveals deficiencies in the representation of spatiotemporal patterns of gridded climate data sets, which cannot be corrected by simply rescaling the meteorological forcing fields, as often done in bias correction of climate model outputs. We recommend this framework to assess the hydrological coherence of gridded data sets to be used in large-scale hydroclimatic studies.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFMGC21A1055D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFMGC21A1055D"><span>A Variable Resolution Atmospheric General Circulation Model for a Megasite at the North Slope of Alaska</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Dennis, L.; Roesler, E. L.; Guba, O.; Hillman, B. R.; McChesney, M.</p> <p>2016-12-01</p> <p>The Atmospheric Radiation Measurement (ARM) climate research facility has three siteslocated on the North Slope of Alaska (NSA): Barrrow, Oliktok, and Atqasuk. These sites, incombination with one other at Toolik Lake, have the potential to become a "megasite" whichwould combine observational data and high resolution modeling to produce high resolutiondata products for the climate community. Such a data product requires high resolutionmodeling over the area of the megasite. We present three variable resolution atmosphericgeneral circulation model (AGCM) configurations as potential alternatives to stand-alonehigh-resolution regional models. Each configuration is based on a global cubed-sphere gridwith effective resolution of 1 degree, with a refinement in resolution down to 1/8 degree overan area surrounding the ARM megasite. The three grids vary in the size of the refined areawith 13k, 9k, and 7k elements. SquadGen, NCL, and GIMP are used to create the grids.Grids vary based upon the selection of areas of refinement which capture climate andweather processes that may affect a proposed NSA megasite. A smaller area of highresolution may not fully resolve climate and weather processes before they reach the NSA,however grids with smaller areas of refinement have a significantly reduced computationalcost compared with grids with larger areas of refinement. Optimal size and shape of thearea of refinement for a variable resolution model at the NSA is investigated.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013ClDy...40.1903B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013ClDy...40.1903B"><span>Simulating the impact of the large-scale circulation on the 2-m temperature and precipitation climatology</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bowden, Jared H.; Nolte, Christopher G.; Otte, Tanya L.</p> <p>2013-04-01</p> <p>The impact of the simulated large-scale atmospheric circulation on the regional climate is examined using the Weather Research and Forecasting (WRF) model as a regional climate model. The purpose is to understand the potential need for interior grid nudging for dynamical downscaling of global climate model (GCM) output for air quality applications under a changing climate. In this study we downscale the NCEP-Department of Energy Atmospheric Model Intercomparison Project (AMIP-II) Reanalysis using three continuous 20-year WRF simulations: one simulation without interior grid nudging and two using different interior grid nudging methods. The biases in 2-m temperature and precipitation for the simulation without interior grid nudging are unreasonably large with respect to the North American Regional Reanalysis (NARR) over the eastern half of the contiguous United States (CONUS) during the summer when air quality concerns are most relevant. This study examines how these differences arise from errors in predicting the large-scale atmospheric circulation. It is demonstrated that the Bermuda high, which strongly influences the regional climate for much of the eastern half of the CONUS during the summer, is poorly simulated without interior grid nudging. In particular, two summers when the Bermuda high was west (1993) and east (2003) of its climatological position are chosen to illustrate problems in the large-scale atmospheric circulation anomalies. For both summers, WRF without interior grid nudging fails to simulate the placement of the upper-level anticyclonic (1993) and cyclonic (2003) circulation anomalies. The displacement of the large-scale circulation impacts the lower atmosphere moisture transport and precipitable water, affecting the convective environment and precipitation. Using interior grid nudging improves the large-scale circulation aloft and moisture transport/precipitable water anomalies, thereby improving the simulated 2-m temperature and precipitation. The results demonstrate that constraining the RCM to the large-scale features in the driving fields improves the overall accuracy of the simulated regional climate, and suggest that in the absence of such a constraint, the RCM will likely misrepresent important large-scale shifts in the atmospheric circulation under a future climate.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20000021420','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20000021420"><span>Simulation of Anomalous Regional Climate Events with a Variable Resolution Stretched Grid GCM</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Fox-Rabinovitz, Michael S.</p> <p>1999-01-01</p> <p>The stretched-grid approach provides an efficient down-scaling and consistent interactions between global and regional scales due to using one variable-resolution model for integrations. It is a workable alternative to the widely used nested-grid approach introduced over a decade ago as a pioneering step in regional climate modeling. A variable-resolution General Circulation Model (GCM) employing a stretched grid, with enhanced resolution over the US as the area of interest, is used for simulating two anomalous regional climate events, the US summer drought of 1988 and flood of 1993. The special mode of integration using a stretched-grid GCM and data assimilation system is developed that allows for imitating the nested-grid framework. The mode is useful for inter-comparison purposes and for underlining the differences between these two approaches. The 1988 and 1993 integrations are performed for the two month period starting from mid May. Regional resolutions used in most of the experiments is 60 km. The major goal and the result of the study is obtaining the efficient down-scaling over the area of interest. The monthly mean prognostic regional fields for the stretched-grid integrations are remarkably close to those of the verifying analyses. Simulated precipitation patterns are successfully verified against gauge precipitation observations. The impact of finer 40 km regional resolution is investigated for the 1993 integration and an example of recovering subregional precipitation is presented. The obtained results show that the global variable-resolution stretched-grid approach is a viable candidate for regional and subregional climate studies and applications.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/893815','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/893815"><span>Final Report: Closeout of the Award NO. DE-FG02-98ER62618 (M.S. Fox-Rabinovitz, P.I.)</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Fox-Rabinovitz, M. S.</p> <p></p> <p>The final report describes the study aimed at exploring the variable-resolution stretched-grid (SG) approach to decadal regional climate modeling using advanced numerical techniques. The obtained results have shown that variable-resolution SG-GCMs using stretched grids with fine resolution over the area(s) of interest, is a viable established approach to regional climate modeling. The developed SG-GCMs have been extensively used for regional climate experimentation. The SG-GCM simulations are aimed at studying the U.S. regional climate variability with an emphasis on studying anomalous summer climate events, the U.S. droughts and floods.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017JAMES...9..501D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017JAMES...9..501D"><span>An approach to secure weather and climate models against hardware faults</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Düben, Peter D.; Dawson, Andrew</p> <p>2017-03-01</p> <p>Enabling Earth System models to run efficiently on future supercomputers is a serious challenge for model development. Many publications study efficient parallelization to allow better scaling of performance on an increasing number of computing cores. However, one of the most alarming threats for weather and climate predictions on future high performance computing architectures is widely ignored: the presence of hardware faults that will frequently hit large applications as we approach exascale supercomputing. Changes in the structure of weather and climate models that would allow them to be resilient against hardware faults are hardly discussed in the model development community. In this paper, we present an approach to secure the dynamical core of weather and climate models against hardware faults using a backup system that stores coarse resolution copies of prognostic variables. Frequent checks of the model fields on the backup grid allow the detection of severe hardware faults, and prognostic variables that are changed by hardware faults on the model grid can be restored from the backup grid to continue model simulations with no significant delay. To justify the approach, we perform model simulations with a C-grid shallow water model in the presence of frequent hardware faults. As long as the backup system is used, simulations do not crash and a high level of model quality can be maintained. The overhead due to the backup system is reasonable and additional storage requirements are small. Runtime is increased by only 13 % for the shallow water model.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017EGUGA..19.4961D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017EGUGA..19.4961D"><span>An approach to secure weather and climate models against hardware faults</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Düben, Peter; Dawson, Andrew</p> <p>2017-04-01</p> <p>Enabling Earth System models to run efficiently on future supercomputers is a serious challenge for model development. Many publications study efficient parallelisation to allow better scaling of performance on an increasing number of computing cores. However, one of the most alarming threats for weather and climate predictions on future high performance computing architectures is widely ignored: the presence of hardware faults that will frequently hit large applications as we approach exascale supercomputing. Changes in the structure of weather and climate models that would allow them to be resilient against hardware faults are hardly discussed in the model development community. We present an approach to secure the dynamical core of weather and climate models against hardware faults using a backup system that stores coarse resolution copies of prognostic variables. Frequent checks of the model fields on the backup grid allow the detection of severe hardware faults, and prognostic variables that are changed by hardware faults on the model grid can be restored from the backup grid to continue model simulations with no significant delay. To justify the approach, we perform simulations with a C-grid shallow water model in the presence of frequent hardware faults. As long as the backup system is used, simulations do not crash and a high level of model quality can be maintained. The overhead due to the backup system is reasonable and additional storage requirements are small. Runtime is increased by only 13% for the shallow water model.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/pages/biblio/1268219-impact-surface-coupling-grids-tropical-cyclone-extremes-high-resolution-atmospheric-simulations','SCIGOV-DOEP'); return false;" href="https://www.osti.gov/pages/biblio/1268219-impact-surface-coupling-grids-tropical-cyclone-extremes-high-resolution-atmospheric-simulations"><span>Impact of surface coupling grids on tropical cyclone extremes in high-resolution atmospheric simulations</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/pages">DOE PAGES</a></p> <p>Zarzycki, Colin M.; Reed, Kevin A.; Bacmeister, Julio T.; ...</p> <p>2016-02-25</p> <p>This article discusses the sensitivity of tropical cyclone climatology to surface coupling strategy in high-resolution configurations of the Community Earth System Model. Using two supported model setups, we demonstrate that the choice of grid on which the lowest model level wind stress and surface fluxes are computed may lead to differences in cyclone strength in multi-decadal climate simulations, particularly for the most intense cyclones. Using a deterministic framework, we show that when these surface quantities are calculated on an ocean grid that is coarser than the atmosphere, the computed frictional stress is misaligned with wind vectors in individual atmospheric gridmore » cells. This reduces the effective surface drag, and results in more intense cyclones when compared to a model configuration where the ocean and atmosphere are of equivalent resolution. Our results demonstrate that the choice of computation grid for atmosphere–ocean interactions is non-negligible when considering climate extremes at high horizontal resolution, especially when model components are on highly disparate grids.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19940038873&hterms=atmospheric+rivers&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D20%26Ntt%3Datmospheric%2Brivers','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19940038873&hterms=atmospheric+rivers&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D20%26Ntt%3Datmospheric%2Brivers"><span>Continental-scale river flow in climate models</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Miller, James R.; Russell, Gary L.; Caliri, Guilherme</p> <p>1994-01-01</p> <p>The hydrologic cycle is a major part of the global climate system. There is an atmospheric flux of water from the ocean surface to the continents. The cycle is closed by return flow in rivers. In this paper a river routing model is developed to use with grid box climate models for the whole earth. The routing model needs an algorithm for the river mass flow and a river direction file, which has been compiled for 4 deg x 5 deg and 2 deg x 2.5 deg resolutions. River basins are defined by the direction files. The river flow leaving each grid box depends on river and lake mass, downstream distance, and an effective flow speed that depends on topography. As input the routing model uses monthly land source runoff from a 5-yr simulation of the NASA/GISS atmospheric climate model (Hansen et al.). The land source runoff from the 4 deg x 5 deg resolution model is quartered onto a 2 deg x 2.5 deg grid, and the effect of grid resolution is examined. Monthly flow at the mouth of the world's major rivers is compared with observations, and a global error function for river flow is used to evaluate the routing model and its sensitivity to physical parameters. Three basinwide parameters are introduced: the river length weighted by source runoff, the turnover rate, and the basinwide speed. Although the values of these parameters depend on the resolution at which the rivers are defined, the values should converge as the grid resolution becomes finer. When the routing scheme described here is coupled with a climate model's source runoff, it provides the basis for closing the hydrologic cycle in coupled atmosphere-ocean models by realistically allowing water to return to the ocean at the correct location and with the proper magnitude and timing.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://pubs.usgs.gov/circ/1998/c1153/','USGSPUBS'); return false;" href="https://pubs.usgs.gov/circ/1998/c1153/"><span>A strategy for assessing potential future changes in climate, hydrology, and vegetation in the Western United States</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Thompson, Robert Stephen; Hostetler, Steven W.; Bartlein, Patrick J.; Anderson, Katherine H.</p> <p>1998-01-01</p> <p>Historical and geological data indicate that significant changes can occur in the Earth's climate on time scales ranging from years to millennia. In addition to natural climatic change, climatic changes may occur in the near future due to increased concentrations of carbon dioxide and other trace gases in the atmosphere that are the result of human activities. International research efforts using atmospheric general circulation models (AGCM's) to assess potential climatic conditions under atmospheric carbon dioxide concentrations of twice the pre-industrial level (a '2 X CO2' atmosphere) conclude that climate would warm on a global basis. However, it is difficult to assess how the projected warmer climatic conditions would be distributed on a regional scale and what the effects of such warming would be on the landscape, especially for temperate mountainous regions such as the Western United States. In this report, we present a strategy to assess the regional sensitivity to global climatic change. The strategy makes use of a hierarchy of models ranging from an AGCM, to a regional climate model, to landscape-scale process models of hydrology and vegetation. A 2 X CO2 global climate simulation conducted with the National Center for Atmospheric Research (NCAR) GENESIS AGCM on a grid of approximately 4.5o of latitude by 7.5o of longitude was used to drive the NCAR regional climate model (RegCM) over the Western United States on a grid of 60 km by 60 km. The output from the RegCM is used directly (for hydrologic models) or interpolated onto a 15-km grid (for vegetation models) to quantify possible future environmental conditions on a spatial scale relevant to policy makers and land managers.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2008JPhCS.125a2072W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2008JPhCS.125a2072W"><span>Data management and analysis for the Earth System Grid</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Williams, D. N.; Ananthakrishnan, R.; Bernholdt, D. E.; Bharathi, S.; Brown, D.; Chen, M.; Chervenak, A. L.; Cinquini, L.; Drach, R.; Foster, I. T.; Fox, P.; Hankin, S.; Henson, V. E.; Jones, P.; Middleton, D. E.; Schwidder, J.; Schweitzer, R.; Schuler, R.; Shoshani, A.; Siebenlist, F.; Sim, A.; Strand, W. G.; Wilhelmi, N.; Su, M.</p> <p>2008-07-01</p> <p>The international climate community is expected to generate hundreds of petabytes of simulation data within the next five to seven years. This data must be accessed and analyzed by thousands of analysts worldwide in order to provide accurate and timely estimates of the likely impact of climate change on physical, biological, and human systems. Climate change is thus not only a scientific challenge of the first order but also a major technological challenge. In order to address this technological challenge, the Earth System Grid Center for Enabling Technologies (ESG-CET) has been established within the U.S. Department of Energy's Scientific Discovery through Advanced Computing (SciDAC)-2 program, with support from the offices of Advanced Scientific Computing Research and Biological and Environmental Research. ESG-CET's mission is to provide climate researchers worldwide with access to the data, information, models, analysis tools, and computational capabilities required to make sense of enormous climate simulation datasets. Its specific goals are to (1) make data more useful to climate researchers by developing Grid technology that enhances data usability; (2) meet specific distributed database, data access, and data movement needs of national and international climate projects; (3) provide a universal and secure web-based data access portal for broad multi-model data collections; and (4) provide a wide-range of Grid-enabled climate data analysis tools and diagnostic methods to international climate centers and U.S. government agencies. Building on the successes of the previous Earth System Grid (ESG) project, which has enabled thousands of researchers to access tens of terabytes of data from a small number of ESG sites, ESG-CET is working to integrate a far larger number of distributed data providers, high-bandwidth wide-area networks, and remote computers in a highly collaborative problem-solving environment.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/pages/biblio/1349062-global-gridded-crop-model-intercomparison-data-modeling-protocols-phase-v1','SCIGOV-DOEP'); return false;" href="https://www.osti.gov/pages/biblio/1349062-global-gridded-crop-model-intercomparison-data-modeling-protocols-phase-v1"><span>The global gridded crop model intercomparison: Data and modeling protocols for Phase 1 (v1.0)</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/pages">DOE PAGES</a></p> <p>Elliott, J.; Müller, C.; Deryng, D.; ...</p> <p>2015-02-11</p> <p>We present protocols and input data for Phase 1 of the Global Gridded Crop Model Intercomparison, a project of the Agricultural Model Intercomparison and Improvement Project (AgMIP). The project consist of global simulations of yields, phenologies, and many land-surface fluxes using 12–15 modeling groups for many crops, climate forcing data sets, and scenarios over the historical period from 1948 to 2012. The primary outcomes of the project include (1) a detailed comparison of the major differences and similarities among global models commonly used for large-scale climate impact assessment, (2) an evaluation of model and ensemble hindcasting skill, (3) quantification ofmore » key uncertainties from climate input data, model choice, and other sources, and (4) a multi-model analysis of the agricultural impacts of large-scale climate extremes from the historical record.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..1810050O','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..1810050O"><span>ESMPy and OpenClimateGIS: Python Interfaces for High Performance Grid Remapping and Geospatial Dataset Manipulation</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>O'Kuinghttons, Ryan; Koziol, Benjamin; Oehmke, Robert; DeLuca, Cecelia; Theurich, Gerhard; Li, Peggy; Jacob, Joseph</p> <p>2016-04-01</p> <p>The Earth System Modeling Framework (ESMF) Python interface (ESMPy) supports analysis and visualization in Earth system modeling codes by providing access to a variety of tools for data manipulation. ESMPy started as a Python interface to the ESMF grid remapping package, which provides mature and robust high-performance and scalable grid remapping between 2D and 3D logically rectangular and unstructured grids and sets of unconnected data. ESMPy now also interfaces with OpenClimateGIS (OCGIS), a package that performs subsetting, reformatting, and computational operations on climate datasets. ESMPy exposes a subset of ESMF grid remapping utilities. This includes bilinear, finite element patch recovery, first-order conservative, and nearest neighbor grid remapping methods. There are also options to ignore unmapped destination points, mask points on source and destination grids, and provide grid structure in the polar regions. Grid remapping on the sphere takes place in 3D Cartesian space, so the pole problem is not an issue as it can be with other grid remapping software. Remapping can be done between any combination of 2D and 3D logically rectangular and unstructured grids with overlapping domains. Grid pairs where one side of the regridding is represented by an appropriate set of unconnected data points, as is commonly found with observational data streams, is also supported. There is a developing interoperability layer between ESMPy and OpenClimateGIS (OCGIS). OCGIS is a pure Python, open source package designed for geospatial manipulation, subsetting, and computation on climate datasets stored in local NetCDF files or accessible remotely via the OPeNDAP protocol. Interfacing with OCGIS has brought GIS-like functionality to ESMPy (i.e. subsetting, coordinate transformations) as well as additional file output formats (i.e. CSV, ESRI Shapefile). ESMPy is distinguished by its strong emphasis on open source, community governance, and distributed development. The user base has grown quickly, and the package is integrating with several other software tools and frameworks. These include the Ultrascale Visualization Climate Data Analysis Tools (UV-CDAT), Iris, PyFerret, cfpython, and the Community Surface Dynamics Modeling System (CSDMS). ESMPy minimum requirements include Python 2.6, Numpy 1.6.1 and an ESMF installation. Optional dependencies include NetCDF and OCGIS-related dependencies: GDAL, Shapely, and Fiona. ESMPy is regression tested nightly, and supported on Darwin, Linux and Cray systems with the GNU compiler suite and MPI communications. OCGIS is supported on Linux, and also undergoes nightly regression testing. Both packages are installable from Anaconda channels. Upcoming development plans for ESMPy involve development of a higher order conservative grid remapping method. Future OCGIS development will focus on mesh and location stream interoperability and streamlined access to ESMPy's MPI implementation.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=1569574','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=1569574"><span>From GCM grid cell to agricultural plot: scale issues affecting modelling of climate impact</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Baron, Christian; Sultan, Benjamin; Balme, Maud; Sarr, Benoit; Traore, Seydou; Lebel, Thierry; Janicot, Serge; Dingkuhn, Michael</p> <p>2005-01-01</p> <p>General circulation models (GCM) are increasingly capable of making relevant predictions of seasonal and long-term climate variability, thus improving prospects of predicting impact on crop yields. This is particularly important for semi-arid West Africa where climate variability and drought threaten food security. Translating GCM outputs into attainable crop yields is difficult because GCM grid boxes are of larger scale than the processes governing yield, involving partitioning of rain among runoff, evaporation, transpiration, drainage and storage at plot scale. This study analyses the bias introduced to crop simulation when climatic data is aggregated spatially or in time, resulting in loss of relevant variation. A detailed case study was conducted using historical weather data for Senegal, applied to the crop model SARRA-H (version for millet). The study was then extended to a 10°N–17° N climatic gradient and a 31 year climate sequence to evaluate yield sensitivity to the variability of solar radiation and rainfall. Finally, a down-scaling model called LGO (Lebel–Guillot–Onibon), generating local rain patterns from grid cell means, was used to restore the variability lost by aggregation. Results indicate that forcing the crop model with spatially aggregated rainfall causes yield overestimations of 10–50% in dry latitudes, but nearly none in humid zones, due to a biased fraction of rainfall available for crop transpiration. Aggregation of solar radiation data caused significant bias in wetter zones where radiation was limiting yield. Where climatic gradients are steep, these two situations can occur within the same GCM grid cell. Disaggregation of grid cell means into a pattern of virtual synoptic stations having high-resolution rainfall distribution removed much of the bias caused by aggregation and gave realistic simulations of yield. It is concluded that coupling of GCM outputs with plot level crop models can cause large systematic errors due to scale incompatibility. These errors can be avoided by transforming GCM outputs, especially rainfall, to simulate the variability found at plot level. PMID:16433096</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011AGUFMIN41A1391Y','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011AGUFMIN41A1391Y"><span>A Computing Infrastructure for Supporting Climate Studies</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Yang, C.; Bambacus, M.; Freeman, S. M.; Huang, Q.; Li, J.; Sun, M.; Xu, C.; Wojcik, G. S.; Cahalan, R. F.; NASA Climate @ Home Project Team</p> <p>2011-12-01</p> <p>Climate change is one of the major challenges facing us on the Earth planet in the 21st century. Scientists build many models to simulate the past and predict the climate change for the next decades or century. Most of the models are at a low resolution with some targeting high resolution in linkage to practical climate change preparedness. To calibrate and validate the models, millions of model runs are needed to find the best simulation and configuration. This paper introduces the NASA effort on Climate@Home project to build a supercomputer based-on advanced computing technologies, such as cloud computing, grid computing, and others. Climate@Home computing infrastructure includes several aspects: 1) a cloud computing platform is utilized to manage the potential spike access to the centralized components, such as grid computing server for dispatching and collecting models runs results; 2) a grid computing engine is developed based on MapReduce to dispatch models, model configuration, and collect simulation results and contributing statistics; 3) a portal serves as the entry point for the project to provide the management, sharing, and data exploration for end users; 4) scientists can access customized tools to configure model runs and visualize model results; 5) the public can access twitter and facebook to get the latest about the project. This paper will introduce the latest progress of the project and demonstrate the operational system during the AGU fall meeting. It will also discuss how this technology can become a trailblazer for other climate studies and relevant sciences. It will share how the challenges in computation and software integration were solved.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018GeoRL..45.2805J','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018GeoRL..45.2805J"><span>Dynamical Core in Atmospheric Model Does Matter in the Simulation of Arctic Climate</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Jun, Sang-Yoon; Choi, Suk-Jin; Kim, Baek-Min</p> <p>2018-03-01</p> <p>Climate models using different dynamical cores can simulate significantly different winter Arctic climates even if equipped with virtually the same physics schemes. Current climate simulated by the global climate model using cubed-sphere grid with spectral element method (SE core) exhibited significantly warmer Arctic surface air temperature compared to that using latitude-longitude grid with finite volume method core. Compared to the finite volume method core, SE core simulated additional adiabatic warming in the Arctic lower atmosphere, and this was consistent with the eddy-forced secondary circulation. Downward longwave radiation further enhanced Arctic near-surface warming with a higher surface air temperature of about 1.9 K. Furthermore, in the atmospheric response to the reduced sea ice conditions with the same physical settings, only the SE core showed a robust cooling response over North America. We emphasize that special attention is needed in selecting the dynamical core of climate models in the simulation of the Arctic climate and associated teleconnection patterns.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3948251','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3948251"><span>Assessing agricultural risks of climate change in the 21st century in a global gridded crop model intercomparison</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Rosenzweig, Cynthia; Elliott, Joshua; Deryng, Delphine; Ruane, Alex C.; Müller, Christoph; Arneth, Almut; Boote, Kenneth J.; Folberth, Christian; Glotter, Michael; Khabarov, Nikolay; Neumann, Kathleen; Piontek, Franziska; Pugh, Thomas A. M.; Schmid, Erwin; Stehfest, Elke; Yang, Hong; Jones, James W.</p> <p>2014-01-01</p> <p>Here we present the results from an intercomparison of multiple global gridded crop models (GGCMs) within the framework of the Agricultural Model Intercomparison and Improvement Project and the Inter-Sectoral Impacts Model Intercomparison Project. Results indicate strong negative effects of climate change, especially at higher levels of warming and at low latitudes; models that include explicit nitrogen stress project more severe impacts. Across seven GGCMs, five global climate models, and four representative concentration pathways, model agreement on direction of yield changes is found in many major agricultural regions at both low and high latitudes; however, reducing uncertainty in sign of response in mid-latitude regions remains a challenge. Uncertainties related to the representation of carbon dioxide, nitrogen, and high temperature effects demonstrated here show that further research is urgently needed to better understand effects of climate change on agricultural production and to devise targeted adaptation strategies. PMID:24344314</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20150004143','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20150004143"><span>Assessing Agricultural Risks of Climate Change in the 21st Century in a Global Gridded Crop Model Intercomparison</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Rosenzweig, Cynthia E.; Elliott, Joshua; Deryng, Delphine; Ruane, Alex C.; Mueller, Christoph; Arneth, Almut; Boote, Kenneth J.; Folberth, Christian; Glotter, Michael; Khabarov, Nikolay</p> <p>2014-01-01</p> <p>Here we present the results from an intercomparison of multiple global gridded crop models (GGCMs) within the framework of the Agricultural Model Intercomparison and Improvement Project and the Inter-Sectoral Impacts Model Intercomparison Project. Results indicate strong negative effects of climate change, especially at higher levels of warming and at low latitudes; models that include explicit nitrogen stress project more severe impacts. Across seven GGCMs, five global climate models, and four representative concentration pathways, model agreement on direction of yield changes is found in many major agricultural regions at both low and high latitudes; however, reducing uncertainty in sign of response in mid-latitude regions remains a challenge. Uncertainties related to the representation of carbon dioxide, nitrogen, and high temperature effects demonstrated here show that further research is urgently needed to better understand effects of climate change on agricultural production and to devise targeted adaptation strategies.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=310604&keyword=topography&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50','EPA-EIMS'); return false;" href="https://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=310604&keyword=topography&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50"><span>: “Developing Regional Modeling Techniques Applicable for Simulating Future Climate Conditions in the Carolinas”</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://oaspub.epa.gov/eims/query.page">EPA Science Inventory</a></p> <p></p> <p></p> <p>Global climate models (GCMs) are currently used to obtain information about future changes in the large-scale climate. However, such simulations are typically done at coarse spatial resolutions, with model grid boxes on the order of 100 km on a horizontal side. Therefore, techniq...</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..18.6083H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..18.6083H"><span>Regionalisation of statistical model outputs creating gridded data sets for Germany</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Höpp, Simona Andrea; Rauthe, Monika; Deutschländer, Thomas</p> <p>2016-04-01</p> <p>The goal of the German research program ReKliEs-De (regional climate projection ensembles for Germany, http://.reklies.hlug.de) is to distribute robust information about the range and the extremes of future climate for Germany and its neighbouring river catchment areas. This joint research project is supported by the German Federal Ministry of Education and Research (BMBF) and was initiated by the German Federal States. The Project results are meant to support the development of adaptation strategies to mitigate the impacts of future climate change. The aim of our part of the project is to adapt and transfer the regionalisation methods of the gridded hydrological data set (HYRAS) from daily station data to the station based statistical regional climate model output of WETTREG (regionalisation method based on weather patterns). The WETTREG model output covers the period of 1951 to 2100 with a daily temporal resolution. For this, we generate a gridded data set of the WETTREG output for precipitation, air temperature and relative humidity with a spatial resolution of 12.5 km x 12.5 km, which is common for regional climate models. Thus, this regionalisation allows comparing statistical to dynamical climate model outputs. The HYRAS data set was developed by the German Meteorological Service within the German research program KLIWAS (www.kliwas.de) and consists of daily gridded data for Germany and its neighbouring river catchment areas. It has a spatial resolution of 5 km x 5 km for the entire domain for the hydro-meteorological elements precipitation, air temperature and relative humidity and covers the period of 1951 to 2006. After conservative remapping the HYRAS data set is also convenient for the validation of climate models. The presentation will consist of two parts to present the actual state of the adaptation of the HYRAS regionalisation methods to the statistical regional climate model WETTREG: First, an overview of the HYRAS data set and the regionalisation methods for precipitation (REGNIE method based on a combination of multiple linear regression with 5 predictors and inverse distance weighting), air temperature and relative humidity (optimal interpolation) will be given. Finally, results of the regionalisation of WETTREG model output will be shown.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_1");'>1</a></li> <li class="active"><span>2</span></li> <li><a href="#" onclick='return showDiv("page_3");'>3</a></li> <li><a href="#" onclick='return showDiv("page_4");'>4</a></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_2 --> <div id="page_3" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_1");'>1</a></li> <li><a href="#" onclick='return showDiv("page_2");'>2</a></li> <li class="active"><span>3</span></li> <li><a href="#" onclick='return showDiv("page_4");'>4</a></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="41"> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012AGUFMIN41A1467H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012AGUFMIN41A1467H"><span>Enhancement of Local Climate Analysis Tool</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Horsfall, F. M.; Timofeyeva, M. M.; Dutton, J.</p> <p>2012-12-01</p> <p>The National Oceanographic and Atmospheric Administration (NOAA) National Weather Service (NWS) will enhance its Local Climate Analysis Tool (LCAT) to incorporate specific capabilities to meet the needs of various users including energy, health, and other communities. LCAT is an online interactive tool that provides quick and easy access to climate data and allows users to conduct analyses at the local level such as time series analysis, trend analysis, compositing, correlation and regression techniques, with others to be incorporated as needed. LCAT uses principles of Artificial Intelligence in connecting human and computer perceptions on application of data and scientific techniques in multiprocessing simultaneous users' tasks. Future development includes expanding the type of data currently imported by LCAT (historical data at stations and climate divisions) to gridded reanalysis and General Circulation Model (GCM) data, which are available on global grids and thus will allow for climate studies to be conducted at international locations. We will describe ongoing activities to incorporate NOAA Climate Forecast System (CFS) reanalysis data (CFSR), NOAA model output data, including output from the National Multi Model Ensemble Prediction System (NMME) and longer term projection models, and plans to integrate LCAT into the Earth System Grid Federation (ESGF) and its protocols for accessing model output and observational data to ensure there is no redundancy in development of tools that facilitate scientific advancements and use of climate model information in applications. Validation and inter-comparison of forecast models will be included as part of the enhancement to LCAT. To ensure sustained development, we will investigate options for open sourcing LCAT development, in particular, through the University Corporation for Atmospheric Research (UCAR).</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013JGRD..11810944L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013JGRD..11810944L"><span>Nonhydrostatic nested climate modeling: A case study of the 2010 summer season over the western United States</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Lebassi-Habtezion, Bereket; Diffenbaugh, Noah S.</p> <p>2013-10-01</p> <p>potential importance of local-scale climate phenomena motivates development of approaches to enable computationally feasible nonhydrostatic climate simulations. To that end, we evaluate the potential viability of nested nonhydrostatic model approaches, using the summer climate of the western United States (WUSA) as a case study. We use the Weather Research and Forecast (WRF) model to carry out five simulations of summer 2010. This suite allows us to test differences between nonhydrostatic and hydrostatic resolutions, single and multiple nesting approaches, and high- and low-resolution reanalysis boundary conditions. WRF simulations were evaluated against station observations, gridded observations, and reanalysis data over domains that cover the 11 WUSA states at nonhydrostatic grid spacing of 4 km and hydrostatic grid spacing of 25 km and 50 km. Results show that the nonhydrostatic simulations more accurately resolve the heterogeneity of surface temperature, precipitation, and wind speed features associated with the topography and orography of the WUSA region. In addition, we find that the simulation in which the nonhydrostatic grid is nested directly within the regional reanalysis exhibits the greatest overall agreement with observational data. Results therefore indicate that further development of nonhydrostatic nesting approaches is likely to yield important insights into the response of local-scale climate phenomena to increases in global greenhouse gas concentrations. However, the biases in regional precipitation, atmospheric circulation, and moisture flux identified in a subset of the nonhydrostatic simulations suggest that alternative nonhydrostatic modeling approaches such as superparameterization and variable-resolution global nonhydrostatic modeling will provide important complements to the nested approaches tested here.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2002AdAtS..19..487L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2002AdAtS..19..487L"><span>Climate Simulations based on a different-grid nested and coupled model</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Li, Dan; Ji, Jinjun; Li, Yinpeng</p> <p>2002-05-01</p> <p>An atmosphere-vegetation interaction model (A VIM) has been coupled with a nine-layer General Cir-culation Model (GCM) of Institute of Atmospheic Physics/State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (IAP/LASG), which is rhomboidally truncated at zonal wave number 15, to simulate global climatic mean states. A VIM is a model having inter-feedback between land surface processes and eco-physiological processes on land. As the first step to couple land with atmosphere completely, the physiological processes are fixed and only the physical part (generally named the SVAT (soil-vegetation-atmosphere-transfer scheme) model) of AVIM is nested into IAP/LASG L9R15 GCM. The ocean part of GCM is prescribed and its monthly sea surface temperature (SST) is the climatic mean value. With respect to the low resolution of GCM, i.e., each grid cell having lon-gitude 7.5° and latitude 4.5°, the vegetation is given a high resolution of 1.5° by 1.5° to nest and couple the fine grid cells of land with the coarse grid cells of atmosphere. The coupling model has been integrated for 15 years and its last ten-year mean of outputs was chosen for analysis. Compared with observed data and NCEP reanalysis, the coupled model simulates the main characteris-tics of global atmospheric circulation and the fields of temperature and moisture. In particular, the simu-lated precipitation and surface air temperature have sound results. The work creates a solid base on coupling climate models with the biosphere.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013EGUGA..15.7300D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013EGUGA..15.7300D"><span>Linking the Weather Generator with Regional Climate Model</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Dubrovsky, Martin; Farda, Ales; Skalak, Petr; Huth, Radan</p> <p>2013-04-01</p> <p>One of the downscaling approaches, which transform the raw outputs from the climate models (GCMs or RCMs) into data with more realistic structure, is based on linking the stochastic weather generator with the climate model output. The present contribution, in which the parametric daily surface weather generator (WG) M&Rfi is linked to the RCM output, follows two aims: (1) Validation of the new simulations of the present climate (1961-1990) made by the ALADIN-Climate Regional Climate Model at 25 km resolution. The WG parameters are derived from the RCM-simulated surface weather series and compared to those derived from weather series observed in 125 Czech meteorological stations. The set of WG parameters will include statistics of the surface temperature and precipitation series (including probability of wet day occurrence). (2) Presenting a methodology for linking the WG with RCM output. This methodology, which is based on merging information from observations and RCM, may be interpreted as a downscaling procedure, whose product is a gridded WG capable of producing realistic synthetic multivariate weather series for weather-ungauged locations. In this procedure, WG is calibrated with RCM-simulated multi-variate weather series in the first step, and the grid specific WG parameters are then de-biased by spatially interpolated correction factors based on comparison of WG parameters calibrated with gridded RCM weather series and spatially scarcer observations. The quality of the weather series produced by the resultant gridded WG will be assessed in terms of selected climatic characteristics (focusing on characteristics related to variability and extremes of surface temperature and precipitation). Acknowledgements: The present experiment is made within the frame of projects ALARO-Climate (project P209/11/2405 sponsored by the Czech Science Foundation), WG4VALUE (project LD12029 sponsored by the Ministry of Education, Youth and Sports of CR) and VALUE (COST ES 1102 action).</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2003AGUFMNG11A0178W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2003AGUFMNG11A0178W"><span>Earth System Grid II (ESG): Turning Climate Model Datasets Into Community Resources</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Williams, D.; Middleton, D.; Foster, I.; Nevedova, V.; Kesselman, C.; Chervenak, A.; Bharathi, S.; Drach, B.; Cinquni, L.; Brown, D.; Strand, G.; Fox, P.; Garcia, J.; Bernholdte, D.; Chanchio, K.; Pouchard, L.; Chen, M.; Shoshani, A.; Sim, A.</p> <p>2003-12-01</p> <p>High-resolution, long-duration simulations performed with advanced DOE SciDAC/NCAR climate models will produce tens of petabytes of output. To be useful, this output must be made available to global change impacts researchers nationwide, both at national laboratories and at universities, other research laboratories, and other institutions. To this end, we propose to create a new Earth System Grid, ESG-II - a virtual collaborative environment that links distributed centers, users, models, and data. ESG-II will provide scientists with virtual proximity to the distributed data and resources that they require to perform their research. The creation of this environment will significantly increase the scientific productivity of U.S. climate researchers by turning climate datasets into community resources. In creating ESG-II, we will integrate and extend a range of Grid and collaboratory technologies, including the DODS remote access protocols for environmental data, Globus Toolkit technologies for authentication, resource discovery, and resource access, and Data Grid technologies developed in other projects. We will develop new technologies for (1) creating and operating "filtering servers" capable of performing sophisticated analyses, and (2) delivering results to users. In so doing, we will simultaneously contribute to climate science and advance the state of the art in collaboratory technology. We expect our results to be useful to numerous other DOE projects. The three-year R&D program will be undertaken by a talented and experienced team of computer scientists at five laboratories (ANL, LBNL, LLNL, NCAR, ORNL) and one university (ISI), working in close collaboration with climate scientists at several sites.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018WRR....54..827N','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018WRR....54..827N"><span>Addressing Spatial Dependence Bias in Climate Model Simulations—An Independent Component Analysis Approach</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Nahar, Jannatun; Johnson, Fiona; Sharma, Ashish</p> <p>2018-02-01</p> <p>Conventional bias correction is usually applied on a grid-by-grid basis, meaning that the resulting corrections cannot address biases in the spatial distribution of climate variables. To solve this problem, a two-step bias correction method is proposed here to correct time series at multiple locations conjointly. The first step transforms the data to a set of statistically independent univariate time series, using a technique known as independent component analysis (ICA). The mutually independent signals can then be bias corrected as univariate time series and back-transformed to improve the representation of spatial dependence in the data. The spatially corrected data are then bias corrected at the grid scale in the second step. The method has been applied to two CMIP5 General Circulation Model simulations for six different climate regions of Australia for two climate variables—temperature and precipitation. The results demonstrate that the ICA-based technique leads to considerable improvements in temperature simulations with more modest improvements in precipitation. Overall, the method results in current climate simulations that have greater equivalency in space and time with observational data.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20020069127','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20020069127"><span>A Variable-Resolution Stretched-Grid General Circulation Model and Data Assimilation System with Multiple Areas of Interest: Studying the Anomalous Regional Climate Events of 1998</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Fox-Rabinovitz, Michael S.; Takacs, Lawrence; Govindaraju, Ravi C.; Atlas, Robert (Technical Monitor)</p> <p>2002-01-01</p> <p>The new stretched-grid design with multiple (four) areas of interest, one at each global quadrant, is implemented into both a stretched-grid GCM (general circulation model) and a stretched-grid data assimilation system (DAS). The four areas of interest include: the U.S./Northern Mexico, the El Nino area/Central South America, India/China, and the Eastern Indian Ocean/Australia. Both the stretched-grid GCM and DAS annual (November 1997 through December 1998) integrations are performed with 50 km regional resolution. The efficient regional down-scaling to mesoscales is obtained for each of the four areas of interest while the consistent interactions between regional and global scales and the high quality of global circulation, are preserved. This is the advantage of the stretched-grid approach. The global variable resolution DAS incorporating the stretched-grid GCM has been developed and tested as an efficient tool for producing regional analyses and diagnostics with enhanced mesoscale resolution. The anomalous regional climate events of 1998 that occurred over the U.S., Mexico, South America, China, India, African Sahel, and Australia are investigated in both simulation and data assimilation modes. Tree assimilated products are also used, along with gauge precipitation data, for validating the simulation results. The obtained results show that the stretched-grid GCM and DAS are capable of producing realistic high quality simulated and assimilated products at mesoscale resolution for regional climate studies and applications.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=234244&keyword=nudging&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50','EPA-EIMS'); return false;" href="https://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=234244&keyword=nudging&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50"><span>Examining Interior Grid Nudging Techniques Using Two-Way Nesting in the WRF Model for Regional Climate Modeling</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://oaspub.epa.gov/eims/query.page">EPA Science Inventory</a></p> <p></p> <p></p> <p>This study evaluates interior nudging techniques using the Weather Research and Forecasting (WRF) model for regional climate modeling over the conterminous United States (CONUS) using a two-way nested configuration. NCEP–Department of Energy Atmospheric Model Intercomparison Pro...</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFMGC43F1117V','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFMGC43F1117V"><span>Bayesian Non-Stationary Index Gauge Modeling of Gridded Precipitation Extremes</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Verdin, A.; Bracken, C.; Caldwell, J.; Balaji, R.; Funk, C. C.</p> <p>2017-12-01</p> <p>We propose a Bayesian non-stationary model to generate watershed scale gridded estimates of extreme precipitation return levels. The Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) dataset is used to obtain gridded seasonal precipitation extremes over the Taylor Park watershed in Colorado for the period 1981-2016. For each year, grid cells within the Taylor Park watershed are aggregated to a representative "index gauge," which is input to the model. Precipitation-frequency curves for the index gauge are estimated for each year, using climate variables with significant teleconnections as proxies. Such proxies enable short-term forecasting of extremes for the upcoming season. Disaggregation ratios of the index gauge to the grid cells within the watershed are computed for each year and preserved to translate the index gauge precipitation-frequency curve to gridded precipitation-frequency maps for select return periods. Gridded precipitation-frequency maps are of the same spatial resolution as CHIRPS (0.05° x 0.05°). We verify that the disaggregation method preserves spatial coherency of extremes in the Taylor Park watershed. Validation of the index gauge extreme precipitation-frequency method consists of ensuring extreme value statistics are preserved on a grid cell basis. To this end, a non-stationary extreme precipitation-frequency analysis is performed on each grid cell individually, and the resulting frequency curves are compared to those produced by the index gauge disaggregation method.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016GMD.....9.4111R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016GMD.....9.4111R"><span>OBLIMAP 2.0: a fast climate model-ice sheet model coupler including online embeddable mapping routines</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Reerink, Thomas J.; van de Berg, Willem Jan; van de Wal, Roderik S. W.</p> <p>2016-11-01</p> <p>This paper accompanies the second OBLIMAP open-source release. The package is developed to map climate fields between a general circulation model (GCM) and an ice sheet model (ISM) in both directions by using optimal aligned oblique projections, which minimize distortions. The curvature of the surfaces of the GCM and ISM grid differ, both grids may be irregularly spaced and the ratio of the grids is allowed to differ largely. OBLIMAP's stand-alone version is able to map data sets that differ in various aspects on the same ISM grid. Each grid may either coincide with the surface of a sphere, an ellipsoid or a flat plane, while the grid types might differ. Re-projection of, for example, ISM data sets is also facilitated. This is demonstrated by relevant applications concerning the major ice caps. As the stand-alone version also applies to the reverse mapping direction, it can be used as an offline coupler. Furthermore, OBLIMAP 2.0 is an embeddable GCM-ISM coupler, suited for high-frequency online coupled experiments. A new fast scan method is presented for structured grids as an alternative for the former time-consuming grid search strategy, realising a performance gain of several orders of magnitude and enabling the mapping of high-resolution data sets with a much larger number of grid nodes. Further, a highly flexible masked mapping option is added. The limitation of the fast scan method with respect to unstructured and adaptive grids is discussed together with a possible future parallel Message Passing Interface (MPI) implementation.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.H41A1425V','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.H41A1425V"><span>Sensitivity of power system operations to projected changes in water availability due to climate change: the Western U.S. case study</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Voisin, N.; Macknick, J.; Fu, T.; O'Connell, M.; Zhou, T.; Brinkman, G.</p> <p>2017-12-01</p> <p>Water resources provide multiple critical services to the electrical grid through hydropower technologies, from generation to regulation of the electric grid (frequency, capacity reserve). Water resources can also represent vulnerabilities to the electric grid, as hydropower and thermo-electric facilities require water for operations. In the Western U.S., hydropower and thermo-electric plants that rely on fresh surface water represent 67% of the generating capacity. Prior studies have looked at the impact of change in water availability under future climate conditions on expected generating capacity in the Western U.S., but have not evaluated operational risks or changes resulting from climate. In this study, we systematically assess the impact of change in water availability and air temperatures on power operations, i.e. we take into account the different grid services that water resources can provide to the electric grid (generation, regulation) in the system-level context of inter-regional coordination through the electric transmission network. We leverage the Coupled Model Intercomparison Project Phase 5 (CMIP5) hydrology simulations under historical and future climate conditions, and force the large scale river routing- water management model MOSART-WM along with 2010-level sectoral water demands. Changes in monthly hydropower potential generation (including generation and reserves), as well as monthly generation capacity of thermo-electric plants are derived for each power plant in the Western U.S. electric grid. We then utilize the PLEXOS electricity production cost model to optimize power system dispatch and cost decisions for the 2010 infrastructure under 100 years of historical and future (2050 horizon) hydroclimate conditions. We use economic metrics as well as operational metrics such as generation portfolio, emissions, and reserve margins to assess the changes in power system operations between historical and future normal and extreme water availability conditions. We provide insight on how this information can be used to support resource adequacy and grid expansion studies over the Western U.S. in the context of inter-annual variability and climate change.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012AGUFM.A53D0178K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012AGUFM.A53D0178K"><span>GEM-AC, a stratospheric-tropospheric global and regional model for air quality and climate change: evaluation of gas phase properties</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kaminski, J. W.; Semeniuk, K.; McConnell, J. C.; Lupu, A.; Mamun, A.</p> <p>2012-12-01</p> <p>The Global Environmental Multiscale model for Air Quality and climate change (GEM-AC) is a global general circulation model based on the GEM model developed by the Meteorological Service of Canada for operational weather forecasting. It can be run with a global uniform (GU) grid or a global variable (GV) grid where the core has uniform grid spacing and the exterior grid expands. With a GV grid high resolution regional runs can be accomplished without a concern for boundary conditions. The work described here uses GEM version 3.3.2. The gas-phase chemistry consists in detailed reactions of Ox, NOx, HOx, CO, CH4, NMVOCs, halocarbons, ClOx and BrO. We have recently added elements of the Global Modal-aerosol eXtension (GMXe) scheme to address aerosol microphysics and gas-aerosol partitioning. The evaluation of the MESSY GMXe aerosol scheme is addressed in another poster. The Canadian aerosol module (CAM) is also available. Tracers are advected using the semi-Lagrangian scheme native to GEM. The vertical transport includes parameterized subgrid scale turbulence and large scale convection. Dry deposition is implemented as a flux boundary condition in the vertical diffusion equation. For climate runs the GHGs CO2, CH4, N2O, CFCs in the radiation scheme are adjusted to the scenario considered. In GV regional mode at high resolutions a lake model, FLAKE is also included. Wet removal comprises both in-cloud and below-cloud scavenging. With the gas phase chemistry the model has been run for a series of ten year time slices on a 3°×3° global grid with 77 hybrid levels from the surface to 0.15 hPa. The tropospheric and stratospheric gas phase results are compared with satellite measurements including, ACE, MIPAS, MOPITT, and OSIRIS. Current evaluations of the ozone field and other stratospheric fields are encouraging and tropospheric lifetimes for CH4 and CH3CCl3 are in reasonable accord with tropospheric models. We will present results for current and future climate conditions forced by SST for 2050.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010JGRD..11523118K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010JGRD..11523118K"><span>A critical remark on the applicability of E-OBS European gridded temperature data set for validating control climate simulations</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kyselý, Jan; Plavcová, Eva</p> <p>2010-12-01</p> <p>The study compares daily maximum (Tmax) and minimum (Tmin) temperatures in two data sets interpolated from irregularly spaced meteorological stations to a regular grid: the European gridded data set (E-OBS), produced from a relatively sparse network of stations available in the European Climate Assessment and Dataset (ECA&D) project, and a data set gridded onto the same grid from a high-density network of stations in the Czech Republic (GriSt). We show that large differences exist between the two gridded data sets, particularly for Tmin. The errors tend to be larger in tails of the distributions. In winter, temperatures below the 10% quantile of Tmin, which is still far from the very tail of the distribution, are too warm by almost 2°C in E-OBS on average. A large bias is found also for the diurnal temperature range. Comparison with simple average series from stations in two regions reveals that differences between GriSt and the station averages are minor relative to differences between E-OBS and either of the two data sets. The large deviations between the two gridded data sets affect conclusions concerning validation of temperature characteristics in regional climate model (RCM) simulations. The bias of the E-OBS data set and limitations with respect to its applicability for evaluating RCMs stem primarily from (1) insufficient density of information from station observations used for the interpolation, including the fact that the stations available may not be representative for a wider area, and (2) inconsistency between the radii of the areal average values in high-resolution RCMs and E-OBS. Further increases in the amount and quality of station data available within ECA&D and used in the E-OBS data set are essentially needed for more reliable validation of climate models against recent climate on a continental scale.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19950052925&hterms=bats&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D50%26Ntt%3Dbats','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19950052925&hterms=bats&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D50%26Ntt%3Dbats"><span>Simulating fluxes from heterogeneous land surfaces: Explicit subgrid method employing the biosphere-atmosphere transfer scheme (BATS)</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Seth, Anji; Giorgi, Filippo; Dickinson, Robert E.</p> <p>1994-01-01</p> <p>A vectorized version of the biosphere-atmosphere transfer scheme (VBATS) is used to study moisture, energy, and momentum fluxes from heterogeneous land surfaces st the scale of an atmospheric model (AM) grid cells. To incorporate subgrid scale inhomogeneity, VBATS includes two important features: (1) characterization of the land surface (vegetation and soil parameters) at N subgrid points within an AM grid cell and (2) explicit distribution of climate forcing (precipitation, clouds, etc.) over the subgrid. In this study, VBATS is used in stand-alone mode to simulate a single AM grid cell and to evaluate the effects of subgrid scale vegetation and climate specification on the surface fluxes and hydrology. It is found that the partitioning of energy can be affected by up to 30%, runoff by 50%, and surface stress in excess of 60%. Distributing climate forcing over the AM grid cell increases the Bowen ratio, as a result of enhanced sensible heat flux and reduced latent heat flux. The combined effect of heterogeneous vegetation and distribution of climate is found to be dependent on the dominat vegetation class in the AM grid cell. Development of this method is part of a larger program to explore the importance of subgrid scale processes in regional and global climate simulations.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20150011473&hterms=Initiative&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D40%26Ntt%3DInitiative','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20150011473&hterms=Initiative&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D40%26Ntt%3DInitiative"><span>The AgMIP GRIDded Crop Modeling Initiative (AgGRID) and the Global Gridded Crop Model Intercomparison (GGCMI)</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Elliott, Joshua; Muller, Christoff</p> <p>2015-01-01</p> <p>Climate change is a significant risk for agricultural production. Even under optimistic scenarios for climate mitigation action, present-day agricultural areas are likely to face significant increases in temperatures in the coming decades, in addition to changes in precipitation, cloud cover, and the frequency and duration of extreme heat, drought, and flood events (IPCC, 2013). These factors will affect the agricultural system at the global scale by impacting cultivation regimes, prices, trade, and food security (Nelson et al., 2014a). Global-scale evaluation of crop productivity is a major challenge for climate impact and adaptation assessment. Rigorous global assessments that are able to inform planning and policy will benefit from consistent use of models, input data, and assumptions across regions and time that use mutually agreed protocols designed by the modeling community. To ensure this consistency, large-scale assessments are typically performed on uniform spatial grids, with spatial resolution of typically 10 to 50 km, over specified time-periods. Many distinct crop models and model types have been applied on the global scale to assess productivity and climate impacts, often with very different results (Rosenzweig et al., 2014). These models are based to a large extent on field-scale crop process or ecosystems models and they typically require resolved data on weather, environmental, and farm management conditions that are lacking in many regions (Bondeau et al., 2007; Drewniak et al., 2013; Elliott et al., 2014b; Gueneau et al., 2012; Jones et al., 2003; Liu et al., 2007; M¨uller and Robertson, 2014; Van den Hoof et al., 2011;Waha et al., 2012; Xiong et al., 2014). Due to data limitations, the requirements of consistency, and the computational and practical limitations of running models on a large scale, a variety of simplifying assumptions must generally be made regarding prevailing management strategies on the grid scale in both the baseline and future periods. Implementation differences in these and other modeling choices contribute to significant variation among global-scale crop model assessments in addition to differences in crop model implementations that also cause large differences in site-specific crop modeling (Asseng et al., 2013; Bassu et al., 2014).</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.fs.usda.gov/treesearch/pubs/49862','TREESEARCH'); return false;" href="https://www.fs.usda.gov/treesearch/pubs/49862"><span>The Dynamic General Vegetation Model MC1 over the United States and Canada at a 5-arcminute resolution: model inputs and outputs</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.fs.usda.gov/treesearch/">Treesearch</a></p> <p>Ray Drapek; John B. Kim; Ronald P. Neilson</p> <p>2015-01-01</p> <p>Land managers need to include climate change in their decisionmaking, but the climate models that project future climates operate at spatial scales that are too coarse to be of direct use. To create a dataset more useful to managers, soil and historical climate were assembled for the United States and Canada at a 5-arcminute grid resolution. Nine CMIP3 future climate...</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19950032432&hterms=regional+impacts+climate+change&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D90%26Ntt%3Dregional%2Bimpacts%2Bclimate%2Bchange','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19950032432&hterms=regional+impacts+climate+change&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D90%26Ntt%3Dregional%2Bimpacts%2Bclimate%2Bchange"><span>Regional climates in the GISS general circulation model: Surface air temperature</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Hewitson, Bruce</p> <p>1994-01-01</p> <p>One of the more viable research techniques into global climate change for the purpose of understanding the consequent environmental impacts is based on the use of general circulation models (GCMs). However, GCMs are currently unable to reliably predict the regional climate change resulting from global warming, and it is at the regional scale that predictions are required for understanding human and environmental responses. Regional climates in the extratropics are in large part governed by the synoptic-scale circulation and the feasibility of using this interscale relationship is explored to provide a way of moving to grid cell and sub-grid cell scales in the model. The relationships between the daily circulation systems and surface air temperature for points across the continental United States are first developed in a quantitative form using a multivariate index based on principal components analysis (PCA) of the surface circulation. These relationships are then validated by predicting daily temperature using observed circulation and comparing the predicted values with the observed temperatures. The relationships predict surface temperature accurately over the major portion of the country in winter, and for half the country in summer. These relationships are then applied to the surface synoptic circulation of the Goddard Institute for Space Studies (GISS) GCM control run, and a set of surface grid cell temperatures are generated. These temperatures, based on the larger-scale validated circulation, may now be used with greater confidence at the regional scale. The generated temperatures are compared to those of the model and show that the model has regional errors of up to 10 C in individual grid cells.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFM.H53G0943C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFM.H53G0943C"><span>Multiresolution comparison of precipitation datasets for large-scale models</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Chun, K. P.; Sapriza Azuri, G.; Davison, B.; DeBeer, C. M.; Wheater, H. S.</p> <p>2014-12-01</p> <p>Gridded precipitation datasets are crucial for driving large-scale models which are related to weather forecast and climate research. However, the quality of precipitation products is usually validated individually. Comparisons between gridded precipitation products along with ground observations provide another avenue for investigating how the precipitation uncertainty would affect the performance of large-scale models. In this study, using data from a set of precipitation gauges over British Columbia and Alberta, we evaluate several widely used North America gridded products including the Canadian Gridded Precipitation Anomalies (CANGRD), the National Center for Environmental Prediction (NCEP) reanalysis, the Water and Global Change (WATCH) project, the thin plate spline smoothing algorithms (ANUSPLIN) and Canadian Precipitation Analysis (CaPA). Based on verification criteria for various temporal and spatial scales, results provide an assessment of possible applications for various precipitation datasets. For long-term climate variation studies (~100 years), CANGRD, NCEP, WATCH and ANUSPLIN have different comparative advantages in terms of their resolution and accuracy. For synoptic and mesoscale precipitation patterns, CaPA provides appealing performance of spatial coherence. In addition to the products comparison, various downscaling methods are also surveyed to explore new verification and bias-reduction methods for improving gridded precipitation outputs for large-scale models.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.fs.usda.gov/treesearch/pubs/37845','TREESEARCH'); return false;" href="https://www.fs.usda.gov/treesearch/pubs/37845"><span>Analysis of potential impacts of climate change on forests of the United States Pacific Northwest</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.fs.usda.gov/treesearch/">Treesearch</a></p> <p>Gregory Latta; Hailemariam Temesgen; Darius Adams; Tara Barrett</p> <p>2010-01-01</p> <p>As global climate changes over the next century, forest productivity is expected to change as well. Using PRISM climate and productivity data measured on a grid of 3356 plots, we developed a simultaneous autoregressive model to estimate the impacts of climate change on potential productivity of Pacific Northwest forests of the United States. The model, coupled with...</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/1195406','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/1195406"><span>Introducing Enabling Computational Tools to the Climate Sciences: Multi-Resolution Climate Modeling with Adaptive Cubed-Sphere Grids</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Jablonowski, Christiane</p> <p></p> <p>The research investigates and advances strategies how to bridge the scale discrepancies between local, regional and global phenomena in climate models without the prohibitive computational costs of global cloud-resolving simulations. In particular, the research explores new frontiers in computational geoscience by introducing high-order Adaptive Mesh Refinement (AMR) techniques into climate research. AMR and statically-adapted variable-resolution approaches represent an emerging trend for atmospheric models and are likely to become the new norm in future-generation weather and climate models. The research advances the understanding of multi-scale interactions in the climate system and showcases a pathway how to model these interactions effectively withmore » advanced computational tools, like the Chombo AMR library developed at the Lawrence Berkeley National Laboratory. The research is interdisciplinary and combines applied mathematics, scientific computing and the atmospheric sciences. In this research project, a hierarchy of high-order atmospheric models on cubed-sphere computational grids have been developed that serve as an algorithmic prototype for the finite-volume solution-adaptive Chombo-AMR approach. The foci of the investigations have lied on the characteristics of both static mesh adaptations and dynamically-adaptive grids that can capture flow fields of interest like tropical cyclones. Six research themes have been chosen. These are (1) the introduction of adaptive mesh refinement techniques into the climate sciences, (2) advanced algorithms for nonhydrostatic atmospheric dynamical cores, (3) an assessment of the interplay between resolved-scale dynamical motions and subgrid-scale physical parameterizations, (4) evaluation techniques for atmospheric model hierarchies, (5) the comparison of AMR refinement strategies and (6) tropical cyclone studies with a focus on multi-scale interactions and variable-resolution modeling. The results of this research project demonstrate significant advances in all six research areas. The major conclusions are that statically-adaptive variable-resolution modeling is currently becoming mature in the climate sciences, and that AMR holds outstanding promise for future-generation weather and climate models on high-performance computing architectures.« less</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_1");'>1</a></li> <li><a href="#" onclick='return showDiv("page_2");'>2</a></li> <li class="active"><span>3</span></li> <li><a href="#" onclick='return showDiv("page_4");'>4</a></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_3 --> <div id="page_4" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_2");'>2</a></li> <li><a href="#" onclick='return showDiv("page_3");'>3</a></li> <li class="active"><span>4</span></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li><a href="#" onclick='return showDiv("page_6");'>6</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="61"> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20030022773','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20030022773"><span>Snow and Ice Products from the Moderate Resolution Imaging Spectroradiometer</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Hall, Dorothy K.; Salomonson, Vincent V.; Riggs, George A.; Klein, Andrew G.</p> <p>2003-01-01</p> <p>Snow and sea ice products, derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument, flown on the Terra and Aqua satellites, are or will be available through the National Snow and Ice Data Center Distributed Active Archive Center (DAAC). The algorithms that produce the products are automated, thus providing a consistent global data set that is suitable for climate studies. The suite of MODIS snow products begins with a 500-m resolution, 2330-km swath snow-cover map that is then projected onto a sinusoidal grid to produce daily and 8-day composite tile products. The sequence proceeds to daily and 8-day composite climate-modeling grid (CMG) products at 0.05 resolution. A daily snow albedo product will be available in early 2003 as a beta test product. The sequence of sea ice products begins with a swath product at 1-km resolution that provides sea ice extent and ice-surface temperature (IST). The sea ice swath products are then mapped onto the Lambert azimuthal equal area or EASE-Grid projection to create a daily and 8-day composite sea ice tile product, also at 1 -km resolution. Climate-Modeling Grid (CMG) sea ice products in the EASE-Grid projection at 4-km resolution are planned for early 2003.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018JHyd..560..202T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018JHyd..560..202T"><span>Optimal Interpolation scheme to generate reference crop evapotranspiration</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Tomas-Burguera, Miquel; Beguería, Santiago; Vicente-Serrano, Sergio; Maneta, Marco</p> <p>2018-05-01</p> <p>We used an Optimal Interpolation (OI) scheme to generate a reference crop evapotranspiration (ETo) grid, forcing meteorological variables, and their respective error variance in the Iberian Peninsula for the period 1989-2011. To perform the OI we used observational data from the Spanish Meteorological Agency (AEMET) and outputs from a physically-based climate model. To compute ETo we used five OI schemes to generate grids for the five observed climate variables necessary to compute ETo using the FAO-recommended form of the Penman-Monteith equation (FAO-PM). The granularity of the resulting grids are less sensitive to variations in the density and distribution of the observational network than those generated by other interpolation methods. This is because our implementation of the OI method uses a physically-based climate model as prior background information about the spatial distribution of the climatic variables, which is critical for under-observed regions. This provides temporal consistency in the spatial variability of the climatic fields. We also show that increases in the density and improvements in the distribution of the observational network reduces substantially the uncertainty of the climatic and ETo estimates. Finally, a sensitivity analysis of observational uncertainties and network densification suggests the existence of a trade-off between quantity and quality of observations.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=336404','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=336404"><span>Assessing long-term hydrologic impact of climate change using ensemble approach and comparison with Global Gridded Model-A case study on Goodwater Creek Experimental Watershed</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ars.usda.gov/research/publications/find-a-publication/">USDA-ARS?s Scientific Manuscript database</a></p> <p></p> <p></p> <p>Potential impacts of climate change on hydrologic components of Goodwater Creek Experimental Watershed were assessed using climate datasets from the Coupled Model Intercomparison Project Phase 5 and Soil and Water Assessment Tool (SWAT). Historical and future ensembles of downscaled precipitation an...</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017ClDy..tmp..834P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017ClDy..tmp..834P"><span>Simulating North American mesoscale convective systems with a convection-permitting climate model</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Prein, Andreas F.; Liu, Changhai; Ikeda, Kyoko; Bullock, Randy; Rasmussen, Roy M.; Holland, Greg J.; Clark, Martyn</p> <p>2017-10-01</p> <p>Deep convection is a key process in the climate system and the main source of precipitation in the tropics, subtropics, and mid-latitudes during summer. Furthermore, it is related to high impact weather causing floods, hail, tornadoes, landslides, and other hazards. State-of-the-art climate models have to parameterize deep convection due to their coarse grid spacing. These parameterizations are a major source of uncertainty and long-standing model biases. We present a North American scale convection-permitting climate simulation that is able to explicitly simulate deep convection due to its 4-km grid spacing. We apply a feature-tracking algorithm to detect hourly precipitation from Mesoscale Convective Systems (MCSs) in the model and compare it with radar-based precipitation estimates east of the US Continental Divide. The simulation is able to capture the main characteristics of the observed MCSs such as their size, precipitation rate, propagation speed, and lifetime within observational uncertainties. In particular, the model is able to produce realistically propagating MCSs, which was a long-standing challenge in climate modeling. However, the MCS frequency is significantly underestimated in the central US during late summer. We discuss the origin of this frequency biases and suggest strategies for model improvements.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018ClDy...50.1581M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018ClDy...50.1581M"><span>Simulating the characteristics of tropical cyclones over the South West Indian Ocean using a Stretched-Grid Global Climate Model</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Maoyi, Molulaqhooa L.; Abiodun, Babatunde J.; Prusa, Joseph M.; Veitch, Jennifer J.</p> <p>2018-03-01</p> <p>Tropical cyclones (TCs) are one of the most devastating natural phenomena. This study examines the capability of a global climate model with grid stretching (CAM-EULAG, hereafter CEU) in simulating the characteristics of TCs over the South West Indian Ocean (SWIO). In the study, CEU is applied with a variable increment global grid that has a fine horizontal grid resolution (0.5° × 0.5°) over the SWIO and coarser resolution (1° × 1°—2° × 2.25°) over the rest of the globe. The simulation is performed for the 11 years (1999-2010) and validated against the Joint Typhoon Warning Center (JTWC) best track data, global precipitation climatology project (GPCP) satellite data, and ERA-Interim (ERAINT) reanalysis. CEU gives a realistic simulation of the SWIO climate and shows some skill in simulating the spatial distribution of TC genesis locations and tracks over the basin. However, there are some discrepancies between the observed and simulated climatic features over the Mozambique channel (MC). Over MC, CEU simulates a substantial cyclonic feature that produces a higher number of TC than observed. The dynamical structure and intensities of the CEU TCs compare well with observation, though the model struggles to produce TCs with a deep pressure centre as low as the observed. The reanalysis has the same problem. The model captures the monthly variation of TC occurrence well but struggles to reproduce the interannual variation. The results of this study have application in improving and adopting CEU for seasonal forecasting over the SWIO.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/27755746','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/27755746"><span>The effects of climate downscaling technique and observational data set on modeled ecological responses.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Pourmokhtarian, Afshin; Driscoll, Charles T; Campbell, John L; Hayhoe, Katharine; Stoner, Anne M K</p> <p>2016-07-01</p> <p>Assessments of future climate change impacts on ecosystems typically rely on multiple climate model projections, but often utilize only one downscaling approach trained on one set of observations. Here, we explore the extent to which modeled biogeochemical responses to changing climate are affected by the selection of the climate downscaling method and training observations used at the montane landscape of the Hubbard Brook Experimental Forest, New Hampshire, USA. We evaluated three downscaling methods: the delta method (or the change factor method), monthly quantile mapping (Bias Correction-Spatial Disaggregation, or BCSD), and daily quantile regression (Asynchronous Regional Regression Model, or ARRM). Additionally, we trained outputs from four atmosphere-ocean general circulation models (AOGCMs) (CCSM3, HadCM3, PCM, and GFDL-CM2.1) driven by higher (A1fi) and lower (B1) future emissions scenarios on two sets of observations (1/8º resolution grid vs. individual weather station) to generate the high-resolution climate input for the forest biogeochemical model PnET-BGC (eight ensembles of six runs).The choice of downscaling approach and spatial resolution of the observations used to train the downscaling model impacted modeled soil moisture and streamflow, which in turn affected forest growth, net N mineralization, net soil nitrification, and stream chemistry. All three downscaling methods were highly sensitive to the observations used, resulting in projections that were significantly different between station-based and grid-based observations. The choice of downscaling method also slightly affected the results, however not as much as the choice of observations. Using spatially smoothed gridded observations and/or methods that do not resolve sub-monthly shifts in the distribution of temperature and/or precipitation can produce biased results in model applications run at greater temporal and/or spatial resolutions. These results underscore the importance of carefully considering field observations used for training, as well as the downscaling method used to generate climate change projections, for smaller-scale modeling studies. Different sources of variability including selection of AOGCM, emissions scenario, downscaling technique, and data used for training downscaling models, result in a wide range of projected forest ecosystem responses to future climate change. © 2016 by the Ecological Society of America.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.A21P..08S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.A21P..08S"><span>A satellite simulator for TRMM PR applied to climate model simulations</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Spangehl, T.; Schroeder, M.; Bodas-Salcedo, A.; Hollmann, R.; Riley Dellaripa, E. M.; Schumacher, C.</p> <p>2017-12-01</p> <p>Climate model simulations have to be compared against observation based datasets in order to assess their skill in representing precipitation characteristics. Here we use a satellite simulator for TRMM PR in order to evaluate simulations performed with MPI-ESM (Earth system model of the Max Planck Institute for Meteorology in Hamburg, Germany) performed within the MiKlip project (https://www.fona-miklip.de/, funded by Federal Ministry of Education and Research in Germany). While classical evaluation methods focus on geophysical parameters such as precipitation amounts, the application of the satellite simulator enables an evaluation in the instrument's parameter space thereby reducing uncertainties on the reference side. The CFMIP Observation Simulator Package (COSP) provides a framework for the application of satellite simulators to climate model simulations. The approach requires the introduction of sub-grid cloud and precipitation variability. Radar reflectivities are obtained by applying Mie theory, with the microphysical assumptions being chosen to match the atmosphere component of MPI-ESM (ECHAM6). The results are found to be sensitive to the methods used to distribute the convective precipitation over the sub-grid boxes. Simple parameterization methods are used to introduce sub-grid variability of convective clouds and precipitation. In order to constrain uncertainties a comprehensive comparison with sub-grid scale convective precipitation variability which is deduced from TRMM PR observations is carried out.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://pubs.er.usgs.gov/publication/70017774','USGSPUBS'); return false;" href="https://pubs.er.usgs.gov/publication/70017774"><span>Middle Pliocene vegetation: Reconstructions, paleoclimatic inferences, and boundary conditions for climate modeling</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Thompson, R.S.; Fleming, R.F.</p> <p>1996-01-01</p> <p>The general characteristics of global vegetation during the middle Pliocene warm period can be reconstructed from fossil pollen and plant megafossil data. The largest differences between Pliocene vegetation and that of today occurred at high latitudes in both hemispheres, where warming was pronounced relative to today. In the Northern Hemisphere coniferous forests lived in the modern tundra and polar desert regions, whereas in the Southern Hemisphere southern beech apparently grew in coastal areas of Antarctica. Pliocene middle latitude vegetation differed less, although moister-than-modern conditions supported forest and woodland growth in some regions now covered by steppe or grassland. Pliocene tropical vegetation reflects essentially modern conditions in some regions and slightly cooler-than-or warmer-than- modern climates in other areas. Changes in topography induced by tectonics may be responsible for many of the climatic changes since the Pliocene in both middle and lower latitudes. However, the overall latitudinal progression of climatic conditions on land parallels that seen in the reconstruction of middle Pliocene sea-surface temperatures. Pliocene paleovegetational data was employed to construct a 2????2?? global grid of estimated mid-Pliocene vegetational cover for use as boundary conditions for numerical General Circulation Model simulations of middle Pliocene climates. Continental outlines and topography were first modified to represent the Pliocene landscape on the 2????2?? grid. A modern 1????1?? vegetation grid was simplified and mapped on this Pliocene grid, and then modified following general geographic trends evident in the Pliocene paleovegetation data set.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010EGUGA..12.9008F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010EGUGA..12.9008F"><span>A Grid Metadata Service for Earth and Environmental Sciences</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Fiore, Sandro; Negro, Alessandro; Aloisio, Giovanni</p> <p>2010-05-01</p> <p>Critical challenges for climate modeling researchers are strongly connected with the increasingly complex simulation models and the huge quantities of produced datasets. Future trends in climate modeling will only increase computational and storage requirements. For this reason the ability to transparently access to both computational and data resources for large-scale complex climate simulations must be considered as a key requirement for Earth Science and Environmental distributed systems. From the data management perspective (i) the quantity of data will continuously increases, (ii) data will become more and more distributed and widespread, (iii) data sharing/federation will represent a key challenging issue among different sites distributed worldwide, (iv) the potential community of users (large and heterogeneous) will be interested in discovery experimental results, searching of metadata, browsing collections of files, compare different results, display output, etc.; A key element to carry out data search and discovery, manage and access huge and distributed amount of data is the metadata handling framework. What we propose for the management of distributed datasets is the GRelC service (a data grid solution focusing on metadata management). Despite the classical approaches, the proposed data-grid solution is able to address scalability, transparency, security and efficiency and interoperability. The GRelC service we propose is able to provide access to metadata stored in different and widespread data sources (relational databases running on top of MySQL, Oracle, DB2, etc. leveraging SQL as query language, as well as XML databases - XIndice, eXist, and libxml2 based documents, adopting either XPath or XQuery) providing a strong data virtualization layer in a grid environment. Such a technological solution for distributed metadata management leverages on well known adopted standards (W3C, OASIS, etc.); (ii) supports role-based management (based on VOMS), which increases flexibility and scalability; (iii) provides full support for Grid Security Infrastructure, which means (authorization, mutual authentication, data integrity, data confidentiality and delegation); (iv) is compatible with existing grid middleware such as gLite and Globus and finally (v) is currently adopted at the Euro-Mediterranean Centre for Climate Change (CMCC - Italy) to manage the entire CMCC data production activity as well as in the international Climate-G testbed.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.A24F..02L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.A24F..02L"><span>Aerosol-cloud interactions in a multi-scale modeling framework</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Lin, G.; Ghan, S. J.</p> <p>2017-12-01</p> <p>Atmospheric aerosols play an important role in changing the Earth's climate through scattering/absorbing solar and terrestrial radiation and interacting with clouds. However, quantification of the aerosol effects remains one of the most uncertain aspects of current and future climate projection. Much of the uncertainty results from the multi-scale nature of aerosol-cloud interactions, which is very challenging to represent in traditional global climate models (GCMs). In contrast, the multi-scale modeling framework (MMF) provides a viable solution, which explicitly resolves the cloud/precipitation in the cloud resolved model (CRM) embedded in the GCM grid column. In the MMF version of community atmospheric model version 5 (CAM5), aerosol processes are treated with a parameterization, called the Explicit Clouds Parameterized Pollutants (ECPP). It uses the cloud/precipitation statistics derived from the CRM to treat the cloud processing of aerosols on the GCM grid. However, this treatment treats clouds on the CRM grid but aerosols on the GCM grid, which is inconsistent with the reality that cloud-aerosol interactions occur on the cloud scale. To overcome the limitation, here, we propose a new aerosol treatment in the MMF: Explicit Clouds Explicit Aerosols (ECEP), in which we resolve both clouds and aerosols explicitly on the CRM grid. We first applied the MMF with ECPP to the Accelerated Climate Modeling for Energy (ACME) model to have an MMF version of ACME. Further, we also developed an alternative version of ACME-MMF with ECEP. Based on these two models, we have conducted two simulations: one with the ECPP and the other with ECEP. Preliminary results showed that the ECEP simulations tend to predict higher aerosol concentrations than ECPP simulations, because of the more efficient vertical transport from the surface to the higher atmosphere but the less efficient wet removal. We also found that the cloud droplet number concentrations are also different between the two simulations due to the difference in the cloud droplet lifetime. Next, we will explore how the ECEP treatment affects the anthropogenic aerosol forcing, particularly the aerosol indirect forcing, by comparing present-day and pre-industrial simulations.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011HESSD...8.8399T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011HESSD...8.8399T"><span>On the uncertainties associated with using gridded rainfall data as a proxy for observed</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Tozer, C. R.; Kiem, A. S.; Verdon-Kidd, D. C.</p> <p>2011-09-01</p> <p>Gridded rainfall datasets are used in many hydrological and climatological studies, in Australia and elsewhere, including for hydroclimatic forecasting, climate attribution studies and climate model performance assessments. The attraction of the spatial coverage provided by gridded data is clear, particularly in Australia where the spatial and temporal resolution of the rainfall gauge network is sparse. However, the question that must be asked is whether it is suitable to use gridded data as a proxy for observed point data, given that gridded data is inherently "smoothed" and may not necessarily capture the temporal and spatial variability of Australian rainfall which leads to hydroclimatic extremes (i.e. droughts, floods)? This study investigates this question through a statistical analysis of three monthly gridded Australian rainfall datasets - the Bureau of Meteorology (BOM) dataset, the Australian Water Availability Project (AWAP) and the SILO dataset. To demonstrate the hydrological implications of using gridded data as a proxy for gauged data, a rainfall-runoff model is applied to one catchment in South Australia (SA) initially using gridded data as the source of rainfall input and then gauged rainfall data. The results indicate a markedly different runoff response associated with each of the different sources of rainfall data. It should be noted that this study does not seek to identify which gridded dataset is the "best" for Australia, as each gridded data source has its pros and cons, as does gauged or point data. Rather the intention is to quantify differences between various gridded data sources and how they compare with gauged data so that these differences can be considered and accounted for in studies that utilise these gridded datasets. Ultimately, if key decisions are going to be based on the outputs of models that use gridded data, an estimate (or at least an understanding) of the uncertainties relating to the assumptions made in the development of gridded data and how that gridded data compares with reality should be made.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/1113798','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/1113798"><span>Earth System Grid II, Turning Climate Datasets into Community Resources</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Middleton, Don</p> <p>2006-08-01</p> <p>The Earth System Grid (ESG) II project, funded by the Department of Energy’s Scientific Discovery through Advanced Computing program, has transformed climate data into community resources. ESG II has accomplished this goal by creating a virtual collaborative environment that links climate centers and users around the world to models and data via a computing Grid, which is based on the Department of Energy’s supercomputing resources and the Internet. Our project’s success stems from partnerships between climate researchers and computer scientists to advance basic and applied research in the terrestrial, atmospheric, and oceanic sciences. By interfacing with other climate science projects,more » we have learned that commonly used methods to manage and remotely distribute data among related groups lack infrastructure and under-utilize existing technologies. Knowledge and expertise gained from ESG II have helped the climate community plan strategies to manage a rapidly growing data environment more effectively. Moreover, approaches and technologies developed under the ESG project have impacted datasimulation integration in other disciplines, such as astrophysics, molecular biology and materials science.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=308471&keyword=Scheme&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50','EPA-EIMS'); return false;" href="https://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=308471&keyword=Scheme&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50"><span>Grid-scale Indirect Radiative Forcing of Climate due to aerosols over the northern hemisphere simulated by the integrated WRF-CMAQ model: Preliminary results</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://oaspub.epa.gov/eims/query.page">EPA Science Inventory</a></p> <p></p> <p></p> <p>In this study, indirect aerosol effects on grid-scale clouds were implemented in the integrated WRF3.3-CMAQ5.0 modeling system by including parameterizations for both cloud droplet and ice number concentrations calculated from the CMAQ-predicted aerosol particles. The resulting c...</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.A13H2212M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.A13H2212M"><span>Process-Oriented Diagnostics of Tropical Cyclones in Global Climate Models</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Moon, Y.; Kim, D.; Camargo, S. J.; Wing, A. A.; Sobel, A. H.; Bosilovich, M. G.; Murakami, H.; Reed, K. A.; Vecchi, G. A.; Wehner, M. F.; Zarzycki, C. M.; Zhao, M.</p> <p>2017-12-01</p> <p>Simulating tropical cyclone (TC) activity with global climate models (GCMs) remains a challenging problem. While some GCMs are able to simulate TC activity that is in good agreement with the observations, many other models exhibit strong biases. Decreasing horizontal grid spacing of the GCM simulations tends to improve the characteristics of simulated TCs, but this enhancement alone does not necessarily lead to greater skill in simulating TC activity. This study uses process-based diagnostics to identify model characteristics that could explain why some GCM simulations are able to produce more realistic TC activity than others. The diagnostics examine how convection, moisture, clouds and related processes are coupled at individual grid points, which yields useful information into how convective parameterizations interact with resolved model dynamics. These diagnostics share similarities with those originally developed to examine the Madden-Julian Oscillations in climate models. This study will examine TCs in eight different GCM simulations performed at NOAA/GFDL, NCAR and NASA that have different horizontal resolutions and ocean coupling. Preliminary results suggest that stronger TCs are closely associated with greater rainfall - thus greater diabatic heating - in the inner-core regions of the storms, which is consistent with previous theoretical studies. Other storm characteristics that can be used to infer why GCM simulations with comparable horizontal grid spacings produce different TC activity will be examined.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014GMD.....7..387F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014GMD.....7..387F"><span>TopoSCALE v.1.0: downscaling gridded climate data in complex terrain</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Fiddes, J.; Gruber, S.</p> <p>2014-02-01</p> <p>Simulation of land surface processes is problematic in heterogeneous terrain due to the the high resolution required of model grids to capture strong lateral variability caused by, for example, topography, and the lack of accurate meteorological forcing data at the site or scale it is required. Gridded data products produced by atmospheric models can fill this gap, however, often not at an appropriate spatial resolution to drive land-surface simulations. In this study we describe a method that uses the well-resolved description of the atmospheric column provided by climate models, together with high-resolution digital elevation models (DEMs), to downscale coarse-grid climate variables to a fine-scale subgrid. The main aim of this approach is to provide high-resolution driving data for a land-surface model (LSM). The method makes use of an interpolation of pressure-level data according to topographic height of the subgrid. An elevation and topography correction is used to downscale short-wave radiation. Long-wave radiation is downscaled by deriving a cloud-component of all-sky emissivity at grid level and using downscaled temperature and relative humidity fields to describe variability with elevation. Precipitation is downscaled with a simple non-linear lapse and optionally disaggregated using a climatology approach. We test the method in comparison with unscaled grid-level data and a set of reference methods, against a large evaluation dataset (up to 210 stations per variable) in the Swiss Alps. We demonstrate that the method can be used to derive meteorological inputs in complex terrain, with most significant improvements (with respect to reference methods) seen in variables derived from pressure levels: air temperature, relative humidity, wind speed and incoming long-wave radiation. This method may be of use in improving inputs to numerical simulations in heterogeneous and/or remote terrain, especially when statistical methods are not possible, due to lack of observations (i.e. remote areas or future periods).</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016JAMES...8.1358X','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016JAMES...8.1358X"><span>Calibration-induced uncertainty of the EPIC model to estimate climate change impact on global maize yield</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Xiong, Wei; Skalský, Rastislav; Porter, Cheryl H.; Balkovič, Juraj; Jones, James W.; Yang, Di</p> <p>2016-09-01</p> <p>Understanding the interactions between agricultural production and climate is necessary for sound decision-making in climate policy. Gridded and high-resolution crop simulation has emerged as a useful tool for building this understanding. Large uncertainty exists in this utilization, obstructing its capacity as a tool to devise adaptation strategies. Increasing focus has been given to sources of uncertainties for climate scenarios, input-data, and model, but uncertainties due to model parameter or calibration are still unknown. Here, we use publicly available geographical data sets as input to the Environmental Policy Integrated Climate model (EPIC) for simulating global-gridded maize yield. Impacts of climate change are assessed up to the year 2099 under a climate scenario generated by HadEM2-ES under RCP 8.5. We apply five strategies by shifting one specific parameter in each simulation to calibrate the model and understand the effects of calibration. Regionalizing crop phenology or harvest index appears effective to calibrate the model for the globe, but using various values of phenology generates pronounced difference in estimated climate impact. However, projected impacts of climate change on global maize production are consistently negative regardless of the parameter being adjusted. Different values of model parameter result in a modest uncertainty at global level, with difference of the global yield change less than 30% by the 2080s. The uncertainty subjects to decrease if applying model calibration or input data quality control. Calibration has a larger effect at local scales, implying the possible types and locations for adaptation.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/pages/biblio/1329440-atmospheric-updrafts-key-unlocking-climate-forcing-sensitivity','SCIGOV-DOEP'); return false;" href="https://www.osti.gov/pages/biblio/1329440-atmospheric-updrafts-key-unlocking-climate-forcing-sensitivity"><span>Are atmospheric updrafts a key to unlocking climate forcing and sensitivity?</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/pages">DOE PAGES</a></p> <p>Donner, Leo J.; O'Brien, Travis A.; Rieger, Daniel; ...</p> <p>2016-10-20</p> <p>Both climate forcing and climate sensitivity persist as stubborn uncertainties limiting the extent to which climate models can provide actionable scientific scenarios for climate change. A key, explicit control on cloud–aerosol interactions, the largest uncertainty in climate forcing, is the vertical velocity of cloud-scale updrafts. Model-based studies of climate sensitivity indicate that convective entrainment, which is closely related to updraft speeds, is an important control on climate sensitivity. Updraft vertical velocities also drive many physical processes essential to numerical weather prediction. Vertical velocities and their role in atmospheric physical processes have been given very limited attention in models for climatemore » and numerical weather prediction. The relevant physical scales range down to tens of meters and are thus frequently sub-grid and require parameterization. Many state-of-science convection parameterizations provide mass fluxes without specifying Vertical velocities and their role in atmospheric physical processes have been given very limited attention in models for climate and numerical weather prediction. The relevant physical scales range down to tens of meters and are thus frequently sub-grid and require parameterization. Many state-of-science convection parameterizations provide mass fluxes without specifying vs in climate models may capture this behavior, but it has not been accounted for when parameterizing cloud and precipitation processes in current models. New observations of convective vertical velocities offer a potentially promising path toward developing process-level cloud models and parameterizations for climate and numerical weather prediction. Taking account of the scale dependence of resolved vertical velocities offers a path to matching cloud-scale physical processes and their driving dynamics more realistically, with a prospect of reduced uncertainty in both climate forcing and sensitivity.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/biblio/1329440-atmospheric-updrafts-key-unlocking-climate-forcing-sensitivity','SCIGOV-STC'); return false;" href="https://www.osti.gov/biblio/1329440-atmospheric-updrafts-key-unlocking-climate-forcing-sensitivity"><span>Are atmospheric updrafts a key to unlocking climate forcing and sensitivity?</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Donner, Leo J.; O'Brien, Travis A.; Rieger, Daniel</p> <p></p> <p>Both climate forcing and climate sensitivity persist as stubborn uncertainties limiting the extent to which climate models can provide actionable scientific scenarios for climate change. A key, explicit control on cloud–aerosol interactions, the largest uncertainty in climate forcing, is the vertical velocity of cloud-scale updrafts. Model-based studies of climate sensitivity indicate that convective entrainment, which is closely related to updraft speeds, is an important control on climate sensitivity. Updraft vertical velocities also drive many physical processes essential to numerical weather prediction. Vertical velocities and their role in atmospheric physical processes have been given very limited attention in models for climatemore » and numerical weather prediction. The relevant physical scales range down to tens of meters and are thus frequently sub-grid and require parameterization. Many state-of-science convection parameterizations provide mass fluxes without specifying Vertical velocities and their role in atmospheric physical processes have been given very limited attention in models for climate and numerical weather prediction. The relevant physical scales range down to tens of meters and are thus frequently sub-grid and require parameterization. Many state-of-science convection parameterizations provide mass fluxes without specifying vs in climate models may capture this behavior, but it has not been accounted for when parameterizing cloud and precipitation processes in current models. New observations of convective vertical velocities offer a potentially promising path toward developing process-level cloud models and parameterizations for climate and numerical weather prediction. Taking account of the scale dependence of resolved vertical velocities offers a path to matching cloud-scale physical processes and their driving dynamics more realistically, with a prospect of reduced uncertainty in both climate forcing and sensitivity.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=269553&Lab=NERL&keyword=health+AND+physics&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50','EPA-EIMS'); return false;" href="https://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=269553&Lab=NERL&keyword=health+AND+physics&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50"><span>An Observation-base investigation of nudging in WRF for downscaling surface climate information to 12-km Grid Spacing</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://oaspub.epa.gov/eims/query.page">EPA Science Inventory</a></p> <p></p> <p></p> <p>Previous research has demonstrated the ability to use the Weather Research and Forecast (WRF) model and contemporary dynamical downscaling methods to refine global climate modeling results to a horizontal resolution of 36 km. Environmental managers and urban planners have expre...</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=241245&Lab=NERL&keyword=displacement&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50','EPA-EIMS'); return false;" href="https://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=241245&Lab=NERL&keyword=displacement&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50"><span>Simulating the impact of the large-scale circulation on the 2-m temperature and precipitation climatology</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://oaspub.epa.gov/eims/query.page">EPA Science Inventory</a></p> <p></p> <p></p> <p>The impact of the simulated large-scale atmospheric circulation on the regional climate is examined using the Weather Research and Forecasting (WRF) model as a regional climate model. The purpose is to understand the potential need for interior grid nudging for dynamical downscal...</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_2");'>2</a></li> <li><a href="#" onclick='return showDiv("page_3");'>3</a></li> <li class="active"><span>4</span></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li><a href="#" onclick='return showDiv("page_6");'>6</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_4 --> <div id="page_5" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_3");'>3</a></li> <li><a href="#" onclick='return showDiv("page_4");'>4</a></li> <li class="active"><span>5</span></li> <li><a href="#" onclick='return showDiv("page_6");'>6</a></li> <li><a href="#" onclick='return showDiv("page_7");'>7</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="81"> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFM.H43P..02R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFM.H43P..02R"><span>High Resolution Climate Modeling of the Water Cycle over the Western United States Including Potential Climate Change Impacts</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Rasmussen, R.; Liu, C.; Ikeda, K.</p> <p>2016-12-01</p> <p>The NCAR Water System program strives to improve the full representation of the water cycle in both regional and global models. Our previous high-resolution simulations using the WRF model over the Rocky Mountains revealed that proper spatial and temporal depiction of snowfall adequate for water resource and climate change purposes can be achieved with the appropriate choice of model grid spacing (< 6 km horizontal) and parameterizations. The climate sensitivity experiment consistent with expected climate change showed an altered hydrological cycle with increased fraction of rain versus snow, increased snowfall at high altitudes, earlier melting of snowpack, and decreased total runoff. In order to investigate regional differences between the Rockies and other major mountain barriers and to study climate change impacts over other regions of the contiguous U.S. (CONUS), we have expanded our prior CO Headwaters modeling study to encompass most of North America at a horizontal grid spacing of 4 km (see figure below). A domain expansion provides the opportunity to assess changes in orographic precipitation across different mountain ranges in the western USA. This study will examine the water cycle over Western U.S. seven U.S. mountain ranges, including likely changes to amount of snowpack and spring melt-off, critical to agriculture in the western U.S.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFM.A32G..01P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFM.A32G..01P"><span>A Review on Regional Convection-Permitting Climate Modeling: Demonstrations, Prospects, and Challenges</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Prein, A. F.; Langhans, W.; Fosser, G.; Ferrone, A.; Ban, N.; Goergen, K.; Keller, M.; Tölle, M.; Gutjahr, O.; Feser, F.; Brisson, E.; Kollet, S. J.; Schmidli, J.; Van Lipzig, N. P. M.; Leung, L. R.</p> <p>2015-12-01</p> <p>Regional climate modeling using convection-permitting models (CPMs; horizontal grid spacing <4 km) emerges as a promising framework to provide more reliable climate information on regional to local scales compared to traditionally used large-scale models (LSMs; horizontal grid spacing >10 km). CPMs no longer rely on convection parameterization schemes, which had been identified as a major source of errors and uncertainties in LSMs. Moreover, CPMs allow for a more accurate representation of surface and orography fields. The drawback of CPMs is the high demand on computational resources. For this reason, first CPM climate simulations only appeared a decade ago. We aim to provide a common basis for CPM climate simulations by giving a holistic review of the topic. The most important components in CPMs such as physical parameterizations and dynamical formulations are discussed critically. An overview of weaknesses and an outlook on required future developments is provided. Most importantly, this review presents the consolidated outcome of studies that addressed the added value of CPM climate simulations compared to LSMs. Improvements are evident mostly for climate statistics related to deep convection, mountainous regions, or extreme events. The climate change signals of CPM simulations suggest an increase in flash floods, changes in hail storm characteristics, and reductions in the snowpack over mountains. In conclusion, CPMs are a very promising tool for future climate research. However, coordinated modeling programs are crucially needed to advance parameterizations of unresolved physics and to assess the full potential of CPMs.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/27478878','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/27478878"><span>A review on regional convection-permitting climate modeling: Demonstrations, prospects, and challenges.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Prein, Andreas F; Langhans, Wolfgang; Fosser, Giorgia; Ferrone, Andrew; Ban, Nikolina; Goergen, Klaus; Keller, Michael; Tölle, Merja; Gutjahr, Oliver; Feser, Frauke; Brisson, Erwan; Kollet, Stefan; Schmidli, Juerg; van Lipzig, Nicole P M; Leung, Ruby</p> <p>2015-06-01</p> <p>Regional climate modeling using convection-permitting models (CPMs; horizontal grid spacing <4 km) emerges as a promising framework to provide more reliable climate information on regional to local scales compared to traditionally used large-scale models (LSMs; horizontal grid spacing >10 km). CPMs no longer rely on convection parameterization schemes, which had been identified as a major source of errors and uncertainties in LSMs. Moreover, CPMs allow for a more accurate representation of surface and orography fields. The drawback of CPMs is the high demand on computational resources. For this reason, first CPM climate simulations only appeared a decade ago. In this study, we aim to provide a common basis for CPM climate simulations by giving a holistic review of the topic. The most important components in CPMs such as physical parameterizations and dynamical formulations are discussed critically. An overview of weaknesses and an outlook on required future developments is provided. Most importantly, this review presents the consolidated outcome of studies that addressed the added value of CPM climate simulations compared to LSMs. Improvements are evident mostly for climate statistics related to deep convection, mountainous regions, or extreme events. The climate change signals of CPM simulations suggest an increase in flash floods, changes in hail storm characteristics, and reductions in the snowpack over mountains. In conclusion, CPMs are a very promising tool for future climate research. However, coordinated modeling programs are crucially needed to advance parameterizations of unresolved physics and to assess the full potential of CPMs.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015RvGeo..53..323P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015RvGeo..53..323P"><span>A review on regional convection-permitting climate modeling: Demonstrations, prospects, and challenges</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Prein, Andreas F.; Langhans, Wolfgang; Fosser, Giorgia; Ferrone, Andrew; Ban, Nikolina; Goergen, Klaus; Keller, Michael; Tölle, Merja; Gutjahr, Oliver; Feser, Frauke; Brisson, Erwan; Kollet, Stefan; Schmidli, Juerg; van Lipzig, Nicole P. M.; Leung, Ruby</p> <p>2015-06-01</p> <p>Regional climate modeling using convection-permitting models (CPMs; horizontal grid spacing <4 km) emerges as a promising framework to provide more reliable climate information on regional to local scales compared to traditionally used large-scale models (LSMs; horizontal grid spacing >10 km). CPMs no longer rely on convection parameterization schemes, which had been identified as a major source of errors and uncertainties in LSMs. Moreover, CPMs allow for a more accurate representation of surface and orography fields. The drawback of CPMs is the high demand on computational resources. For this reason, first CPM climate simulations only appeared a decade ago. In this study, we aim to provide a common basis for CPM climate simulations by giving a holistic review of the topic. The most important components in CPMs such as physical parameterizations and dynamical formulations are discussed critically. An overview of weaknesses and an outlook on required future developments is provided. Most importantly, this review presents the consolidated outcome of studies that addressed the added value of CPM climate simulations compared to LSMs. Improvements are evident mostly for climate statistics related to deep convection, mountainous regions, or extreme events. The climate change signals of CPM simulations suggest an increase in flash floods, changes in hail storm characteristics, and reductions in the snowpack over mountains. In conclusion, CPMs are a very promising tool for future climate research. However, coordinated modeling programs are crucially needed to advance parameterizations of unresolved physics and to assess the full potential of CPMs.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012ERL.....7b4017F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012ERL.....7b4017F"><span>Assessment of gridded observations used for climate model validation in the Mediterranean region: the HyMeX and MED-CORDEX framework</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Flaounas, Emmanouil; Drobinski, Philippe; Borga, Marco; Calvet, Jean-Christophe; Delrieu, Guy; Morin, Efrat; Tartari, Gianni; Toffolon, Roberta</p> <p>2012-06-01</p> <p>This letter assesses the quality of temperature and rainfall daily retrievals of the European Climate Assessment and Dataset (ECA&D) with respect to measurements collected locally in various parts of the Euro-Mediterranean region in the framework of the Hydrological Cycle in the Mediterranean Experiment (HyMeX), endorsed by the Global Energy and Water Cycle Experiment (GEWEX) of the World Climate Research Program (WCRP). The ECA&D, among other gridded datasets, is very often used as a reference for model calibration and evaluation. This is for instance the case in the context of the WCRP Coordinated Regional Downscaling Experiment (CORDEX) and its Mediterranean declination MED-CORDEX. This letter quantifies ECA&D dataset uncertainties associated with temperature and precipitation intra-seasonal variability, seasonal distribution and extremes. Our motivation is to help the interpretation of the results when validating or calibrating downscaling models by the ECA&D dataset in the context of regional climate research in the Euro-Mediterranean region.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/biblio/1091986-hurricanes-aquaplanet-world-implications-impacts-external-forcing-model-horizontal-resolution','SCIGOV-STC'); return false;" href="https://www.osti.gov/biblio/1091986-hurricanes-aquaplanet-world-implications-impacts-external-forcing-model-horizontal-resolution"><span></span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Li, Fuyu; Collins, William D.; Wehner, Michael F.</p> <p></p> <p>High-resolution climate models have been shown to improve the statistics of tropical storms and hurricanes compared to low-resolution models. The impact of increasing horizontal resolution in the tropical storm simulation is investigated exclusively using a series of Atmospheric Global Climate Model (AGCM) runs with idealized aquaplanet steady-state boundary conditions and a fixed operational storm-tracking algorithm. The results show that increasing horizontal resolution helps to detect more hurricanes, simulate stronger extreme rainfall, and emulate better storm structures in the models. However, increasing model resolution does not necessarily produce stronger hurricanes in terms of maximum wind speed, minimum sea level pressure, andmore » mean precipitation, as the increased number of storms simulated by high-resolution models is mainly associated with weaker storms. The spatial scale at which the analyses are conducted appears to have more important control on these meteorological statistics compared to horizontal resolution of the model grid. When the simulations are analyzed on common low-resolution grids, the statistics of the hurricanes, particularly the hurricane counts, show reduced sensitivity to the horizontal grid resolution and signs of scale invariant.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018GMD....11..453Q','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018GMD....11..453Q"><span>Online dynamical downscaling of temperature and precipitation within the iLOVECLIM model (version 1.1)</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Quiquet, Aurélien; Roche, Didier M.; Dumas, Christophe; Paillard, Didier</p> <p>2018-02-01</p> <p>This paper presents the inclusion of an online dynamical downscaling of temperature and precipitation within the model of intermediate complexity iLOVECLIM v1.1. We describe the following methodology to generate temperature and precipitation fields on a 40 km × 40 km Cartesian grid of the Northern Hemisphere from the T21 native atmospheric model grid. Our scheme is not grid specific and conserves energy and moisture in the same way as the original climate model. We show that we are able to generate a high-resolution field which presents a spatial variability in better agreement with the observations compared to the standard model. Although the large-scale model biases are not corrected, for selected model parameters, the downscaling can induce a better overall performance compared to the standard version on both the high-resolution grid and on the native grid. Foreseen applications of this new model feature include the improvement of ice sheet model coupling and high-resolution land surface models.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20000012298&hterms=imitation&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3Dimitation','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20000012298&hterms=imitation&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3Dimitation"><span>Regional Climate Simulation with a Variable Resolution Stretched Grid GCM: The Regional Down-Scaling Effects</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Fox-Rabinovitz, Michael S.; Takacs, Lawrence L.; Suarez, Max; Sawyer, William; Govindaraju, Ravi C.</p> <p>1999-01-01</p> <p>The results obtained with the variable resolution stretched grid (SG) GEOS GCM (Goddard Earth Observing System General Circulation Models) are discussed, with the emphasis on the regional down-scaling effects and their dependence on the stretched grid design and parameters. A variable resolution SG-GCM and SG-DAS using a global stretched grid with fine resolution over an area of interest, is a viable new approach to REGIONAL and subregional CLIMATE studies and applications. The stretched grid approach is an ideal tool for representing regional to global scale interactions. It is an alternative to the widely used nested grid approach introduced a decade ago as a pioneering step in regional climate modeling. The GEOS SG-GCM is used for simulations of the anomalous U.S. climate events of 1988 drought and 1993 flood, with enhanced regional resolution. The height low level jet, precipitation and other diagnostic patterns are successfully simulated and show the efficient down-scaling over the area of interest the U.S. An imitation of the nested grid approach is performed using the developed SG-DAS (Data Assimilation System) that incorporates the SG-GCM. The SG-DAS is run with withholding data over the area of interest. The design immitates the nested grid framework with boundary conditions provided from analyses. No boundary condition buffer is needed for the case due to the global domain of integration used for the SG-GCM and SG-DAS. The experiments based on the newly developed versions of the GEOS SG-GCM and SG-DAS, with finer 0.5 degree (and higher) regional resolution, are briefly discussed. The major aspects of parallelization of the SG-GCM code are outlined. The KEY OBJECTIVES of the study are: 1) obtaining an efficient DOWN-SCALING over the area of interest with fine and very fine resolution; 2) providing CONSISTENT interactions between regional and global scales including the consistent representation of regional ENERGY and WATER BALANCES; 3) providing a high computational efficiency for future SG-GCM and SG-DAS versions using PARALLEL codes.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFMGC33F..06V','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFMGC33F..06V"><span>Compounded effects of heat waves and droughts over the Western Electricity Grid: spatio-temporal scales of impacts and predictability toward mitigation and adaptation.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Voisin, N.; Kintner-Meyer, M.; Skaggs, R.; Xie, Y.; Wu, D.; Nguyen, T. B.; Fu, T.; Zhou, T.</p> <p>2016-12-01</p> <p>Heat waves and droughts are projected to be more frequent and intense. We have seen in the past the effects of each of those extreme climate events on electricity demand and constrained electricity generation, challenging power system operations. Our aim here is to understand the compounding effects under historical conditions. We present a benchmark of Western US grid performance under 55 years of historical climate, and including droughts, using 2010-level of water demand and water management infrastructure, and 2010-level of electricity grid infrastructure and operations. We leverage CMIP5 historical hydrology simulations and force a large scale river routing- reservoir model with 2010-level sectoral water demands. The regulated flow at each water-dependent generating plants is processed to adjust water-dependent electricity generation parameterization in a production cost model, that represents 2010-level power system operations with hourly energy demand of 2010. The resulting benchmark includes a risk distribution of several grid performance metrics (unserved energy, production cost, carbon emission) as a function of inter-annual variability in regional water availability and predictability using large scale climate oscillations. In the second part of the presentation, we describe an approach to map historical heat waves onto this benchmark grid performance using a building energy demand model. The impact of the heat waves, combined with the impact of droughts, is explored at multiple scales to understand the compounding effects. Vulnerabilities of the power generation and transmission systems are highlighted to guide future adaptation.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://cfpub.epa.gov/si/si_public_record_report.cfm?direntryid=336619&keyword=water&subject=water%20research&showcriteria=2&fed_org_id=111&datebeginpublishedpresented=03/04/2012&dateendpublishedpresented=03/04/2017&sortby=pubdateyear','PESTICIDES'); return false;" href="https://cfpub.epa.gov/si/si_public_record_report.cfm?direntryid=336619&keyword=water&subject=water%20research&showcriteria=2&fed_org_id=111&datebeginpublishedpresented=03/04/2012&dateendpublishedpresented=03/04/2017&sortby=pubdateyear"><span>A Generalized Simple Formulation of Convective Adjustment ...</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.epa.gov/pesticides/search.htm">EPA Pesticide Factsheets</a></p> <p></p> <p></p> <p>Convective adjustment timescale (τ) for cumulus clouds is one of the most influential parameters controlling parameterized convective precipitation in climate and weather simulation models at global and regional scales. Due to the complex nature of deep convection, a prescribed value or ad hoc representation of τ is used in most global and regional climate/weather models making it a tunable parameter and yet still resulting in uncertainties in convective precipitation simulations. In this work, a generalized simple formulation of τ for use in any convection parameterization for shallow and deep clouds is developed to reduce convective precipitation biases at different grid spacing. Unlike existing other methods, our new formulation can be used with field campaign measurements to estimate τ as demonstrated by using data from two different special field campaigns. Then, we implemented our formulation into a regional model (WRF) for testing and evaluation. Results indicate that our simple τ formulation can give realistic temporal and spatial variations of τ across continental U.S. as well as grid-scale and subgrid scale precipitation. We also found that as the grid spacing decreases (e.g., from 36 to 4-km grid spacing), grid-scale precipitation dominants over subgrid-scale precipitation. The generalized τ formulation works for various types of atmospheric conditions (e.g., continental clouds due to heating and large-scale forcing over la</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFM.H11F0918K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFM.H11F0918K"><span>An improved Multimodel Approach for Global Sea Surface Temperature Forecasts</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Khan, M. Z. K.; Mehrotra, R.; Sharma, A.</p> <p>2014-12-01</p> <p>The concept of ensemble combinations for formulating improved climate forecasts has gained popularity in recent years. However, many climate models share similar physics or modeling processes, which may lead to similar (or strongly correlated) forecasts. Recent approaches for combining forecasts that take into consideration differences in model accuracy over space and time have either ignored the similarity of forecast among the models or followed a pairwise dynamic combination approach. Here we present a basis for combining model predictions, illustrating the improvements that can be achieved if procedures for factoring in inter-model dependence are utilised. The utility of the approach is demonstrated by combining sea surface temperature (SST) forecasts from five climate models over a period of 1960-2005. The variable of interest, the monthly global sea surface temperature anomalies (SSTA) at a 50´50 latitude-longitude grid, is predicted three months in advance to demonstrate the utility of the proposed algorithm. Results indicate that the proposed approach offers consistent and significant improvements for majority of grid points compared to the case where the dependence among the models is ignored. Therefore, the proposed approach of combining multiple models by taking into account the existing interdependence, provides an attractive alternative to obtain improved climate forecast. In addition, an approach to combine seasonal forecasts from multiple climate models with varying periods of availability is also demonstrated.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2003HyPr...17.3703B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2003HyPr...17.3703B"><span>An assessment of global climate model-simulated climate for the western cordillera of Canada (1961-90)</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bonsal, Barrie R.; Prowse, Terry D.; Pietroniro, Alain</p> <p>2003-12-01</p> <p>Climate change is projected to significantly affect future hydrologic processes over many regions of the world. This is of particular importance for alpine systems that provide critical water supplies to lower-elevation regions. The western cordillera of Canada is a prime example where changes to temperature and precipitation could have profound hydro-climatic impacts not only for the cordillera itself, but also for downstream river systems and the drought-prone Canadian Prairies. At present, impact researchers primarily rely on global climate models (GCMs) for future climate projections. The main objective of this study is to assess several GCMs in their ability to simulate the magnitude and spatial variability of current (1961-90) temperature and precipitation over the western cordillera of Canada. In addition, several gridded data sets of observed climate for the study region are evaluated.Results reveal a close correspondence among the four gridded data sets of observed climate, particularly for temperature. There is, however, considerable variability regarding the various GCM simulations of this observed climate. The British, Canadian, German, Australian, and US GFDL models are superior at simulating the magnitude and spatial variability of mean temperature. The Japanese GCM is of intermediate ability, and the US NCAR model is least representative of temperature in this region. Nearly all the models substantially overestimate the magnitude of total precipitation, both annually and on a seasonal basis. An exception involves the British (Hadley) model, which best represents the observed magnitude and spatial variability of precipitation. This study improves our understanding regarding the accuracy of GCM climate simulations over the western cordillera of Canada. The findings may assist in producing more reliable future scenarios of hydro-climatic conditions over various regions of the country. Copyright</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2002AGUFM.A61C0088K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2002AGUFM.A61C0088K"><span>Development of a High-Resolution Climate Model for Future Climate Change Projection on the Earth Simulator</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kanzawa, H.; Emori, S.; Nishimura, T.; Suzuki, T.; Inoue, T.; Hasumi, H.; Saito, F.; Abe-Ouchi, A.; Kimoto, M.; Sumi, A.</p> <p>2002-12-01</p> <p>The fastest supercomputer of the world, the Earth Simulator (total peak performance 40TFLOPS) has recently been available for climate researches in Yokohama, Japan. We are planning to conduct a series of future climate change projection experiments on the Earth Simulator with a high-resolution coupled ocean-atmosphere climate model. The main scientific aims for the experiments are to investigate 1) the change in global ocean circulation with an eddy-permitting ocean model, 2) the regional details of the climate change including Asian monsoon rainfall pattern, tropical cyclones and so on, and 3) the change in natural climate variability with a high-resolution model of the coupled ocean-atmosphere system. To meet these aims, an atmospheric GCM, CCSR/NIES AGCM, with T106(~1.1o) horizontal resolution and 56 vertical layers is to be coupled with an oceanic GCM, COCO, with ~ 0.28ox 0.19o horizontal resolution and 48 vertical layers. This coupled ocean-atmosphere climate model, named MIROC, also includes a land-surface model, a dynamic-thermodynamic seaice model, and a river routing model. The poles of the oceanic model grid system are rotated from the geographic poles so that they are placed in Greenland and Antarctic land masses to avoild the singularity of the grid system. Each of the atmospheric and the oceanic parts of the model is parallelized with the Message Passing Interface (MPI) technique. The coupling of the two is to be done with a Multi Program Multi Data (MPMD) fashion. A 100-model-year integration will be possible in one actual month with 720 vector processors (which is only 14% of the full resources of the Earth Simulator).</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..17.4514M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..17.4514M"><span>Building a statistical emulator for prediction of crop yield response to climate change: a global gridded panel data set approach</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Mistry, Malcolm; De Cian, Enrica; Wing, Ian Sue</p> <p>2015-04-01</p> <p>There is widespread concern that trends and variability in weather induced by climate change will detrimentally affect global agricultural productivity and food supplies. Reliable quantification of the risks of negative impacts at regional and global scales is a critical research need, which has so far been met by forcing state-of-the-art global gridded crop models with outputs of global climate model (GCM) simulations in exercises such as the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP)-Fastrack. Notwithstanding such progress, it remains challenging to use these simulation-based projections to assess agricultural risk because their gridded fields of crop yields are fundamentally denominated as discrete combinations of warming scenarios, GCMs and crop models, and not as model-specific or model-averaged yield response functions of meteorological shifts, which may have their own independent probability of occurrence. By contrast, the empirical climate economics literature has adeptly represented agricultural responses to meteorological variables as reduced-form statistical response surfaces which identify the crop productivity impacts of additional exposure to different intervals of temperature and precipitation [cf Schlenker and Roberts, 2009]. This raises several important questions: (1) what do the equivalent reduced-form statistical response surfaces look like for crop model outputs, (2) do they exhibit systematic variation over space (e.g., crop suitability zones) or across crop models with different characteristics, (3) how do they compare to estimates based on historical observations, and (4) what are the implications for the characterization of climate risks? We address these questions by estimating statistical yield response functions for four major crops (maize, rice, wheat and soybeans) over the historical period (1971-2004) as well as future climate change scenarios (2005-2099) using ISIMIP-Fastrack data for five GCMs and seven crop models under rain-fed and irrigated management regimes. Our approach, which is patterned after Lobell and Burke [2010], is a novel application of cross-section/time-series statistical techniques from the climate economics literature to large, high-dimension, multi-model datasets, and holds considerable promise as a diagnostic methodology to elucidate uncertainties in the processes simulated by crop models, and to support the development of climate impact intercomparison exercises.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012EGUGA..1413758C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012EGUGA..1413758C"><span>Mesoscale Climate Evaluation Using Grid Computing</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Campos Velho, H. F.; Freitas, S. R.; Souto, R. P.; Charao, A. S.; Ferraz, S.; Roberti, D. R.; Streck, N.; Navaux, P. O.; Maillard, N.; Collischonn, W.; Diniz, G.; Radin, B.</p> <p>2012-04-01</p> <p>The CLIMARS project is focused to establish an operational environment for seasonal climate prediction for the Rio Grande do Sul state, Brazil. The dynamical downscaling will be performed with the use of several software platforms and hardware infrastructure to carry out the investigation on mesoscale of the global change impact. The grid computing takes advantage of geographically spread out computer systems, connected by the internet, for enhancing the power of computation. The ensemble climate prediction is an appropriated application for processing on grid computing, because the integration of each ensemble member does not have a dependency on information from another ensemble members. The grid processing is employed to compute the 20-year climatology and the long range simulations under ensemble methodology. BRAMS (Brazilian Regional Atmospheric Model) is a mesoscale model developed from a version of the RAMS (from the Colorado State University - CSU, USA). BRAMS model is the tool for carrying out the dynamical downscaling from the IPCC scenarios. Long range BRAMS simulations will provide data for some climate (data) analysis, and supply data for numerical integration of different models: (a) Regime of the extreme events for temperature and precipitation fields: statistical analysis will be applied on the BRAMS data, (b) CCATT-BRAMS (Coupled Chemistry Aerosol Tracer Transport - BRAMS) is an environmental prediction system that will be used to evaluate if the new standards of temperature, rain regime, and wind field have a significant impact on the pollutant dispersion in the analyzed regions, (c) MGB-IPH (Portuguese acronym for the Large Basin Model (MGB), developed by the Hydraulic Research Institute, (IPH) from the Federal University of Rio Grande do Sul (UFRGS), Brazil) will be employed to simulate the alteration of the river flux under new climate patterns. Important meteorological input variables for the MGB-IPH are the precipitation (most relevant), temperature, and wind field, all provided by BRAMS. The Uruguay river basin will be analyzed in the scope of this proposal, (d) INFOCROP: this crop model has been calibrated for Southern Brazil, three agriculture cropswill be analyzed: rice, soybean and corn.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018ClDy..tmp.2398L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018ClDy..tmp.2398L"><span>CWRF performance at downscaling China climate characteristics</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Liang, Xin-Zhong; Sun, Chao; Zheng, Xiaohui; Dai, Yongjiu; Xu, Min; Choi, Hyun I.; Ling, Tiejun; Qiao, Fengxue; Kong, Xianghui; Bi, Xunqiang; Song, Lianchun; Wang, Fang</p> <p>2018-05-01</p> <p>The performance of the regional Climate-Weather Research and Forecasting model (CWRF) for downscaling China climate characteristics is evaluated using a 1980-2015 simulation at 30 km grid spacing driven by the ECMWF Interim reanalysis (ERI). It is shown that CWRF outperforms the popular Regional Climate Modeling system (RegCM4.6) in key features including monsoon rain bands, diurnal temperature ranges, surface winds, interannual precipitation and temperature anomalies, humidity couplings, and 95th percentile daily precipitation. Even compared with ERI, which assimilates surface observations, CWRF better represents the geographic distributions of seasonal mean climate and extreme precipitation. These results indicate that CWRF may significantly enhance China climate modeling capabilities.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.H11C1194J','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.H11C1194J"><span>Development of a Distributed Parallel Computing Framework to Facilitate Regional/Global Gridded Crop Modeling with Various Scenarios</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Jang, W.; Engda, T. A.; Neff, J. C.; Herrick, J.</p> <p>2017-12-01</p> <p>Many crop models are increasingly used to evaluate crop yields at regional and global scales. However, implementation of these models across large areas using fine-scale grids is limited by computational time requirements. In order to facilitate global gridded crop modeling with various scenarios (i.e., different crop, management schedule, fertilizer, and irrigation) using the Environmental Policy Integrated Climate (EPIC) model, we developed a distributed parallel computing framework in Python. Our local desktop with 14 cores (28 threads) was used to test the distributed parallel computing framework in Iringa, Tanzania which has 406,839 grid cells. High-resolution soil data, SoilGrids (250 x 250 m), and climate data, AgMERRA (0.25 x 0.25 deg) were also used as input data for the gridded EPIC model. The framework includes a master file for parallel computing, input database, input data formatters, EPIC model execution, and output analyzers. Through the master file for parallel computing, the user-defined number of threads of CPU divides the EPIC simulation into jobs. Then, Using EPIC input data formatters, the raw database is formatted for EPIC input data and the formatted data moves into EPIC simulation jobs. Then, 28 EPIC jobs run simultaneously and only interesting results files are parsed and moved into output analyzers. We applied various scenarios with seven different slopes and twenty-four fertilizer ranges. Parallelized input generators create different scenarios as a list for distributed parallel computing. After all simulations are completed, parallelized output analyzers are used to analyze all outputs according to the different scenarios. This saves significant computing time and resources, making it possible to conduct gridded modeling at regional to global scales with high-resolution data. For example, serial processing for the Iringa test case would require 113 hours, while using the framework developed in this study requires only approximately 6 hours, a nearly 95% reduction in computing time.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3948307','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3948307"><span>First look at changes in flood hazard in the Inter-Sectoral Impact Model Intercomparison Project ensemble</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Dankers, Rutger; Arnell, Nigel W.; Clark, Douglas B.; Falloon, Pete D.; Fekete, Balázs M.; Gosling, Simon N.; Heinke, Jens; Kim, Hyungjun; Masaki, Yoshimitsu; Satoh, Yusuke; Stacke, Tobias; Wada, Yoshihide; Wisser, Dominik</p> <p>2014-01-01</p> <p>Climate change due to anthropogenic greenhouse gas emissions is expected to increase the frequency and intensity of precipitation events, which is likely to affect the probability of flooding into the future. In this paper we use river flow simulations from nine global hydrology and land surface models to explore uncertainties in the potential impacts of climate change on flood hazard at global scale. As an indicator of flood hazard we looked at changes in the 30-y return level of 5-d average peak flows under representative concentration pathway RCP8.5 at the end of this century. Not everywhere does climate change result in an increase in flood hazard: decreases in the magnitude and frequency of the 30-y return level of river flow occur at roughly one-third (20–45%) of the global land grid points, particularly in areas where the hydrograph is dominated by the snowmelt flood peak in spring. In most model experiments, however, an increase in flooding frequency was found in more than half of the grid points. The current 30-y flood peak is projected to occur in more than 1 in 5 y across 5–30% of land grid points. The large-scale patterns of change are remarkably consistent among impact models and even the driving climate models, but at local scale and in individual river basins there can be disagreement even on the sign of change, indicating large modeling uncertainty which needs to be taken into account in local adaptation studies. PMID:24344290</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/biblio/15010453-hydrologic-implications-dynamical-statistical-approaches-downscaling-climate-model-outputs','SCIGOV-STC'); return false;" href="https://www.osti.gov/biblio/15010453-hydrologic-implications-dynamical-statistical-approaches-downscaling-climate-model-outputs"><span>Hydrologic Implications of Dynamical and Statistical Approaches to Downscaling Climate Model Outputs</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Wood, Andrew W; Leung, Lai R; Sridhar, V</p> <p></p> <p>Six approaches for downscaling climate model outputs for use in hydrologic simulation were evaluated, with particular emphasis on each method's ability to produce precipitation and other variables used to drive a macroscale hydrology model applied at much higher spatial resolution than the climate model. Comparisons were made on the basis of a twenty-year retrospective (1975–1995) climate simulation produced by the NCAR-DOE Parallel Climate Model (PCM), and the implications of the comparison for a future (2040–2060) PCM climate scenario were also explored. The six approaches were made up of three relatively simple statistical downscaling methods – linear interpolation (LI), spatial disaggregationmore » (SD), and bias-correction and spatial disaggregation (BCSD) – each applied to both PCM output directly (at T42 spatial resolution), and after dynamical downscaling via a Regional Climate Model (RCM – at ½-degree spatial resolution), for downscaling the climate model outputs to the 1/8-degree spatial resolution of the hydrological model. For the retrospective climate simulation, results were compared to an observed gridded climatology of temperature and precipitation, and gridded hydrologic variables resulting from forcing the hydrologic model with observations. The most significant findings are that the BCSD method was successful in reproducing the main features of the observed hydrometeorology from the retrospective climate simulation, when applied to both PCM and RCM outputs. Linear interpolation produced better results using RCM output than PCM output, but both methods (PCM-LI and RCM-LI) lead to unacceptably biased hydrologic simulations. Spatial disaggregation of the PCM output produced results similar to those achieved with the RCM interpolated output; nonetheless, neither PCM nor RCM output was useful for hydrologic simulation purposes without a bias-correction step. For the future climate scenario, only the BCSD-method (using PCM or RCM) was able to produce hydrologically plausible results. With the BCSD method, the RCM-derived hydrology was more sensitive to climate change than the PCM-derived hydrology.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/28573477','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/28573477"><span>Vulnerability of Forests in India: A National Scale Assessment.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Sharma, Jagmohan; Upgupta, Sujata; Jayaraman, Mathangi; Chaturvedi, Rajiv Kumar; Bala, Govindswamy; Ravindranath, N H</p> <p>2017-09-01</p> <p>Forests are subjected to stress from climatic and non-climatic sources. In this study, we have reported the results of inherent, as well as climate change driven vulnerability assessments for Indian forests. To assess inherent vulnerability of forests under current climate, we have used four indicators, namely biological richness, disturbance index, canopy cover, and slope. The assessment is presented as spatial profile of inherent vulnerability in low, medium, high and very high vulnerability classes. Fourty percent forest grid points in India show high or very high inherent vulnerability. Plantation forests show higher inherent vulnerability than natural forests. We assess the climate change driven vulnerability by combining the results of inherent vulnerability assessment with the climate change impact projections simulated by the Integrated Biosphere Simulator dynamic global vegetation model. While 46% forest grid points show high, very high, or extremely high vulnerability under future climate in the short term (2030s) under both representative concentration pathways 4.5 and 8.5, such grid points are 49 and 54%, respectively, in the long term (2080s). Generally, forests in the higher rainfall zones show lower vulnerability as compared to drier forests under future climate. Minimizing anthropogenic disturbance and conserving biodiversity can potentially reduce forest vulnerability under climate change. For disturbed forests and plantations, adaptive management aimed at forest restoration is necessary to build long-term resilience.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_3");'>3</a></li> <li><a href="#" onclick='return showDiv("page_4");'>4</a></li> <li class="active"><span>5</span></li> <li><a href="#" onclick='return showDiv("page_6");'>6</a></li> <li><a href="#" onclick='return showDiv("page_7");'>7</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_5 --> <div id="page_6" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_4");'>4</a></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li class="active"><span>6</span></li> <li><a href="#" onclick='return showDiv("page_7");'>7</a></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="101"> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017EnMan..60..544S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017EnMan..60..544S"><span>Vulnerability of Forests in India: A National Scale Assessment</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Sharma, Jagmohan; Upgupta, Sujata; Jayaraman, Mathangi; Chaturvedi, Rajiv Kumar; Bala, Govindswamy; Ravindranath, N. H.</p> <p>2017-09-01</p> <p>Forests are subjected to stress from climatic and non-climatic sources. In this study, we have reported the results of inherent, as well as climate change driven vulnerability assessments for Indian forests. To assess inherent vulnerability of forests under current climate, we have used four indicators, namely biological richness, disturbance index, canopy cover, and slope. The assessment is presented as spatial profile of inherent vulnerability in low, medium, high and very high vulnerability classes. Fourty percent forest grid points in India show high or very high inherent vulnerability. Plantation forests show higher inherent vulnerability than natural forests. We assess the climate change driven vulnerability by combining the results of inherent vulnerability assessment with the climate change impact projections simulated by the Integrated Biosphere Simulator dynamic global vegetation model. While 46% forest grid points show high, very high, or extremely high vulnerability under future climate in the short term (2030s) under both representative concentration pathways 4.5 and 8.5, such grid points are 49 and 54%, respectively, in the long term (2080s). Generally, forests in the higher rainfall zones show lower vulnerability as compared to drier forests under future climate. Minimizing anthropogenic disturbance and conserving biodiversity can potentially reduce forest vulnerability under climate change. For disturbed forests and plantations, adaptive management aimed at forest restoration is necessary to build long-term resilience.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012AGUFM.A31L..02L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012AGUFM.A31L..02L"><span>Comparison of Two Grid Refinement Approaches for High Resolution Regional Climate Modeling: MPAS vs WRF</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Leung, L.; Hagos, S. M.; Rauscher, S.; Ringler, T.</p> <p>2012-12-01</p> <p>This study compares two grid refinement approaches using global variable resolution model and nesting for high-resolution regional climate modeling. The global variable resolution model, Model for Prediction Across Scales (MPAS), and the limited area model, Weather Research and Forecasting (WRF) model, are compared in an idealized aqua-planet context with a focus on the spatial and temporal characteristics of tropical precipitation simulated by the models using the same physics package from the Community Atmosphere Model (CAM4). For MPAS, simulations have been performed with a quasi-uniform resolution global domain at coarse (1 degree) and high (0.25 degree) resolution, and a variable resolution domain with a high-resolution region at 0.25 degree configured inside a coarse resolution global domain at 1 degree resolution. Similarly, WRF has been configured to run on a coarse (1 degree) and high (0.25 degree) resolution tropical channel domain as well as a nested domain with a high-resolution region at 0.25 degree nested two-way inside the coarse resolution (1 degree) tropical channel. The variable resolution or nested simulations are compared against the high-resolution simulations that serve as virtual reality. Both MPAS and WRF simulate 20-day Kelvin waves propagating through the high-resolution domains fairly unaffected by the change in resolution. In addition, both models respond to increased resolution with enhanced precipitation. Grid refinement induces zonal asymmetry in precipitation (heating), accompanied by zonal anomalous Walker like circulations and standing Rossby wave signals. However, there are important differences between the anomalous patterns in MPAS and WRF due to differences in the grid refinement approaches and sensitivity of model physics to grid resolution. This study highlights the need for "scale aware" parameterizations in variable resolution and nested regional models.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/pages/biblio/1262453-health-climate-benefits-offshore-wind-facilities-mid-atlantic-united-states','SCIGOV-DOEP'); return false;" href="https://www.osti.gov/pages/biblio/1262453-health-climate-benefits-offshore-wind-facilities-mid-atlantic-united-states"><span>Health and climate benefits of offshore wind facilities in the Mid-Atlantic United States</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/pages">DOE PAGES</a></p> <p>Buonocore, Jonathan J.; Luckow, Patrick; Fisher, Jeremy; ...</p> <p>2016-07-14</p> <p>Electricity from fossil fuels contributes substantially to both climate change and the health burden of air pollution. Renewable energy sources are capable of displacing electricity from fossil fuels, but the quantity of health and climate benefits depend on site-specific attributes that are not often included in quantitative models. Here, we link an electrical grid simulation model to an air pollution health impact assessment model and US regulatory estimates of the impacts of carbon to estimate the health and climate benefits of offshore wind facilities of different sizes in two different locations. We find that offshore wind in the Mid-Atlantic ismore » capable of producing health and climate benefits of between $54 and $120 per MWh of generation, with the largest simulated facility (3000 MW off the coast of New Jersey) producing approximately $690 million in benefits in 2017. The variability in benefits per unit generation is a function of differences in locations (Maryland versus New Jersey), simulated years (2012 versus 2017), and facility generation capacity, given complexities of the electrical grid and differences in which power plants are offset. In the end, this work demonstrates health and climate benefits of off shore wind, provides further evidence of the utility of geographically-refined modeling frameworks, and yields quantitative insights that would allow for inclusion of both climate and public health in benefits assessments of renewable energy.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/biblio/1262453-health-climate-benefits-offshore-wind-facilities-mid-atlantic-united-states','SCIGOV-STC'); return false;" href="https://www.osti.gov/biblio/1262453-health-climate-benefits-offshore-wind-facilities-mid-atlantic-united-states"><span>Health and climate benefits of offshore wind facilities in the Mid-Atlantic United States</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Buonocore, Jonathan J.; Luckow, Patrick; Fisher, Jeremy</p> <p></p> <p>Electricity from fossil fuels contributes substantially to both climate change and the health burden of air pollution. Renewable energy sources are capable of displacing electricity from fossil fuels, but the quantity of health and climate benefits depend on site-specific attributes that are not often included in quantitative models. Here, we link an electrical grid simulation model to an air pollution health impact assessment model and US regulatory estimates of the impacts of carbon to estimate the health and climate benefits of offshore wind facilities of different sizes in two different locations. We find that offshore wind in the Mid-Atlantic ismore » capable of producing health and climate benefits of between $54 and $120 per MWh of generation, with the largest simulated facility (3000 MW off the coast of New Jersey) producing approximately $690 million in benefits in 2017. The variability in benefits per unit generation is a function of differences in locations (Maryland versus New Jersey), simulated years (2012 versus 2017), and facility generation capacity, given complexities of the electrical grid and differences in which power plants are offset. In the end, this work demonstrates health and climate benefits of off shore wind, provides further evidence of the utility of geographically-refined modeling frameworks, and yields quantitative insights that would allow for inclusion of both climate and public health in benefits assessments of renewable energy.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016ERL....11g4019B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016ERL....11g4019B"><span>Health and climate benefits of offshore wind facilities in the Mid-Atlantic United States</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Buonocore, Jonathan J.; Luckow, Patrick; Fisher, Jeremy; Kempton, Willett; Levy, Jonathan I.</p> <p>2016-07-01</p> <p>Electricity from fossil fuels contributes substantially to both climate change and the health burden of air pollution. Renewable energy sources are capable of displacing electricity from fossil fuels, but the quantity of health and climate benefits depend on site-specific attributes that are not often included in quantitative models. Here, we link an electrical grid simulation model to an air pollution health impact assessment model and US regulatory estimates of the impacts of carbon to estimate the health and climate benefits of offshore wind facilities of different sizes in two different locations. We find that offshore wind in the Mid-Atlantic is capable of producing health and climate benefits of between 54 and 120 per MWh of generation, with the largest simulated facility (3000 MW off the coast of New Jersey) producing approximately 690 million in benefits in 2017. The variability in benefits per unit generation is a function of differences in locations (Maryland versus New Jersey), simulated years (2012 versus 2017), and facility generation capacity, given complexities of the electrical grid and differences in which power plants are offset. This work demonstrates health and climate benefits of offshore wind, provides further evidence of the utility of geographically-refined modeling frameworks, and yields quantitative insights that would allow for inclusion of both climate and public health in benefits assessments of renewable energy.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017ESSD....9..529M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017ESSD....9..529M"><span>A global data set of soil hydraulic properties and sub-grid variability of soil water retention and hydraulic conductivity curves</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Montzka, Carsten; Herbst, Michael; Weihermüller, Lutz; Verhoef, Anne; Vereecken, Harry</p> <p>2017-07-01</p> <p>Agroecosystem models, regional and global climate models, and numerical weather prediction models require adequate parameterization of soil hydraulic properties. These properties are fundamental for describing and predicting water and energy exchange processes at the transition zone between solid earth and atmosphere, and regulate evapotranspiration, infiltration and runoff generation. Hydraulic parameters describing the soil water retention (WRC) and hydraulic conductivity (HCC) curves are typically derived from soil texture via pedotransfer functions (PTFs). Resampling of those parameters for specific model grids is typically performed by different aggregation approaches such a spatial averaging and the use of dominant textural properties or soil classes. These aggregation approaches introduce uncertainty, bias and parameter inconsistencies throughout spatial scales due to nonlinear relationships between hydraulic parameters and soil texture. Therefore, we present a method to scale hydraulic parameters to individual model grids and provide a global data set that overcomes the mentioned problems. The approach is based on Miller-Miller scaling in the relaxed form by Warrick, that fits the parameters of the WRC through all sub-grid WRCs to provide an effective parameterization for the grid cell at model resolution; at the same time it preserves the information of sub-grid variability of the water retention curve by deriving local scaling parameters. Based on the Mualem-van Genuchten approach we also derive the unsaturated hydraulic conductivity from the water retention functions, thereby assuming that the local parameters are also valid for this function. In addition, via the Warrick scaling parameter λ, information on global sub-grid scaling variance is given that enables modellers to improve dynamical downscaling of (regional) climate models or to perturb hydraulic parameters for model ensemble output generation. The present analysis is based on the ROSETTA PTF of Schaap et al. (2001) applied to the SoilGrids1km data set of Hengl et al. (2014). The example data set is provided at a global resolution of 0.25° at <a href="https://doi.org/10.1594/PANGAEA.870605" target="_blank">https://doi.org/10.1594/PANGAEA.870605</a>.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/26438280','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/26438280"><span>Insights into low-latitude cloud feedbacks from high-resolution models.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Bretherton, Christopher S</p> <p>2015-11-13</p> <p>Cloud feedbacks are a leading source of uncertainty in the climate sensitivity simulated by global climate models (GCMs). Low-latitude boundary-layer and cumulus cloud regimes are particularly problematic, because they are sustained by tight interactions between clouds and unresolved turbulent circulations. Turbulence-resolving models better simulate such cloud regimes and support the GCM consensus that they contribute to positive global cloud feedbacks. Large-eddy simulations using sub-100 m grid spacings over small computational domains elucidate marine boundary-layer cloud response to greenhouse warming. Four observationally supported mechanisms contribute: 'thermodynamic' cloudiness reduction from warming of the atmosphere-ocean column, 'radiative' cloudiness reduction from CO2- and H2O-induced increase in atmospheric emissivity aloft, 'stability-induced' cloud increase from increased lower tropospheric stratification, and 'dynamical' cloudiness increase from reduced subsidence. The cloudiness reduction mechanisms typically dominate, giving positive shortwave cloud feedback. Cloud-resolving models with horizontal grid spacings of a few kilometres illuminate how cumulonimbus cloud systems affect climate feedbacks. Limited-area simulations and superparameterized GCMs show upward shift and slight reduction of cloud cover in a warmer climate, implying positive cloud feedbacks. A global cloud-resolving model suggests tropical cirrus increases in a warmer climate, producing positive longwave cloud feedback, but results are sensitive to subgrid turbulence and ice microphysics schemes. © 2015 The Author(s).</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017NatSR...746399S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017NatSR...746399S"><span>The relationship between climate change and the endangered rainforest shrub Triunia robusta (Proteaceae) endemic to southeast Queensland, Australia</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Shimizu-Kimura, Yoko; Accad, Arnon; Shapcott, Alison</p> <p>2017-04-01</p> <p>Threatened species in rainforests may be vulnerable to climate change, because of their potentially narrow thermal tolerances, small population sizes and restricted distributions. This study modelled climate induced changes on the habitat distribution of the endangered rainforest plant Triunia robusta, endemic to southeast Queensland, Australia. Species distribution models were developed for eastern Australia at 250 m grids and southeast Queensland at 25 m grids using ground-truthed presence records and environmental predictor data. The species’ habitat distribution under the current climate was modelled, and the future potential habitat distributions were projected for the epochs 2030, 2050 and 2070. The eastern Australia model identified several spatially disjunct, broad habitat areas of coastal eastern Australia consistent with the current distribution of rainforests, and projected a southward and upslope contraction driven mainly by average temperatures exceeding current range limits. The southeast Queensland models suggest a dramatic upslope contraction toward locations where the majority of known populations are found. Populations located in the Sunshine Coast hinterland, consistent with past rainforest refugia, are likely to persist long-term. Upgrading the level of protection for less formal nature reserves containing viable populations is a high priority to better protect refugial T. robusta populations with respect to climate change.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5395822','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5395822"><span>The relationship between climate change and the endangered rainforest shrub Triunia robusta (Proteaceae) endemic to southeast Queensland, Australia</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Shimizu-Kimura, Yoko; Accad, Arnon; Shapcott, Alison</p> <p>2017-01-01</p> <p>Threatened species in rainforests may be vulnerable to climate change, because of their potentially narrow thermal tolerances, small population sizes and restricted distributions. This study modelled climate induced changes on the habitat distribution of the endangered rainforest plant Triunia robusta, endemic to southeast Queensland, Australia. Species distribution models were developed for eastern Australia at 250 m grids and southeast Queensland at 25 m grids using ground-truthed presence records and environmental predictor data. The species’ habitat distribution under the current climate was modelled, and the future potential habitat distributions were projected for the epochs 2030, 2050 and 2070. The eastern Australia model identified several spatially disjunct, broad habitat areas of coastal eastern Australia consistent with the current distribution of rainforests, and projected a southward and upslope contraction driven mainly by average temperatures exceeding current range limits. The southeast Queensland models suggest a dramatic upslope contraction toward locations where the majority of known populations are found. Populations located in the Sunshine Coast hinterland, consistent with past rainforest refugia, are likely to persist long-term. Upgrading the level of protection for less formal nature reserves containing viable populations is a high priority to better protect refugial T. robusta populations with respect to climate change. PMID:28422136</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013EGUGA..15.7969E','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013EGUGA..15.7969E"><span>Potential of knowledge discovery using workflows implemented in the C3Grid</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Engel, Thomas; Fink, Andreas; Ulbrich, Uwe; Schartner, Thomas; Dobler, Andreas; Fritzsch, Bernadette; Hiller, Wolfgang; Bräuer, Benny</p> <p>2013-04-01</p> <p>With the increasing number of climate simulations, reanalyses and observations, new infrastructures to search and analyse distributed data are necessary. In recent years, the Grid architecture became an important technology to fulfill these demands. For the German project "Collaborative Climate Community Data and Processing Grid" (C3Grid) computer scientists and meteorologists developed a system that offers its users a webinterface to search and download climate data and use implemented analysis tools (called workflows) to further investigate them. In this contribution, two workflows that are implemented in the C3Grid architecture are presented: the Cyclone Tracking (CT) and Stormtrack workflow. They shall serve as an example on how to perform numerous investigations on midlatitude winterstorms on a large amount of analysis and climate model data without having an insight into the data source, program code and a low-to-moderate understanding of the theortical background. CT is based on the work of Murray and Simmonds (1991) to identify and track local minima in the mean sea level pressure (MSLP) field of the selected dataset. Adjustable thresholds for the curvature of the isobars as well as the minimum lifetime of a cyclone allow the distinction of weak subtropical heat low systems and stronger midlatitude cyclones e.g. in the Northern Atlantic. The user gets the resulting track data including statistics about the track density, average central pressure, average central curvature, cyclogenesis and cyclolysis as well as pre-built visualizations of these results. Stormtrack calculates the 2.5-6 day bandpassfiltered standard deviation of the geopotential height on a selected pressure level. Although this workflow needs much less computational effort compared to CT it shows structures that are in good agreement with the track density of the CT workflow. To what extent changes in the mid-level tropospheric storm track are reflected in trough density and intensity alteration of surface cyclones. A specific feature of C3Grid is the flexible Workflow Scheduling Service (WSS) which also allows for automated nightly analysis runs of CT, Stormtrack, etc. with different input parameter sets. The statistical results of these workflows can be accumulated afterwards by a scheduled final analysis step, thereby providing a tool for data intensive analytics for the massive amounts of climate model data accessible through C3Grid. First tests with these automated analysis workflows show promising results to speed up the investigation of high volume modeling data. This example is relevant to the thorough analysis of future changes in storminess in Europe and is just one example of the potential of knowledge discovery using automated workflows implemented in the C3Grid architecture.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFM.A41D0095R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFM.A41D0095R"><span>High Resolution Climate Modeling of the Water Cycle over the Contiguous United States Including Potential Climate Change Scenarios</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Rasmussen, R.; Ikeda, K.; Liu, C.; Gochis, D.; Chen, F.; Barlage, M. J.; Dai, A.; Dudhia, J.; Clark, M. P.; Gutmann, E. D.; Li, Y.</p> <p>2015-12-01</p> <p>The NCAR Water System program strives to improve the full representation of the water cycle in both regional and global models. Our previous high-resolution simulations using the WRF model over the Rocky Mountains revealed that proper spatial and temporal depiction of snowfall adequate for water resource and climate change purposes can be achieved with the appropriate choice of model grid spacing (< 6 km horizontal) and parameterizations. The climate sensitivity experiment consistent with expected climate change showed an altered hydrological cycle with increased fraction of rain versus snow, increased snowfall at high altitudes, earlier melting of snowpack, and decreased total runoff. In order to investigate regional differences between the Rockies and other major mountain barriers and to study climate change impacts over other regions of the contiguous U.S. (CONUS), we have expanded our prior CO Headwaters modeling study to encompass most of North America at a horizontal grid spacing of 4 km. A domain expansion provides the opportunity to assess changes in orographic precipitation across different mountain ranges in the western USA, as well as the very dominant role of convection in the eastern half of the USA. The high resolution WRF-downscaled climate change data will also become a valuable community resource for many university groups who are interested in studying regional climate changes and impacts but unable to perform such long-duration and high-resolution WRF-based downscaling simulations of their own. The scientific goals and details of the model dataset will be presented including some preliminary results.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017PhDT........17X','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017PhDT........17X"><span>Improving and Understanding Climate Models: Scale-Aware Parameterization of Cloud Water Inhomogeneity and Sensitivity of MJO Simulation to Physical Parameters in a Convection Scheme</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Xie, Xin</p> <p></p> <p>Microphysics and convection parameterizations are two key components in a climate model to simulate realistic climatology and variability of cloud distribution and the cycles of energy and water. When a model has varying grid size or simulations have to be run with different resolutions, scale-aware parameterization is desirable so that we do not have to tune model parameters tailored to a particular grid size. The subgrid variability of cloud hydrometers is known to impact microphysics processes in climate models and is found to highly depend on spatial scale. A scale- aware liquid cloud subgrid variability parameterization is derived and implemented in the Community Earth System Model (CESM) in this study using long-term radar-based ground measurements from the Atmospheric Radiation Measurement (ARM) program. When used in the default CESM1 with the finite-volume dynamic core where a constant liquid inhomogeneity parameter was assumed, the newly developed parameterization reduces the cloud inhomogeneity in high latitudes and increases it in low latitudes. This is due to both the smaller grid size in high latitudes, and larger grid size in low latitudes in the longitude-latitude grid setting of CESM as well as the variation of the stability of the atmosphere. The single column model and general circulation model (GCM) sensitivity experiments show that the new parameterization increases the cloud liquid water path in polar regions and decreases it in low latitudes. Current CESM1 simulation suffers from the bias of both the pacific double ITCZ precipitation and weak Madden-Julian oscillation (MJO). Previous studies show that convective parameterization with multiple plumes may have the capability to alleviate such biases in a more uniform and physical way. A multiple-plume mass flux convective parameterization is used in Community Atmospheric Model (CAM) to investigate the sensitivity of MJO simulations. We show that MJO simulation is sensitive to entrainment rate specification. We found that shallow plumes can generate and sustain the MJO propagation in the model.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018ThApC.131..899K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018ThApC.131..899K"><span>High-resolution grids of hourly meteorological variables for Germany</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Krähenmann, S.; Walter, A.; Brienen, S.; Imbery, F.; Matzarakis, A.</p> <p>2018-02-01</p> <p>We present a 1-km2 gridded German dataset of hourly surface climate variables covering the period 1995 to 2012. The dataset comprises 12 variables including temperature, dew point, cloud cover, wind speed and direction, global and direct shortwave radiation, down- and up-welling longwave radiation, sea level pressure, relative humidity and vapour pressure. This dataset was constructed statistically from station data, satellite observations and model data. It is outstanding in terms of spatial and temporal resolution and in the number of climate variables. For each variable, we employed the most suitable gridding method and combined the best of several information sources, including station records, satellite-derived data and data from a regional climate model. A module to estimate urban heat island intensity was integrated for air and dew point temperature. Owing to the low density of available synop stations, the gridded dataset does not capture all variations that may occur at a resolution of 1 km2. This applies to areas of complex terrain (all the variables), and in particular to wind speed and the radiation parameters. To achieve maximum precision, we used all observational information when it was available. This, however, leads to inhomogeneities in station network density and affects the long-term consistency of the dataset. A first climate analysis for Germany was conducted. The Rhine River Valley, for example, exhibited more than 100 summer days in 2003, whereas in 1996, the number was low everywhere in Germany. The dataset is useful for applications in various climate-related studies, hazard management and for solar or wind energy applications and it is available via doi: 10.5676/DWD_CDC/TRY_Basis_v001.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.fs.usda.gov/treesearch/pubs/39063','TREESEARCH'); return false;" href="https://www.fs.usda.gov/treesearch/pubs/39063"><span>Seasonal predictions for wildland fire severity</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.fs.usda.gov/treesearch/">Treesearch</a></p> <p>Shyh-Chin Chen; Haiganoush Preisler; Francis Fujioka; John W. Benoit; John O. Roads</p> <p>2009-01-01</p> <p>The National Fire Danger Rating System (NFDRS) indices deduced from the monthly to seasonal predictions of a meteorological climate model at 50-km grid space from January 1998 through December 2003 were used in conjunction with a probability model to predict the expected number of fire occurrences and large fires over the U.S. West. The short-term climate forecasts are...</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFM.A53G..01O','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFM.A53G..01O"><span>Challenges of Representing Sub-Grid Physics in an Adaptive Mesh Refinement Atmospheric Model</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>O'Brien, T. A.; Johansen, H.; Johnson, J. N.; Rosa, D.; Benedict, J. J.; Keen, N. D.; Collins, W.; Goodfriend, E.</p> <p>2015-12-01</p> <p>Some of the greatest potential impacts from future climate change are tied to extreme atmospheric phenomena that are inherently multiscale, including tropical cyclones and atmospheric rivers. Extremes are challenging to simulate in conventional climate models due to existing models' coarse resolutions relative to the native length-scales of these phenomena. Studying the weather systems of interest requires an atmospheric model with sufficient local resolution, and sufficient performance for long-duration climate-change simulations. To this end, we have developed a new global climate code with adaptive spatial and temporal resolution. The dynamics are formulated using a block-structured conservative finite volume approach suitable for moist non-hydrostatic atmospheric dynamics. By using both space- and time-adaptive mesh refinement, the solver focuses computational resources only where greater accuracy is needed to resolve critical phenomena. We explore different methods for parameterizing sub-grid physics, such as microphysics, macrophysics, turbulence, and radiative transfer. In particular, we contrast the simplified physics representation of Reed and Jablonowski (2012) with the more complex physics representation used in the System for Atmospheric Modeling of Khairoutdinov and Randall (2003). We also explore the use of a novel macrophysics parameterization that is designed to be explicitly scale-aware.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFMPP43B1344H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFMPP43B1344H"><span>Paleobathymetric grids of the Cenozoic Southern Ocean - Opening the door towards improved reconstructions of the Southern Ocean's past</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Hochmuth, K.; Gohl, K.; Leitchenkov, G. L.; Sauermilch, I.; Whittaker, J. M.; De Santis, L.; Olivo, E.; Uenzelmann-Neben, G.; Davy, B. W.</p> <p>2017-12-01</p> <p>Although the Southern Ocean plays a fundamental role in the global climate and ocean current system, paleo-ocean circulation models of the Southern Ocean suffer from missing boundary conditions. A more accurate representation of the geometry of the seafloor and their dynamics over long time-scales are key for enabling more precise reconstructions of the development of the paleo-currents, the paleo-environment and the Antarctic ice sheets. The accurate parameterisation of these models controls the meaning and implications of regional and global paleo-climate models. The dynamics of ocean currents in proximity of the continental margins is also controlled by the development of the regional seafloor morphology of the conjugate continental shelves, slopes and rises. The reassessment of all available reflection seismic and borehole data from Antarctica as well as its conjugate margins of Australia, New Zealand, South Africa and South America, allows us to create paleobathymetric grids for various time slices during the Cenozoic. Those grids inform us about sediment distribution and volume as well a local sedimentation rates. The earliest targeted time slice of the Eocene/Oligocene Boundary marks a significant turning point towards an icehouse climate. From latest Eocene to earliest Oligocene the Southern Ocean changes fundamentally from a post greenhouse to an icehouse environment with the establishment of a vast continental ice sheet on the Antarctic continent. With the calculated sediment distribution maps, we can evaluate the dynamics of the sedimentary cover as well as the development of structural obstacles such as oceanic plateaus and ridges. The ultimate aim of this project is - as a community based effort - to create paleobathymetric grids at various time slices such as the Mid-Miocene Climatic Optimum and the Pliocene/Pleistocene, and eventually mimic the time steps used within the modelling community. The observation of sediment distribution and local sediment volumes open the door towards more sophisticated paleo-topograpy studies of the Antarctic continent and more detailed studies of the paleo-circulation. Local paleo - water depths at the oceanic gateways or the position of paleo-shelf edges highly influence the regional circulation patterns supporting more elaborated climate models.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012HESS...16.1481T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012HESS...16.1481T"><span>On the uncertainties associated with using gridded rainfall data as a proxy for observed</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Tozer, C. R.; Kiem, A. S.; Verdon-Kidd, D. C.</p> <p>2012-05-01</p> <p>Gridded rainfall datasets are used in many hydrological and climatological studies, in Australia and elsewhere, including for hydroclimatic forecasting, climate attribution studies and climate model performance assessments. The attraction of the spatial coverage provided by gridded data is clear, particularly in Australia where the spatial and temporal resolution of the rainfall gauge network is sparse. However, the question that must be asked is whether it is suitable to use gridded data as a proxy for observed point data, given that gridded data is inherently "smoothed" and may not necessarily capture the temporal and spatial variability of Australian rainfall which leads to hydroclimatic extremes (i.e. droughts, floods). This study investigates this question through a statistical analysis of three monthly gridded Australian rainfall datasets - the Bureau of Meteorology (BOM) dataset, the Australian Water Availability Project (AWAP) and the SILO dataset. The results of the monthly, seasonal and annual comparisons show that not only are the three gridded datasets different relative to each other, there are also marked differences between the gridded rainfall data and the rainfall observed at gauges within the corresponding grids - particularly for extremely wet or extremely dry conditions. Also important is that the differences observed appear to be non-systematic. To demonstrate the hydrological implications of using gridded data as a proxy for gauged data, a rainfall-runoff model is applied to one catchment in South Australia initially using gauged data as the source of rainfall input and then gridded rainfall data. The results indicate a markedly different runoff response associated with each of the different sources of rainfall data. It should be noted that this study does not seek to identify which gridded dataset is the "best" for Australia, as each gridded data source has its pros and cons, as does gauged data. Rather, the intention is to quantify differences between various gridded data sources and how they compare with gauged data so that these differences can be considered and accounted for in studies that utilise these gridded datasets. Ultimately, if key decisions are going to be based on the outputs of models that use gridded data, an estimate (or at least an understanding) of the uncertainties relating to the assumptions made in the development of gridded data and how that gridded data compares with reality should be made.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012AGUFM.A53T..05P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012AGUFM.A53T..05P"><span>The evaluation and development of the Met Office Unified Model using surface and space borne radar.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Petch, J.</p> <p>2012-12-01</p> <p>The Met Office Unified Model is used for the prediction of weather and climate on time scales of hours through to centuries. Therefore, the parametrizations in that model need to work on weather and climate timescale, and with grid-lengths from hundres of meters through to several hundred kilometres. Focusing on the development of the cloud and radiation schemes I will discuss how we are using ground-based remote-sensing observations from Chilbolton (England) and a combination of Cloudsat and Calipso data to evaluate and improve the performance of the model. I will show how the prediction of the clouds has improved since the AR5 version of the model and how we have developed an improved cloud generator to rebresent the sub-grid variability of clouds for radiative transfer.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010AGUFMGC23C0921I','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010AGUFMGC23C0921I"><span>Simulating large-scale crop yield by using perturbed-parameter ensemble method</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Iizumi, T.; Yokozawa, M.; Sakurai, G.; Nishimori, M.</p> <p>2010-12-01</p> <p>Toshichika Iizumi, Masayuki Yokozawa, Gen Sakurai, Motoki Nishimori Agro-Meteorology Division, National Institute for Agro-Environmental Sciences, Japan Abstract One of concerning issues of food security under changing climate is to predict the inter-annual variation of crop production induced by climate extremes and modulated climate. To secure food supply for growing world population, methodology that can accurately predict crop yield on a large scale is needed. However, for developing a process-based large-scale crop model with a scale of general circulation models (GCMs), 100 km in latitude and longitude, researchers encounter the difficulties in spatial heterogeneity of available information on crop production such as cultivated cultivars and management. This study proposed an ensemble-based simulation method that uses a process-based crop model and systematic parameter perturbation procedure, taking maize in U.S., China, and Brazil as examples. The crop model was developed modifying the fundamental structure of the Soil and Water Assessment Tool (SWAT) to incorporate the effect of heat stress on yield. We called the new model PRYSBI: the Process-based Regional-scale Yield Simulator with Bayesian Inference. The posterior probability density function (PDF) of 17 parameters, which represents the crop- and grid-specific features of the crop and its uncertainty under given data, was estimated by the Bayesian inversion analysis. We then take 1500 ensemble members of simulated yield values based on the parameter sets sampled from the posterior PDF to describe yearly changes of the yield, i.e. perturbed-parameter ensemble method. The ensemble median for 27 years (1980-2006) was compared with the data aggregated from the county yield. On a country scale, the ensemble median of the simulated yield showed a good correspondence with the reported yield: the Pearson’s correlation coefficient is over 0.6 for all countries. In contrast, on a grid scale, the correspondence is still high in most grids regardless of the countries. However, the model showed comparatively low reproducibility in the slope areas, such as around the Rocky Mountains in South Dakota, around the Great Xing'anling Mountains in Heilongjiang, and around the Brazilian Plateau. As there is a wide-ranging local climate conditions in the complex terrain, such as the slope of mountain, the GCM grid-scale weather inputs is likely one of major sources of error. The results of this study highlight the benefits of the perturbed-parameter ensemble method in simulating crop yield on a GCM grid scale: (1) the posterior PDF of parameter could quantify the uncertainty of parameter value of the crop model associated with the local crop production aspects; (2) the method can explicitly account for the uncertainty of parameter value in the crop model simulations; (3) the method achieve a Monte Carlo approximation of probability of sub-grid scale yield, accounting for the nonlinear response of crop yield to weather and management; (4) the method is therefore appropriate to aggregate the simulated sub-grid scale yields to a grid-scale yield and it may be a reason for high performance of the model in capturing inter-annual variation of yield.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2007AGUFM.A12D..01K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2007AGUFM.A12D..01K"><span>High Resolution Modeling of Hurricanes in a Climate Context</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Knutson, T. R.</p> <p>2007-12-01</p> <p>Modeling of tropical cyclone activity in a climate context initially focused on simulation of relatively weak tropical storm-like disturbances as resolved by coarse grid (200 km) global models. As computing power has increased, multi-year simulations with global models of grid spacing 20-30 km have become feasible. Increased resolution also allowed for simulation storms of increasing intensity, and some global models generate storms of hurricane strength, depending on their resolution and other factors, although detailed hurricane structure is not simulated realistically. Results from some recent high resolution global model studies are reviewed. An alternative for hurricane simulation is regional downscaling. An early approach was to embed an operational (GFDL) hurricane prediction model within a global model solution, either for 5-day case studies of particular model storm cases, or for "idealized experiments" where an initial vortex is inserted into an idealized environments derived from global model statistics. Using this approach, hurricanes up to category five intensity can be simulated, owing to the model's relatively high resolution (9 km grid) and refined physics. Variants on this approach have been used to provide modeling support for theoretical predictions that greenhouse warming will increase the maximum intensities of hurricanes. These modeling studies also simulate increased hurricane rainfall rates in a warmer climate. The studies do not address hurricane frequency issues, and vertical shear is neglected in the idealized studies. A recent development is the use of regional model dynamical downscaling for extended (e.g., season-length) integrations of hurricane activity. In a study for the Atlantic basin, a non-hydrostatic model with grid spacing of 18km is run without convective parameterization, but with internal spectral nudging toward observed large-scale (basin wavenumbers 0-2) atmospheric conditions from reanalyses. Using this approach, our model reproduces the observed increase in Atlantic hurricane activity (numbers, Accumulated Cyclone Energy (ACE), Power Dissipation Index (PDI), etc.) over the period 1980-2006 fairly realistically, and also simulates ENSO-related interannual variations in hurricane counts. Annual simulated hurricane counts from a two-member ensemble correlate with observed counts at r=0.86. However, the model does not simulate hurricanes as intense as those observed, with minimum central pressures of 937 hPa (category 4) and maximum surface winds of 47 m/s (category 2) being the most intense simulated so far in these experiments. To explore possible impacts of future climate warming on Atlantic hurricane activity, we are re-running the 1980- 2006 seasons, keeping the interannual to multidecadal variations unchanged, but altering the August-October mean climate according to changes simulated by an 18-member ensemble of AR4 climate models (years 2080- 2099, A1B emission scenario). The warmer climate state features higher Atlantic SSTs, and also increased vertical wind shear across the Caribbean (Vecchi and Soden, GRL 2007). A key assumption of this approach is that the 18-model ensemble-mean climate change is the best available projection of future climate change in the Atlantic. Some of the 18 global models show little increase in wind shear, or even a decrease, and thus there will be considerable uncertainty associated with the hurricane frequency results, which will require further exploration. Results from our simulations will be presented at the meeting.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_4");'>4</a></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li class="active"><span>6</span></li> <li><a href="#" onclick='return showDiv("page_7");'>7</a></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_6 --> <div id="page_7" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li><a href="#" onclick='return showDiv("page_6");'>6</a></li> <li class="active"><span>7</span></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="121"> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/biblio/1439707-agmip-framework-improved-agricultural-representation-integrated-assessment-models','SCIGOV-STC'); return false;" href="https://www.osti.gov/biblio/1439707-agmip-framework-improved-agricultural-representation-integrated-assessment-models"><span>An AgMIP framework for improved agricultural representation in integrated assessment models</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Ruane, Alex C.; Rosenzweig, Cynthia; Asseng, Senthold</p> <p></p> <p>Integrated assessment models (IAMs) hold great potential to assess how future agricultural systems will be shaped by socioeconomic development, technological innovation, and changing climate conditions. By coupling with climate and crop model emulators, IAMs have the potential to resolve important agricultural feedback loops and identify unintended consequences of socioeconomic development for agricultural systems. Here we propose a framework to develop robust representation of agricultural system responses within IAMs, linking downstream applications with model development and the coordinated evaluation of key climate responses from local to global scales. We survey the strengths and weaknesses of protocol-based assessments linked to the Agriculturalmore » Model Intercomparison and Improvement Project (AgMIP), each utilizing multiple sites and models to evaluate crop response to core climate changes including shifts in carbon dioxide concentration, temperature, and water availability, with some studies further exploring how climate responses are affected by nitrogen levels and adaptation in farm systems. Site-based studies with carefully calibrated models encompass the largest number of activities; however they are limited in their ability to capture the full range of global agricultural system diversity. Representative site networks provide more targeted response information than broadly-sampled networks, with limitations stemming from difficulties in covering the diversity of farming systems. Global gridded crop models provide comprehensive coverage, although with large challenges for calibration and quality control of inputs. Diversity in climate responses underscores that crop model emulators must distinguish between regions and farming system while recognizing model uncertainty. Finally, to bridge the gap between bottom-up and top-down approaches we recommend the deployment of a hybrid climate response system employing a representative network of sites to bias-correct comprehensive gridded simulations, opening the door to accelerated development and a broad range of applications.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017ERL....12l5003R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017ERL....12l5003R"><span>An AgMIP framework for improved agricultural representation in integrated assessment models</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ruane, Alex C.; Rosenzweig, Cynthia; Asseng, Senthold; Boote, Kenneth J.; Elliott, Joshua; Ewert, Frank; Jones, James W.; Martre, Pierre; McDermid, Sonali P.; Müller, Christoph; Snyder, Abigail; Thorburn, Peter J.</p> <p>2017-12-01</p> <p>Integrated assessment models (IAMs) hold great potential to assess how future agricultural systems will be shaped by socioeconomic development, technological innovation, and changing climate conditions. By coupling with climate and crop model emulators, IAMs have the potential to resolve important agricultural feedback loops and identify unintended consequences of socioeconomic development for agricultural systems. Here we propose a framework to develop robust representation of agricultural system responses within IAMs, linking downstream applications with model development and the coordinated evaluation of key climate responses from local to global scales. We survey the strengths and weaknesses of protocol-based assessments linked to the Agricultural Model Intercomparison and Improvement Project (AgMIP), each utilizing multiple sites and models to evaluate crop response to core climate changes including shifts in carbon dioxide concentration, temperature, and water availability, with some studies further exploring how climate responses are affected by nitrogen levels and adaptation in farm systems. Site-based studies with carefully calibrated models encompass the largest number of activities; however they are limited in their ability to capture the full range of global agricultural system diversity. Representative site networks provide more targeted response information than broadly-sampled networks, with limitations stemming from difficulties in covering the diversity of farming systems. Global gridded crop models provide comprehensive coverage, although with large challenges for calibration and quality control of inputs. Diversity in climate responses underscores that crop model emulators must distinguish between regions and farming system while recognizing model uncertainty. Finally, to bridge the gap between bottom-up and top-down approaches we recommend the deployment of a hybrid climate response system employing a representative network of sites to bias-correct comprehensive gridded simulations, opening the door to accelerated development and a broad range of applications.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015ESD.....6..311F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015ESD.....6..311F"><span>Exploring objective climate classification for the Himalayan arc and adjacent regions using gridded data sources</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Forsythe, N.; Blenkinsop, S.; Fowler, H. J.</p> <p>2015-05-01</p> <p>A three-step climate classification was applied to a spatial domain covering the Himalayan arc and adjacent plains regions using input data from four global meteorological reanalyses. Input variables were selected based on an understanding of the climatic drivers of regional water resource variability and crop yields. Principal component analysis (PCA) of those variables and k-means clustering on the PCA outputs revealed a reanalysis ensemble consensus for eight macro-climate zones. Spatial statistics of input variables for each zone revealed consistent, distinct climatologies. This climate classification approach has potential for enhancing assessment of climatic influences on water resources and food security as well as for characterising the skill and bias of gridded data sets, both meteorological reanalyses and climate models, for reproducing subregional climatologies. Through their spatial descriptors (area, geographic centroid, elevation mean range), climate classifications also provide metrics, beyond simple changes in individual variables, with which to assess the magnitude of projected climate change. Such sophisticated metrics are of particular interest for regions, including mountainous areas, where natural and anthropogenic systems are expected to be sensitive to incremental climate shifts.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..1713381S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..1713381S"><span>PDF added value of a high resolution climate simulation for precipitation</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Soares, Pedro M. M.; Cardoso, Rita M.</p> <p>2015-04-01</p> <p>General Circulation Models (GCMs) are models suitable to study the global atmospheric system, its evolution and response to changes in external forcing, namely to increasing emissions of CO2. However, the resolution of GCMs, of the order of 1o, is not sufficient to reproduce finer scale features of the atmospheric flow related to complex topography, coastal processes and boundary layer processes, and higher resolution models are needed to describe observed weather and climate. The latter are known as Regional Climate Models (RCMs) and are widely used to downscale GCMs results for many regions of the globe and are able to capture physically consistent regional and local circulations. Most of the RCMs evaluations rely on the comparison of its results with observations, either from weather stations networks or regular gridded datasets, revealing the ability of RCMs to describe local climatic properties, and assuming most of the times its higher performance in comparison with the forcing GCMs. The additional climatic details given by RCMs when compared with the results of the driving models is usually named as added value, and it's evaluation is still scarce and controversial in the literuature. Recently, some studies have proposed different methodologies to different applications and processes to characterize the added value of specific RCMs. A number of examples reveal that some RCMs do add value to GCMs in some properties or regions, and also the opposite, elighnening that RCMs may add value to GCM resuls, but improvements depend basically on the type of application, model setup, atmospheric property and location. The precipitation can be characterized by histograms of daily precipitation, or also known as probability density functions (PDFs). There are different strategies to evaluate the quality of both GCMs and RCMs in describing the precipitation PDFs when compared to observations. Here, we present a new method to measure the PDF added value obtained from dynamical downscaling, based on simple PDF skill scores. The measure can assess the full quality of the PDFs and at the same time integrates a flexible manner to weight differently the PDF tails. In this study we apply the referred method to characaterize the PDF added value of a high resolution simulation with the WRF model. Results from a WRF climate simulation centred at the Iberian Penisnula with two nested grids, a larger one at 27km and a smaller one at 9km. This simulation is forced by ERA-Interim. The observational data used covers from rain gauges precipitation records to observational regular grids of daily precipitation. Two regular gridded precipitation datasets are used. A Portuguese grid precipitation dataset developed at 0.2°× 0.2°, from observed rain gauges daily precipitation. A second one corresponding to the ENSEMBLES observational gridded dataset for Europe, which includes daily precipitation values at 0.25°. The analisys shows an important PDF added value from the higher resolution simulation, regarding the full PDF and the extremes. This method shows higher potential to be applied to other simulation exercises and to evaluate other variables.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFM.A13A0207K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFM.A13A0207K"><span>A New High Resolution Climate Dataset for Climate Change Impacts Assessments in New England</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Komurcu, M.; Huber, M.</p> <p>2016-12-01</p> <p>Assessing regional impacts of climate change (such as changes in extreme events, land surface hydrology, water resources, energy, ecosystems and economy) requires much higher resolution climate variables than those available from global model projections. While it is possible to run global models in higher resolution, the high computational cost associated with these simulations prevent their use in such manner. To alleviate this problem, dynamical downscaling offers a method to deliver higher resolution climate variables. As part of an NSF EPSCoR funded interdisciplinary effort to assess climate change impacts on New Hampshire ecosystems, hydrology and economy (the New Hampshire Ecosystems and Society project), we create a unique high-resolution climate dataset for New England. We dynamically downscale global model projections under a high impact emissions scenario using the Weather Research and Forecasting model (WRF) with three nested grids of 27, 9 and 3 km horizontal resolution with the highest resolution innermost grid focusing over New England. We prefer dynamical downscaling over other methods such as statistical downscaling because it employs physical equations to progressively simulate climate variables as atmospheric processes interact with surface processes, emissions, radiation, clouds, precipitation and other model components, hence eliminates fix relationships between variables. In addition to simulating mean changes in regional climate, dynamical downscaling also allows for the simulation of climate extremes that significantly alter climate change impacts. We simulate three time slices: 2006-2015, 2040-2060 and 2080-2100. This new high-resolution climate dataset (with more than 200 variables saved in hourly (six hourly) intervals for the highest resolution domain (outer two domains)) along with model input and restart files used in our WRF simulations will be publicly available for use to the broader scientific community to support in-depth climate change impacts assessments for New England. We present results focusing on future changes in New England extreme events.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015QSRv..112...78L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015QSRv..112...78L"><span>The impact of using different modern climate data sets in pollen-based paleoclimate reconstructions of North America</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ladd, M.; Way, R. G.; Viau, A. E.</p> <p>2015-03-01</p> <p>The use of different modern climate data sets is shown to impact a continental-scale pollen-based reconstruction of mean July temperature (TJUL) over the last 2000 years for North America. Data from climate stations, physically modeled from climate stations and reanalysis products are used to calibrate the reconstructions. Results show that the use of reanalysis products produces warmer and/or smoother reconstructions as compared to the use of station based data sets. The reconstructions during the period of 1050-1550 CE are shown to be more variable because of a high latitude cold-bias in the modern TJUL data. The ultra-high resolution WorldClim gridded data may only useful if the modern pollen sites have at least the same spatial precision as the gridded dataset. Hence we justify the use of the lapse-rate corrected University of East Anglia Climate Research Unit (CRU) based Whitmore modern climate data set for North American pollen-based climate reconstructions.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://pubs.er.usgs.gov/publication/70176785','USGSPUBS'); return false;" href="https://pubs.er.usgs.gov/publication/70176785"><span>Climatic water deficit, tree species ranges, and climate change in Yosemite National Park</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Lutz, James A.; Van Wagtendonk, Jan W.; Franklin, Jerry F.</p> <p>2010-01-01</p> <p>Aim (1) To calculate annual potential evapotranspiration (PET), actual evapotranspiration (AET) and climatic water deficit (Deficit) with high spatial resolution; (2) to describe distributions for 17 tree species over a 2300-m elevation gradient in a 3000-km2 landscape relative to AET and Deficit; (3) to examine changes in AET and Deficit between past (c. 1700), present (1971–2000) and future (2020–49) climatological means derived from proxies, observations and projections; and (4) to infer how the magnitude of changing Deficit may contribute to changes in forest structure and composition.Location Yosemite National Park, California, USA.Methods We calculated the water balance within Yosemite National Park using a modified Thornthwaite-type method and correlated AET and Deficit with tree species distribution. We used input data sets with different spatial resolutions parameterized for variation in latitude, precipitation, temperature, soil water-holding capacity, slope and aspect. We used climate proxies and climate projections to model AET and Deficit for past and future climate. We compared the modelled future water balance in Yosemite with current species water-balance ranges in North America.Results We calculated species climatic envelopes over broad ranges of environmental gradients – a range of 310 mm for soil water-holding capacity, 48.3°C for mean monthly temperature (January minima to July maxima), and 918 mm yr−1 for annual precipitation. Tree species means were differentiated by AET and Deficit, and at higher levels of Deficit, species means were increasingly differentiated. Modelled Deficit for all species increased by a mean of 5% between past (c. 1700) and present (1971–2000). Projected increases in Deficit between present and future (2020–49) were 23% across all plots.Main conclusions Modelled changes in Deficit between past, present and future climate scenarios suggest that recent past changes in forest structure and composition may accelerate in the future, with species responding individualistically to further declines in water availability. Declining water availability may disproportionately affect Pinus monticola and Tsuga mertensiana. Fine-scale heterogeneity in soil water-holding capacity, aspect and slope implies that plant water balance may vary considerably within the grid cells of kilometre-scale climate models. Sub-grid-cell soil and topographical data can partially compensate for the lack of spatial heterogeneity in gridded climate data, potentially improving vegetation-change projections in mountainous landscapes with heterogeneous topography.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/1124881','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/1124881"><span>3rd Annual Earth System Grid Federation and 3rd Annual Earth System Grid Federation and Ultrascale Visualization Climate Data Analysis Tools Face-to-Face Meeting Report December 2013</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Williams, Dean N.</p> <p></p> <p>The climate and weather data science community gathered December 3–5, 2013, at Lawrence Livermore National Laboratory, in Livermore, California, for the third annual Earth System Grid Federation (ESGF) and Ultra-scale Visualization Climate Data Analysis Tools (UV-CDAT) Face-to-Face (F2F) Meeting, which was hosted by the Department of Energy, National Aeronautics and Space Administration, National Oceanic and Atmospheric Administration, the European Infrastructure for the European Network of Earth System Modelling, and the Australian Department of Education. Both ESGF and UV-CDAT are global collaborations designed to develop a new generation of open-source software infrastructure that provides distributed access and analysis to observed andmore » simulated data from the climate and weather communities. The tools and infrastructure developed under these international multi-agency collaborations are critical to understanding extreme weather conditions and long-term climate change, while the F2F meetings help to build a stronger climate and weather data science community and stronger federated software infrastructure. The 2013 F2F meeting determined requirements for existing and impending national and international community projects; enhancements needed for data distribution, analysis, and visualization infrastructure; and standards and resources needed for better collaborations.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFM.A51E0079P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFM.A51E0079P"><span>Filtering and Gridding Satellite Observations of Cloud Variables to Compare with Climate Model Output</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Pitts, K.; Nasiri, S. L.; Smith, N.</p> <p>2013-12-01</p> <p>Global climate models have improved considerably over the years, yet clouds still represent a large factor of uncertainty for these models. Comparisons of model-simulated cloud variables with equivalent satellite cloud products are the best way to start diagnosing the differences between model output and observations. Gridded (level 3) cloud products from many different satellites and instruments are required for a full analysis, but these products are created by different science teams using different algorithms and filtering criteria to create similar, but not directly comparable, cloud products. This study makes use of a recently developed uniform space-time gridding algorithm to create a new set of gridded cloud products from each satellite instrument's level 2 data of interest which are each filtered using the same criteria, allowing for a more direct comparison between satellite products. The filtering is done via several variables such as cloud top pressure/height, thermodynamic phase, optical properties, satellite viewing angle, and sun zenith angle. The filtering criteria are determined based on the variable being analyzed and the science question at hand. Each comparison of different variables may require different filtering strategies as no single approach is appropriate for all problems. Beyond inter-satellite data comparison, these new sets of uniformly gridded satellite products can also be used for comparison with model-simulated cloud variables. Of particular interest to this study are the differences in the vertical distributions of ice and liquid water content between the satellite retrievals and model simulations, especially in the mid-troposphere where there are mixed-phase clouds to consider. This presentation will demonstrate the proof of concept through comparisons of cloud water path from Aqua MODIS retrievals and NASA GISS-E2-[R/H] model simulations archived in the CMIP5 data portal.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..17.7462W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..17.7462W"><span>Scales of variability of black carbon plumes and their dependence on resolution of ECHAM6-HAM</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Weigum, Natalie; Stier, Philip; Schutgens, Nick; Kipling, Zak</p> <p>2015-04-01</p> <p>Prediction of the aerosol effect on climate depends on the ability of three-dimensional numerical models to accurately estimate aerosol properties. However, a limitation of traditional grid-based models is their inability to resolve variability on scales smaller than a grid box. Past research has shown that significant aerosol variability exists on scales smaller than these grid-boxes, which can lead to discrepancies between observations and aerosol models. The aim of this study is to understand how a global climate model's (GCM) inability to resolve sub-grid scale variability affects simulations of important aerosol features. This problem is addressed by comparing observed black carbon (BC) plume scales from the HIPPO aircraft campaign to those simulated by ECHAM-HAM GCM, and testing how model resolution affects these scales. This study additionally investigates how model resolution affects BC variability in remote and near-source regions. These issues are examined using three different approaches: comparison of observed and simulated along-flight-track plume scales, two-dimensional autocorrelation analysis, and 3-dimensional plume analysis. We find that the degree to which GCMs resolve variability can have a significant impact on the scales of BC plumes, and it is important for models to capture the scales of aerosol plume structures, which account for a large degree of aerosol variability. In this presentation, we will provide further results from the three analysis techniques along with a summary of the implication of these results on future aerosol model development.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014JHyd..510..436K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014JHyd..510..436K"><span>Precipitation frequency analysis based on regional climate simulations in Central Alberta</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kuo, Chun-Chao; Gan, Thian Yew; Hanrahan, Janel L.</p> <p>2014-03-01</p> <p>A Regional Climate Model (RCM), MM5 (the Fifth Generation Pennsylvania State University/National Center for Atmospheric Research mesoscale model), is used to simulate summer precipitation in Central Alberta. MM5 was set up with a one-way, three-domain nested framework, with domain resolutions of 27, 9, and 3 km, respectively, and forced with ERA-Interim reanalysis data of ECMWF (European Centre for Medium-Range Weather Forecasts). The objective is to develop high resolution, grid-based Intensity-Duration-Frequency (IDF) curves based on the simulated annual maximums of precipitation (AMP) data for durations ranging from 15-min to 24-h. The performance of MM5 was assessed in terms of simulated rainfall intensity, precipitable water, and 2-m air temperature. Next, the grid-based IDF curves derived from MM5 were compared to IDF curves derived from six RCMs of the North American Regional Climate Change Assessment Program (NARCCAP) set up with 50-km grids, driven with NCEP-DOE (National Centers for Environmental Prediction-Department of Energy) Reanalysis II data, and regional IDF curves derived from observed rain gauge data (RG-IDF). The analyzed results indicate that 6-h simulated precipitable water and 2-m temperature agree well with the ERA-Interim reanalysis data. However, compared to RG-IDF curves, IDF curves based on simulated precipitation data of MM5 are overestimated especially for IDF curves of 2-year return period. In contract, IDF curves developed from NARCCAP data suffer from under-estimation and differ more from RG-IDF curves than the MM5 IDF curves. The over-estimation of IDF curves of MM5 was corrected by a quantile-based, bias correction method. By dynamically downscale the ERA-Interim and after bias correction, it is possible to develop IDF curves useful for regions with limited or no rain gauge data. This estimation process can be further extended to predict future grid-based IDF curves subjected to possible climate change impacts based on climate change projections of GCMs (general circulation models) of IPCC (Intergovernmental Panel on Climate Change).</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2008AGUFMGC21B..06W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2008AGUFMGC21B..06W"><span>Projecting Climate Change Impacts on Wildfire Probabilities</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Westerling, A. L.; Bryant, B. P.; Preisler, H.</p> <p>2008-12-01</p> <p>We present preliminary results of the 2008 Climate Change Impact Assessment for wildfire in California, part of the second biennial science report to the California Climate Action Team organized via the California Climate Change Center by the California Energy Commission's Public Interest Energy Research Program pursuant to Executive Order S-03-05 of Governor Schwarzenegger. In order to support decision making by the State pertaining to mitigation of and adaptation to climate change and its impacts, we model wildfire occurrence monthly from 1950 to 2100 under a range of climate scenarios from the Intergovernmental Panel on Climate Change. We use six climate change models (GFDL CM2.1, NCAR PCM1, CNRM CM3, MPI ECHAM5, MIROC3.2 med, NCAR CCSM3) under two emissions scenarios--A2 (C02 850ppm max atmospheric concentration) and B1(CO2 550ppm max concentration). Climate model output has been downscaled to a 1/8 degree (~12 km) grid using two alternative methods: a Bias Correction and Spatial Donwscaling (BCSD) and a Constructed Analogues (CA) downscaling. Hydrologic variables have been simulated from temperature, precipitation, wind and radiation forcing data using the Variable Infiltration Capacity (VIC) Macroscale Hydrologic Model. We model wildfire as a function of temperature, moisture deficit, and land surface characteristics using nonlinear logistic regression techniques. Previous work on wildfire climatology and seasonal forecasting has demonstrated that these variables account for much of the inter-annual and seasonal variation in wildfire. The results of this study are monthly gridded probabilities of wildfire occurrence by fire size class, and estimates of the number of structures potentially affected by fires. In this presentation we will explore the range of modeled outcomes for wildfire in California, considering the effects of emissions scenarios, climate model sensitivities, downscaling methods, hydrologic simulations, statistical model specifications for california wildfire, and their intersection with a range of development scenarios for California.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/1992AIPC..277..112H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/1992AIPC..277..112H"><span>An approach for assessing the sensitivity of floods to regional climate change</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Hughes, James P.; Lettenmaier, Dennis P.; Wood, Eric F.</p> <p>1992-06-01</p> <p>A high visibility afforded climate change issues is recent years has led to conflicts between and among decision makers and scientists. Decision makers inevitably feel pressure to assess the effect of climate change on the public welfare, while most climate modelers are, to a greater or lesser degree, concerned about the extent to which known inaccuracies in their models limit or preclude the use of modeling results for policy making. The water resources sector affords a good example of the limitations of the use of alternative climate scenarios derived from GCMs for decision making. GCM simulations of precipitation agree poorly between GCMs, and GCM predictions of runoff and evapotranspiration are even more uncertain. Further, water resources managers must be concerned about hydrologic extremes (floods and droughts) which are much more difficult to predict than ``average'' conditions. Most studies of the sensitivity of water resource systems and operating policies to climate change to data have been based on simple perturbations of historic hydroclimatological time series to reflect the difference between large area GCM simulations for an altered climate (e.g., CO2 doubling) and a GCM simulation of present climate. Such approaches are especially limited for assessment of the sensitivity of water resources systems under extreme conditions, conditions, since the distribution of storm inter-arrival times, for instance, is kept identical to that observed in the historic past. Further, such approaches have generally been based on the difference between the GCM altered and present climates for a single grid cell, primarily because the GCM spatial scale is often much larger than the scale at which climate interpretations are desired. The use of single grid cell GCM results is considered inadvisable by many GCM modelers, who feel the spatial scale for which interpretation of GCM results is most reasonable is on the order of several grid cells. In this paper, we demonstrate an alternative approach to assessing the implications of altered climates as predicted by GCMs for extreme (flooding) conditions. The approach is based on the characterization of regional atmospheric circulation patterns through a weather typing procedure, from which a stochastic model of the weather class occurrences is formulated. Weather types are identified through a CART (Classification and Regression Tree) approach. Precipitation occurence/non-occurence at multiple precipitation station is then predicted through a second stage stochastic model. Precipitation amounts are predicted conditional on the weather class identified from the large area circulation information.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012AGUFMNH23C..05R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012AGUFMNH23C..05R"><span>Modeling Storm-Induced Inundation on the Yukon-Kuskokwim Delta for Present and Future Climates</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ravens, T. M.; Allen, J.</p> <p>2012-12-01</p> <p>The Yukon-Kuskokwim (YK) Delta is a large delta on the west coast of Alaska and one of the few remaining deltas that is largely free of anthropogenic impacts. The delta hosts a wide-range of nesting birds including the endangered Spectacled Eider. The delta plain, with an elevation of about 2 m (m.s.l.) - and an average tidal range of 2.7 m - is subject to frequent inundation by storm surges originating from the adjacent Bering Sea. Here, we report on our efforts to validate a storm-surge modeling system consisting of a course-grid ADCIRC model covering the Bering and Chukchi Seas and a Delft3D fine-grid model of the southern YK Delta. The storm surge models are validated based on measured water levels from 2007-2010 and using satellite observations of inundation due to large storms in 2005 and 2006. About 10 storms over the past 30 years are modeled. Based on model output, we computed a spatially distributed inundation index which is a time-integral of water level throughout the fine-grid model domain from individual storms and from the 30 year period. In order to examine the change in inundation in future climates, the models of the 30 year period were re-run assuming a 1 and 2 meter sea level rise. The impact of climate change on inundation frequency and intensity - using the inundation index - is reported. Future work will relate the present and projected inundation index to ecological parameters such as bird-nest concentration and vegetation type.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/biblio/1222892-exploring-multi-resolution-approach-using-amip-simulations','SCIGOV-STC'); return false;" href="https://www.osti.gov/biblio/1222892-exploring-multi-resolution-approach-using-amip-simulations"><span></span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Sakaguchi, Koichi; Leung, Lai-Yung R.; Zhao, Chun</p> <p></p> <p>This study presents a diagnosis of a multi-resolution approach using the Model for Prediction Across Scales - Atmosphere (MPAS-A) for simulating regional climate. Four AMIP experiments are conducted for 1999-2009. In the first two experiments, MPAS-A is configured using global quasi-uniform grids at 120 km and 30 km grid spacing. In the other two experiments, MPAS-A is configured using variable-resolution (VR) mesh with local refinement at 30 km over North America and South America embedded inside a quasi-uniform domain at 120 km elsewhere. Precipitation and related fields in the four simulations are examined to determine how well the VR simulationsmore » reproduce the features simulated by the globally high-resolution model in the refined domain. In previous analyses of idealized aqua-planet simulations, the characteristics of the global high-resolution simulation in moist processes only developed near the boundary of the refined region. In contrast, the AMIP simulations with VR grids are able to reproduce the high-resolution characteristics across the refined domain, particularly in South America. This indicates the importance of finely resolved lower-boundary forcing such as topography and surface heterogeneity for the regional climate, and demonstrates the ability of the MPAS-A VR to replicate the large-scale moisture transport as simulated in the quasi-uniform high-resolution model. Outside of the refined domain, some upscale effects are detected through large-scale circulation but the overall climatic signals are not significant at regional scales. Our results provide support for the multi-resolution approach as a computationally efficient and physically consistent method for modeling regional climate.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..18.2290W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..18.2290W"><span>Improved Climate Simulations through a Stochastic Parameterization of Ocean Eddies</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Williams, Paul; Howe, Nicola; Gregory, Jonathan; Smith, Robin; Joshi, Manoj</p> <p>2016-04-01</p> <p>In climate simulations, the impacts of the sub-grid scales on the resolved scales are conventionally represented using deterministic closure schemes, which assume that the impacts are uniquely determined by the resolved scales. Stochastic parameterization relaxes this assumption, by sampling the sub-grid variability in a computationally inexpensive manner. This presentation shows that the simulated climatological state of the ocean is improved in many respects by implementing a simple stochastic parameterization of ocean eddies into a coupled atmosphere-ocean general circulation model. Simulations from a high-resolution, eddy-permitting ocean model are used to calculate the eddy statistics needed to inject realistic stochastic noise into a low-resolution, non-eddy-permitting version of the same model. A suite of four stochastic experiments is then run to test the sensitivity of the simulated climate to the noise definition, by varying the noise amplitude and decorrelation time within reasonable limits. The addition of zero-mean noise to the ocean temperature tendency is found to have a non-zero effect on the mean climate. Specifically, in terms of the ocean temperature and salinity fields both at the surface and at depth, the noise reduces many of the biases in the low-resolution model and causes it to more closely resemble the high-resolution model. The variability of the strength of the global ocean thermohaline circulation is also improved. It is concluded that stochastic ocean perturbations can yield reductions in climate model error that are comparable to those obtained by refining the resolution, but without the increased computational cost. Therefore, stochastic parameterizations of ocean eddies have the potential to significantly improve climate simulations. Reference PD Williams, NJ Howe, JM Gregory, RS Smith, and MM Joshi (2016) Improved Climate Simulations through a Stochastic Parameterization of Ocean Eddies. Journal of Climate, under revision.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFMGC43B1166H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFMGC43B1166H"><span>Discerning spatial and temporal LAI and clear-sky FAPAR variability during summer at the Toolik Lake vegetation monitoring grid (North Slope, Alaska)</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Heim, B.; Beamish, A. L.; Walker, D. A.; Epstein, H. E.; Sachs, T.; Chabrillat, S.; Buchhorn, M.; Prakash, A.</p> <p>2016-12-01</p> <p>Ground data for the validation of satellite-derived terrestrial Essential Climate Variables (ECVs) at high latitudes are sparse. Also for regional model evaluation (e.g. climate models, land surface models, permafrost models), we lack accurate ranges of terrestrial ground data and face the problem of a large mismatch in scale. Within the German research programs `Regional Climate Change' (REKLIM) and the Environmental Mapping and Analysis Program (EnMAP), we conducted a study on ground data representativeness for vegetation-related variables within a monitoring grid at the Toolik Lake Long-Term Ecological Research station; the Toolik Lake station lies in the Kuparuk River watershed on the North Slope of the Brooks Mountain Range in Alaska. The Toolik Lake grid covers an area of 1 km2 containing Eight five grid points spaced 100 meters apart. Moist acidic tussock tundra is the most dominant vegetation type within the grid. Eight five permanent 1 m2 plots were also established to be representative of the individual gridpoints. Researchers from the University of Alaska Fairbanks have undertaken assessments at these plots, including Leaf Area Index (LAI) and field spectrometry to derive the Normalized Difference Vegetation Index (NDVI). During summer 2016, we conducted field spectrometry and LAI measurements at selected plots during early, peak and late summer. We experimentally measured LAI on more spatially extensive Elementary Sampling Units (ESUs) to investigate the spatial representativeness of the permanent 1 m2 plots and to map ESUs for various tundra types. LAI measurements are potentially influenced by landscape-inherent microtopography, sparse vascular plant cover, and dead woody matter. From field spectrometer measurements, we derived a clear-sky mid-day Fraction of Absorbed Photosynthetically Active Radiation (FAPAR). We will present the first data analyses comparing FAPAR and LAI, and maps of biophysically-focused ESUs for evaluation of the use of remote sensing data to estimate these ecosystem properties.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20160010309&hterms=climate&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D90%26Ntt%3Dclimate','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20160010309&hterms=climate&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D90%26Ntt%3Dclimate"><span>The Impact of ARM on Climate Modeling. Chapter 26</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Randall, David A.; Del Genio, Anthony D.; Donner, Leo J.; Collins, William D.; Klein, Stephen A.</p> <p>2016-01-01</p> <p>Climate models are among humanity's most ambitious and elaborate creations. They are designed to simulate the interactions of the atmosphere, ocean, land surface, and cryosphere on time scales far beyond the limits of deterministic predictability, and including the effects of time-dependent external forcings. The processes involved include radiative transfer, fluid dynamics, microphysics, and some aspects of geochemistry, biology, and ecology. The models explicitly simulate processes on spatial scales ranging from the circumference of the Earth down to one hundred kilometers or smaller, and implicitly include the effects of processes on even smaller scales down to a micron or so. The atmospheric component of a climate model can be called an atmospheric global circulation model (AGCM). In an AGCM, calculations are done on a three-dimensional grid, which in some of today's climate models consists of several million grid cells. For each grid cell, about a dozen variables are time-stepped as the model integrates forward from its initial conditions. These so-called prognostic variables have special importance because they are the only things that a model remembers from one time step to the next; everything else is recreated on each time step by starting from the prognostic variables and the boundary conditions. The prognostic variables typically include information about the mass of dry air, the temperature, the wind components, water vapor, various condensed-water species, and at least a few chemical species such as ozone. A good way to understand how climate models work is to consider the lengthy and complex process used to develop one. Lets imagine that a new AGCM is to be created, starting from a blank piece of paper. The model may be intended for a particular class of applications, e.g., high-resolution simulations on time scales of a few decades. Before a single line of code is written, the conceptual foundation of the model must be designed through a creative envisioning that starts from the intended application and is based on current understanding of how the atmosphere works and the inventory of mathematical methods available.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..1813211K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..1813211K"><span>Evaluation of climatic changes in South-Asia</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kjellstrom, Erik; Rana, Arun; Grigory, Nikulin; Renate, Wilcke; Hansson, Ulf; Kolax, Michael</p> <p>2016-04-01</p> <p>Literature has sufficient evidences of climate change impact all over the world and its impact on various sectors. In light of new advancements made in climate modeling, availability of several climate downscaling approaches, the more robust bias correction methods with varying complexities and strengths, in the present study we performed a systematic evaluation of climate change impact over South-Asia region. We have used different Regional Climate Models (RCMs) (from CORDEX domain), (Global Climate Models GCMs) and gridded observations for the study area to evaluate the models in historical/control period (1980-2010) and changes in future period (2010-2099). Firstly, GCMs and RCMs are evaluated against the Gridded observational datasets in the area using precipitation and temperature as indicative variables. Observational dataset are also evaluated against the reliable set of observational dataset, as pointed in literature. Bias, Correlation, and changes (among other statistical measures) are calculated for the entire region and both the variables. Eventually, the region was sub-divided into various smaller domains based on homogenous precipitation zones to evaluate the average changes over time period. Spatial and temporal changes for the region are then finally calculated to evaluate the future changes in the region. Future changes are calculated for 2 Representative Concentration Pathways (RCPs), the middle emission (RCP4.5) and high emission (RCP8.5) and for both climatic variables, precipitation and temperature. Lastly, Evaluation of Extremes is performed based on precipitation and temperature based indices for whole region in future dataset. Results have indicated that the whole study region is under extreme stress in future climate scenarios for both climatic variables i.e. precipitation and temperature. Precipitation variability is dependent on the location in the area leading to droughts and floods in various regions in future. Temperature is hinting towards a constant increase throughout the region regardless of location.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFMGC33A1095D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFMGC33A1095D"><span>Construction of Gridded Daily Weather Data and its Use in Central-European Agroclimatic Study</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Dubrovsky, M.; Trnka, M.; Skalak, P.</p> <p>2013-12-01</p> <p>The regional-scale simulations of weather-sensitive processes (e.g. hydrology, agriculture and forestry) for the present and/or future climate often require high resolution meteorological inputs in terms of the time series of selected surface weather characteristics (typically temperature, precipitation, solar radiation, humidity, wind) for a set of stations or on a regular grid. As even the latest Global and Regional Climate Models (GCMs and RCMs) do not provide realistic representation of statistical structure of the surface weather, the model outputs must be postprocessed (downscaled) to achieve the desired statistical structure of the weather data before being used as an input to the follow-up simulation models. One of the downscaling approaches, which is employed also here, is based on a weather generator (WG), which is calibrated using the observed weather series, interpolated, and then modified according to the GCM- or RCM-based climate change scenarios. The present contribution, in which the parametric daily weather generator M&Rfi is linked to the high-resolution RCM output (ALADIN-Climate/CZ model) and GCM-based climate change scenarios, consists of two parts: The first part focuses on a methodology. Firstly, the gridded WG representing the baseline climate is created by merging information from observations and high resolution RCM outputs. In this procedure, WG is calibrated with RCM-simulated multi-variate weather series, and the grid specific WG parameters are then de-biased by spatially interpolated correction factors based on comparison of WG parameters calibrated with RCM-simulated weather series vs. spatially scarcer observations. To represent the future climate, the WG parameters are modified according to the 'WG-friendly' climate change scenarios. These scenarios are defined in terms of changes in WG parameters and include - apart from changes in the means - changes in WG parameters, which represent the additional characteristics of the weather series (e.g. probability of wet day occurrence and lag-1 autocorrelation of daily mean temperature). The WG-friendly scenarios for the present experiment are based on comparison of future vs baseline surface weather series simulated by GCMs from a CMIP3 database. The second part will present results of climate change impact study based on an above methodology applied to Central Europe. The changes in selected climatic (focusing on the extreme precipitation and temperature characteristics) and agroclimatic (including number of days during vegetation season with heat and drought stresses) characteristics will be analysed. In discussing the results, the emphasis will be put on 'added value' of various aspects of above methodology (e.g. inclusion of changes in 'advanced' WG parameters into the climate change scenarios). Acknowledgements: The present experiment is made within the frame of projects WG4VALUE (project LD12029 sponsored by the Ministry of Education, Youth and Sports of CR), ALARO-Climate (project P209/11/2405 sponsored by the Czech Science Foundation), and VALUE (COST ES 1102 action).</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li><a href="#" onclick='return showDiv("page_6");'>6</a></li> <li class="active"><span>7</span></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_7 --> <div id="page_8" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_6");'>6</a></li> <li><a href="#" onclick='return showDiv("page_7");'>7</a></li> <li class="active"><span>8</span></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="141"> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/biblio/991575-historical-gridded-anthropogenic-biomass-burning-emissions-reactive-gases-aerosols-methodology-application','SCIGOV-STC'); return false;" href="https://www.osti.gov/biblio/991575-historical-gridded-anthropogenic-biomass-burning-emissions-reactive-gases-aerosols-methodology-application"><span>Historical (1850-2000) gridded anthropogenic and biomass burning emissions of reactive gases and aerosols:methodology and application</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Lamarque, J. F.; Bond, Tami C.; Eyring, Veronika</p> <p>2010-08-11</p> <p>We present and discuss a new dataset of gridded emissions covering the historical period (1850-2000) in decadal increments at a horizontal resolution of 0.5° in latitude and longitude. The primary purpose of this inventory is to provide consistent gridded emissions of reactive gases and aerosols for use in chemistry model simulations needed by climate models for the Climate Model Intercomparison Program #5 (CMIP5) in support of the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment report. Our best estimate for the year 2000 inventory represents a combination of existing regional and global inventories to capture the best information available atmore » this point; 40 regions and 12 sectors were used to combine the various sources. The historical reconstruction of each emitted compound, for each region and sector, was then forced to agree with our 2000 estimate, ensuring continuity between past and 2000 emissions. Application of these emissions into two chemistry-climate models is used to test their ability to capture long-term changes in atmospheric ozone, carbon monoxide and aerosols distributions. The simulated long-term change in the Northern mid-latitudes surface and mid-troposphere ozone is not quite as rapid as observed. However, stations outside this latitude band show much better agreement in both present-day and long-term trend. The model simulations consistently underestimate the carbon monoxide trend, while capturing the long-term trend at the Mace Head station. The simulated sulfate and black carbon deposition over Greenland is in very good agreement with the ice-core observations spanning the simulation period. Finally, aerosol optical depth and additional aerosol diagnostics are shown to be in good agreement with previously published estimates.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.A21J2296S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.A21J2296S"><span>Particulate Matter and Ozone Prediction and Source Attribution for U.S. Air Quality Management in a Changing World</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Sanyal, S.; Wuebbles, D. J.</p> <p>2017-12-01</p> <p>In this study, the focus is on how global changes in climate and emissions will affect the U.S. air quality, especially on fine particulate matter and ozone, projecting their future trends and quantifying key source attribution. We are conducting three primary experiments : (1) historical simulations for period 1994-2013 to establish the credibility of the system and refine process-level understanding of U.S. regional air quality; (2) projections for period 2041-2060 to quantify individual and combined impacts of global climate and emissions changes under multiple scenarios; (3) sensitivity analyses to determine future changes in pollution sources and their relative contributions from anthropogenic and natural emissions, long-range pollutant transport, and climate change effects. Here we will present the result from the first experiment with the global model CESM1.2 (with fully coupled chemistry using CAM-chem5) driven by NASA Modern-Era Retrospective analysis for Research and Applications (MERRA) reanalysis data at 0.9o x 1.25o resolution. We will present the comparison between the results from model simulation with observation data from EPA database. Since there is always a challenge in comparing gridded prediction from model data with point data from the observation databases, because the model simulations calculate the average outcome over a grid for a given set of conditions while the stochastic component (e.g. sub-grid variations) embedded in the observations are not accounted for, we are using extensive statistical measure to do the comparison. We will also determine relative contributions from multiscale (local, regional, global) processes, major source regions (Mexico, Canada, Asia, Africa) and types (natural, anthropogenic) and associated uncertainties (climate decadal oscillations/interannual variations, emissions and model structure errors).</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.H22B..08C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.H22B..08C"><span>Using Four Downscaling Techniques to Characterize Uncertainty in Updating Intensity-Duration-Frequency Curves Under Climate Change</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Cook, L. M.; Samaras, C.; McGinnis, S. A.</p> <p>2017-12-01</p> <p>Intensity-duration-frequency (IDF) curves are a common input to urban drainage design, and are used to represent extreme rainfall in a region. As rainfall patterns shift into a non-stationary regime as a result of climate change, these curves will need to be updated with future projections of extreme precipitation. Many regions have begun to update these curves to reflect the trends from downscaled climate models; however, few studies have compared the methods for doing so, as well as the uncertainty that results from the selection of the native grid scale and temporal resolution of the climate model. This study examines the variability in updated IDF curves for Pittsburgh using four different methods for adjusting gridded regional climate model (RCM) outputs into station scale precipitation extremes: (1) a simple change factor applied to observed return levels, (2) a naïve adjustment of stationary and non-stationary Generalized Extreme Value (GEV) distribution parameters, (3) a transfer function of the GEV parameters from the annual maximum series, and (4) kernel density distribution mapping bias correction of the RCM time series. Return level estimates (rainfall intensities) and confidence intervals from these methods for the 1-hour to 48-hour duration are tested for sensitivity to the underlying spatial and temporal resolution of the climate ensemble from the NA-CORDEX project, as well as, the future time period for updating. The first goal is to determine if uncertainty is highest for: (i) the downscaling method, (ii) the climate model resolution, (iii) the climate model simulation, (iv) the GEV parameters, or (v) the future time period examined. Initial results of the 6-hour, 10-year return level adjusted with the simple change factor method using four climate model simulations of two different spatial resolutions show that uncertainty is highest in the estimation of the GEV parameters. The second goal is to determine if complex downscaling methods and high-resolution climate models are necessary for updating, or if simpler methods and lower resolution climate models will suffice. The final results can be used to inform the most appropriate method and climate model resolutions to use for updating IDF curves for urban drainage design.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018ThApC.132..327H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018ThApC.132..327H"><span>Daily precipitation grids for Austria since 1961—development and evaluation of a spatial dataset for hydroclimatic monitoring and modelling</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Hiebl, Johann; Frei, Christoph</p> <p>2018-04-01</p> <p>Spatial precipitation datasets that are long-term consistent, highly resolved and extend over several decades are an increasingly popular basis for modelling and monitoring environmental processes and planning tasks in hydrology, agriculture, energy resources management, etc. Here, we present a grid dataset of daily precipitation for Austria meant to promote such applications. It has a grid spacing of 1 km, extends back till 1961 and is continuously updated. It is constructed with the classical two-tier analysis, involving separate interpolations for mean monthly precipitation and daily relative anomalies. The former was accomplished by kriging with topographic predictors as external drift utilising 1249 stations. The latter is based on angular distance weighting and uses 523 stations. The input station network was kept largely stationary over time to avoid artefacts on long-term consistency. Example cases suggest that the new analysis is at least as plausible as previously existing datasets. Cross-validation and comparison against experimental high-resolution observations (WegenerNet) suggest that the accuracy of the dataset depends on interpretation. Users interpreting grid point values as point estimates must expect systematic overestimates for light and underestimates for heavy precipitation as well as substantial random errors. Grid point estimates are typically within a factor of 1.5 from in situ observations. Interpreting grid point values as area mean values, conditional biases are reduced and the magnitude of random errors is considerably smaller. Together with a similar dataset of temperature, the new dataset (SPARTACUS) is an interesting basis for modelling environmental processes, studying climate change impacts and monitoring the climate of Austria.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/19940011553','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19940011553"><span>Research into the influence of spatial variability and scale on the parameterization of hydrological processes</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Wood, Eric F.</p> <p>1993-01-01</p> <p>The objectives of the research were as follows: (1) Extend the Representative Elementary Area (RE) concept, first proposed and developed in Wood et al, (1988), to the water balance fluxes of the interstorm period (redistribution, evapotranspiration and baseflow) necessary for the analysis of long-term water balance processes. (2) Derive spatially averaged water balance model equations for spatially variable soil, topography and vegetation, over A RANGE OF CLIMATES. This is a necessary step in our goal to derive consistent hydrologic results up to GCM grid scales necessary for global climate modeling. (3) Apply the above macroscale water balance equations with remotely sensed data and begin to explore the feasibility of parameterizing the water balance constitutive equations at GCM grid scale.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.C23B1217C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.C23B1217C"><span>An iceberg model implementation in ACME.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Comeau, D.; Turner, A. K.; Hunke, E. C.</p> <p>2017-12-01</p> <p>Icebergs represent approximately half of the mass flux from the Antarctic ice sheet, transporting freshwater and nutrients away from the coast to the Southern Ocean. Icebergs impact the surrounding ocean and sea ice environment, and serve as nutrient sources for biogeochemical activity, yet these processes are typically not resolved in current climate models. We have implemented a parameterization for iceberg drift and decay into the Department of Energy's Accelerated Climate Model for Energy (ACME), where the ocean, sea ice, and land ice components are based on the unstructured grid modeling framework Multiple Prediction Across Scales (MPAS), to improve the representation of Antarctic mass flux to the Southern Ocean and its impacts on ocean stratification and circulation, sea ice, and biogeochemical processes in a fully coupled global climate model. The iceberg model is implemented in two frameworks: Lagrangian and Eulerian. The Lagrangian framework embeds individual icebergs into the ocean and sea ice grids, and will be useful in modeling `giant' (>10 nautical miles) iceberg events, which may have highly localized impacts on ocean and sea ice. The Eulerian framework allows us to model a realistic population of Antarctic icebergs without the computational expense of individual particle tracking to simulate the aggregate impact on the Southern Ocean climate system. This capability, together with under ice-shelf ocean cavities and dynamic ice-shelf fronts, will allow for extremely high fidelity simulation of the southern cryosphere within ACME.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..1810489D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..1810489D"><span>Development of an analytical model for estimating global terrestrial carbon assimilation using a rate-limitation framework</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Donohue, Randall; Yang, Yuting; McVicar, Tim; Roderick, Michael</p> <p>2016-04-01</p> <p>A fundamental question in climate and ecosystem science is "how does climate regulate the land surface carbon budget?" To better answer that question, here we develop an analytical model for estimating mean annual terrestrial gross primary productivity (GPP), which is the largest carbon flux over land, based on a rate-limitation framework. Actual GPP (climatological mean from 1982 to 2010) is calculated as a function of the balance between two GPP potentials defined by the climate (i.e., precipitation and solar radiation) and a third parameter that encodes other environmental variables and modifies the GPP-climate relationship. The developed model was tested at three spatial scales using different GPP sources, i.e., (1) observed GPP from 94 flux-sites, (2) modelled GPP (using the model-tree-ensemble approach) at 48654 (0.5 degree) grid-cells and (3) at 32 large catchments across the globe. Results show that the proposed model could account for the spatial GPP patterns, with a root-mean-square error of 0.70, 0.65 and 0.3 g C m-2 d-1 and R2 of 0.79, 0.92 and 0.97 for the flux-site, grid-cell and catchment scales, respectively. This analytical GPP model shares a similar form with the Budyko hydroclimatological model, which opens the possibility of a general analytical framework to analyze the linked carbon-water-energy cycles.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014EGUGA..16.2552Z','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014EGUGA..16.2552Z"><span>STAMMEX high resolution gridded daily precipitation dataset over Germany: a new potential for regional precipitation climate research</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Zolina, Olga; Simmer, Clemens; Kapala, Alice; Mächel, Hermann; Gulev, Sergey; Groisman, Pavel</p> <p>2014-05-01</p> <p>We present new high resolution precipitation daily grids developed at Meteorological Institute, University of Bonn and German Weather Service (DWD) under the STAMMEX project (Spatial and Temporal Scales and Mechanisms of Extreme Precipitation Events over Central Europe). Daily precipitation grids have been developed from the daily-observing precipitation network of DWD, which runs one of the World's densest rain gauge networks comprising more than 7500 stations. Several quality-controlled daily gridded products with homogenized sampling were developed covering the periods 1931-onwards (with 0.5 degree resolution), 1951-onwards (0.25 degree and 0.5 degree), and 1971-2000 (0.1 degree). Different methods were tested to select the best gridding methodology that minimizes errors of integral grid estimates over hilly terrain. Besides daily precipitation values with uncertainty estimates (which include standard estimates of the kriging uncertainty as well as error estimates derived by a bootstrapping algorithm), the STAMMEX data sets include a variety of statistics that characterize temporal and spatial dynamics of the precipitation distribution (quantiles, extremes, wet/dry spells, etc.). Comparisons with existing continental-scale daily precipitation grids (e.g., CRU, ECA E-OBS, GCOS) which include considerably less observations compared to those used in STAMMEX, demonstrate the added value of high-resolution grids for extreme rainfall analyses. These data exhibit spatial variability pattern and trends in precipitation extremes, which are missed or incorrectly reproduced over Central Europe from coarser resolution grids based on sparser networks. The STAMMEX dataset can be used for high-quality climate diagnostics of precipitation variability, as a reference for reanalyses and remotely-sensed precipitation products (including the upcoming Global Precipitation Mission products), and for input into regional climate and operational weather forecast models. We will present numerous application of the STAMMEX grids spanning from case studies of the major Central European floods to long-term changes in different precipitation statistics, including those accounting for the alternation of dry and wet periods and precipitation intensities associated with prolonged rainy episodes.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.fs.usda.gov/treesearch/pubs/41212','TREESEARCH'); return false;" href="https://www.fs.usda.gov/treesearch/pubs/41212"><span>Regional Spectral Model simulations of the summertime regional climate over Taiwan and adjacent areas</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.fs.usda.gov/treesearch/">Treesearch</a></p> <p>Ching-Teng Lee; Ming-Chin Wu; Shyh-Chin Chen</p> <p>2005-01-01</p> <p>The National Centers for Environmental Prediction (NCEP) regional spectral model (RSM) version 97 was used to investigate the regional summertime climate over Taiwan and adjacent areas for June-July-August of 1990 through 2000. The simulated sea-level-pressure and wind fields of RSM1 with 50-km grid space are similar to the reanalysis, but the strength of the...</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014EGUGA..16.8512A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014EGUGA..16.8512A"><span>Recent Advances in High-Resolution Regional Climate Modeling at the U.S. Environmental Protection Agency</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Alapaty, Kiran; Bullock, O. Russell; Herwehe, Jerold; Spero, Tanya; Nolte, Christopher; Mallard, Megan</p> <p>2014-05-01</p> <p>The Regional Climate Modeling Team at the U.S. Environmental Protection Agency has been improving the quality of regional climate fields generated by the Weather Research and Forecasting (WRF) model. Active areas of research include improving core physics within the WRF model and adapting the physics for regional climate applications, improving the representation of inland lakes that are unresolved by the driving fields, evaluating nudging strategies, and devising techniques to demonstrate value added by dynamical downscaling. These research efforts have been conducted using reanalysis data as driving fields, and then their results have been applied to downscale data from global climate models. The goals of this work are to equip environmental managers and policy/decision makers in the U.S. with science, tools, and data to inform decisions related to adapting to and mitigating the potential impacts of climate change on air quality, ecosystems, and human health. Our presentation will focus mainly on one area of the Team's research: Development and testing of a seamless convection parameterization scheme. For the continental U.S., one of the impediments to high-resolution (~3 to 15 km) climate modeling is related to the lack of a seamless convection parameterization that works across many scales. Since many convection schemes are not developed to work at those "gray scales", they often lead to excessive precipitation during warm periods (e.g., summer). The Kain-Fritsch (KF) convection parameterization in the WRF model has been updated such that it can be used seamlessly across spatial scales down to ~1 km grid spacing. First, we introduced subgrid-scale cloud and radiation interactions that had not been previously considered in the KF scheme. Then, a scaling parameter was developed to introduce scale-dependency in the KF scheme for use with various processes. In addition, we developed new formulations for: (1) convective adjustment timescale; (2) entrainment of environmental air; (3) impacts of convective updraft on grid-scale vertical velocity; (4) convective cloud microphysics; (5) stabilizing capacity; (6) elimination of double counting of precipitation; and (7) estimation of updraft mass flux at the lifting condensation level. Some of these scale-dependent formulations make the KF scheme operable at all scales up to about sub-kilometer grid resolution. In this presentation, regional climate simulations using the WRF model will be presented to demonstrate the effects of these changes to the KF scheme. Additionally, we briefly present results obtained from the improved representation of inland lakes, various nudging strategies, and added value of dynamical downscaling of regional climate. Requesting for a plenary talk for the session: "Regional climate modeling, including CORDEX" (session number CL6.4) at the EGU 2014 General Assembly, to be held 27 April - 2 May 2014 in Vienna, Austria.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017ThApC.128...71A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017ThApC.128...71A"><span>Multi-criteria evaluation of CMIP5 GCMs for climate change impact analysis</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ahmadalipour, Ali; Rana, Arun; Moradkhani, Hamid; Sharma, Ashish</p> <p>2017-04-01</p> <p>Climate change is expected to have severe impacts on global hydrological cycle along with food-water-energy nexus. Currently, there are many climate models used in predicting important climatic variables. Though there have been advances in the field, there are still many problems to be resolved related to reliability, uncertainty, and computing needs, among many others. In the present work, we have analyzed performance of 20 different global climate models (GCMs) from Climate Model Intercomparison Project Phase 5 (CMIP5) dataset over the Columbia River Basin (CRB) in the Pacific Northwest USA. We demonstrate a statistical multicriteria approach, using univariate and multivariate techniques, for selecting suitable GCMs to be used for climate change impact analysis in the region. Univariate methods includes mean, standard deviation, coefficient of variation, relative change (variability), Mann-Kendall test, and Kolmogorov-Smirnov test (KS-test); whereas multivariate methods used were principal component analysis (PCA), singular value decomposition (SVD), canonical correlation analysis (CCA), and cluster analysis. The analysis is performed on raw GCM data, i.e., before bias correction, for precipitation and temperature climatic variables for all the 20 models to capture the reliability and nature of the particular model at regional scale. The analysis is based on spatially averaged datasets of GCMs and observation for the period of 1970 to 2000. Ranking is provided to each of the GCMs based on the performance evaluated against gridded observational data on various temporal scales (daily, monthly, and seasonal). Results have provided insight into each of the methods and various statistical properties addressed by them employed in ranking GCMs. Further; evaluation was also performed for raw GCM simulations against different sets of gridded observational dataset in the area.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018ThApC.132..239I','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018ThApC.132..239I"><span>Field significance of performance measures in the context of regional climate model evaluation. Part 2: precipitation</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ivanov, Martin; Warrach-Sagi, Kirsten; Wulfmeyer, Volker</p> <p>2018-04-01</p> <p>A new approach for rigorous spatial analysis of the downscaling performance of regional climate model (RCM) simulations is introduced. It is based on a multiple comparison of the local tests at the grid cells and is also known as `field' or `global' significance. The block length for the local resampling tests is precisely determined to adequately account for the time series structure. New performance measures for estimating the added value of downscaled data relative to the large-scale forcing fields are developed. The methodology is exemplarily applied to a standard EURO-CORDEX hindcast simulation with the Weather Research and Forecasting (WRF) model coupled with the land surface model NOAH at 0.11 ∘ grid resolution. Daily precipitation climatology for the 1990-2009 period is analysed for Germany for winter and summer in comparison with high-resolution gridded observations from the German Weather Service. The field significance test controls the proportion of falsely rejected local tests in a meaningful way and is robust to spatial dependence. Hence, the spatial patterns of the statistically significant local tests are also meaningful. We interpret them from a process-oriented perspective. While the downscaled precipitation distributions are statistically indistinguishable from the observed ones in most regions in summer, the biases of some distribution characteristics are significant over large areas in winter. WRF-NOAH generates appropriate stationary fine-scale climate features in the daily precipitation field over regions of complex topography in both seasons and appropriate transient fine-scale features almost everywhere in summer. As the added value of global climate model (GCM)-driven simulations cannot be smaller than this perfect-boundary estimate, this work demonstrates in a rigorous manner the clear additional value of dynamical downscaling over global climate simulations. The evaluation methodology has a broad spectrum of applicability as it is distribution-free, robust to spatial dependence, and accounts for time series structure.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.A12C..03P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.A12C..03P"><span>Evaluation of a Mesoscale Convective System in Variable-Resolution CESM</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Payne, A. E.; Jablonowski, C.</p> <p>2017-12-01</p> <p>Warm season precipitation over the Southern Great Plains (SGP) follows a well observed diurnal pattern of variability, peaking at night-time, due to the eastward propagation of mesoscale convection systems that develop over the eastern slopes of the Rockies in the late afternoon. While most climate models are unable to adequately capture the organization of convection and characteristic pattern of precipitation over this region, models with high enough resolution to explicitly resolve convection show improvement. However, high resolution simulations are computationally expensive and, in the case of regional climate models, are subject to boundary conditions. Newly developed variable resolution global climate models strike a balance between the benefits of high-resolution regional climate models and the large-scale dynamics of global climate models and low computational cost. Recently developed parameterizations that are insensitive to the model grid scale provide a way to improve model performance. Here, we present an evaluation of the newly available Cloud Layers Unified by Binormals (CLUBB) parameterization scheme in a suite of variable-resolution CESM simulations with resolutions ranging from 110 km to 7 km within a regionally refined region centered over the SGP Atmospheric Radiation Measurement (ARM) site. Simulations utilize the hindcast approach developed by the Department of Energy's Cloud-Associated Parameterizations Testbed (CAPT) for the assessment of climate models. We limit our evaluation to a single mesoscale convective system that passed over the region on May 24, 2008. The effects of grid-resolution on the timing and intensity of precipitation, as well as, on the transition from shallow to deep convection are assessed against ground-based observations from the SGP ARM site, satellite observations and ERA-Interim reanalysis.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFM.A11O..01P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFM.A11O..01P"><span>Changing Characteristics of convective storms: Results from a continental-scale convection-permitting climate simulations</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Prein, A. F.; Ikeda, K.; Liu, C.; Bullock, R.; Rasmussen, R.</p> <p>2016-12-01</p> <p>Convective storms are causing extremes such as flooding, landslides, and wind gusts and are related to the development of tornadoes and hail. Convective storms are also the dominant source of summer precipitation in most regions of the Contiguous United States. So far little is known about how convective storms might change due to global warming. This is mainly because of the coarse grid spacing of state-of-the-art climate models that are not able to resolve deep convection explicitly. Instead, coarse resolution models rely on convective parameterization schemes that are a major source of errors and uncertainties in climate change projections. Convection-permitting climate simulations, with grid-spacings smaller than 4 km, show significant improvements in the simulation of convective storms by representing deep convection explicitly. Here we use a pair of 13-year long current and future convection-permitting climate simulations that cover large parts of North America. We use the Method for Object-Based Diagnostic Evaluation (MODE) that incorporates the time dimension (MODE-TD) to analyze the model performance in reproducing storm features in the current climate and to investigate their potential future changes. We show that the model is able to accurately reproduce the main characteristics of convective storms in the present climate. The comparison with the future climate simulation shows that convective storms significantly increase in frequency, intensity, and size. Furthermore, they are projected to move slower which could result in a substantial increase in convective storm-related hazards such as flash floods, debris flows, and landslides. Some regions, such as the North Atlantic, might experience a regime shift that leads to significantly stronger storms that are unrepresented in the current climate.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/27035575','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/27035575"><span>Photovoltaic Grid-Connected Modeling and Characterization Based on Experimental Results.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Humada, Ali M; Hojabri, Mojgan; Sulaiman, Mohd Herwan Bin; Hamada, Hussein M; Ahmed, Mushtaq N</p> <p>2016-01-01</p> <p>A grid-connected photovoltaic (PV) system operates under fluctuated weather condition has been modeled and characterized based on specific test bed. A mathematical model of a small-scale PV system has been developed mainly for residential usage, and the potential results have been simulated. The proposed PV model based on three PV parameters, which are the photocurrent, IL, the reverse diode saturation current, Io, the ideality factor of diode, n. Accuracy of the proposed model and its parameters evaluated based on different benchmarks. The results showed that the proposed model fitting the experimental results with high accuracy compare to the other models, as well as the I-V characteristic curve. The results of this study can be considered valuable in terms of the installation of a grid-connected PV system in fluctuated climatic conditions.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4817980','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4817980"><span>Photovoltaic Grid-Connected Modeling and Characterization Based on Experimental Results</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Humada, Ali M.; Hojabri, Mojgan; Sulaiman, Mohd Herwan Bin; Hamada, Hussein M.; Ahmed, Mushtaq N.</p> <p>2016-01-01</p> <p>A grid-connected photovoltaic (PV) system operates under fluctuated weather condition has been modeled and characterized based on specific test bed. A mathematical model of a small-scale PV system has been developed mainly for residential usage, and the potential results have been simulated. The proposed PV model based on three PV parameters, which are the photocurrent, IL, the reverse diode saturation current, Io, the ideality factor of diode, n. Accuracy of the proposed model and its parameters evaluated based on different benchmarks. The results showed that the proposed model fitting the experimental results with high accuracy compare to the other models, as well as the I-V characteristic curve. The results of this study can be considered valuable in terms of the installation of a grid-connected PV system in fluctuated climatic conditions. PMID:27035575</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/biblio/22590894-development-smart-dc-grid-model','SCIGOV-STC'); return false;" href="https://www.osti.gov/biblio/22590894-development-smart-dc-grid-model"><span></span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Dalimunthe, Amty Ma’rufah Ardhiyah; Mindara, Jajat Yuda; Panatarani, Camellia</p> <p></p> <p>Smart grid and distributed generation should be the solution of the global climate change and the crisis energy of the main source of electrical power generation which is fossil fuel. In order to meet the rising electrical power demand and increasing service quality demands, as well as reduce pollution, the existing power grid infrastructure should be developed into a smart grid and distributed power generation which provide a great opportunity to address issues related to energy efficiency, energy security, power quality and aging infrastructure systems. The conventional of the existing distributed generation system is an AC grid while for amore » renewable resources requires a DC grid system. This paper explores the model of smart DC grid by introducing a model of smart DC grid with the stable power generation give a minimal and compressed circuitry that can be implemented very cost-effectively with simple components. The PC based application software for controlling was developed to show the condition of the grid and to control the grid become ‘smart’. The model is then subjected to a severe system perturbation, such as incremental change in loads to test the performance of the system again stability. It is concluded that the system able to detect and controlled the voltage stability which indicating the ability of power system to maintain steady voltage within permissible rangers in normal condition.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AdSR...12..103D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AdSR...12..103D"><span>The FORBIO Climate data set for climate analyses</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Delvaux, C.; Journée, M.; Bertrand, C.</p> <p>2015-06-01</p> <p>In the framework of the interdisciplinary FORBIO Climate research project, the Royal Meteorological Institute of Belgium is in charge of providing high resolution gridded past climate data (i.e. temperature and precipitation). This climate data set will be linked to the measurements on seedlings, saplings and mature trees to assess the effects of climate variation on tree performance. This paper explains how the gridded daily temperature (minimum and maximum) data set was generated from a consistent station network between 1980 and 2013. After station selection, data quality control procedures were developed and applied to the station records to ensure that only valid measurements will be involved in the gridding process. Thereafter, the set of unevenly distributed validated temperature data was interpolated on a 4 km × 4 km regular grid over Belgium. The performance of different interpolation methods has been assessed. The method of kriging with external drift using correlation between temperature and altitude gave the most relevant results.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://pubs.er.usgs.gov/publication/70018145','USGSPUBS'); return false;" href="https://pubs.er.usgs.gov/publication/70018145"><span>Comparison of alternative spatial resolutions in the application of a spatially distributed biogeochemical model over complex terrain</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Turner, D.P.; Dodson, R.; Marks, D.</p> <p>1996-01-01</p> <p>Spatially distributed biogeochemical models may be applied over grids at a range of spatial resolutions, however, evaluation of potential errors and loss of information at relatively coarse resolutions is rare. In this study, a georeferenced database at the 1-km spatial resolution was developed to initialize and drive a process-based model (Forest-BGC) of water and carbon balance over a gridded 54976 km2 area covering two river basins in mountainous western Oregon. Corresponding data sets were also prepared at 10-km and 50-km spatial resolutions using commonly employed aggregation schemes. Estimates were made at each grid cell for climate variables including daily solar radiation, air temperature, humidity, and precipitation. The topographic structure, water holding capacity, vegetation type and leaf area index were likewise estimated for initial conditions. The daily time series for the climatic drivers was developed from interpolations of meteorological station data for the water year 1990 (1 October 1989-30 September 1990). Model outputs at the 1-km resolution showed good agreement with observed patterns in runoff and productivity. The ranges for model inputs at the 10-km and 50-km resolutions tended to contract because of the smoothed topography. Estimates for mean evapotranspiration and runoff were relatively insensitive to changing the spatial resolution of the grid whereas estimates of mean annual net primary production varied by 11%. The designation of a vegetation type and leaf area at the 50-km resolution often subsumed significant heterogeneity in vegetation, and this factor accounted for much of the difference in the mean values for the carbon flux variables. Although area wide means for model outputs were generally similar across resolutions, difference maps often revealed large areas of disagreement. Relatively high spatial resolution analyses of biogeochemical cycling are desirable from several perspectives and may be particularly important in the study of the potential impacts of climate change.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.fs.usda.gov/treesearch/pubs/34936','TREESEARCH'); return false;" href="https://www.fs.usda.gov/treesearch/pubs/34936"><span>Mapping and imputing potential productivity of Pacific Northwest forests using climate variables</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.fs.usda.gov/treesearch/">Treesearch</a></p> <p>Gregory Latta; Hailemariam Temesgen; Tara Barrett</p> <p>2009-01-01</p> <p>Regional estimation of potential forest productivity is important to diverse applications, including biofuels supply, carbon sequestration, and projections of forest growth. Using PRISM (Parameter-elevation Regressions on Independent Slopes Model) climate and productivity data measured on a grid of 3356 Forest Inventory and Analysis plots in Oregon and Washington, we...</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_6");'>6</a></li> <li><a href="#" onclick='return showDiv("page_7");'>7</a></li> <li class="active"><span>8</span></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_8 --> <div id="page_9" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_7");'>7</a></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li class="active"><span>9</span></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="161"> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017EGUGA..1910673M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017EGUGA..1910673M"><span>Climate Signal Detection in Wine Quality Using Gridded vs. Station Data in North-East Hungary</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Mika, Janos; Razsi, Andras; Gal, Lajos</p> <p>2017-04-01</p> <p>The grapevine is one of the oldest cultivated plants. Today's viticultural regions for quality wine production are located in relatively narrow geographical and therefore climatic niches. Our target area, the Matra Region in NE Hungary is fairly close to the edge of optimal wine production concerning its climate conditions. Fifty year (1961-2010) wine and quality (natural sugar content, in weight % of must) data are analysed and compared to parallel climate variables. Two sets of station-based monthly temperature, sunshine duration and precipitation data, taken from neighbouring stations, Eger-Kőlyuktető (1961-2010) and Kompolt (1976-2006) are used in 132 combinations, together with daily grid-point data provided by the CarpatClim Project (www.carpatclim-eu.org/pages/home). By now it is clear that (1) wine quality, is in significant negative correlation with the annual precipitation and in positive correlation with temperature and sunshine duration. (2) Applying a wide combination of monthly data we obtain even stronger correlations (higher significance according to t-tests) even from the station-based data, but it is difficult to select and optimum model from the many proper combinations differing in performance over the test sample just slightly. (3) The interpolated site-specific areal averages from the grid-point data provide even better results and stronger differences between the best models and the few other candidates. (4) Further improvement of statistical signal detection capacity of the above climate variables by using 5-day averages, point at the strong vulnerability of wine quality on climate anomalies of some key phenological phases of the investigated grapevine-mixes. Enhanced spatial and temporal resolution provides much better fit to the observed wine quality data. The study has been supported by the OTKA-113209 national project.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFM.A53D0204H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFM.A53D0204H"><span>Relating health and climate impacts to grid-scale emissions using adjoint sensitivity modeling for the Climate and Clean Air Coalition</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Henze, D. K.; Lacey, F.; Seltzer, M.; Vallack, H.; Kuylenstierna, J.; Bowman, K. W.; Anenberg, S.; Sasser, E.; Lee, C. J.; Martin, R.</p> <p>2013-12-01</p> <p>The Climate and Clean Air Coalition (CCAC) was initiated in 2012 to develop, understand and promote measures to reduce short lived climate forcers such as aerosol, ozone and methane. The Coalition now includes over 30 nations, and as a service to these nations is committed to providing a decision support toolkit that allows member nations to explore the benefits of a range of emissions mitigation measures in terms of the combined impacts on air quality and climate and so help in the development of their National Action Plans. Here we will present recent modeling work to support the development of the CCAC National Action Plans toolkit. Adjoint sensitivity analysis is presented as a means of efficiently relating air quality, climate and crop impacts back to changes in emissions from each species, sector and location at the grid-scale resolution of typical global air quality model applications. The GEOS-Chem adjoint model is used to estimate the damages per ton of emissions of PM2.5 related mortality, the impacts of ozone precursors on crops and ozone-related health effects, and the combined impacts of these species on regional surface temperature changes. We show how the benefits-per-emission vary spatially as a function of the surrounding environment, and how this impacts the overall benefit of sector-specific control strategies. We present initial findings for Bangladesh, as well as Mexico, Ghana and Colombia, some of the first countries to join the CCAC, and discuss general issues related to adjoint-based metrics for quantifying air quality and climate co-benefits.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20040016048&hterms=ensemble&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D70%26Ntt%3Densemble','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20040016048&hterms=ensemble&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D70%26Ntt%3Densemble"><span>A 12-year (1987-1998) Ensemble Simulation of the US Climate with a Variable Resolution Stretched Grid GCM</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Fox-Rabinovitz, Michael S.; Takacs, Lawrence L.; Govindaraju, Ravi C.</p> <p>2002-01-01</p> <p>The variable-resolution stretched-grid (SG) GEOS (Goddard Earth Observing System) GCM has been used for limited ensemble integrations with a relatively coarse, 60 to 100 km, regional resolution over the U.S. The experiments have been run for the 12-year period, 1987-1998, that includes the recent ENSO cycles. Initial conditions 1-2 days apart are used for ensemble members. The goal of the experiments is analyzing the long-term SG-GCM ensemble integrations in terms of their potential in reducing the uncertainties of regional climate simulation while producing realistic mesoscales. The ensemble integration results are analyzed for both prognostic and diagnostic fields. A special attention is devoted to analyzing the variability of precipitation over the U.S. The internal variability of the SG-GCM has been assessed. The ensemble means appear to be closer to the verifying analyses than the individual ensemble members. The ensemble means capture realistic mesoscale patterns, especially those of induced by orography. Two ENSO cycles have been analyzed in terms their impact on the U.S. climate, especially on precipitation. The ability of the SG-GCM simulations to produce regional climate anomalies has been confirmed. However, the optimal size of the ensembles depending on fine regional resolution used, is still to be determined. The SG-GCM ensemble simulations are performed as a preparation or a preliminary stage for the international SGMIP (Stretched-Grid Model Intercomparison Project) that is under way with participation of the major centers and groups employing the SG-approach for regional climate modeling.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFM.A11F0100L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFM.A11F0100L"><span>GFDL's unified regional-global weather-climate modeling system with variable resolution capability for severe weather predictions and regional climate simulations</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Lin, S. J.</p> <p>2015-12-01</p> <p>The NOAA/Geophysical Fluid Dynamics Laboratory has been developing a unified regional-global modeling system with variable resolution capabilities that can be used for severe weather predictions (e.g., tornado outbreak events and cat-5 hurricanes) and ultra-high-resolution (1-km) regional climate simulations within a consistent global modeling framework. The fundation of this flexible regional-global modeling system is the non-hydrostatic extension of the vertically Lagrangian dynamical core (Lin 2004, Monthly Weather Review) known in the community as FV3 (finite-volume on the cubed-sphere). Because of its flexability and computational efficiency, the FV3 is one of the final candidates of NOAA's Next Generation Global Prediction System (NGGPS). We have built into the modeling system a stretched (single) grid capability, a two-way (regional-global) multiple nested grid capability, and the combination of the stretched and two-way nests, so as to make convection-resolving regional climate simulation within a consistent global modeling system feasible using today's High Performance Computing System. One of our main scientific goals is to enable simulations of high impact weather phenomena (such as tornadoes, thunderstorms, category-5 hurricanes) within an IPCC-class climate modeling system previously regarded as impossible. In this presentation I will demonstrate that it is computationally feasible to simulate not only super-cell thunderstorms, but also the subsequent genesis of tornadoes using a global model that was originally designed for century long climate simulations. As a unified weather-climate modeling system, we evaluated the performance of the model with horizontal resolution ranging from 1 km to as low as 200 km. In particular, for downscaling studies, we have developed various tests to ensure that the large-scale circulation within the global varaible resolution system is well simulated while at the same time the small-scale can be accurately captured within the targeted high resolution region.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20150002140','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20150002140"><span>The Parallel System for Integrating Impact Models and Sectors (pSIMS)</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Elliott, Joshua; Kelly, David; Chryssanthacopoulos, James; Glotter, Michael; Jhunjhnuwala, Kanika; Best, Neil; Wilde, Michael; Foster, Ian</p> <p>2014-01-01</p> <p>We present a framework for massively parallel climate impact simulations: the parallel System for Integrating Impact Models and Sectors (pSIMS). This framework comprises a) tools for ingesting and converting large amounts of data to a versatile datatype based on a common geospatial grid; b) tools for translating this datatype into custom formats for site-based models; c) a scalable parallel framework for performing large ensemble simulations, using any one of a number of different impacts models, on clusters, supercomputers, distributed grids, or clouds; d) tools and data standards for reformatting outputs to common datatypes for analysis and visualization; and e) methodologies for aggregating these datatypes to arbitrary spatial scales such as administrative and environmental demarcations. By automating many time-consuming and error-prone aspects of large-scale climate impacts studies, pSIMS accelerates computational research, encourages model intercomparison, and enhances reproducibility of simulation results. We present the pSIMS design and use example assessments to demonstrate its multi-model, multi-scale, and multi-sector versatility.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.A53G2345R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.A53G2345R"><span>Gridding Cloud and Irradiance to Quantify Variability at the ARM Southern Great Plains Site</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Riihimaki, L.; Long, C. N.; Gaustad, K.</p> <p>2017-12-01</p> <p>Ground-based radiometers provide the most accurate measurements of surface irradiance. However, geometry differences between surface point measurements and large area climate model grid boxes or satellite-based footprints can cause systematic differences in surface irradiance comparisons. In this work, irradiance measurements from a network of ground stations around Kansas and Oklahoma at the US Department of Energy Atmospheric Radiation Measurement (ARM) Southern Great Plains facility are examined. Upwelling and downwelling broadband shortwave and longwave radiometer measurements are available at each site as well as surface meteorological measurements. In addition to the measured irradiances, clear sky irradiance and cloud fraction estimates are analyzed using well established methods based on empirical fits to measured clear sky irradiances. Measurements are interpolated onto a 0.25 degree latitude and longitude grid using a Gaussian weight scheme in order to provide a more accurate statistical comparison between ground measurements and a larger area such as that used in climate models, plane parallel radiative transfer calculations, and other statistical and climatological research. Validation of the gridded product will be shown, as well as analysis that quantifies the impact of site location, cloud type, and other factors on the resulting surface irradiance estimates. The results of this work are being incorporated into the Surface Cloud Grid operational data product produced by ARM, and will be made publicly available for use by others.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014IJBm...58..509C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014IJBm...58..509C"><span>Spatial heterogeneity in the timing of birch budburst in response to future climate warming in Ireland</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Caffarra, Amelia; Zottele, Fabio; Gleeson, Emily; Donnelly, Alison</p> <p>2014-05-01</p> <p>In order to predict the impact of future climate warming on trees it is important to quantify the effect climate has on their development. Our understanding of the phenological response to environmental drivers has given rise to various mathematical models of the annual growth cycle of plants. These models simulate the timing of phenophases by quantifying the relationship between development and its triggers, typically temperature. In addition, other environmental variables have an important role in determining the timing of budburst. For example, photoperiod has been shown to have a strong influence on phenological events of a number of tree species, including Betula pubescens (birch). A recently developed model for birch (DORMPHOT), which integrates the effects of temperature and photoperiod on budburst, was applied to future temperature projections from a 19-member ensemble of regional climate simulations (on a 25 km grid) generated as part of the ENSEMBLES project, to simulate the timing of birch budburst in Ireland each year up to the end of the present century. Gridded temperature time series data from the climate simulations were used as input to the DORMPHOT model to simulate future budburst timing. The results showed an advancing trend in the timing of birch budburst over most regions in Ireland up to 2100. Interestingly, this trend appeared greater in the northeast of the country than in the southwest, where budburst is currently relatively early. These results could have implications for future forest planning, species distribution modeling, and the birch allergy season.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014EGUGA..16.1756S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014EGUGA..16.1756S"><span>Extraction of Urban Morphology Parameters from Generic European Datasets: A Case Study for Antwerp, Berlin and Almada</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Stevens, Catherine; Thomas, Bart</p> <p>2014-05-01</p> <p>Climate change is driven by global processes such as the global ocean circulation and its variability over time leading to changing weather patterns on regional scales as well as changes in the severity and occurrence of extreme events such as heat waves. The response of urban societies to the evolving climate depends not only on their regional climate characteristics but also on other local factors such as the urban heat island effect. Simulation of this phenomenon with local urban climate models requires comprehensive information about the urban morphology. This study focusses on the extraction of the planar and frontal area indices from detailed 3D city models and their relationship with the European Soil Sealing Level database from the European Environment Agency. These parameters have been calculated on a 1km2 grid and compared with soil sealing values aggregated at the same spatial resolution. The optimal size of the grid is a trade-off between the level of detail and the robustness of the established relationships by reducing the scatter at small scales. Moreover, the transferability of the results to other geographical areas has been investigated. The analyses have been conducted in the framework of the NACLIM FP7 project funded by the European Commission and include the cities of Antwerp (BE), Berlin (DE) and Almada (PT) represented by different climate and urban characteristics. First results show a correlation of 70% between the planar area index and the averaged soil sealing using a linear regression model at a 1km scale. Moreover, a good correspondence has been found between the relationships for Antwerp and Berlin which is promising for urban climate modellers to reduce model complexity and analyse various climate scenarios in an effective way.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFM.A33B0207C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFM.A33B0207C"><span>Improvements in sub-grid, microphysics averages using quadrature based approaches</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Chowdhary, K.; Debusschere, B.; Larson, V. E.</p> <p>2013-12-01</p> <p>Sub-grid variability in microphysical processes plays a critical role in atmospheric climate models. In order to account for this sub-grid variability, Larson and Schanen (2013) propose placing a probability density function on the sub-grid cloud microphysics quantities, e.g. autoconversion rate, essentially interpreting the cloud microphysics quantities as a random variable in each grid box. Random sampling techniques, e.g. Monte Carlo and Latin Hypercube, can be used to calculate statistics, e.g. averages, on the microphysics quantities, which then feed back into the model dynamics on the coarse scale. We propose an alternate approach using numerical quadrature methods based on deterministic sampling points to compute the statistical moments of microphysics quantities in each grid box. We have performed a preliminary test on the Kessler autoconversion formula, and, upon comparison with Latin Hypercube sampling, our approach shows an increased level of accuracy with a reduction in sample size by almost two orders of magnitude. Application to other microphysics processes is the subject of ongoing research.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017ClDy..tmp..873H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017ClDy..tmp..873H"><span>Climate projections and extremes in dynamically downscaled CMIP5 model outputs over the Bengal delta: a quartile based bias-correction approach with new gridded data</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Hasan, M. Alfi; Islam, A. K. M. Saiful; Akanda, Ali Shafqat</p> <p>2017-11-01</p> <p>In the era of global warning, the insight of future climate and their changing extremes is critical for climate-vulnerable regions of the world. In this study, we have conducted a robust assessment of Regional Climate Model (RCM) results in a monsoon-dominated region within the new Coupled Model Intercomparison Project Phase 5 (CMIP5) and the latest Representative Concentration Pathways (RCP) scenarios. We have applied an advanced bias correction approach to five RCM simulations in order to project future climate and associated extremes over Bangladesh, a critically climate-vulnerable country with a complex monsoon system. We have also generated a new gridded product that performed better in capturing observed climatic extremes than existing products. The bias-correction approach provided a notable improvement in capturing the precipitation extremes as well as mean climate. The majority of projected multi-model RCMs indicate an increase of rainfall, where one model shows contrary results during the 2080s (2071-2100) era. The multi-model mean shows that nighttime temperatures will increase much faster than daytime temperatures and the average annual temperatures are projected to be as hot as present-day summer temperatures. The expected increase of precipitation and temperature over the hilly areas are higher compared to other parts of the country. Overall, the projected extremities of future rainfall are more variable than temperature. According to the majority of the models, the number of the heavy rainy days will increase in future years. The severity of summer-day temperatures will be alarming, especially over hilly regions, where winters are relatively warm. The projected rise of both precipitation and temperature extremes over the intense rainfall-prone northeastern region of the country creates a possibility of devastating flash floods with harmful impacts on agriculture. Moreover, the effect of bias-correction, as presented in probable changes of both bias-corrected and uncorrected extremes, can be considered in future policy making.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..18.8801G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..18.8801G"><span>Towards an automatic statistical model for seasonal precipitation prediction and its application to Central and South Asian headwater catchments</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Gerlitz, Lars; Gafurov, Abror; Apel, Heiko; Unger-Sayesteh, Katy; Vorogushyn, Sergiy; Merz, Bruno</p> <p>2016-04-01</p> <p>Statistical climate forecast applications typically utilize a small set of large scale SST or climate indices, such as ENSO, PDO or AMO as predictor variables. If the predictive skill of these large scale modes is insufficient, specific predictor variables such as customized SST patterns are frequently included. Hence statistically based climate forecast models are either based on a fixed number of climate indices (and thus might not consider important predictor variables) or are highly site specific and barely transferable to other regions. With the aim of developing an operational seasonal forecast model, which is easily transferable to any region in the world, we present a generic data mining approach which automatically selects potential predictors from gridded SST observations and reanalysis derived large scale atmospheric circulation patterns and generates robust statistical relationships with posterior precipitation anomalies for user selected target regions. Potential predictor variables are derived by means of a cellwise correlation analysis of precipitation anomalies with gridded global climate variables under consideration of varying lead times. Significantly correlated grid cells are subsequently aggregated to predictor regions by means of a variability based cluster analysis. Finally for every month and lead time, an individual random forest based forecast model is automatically calibrated and evaluated by means of the preliminary generated predictor variables. The model is exemplarily applied and evaluated for selected headwater catchments in Central and South Asia. Particularly the for winter and spring precipitation (which is associated with westerly disturbances in the entire target domain) the model shows solid results with correlation coefficients up to 0.7, although the variability of precipitation rates is highly underestimated. Likewise for the monsoonal precipitation amounts in the South Asian target areas a certain skill of the model could be detected. The skill of the model for the dry summer season in Central Asia and the transition seasons over South Asia is found to be low. A sensitivity analysis by means on well known climate indices reveals the major large scale controlling mechanisms for the seasonal precipitation climate of each target area. For the Central Asian target areas, both, the El Nino Southern Oscillation and the North Atlantic Oscillation are identified as important controlling factors for precipitation totals during moist spring season. Drought conditions are found to be triggered by a warm ENSO phase in combination with a positive phase of the NAO. For the monsoonal summer precipitation amounts over Southern Asia, the model suggests a distinct negative response to El Nino events.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015CliPD..11.2585S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015CliPD..11.2585S"><span>Gridded climate data from 5 GCMs of the Last Glacial Maximum downscaled to 30 arc s for Europe</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Schmatz, D. R.; Luterbacher, J.; Zimmermann, N. E.; Pearman, P. B.</p> <p>2015-06-01</p> <p>Studies of the impacts of historical, current and future global change require very high-resolution climate data (≤ 1 km) as a basis for modelled responses, meaning that data from digital climate models generally require substantial rescaling. Another shortcoming of available datasets on past climate is that the effects of sea level rise and fall are not considered. Without such information, the study of glacial refugia or early Holocene plant and animal migration are incomplete if not impossible. Sea level at the last glacial maximum (LGM) was approximately 125 m lower, creating substantial additional terrestrial area for which no current baseline data exist. Here, we introduce the development of a novel, gridded climate dataset for LGM that is both very high resolution (1 km) and extends to the LGM sea and land mask. We developed two methods to extend current terrestrial precipitation and temperature data to areas between the current and LGM coastlines. The absolute interpolation error is less than 1 and 0.5 °C for 98.9 and 87.8 %, respectively, of all pixels within two arc degrees of the current coastline. We use the change factor method with these newly assembled baseline data to downscale five global circulation models of LGM climate to a resolution of 1 km for Europe. As additional variables we calculate 19 "bioclimatic" variables, which are often used in climate change impact studies on biological diversity. The new LGM climate maps are well suited for analysing refugia and migration during Holocene warming following the LGM.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018ClDy...50.4455B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018ClDy...50.4455B"><span>Near-surface wind variability over the broader Adriatic region: insights from an ensemble of regional climate models</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Belušić, Andreina; Prtenjak, Maja Telišman; Güttler, Ivan; Ban, Nikolina; Leutwyler, David; Schär, Christoph</p> <p>2018-06-01</p> <p>Over the past few decades the horizontal resolution of regional climate models (RCMs) has steadily increased, leading to a better representation of small-scale topographic features and more details in simulating dynamical aspects, especially in coastal regions and over complex terrain. Due to its complex terrain, the broader Adriatic region represents a major challenge to state-of-the-art RCMs in simulating local wind systems realistically. The objective of this study is to identify the added value in near-surface wind due to the refined grid spacing of RCMs. For this purpose, we use a multi-model ensemble composed of CORDEX regional climate simulations at 0.11° and 0.44° grid spacing, forced by the ERA-Interim reanalysis, a COSMO convection-parameterizing simulation at 0.11° and a COSMO convection-resolving simulation at 0.02° grid spacing. Surface station observations from this region and satellite QuikSCAT data over the Adriatic Sea have been compared against daily output obtained from the available simulations. Both day-to-day wind and its frequency distribution are examined. The results indicate that the 0.44° RCMs rarely outperform ERA-Interim reanalysis, while the performance of the high-resolution simulations surpasses that of ERA-Interim. We also disclose that refining the grid spacing to a few km is needed to properly capture the small-scale wind systems. Finally, we show that the simulations frequently yield the accurate angle of local wind regimes, such as for the Bora flow, but overestimate the associated wind magnitude. Finally, spectral analysis shows good agreement between measurements and simulations, indicating the correct temporal variability of the wind speed.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.H23J1812H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.H23J1812H"><span>Coupled Crop/Hydrology Model to Estimate Expanded Irrigation Impact on Water Resources</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Handyside, C. T.; Cruise, J.</p> <p>2017-12-01</p> <p>A coupled agricultural and hydrologic systems model is used to examine the environmental impact of irrigation in the Southeast. A gridded crop model for the Southeast is used to determine regional irrigation demand. This irrigation demand is used in a regional hydrologic model to determine the hydrologic impact of irrigation. For the Southeast to maintain/expand irrigated agricultural production and provide adaptation to climate change and climate variability it will require integrated agricultural and hydrologic system models that can calculate irrigation demand and the impact of the this demand on the river hydrology. These integrated models can be used as (1) historical tools to examine vulnerability of expanded irrigation to past climate extremes (2) future tools to examine the sustainability of expanded irrigation under future climate scenarios and (3) a real-time tool to allow dynamic water resource management. Such tools are necessary to assure stakeholders and the public that irrigation can be carried out in a sustainable manner. The system tools to be discussed include a gridded version of the crop modeling system (DSSAT). The gridded model is referred to as GriDSSAT. The irrigation demand from GriDSSAT is coupled to a regional hydrologic model developed by the Eastern Forest Environmental Threat Assessment Center of the USDA Forest Service) (WaSSI). The crop model provides the dynamic irrigation demand which is a function of the weather. The hydrologic model includes all other competing uses of water. Examples of use the crop model coupled with the hydrologic model include historical analyses which show the change in hydrology as additional acres of irrigated land are added to water sheds. The first order change in hydrology is computed in terms of changes in the Water Availability Stress Index (WASSI) which is the ratio of water demand (irrigation, public water supply, industrial use, etc.) and water availability from the hydrologic model. Also, statistics such as the number of times certain WASSI thresholds are exceeded are calculated to show the impact of expanded irrigation during times of hydrologic drought and the coincident use of water by other sectors. Also, integrated downstream impacts of irrigation are also calculated through changes in flows through the whole river systems.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://cfpub.epa.gov/si/si_public_record_report.cfm?direntryid=336801&showcriteria=2&fed_org_id=111&timstype=presentation&datebeginpublishedpresented=01/18/2012&dateendpublishedpresented=01/18/2017&sortby=pubdateyear','PESTICIDES'); return false;" href="https://cfpub.epa.gov/si/si_public_record_report.cfm?direntryid=336801&showcriteria=2&fed_org_id=111&timstype=presentation&datebeginpublishedpresented=01/18/2012&dateendpublishedpresented=01/18/2017&sortby=pubdateyear"><span>A Coupled Surface Nudging Scheme for use in Retrospective ...</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.epa.gov/pesticides/search.htm">EPA Pesticide Factsheets</a></p> <p></p> <p></p> <p>A surface analysis nudging scheme coupling atmospheric and land surface thermodynamic parameters has been implemented into WRF v3.8 (latest version) for use with retrospective weather and climate simulations, as well as for applications in air quality, hydrology, and ecosystem modeling. This scheme is known as the flux-adjusting surface data assimilation system (FASDAS) developed by Alapaty et al. (2008). This scheme provides continuous adjustments for soil moisture and temperature (via indirect nudging) and for surface air temperature and water vapor mixing ratio (via direct nudging). The simultaneous application of indirect and direct nudging maintains greater consistency between the soil temperature–moisture and the atmospheric surface layer mass-field variables. The new method, FASDAS, consistently improved the accuracy of the model simulations at weather prediction scales for different horizontal grid resolutions, as well as for high resolution regional climate predictions. This new capability has been released in WRF Version 3.8 as option grid_sfdda = 2. This new capability increased the accuracy of atmospheric inputs for use air quality, hydrology, and ecosystem modeling research to improve the accuracy of respective end-point research outcome. IMPACT: A new method, FASDAS, was implemented into the WRF model to consistently improve the accuracy of the model simulations at weather prediction scales for different horizontal grid resolutions, as wel</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFM.A13A0215X','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFM.A13A0215X"><span>High resolution regional climate simulation of the Hawaiian Islands - Validation of the historical run from 2003 to 2012</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Xue, L.; Newman, A. J.; Ikeda, K.; Rasmussen, R.; Clark, M. P.; Monaghan, A. J.</p> <p>2016-12-01</p> <p>A high-resolution (a 1.5 km grid spacing domain nested within a 4.5 km grid spacing domain) 10-year regional climate simulation over the entire Hawaiian archipelago is being conducted at the National Center for Atmospheric Research (NCAR) using the Weather Research and Forecasting (WRF) model version 3.7.1. Numerical sensitivity simulations of the Hawaiian Rainband Project (HaRP, a filed experiment from July to August in 1990) showed that the simulated precipitation properties are sensitive to initial and lateral boundary conditions, sea surface temperature (SST), land surface models, vertical resolution and cloud droplet concentration. The validations of model simulated statistics of the trade wind inversion, temperature, wind field, cloud cover, and precipitation over the islands against various observations from soundings, satellites, weather stations and rain gauges during the period from 2003 to 2012 will be presented at the meeting.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018E%26ES..140a2005Y','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018E%26ES..140a2005Y"><span>CFD Simulation On The Pressure Distribution For An Isolated Single-Story House With Extension: Grid Sensitivity Analysis</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Yahya, W. N. W.; Zaini, S. S.; Ismail, M. A.; Majid, T. A.; Deraman, S. N. C.; Abdullah, J.</p> <p>2018-04-01</p> <p>Damage due to wind-related disasters is increasing due to global climate change. Many studies have been conducted to study the wind effect surrounding low-rise building using wind tunnel tests or numerical simulations. The use of numerical simulation is relatively cheap but requires very good command in handling the software, acquiring the correct input parameters and obtaining the optimum grid or mesh. However, before a study can be conducted, a grid sensitivity test must be conducted to get a suitable cell number for the final to ensure an accurate result with lesser computing time. This study demonstrates the numerical procedures for conducting a grid sensitivity analysis using five models with different grid schemes. The pressure coefficients (CP) were observed along the wall and roof profile and compared between the models. The results showed that medium grid scheme can be used and able to produce high accuracy results compared to finer grid scheme as the difference in terms of the CP values was found to be insignificant.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://pubs.usgs.gov/wri/wri034090/','USGSPUBS'); return false;" href="https://pubs.usgs.gov/wri/wri034090/"><span>Simulation of net infiltration and potential recharge using a distributed-parameter watershed model of the Death Valley region, Nevada and California</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Hevesi, Joseph A.; Flint, Alan L.; Flint, Lorraine E.</p> <p>2003-01-01</p> <p>This report presents the development and application of the distributed-parameter watershed model, INFILv3, for estimating the temporal and spatial distribution of net infiltration and potential recharge in the Death Valley region, Nevada and California. The estimates of net infiltration quantify the downward drainage of water across the lower boundary of the root zone and are used to indicate potential recharge under variable climate conditions and drainage basin characteristics. Spatial variability in recharge in the Death Valley region likely is high owing to large differences in precipitation, potential evapotranspiration, bedrock permeability, soil thickness, vegetation characteristics, and contributions to recharge along active stream channels. The quantity and spatial distribution of recharge representing the effects of variable climatic conditions and drainage basin characteristics on recharge are needed to reduce uncertainty in modeling ground-water flow. The U.S. Geological Survey, in cooperation with the Department of Energy, developed a regional saturated-zone ground-water flow model of the Death Valley regional ground-water flow system to help evaluate the current hydrogeologic system and the potential effects of natural or human-induced changes. Although previous estimates of recharge have been made for most areas of the Death Valley region, including the area defined by the boundary of the Death Valley regional ground-water flow system, the uncertainty of these estimates is high, and the spatial and temporal variability of the recharge in these basins has not been quantified. To estimate the magnitude and distribution of potential recharge in response to variable climate and spatially varying drainage basin characteristics, the INFILv3 model uses a daily water-balance model of the root zone with a primarily deterministic representation of the processes controlling net infiltration and potential recharge. The daily water balance includes precipitation (as either rain or snow), snow accumulation, sublimation, snowmelt, infiltration into the root zone, evapotranspiration, drainage, water content change throughout the root-zone profile (represented as a 6-layered system), runoff (defined as excess rainfall and snowmelt) and surface water run-on (defined as runoff that is routed downstream), and net infiltration (simulated as drainage from the bottom root-zone layer). Potential evapotranspiration is simulated using an hourly solar radiation model to simulate daily net radiation, and daily evapotranspiration is simulated as an empirical function of root zone water content and potential evapotranspiration. The model uses daily climate records of precipitation and air temperature from a regionally distributed network of 132 climate stations and a spatially distributed representation of drainage basin characteristics defined by topography, geology, soils, and vegetation to simulate daily net infiltration at all locations, including stream channels with intermittent streamflow in response to runoff from rain and snowmelt. The temporal distribution of daily, monthly, and annual net infiltration can be used to evaluate the potential effect of future climatic conditions on potential recharge. The INFILv3 model inputs representing drainage basin characteristics were developed using a geographic information system (GIS) to define a set of spatially distributed input parameters uniquely assigned to each grid cell of the INFILv3 model grid. The model grid, which was defined by a digital elevation model (DEM) of the Death Valley region, consists of 1,252,418 model grid cells with a uniform grid cell dimension of 278.5 meters in the north-south and east-west directions. The elevation values from the DEM were used with monthly regression models developed from the daily climate data to estimate the spatial distribution of daily precipitation and air temperature. The elevation values were also used to simulate atmosp</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012HESSD...9.9425C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012HESSD...9.9425C"><span>Regional climate models downscaling in the Alpine area with Multimodel SuperEnsemble</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Cane, D.; Barbarino, S.; Renier, L. A.; Ronchi, C.</p> <p>2012-08-01</p> <p>The climatic scenarios show a strong signal of warming in the Alpine area already for the mid XXI century. The climate simulations, however, even when obtained with Regional Climate Models (RCMs), are affected by strong errors where compared with observations, due to their difficulties in representing the complex orography of the Alps and limitations in their physical parametrization. Therefore the aim of this work is reducing these model biases using a specific post processing statistic technique to obtain a more suitable projection of climate change scenarios in the Alpine area. For our purposes we use a selection of RCMs runs from the ENSEMBLES project, carefully chosen in order to maximise the variety of leading Global Climate Models and of the RCMs themselves, calculated on the SRES scenario A1B. The reference observation for the Greater Alpine Area are extracted from the European dataset E-OBS produced by the project ENSEMBLES with an available resolution of 25 km. For the study area of Piedmont daily temperature and precipitation observations (1957-present) were carefully gridded on a 14-km grid over Piedmont Region with an Optimal Interpolation technique. Hence, we applied the Multimodel SuperEnsemble technique to temperature fields, reducing the high biases of RCMs temperature field compared to observations in the control period. We propose also the first application to RCMS of a brand new probabilistic Multimodel SuperEnsemble Dressing technique to estimate precipitation fields, already applied successfully to weather forecast models, with careful description of precipitation Probability Density Functions conditioned to the model outputs. This technique reduces the strong precipitation overestimation by RCMs over the alpine chain and reproduces well the monthly behaviour of precipitation in the control period.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018ESSD...10..235L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018ESSD...10..235L"><span>seNorge2 daily precipitation, an observational gridded dataset over Norway from 1957 to the present day</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Lussana, Cristian; Saloranta, Tuomo; Skaugen, Thomas; Magnusson, Jan; Tveito, Ole Einar; Andersen, Jess</p> <p>2018-02-01</p> <p>The conventional climate gridded datasets based on observations only are widely used in atmospheric sciences; our focus in this paper is on climate and hydrology. On the Norwegian mainland, seNorge2 provides high-resolution fields of daily total precipitation for applications requiring long-term datasets at regional or national level, where the challenge is to simulate small-scale processes often taking place in complex terrain. The dataset constitutes a valuable meteorological input for snow and hydrological simulations; it is updated daily and presented on a high-resolution grid (1 km of grid spacing). The climate archive goes back to 1957. The spatial interpolation scheme builds upon classical methods, such as optimal interpolation and successive-correction schemes. An original approach based on (spatial) scale-separation concepts has been implemented which uses geographical coordinates and elevation as complementary information in the interpolation. seNorge2 daily precipitation fields represent local precipitation features at spatial scales of a few kilometers, depending on the station network density. In the surroundings of a station or in dense station areas, the predictions are quite accurate even for intense precipitation. For most of the grid points, the performances are comparable to or better than a state-of-the-art pan-European dataset (E-OBS), because of the higher effective resolution of seNorge2. However, in very data-sparse areas, such as in the mountainous region of southern Norway, seNorge2 underestimates precipitation because it does not make use of enough geographical information to compensate for the lack of observations. The evaluation of seNorge2 as the meteorological forcing for the seNorge snow model and the DDD (Distance Distribution Dynamics) rainfall-runoff model shows that both models have been able to make profitable use of seNorge2, partly because of the automatic calibration procedure they incorporate for precipitation. The seNorge2 dataset 1957-2015 is available at <a href="https://doi.org/10.5281/zenodo.845733" target="_blank">https://doi.org/10.5281/zenodo.845733</a>. Daily updates from 2015 onwards are available at <a href="http://thredds.met.no/thredds/catalog/metusers/senorge2/seNorge2/provisional_archive/PREC1d/gridded_dataset/catalog.html" target="_blank">http://thredds.met.no/thredds/catalog/metusers/senorge2/seNorge2/provisional_archive/PREC1d/gridded_dataset/catalog.html</a>.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_7");'>7</a></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li class="active"><span>9</span></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_9 --> <div id="page_10" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li class="active"><span>10</span></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="181"> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/pages/biblio/1380040-statistical-emulators-maize-rice-soybean-wheat-yields-from-global-gridded-crop-models','SCIGOV-DOEP'); return false;" href="https://www.osti.gov/pages/biblio/1380040-statistical-emulators-maize-rice-soybean-wheat-yields-from-global-gridded-crop-models"><span>Statistical emulators of maize, rice, soybean and wheat yields from global gridded crop models</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/pages">DOE PAGES</a></p> <p>Blanc, Élodie</p> <p>2017-01-26</p> <p>This study provides statistical emulators of crop yields based on global gridded crop model simulations from the Inter-Sectoral Impact Model Intercomparison Project Fast Track project. The ensemble of simulations is used to build a panel of annual crop yields from five crop models and corresponding monthly summer weather variables for over a century at the grid cell level globally. This dataset is then used to estimate, for each crop and gridded crop model, the statistical relationship between yields, temperature, precipitation and carbon dioxide. This study considers a new functional form to better capture the non-linear response of yields to weather,more » especially for extreme temperature and precipitation events, and now accounts for the effect of soil type. In- and out-of-sample validations show that the statistical emulators are able to replicate spatial patterns of yields crop levels and changes overtime projected by crop models reasonably well, although the accuracy of the emulators varies by model and by region. This study therefore provides a reliable and accessible alternative to global gridded crop yield models. By emulating crop yields for several models using parsimonious equations, the tools provide a computationally efficient method to account for uncertainty in climate change impact assessments.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/biblio/1380040-statistical-emulators-maize-rice-soybean-wheat-yields-from-global-gridded-crop-models','SCIGOV-STC'); return false;" href="https://www.osti.gov/biblio/1380040-statistical-emulators-maize-rice-soybean-wheat-yields-from-global-gridded-crop-models"><span>Statistical emulators of maize, rice, soybean and wheat yields from global gridded crop models</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Blanc, Élodie</p> <p></p> <p>This study provides statistical emulators of crop yields based on global gridded crop model simulations from the Inter-Sectoral Impact Model Intercomparison Project Fast Track project. The ensemble of simulations is used to build a panel of annual crop yields from five crop models and corresponding monthly summer weather variables for over a century at the grid cell level globally. This dataset is then used to estimate, for each crop and gridded crop model, the statistical relationship between yields, temperature, precipitation and carbon dioxide. This study considers a new functional form to better capture the non-linear response of yields to weather,more » especially for extreme temperature and precipitation events, and now accounts for the effect of soil type. In- and out-of-sample validations show that the statistical emulators are able to replicate spatial patterns of yields crop levels and changes overtime projected by crop models reasonably well, although the accuracy of the emulators varies by model and by region. This study therefore provides a reliable and accessible alternative to global gridded crop yield models. By emulating crop yields for several models using parsimonious equations, the tools provide a computationally efficient method to account for uncertainty in climate change impact assessments.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017GMD....10.1961T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017GMD....10.1961T"><span>Vorticity-divergence semi-Lagrangian global atmospheric model SL-AV20: dynamical core</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Tolstykh, Mikhail; Shashkin, Vladimir; Fadeev, Rostislav; Goyman, Gordey</p> <p>2017-05-01</p> <p>SL-AV (semi-Lagrangian, based on the absolute vorticity equation) is a global hydrostatic atmospheric model. Its latest version, SL-AV20, provides global operational medium-range weather forecast with 20 km resolution over Russia. The lower-resolution configurations of SL-AV20 are being tested for seasonal prediction and climate modeling. The article presents the model dynamical core. Its main features are a vorticity-divergence formulation at the unstaggered grid, high-order finite-difference approximations, semi-Lagrangian semi-implicit discretization and the reduced latitude-longitude grid with variable resolution in latitude. The accuracy of SL-AV20 numerical solutions using a reduced lat-lon grid and the variable resolution in latitude is tested with two idealized test cases. Accuracy and stability of SL-AV20 in the presence of the orography forcing are tested using the mountain-induced Rossby wave test case. The results of all three tests are in good agreement with other published model solutions. It is shown that the use of the reduced grid does not significantly affect the accuracy up to the 25 % reduction in the number of grid points with respect to the regular grid. Variable resolution in latitude allows us to improve the accuracy of a solution in the region of interest.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFMNG41A0115D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFMNG41A0115D"><span>SPAGETTA: a Multi-Purpose Gridded Stochastic Weather Generator</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Dubrovsky, M.; Huth, R.; Rotach, M. W.; Dabhi, H.</p> <p>2017-12-01</p> <p>SPAGETTA is a new multisite/gridded multivariate parametric stochastic weather generator (WG). Site-specific precipitation occurrence and amount are modelled by Markov chain and Gamma distribution, the non-precipitation variables are modelled by an autoregressive (AR) model conditioned on precipitation occurrence, and the spatial coherence of all variables is modelled following the Wilks' (2009) approach. SPAGETTA may be run in two modes. Mode 1: it is run as a classical WG, which is calibrated using weather series from multiple sites, and only then it may produce arbitrarily long synthetic series mimicking the spatial and temporal structure of the calibration data. To generate the weather series representing the future climate, the WG parameters are modified according to the climate change scenario, typically derived from GCM or RCM simulations. Mode 2: the user provides only basic information (not necessarily to be realistic) on the temporal and spatial auto-correlation structure of the weather variables and their mean annual cycle; the generator itself derives the parameters of the underlying AR model, which produces the multi-site weather series. Optionally, the user may add the spatially varying trend, which is superimposed to the synthetic series. The contribution consists of following parts: (a) Model of the WG. (b) Validation of WG in terms of the spatial temperature and precipitation characteristics, including characteristics of spatial hot/cold/dry/wet spells. (c) Results of the climate change impact experiment, in which the WG parameters representing the spatial and temporal variability are modified using the climate change scenarios and the effect on the above spatial validation indices is analysed. In this experiment, the WG is calibrated using the E-OBS gridded daily weather data for several European regions, and the climate change scenarios are derived from the selected RCM simulations (CORDEX database). (d) The second mode of operation will be demonstrated by results obtained while developing the methodology for assessing collective significance of trends in multi-site weather series. The performance of the proposed test statistics is assessed based on large number of realisations of synthetic series produced by WG assuming a given statistical structure and trend of the weather series.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012EGUGA..14.2843C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012EGUGA..14.2843C"><span>Regional Climate Models Downscaling in the Alpine Area with Multimodel SuperEnsemble</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Cane, D.; Barbarino, S.; Renier, L.; Ronchi, C.</p> <p>2012-04-01</p> <p>The climatic scenarios show a strong signal of warming in the Alpine area already for the mid XXI century. The climate simulation, however, even when obtained with Regional Climate Models (RCMs), are affected by strong errors where compared with observations in the control period, due to their difficulties in representing the complex orography of the Alps and limitations in their physical parametrization. In this work we use a selection of RCMs runs from the ENSEMBLES project, carefully chosen in order to maximise the variety of leading Global Climate Models and of the RCMs themselves, calculated on the SRES scenario A1B. The reference observation for the Greater Alpine Area are extracted from the European dataset E-OBS produced by the project ENSEMBLES with an available resolution of 25 km. For the study area of Piemonte daily temperature and precipitation observations (1957-present) were carefully gridded on a 14-km grid over Piemonte Region with an Optimal Interpolation technique. We applied the Multimodel SuperEnsemble technique to temperature fields, reducing the high biases of RCMs temperature field compared to observations in the control period. We propose also the first application to RCMs of a brand new probabilistic Multimodel SuperEnsemble Dressing technique to estimate precipitation fields, already applied successfully to weather forecast models, with careful description of precipitation Probability Density Functions conditioned to the model outputs. This technique reduces the strong precipitation overestimation by RCMs over the alpine chain and reproduces the monthly behaviour of observed precipitation in the control period far better than the direct model outputs.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017EGUGA..1915344Z','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017EGUGA..1915344Z"><span>Downscaling CESM1 climate change projections for the MENA-CORDEX domain using WRF</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Zittis, George; Hadjinicolaou, Panos; Lelieveld, Jos</p> <p>2017-04-01</p> <p>According to analysis of observations and global climate model projections, the broader Middle East, North Africa and Mediterranean region is found to be a climate change hotspot. Substantial changes in precipitation amounts and patterns and strong summer warming (including an intensification of heat extremes) is a likely future scenario for the region, but a recent uncertainty analysis indicated good model agreement for temperature but much less for precipitation. Although the horizontal resolution of global models has increased over the last years, it is still not adequate for impact and adaptation assessments of regional or national level and further downscaling of the climate information is required. The region is now studied within the CORDEX initiative (Coordinated Regional Climate Downscaling Experiment) with the establishment of a domain covering the Middle East - North Africa (MENA-CORDEX) region (http://mena-cordex.cyi.ac.cy/). In this study, we present the first climate change projections for the MENA produced by dynamically downscaling a bias-corrected output of the CESM1 global earth system model. For the downscaling, we use a climate configuration of the Weather, Research and Forecasting model (WRF). Our simulations use a standard CORDEX Phase I 50-km grid in three simulations, a historical (1950-2005) and two scenario runs (2006-2100) with the greenhouse gas forcing following the RCP 4.5 and 8.5. We evaluate precipitation, temperature and other surface meteorological variables from the historical using gridded and station observational datasets. Maps of projected changes are constructed for different periods in the future as differences of the two scenarios model output against the data from the historical run. The main spatial and temporal patterns of change are discussed, especially in the context of the United Nations Framework Convention on Climate Change agreement in Paris to limit the global average temperature increase to 1.5 degrees above pre-industrial levels.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009AGUFM.H24B..01W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009AGUFM.H24B..01W"><span>Fire Regime and Ecosystem Effects of Climate-driven Changes in Rocky Mountains Hydrology</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Westerling, A. L.; Das, T.; Lubetkin, K.; Romme, W.; Ryan, M. G.; Smithwick, E. A.; Turner, M.</p> <p>2009-12-01</p> <p>Western US Forest managers face more wildfires than ever before, and it is increasingly imperative to anticipate the consequences of this trend. Large fires in the northern Rocky Mountains have increased in association with warmer temperatures, earlier snowmelt, and longer fire seasons (1), and this trend is likely to continue with global warming (2). Increased wildfire occurrence is already a concern shared by managers from many federal land-management agencies (3). However, new analyses for the western US suggest that future climate could diverge even more rapidly from past climate than previously suggested. Current model projections suggest end-of-century hydroclimatic conditions like those of 1988 (the year of the well-known Yellowstone Fires) may represent close to the average year rather than an extreme year. The consequences of a shift of this magnitude for the fire regime, post-fire succession and carbon (C) balance of western forest ecosystems are well beyond what scientists have explored to date, and may fundamentally change the potential of western forests to sequester atmospheric C. We link hydroclimatic extremes (spring and summer temperature and cumulative water-year moisture deficit) to extreme fire years in northern Rockies forests, using large forest fire histories and 1/8-degree gridded historical hydrologic simulations (1950 - 2005) (4) forced with historical gridded temperature and precipitation (5). The frequency of extremes in hydroclimate associated with historic severe fire years in the northern Rocky Mountains is compared to those projected under a range of climate change projections, using global climate model runs for the A2 and B1 emissions pathways for three global climate models (NCAR PCM1, GFDL CM2.1, CNRM CM3). Coarse-scale climatic variables are downscaled to a 1/8 degree grid and used to force hydrologic simulations (6, 7). We will present preliminary results using these hydrologic simulations to model spatially explicit annual wildfire occurrence historically and under the above-cited future climate scenarios, and discuss how these results are being integrated with process-based ecosystem models and field data to model changes in carbon flux across the Greater Yellowstone Ecosystem landscape (8). 1. Westerling, Hidalgo, Cayan, Swetnam, Science 313, 940 (2006). 2. Tymstra, Flannigan, Armitage, Logan, Int’l J. Wildland Fire 16, 153 (2007). 3. U. S. G. A. O. GAO. (2007). 4. Liang, Lettenmaier, Wood, Burges. J. Geophys. Res. 99(D7), 14,415 (1994). 5. Maurer, Wood, Adam, Lettenmaier, Nijssen. J. Climate 15:3237 (2002). 6. Cayan, Maurer, Dettinger, Tyree, Hayhoe. Climatic Change 87(Suppl. 1) 21 (2008). 7. Hidalgo, Dettinger Cayan, CEC Report CEC-500-2007-123 (2008). 8. We acknowledge support from the Joint Fire Science Program (Project ID 09-3-01-47), the NOAA RISA program for California, and the US Forest Service.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010AGUFM.H51A0874B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010AGUFM.H51A0874B"><span>Optimal selection of MULTI-model downscaled ensembles for interannual and seasonal climate prediction in the eastern seaboard of Thailand</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bejranonda, W.; Koch, M.</p> <p>2010-12-01</p> <p>Because of the imminent threat of the water resources of the eastern seaboard of Thailand, a climate impact study has been carried out there. To that avail, a hydrological watershed model is being used to simulate the future water availability in the wake of possible climate change in the region. The hydrological model is forced by predictions from global climate models (GCMs) that are to be downscaled in an appropriate manner. The challenge at that stage of the climate impact analysis lies then the in the choice of the best GCM and the (statistical) downscaling method. In this study the selection of coarse grid resolution output of the GCMs, transferring information to the fine grid of local climate-hydrology is achieved by cross-correlation and multiple linear regression using meteorological data in the eastern seaboard of Thailand observed between 1970-1999. The grids of 20 atmosphere/ocean global climate models (AOGCM), covering latitude 12.5-15.0 N and longitude 100.0-102.5 E were examined using the Climate-Change Scenario Generator (SCENGEN). With that tool the model efficiency of the prediction of daily precipitation and mean temperature was calculated by comparing the 1980-1999 ECMWF reanalysis predictions with the observed data during that time period. The root means square errors of the predictions were considered and ranked to select the top 5 models, namely, BCCR-BCM2.0, GISS-ER, ECHO-G, ECHAM5/MPI-OM and PCM. The daily time-series of 338 predictors in 9 runs of the 5 selected models were gathered from the CMIP3 multi-model database. Monthly time-serial cross-correlations between the climate predictors and the meteorological measurements from 25 rainfall, 4 minimum and maximum temperature, 4 humidity and 2 solar radiation stations in the study area were then computed and ranked. Using the ranked predictors, a multiple-linear regression model (downscaling transfer model) to forecast the local climate was set up. To improve the prediction power of this GCM downscaling approach, the regression equations were considered as a dynamic regression model that can alter the predictor by seasonal variation. The possible seasonal effect was examined for the 1974-1999 period which was equally divided into a calibration and verification sub-period. The calibrated model using the whole observed time-series was compared with the models separated into 2 seasons; dry and wet, 3 seasons; winter, summer and rainy, and 4 seasons; dry, pre-monsoon, first monsoon and second monsoon. The verification power of the various model variants was measured considering Akaike's information criterion (AIC) and the Nash-Sutcliffe coefficient of the corresponding model fit. The results show that the 4-seasons-variation prediction works best. The highest efficiency for the prediction of rainfall is achieved for the dry season, Oct-Mar, whereas the smallest efficiency is obtained in the monsoon seasons. The overall number of predictor giving top efficiency lies between 3 and 20 in the regression models. In the next, still ongoing stage of the climate impact study the predictions from this new, seasonally optimized downscaling transfer model are being used in the simulations of the future hydrological water budget in that region of Thailand.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/biblio/1426362-opportunities-joint-waterenergy-management-sensitivity-western-electricity-grid-operations-climate-oscillations','SCIGOV-STC'); return false;" href="https://www.osti.gov/biblio/1426362-opportunities-joint-waterenergy-management-sensitivity-western-electricity-grid-operations-climate-oscillations"><span>Opportunities for Joint Water–Energy Management: Sensitivity of the 2010 Western U.S. Electricity Grid Operations to Climate Oscillations</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Voisin, N.; Kintner-Meyer, M.; Wu, D.</p> <p></p> <p>The 2016 SECURE Water Act report’s natural water availability benchmark, combined with the 2010 level of water demand from an integrated assessment model, is used as input to drive a large-scale water management model. The regulated flow at hydropower plants and thermoelectric plants in the Western U.S. electricity grid (WECC) is translated into potential hydropower generation and generation capacity constraints. The impact on reliability (unserved energy, reserve margin) and cost (production cost, carbon emissions) of water constraints on 2010-level WECC power system operations is assessed using an electricity production cost model (PCM). Use of the PCM reveals the changes inmore » generation dispatch that reflect the inter-regional interdependencies in water-constrained generation and the ability to use other generation resources to meet all electricity loads in the WECC. August grid operational benchmarks show a range of sensitivity in production cost (-8 to +11%) and carbon emissions (-7 to 11%). The reference reserve margin threshold of 15% above peak load is maintained in the scenarios analyzed, but in 5 out of 55 years unserved energy is observed when normal operations are maintained. There is 1 chance in 10 that a year will demonstrate unserved energy in August, which defines the system’s historical performance threshold to support impact, vulnerability, and adaptation analysis. For seasonal and longer term planning, i.e., multi-year drought, we demonstrate how the Water Scarcity Grid Impact Factor and climate oscillations (ENSO, PDO) can be used to plan for joint water-electricity management to maintain grid reliability.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.A23D2380K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.A23D2380K"><span>Future projections of total snowfall and heavy snowfall in Japan simulated by large ensemble regional climate simulations.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kawase, H.; Sasaki, H.; Murata, A.; Nosaka, M.; Ito, R.; Dairaku, K.; Sasai, T.; Yamazaki, T.; Sugimoto, S.; Watanabe, S.; Fujita, M.; Kawazoe, S.; Okada, Y.; Ishii, M.; Mizuta, R.; Takayabu, I.</p> <p>2017-12-01</p> <p>We performed large ensemble climate experiments to investigate future changes in extreme weather events using Meteorological Research Institute-Atmospheric General Circulation Model (MRI-AGCM) with about 60 km grid spacing and Non-Hydrostatic Regional Climate Model with 20 km grid spacing (NHRCM20). The global climate simulations are prescribed by the past and future sea surface temperature (SST). Two future climate simulations are conducted so that the global-mean surface air temperature rise 2 K and 4 K from the pre-industrial period. The non-warming simulations are also conducted by MRI-AGCM and NHRCM20. We focus on the future changes in snowfall in Japan. In winter, the Sea of Japan coast experiences heavy snowfall due to East Asian winter monsoon. The cold and dry air from the continent obtains abundant moisture from the warm Sea of Japan, causing enormous amount of snowfall especially in the mountainous area. The NHRCM20 showed winter total snowfall decreases in the most parts of Japan. In contrast, extremely heavy daily snowfall could increase at mountainous areas in the Central Japan and Northern parts of Japan when strong cold air outbreak occurs and the convergence zone appears over the Sea of Japan. The warmer Sea of Japan in the future climate could supply more moisture than that in the present climate, indicating that the cumulus convections could be enhanced around the convergence zone in the Sea of Japan. However, the horizontal resolution of 20 km is not enough to resolve Japan`s complex topography. Therefore, dynamical downscaling with 5 km grid spacing (NHRCM05) is also conducted using NHRCM20. The NHRCM05 does a better job simulating the regional boundary of snowfall and shows more detailed changes in future snowfall characteristics. The future changes in total and extremely heavy snowfall depend on the regions, elevations, and synoptic conditions around Japan.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFMGC33E1118L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFMGC33E1118L"><span>Evaluation of CMIP5 Ability to Reproduce 20th Century Regional Trends in Surface Air Temperature and Precipitation over CONUS</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Lee, J.; Waliser, D. E.; Lee, H.; Loikith, P. C.; Kunkel, K.</p> <p>2017-12-01</p> <p>Monitoring temporal changes in key climate variables, such as surface air temperature and precipitation, is an integral part of the ongoing efforts of the United States National Climate Assessment (NCA). Climate models participating in CMIP5 provide future trends for four different emissions scenarios. In order to have confidence in the future projections of surface air temperature and precipitation, it is crucial to evaluate the ability of CMIP5 models to reproduce observed trends for three different time periods (1895-1939, 1940-1979, and 1980-2005). Towards this goal, trends in surface air temperature and precipitation obtained from the NOAA nClimGrid 5 km gridded station observation-based product are compared during all three time periods to the 206 CMIP5 historical simulations from 48 unique GCMs and their multi-model ensemble (MME) for NCA-defined climate regions during summer (JJA) and winter (DJF). This evaluation quantitatively examines the biases of simulated trends of the spatially averaged temperature and precipitation in the NCA climate regions. The CMIP5 MME reproduces historical surface air temperature trends for JJA for all time period and all regions, except the Northern Great Plains from 1895-1939 and Southeast during 1980-2005. Likewise, for DJF, the MME reproduces historical surface air temperature trends across all time periods over all regions except the Southeast from 1895-1939 and the Midwest during 1940-1979. The Regional Climate Model Evaluation System (RCMES), an analysis tool which supports the NCA by providing access to data and tools for regional climate model validation, facilitates the comparisons between the models and observation. The RCMES Toolkit is designed to assist in the analysis of climate variables and the procedure of the evaluation of climate projection models to support the decision-making processes. This tool is used in conjunction with the above analysis and results will be presented to demonstrate its capability to access observation and model datasets, calculate evaluation metrics, and visualize the results. Several other examples of the RCMES capabilities can be found at https://rcmes.jpl.nasa.gov.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFMGC31E1233C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFMGC31E1233C"><span>Data Management System for the National Energy-Water System (NEWS) Assessment Framework</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Corsi, F.; Prousevitch, A.; Glidden, S.; Piasecki, M.; Celicourt, P.; Miara, A.; Fekete, B. M.; Vorosmarty, C. J.; Macknick, J.; Cohen, S. M.</p> <p>2015-12-01</p> <p>Aiming at providing a comprehensive assessment of the water-energy nexus, the National Energy-Water System (NEWS) project requires the integration of data to support a modeling framework that links climate, hydrological, power production, transmission, and economical models. Large amounts of Georeferenced data has to be streamed to the components of the inter-disciplinary model to explore future challenges and tradeoffs in the US power production, based on climate scenarios, power plant locations and technologies, available water resources, ecosystem sustainability, and economic demand. We used open source and in-house build software components to build a system that addresses two major data challenges: On-the-fly re-projection, re-gridding, interpolation, extrapolation, nodata patching, merging, temporal and spatial aggregation, of static and time series datasets in virtually any file formats and file structures, and any geographic extent for the models I/O, directly at run time; Comprehensive data management based on metadata cataloguing and discovery in repositories utilizing the MAGIC Table (Manipulation and Geographic Inquiry Control database). This innovative concept allows models to access data on-the-fly by data ID, irrespective of file path, file structure, file format and regardless its GIS specifications. In addition, a web-based information and computational system is being developed to control the I/O of spatially distributed Earth system, climate, and hydrological, power grid, and economical data flow within the NEWS framework. The system allows scenario building, data exploration, visualization, querying, and manipulation any loaded gridded, point, and vector polygon dataset. The system has demonstrated its potential for applications in other fields of Earth science modeling, education, and outreach. Over time, this implementation of the system will provide near real-time assessment of various current and future scenarios of the water-energy nexus.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFM.A12A..02L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFM.A12A..02L"><span>Towards a unified Global Weather-Climate Prediction System</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Lin, S. J.</p> <p>2016-12-01</p> <p>The Geophysical Fluid Dynamics Laboratory has been developing a unified regional-global modeling system with variable resolution capabilities that can be used for severe weather predictions and kilometer scale regional climate simulations within a unified global modeling system. The foundation of this flexible modeling system is the nonhydrostatic Finite-Volume Dynamical Core on the Cubed-Sphere (FV3). A unique aspect of FV3 is that it is "vertically Lagrangian" (Lin 2004), essentially reducing the equation sets to two dimensions, and is the single most important reason why FV3 outperforms other non-hydrostatic cores. Owning to its accuracy, adaptability, and computational efficiency, the FV3 has been selected as the "engine" for NOAA's Next Generation Global Prediction System (NGGPS). We have built into the modeling system a stretched grid, a two-way regional-global nested grid, and an optimal combination of the stretched and two-way nests capability, making kilometer-scale regional simulations within a global modeling system feasible. Our main scientific goal is to enable simulations of high impact weather phenomena (such as tornadoes, thunderstorms, category-5 hurricanes) within an IPCC-class climate modeling system previously regarded as impossible. In this presentation I will demonstrate that, with the FV3, it is computationally feasible to simulate not only super-cell thunderstorms, but also the subsequent genesis of tornado-like vortices using a global model that was originally designed for climate simulations. The development and tuning strategy between traditional weather and climate models are fundamentally different due to different metrics. We were able to adapt and use traditional "climate" metrics or standards, such as angular momentum conservation, energy conservation, and flux balance at top of the atmosphere, and gain insight into problems of traditional weather prediction model for medium-range weather prediction, and vice versa. Therefore, the unification in weather and climate models can happen not just at the algorithm or parameterization level, but also in the metric and tuning strategy used for both applications, and ultimately, with benefits to both weather and climate applications.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFMNG23A1778U','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFMNG23A1778U"><span>Tempest: Tools for Addressing the Needs of Next-Generation Climate Models</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ullrich, P. A.; Guerra, J. E.; Pinheiro, M. C.; Fong, J.</p> <p>2015-12-01</p> <p>Tempest is a comprehensive simulation-to-science infrastructure that tackles the needs of next-generation, high-resolution, data intensive climate modeling activities. This project incorporates three key components: TempestDynamics, a global modeling framework for experimental numerical methods and high-performance computing; TempestRemap, a toolset for arbitrary-order conservative and consistent remapping between unstructured grids; and TempestExtremes, a suite of detection and characterization tools for identifying weather extremes in large climate datasets. In this presentation, the latest advances with the implementation of this framework will be discussed, and a number of projects now utilizing these tools will be featured.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFMIN31A1748D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFMIN31A1748D"><span>Integration and Exposure of Large Scale Computational Resources Across the Earth System Grid Federation (ESGF)</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Duffy, D.; Maxwell, T. P.; Doutriaux, C.; Williams, D. N.; Chaudhary, A.; Ames, S.</p> <p>2015-12-01</p> <p>As the size of remote sensing observations and model output data grows, the volume of the data has become overwhelming, even to many scientific experts. As societies are forced to better understand, mitigate, and adapt to climate changes, the combination of Earth observation data and global climate model projects is crucial to not only scientists but to policy makers, downstream applications, and even the public. Scientific progress on understanding climate is critically dependent on the availability of a reliable infrastructure that promotes data access, management, and provenance. The Earth System Grid Federation (ESGF) has created such an environment for the Intergovernmental Panel on Climate Change (IPCC). ESGF provides a federated global cyber infrastructure for data access and management of model outputs generated for the IPCC Assessment Reports (AR). The current generation of the ESGF federated grid allows consumers of the data to find and download data with limited capabilities for server-side processing. Since the amount of data for future AR is expected to grow dramatically, ESGF is working on integrating server-side analytics throughout the federation. The ESGF Compute Working Team (CWT) has created a Web Processing Service (WPS) Application Programming Interface (API) to enable access scalable computational resources. The API is the exposure point to high performance computing resources across the federation. Specifically, the API allows users to execute simple operations, such as maximum, minimum, average, and anomalies, on ESGF data without having to download the data. These operations are executed at the ESGF data node site with access to large amounts of parallel computing capabilities. This presentation will highlight the WPS API, its capabilities, provide implementation details, and discuss future developments.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013HESS...17.2017C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013HESS...17.2017C"><span>Regional climate models downscaling in the Alpine area with multimodel superensemble</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Cane, D.; Barbarino, S.; Renier, L. A.; Ronchi, C.</p> <p>2013-05-01</p> <p>The climatic scenarios show a strong signal of warming in the Alpine area already for the mid-XXI century. The climate simulations, however, even when obtained with regional climate models (RCMs), are affected by strong errors when compared with observations, due both to their difficulties in representing the complex orography of the Alps and to limitations in their physical parametrization. Therefore, the aim of this work is to reduce these model biases by using a specific post processing statistic technique, in order to obtain a more suitable projection of climate change scenarios in the Alpine area. For our purposes we used a selection of regional climate models (RCMs) runs which were developed in the framework of the ENSEMBLES project. They were carefully chosen with the aim to maximise the variety of leading global climate models and of the RCMs themselves, calculated on the SRES scenario A1B. The reference observations for the greater Alpine area were extracted from the European dataset E-OBS (produced by the ENSEMBLES project), which have an available resolution of 25 km. For the study area of Piedmont daily temperature and precipitation observations (covering the period from 1957 to the present) were carefully gridded on a 14 km grid over Piedmont region through the use of an optimal interpolation technique. Hence, we applied the multimodel superensemble technique to temperature fields, reducing the high biases of RCMs temperature field compared to observations in the control period. We also proposed the application of a brand new probabilistic multimodel superensemble dressing technique, already applied to weather forecast models successfully, to RCMS: the aim was to estimate precipitation fields, with careful description of precipitation probability density functions conditioned to the model outputs. This technique allowed for reducing the strong precipitation overestimation, arising from the use of RCMs, over the Alpine chain and to reproduce well the monthly behaviour of precipitation in the control period.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017E%26SS....4..554C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017E%26SS....4..554C"><span>Factorial inferential grid grouping and representativeness analysis for a systematic selection of representative grids</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Cheng, Guanhui; Huang, Guohe; Dong, Cong; Xu, Ye; Yao, Yao</p> <p>2017-08-01</p> <p>A factorial inferential grid grouping and representativeness analysis (FIGGRA) approach is developed to achieve a systematic selection of representative grids in large-scale climate change impact assessment and adaptation (LSCCIAA) studies and other fields of Earth and space sciences. FIGGRA is applied to representative-grid selection for temperature (Tas) and precipitation (Pr) over the Loess Plateau (LP) to verify methodological effectiveness. FIGGRA is effective at and outperforms existing grid-selection approaches (e.g., self-organizing maps) in multiple aspects such as clustering similar grids, differentiating dissimilar grids, and identifying representative grids for both Tas and Pr over LP. In comparison with Pr, the lower spatial heterogeneity and higher spatial discontinuity of Tas over LP lead to higher within-group similarity, lower between-group dissimilarity, lower grid grouping effectiveness, and higher grid representativeness; the lower interannual variability of the spatial distributions of Tas results in lower impacts of the interannual variability on the effectiveness of FIGGRA. For LP, the spatial climatic heterogeneity is the highest in January for Pr and in October for Tas; it decreases from spring, autumn, summer to winter for Tas and from summer, spring, autumn to winter for Pr. Two parameters, i.e., the statistical significance level (α) and the minimum number of grids in every climate zone (Nmin), and their joint effects are significant for the effectiveness of FIGGRA; normalization of a nonnormal climate-variable distribution is helpful for the effectiveness only for Pr. For FIGGRA-based LSCCIAA studies, a low value of Nmin is recommended for both Pr and Tas, and a high and medium value of α for Pr and Tas, respectively.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009EGUGA..1112296M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009EGUGA..1112296M"><span>Calculating distributed glacier mass balance for the Swiss Alps from RCM output: Development and testing of downscaling and validation methods</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Machguth, H.; Paul, F.; Kotlarski, S.; Hoelzle, M.</p> <p>2009-04-01</p> <p>Climate model output has been applied in several studies on glacier mass balance calculation. Hereby, computation of mass balance has mostly been performed at the native resolution of the climate model output or data from individual cells were selected and statistically downscaled. Little attention has been given to the issue of downscaling entire fields of climate model output to a resolution fine enough to compute glacier mass balance in rugged high-mountain terrain. In this study we explore the use of gridded output from a regional climate model (RCM) to drive a distributed mass balance model for the perimeter of the Swiss Alps and the time frame 1979-2003. Our focus lies on the development and testing of downscaling and validation methods. The mass balance model runs at daily steps and 100 m spatial resolution while the RCM REMO provides daily grids (approx. 18 km resolution) of dynamically downscaled re-analysis data. Interpolation techniques and sub-grid parametrizations are combined to bridge the gap in spatial resolution and to obtain daily input fields of air temperature, global radiation and precipitation. The meteorological input fields are compared to measurements at 14 high-elevation weather stations. Computed mass balances are compared to various sets of direct measurements, including stake readings and mass balances for entire glaciers. The validation procedure is performed separately for annual, winter and summer balances. Time series of mass balances for entire glaciers obtained from the model run agree well with observed time series. On the one hand, summer melt measured at stakes on several glaciers is well reproduced by the model, on the other hand, observed accumulation is either over- or underestimated. It is shown that these shifts are systematic and correlated to regional biases in the meteorological input fields. We conclude that the gap in spatial resolution is not a large drawback, while biases in RCM output are a major limitation to model performance. The development and testing of methods to reduce regionally variable biases in entire fields of RCM output should be a focus of pursuing studies.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010ems..confE.530S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010ems..confE.530S"><span>New planetary boundary layer parametrization in ECHAM5-HAM: Dynamical refinement of the vertical resolution</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Siegenthaler-Le Drian, C.; Spichtinger, P.; Lohmann, U.</p> <p>2010-09-01</p> <p>Marine stratocumulus-capped boundary layers exhibit a strong net cooling impact on the Earth-Atmosphere system. Moreover, they are highly persistent over subtropical oceans. Therefore climate models need to represent them well in order to make reliable projections of future climate. One of the reasons for the absence of stratocumuli in the general circulation model ECHAM5-HAM (Roeckner et al., 2003; Stier et al., 2005) is due to the limited vertical resolution. In the current model version, no vertical sub-grid scale variability of clouds is taken into account, such that clouds occupy the full vertical layer. Around the inversion on top of the planetary boundary layer (PBL), conserved variables often have a steep gradient, which in a GCM may produce large discretization errors (Bretherton and Park, 2009). This inversion has a large diurnal cycle and varies with location around the globe, which is difficult to represent in a classical, coarse Eulerian approach. Furthermore, Lenderink and Holtslag (2000) and Lock (2001) showed that an inconsistent numerical representation between the entrainment parametrization and the other schemes, particularly with the vertical advection can lead to the occurrence of 'numerical entrainment'. The problem can be resolved by introducing a dynamical inversion as introduced by Grenier and Bretherton (2001) and Lock (2001). As these features can be seen in our version of ECHAM5-HAM, our implementation is aimed to reduce the numerical entrainment and to better represent stratocumuli in ECHAM5-HAM. To better resolve stratocumulus clouds, their inversion and the interaction between the turbulent diffusion and the vertical advection, the vertical grid is dynamically refined. The new grid is based on the reconstruction of the profiles of variables experiencing a sharp gradient (temperature, mixing ratio) applying the method presented in Grenier and Bretherton (2001). In typical stratocumulus regions, an additional grid level is thus associated with the PBL top. In case a cloud can be formed, a new level is associated with the lifting condensation level as well. The regular grid plus the two additional levels define the new dynamical grid, which varies geographically and temporally. The physical processes are computed on this new dynamical grid, Consequently, the sharp gradients and the interaction between the different processes can be better resolved. Some results of this new parametrization will be presented. On a single column model set-up, the reconstruction method accurately finds the inversion at the PBL top for the EPIC stratocumulus case. Also, on a global scale, the occurrence of a successful reconstruction, which is restricted in typical stratocumulus regions, occurs with a high frequency. The impact of the new dynamical grid on clouds and the radiation balance will be presented in the talk. References [Bretherton and Park, 2009] Bretherton, C. S. and Park, S. (2009). A new moist turbulence parametrization in the community atmosphere model. J. Climate, 22:3422-3448. [Grenier and Bretherton, 2001] Grenier, H. and Bretherton, C. S. (2001). A moist parametrization for large-scale models and its application to subtropical cloud-topped marine boundary layers. Mon. Wea. Rev., 129:357-377. [Lenderink and Holtslag, 2000] Lenderink, G. and Holtslag, A. M. (2000). Evaluation of the kinetic energy approach for modeling turbulent fluxes in stratocumulus. Mon. Wea. Rev., 128:244-258. [Lock, 2001] Lock, A. P. (2001). The numerical representation of entrainment in parametrizations of boundary layer turbulent mixing. Mon. Wea. Rev., 129:1148-1163. [Roeckner et al., 2003] Roeckner, E., Bäuml, G., Bonaventura, L. et al. (2003). The atmospheric general circulation model echam5, part I: Model description. Technical Report 349, Max-Planck-Institute for Meteorology, Hamburg,Germany. [Stier et al., 2005] Stier, P., Feichter, J., Kinne, S. et al. (2005). The aerosol-climate model ECHAM5-HAM. Atmos. Chem. Phys., 5:1125-1156.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.fs.usda.gov/treesearch/pubs/25706','TREESEARCH'); return false;" href="https://www.fs.usda.gov/treesearch/pubs/25706"><span>Empirical analyses of plant-climate relationships for the western United States</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.fs.usda.gov/treesearch/">Treesearch</a></p> <p>Gerald E. Rehfeldt; Nicholas L. Crookston; Marcus V. Warwell; Jeffrey S. Evans</p> <p>2006-01-01</p> <p>The Random Forests multiple-regression tree was used to model climate profiles of 25 biotic communities of the western United States and nine of their constituent species. Analyses of the communities were based on a gridded sample of ca. 140,000 points, while those for the species used presence-absence data from ca. 120,000 locations. Independent variables included 35...</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li class="active"><span>10</span></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_10 --> <div id="page_11" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li class="active"><span>11</span></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="201"> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/1182238','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/1182238"><span>2014 Earth System Grid Federation and Ultrascale Visualization Climate Data Analysis Tools Conference Report</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Williams, Dean N.</p> <p>2015-01-27</p> <p>The climate and weather data science community met December 9–11, 2014, in Livermore, California, for the fourth annual Earth System Grid Federation (ESGF) and Ultrascale Visualization Climate Data Analysis Tools (UV-CDAT) Face-to-Face (F2F) Conference, hosted by the Department of Energy, National Aeronautics and Space Administration, National Oceanic and Atmospheric Administration, the European Infrastructure for the European Network of Earth System Modelling, and the Australian Department of Education. Both ESGF and UVCDATremain global collaborations committed to developing a new generation of open-source software infrastructure that provides distributed access and analysis to simulated and observed data from the climate and weather communities.more » The tools and infrastructure created under these international multi-agency collaborations are critical to understanding extreme weather conditions and long-term climate change. In addition, the F2F conference fosters a stronger climate and weather data science community and facilitates a stronger federated software infrastructure. The 2014 F2F conference detailed the progress of ESGF, UV-CDAT, and other community efforts over the year and sets new priorities and requirements for existing and impending national and international community projects, such as the Coupled Model Intercomparison Project Phase Six. Specifically discussed at the conference were project capabilities and enhancements needs for data distribution, analysis, visualization, hardware and network infrastructure, standards, and resources.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.A14F..01S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.A14F..01S"><span>High-resolution RCMs as pioneers for future GCMs</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Schar, C.; Ban, N.; Arteaga, A.; Charpilloz, C.; Di Girolamo, S.; Fuhrer, O.; Hoefler, T.; Leutwyler, D.; Lüthi, D.; Piaget, N.; Ruedisuehli, S.; Schlemmer, L.; Schulthess, T. C.; Wernli, H.</p> <p>2017-12-01</p> <p>Currently large efforts are underway to refine the horizontal resolution of global and regional climate models to O(1 km), with the intent to represent convective clouds explicitly rather than using semi-empirical parameterizations. This refinement will move the governing equations closer to first principles and is expected to reduce the uncertainties of climate models. High resolution is particularly attractive in order to better represent critical cloud feedback processes (e.g. related to global climate sensitivity and extratropical summer convection) and extreme events (such as heavy precipitation events, floods, and hurricanes). The presentation will be illustrated using decade-long simulations at 2 km horizontal grid spacing, some of these covering the European continent on a computational mesh with 1536x1536x60 grid points. To accomplish such simulations, use is made of emerging heterogeneous supercomputing architectures, using a version of the COSMO limited-area weather and climate model that is able to run entirely on GPUs. Results show that kilometer-scale resolution dramatically improves the simulation of precipitation in terms of the diurnal cycle and short-term extremes. The modeling framework is used to address changes of precipitation scaling with climate change. It is argued that already today, modern supercomputers would in principle enable global atmospheric convection-resolving climate simulations, provided appropriately refactored codes were available, and provided solutions were found to cope with the rapidly growing output volume. A discussion will be provided of key challenges affecting the design of future high-resolution climate models. It is suggested that km-scale RCMs should be exploited to pioneer this terrain, at a time when GCMs are not yet available at such resolutions. Areas of interest include the development of new parameterization schemes adequate for km-scale resolution, the exploration of new validation methodologies and data sets, the assessment of regional-scale climate feedback processes, and the development of alternative output analysis methodologies.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018AIPC.1952b0047S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018AIPC.1952b0047S"><span>A study on economic power dispatch grid connected PV power plant in educational institutes</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Singh, Kuldip; Kumar, M. Narendra; Mishra, Satyasis</p> <p>2018-04-01</p> <p>India has main concerns on environment and escalation of fuel prices with respect to diminution of fossil fuel reserves and the major focus on renewable Energy sources for power generation to fulfill the present and future energy demand. Installation of PV power plants in the Educational Institutions has grown up drastically throughout India. More PV power plant are integrated with load and grid through net metering. Therefore, this paper is an analysis of the 75kWp PV plant at chosen buses, considering the need of minimum demand from the grid. The case study is carried out for different generation level throughout the day and year w.r.t load and climate changes, load sharing on grid. The economic dispatch model developed for PV plant integrated with Grid.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFMIN11G..02W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFMIN11G..02W"><span>The Earth System Grid Federation (ESGF): Climate Science Infrastructure for Large-scale Data Management and Dissemination</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Williams, D. N.</p> <p>2015-12-01</p> <p>Progress in understanding and predicting climate change requires advanced tools to securely store, manage, access, process, analyze, and visualize enormous and distributed data sets. Only then can climate researchers understand the effects of climate change across all scales and use this information to inform policy decisions. With the advent of major international climate modeling intercomparisons, a need emerged within the climate-change research community to develop efficient, community-based tools to obtain relevant meteorological and other observational data, develop custom computational models, and export analysis tools for climate-change simulations. While many nascent efforts to fill these gaps appeared, they were not integrated and therefore did not benefit from collaborative development. Sharing huge data sets was difficult, and the lack of data standards prevented the merger of output data from different modeling groups. Thus began one of the largest-ever collaborative data efforts in climate science, resulting in the Earth System Grid Federation (ESGF), which is now used to disseminate model, observational, and reanalysis data for research assessed by the Intergovernmental Panel on Climate Change (IPCC). Today, ESGF is an open-source petabyte-level data storage and dissemination operational code-base that manages secure resources essential for climate change study. It is designed to remain robust even as data volumes grow exponentially. The internationally distributed, peer-to-peer ESGF "data cloud" archive represents the culmination of an effort that began in the late 1990s. ESGF portals are gateways to scientific data collections hosted at sites around the globe that allow the user to register and potentially access the entire ESGF network of data and services. The growing international interest in ESGF development efforts has attracted many others who want to make their data more widely available and easy to use. For example, the World Climate Research Program, which provides governance for CMIP, has now endorsed the ESGF software foundation to be used for ~70 other model intercomparison projects (MIPs), such as obs4MIPs, TAMIP, CFMIP, and GeoMIP. At present, more than 40 projects disseminate their data via ESGF.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5839517','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5839517"><span>Avoided climate impacts of urban and rural heat and cold waves over the U.S. using large climate model ensembles for RCP8.5 and RCP4.5</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Anderson, G.B.; Jones, B.; McGinnis, S.A.; Sanderson, B.</p> <p>2015-01-01</p> <p>Previous studies examining future changes in heat/cold waves using climate model ensembles have been limited to grid cell-average quantities. Here, we make use of an urban parameterization in the Community Earth System Model (CESM) that represents the urban heat island effect, which can exacerbate extreme heat but may ameliorate extreme cold in urban relative to rural areas. Heat/cold wave characteristics are derived for U.S. regions from a bias-corrected CESM 30-member ensemble for climate outcomes driven by the RCP8.5 forcing scenario and a 15-member ensemble driven by RCP4.5. Significant differences are found between urban and grid cell-average heat/cold wave characteristics. Most notably, urban heat waves for 1981–2005 are more intense than grid cell-average by 2.1°C (southeast) to 4.6°C (southwest), while cold waves are less intense. We assess the avoided climate impacts of urban heat/cold waves in 2061–2080 when following the lower forcing scenario. Urban heat wave days per year increase from 6 in 1981–2005 to up to 92 (southeast) in RCP8.5. Following RCP4.5 reduces heat wave days by about 50%. Large avoided impacts are demonstrated for individual communities; e.g., the longest heat wave for Houston in RCP4.5 is 38 days while in RCP8.5 there is one heat wave per year that is longer than a month with some lasting the entire summer. Heat waves also start later in the season in RCP4.5 (earliest are in early May) than RCP8.5 (mid-April), compared to 1981–2005 (late May). In some communities, cold wave events decrease from 2 per year for 1981–2005 to one-in-five year events in RCP4.5 and one-in-ten year events in RCP8.5. PMID:29520121</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFMGC13C0789S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFMGC13C0789S"><span>Resiliency of the Nation's Power Grid: Assessing Risks of Premature Failure of Large Power Transformers Under Climate Warming and Increased Heat Waves</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Schlosser, C. A.; Gao, X.; Morgan, E.</p> <p>2017-12-01</p> <p>The aging pieces of our nation's power grid - the largest machine ever built - are at a critical time. Key assets in the transmission system, including large power transformers (LPTs), are approaching their originally designed lifetimes. Moreover, extreme weather and climate events upon which these design lifetimes are partially based are expected to change. In particular, more frequent and intense heat waves can accelerate the degradation of LPTs' insulation/cooling system. Thus, there are likely thousands of LPTs across the United States under increasing risk of premature failure - yet this risk has not been assessed. In this study, we investigate the impact of climate warming and corresponding shifts in heat waves for critical LPTs located in the Northeast corridor of the United States to assess: To what extent do changes in heat waves/events present a rising threat to the transformer network over the Northeast U.S. and to what extent can climate mitigation reduce this risk? This study focuses on a collection of LPTs with a high degree of "betweenness" - while recognizing other factors such as: connectivity, voltage rating, MVA rating, approximate price, weight, location/proximity to major transportation routes, and age. To assess the risk of future change in heat wave occurrence we use an analogue method, which detects the occurrence of heat waves based on associated large-scale atmospheric conditions. This method is compared to the more conventional approach that uses model-simulated daily maximum temperature. Under future climate warming scenarios, multi-model medians of both methods indicate strong increases in heat wave frequency during the latter half of this century. Under weak climate mitigation - the risks imposed from heat wave occurrence could quadruple, but a modest mitigation scenario cuts the increasing threat in half. As important, the analogue method substantially improves the model consensus through reduction of the interquartile range by a factor of three. The improved inter-model consensus is viewed as a promising step toward providing more actionable climate information. Ultimately - this technique could be applied to the entirety of the U.S. power grid as well as other weather extrema (e.g. precipitation, ice, and wind) as well as assess current and future topologies of any electricity system.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2008EPJST.161..167V','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2008EPJST.161..167V"><span>Modelling the spread of ragweed: Effects of habitat, climate change and diffusion</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Vogl, G.; Smolik, M.; Stadler, L.-M.; Leitner, M.; Essl, F.; Dullinger, S.; Kleinbauer, I.; Peterseil, J.</p> <p>2008-07-01</p> <p>Ragweed (Ambrosia artemisiifolia L.) is an annual plant native in North America which has been invading Central Europe for 150 years. Caused by the warming of the European climate its spread process has accelerated in the last few decades. The pollen of ragweed evokes heavy allergies and what probably counts even more because of its bloom rather late in summer causes a second wave of allergy when other pollen allergies have decayed. We have reconstructed the invasion process of ragweed in Austria by collecting all records until the year 2005. Austria was subdivided into more than 2600 grid cells of ≈35 text{km}^2 each. Ragweed records were related to environmental descriptors (average temperatures, land use, etc.) by means of logistic regression models, and the suitability of grid cells as habitat for ragweed was determined. This enabled modelling of the diffusive spread of ragweed from 1990 to 2005. The results of the simulations were compared with the observed data, and thus the model was optimised. We then incorporated regional climate change models, in particular increased July mean temperatures of +2.3 ^circtext{C} in 2050, increasing considerably future habitat suitability. This is used for predicting the drastic dispersal of ragweed during the forthcoming decades.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFM.H21M..06N','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFM.H21M..06N"><span>Multi-Scale Simulations of Past and Future Projections of Hydrology in Lake Tahoe Basin, California-Nevada (Invited)</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Niswonger, R. G.; Huntington, J. L.; Dettinger, M. D.; Rajagopal, S.; Gardner, M.; Morton, C. G.; Reeves, D. M.; Pohll, G. M.</p> <p>2013-12-01</p> <p>Water resources in the Tahoe basin are susceptible to long-term climate change and extreme events because it is a middle-altitude, snow-dominated basin that experiences large inter-annual climate variations. Lake Tahoe provides critical water supply for its basin and downstream populations, but changes in water supply are obscured by complex climatic and hydrologic gradients across the high relief, geologically complex basin. An integrated surface and groundwater model of the Lake Tahoe basin has been developed using GSFLOW to assess the effects of climate change and extreme events on surface and groundwater resources. Key hydrologic mechanisms are identified with this model that explains recent changes in water resources of the region. Critical vulnerabilities of regional water-supplies and hazards also were explored. Maintaining a balance between (a) accurate representation of spatial features (e.g., geology, streams, and topography) and hydrologic response (i.e., groundwater, stream, lake, and wetland flows and storages), and (b) computational efficiency, is a necessity for the desired model applications. Potential climatic influences on water resources are analyzed here in simulations of long-term water-availability and flood responses to selected 100-year climate-model projections. GSFLOW is also used to simulate a scenario depicting an especially extreme storm event that was constructed from a combination of two historical atmospheric-river storm events as part of the USGS MultiHazards Demonstration Project. Historical simulated groundwater levels, streamflow, wetlands, and lake levels compare well with measured values for a 30-year historical simulation period. Results are consistent for both small and large model grid cell sizes, due to the model's ability to represent water table altitude, streams, and other hydrologic features at the sub-grid scale. Simulated hydrologic responses are affected by climate change, where less groundwater resources will be available during more frequent droughts. Simulated floods for the region indicate issues related to drainage in the developed areas around Lake Tahoe, and necessary dam releases that create downstream flood risks.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFM.H51H1590Y','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFM.H51H1590Y"><span>Projected Irrigation Requirement Under Climate Change in Korean Peninsula by Apply Global Hydrologic Model to Local Scale.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Yang, B.; Lee, D. K.</p> <p>2016-12-01</p> <p>Understanding spatial distribution of irrigation requirement is critically important for agricultural water management. However, many studies considering future agricultural water management in Korea assessed irrigation requirement on watershed or administrative district scale, but have not accounted the spatial distribution. Lumped hydrologic model has typically used in Korea for simulating watershed scale irrigation requirement, while distribution hydrologic model can simulate the spatial distribution grid by grid. To overcome this shortcoming, here we applied a grid base global hydrologic model (H08) into local scale to estimate spatial distribution under future irrigation requirement of Korean Peninsula. Korea is one of the world's most densely populated countries, with also high produce and demand of rice which requires higher soil moisture than other crops. Although, most of the precipitation concentrate in particular season and disagree with crop growth season. This precipitation character makes management of agricultural water which is approximately 60% of total water usage critical issue in Korea. Furthermore, under future climate change, the precipitation predicted to be more concentrated and necessary need change of future water management plan. In order to apply global hydrological model into local scale, we selected appropriate major crops under social and local climate condition in Korea to estimate cropping area and yield, and revised the cropping area map more accurately. As a result, future irrigation requirement estimation varies under each projection, however, slightly decreased in most case. The simulation reveals, evapotranspiration increase slightly while effective precipitation also increase to balance the irrigation requirement. This finding suggest practical guideline to decision makers for further agricultural water management plan including future development of water supply plan to resolve water scarcity.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013ClDy...40.2801O','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013ClDy...40.2801O"><span>Multidecadal simulation of coastal fog with a regional climate model</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>O'Brien, Travis A.; Sloan, Lisa C.; Chuang, Patrick Y.; Faloona, Ian C.; Johnstone, James A.</p> <p>2013-06-01</p> <p>In order to model stratocumulus clouds and coastal fog, we have coupled the University of Washington boundary layer model to the regional climate model, RegCM (RegCM-UW). By comparing fog occurrences observed at various coastal airports in the western United States, we show that RegCM-UW has success at modeling the spatial and temporal (diurnal, seasonal, and interannual) climatology of northern California coastal fog. The quality of the modeled fog estimate depends on whether coast-adjacent ocean or land grid cells are used; for the model runs shown here, the oceanic grid cells seem to be most appropriate. The interannual variability of oceanic northern California summertime fog, from a multi-decadal simulation, has a high and statistically significant correlation with the observed interannual variability ( r = 0.72), which indicates that RegCM-UW is capable of investigating the response of fog to long-term climatological forcing. While RegCM-UW has a number of aspects that would benefit from further investigation and development, RegCM-UW is a new tool for investigating the climatology of coastal fog and the physical processes that govern it. We expect that with appropriate physical parameterizations and moderate horizontal resolution, other climate models should be capable of simulating coastal fog. The source code for RegCM-UW is publicly available, under the GNU license, through the International Centre for Theoretical Physics.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/19487199','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/19487199"><span>Multi-objective optimization of GENIE Earth system models.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Price, Andrew R; Myerscough, Richard J; Voutchkov, Ivan I; Marsh, Robert; Cox, Simon J</p> <p>2009-07-13</p> <p>The tuning of parameters in climate models is essential to provide reliable long-term forecasts of Earth system behaviour. We apply a multi-objective optimization algorithm to the problem of parameter estimation in climate models. This optimization process involves the iterative evaluation of response surface models (RSMs), followed by the execution of multiple Earth system simulations. These computations require an infrastructure that provides high-performance computing for building and searching the RSMs and high-throughput computing for the concurrent evaluation of a large number of models. Grid computing technology is therefore essential to make this algorithm practical for members of the GENIE project.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..1715318R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..1715318R"><span>Visualization and Analysis of Climate Simulation Performance Data</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Röber, Niklas; Adamidis, Panagiotis; Behrens, Jörg</p> <p>2015-04-01</p> <p>Visualization is the key process of transforming abstract (scientific) data into a graphical representation, to aid in the understanding of the information hidden within the data. Climate simulation data sets are typically quite large, time varying, and consist of many different variables sampled on an underlying grid. A large variety of climate models - and sub models - exist to simulate various aspects of the climate system. Generally, one is mainly interested in the physical variables produced by the simulation runs, but model developers are also interested in performance data measured along with these simulations. Climate simulation models are carefully developed complex software systems, designed to run in parallel on large HPC systems. An important goal thereby is to utilize the entire hardware as efficiently as possible, that is, to distribute the workload as even as possible among the individual components. This is a very challenging task, and detailed performance data, such as timings, cache misses etc. have to be used to locate and understand performance problems in order to optimize the model implementation. Furthermore, the correlation of performance data to the processes of the application and the sub-domains of the decomposed underlying grid is vital when addressing communication and load imbalance issues. High resolution climate simulations are carried out on tens to hundreds of thousands of cores, thus yielding a vast amount of profiling data, which cannot be analyzed without appropriate visualization techniques. This PICO presentation displays and discusses the ICON simulation model, which is jointly developed by the Max Planck Institute for Meteorology and the German Weather Service and in partnership with DKRZ. The visualization and analysis of the models performance data allows us to optimize and fine tune the model, as well as to understand its execution on the HPC system. We show and discuss our workflow, as well as present new ideas and solutions that greatly aided our understanding. The software employed is based on Avizo Green, ParaView and SimVis, as well as own developed software extensions.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFM.H43J1086J','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFM.H43J1086J"><span>Adapting the iSNOBAL model for improved visualization in a GIS environment</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Johansen, W. J.; Delparte, D.</p> <p>2014-12-01</p> <p>Snowmelt is a primary means of crucial water resources in much of the western United States. Researchers are developing models that estimate snowmelt to aid in water resource management. One such model is the image snowcover energy and mass balance (iSNOBAL) model. It uses input climate grids to simulate the development and melting of snowpack in mountainous regions. This study looks at applying this model to the Reynolds Creek Experimental Watershed in southwestern Idaho, utilizing novel approaches incorporating geographic information systems (GIS). To improve visualization of the iSNOBAL model, we have adapted it to run in a GIS environment. This type of environment is suited to both the input grid creation and the visualization of results. The data used for input grid creation can be stored locally or on a web-server. Kriging interpolation embedded within Python scripts are used to create air temperature, soil temperature, humidity, and precipitation grids, while built-in GIS and existing tools are used to create solar radiation and wind grids. Additional Python scripting is then used to perform model calculations. The final product is a user-friendly and accessible version of the iSNOBAL model, including the ability to easily visualize and interact with model results, all within a web- or desktop-based GIS environment. This environment allows for interactive manipulation of model parameters and visualization of the resulting input grids for the model calculations. Future work is moving towards adapting the model further for use in a 3D gaming engine for improved visualization and interaction.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2003AGUFM.C11B0806H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2003AGUFM.C11B0806H"><span>California's Snow Gun and its implications for mass balance predictions under greenhouse warming</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Howat, I.; Snyder, M.; Tulaczyk, S.; Sloan, L.</p> <p>2003-12-01</p> <p>Precipitation has received limited treatment in glacier and snowpack mass balance models, largely due to the poor resolution and confidence of precipitation predictions relative to temperature predictions derived from atmospheric models. Most snow and glacier mass balance models rely on statistical or lapse rate-based downscaling of general or regional circulation models (GCM's and RCM's), essentially decoupling sub-grid scale, orographically-driven evolution of atmospheric heat and moisture. Such models invariably predict large losses in the snow and ice volume under greenhouse warming. However, positive trends in the mass balance of glaciers in some warming maritime climates, as well as at high elevations of the Greenland Ice Sheet, suggest that increased precipitation may play an important role in snow- and glacier-climate interactions. Here, we present a half century of April snowpack data from the Sierra Nevada and Cascade mountains of California, USA. This high-density network of snow-course data indicates that a gain in winter snow accumulation at higher elevations has compensated loss in snow volume at lower elevations by over 50% and has led to glacier expansion on Mt. Shasta. These trends are concurrent with a region-wide increase in winter temperatures up to 2° C. They result from the orographic lifting and saturation of warmer, more humid air leading to increased precipitation at higher elevations. Previous studies have invoked such a "Snow Gun" effect to explain contemporaneous records of Tertiary ocean warming and rapid glacial expansion. A climatological context of the California's "snow gun" effect is elucidated by correlation between the elevation distribution of April SWE observations and the phase of the Pacific Decadal Oscillation and the El Nino Southern Oscillation, both controlling the heat and moisture delivered to the U.S. Pacific coast. The existence of a significant "Snow Gun" effect presents two challenges to snow and glacier mass balance modeling. Firstly, the link between amplification of orographic precipitation and the temporal evolution of ocean-climate oscillations indicates that prediction of future mass balance trends requires consideration of the timing and amplitude of such oscillations. Only recently have ocean-atmosphere models begun to realistically produce such temporal variability. Secondly, the steepening snow mass-balance elevation-gradient associated with the "Snow Gun" implies greater spatial variability in balance with warming. In a warming climate, orographic processes at a scale finer that the highest resolution RCM (>20km grid) become increasingly important and predictions based on lower elevations become increasingly inaccurate for higher elevations. Therefore, thermodynamic interaction between atmospheric heat, moisture and topography must be included in downscaling techniques. In order to demonstrate the importance of the thermodynamic downscaling in mass balance predictions, we nest a high-resolution (100m grid), coupled Orographic Precipitation and Surface Energy balance Model (OPSEM) into the RegC2.5 RCM (40 km grid) and compare results. We apply this nesting technique to Mt. Shasta, California, an area of high topography (~4000m) relative to its RegCM2.5 grid elevation (1289m). These models compute average April snow volume under present and doubled-present Atmospheric CO2 concentrations. While the RegCM2.5 regional model predicts an 83% decrease in April SWE, OPSEM predicts a 16% increase. These results indicate that thermodynamic interactions between the atmosphere and topography at sub- RCM grid resolution must be considered in mass balance models.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/19900010409','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19900010409"><span>Regional climate change predictions from the Goddard Institute for Space Studies high resolution GCM</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Crane, Robert G.; Hewitson, Bruce</p> <p>1990-01-01</p> <p>Model simulations of global climate change are seen as an essential component of any program aimed at understanding human impact on the global environment. A major weakness of current general circulation models (GCMs), however, is their inability to predict reliably the regional consequences of a global scale change, and it is these regional scale predictions that are necessary for studies of human/environmental response. This research is directed toward the development of a methodology for the validation of the synoptic scale climatology of GCMs. This is developed with regard to the Goddard Institute for Space Studies (GISS) GCM Model 2, with the specific objective of using the synoptic circulation form a doubles CO2 simulation to estimate regional climate change over North America, south of Hudson Bay. This progress report is specifically concerned with validating the synoptic climatology of the GISS GCM, and developing the transfer function to derive grid-point temperatures from the synoptic circulation. Principal Components Analysis is used to characterize the primary modes of the spatial and temporal variability in the observed and simulated climate, and the model validation is based on correlations between component loadings, and power spectral analysis of the component scores. The results show that the high resolution GISS model does an excellent job of simulating the synoptic circulation over the U.S., and that grid-point temperatures can be predicted with reasonable accuracy from the circulation patterns.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19930055336&hterms=coverage&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D80%26Ntt%3Dcoverage','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19930055336&hterms=coverage&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D80%26Ntt%3Dcoverage"><span>Estimation of the fractional coverage of rainfall in climate models</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Eltahir, E. A. B.; Bras, R. L.</p> <p>1993-01-01</p> <p>The fraction of the grid cell area covered by rainfall, mu, is an essential parameter in descriptions of land surface hydrology in climate models. A simple procedure is presented for estimating this fraction, based on extensive observations of storm areas and rainfall volumes. Storm area and rainfall volume are often linearly related; this relation can be used to compute the storm area from the volume of rainfall simulated by a climate model. A formula is developed for computing mu, which describes the dependence of the fractional coverage of rainfall on the season of the year, the geographical region, rainfall volume, and the spatial and temporal resolution of the model. The new formula is applied in computing mu over the Amazon region. Significant temporal variability in the fractional coverage of rainfall is demonstrated. The implications of this variability for the modeling of land surface hydrology in climate models are discussed.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19990103607&hterms=Reasons+Motivation&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3DReasons%2BMotivation','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19990103607&hterms=Reasons+Motivation&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3DReasons%2BMotivation"><span>Physically-Derived Dynamical Cores in Atmospheric General Circulation Models</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Rood, Richard B.; Lin, Shian-Jiann</p> <p>1999-01-01</p> <p>The algorithm chosen to represent the advection in atmospheric models is often used as the primary attribute to classify the model. Meteorological models are generally classified as spectral or grid point, with the term grid point implying discretization using finite differences. These traditional approaches have a number of shortcomings that render them non-physical. That is, they provide approximate solutions to the conservation equations that do not obey the fundamental laws of physics. The most commonly discussed shortcomings are overshoots and undershoots which manifest themselves most overtly in the constituent continuity equation. For this reason many climate models have special algorithms to model water vapor advection. This talk focuses on the development of an atmospheric general circulation model which uses a consistent physically-based advection algorithm in all aspects of the model formulation. The shallow-water model is generalized to three dimensions and combined with the physics parameterizations of NCAR's Community Climate Model. The scientific motivation for the development is to increase the integrity of the underlying fluid dynamics so that the physics terms can be more effectively isolated, examined, and improved. The expected benefits of the new model are discussed and results from the initial integrations will be presented.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19990115820&hterms=Reasons+Motivation&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3DReasons%2BMotivation','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19990115820&hterms=Reasons+Motivation&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3DReasons%2BMotivation"><span>Physically-Derived Dynamical Cores in Atmospheric General Circulation Models</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Rood, Richard B.; Lin, Shian-Kiann</p> <p>1999-01-01</p> <p>The algorithm chosen to represent the advection in atmospheric models is often used as the primary attribute to classify the model. Meteorological models are generally classified as spectral or grid point, with the term grid point implying discretization using finite differences. These traditional approaches have a number of shortcomings that render them non-physical. That is, they provide approximate solutions to the conservation equations that do not obey the fundamental laws of physics. The most commonly discussed shortcomings are overshoots and undershoots which manifest themselves most overtly in the constituent continuity equation. For this reason many climate models have special algorithms to model water vapor advection. This talk focuses on the development of an atmospheric general circulation model which uses a consistent physically-based advection algorithm in all aspects of the model formulation. The shallow-water model of Lin and Rood (QJRMS, 1997) is generalized to three dimensions and combined with the physics parameterizations of NCAR's Community Climate Model. The scientific motivation for the development is to increase the integrity of the underlying fluid dynamics so that the physics terms can be more effectively isolated, examined, and improved. The expected benefits of the new model are discussed and results from the initial integrations will be presented.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20100033542','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20100033542"><span>Characterization of Cloud Water-Content Distribution</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Lee, Seungwon</p> <p>2010-01-01</p> <p>The development of realistic cloud parameterizations for climate models requires accurate characterizations of subgrid distributions of thermodynamic variables. To this end, a software tool was developed to characterize cloud water-content distributions in climate-model sub-grid scales. This software characterizes distributions of cloud water content with respect to cloud phase, cloud type, precipitation occurrence, and geo-location using CloudSat radar measurements. It uses a statistical method called maximum likelihood estimation to estimate the probability density function of the cloud water content.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2008AGUFMIN32A..07W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2008AGUFMIN32A..07W"><span>Assembling Large, Multi-Sensor Climate Datasets Using the SciFlo Grid Workflow System</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wilson, B. D.; Manipon, G.; Xing, Z.; Fetzer, E.</p> <p>2008-12-01</p> <p>NASA's Earth Observing System (EOS) is the world's most ambitious facility for studying global climate change. The mandate now is to combine measurements from the instruments on the A-Train platforms (AIRS, AMSR-E, MODIS, MISR, MLS, and CloudSat) and other Earth probes to enable large-scale studies of climate change over periods of years to decades. However, moving from predominantly single-instrument studies to a multi-sensor, measurement-based model for long-duration analysis of important climate variables presents serious challenges for large-scale data mining and data fusion. For example, one might want to compare temperature and water vapor retrievals from one instrument (AIRS) to another instrument (MODIS), and to a model (ECMWF), stratify the comparisons using a classification of the cloud scenes from CloudSat, and repeat the entire analysis over years of AIRS data. To perform such an analysis, one must discover & access multiple datasets from remote sites, find the space/time matchups between instruments swaths and model grids, understand the quality flags and uncertainties for retrieved physical variables, and assemble merged datasets for further scientific and statistical analysis. To meet these large-scale challenges, we are utilizing a Grid computing and dataflow framework, named SciFlo, in which we are deploying a set of versatile and reusable operators for data query, access, subsetting, co-registration, mining, fusion, and advanced statistical analysis. SciFlo is a semantically-enabled ("smart") Grid Workflow system that ties together a peer-to-peer network of computers into an efficient engine for distributed computation. The SciFlo workflow engine enables scientists to do multi-instrument Earth Science by assembling remotely-invokable Web Services (SOAP or http GET URLs), native executables, command-line scripts, and Python codes into a distributed computing flow. A scientist visually authors the graph of operation in the VizFlow GUI, or uses a text editor to modify the simple XML workflow documents. The SciFlo client & server engines optimize the execution of such distributed workflows and allow the user to transparently find and use datasets and operators without worrying about the actual location of the Grid resources. The engine transparently moves data to the operators, and moves operators to the data (on the dozen trusted SciFlo nodes). SciFlo also deploys a variety of Data Grid services to: query datasets in space and time, locate & retrieve on-line data granules, provide on-the-fly variable and spatial subsetting, and perform pairwise instrument matchups for A-Train datasets. These services are combined into efficient workflows to assemble the desired large-scale, merged climate datasets. SciFlo is currently being applied in several large climate studies: comparisons of aerosol optical depth between MODIS, MISR, AERONET ground network, and U. Michigan's IMPACT aerosol transport model; characterization of long-term biases in microwave and infrared instruments (AIRS, MLS) by comparisons to GPS temperature retrievals accurate to 0.1 degrees Kelvin; and construction of a decade-long, multi-sensor water vapor climatology stratified by classified cloud scene by bringing together datasets from AIRS/AMSU, AMSR-E, MLS, MODIS, and CloudSat (NASA MEASUREs grant, Fetzer PI). The presentation will discuss the SciFlo technologies, their application in these distributed workflows, and the many challenges encountered in assembling and analyzing these massive datasets.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li class="active"><span>11</span></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_11 --> <div id="page_12" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li class="active"><span>12</span></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="221"> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009EGUGA..1111815W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009EGUGA..1111815W"><span>Assembling Large, Multi-Sensor Climate Datasets Using the SciFlo Grid Workflow System</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wilson, B.; Manipon, G.; Xing, Z.; Fetzer, E.</p> <p>2009-04-01</p> <p>NASA's Earth Observing System (EOS) is an ambitious facility for studying global climate change. The mandate now is to combine measurements from the instruments on the "A-Train" platforms (AIRS, AMSR-E, MODIS, MISR, MLS, and CloudSat) and other Earth probes to enable large-scale studies of climate change over periods of years to decades. However, moving from predominantly single-instrument studies to a multi-sensor, measurement-based model for long-duration analysis of important climate variables presents serious challenges for large-scale data mining and data fusion. For example, one might want to compare temperature and water vapor retrievals from one instrument (AIRS) to another instrument (MODIS), and to a model (ECMWF), stratify the comparisons using a classification of the "cloud scenes" from CloudSat, and repeat the entire analysis over years of AIRS data. To perform such an analysis, one must discover & access multiple datasets from remote sites, find the space/time "matchups" between instruments swaths and model grids, understand the quality flags and uncertainties for retrieved physical variables, assemble merged datasets, and compute fused products for further scientific and statistical analysis. To meet these large-scale challenges, we are utilizing a Grid computing and dataflow framework, named SciFlo, in which we are deploying a set of versatile and reusable operators for data query, access, subsetting, co-registration, mining, fusion, and advanced statistical analysis. SciFlo is a semantically-enabled ("smart") Grid Workflow system that ties together a peer-to-peer network of computers into an efficient engine for distributed computation. The SciFlo workflow engine enables scientists to do multi-instrument Earth Science by assembling remotely-invokable Web Services (SOAP or http GET URLs), native executables, command-line scripts, and Python codes into a distributed computing flow. A scientist visually authors the graph of operation in the VizFlow GUI, or uses a text editor to modify the simple XML workflow documents. The SciFlo client & server engines optimize the execution of such distributed workflows and allow the user to transparently find and use datasets and operators without worrying about the actual location of the Grid resources. The engine transparently moves data to the operators, and moves operators to the data (on the dozen trusted SciFlo nodes). SciFlo also deploys a variety of Data Grid services to: query datasets in space and time, locate & retrieve on-line data granules, provide on-the-fly variable and spatial subsetting, perform pairwise instrument matchups for A-Train datasets, and compute fused products. These services are combined into efficient workflows to assemble the desired large-scale, merged climate datasets. SciFlo is currently being applied in several large climate studies: comparisons of aerosol optical depth between MODIS, MISR, AERONET ground network, and U. Michigan's IMPACT aerosol transport model; characterization of long-term biases in microwave and infrared instruments (AIRS, MLS) by comparisons to GPS temperature retrievals accurate to 0.1 degrees Kelvin; and construction of a decade-long, multi-sensor water vapor climatology stratified by classified cloud scene by bringing together datasets from AIRS/AMSU, AMSR-E, MLS, MODIS, and CloudSat (NASA MEASUREs grant, Fetzer PI). The presentation will discuss the SciFlo technologies, their application in these distributed workflows, and the many challenges encountered in assembling and analyzing these massive datasets.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018ThApC.131.1043L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018ThApC.131.1043L"><span>Climate change scenarios of heat waves in Central Europe and their uncertainties</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Lhotka, Ondřej; Kyselý, Jan; Farda, Aleš</p> <p>2018-02-01</p> <p>The study examines climate change scenarios of Central European heat waves with a focus on related uncertainties in a large ensemble of regional climate model (RCM) simulations from the EURO-CORDEX and ENSEMBLES projects. Historical runs (1970-1999) driven by global climate models (GCMs) are evaluated against the E-OBS gridded data set in the first step. Although the RCMs are found to reproduce the frequency of heat waves quite well, those RCMs with the coarser grid (25 and 50 km) considerably overestimate the frequency of severe heat waves. This deficiency is improved in higher-resolution (12.5 km) EURO-CORDEX RCMs. In the near future (2020-2049), heat waves are projected to be nearly twice as frequent in comparison to the modelled historical period, and the increase is even larger for severe heat waves. Uncertainty originates mainly from the selection of RCMs and GCMs because the increase is similar for all concentration scenarios. For the late twenty-first century (2070-2099), a substantial increase in heat wave frequencies is projected, the magnitude of which depends mainly upon concentration scenario. Three to four heat waves per summer are projected in this period (compared to less than one in the recent climate), and severe heat waves are likely to become a regular phenomenon. This increment is primarily driven by a positive shift of temperature distribution, but changes in its scale and enhanced temporal autocorrelation of temperature also contribute to the projected increase in heat wave frequencies.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://eosweb.larc.nasa.gov/project/srb/srb_table','SCIGOV-ASDC'); return false;" href="https://eosweb.larc.nasa.gov/project/srb/srb_table"><span>SRB Data and Information</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://eosweb.larc.nasa.gov/">Atmospheric Science Data Center </a></p> <p></p> <p>2017-01-13</p> <p>... grid. Model inputs of cloud amounts and other atmospheric state parameters are also available in some of the data sets. Primary inputs to ... Analysis (SMOBA), an assimilation product from NOAA's Climate Prediction Center. SRB products are reformatted for the use of ...</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010AGUFM.A21G0180N','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010AGUFM.A21G0180N"><span>Analysis of the Effect of Interior Nudging on Temperature and Precipitation Distributions of Multi-year Regional Climate Simulations</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Nolte, C. G.; Otte, T. L.; Bowden, J. H.; Otte, M. J.</p> <p>2010-12-01</p> <p>There is disagreement in the regional climate modeling community as to the appropriateness of the use of internal nudging. Some investigators argue that the regional model should be minimally constrained and allowed to respond to regional-scale forcing, while others have noted that in the absence of interior nudging, significant large-scale discrepancies develop between the regional model solution and the driving coarse-scale fields. These discrepancies lead to reduced confidence in the ability of regional climate models to dynamically downscale global climate model simulations under climate change scenarios, and detract from the usability of the regional simulations for impact assessments. The advantages and limitations of interior nudging schemes for regional climate modeling are investigated in this study. Multi-year simulations using the WRF model driven by reanalysis data over the continental United States at 36km resolution are conducted using spectral nudging, grid point nudging, and for a base case without interior nudging. The means, distributions, and inter-annual variability of temperature and precipitation will be evaluated in comparison to regional analyses.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012AGUFMGC11C1013M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012AGUFMGC11C1013M"><span>Climatological Impact of Atmospheric River Based on NARCCAP and DRI-RCM Datasets</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Mejia, J. F.; Perryman, N. M.</p> <p>2012-12-01</p> <p>This study evaluates spatial responses of extreme precipitation environments, typically associated with Atmospheric River events, using Regional Climate Model (RCM) output from NARCCAP dataset (50km grid size) and the Desert Research Institute-RCM simulations (36 and 12 km grid size). For this study, a pattern-detection algorithm was developed to characterize Atmospheric Rivers (ARs)-like features from climate models. Topological analysis of the enhanced elongated moisture flux (500-300hPa; daily means) cores is used to objectively characterize such AR features in two distinct groups: (i) zonal, north Pacific ARs, and (ii) subtropical ARs, also known as "Pineapple Express" events. We computed the climatological responses of the different RCMs upon these two AR groups, from which intricate differences among RCMs stand out. This study presents these climatological responses from historical and scenario driven simulations, as well as implications for precipitation extreme-value analyses.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFM.H43P..07M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFM.H43P..07M"><span>Toward Improving Predictability of Extreme Hydrometeorological Events: the Use of Multi-scale Climate Modeling in the Northern High Plains</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Munoz-Arriola, F.; Torres-Alavez, J.; Mohamad Abadi, A.; Walko, R. L.</p> <p>2014-12-01</p> <p>Our goal is to investigate possible sources of predictability of hydrometeorological extreme events in the Northern High Plains. Hydrometeorological extreme events are considered the most costly natural phenomena. Water deficits and surpluses highlight how the water-climate interdependence becomes crucial in areas where single activities drive economies such as Agriculture in the NHP. Nonetheless we recognize the Water-Climate interdependence and the regulatory role that human activities play, we still grapple to identify what sources of predictability could be added to flood and drought forecasts. To identify the benefit of multi-scale climate modeling and the role of initial conditions on flood and drought predictability on the NHP, we use the Ocean Land Atmospheric Model (OLAM). OLAM is characterized by a dynamic core with a global geodesic grid with hexagonal (and variably refined) mesh cells and a finite volume discretization of the full compressible Navier Stokes equations, a cut-grid cell method for topography (that reduces error in computational gradient computation and anomalous vertical dispersion). Our hypothesis is that wet conditions will drive OLAM's simulations of precipitation to wetter conditions affecting both flood forecast and drought forecast. To test this hypothesis we simulate precipitation during identified historical flood events followed by drought events in the NHP (i.e. 2011-2012 years). We initialized OLAM with CFS-data 1-10 days previous to a flooding event (as initial conditions) to explore (1) short-term and high-resolution and (2) long-term and coarse-resolution simulations of flood and drought events, respectively. While floods are assessed during a maximum of 15-days refined-mesh simulations, drought is evaluated during the following 15 months. Simulated precipitation will be compared with the Sub-continental Observation Dataset, a gridded 1/16th degree resolution data obtained from climatological stations in Canada, US, and Mexico. This in-progress research will ultimately contribute to integrate OLAM and VIC models and improve predictability of extreme hydrometeorological events.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=228043&Lab=NHEERL&keyword=evapotranspiration&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50','EPA-EIMS'); return false;" href="https://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=228043&Lab=NHEERL&keyword=evapotranspiration&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50"><span>A temperature-precipitation-based model of thiry-year mean snowpack accumulation and melt in Oregon, USA</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://oaspub.epa.gov/eims/query.page">EPA Science Inventory</a></p> <p></p> <p></p> <p>High-resolution, spatially extensive climate grids can be useful in regional hydrologic applications. However, in regions where precipitation is dominated by snow, snowmelt models are often used to account for timing and magnitude of water delivery. We developed an empirical, non...</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFM.A54D..04P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFM.A54D..04P"><span>Tradeoffs in Acceleration and Initialization of Superparameterized Global Atmospheric Models for MJO and Climate Science</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Pritchard, M. S.; Bretherton, C. S.; DeMott, C. A.</p> <p>2014-12-01</p> <p>New trade-offs are discussed in the cloud superparameterization approach to explicitly representing deep convection in global climate models. Intrinsic predictability tests show that the memory of cloud-resolving-scale organization is not critical for producing desired modes of organized convection such as the Madden-Julian Oscillation (MJO). This has implications for the feasibility of data assimilation and real-world initialization for superparameterized weather forecasting. Climate simulation sensitivity tests demonstrate that 400% acceleration of cloud superparameterization is possible by restricting the 32-128 km scale regime without deteriorating the realism of the simulated MJO but the number of cloud resolving model grid columns is discovered to constrain the efficiency of vertical mixing, with consequences for the simulated liquid cloud climatology. Tuning opportunities for next generation accelerated superparameterized climate models are discussed.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014EPJST.223.2383H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014EPJST.223.2383H"><span>Interdisciplinary challenges in the study of power grid resilience and stability and their relation to extreme weather events</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Heitzig, J.; Fujiwara, N.; Aihara, K.; Kurths, J.</p> <p>2014-10-01</p> <p>This topical issue collects contributions to the interdisciplinary study of power grid stability in face of increasing volatility of energy production and consumption due to increasing renewable energy infeed and changing climatic conditions. The individual papers focus on different aspects of this field and bring together modern achievements from various disciplines, in particular complex systems science, nonlinear data analysis, control theory, electrical engineering, and climatology. Main topics considered here are prediction and volatility of renewable infeed, modelling and theoretical analysis of power grid topology, dynamics and stability, relationships between stability and complex network topology, and improvements via topological changes or control. Impacts for the design of smart power grids are discussed in detail.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFM.A51H0163K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFM.A51H0163K"><span>Widespread, Very Heavy Precipitation Events in Contemporary and Scenario Summer Climates from NARCCAP Simulations</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kawazoe, S.; Gutowski, W. J., Jr.</p> <p>2015-12-01</p> <p>We analyze the ability of regional climate models (RCMs) to simulate very heavy daily precipitation and supporting processes for both contemporary and future-scenario simulations during summer (JJA). RCM output comes from North American Regional Climate Change Assessment Program (NARCCAP) simulations, which are all run at a spatial resolution of 50 km. Analysis focuses on the upper Mississippi basin for summer, between 1982-1998 for the contemporary climate, and 2052-2068 during the scenario climate. We also compare simulated precipitation and supporting processes with those obtained from observed precipitation and reanalysis atmospheric states. Precipitation observations are from the University of Washington (UW) and the Climate Prediction Center (CPC) gridded dataset. Utilizing two observational datasets helps determine if any uncertainties arise from differences in precipitation gridding schemes. Reanalysis fields come from the North American Regional Reanalysis. The NARCCAP models generally reproduce well the precipitation-vs.-intensity spectrum seen in observations, while producing overly strong precipitation at high intensity thresholds. In the future-scenario climate, there is a decrease in frequency for light to moderate precipitation intensities, while an increase in frequency is seen for the higher intensity events. Further analysis focuses on precipitation events exceeding the 99.5 percentile that occur simultaneously at several points in the region, yielding so-called "widespread events". For widespread events, we analyze local and large scale environmental parameters, such as 2-m temperature and specific humidity, 500-hPa geopotential heights, Convective Available Potential Energy (CAPE), vertically integrated moisture flux convergence, among others, to compare atmospheric states and processes leading to such events in the models and observations. The results suggest that an analysis of atmospheric states supporting very heavy precipitation events is a more fruitful path for understanding and detecting changes than simply looking at precipitation itself.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017EGUGA..19.9186K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017EGUGA..19.9186K"><span>Observational uncertainty and regional climate model evaluation: A pan-European perspective</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kotlarski, Sven; Szabó, Péter; Herrera, Sixto; Räty, Olle; Keuler, Klaus; Soares, Pedro M.; Cardoso, Rita M.; Bosshard, Thomas; Pagé, Christian; Boberg, Fredrik; Gutiérrez, José M.; Jaczewski, Adam; Kreienkamp, Frank; Liniger, Mark. A.; Lussana, Cristian; Szepszo, Gabriella</p> <p>2017-04-01</p> <p>Local and regional climate change assessments based on downscaling methods crucially depend on the existence of accurate and reliable observational reference data. In dynamical downscaling via regional climate models (RCMs) observational data can influence model development itself and, later on, model evaluation, parameter calibration and added value assessment. In empirical-statistical downscaling, observations serve as predictand data and directly influence model calibration with corresponding effects on downscaled climate change projections. Focusing on the evaluation of RCMs, we here analyze the influence of uncertainties in observational reference data on evaluation results in a well-defined performance assessment framework and on a European scale. For this purpose we employ three different gridded observational reference grids, namely (1) the well-established EOBS dataset (2) the recently developed EURO4M-MESAN regional re-analysis, and (3) several national high-resolution and quality-controlled gridded datasets that recently became available. In terms of climate models five reanalysis-driven experiments carried out by five different RCMs within the EURO-CORDEX framework are used. Two variables (temperature and precipitation) and a range of evaluation metrics that reflect different aspects of RCM performance are considered. We furthermore include an illustrative model ranking exercise and relate observational spread to RCM spread. The results obtained indicate a varying influence of observational uncertainty on model evaluation depending on the variable, the season, the region and the specific performance metric considered. Over most parts of the continent, the influence of the choice of the reference dataset for temperature is rather small for seasonal mean values and inter-annual variability. Here, model uncertainty (as measured by the spread between the five RCM simulations considered) is typically much larger than reference data uncertainty. For parameters of the daily temperature distribution and for the spatial pattern correlation, however, important dependencies on the reference dataset can arise. The related evaluation uncertainties can be as large or even larger than model uncertainty. For precipitation the influence of observational uncertainty is, in general, larger than for temperature. It often dominates model uncertainty especially for the evaluation of the wet day frequency, the spatial correlation and the shape and location of the distribution of daily values. But even the evaluation of large-scale seasonal mean values can be considerably affected by the choice of the reference. When employing a simple and illustrative model ranking scheme on these results it is found that RCM ranking in many cases depends on the reference dataset employed.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFMGC31F1173L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFMGC31F1173L"><span>Signal to noise quantification of regional climate projections</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Li, S.; Rupp, D. E.; Mote, P.</p> <p>2016-12-01</p> <p>One of the biggest challenges in interpreting climate model outputs for impacts studies and adaptation planning is understanding the sources of disagreement among models (which is often used imperfectly as a stand-in for system uncertainty). Internal variability is a primary source of uncertainty in climate projections, especially for precipitation, for which models disagree about even the sign of changes in large areas like the continental US. Taking advantage of a large initial-condition ensemble of regional climate simulations, this study quantifies the magnitude of changes forced by increasing greenhouse gas concentrations relative to internal variability. Results come from a large initial-condition ensemble of regional climate model simulations generated by weather@home, a citizen science computing platform, where the western United States climate was simulated for the recent past (1985-2014) and future (2030-2059) using a 25-km horizontal resolution regional climate model (HadRM3P) nested in global atmospheric model (HadAM3P). We quantify grid point level signal-to-noise not just in temperature and precipitation responses, but also the energy and moisture flux terms that are related to temperature and precipitation responses, to provide important insights regarding uncertainty in climate change projections at local and regional scales. These results will aid modelers in determining appropriate ensemble sizes for different climate variables and help users of climate model output with interpreting climate model projections.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018ACP....18.5253K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018ACP....18.5253K"><span>The sensitivity of Alpine summer convection to surrogate climate change: an intercomparison between convection-parameterizing and convection-resolving models</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Keller, Michael; Kröner, Nico; Fuhrer, Oliver; Lüthi, Daniel; Schmidli, Juerg; Stengel, Martin; Stöckli, Reto; Schär, Christoph</p> <p>2018-04-01</p> <p>Climate models project an increase in heavy precipitation events in response to greenhouse gas forcing. Important elements of such events are rain showers and thunderstorms, which are poorly represented in models with parameterized convection. In this study, simulations with 12 km horizontal grid spacing (convection-parameterizing model, CPM) and 2 km grid spacing (convection-resolving model, CRM) are employed to investigate the change in the diurnal cycle of convection with warmer climate. For this purpose, simulations of 11 days in June 2007 with a pronounced diurnal cycle of convection are compared with surrogate simulations from the same period. The surrogate climate simulations mimic a future climate with increased temperatures but unchanged relative humidity and similar synoptic-scale circulation. Two temperature scenarios are compared: one with homogeneous warming (HW) using a vertically uniform warming and the other with vertically dependent warming (VW) that enables changes in lapse rate. The two sets of simulations with parameterized and explicit convection exhibit substantial differences, some of which are well known from the literature. These include differences in the timing and amplitude of the diurnal cycle of convection, and the frequency of precipitation with low intensities. The response to climate change is much less studied. We can show that stratification changes have a strong influence on the changes in convection. Precipitation is strongly increasing for HW but decreasing for the VW simulations. For cloud type frequencies, virtually no changes are found for HW, but a substantial reduction in high clouds is found for VW. Further, we can show that the climate change signal strongly depends upon the horizontal resolution. In particular, significant differences between CPM and CRM are found in terms of the radiative feedbacks, with CRM exhibiting a stronger negative feedback in the top-of-the-atmosphere energy budget.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010ems..confE.788H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010ems..confE.788H"><span>Urban impact on air quality in RegCM/CAMx couple for MEGAPOLI project - high resolution sensitivity study</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Halenka, T.; Huszar, P.; Belda, M.</p> <p>2010-09-01</p> <p>Recent studies show considerable effect of atmospheric chemistry and aerosols on climate on regional and local scale. For the purpose of qualifying and quantifying the magnitude of climate forcing due to atmospheric chemistry/aerosols on regional scale, the development of coupling of regional climate model and chemistry/aerosol model was started on the Department of Meteorology and Environmental Protection, Charles University, Prague, for the EC FP6 Project QUANTIFY and EC FP6 Project CECILIA. For this coupling, existing regional climate model and chemistry transport model have been used at very high resolution of 10km grid. Climate is calculated using RegCM while chemistry is solved by CAMx. The experiments with the couple have been prepared for EC FP7 project MEGAPOLI assessing the impact of the megacities and industrialized areas on climate. Meteorological fields generated by RCM drive CAMx transport, chemistry and a dry/wet deposition. A preprocessor utility was developed for transforming RegCM provided fields to CAMx input fields and format. New domain have been settled for MEGAPOLI purpose in 10km resolution including all the European "megacities" regions, i.e. London metropolitan area, Paris region, industrialized Ruhr area, Po valley etc. There is critical issue of the emission inventories available for 10km resolution including the urban hot-spots, TNO emissions are adopted for this sensitivity study in 10km resolution for comparison of the results with the simulation based on merged TNO emissions, i.e. basically original EMEP emissions at 50 km grid. The sensitivity test to switch on/off Paris area emissions is analysed as well. Preliminary results for year 2005 are presented and discussed to reveal whether the concept of effective emission indices could help to parameterize the urban plume effects in lower resolution models. Interactive coupling is compared to study the potential of possible impact of urban air-pollution to the urban area climate.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.A23D2373L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.A23D2373L"><span>Representation of Precipitation in a Decade-long Continental-Scale Convection-Resolving Climate Simulation</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Leutwyler, D.; Fuhrer, O.; Ban, N.; Lapillonne, X.; Lüthi, D.; Schar, C.</p> <p>2017-12-01</p> <p>The representation of moist convection in climate models represents a major challenge, due to the small scales involved. Regional climate simulations using horizontal resolutions of O(1km) allow to explicitly resolve deep convection leading to an improved representation of the water cycle. However, due to their extremely demanding computational requirements, they have so far been limited to short simulations and/or small computational domains. A new version of the Consortium for Small-Scale Modeling weather and climate model (COSMO) is capable of exploiting new supercomputer architectures employing GPU accelerators, and allows convection-resolving climate simulations on computational domains spanning continents and time periods up to one decade. We present results from a decade-long, convection-resolving climate simulation on a European-scale computational domain. The simulation has a grid spacing of 2.2 km, 1536x1536x60 grid points, covers the period 1999-2008, and is driven by the ERA-Interim reanalysis. Specifically we present an evaluation of hourly rainfall using a wide range of data sets, including several rain-gauge networks and a remotely-sensed lightning data set. Substantial improvements are found in terms of the diurnal cycles of precipitation amount, wet-hour frequency and all-hour 99th percentile. However the results also reveal substantial differences between regions with and without strong orographic forcing. Furthermore we present an index for deep-convective activity based on the statistics of vertical motion. Comparison of the index with lightning data shows that the convection-resolving climate simulations are able to reproduce important features of the annual cycle of deep convection in Europe. Leutwyler D., D. Lüthi, N. Ban, O. Fuhrer, and C. Schär (2017): Evaluation of the Convection-Resolving Climate Modeling Approach on Continental Scales , J. Geophys. Res. Atmos., 122, doi:10.1002/2016JD026013.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://pubs.usgs.gov/sir/2010/5117/','USGSPUBS'); return false;" href="https://pubs.usgs.gov/sir/2010/5117/"><span>Implementation of local grid refinement (LGR) for the Lake Michigan Basin regional groundwater-flow model</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Hoard, C.J.</p> <p>2010-01-01</p> <p>The U.S. Geological Survey is evaluating water availability and use within the Great Lakes Basin. This is a pilot effort to develop new techniques and methods to aid in the assessment of water availability. As part of the pilot program, a regional groundwater-flow model for the Lake Michigan Basin was developed using SEAWAT-2000. The regional model was used as a framework for assessing local-scale water availability through grid-refinement techniques. Two grid-refinement techniques, telescopic mesh refinement and local grid refinement, were used to illustrate the capability of the regional model to evaluate local-scale problems. An intermediate model was developed in central Michigan spanning an area of 454 square miles (mi2) using telescopic mesh refinement. Within the intermediate model, a smaller local model covering an area of 21.7 mi2 was developed and simulated using local grid refinement. Recharge was distributed in space and time using a daily output from a modified Thornthwaite-Mather soil-water-balance method. The soil-water-balance method derived recharge estimates from temperature and precipitation data output from an atmosphere-ocean coupled general-circulation model. The particular atmosphere-ocean coupled general-circulation model used, simulated climate change caused by high global greenhouse-gas emissions to the atmosphere. The surface-water network simulated in the regional model was refined and simulated using a streamflow-routing package for MODFLOW. The refined models were used to demonstrate streamflow depletion and potential climate change using five scenarios. The streamflow-depletion scenarios include (1) natural conditions (no pumping), (2) a pumping well near a stream; the well is screened in surficial glacial deposits, (3) a pumping well near a stream; the well is screened in deeper glacial deposits, and (4) a pumping well near a stream; the well is open to a deep bedrock aquifer. Results indicated that a range of 59 to 50 percent of the water pumped originated from the stream for the shallow glacial and deep bedrock pumping scenarios, respectively. The difference in streamflow reduction between the shallow and deep pumping scenarios was compensated for in the deep well by deriving more water from regional sources. The climate-change scenario only simulated natural conditions from 1991-2044, so there was no pumping stress simulated. Streamflows were calculated for the simulated period and indicated that recharge over the period generally increased from the start of the simulation until approximately 2017, and decreased from then to the end of the simulation. Streamflow was highly correlated with recharge so that the lowest streamflows occurred in the later stress periods of the model when recharge was lowest.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.A31J2316H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.A31J2316H"><span>The Impact of Varying the Physics Grid Resolution Relative to the Dynamical Core Resolution in CAM-SE-CSLAM</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Herrington, A. R.; Lauritzen, P. H.; Reed, K. A.</p> <p>2017-12-01</p> <p>The spectral element dynamical core of the Community Atmosphere Model (CAM) has recently been coupled to an approximately isotropic, finite-volume grid per implementation of the conservative semi-Lagrangian multi-tracer transport scheme (CAM-SE-CSLAM; Lauritzen et al. 2017). In this framework, the semi-Lagrangian transport of tracers are computed on the finite-volume grid, while the adiabatic dynamics are solved using the spectral element grid. The physical parameterizations are evaluated on the finite-volume grid, as opposed to the unevenly spaced Gauss-Lobatto-Legendre nodes of the spectral element grid. Computing the physics on the finite-volume grid reduces numerical artifacts such as grid imprinting, possibly because the forcing terms are no longer computed at element boundaries where the resolved dynamics are least smooth. The separation of the physics grid and the dynamics grid allows for a unique opportunity to understand the resolution sensitivity in CAM-SE-CSLAM. The observed large sensitivity of CAM to horizontal resolution is a poorly understood impediment to improved simulations of regional climate using global, variable resolution grids. Here, a series of idealized moist simulations are presented in which the finite-volume grid resolution is varied relative to the spectral element grid resolution in CAM-SE-CSLAM. The simulations are carried out at multiple spectral element grid resolutions, in part to provide a companion set of simulations, in which the spectral element grid resolution is varied relative to the finite-volume grid resolution, but more generally to understand if the sensitivity to the finite-volume grid resolution is consistent across a wider spectrum of resolved scales. Results are interpreted in the context of prior ideas regarding resolution sensitivity of global atmospheric models.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/29398736','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/29398736"><span>Inflated Uncertainty in Multimodel-Based Regional Climate Projections.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Madsen, Marianne Sloth; Langen, Peter L; Boberg, Fredrik; Christensen, Jens Hesselbjerg</p> <p>2017-11-28</p> <p>Multimodel ensembles are widely analyzed to estimate the range of future regional climate change projections. For an ensemble of climate models, the result is often portrayed by showing maps of the geographical distribution of the multimodel mean results and associated uncertainties represented by model spread at the grid point scale. Here we use a set of CMIP5 models to show that presenting statistics this way results in an overestimation of the projected range leading to physically implausible patterns of change on global but also on regional scales. We point out that similar inconsistencies occur in impact analyses relying on multimodel information extracted using statistics at the regional scale, for example, when a subset of CMIP models is selected to represent regional model spread. Consequently, the risk of unwanted impacts may be overestimated at larger scales as climate change impacts will never be realized as the worst (or best) case everywhere.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.fs.usda.gov/treesearch/pubs/33047','TREESEARCH'); return false;" href="https://www.fs.usda.gov/treesearch/pubs/33047"><span>Wildland fire probabilities estimated from weather model-deduced monthly mean fire danger indices</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.fs.usda.gov/treesearch/">Treesearch</a></p> <p>Haiganoush K. Preisler; Shyh-Chin Chen; Francis Fujioka; John W. Benoit; Anthony L. Westerling</p> <p>2008-01-01</p> <p>The National Fire Danger Rating System indices deduced from a regional simulation weather model were used to estimate probabilities and numbers of large fire events on monthly and 1-degree grid scales. The weather model simulations and forecasts are ongoing experimental products from the Experimental Climate Prediction Center at the Scripps Institution of Oceanography...</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20160002946','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20160002946"><span>Big Data Challenges in Climate Science: Improving the Next-Generation Cyberinfrastructure</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Schnase, John L.; Lee, Tsengdar J.; Mattmann, Chris A.; Lynnes, Christopher S.; Cinquini, Luca; Ramirez, Paul M.; Hart, Andre F.; Williams, Dean N.; Waliser, Duane; Rinsland, Pamela; <a style="text-decoration: none; " href="javascript:void(0); " onClick="displayelement('author_20160002946'); toggleEditAbsImage('author_20160002946_show'); toggleEditAbsImage('author_20160002946_hide'); "> <img style="display:inline; width:12px; height:12px; " src="images/arrow-up.gif" width="12" height="12" border="0" alt="hide" id="author_20160002946_show"> <img style="width:12px; height:12px; display:none; " src="images/arrow-down.gif" width="12" height="12" border="0" alt="hide" id="author_20160002946_hide"></p> <p>2016-01-01</p> <p>The knowledge we gain from research in climate science depends on the generation, dissemination, and analysis of high-quality data. This work comprises technical practice as well as social practice, both of which are distinguished by their massive scale and global reach. As a result, the amount of data involved in climate research is growing at an unprecedented rate. Climate model intercomparison (CMIP) experiments, the integration of observational data and climate reanalysis data with climate model outputs, as seen in the Obs4MIPs, Ana4MIPs, and CREATE-IP activities, and the collaborative work of the Intergovernmental Panel on Climate Change (IPCC) provide examples of the types of activities that increasingly require an improved cyberinfrastructure for dealing with large amounts of critical scientific data. This paper provides an overview of some of climate science's big data problems and the technical solutions being developed to advance data publication, climate analytics as a service, and interoperability within the Earth System Grid Federation (ESGF), the primary cyberinfrastructure currently supporting global climate research activities.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li class="active"><span>12</span></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_12 --> <div id="page_13" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li class="active"><span>13</span></li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li><a href="#" onclick='return showDiv("page_15");'>15</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="241"> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015ThApC.119..353W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015ThApC.119..353W"><span>High-resolution spatial databases of monthly climate variables (1961-2010) over a complex terrain region in southwestern China</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wu, Wei; Xu, An-Ding; Liu, Hong-Bin</p> <p>2015-01-01</p> <p>Climate data in gridded format are critical for understanding climate change and its impact on eco-environment. The aim of the current study is to develop spatial databases for three climate variables (maximum, minimum temperatures, and relative humidity) over a large region with complex topography in southwestern China. Five widely used approaches including inverse distance weighting, ordinary kriging, universal kriging, co-kriging, and thin-plate smoothing spline were tested. Root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) showed that thin-plate smoothing spline with latitude, longitude, and elevation outperformed other models. Average RMSE, MAE, and MAPE of the best models were 1.16 °C, 0.74 °C, and 7.38 % for maximum temperature; 0.826 °C, 0.58 °C, and 6.41 % for minimum temperature; and 3.44, 2.28, and 3.21 % for relative humidity, respectively. Spatial datasets of annual and monthly climate variables with 1-km resolution covering the period 1961-2010 were then obtained using the best performance methods. Comparative study showed that the current outcomes were in well agreement with public datasets. Based on the gridded datasets, changes in temperature variables were investigated across the study area. Future study might be needed to capture the uncertainty induced by environmental conditions through remote sensing and knowledge-based methods.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFMGC11D1034S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFMGC11D1034S"><span>Crop Yield Simulations Using Multiple Regional Climate Models in the Southwestern United States</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Stack, D.; Kafatos, M.; Kim, S.; Kim, J.; Walko, R. L.</p> <p>2013-12-01</p> <p>Agricultural productivity (described by crop yield) is strongly dependent on climate conditions determined by meteorological parameters (e.g., temperature, rainfall, and solar radiation). California is the largest producer of agricultural products in the United States, but crops in associated arid and semi-arid regions live near their physiological limits (e.g., in hot summer conditions with little precipitation). Thus, accurate climate data are essential in assessing the impact of climate variability on agricultural productivity in the Southwestern United States and other arid regions. To address this issue, we produced simulated climate datasets and used them as input for the crop production model. For climate data, we employed two different regional climate models (WRF and OLAM) using a fine-resolution (8km) grid. Performances of the two different models are evaluated in a fine-resolution regional climate hindcast experiment for 10 years from 2001 to 2010 by comparing them to the North American Regional Reanalysis (NARR) dataset. Based on this comparison, multi-model ensembles with variable weighting are used to alleviate model bias and improve the accuracy of crop model productivity over large geographic regions (county and state). Finally, by using a specific crop-yield simulation model (APSIM) in conjunction with meteorological forcings from the multi-regional climate model ensemble, we demonstrate the degree to which maize yields are sensitive to the regional climate in the Southwestern United States.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010AGUFMGC23C0924K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010AGUFMGC23C0924K"><span>Analysis of consistency of global net land-use change carbon emission scenario using offline vegetation model and earth system model</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kato, E.; Kawamiya, M.</p> <p>2010-12-01</p> <p>For CMIP5 experiments, emissions scenarios data sets for climate models are prepared as Representative Concentration Pathways (RCPs) by the Integrated Assessment Models (IAMs). IAMs also have depicted regional land-use scenarios based on the socioeconomic assumption of the future scenarios of RCPs. In the land-use harmonization project, gridded land-use transition data has been constructed from the regional IAMs future land-use scenarios which smoothly connects historical reconstructions of land-use based on HYDE 3 data and FAO wood harvest data. In this study, using the gridded transition land-use scenario data, global net CO2 emission from land-use change for each RCPs scenarios is evaluated with a offline version of terrestrial biogeochemical model, VISIT (Vegetation Integrative SImulation Tool), utilizing a protocol to estimate carbon emission from deforested biomass considering delayed decomposition of product pools, and regrowth absorption from the secondary lands with abandoned agricultural lands. From the model output, effect of CO2 fertilization and land-use scenario itself on the emission is assessed to see the consistency of the scenarios. In addition, to see the effect of climate change and the climate-carbon feedback on terrestrial ecosystems, net land-use change CO2 emission is also evaluated with an earth system model, MIROC-ESM incorporating a DGVM with land-use change component. In the simulations with earth system model, RCP 6.0 scenario has been evaluated by model runs with and without land-use change forcing.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011AGUFMGC13A0942W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011AGUFMGC13A0942W"><span>Predicting the Impacts of Climate Change on Central American Agriculture</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Winter, J. M.; Ruane, A. C.; Rosenzweig, C.</p> <p>2011-12-01</p> <p>Agriculture is a vital component of Central America's economy. Poor crop yields and harvest reliability can produce food insecurity, malnutrition, and conflict. Regional climate models (RCMs) and agricultural models have the potential to greatly enhance the efficiency of Central American agriculture and water resources management under both current and future climates. A series of numerical experiments was conducted using Regional Climate Model Version 3 (RegCM3) and the Weather Research and Forecasting Model (WRF) to evaluate the ability of RCMs to reproduce the current climate of Central America and assess changes in temperature and precipitation under multiple future climate scenarios. Control simulations were thoroughly compared to a variety of observational datasets, including local weather station data, gridded meteorological data, and high-resolution satellite-based precipitation products. Future climate simulations were analyzed for both mean shifts in climate and changes in climate variability, including extreme events (droughts, heat waves, floods). To explore the impacts of changing climate on maize, bean, and rice yields in Central America, RCM output was used to force the Decision Support System for Agrotechnology Transfer Model (DSSAT). These results were synthesized to create climate change impacts predictions for Central American agriculture that explicitly account for evolving distributions of precipitation and temperature extremes.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19890064717&hterms=climate+exchange&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D50%26Ntt%3Dclimate%2Bexchange','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19890064717&hterms=climate+exchange&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D50%26Ntt%3Dclimate%2Bexchange"><span>Effects of cumulus entrainment and multiple cloud types on a January global climate model simulation</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Yao, Mao-Sung; Del Genio, Anthony D.</p> <p>1989-01-01</p> <p>An improved version of the GISS Model II cumulus parameterization designed for long-term climate integrations is used to study the effects of entrainment and multiple cloud types on the January climate simulation. Instead of prescribing convective mass as a fixed fraction of the cloud base grid-box mass, it is calculated based on the closure assumption that the cumulus convection restores the atmosphere to a neutral moist convective state at cloud base. This change alone significantly improves the distribution of precipitation, convective mass exchanges, and frequencies in the January climate. The vertical structure of the tropical atmosphere exhibits quasi-equilibrium behavior when this closure is used, even though there is no explicit constraint applied above cloud base.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFMGC33I..04X','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFMGC33I..04X"><span>Impacts of Stratospheric Black Carbon on Agriculture</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Xia, L.; Robock, A.; Elliott, J. W.</p> <p>2017-12-01</p> <p>A regional nuclear war between India and Pakistan could inject 5 Tg of soot into the stratosphere, which would absorb sunlight, decrease global surface temperature by about 1°C for 5-10 years and have major impacts on precipitation and the amount of solar radiation reaching Earth's surface. Using two global gridded crop models forced by one global climate model simulation, we investigate the impacts on agricultural productivity in various nations. The crop model in the Community Land Model 4.5 (CLM-crop4.5) and the parallel Decision Support System for Agricultural Technology (pDSSAT) in the parallel System for Integrating Impact Models and Sectors are participating in the Global Gridded Crop Model Intercomparison. We force these two crop models with output from the Whole Atmospheric Community Climate Model to characterize the global agricultural impact from climate changes due to a regional nuclear war. Crops in CLM-crop4.5 include maize, rice, soybean, cotton and sugarcane, and crops in pDSSAT include maize, rice, soybean and wheat. Although the two crop models require a different time frequency of weather input, we downscale the climate model output to provide consistent temperature, precipitation and solar radiation inputs. In general, CLM-crop4.5 simulates a larger global average reduction of maize and soybean production relative to pDSSAT. Global rice production shows negligible change with climate anomalies from a regional nuclear war. Cotton and sugarcane benefit from a regional nuclear war from CLM-crop4.5 simulation, and global wheat production would decrease significantly in the pDSSAT simulation. The regional crop yield responses to a regional nuclear conflict are different for each crop, and we present the changes in production on a national basis. These models do not include the crop responses to changes in ozone, ultraviolet radiation, or diffuse radiation, and we would like to encourage more modelers to improve crop models to account for those impacts. We present these results as a demonstration of using different crop models to study this problem, and we invite more global crop modeling groups to use the same climate forcing, which we would be happy to provide, to gain a better understanding of global agricultural responses under different future climate scenarios with stratospheric aerosols.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFM.A33C0232A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFM.A33C0232A"><span>Quantifying Co-benefits of Renewable Energy through Integrated Electricity and Air Quality Modeling</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Abel, D.</p> <p>2016-12-01</p> <p>This work focuses on the coordination of electricity sector changes with air quality and health improvement strategies through the integration of electricity and air quality models. Two energy models are used to calculate emission perturbations associated with changes in generation technology (20% generation from solar photovoltaics) and demand (future electricity use under a warmer climate). Impacts from increased solar PV penetration are simulated with the electricity model GridView, in collaboration with the National Renewable Energy Laboratory (NREL). Generation results are used to scale power plant emissions from an inventory developed by the Lake Michigan Air Directors Consortium (LADCO). Perturbed emissions and are used to calculate secondary particulate matter with the Community Multiscale Air Quality (CMAQ) model. We find that electricity NOx and SO2 emissions decrease at a rate similar to the total fraction of electricity supplied by solar. Across the Eastern U.S. region, average PM2.5 is reduced 5% over the summer, with highest reduction in regions and on days of greater PM2.5. A similar approach evaluates the air quality impacts of elevated electricity demand under a warmer climate. Meteorology is selected from the North American Regional Climate Change Assessment Program (NARCCAP) and input to a building energy model, eQUEST, to assess electricity demand as a function of ambient temperature. The associated generation and emissions are calculated on a plant-by-plant basis by the MyPower power sector model. These emissions are referenced to the 2011 National Emissions Inventory to be modeled in CMAQ for the Eastern U.S. and extended to health impact evaluation with the Environmental Benefits Mapping and Analysis Program (BenMAP). All results focus on the air quality and health consequences of energy system changes, considering grid-level changes to meet climate and air quality goals.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20150019892','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20150019892"><span>Ocean-Atmosphere Coupled Model Simulations of Precipitation in the Central Andes</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Nicholls, Stephen D.; Mohr, Karen I.</p> <p>2015-01-01</p> <p>The meridional extent and complex orography of the South American continent contributes to a wide diversity of climate regimes ranging from hyper-arid deserts to tropical rainforests to sub-polar highland regions. In addition, South American meteorology and climate are also made further complicated by ENSO, a powerful coupled ocean-atmosphere phenomenon. Modelling studies in this region have typically resorted to either atmospheric mesoscale or atmosphere-ocean coupled global climate models. The latter offers full physics and high spatial resolution, but it is computationally inefficient typically lack an interactive ocean, whereas the former offers high computational efficiency and ocean-atmosphere coupling, but it lacks adequate spatial and temporal resolution to adequate resolve the complex orography and explicitly simulate precipitation. Explicit simulation of precipitation is vital in the Central Andes where rainfall rates are light (0.5-5 mm hr-1), there is strong seasonality, and most precipitation is associated with weak mesoscale-organized convection. Recent increases in both computational power and model development have led to the advent of coupled ocean-atmosphere mesoscale models for both weather and climate study applications. These modelling systems, while computationally expensive, include two-way ocean-atmosphere coupling, high resolution, and explicit simulation of precipitation. In this study, we use the Coupled Ocean-Atmosphere-Wave-Sediment Transport (COAWST), a fully-coupled mesoscale atmosphere-ocean modeling system. Previous work has shown COAWST to reasonably simulate the entire 2003-2004 wet season (Dec-Feb) as validated against both satellite and model analysis data when ECMWF interim analysis data were used for boundary conditions on a 27-9-km grid configuration (Outer grid extent: 60.4S to 17.7N and 118.6W to 17.4W).</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFMGC11F..05D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFMGC11F..05D"><span>A framework for evaluating statistical downscaling performance under changing climatic conditions (Invited)</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Dixon, K. W.; Balaji, V.; Lanzante, J.; Radhakrishnan, A.; Hayhoe, K.; Stoner, A. K.; Gaitan, C. F.</p> <p>2013-12-01</p> <p>Statistical downscaling (SD) methods may be viewed as generating a value-added product - a refinement of global climate model (GCM) output designed to add finer scale detail and to address GCM shortcomings via a process that gleans information from a combination of observations and GCM-simulated climate change responses. Making use of observational data sets and GCM simulations representing the same historical period, cross-validation techniques allow one to assess how well an SD method meets this goal. However, lacking observations of future, the extent to which a particular SD method's skill might degrade when applied to future climate projections cannot be assessed in the same manner. Here we illustrate and describe extensions to a 'perfect model' experimental design that seeks to quantify aspects of SD method performance both for a historical period (1979-2008) and for late 21st century climate projections. Examples highlighting cases in which downscaling performance deteriorates in future climate projections will be discussed. Also, results will be presented showing how synthetic datasets having known statistical properties may be used to further isolate factors responsible for degradations in SD method skill under changing climatic conditions. We will describe a set of input files used to conduct these analyses that are being made available to researchers who wish to utilize this experimental framework to evaluate SD methods they have developed. The gridded data sets cover a region centered on the contiguous 48 United States with a grid spacing of approximately 25km, have daily time resolution (e.g., maximum and minimum near-surface temperature and precipitation), and represent a total of 120 years of model simulations. This effort is consistent with the 2013 National Climate Predictions and Projections Platform Quantitative Evaluation of Downscaling Workshop goal of supporting a community approach to promote the informed use of downscaled climate projections.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.fs.usda.gov/treesearch/pubs/38486','TREESEARCH'); return false;" href="https://www.fs.usda.gov/treesearch/pubs/38486"><span>Impacts of tropospheric ozone and climate change on net primary productivity and net carbon exchange of China’s forest ecosystems</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.fs.usda.gov/treesearch/">Treesearch</a></p> <p>Wei Ren; Hanqin Tian; Bo Tao; Art Chappelka; Ge Sun; et al</p> <p>2011-01-01</p> <p>Aim We investigated how ozone pollution and climate change/variability have interactively affected net primary productivity (NPP) and net carbon exchange (NCE) across Chinaâs forest ecosystem in the past half century. Location Continental China. Methods Using the dynamic land ecosystem model (DLEM) in conjunction with 10-km-resolution gridded historical data sets (...</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFMPA43B2198H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFMPA43B2198H"><span>Developing Flexible, Integrated Hydrologic Modeling Systems for Multiscale Analysis in the Midwest and Great Lakes Region</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Hamlet, A. F.; Chiu, C. M.; Sharma, A.; Byun, K.; Hanson, Z.</p> <p>2016-12-01</p> <p>Physically based hydrologic modeling of surface and groundwater resources that can be flexibly and efficiently applied to support water resources policy/planning/management decisions at a wide range of spatial and temporal scales are greatly needed in the Midwest, where stakeholder access to such tools is currently a fundamental barrier to basic climate change assessment and adaptation efforts, and also the co-production of useful products to support detailed decision making. Based on earlier pilot studies in the Pacific Northwest Region, we are currently assembling a suite of end-to-end tools and resources to support various kinds of water resources planning and management applications across the region. One of the key aspects of these integrated tools is that the user community can access gridded products at any point along the end-to-end chain of models, looking backwards in time about 100 years (1915-2015), and forwards in time about 85 years using CMIP5 climate model projections. The integrated model is composed of historical and projected future meteorological data based on station observations and statistical and dynamically downscaled climate model output respectively. These gridded meteorological data sets serve as forcing data for the macro-scale VIC hydrologic model implemented over the Midwest at 1/16 degree resolution. High-resolution climate model (4km WRF) output provides inputs for the analyses of urban impacts, hydrologic extremes, agricultural impacts, and impacts to the Great Lakes. Groundwater recharge estimated by the surface water model provides input data for fine-scale and macro-scale groundwater models needed for specific applications. To highlight the multi-scale use of the integrated models in support of co-production of scientific information for decision making, we briefly describe three current case studies addressing different spatial scales of analysis: 1) Effects of climate change on the water balance of the Great Lakes, 2) Future hydropower resources in the St. Joseph River basin, 3) Effects of climate change on carbon cycling in small lakes in the Northern Highland Lakes District.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20010068934','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20010068934"><span>Cloud Modeling</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Tao, Wei-Kuo; Moncrieff, Mitchell; Einaud, Franco (Technical Monitor)</p> <p>2001-01-01</p> <p>Numerical cloud models have been developed and applied extensively to study cloud-scale and mesoscale processes during the past four decades. The distinctive aspect of these cloud models is their ability to treat explicitly (or resolve) cloud-scale dynamics. This requires the cloud models to be formulated from the non-hydrostatic equations of motion that explicitly include the vertical acceleration terms since the vertical and horizontal scales of convection are similar. Such models are also necessary in order to allow gravity waves, such as those triggered by clouds, to be resolved explicitly. In contrast, the hydrostatic approximation, usually applied in global or regional models, does allow the presence of gravity waves. In addition, the availability of exponentially increasing computer capabilities has resulted in time integrations increasing from hours to days, domain grids boxes (points) increasing from less than 2000 to more than 2,500,000 grid points with 500 to 1000 m resolution, and 3-D models becoming increasingly prevalent. The cloud resolving model is now at a stage where it can provide reasonably accurate statistical information of the sub-grid, cloud-resolving processes poorly parameterized in climate models and numerical prediction models.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20150023406&hterms=Summer&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3DSummer','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20150023406&hterms=Summer&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3DSummer"><span>Downscaling GISS ModelE Boreal Summer Climate over Africa</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Druyan, Leonard M.; Fulakeza, Matthew</p> <p>2015-01-01</p> <p>The study examines the perceived added value of downscaling atmosphere-ocean global climate model simulations over Africa and adjacent oceans by a nested regional climate model. NASA/Goddard Institute for Space Studies (GISS) coupled ModelE simulations for June- September 1998-2002 are used to form lateral boundary conditions for synchronous simulations by the GISS RM3 regional climate model. The ModelE computational grid spacing is 2deg latitude by 2.5deg longitude and the RM3 grid spacing is 0.44deg. ModelE precipitation climatology for June-September 1998-2002 is shown to be a good proxy for 30-year means so results based on the 5-year sample are presumed to be generally representative. Comparison with observational evidence shows several discrepancies in ModelE configuration of the boreal summer inter-tropical convergence zone (ITCZ). One glaring shortcoming is that ModelE simulations do not advance the West African rain band northward during the summer to represent monsoon precipitation onset over the Sahel. Results for 1998-2002 show that onset simulation is an important added value produced by downscaling with RM3. ModelE Eastern South Atlantic Ocean computed sea-surface temperatures (SST) are some 4 K warmer than reanalysis, contributing to large positive biases in overlying surface air temperatures (Tsfc). ModelE Tsfc are also too warm over most of Africa. RM3 downscaling somewhat mitigates the magnitude of Tsfc biases over the African continent, it eliminates the ModelE double ITCZ over the Atlantic and it produces more realistic orographic precipitation maxima. Parallel ModelE and RM3 simulations with observed SST forcing (in place of the predicted ocean) lower Tsfc errors but have mixed impacts on circulation and precipitation biases. Downscaling improvements of the meridional movement of the rain band over West Africa and the configuration of orographic precipitation maxima are realized irrespective of the SST biases.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..18.6142M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..18.6142M"><span>Assessing global vegetation activity using spatio-temporal Bayesian modelling</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Mulder, Vera L.; van Eck, Christel M.; Friedlingstein, Pierre; Regnier, Pierre A. G.</p> <p>2016-04-01</p> <p>This work demonstrates the potential of modelling vegetation activity using a hierarchical Bayesian spatio-temporal model. This approach allows modelling changes in vegetation and climate simultaneous in space and time. Changes of vegetation activity such as phenology are modelled as a dynamic process depending on climate variability in both space and time. Additionally, differences in observed vegetation status can be contributed to other abiotic ecosystem properties, e.g. soil and terrain properties. Although these properties do not change in time, they do change in space and may provide valuable information in addition to the climate dynamics. The spatio-temporal Bayesian models were calibrated at a regional scale because the local trends in space and time can be better captured by the model. The regional subsets were defined according to the SREX segmentation, as defined by the IPCC. Each region is considered being relatively homogeneous in terms of large-scale climate and biomes, still capturing small-scale (grid-cell level) variability. Modelling within these regions is hence expected to be less uncertain due to the absence of these large-scale patterns, compared to a global approach. This overall modelling approach allows the comparison of model behavior for the different regions and may provide insights on the main dynamic processes driving the interaction between vegetation and climate within different regions. The data employed in this study encompasses the global datasets for soil properties (SoilGrids), terrain properties (Global Relief Model based on SRTM DEM and ETOPO), monthly time series of satellite-derived vegetation indices (GIMMS NDVI3g) and climate variables (Princeton Meteorological Forcing Dataset). The findings proved the potential of a spatio-temporal Bayesian modelling approach for assessing vegetation dynamics, at a regional scale. The observed interrelationships of the employed data and the different spatial and temporal trends support our hypothesis. That is, the change of vegetation in space and time may be better understood when modelling vegetation change as both a dynamic and multivariate process. Therefore, future research will focus on a multivariate dynamical spatio-temporal modelling approach. This ongoing research is performed within the context of the project "Global impacts of hydrological and climatic extremes on vegetation" (project acronym: SAT-EX) which is part of the Belgian research programme for Earth Observation Stereo III.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018GMD....11.1059K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018GMD....11.1059K"><span>The on-line coupled atmospheric chemistry model system MECO(n) - Part 5: Expanding the Multi-Model-Driver (MMD v2.0) for 2-way data exchange including data interpolation via GRID (v1.0)</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kerkweg, Astrid; Hofmann, Christiane; Jöckel, Patrick; Mertens, Mariano; Pante, Gregor</p> <p>2018-03-01</p> <p>As part of the Modular Earth Submodel System (MESSy), the Multi-Model-Driver (MMD v1.0) was developed to couple online the regional Consortium for Small-scale Modeling (COSMO) model into a driving model, which can be either the regional COSMO model or the global European Centre Hamburg general circulation model (ECHAM) (see Part 2 of the model documentation). The coupled system is called MECO(n), i.e., MESSy-fied ECHAM and COSMO models nested n times. In this article, which is part of the model documentation of the MECO(n) system, the second generation of MMD is introduced. MMD comprises the message-passing infrastructure required for the parallel execution (multiple programme multiple data, MPMD) of different models and the communication of the individual model instances, i.e. between the driving and the driven models. Initially, the MMD library was developed for a one-way coupling between the global chemistry-climate ECHAM/MESSy atmospheric chemistry (EMAC) model and an arbitrary number of (optionally cascaded) instances of the regional chemistry-climate model COSMO/MESSy. Thus, MMD (v1.0) provided only functions for unidirectional data transfer, i.e. from the larger-scale to the smaller-scale models.Soon, extended applications requiring data transfer from the small-scale model back to the larger-scale model became of interest. For instance, the original fields of the larger-scale model can directly be compared to the upscaled small-scale fields to analyse the improvements gained through the small-scale calculations, after the results are upscaled. Moreover, the fields originating from the two different models might be fed into the same diagnostic tool, e.g. the online calculation of the radiative forcing calculated consistently with the same radiation scheme. Last but not least, enabling the two-way data transfer between two models is the first important step on the way to a fully dynamical and chemical two-way coupling of the various model instances.In MMD (v1.0), interpolation between the base model grids is performed via the COSMO preprocessing tool INT2LM, which was implemented into the MMD submodel for online interpolation, specifically for mapping onto the rotated COSMO grid. A more flexible algorithm is required for the backward mapping. Thus, MMD (v2.0) uses the new MESSy submodel GRID for the generalised definition of arbitrary grids and for the transformation of data between them.In this article, we explain the basics of the MMD expansion and the newly developed generic MESSy submodel GRID (v1.0) and show some examples of the abovementioned applications.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/27991912','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/27991912"><span>Plausible rice yield losses under future climate warming.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Zhao, Chuang; Piao, Shilong; Wang, Xuhui; Huang, Yao; Ciais, Philippe; Elliott, Joshua; Huang, Mengtian; Janssens, Ivan A; Li, Tao; Lian, Xu; Liu, Yongwen; Müller, Christoph; Peng, Shushi; Wang, Tao; Zeng, Zhenzhong; Peñuelas, Josep</p> <p>2016-12-19</p> <p>Rice is the staple food for more than 50% of the world's population 1-3 . Reliable prediction of changes in rice yield is thus central for maintaining global food security. This is an extraordinary challenge. Here, we compare the sensitivity of rice yield to temperature increase derived from field warming experiments and three modelling approaches: statistical models, local crop models and global gridded crop models. Field warming experiments produce a substantial rice yield loss under warming, with an average temperature sensitivity of -5.2 ± 1.4% K -1 . Local crop models give a similar sensitivity (-6.3 ± 0.4% K -1 ), but statistical and global gridded crop models both suggest less negative impacts of warming on yields (-0.8 ± 0.3% and -2.4 ± 3.7% K -1 , respectively). Using data from field warming experiments, we further propose a conditional probability approach to constrain the large range of global gridded crop model results for the future yield changes in response to warming by the end of the century (from -1.3% to -9.3% K -1 ). The constraint implies a more negative response to warming (-8.3 ± 1.4% K -1 ) and reduces the spread of the model ensemble by 33%. This yield reduction exceeds that estimated by the International Food Policy Research Institute assessment (-4.2 to -6.4% K -1 ) (ref. 4). Our study suggests that without CO 2 fertilization, effective adaptation and genetic improvement, severe rice yield losses are plausible under intensive climate warming scenarios.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017JGRD..122.2714G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017JGRD..122.2714G"><span>Sensitivity of U.S. summer precipitation to model resolution and convective parameterizations across gray zone resolutions</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Gao, Yang; Leung, L. Ruby; Zhao, Chun; Hagos, Samson</p> <p>2017-03-01</p> <p>Simulating summer precipitation is a significant challenge for climate models that rely on cumulus parameterizations to represent moist convection processes. Motivated by recent advances in computing that support very high-resolution modeling, this study aims to systematically evaluate the effects of model resolution and convective parameterizations across the gray zone resolutions. Simulations using the Weather Research and Forecasting model were conducted at grid spacings of 36 km, 12 km, and 4 km for two summers over the conterminous U.S. The convection-permitting simulations at 4 km grid spacing are most skillful in reproducing the observed precipitation spatial distributions and diurnal variability. Notable differences are found between simulations with the traditional Kain-Fritsch (KF) and the scale-aware Grell-Freitas (GF) convection schemes, with the latter more skillful in capturing the nocturnal timing in the Great Plains and North American monsoon regions. The GF scheme also simulates a smoother transition from convective to large-scale precipitation as resolution increases, resulting in reduced sensitivity to model resolution compared to the KF scheme. Nonhydrostatic dynamics has a positive impact on precipitation over complex terrain even at 12 km and 36 km grid spacings. With nudging of the winds toward observations, we show that the conspicuous warm biases in the Southern Great Plains are related to precipitation biases induced by large-scale circulation biases, which are insensitive to model resolution. Overall, notable improvements in simulating summer rainfall and its diurnal variability through convection-permitting modeling and scale-aware parameterizations suggest promising venues for improving climate simulations of water cycle processes.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFM.B51F0367Y','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFM.B51F0367Y"><span>Paddy rice productivity under climate and land-use change in northern Japan</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Yoshida, R.; Fukui, S.; Shimada, T.; Hasegawa, T.; Iwasaki, T.</p> <p>2013-12-01</p> <p>An evaluation of the best rice cultivar under climate change is an important issue because the projected climate change has a potential to bring a negative impact on crop yield. In this study, we estimate an impact of climate change on rice yield and potential best cultivar in northern Japan where the larger paddy field is located than other regions in Japan. Two global climate model data, MIROC5 (RCP 4.5) and MRI-AGCM (SRES A1B), are applied as the future scenario. These data are too coarse to resolve the regional differences in northern Japan; we conduct the downscale experiments by a regional climate model (JMA-NHM) with a 10-km grid spacing. Considering that rice yield is sensitive to warm season climate, we conduct the downscaling from 28th May to 1st September during 1981-2000 and 2081-2099. The biases of downscaled two scenarios are corrected to match their cumulative distribution functions (CDF) of present climate with that of the station-based observation. The derived CDF-based biases are also used to correct the future scenarios. These corrected scenarios are applied to rice growth model (NIAES-Rice). To take account for the impacts of land use change (LUC) on climate and rice yield, we consider the additional temperature changes due to the LUC. As a reference, we add the LUC-induced temperature change in southwest Japan because the data are available from the previous study. We first check the climate change in northern Japan. General tendencies derived from the bias-corrected-downscaled future climates are that 1) surface warming was approximately twice in low elevation area relative to mountainous area and 2) downward shortwave radiation homogeneously increased 7-8 W m-2. Then, we evaluated the simulated yield through comparing with observation. Using observed ambient conditions as input data, the NIAES-Rice model provides the reasonable performance in simulating the rice yield with biases ranging from -19.0 to 29.2 % in prefecture base. Climate changes enhance the rice yield for 'Koshihikari' from 372.4 to 388.1 g/10a. The cultivar is planted in half of prefectures in Japan. For each analysis grid, we chose the cultivar that held the largest yield among the 10 analyzed cultivars and found that the location of cultivar that achieves the largest yields shifted northward in future climate. Also, the grids that cultivar developed in central Japan become the largest yield among 10 cultivars increase more than triple in the future climate. A LUC-induced temperature impact on prefecture-based yield corresponds to about 18 % of the climate-change-induced yield change, ranging from 0.6 to 33.5 % for six prefectures in the analyzed area. There is unclear tendency between the present rice yield amount and the intensity of LUC impact. However, surface warming due to urbanization decrease the rice yield in all six prefectures. Thus land use management and planting cultivar change are essential for stable food security.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=308468&keyword=Scheme&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50','EPA-EIMS'); return false;" href="https://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=308468&keyword=Scheme&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50"><span>Introducing Subgrid-scale convective cloud and aerosol interactions to the WRF-CMAQ integrated modeling system</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://oaspub.epa.gov/eims/query.page">EPA Science Inventory</a></p> <p></p> <p></p> <p>Many regional and global climate models include aerosol indirect effects (AIE) on grid-scale/resolved clouds. However, the interaction between aerosols and convective clouds remains highly uncertain, as noted in the IPCC AR4 report. The objective of this work is to help fill in ...</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..17.6747S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..17.6747S"><span>Evaluation of global equal-area mass grid solutions from GRACE</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Save, Himanshu; Bettadpur, Srinivas; Tapley, Byron</p> <p>2015-04-01</p> <p>The Gravity Recovery and Climate Experiment (GRACE) range-rate data was inverted into global equal-area mass grid solutions at the Center for Space Research (CSR) using Tikhonov Regularization to stabilize the ill-posed inversion problem. These solutions are intended to be used for applications in Hydrology, Oceanography, Cryosphere etc without any need for post-processing. This paper evaluates these solutions with emphasis on spatial and temporal characteristics of the signal content. These solutions will be validated against multiple models and in-situ data sets.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li class="active"><span>13</span></li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li><a href="#" onclick='return showDiv("page_15");'>15</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_13 --> <div id="page_14" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li class="active"><span>14</span></li> <li><a href="#" onclick='return showDiv("page_15");'>15</a></li> <li><a href="#" onclick='return showDiv("page_16");'>16</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="261"> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/1439208','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/1439208"><span>What are the effects of Agro-Ecological Zones and land use region boundaries on land resource projection using the Global Change Assessment Model?</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Di Vittorio, Alan V.; Kyle, Page; Collins, William D.</p> <p></p> <p>Understanding potential impacts of climate change is complicated by spatially mismatched land representations between gridded datasets and models, and land use models with larger regions defined by geopolitical and/or biophysical criteria. Here in this study, we quantify the sensitivity of Global Change Assessment Model (GCAM) outputs to the delineation of Agro-Ecological Zones (AEZs), which are normally based on historical (1961–1990) climate. We reconstruct GCAM's land regions using projected (2071–2100) climate, and find large differences in estimated future land use that correspond with differences in agricultural commodity prices and production volumes. Importantly, historically delineated AEZs experience spatially heterogeneous climate impacts overmore » time, and do not necessarily provide more homogenous initial land productivity than projected AEZs. Finally, we conclude that non-climatic criteria for land use region delineation are likely preferable for modeling land use change in the context of climate change, and that uncertainty associated with land delineation needs to be quantified.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/pages/biblio/1439208-what-effects-agro-ecological-zones-land-use-region-boundaries-land-resource-projection-using-global-change-assessment-model','SCIGOV-DOEP'); return false;" href="https://www.osti.gov/pages/biblio/1439208-what-effects-agro-ecological-zones-land-use-region-boundaries-land-resource-projection-using-global-change-assessment-model"><span>What are the effects of Agro-Ecological Zones and land use region boundaries on land resource projection using the Global Change Assessment Model?</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/pages">DOE PAGES</a></p> <p>Di Vittorio, Alan V.; Kyle, Page; Collins, William D.</p> <p>2016-09-03</p> <p>Understanding potential impacts of climate change is complicated by spatially mismatched land representations between gridded datasets and models, and land use models with larger regions defined by geopolitical and/or biophysical criteria. Here in this study, we quantify the sensitivity of Global Change Assessment Model (GCAM) outputs to the delineation of Agro-Ecological Zones (AEZs), which are normally based on historical (1961–1990) climate. We reconstruct GCAM's land regions using projected (2071–2100) climate, and find large differences in estimated future land use that correspond with differences in agricultural commodity prices and production volumes. Importantly, historically delineated AEZs experience spatially heterogeneous climate impacts overmore » time, and do not necessarily provide more homogenous initial land productivity than projected AEZs. Finally, we conclude that non-climatic criteria for land use region delineation are likely preferable for modeling land use change in the context of climate change, and that uncertainty associated with land delineation needs to be quantified.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=240472&keyword=polygons&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50','EPA-EIMS'); return false;" href="https://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=240472&keyword=polygons&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50"><span>Climate Change Impacts on Freshwater Recreational Fishing in the United States</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://oaspub.epa.gov/eims/query.page">EPA Science Inventory</a></p> <p></p> <p></p> <p>Using a geographic information system, a spatially explicit modeling framework was developed consisting grid cells organized into 2,099 eight-digit hydrologic unit code (HUC-8) polygons for the coterminous United States. Projected temperature and precipitation changes associated...</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=336795&Lab=NERL&keyword=climatology&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50','EPA-EIMS'); return false;" href="https://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=336795&Lab=NERL&keyword=climatology&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50"><span>Examining Extreme Events Using Dynamically Downscaled 12-km WRF Simulations</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://oaspub.epa.gov/eims/query.page">EPA Science Inventory</a></p> <p></p> <p></p> <p>Continued improvements in the speed and availability of computational resources have allowed dynamical downscaling of global climate model (GCM) projections to be conducted at increasingly finer grid scales and over extended time periods. The implementation of dynamical downscal...</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=305097&keyword=pollution+AND+light&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50','EPA-EIMS'); return false;" href="https://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=305097&keyword=pollution+AND+light&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50"><span>Projecting U.S. climate forcing and criteria pollutant emissions through 2050</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://oaspub.epa.gov/eims/query.page">EPA Science Inventory</a></p> <p></p> <p></p> <p>Presentation highlighting a method for translating emission scenarios to model-ready emission inventories. The presentation highlights new features for spatially allocating emissions to counties and grid cells and identifies areas of potential improvement, such as updating tempor...</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.H14D..05R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.H14D..05R"><span>The effects of spatial heterogeneity and subsurface lateral transfer on evapotranspiration estimates in large scale Earth system models</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Rouholahnejad, E.; Fan, Y.; Kirchner, J. W.; Miralles, D. G.</p> <p>2017-12-01</p> <p>Most Earth system models (ESM) average over considerable sub-grid heterogeneity in land surface properties, and overlook subsurface lateral flow. This could potentially bias evapotranspiration (ET) estimates and has implications for future temperature predictions, since overestimations in ET imply greater latent heat fluxes and potential underestimation of dry and warm conditions in the context of climate change. Here we quantify the bias in evaporation estimates that may arise from the fact that ESMs average over considerable heterogeneity in surface properties, and also neglect lateral transfer of water across the heterogeneous landscapes at global scale. We use a Budyko framework to express ET as a function of P and PET to derive simple sub-grid closure relations that quantify how spatial heterogeneity and lateral transfer could affect average ET as seen from the atmosphere. We show that averaging over sub-grid heterogeneity in P and PET, as typical Earth system models do, leads to overestimation of average ET. Our analysis at global scale shows that the effects of sub-grid heterogeneity will be most pronounced in steep mountainous areas where the topographic gradient is high and where P is inversely correlated with PET across the landscape. In addition, we use the Total Water Storage (TWS) anomaly estimates from the Gravity Recovery and Climate Experiment (GRACE) remote sensing product and assimilate it into the Global Land Evaporation Amsterdam Model (GLEAM) to correct for existing free drainage lower boundary condition in GLEAM and quantify whether, and how much, accounting for changes in terrestrial storage can improve the simulation of soil moisture and regional ET fluxes at global scale.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFM.A51I0201I','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFM.A51I0201I"><span>Probabilistic regional climate projection in Japan using a regression model with CMIP5 multi-model ensemble experiments</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ishizaki, N. N.; Dairaku, K.; Ueno, G.</p> <p>2016-12-01</p> <p>We have developed a statistical downscaling method for estimating probabilistic climate projection using CMIP5 multi general circulation models (GCMs). A regression model was established so that the combination of weights of GCMs reflects the characteristics of the variation of observations at each grid point. Cross validations were conducted to select GCMs and to evaluate the regression model to avoid multicollinearity. By using spatially high resolution observation system, we conducted statistically downscaled probabilistic climate projections with 20-km horizontal grid spacing. Root mean squared errors for monthly mean air surface temperature and precipitation estimated by the regression method were the smallest compared with the results derived from a simple ensemble mean of GCMs and a cumulative distribution function based bias correction method. Projected changes in the mean temperature and precipitation were basically similar to those of the simple ensemble mean of GCMs. Mean precipitation was generally projected to increase associated with increased temperature and consequent increased moisture content in the air. Weakening of the winter monsoon may affect precipitation decrease in some areas. Temperature increase in excess of 4 K was expected in most areas of Japan in the end of 21st century under RCP8.5 scenario. The estimated probability of monthly precipitation exceeding 300 mm would increase around the Pacific side during the summer and the Japan Sea side during the winter season. This probabilistic climate projection based on the statistical method can be expected to bring useful information to the impact studies and risk assessments.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017QSRv..163....1A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017QSRv..163....1A"><span>Last millennium Northern Hemisphere summer temperatures from tree rings: Part II, spatially resolved reconstructions</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Anchukaitis, Kevin J.; Wilson, Rob; Briffa, Keith R.; Büntgen, Ulf; Cook, Edward R.; D'Arrigo, Rosanne; Davi, Nicole; Esper, Jan; Frank, David; Gunnarson, Björn E.; Hegerl, Gabi; Helama, Samuli; Klesse, Stefan; Krusic, Paul J.; Linderholm, Hans W.; Myglan, Vladimir; Osborn, Timothy J.; Zhang, Peng; Rydval, Milos; Schneider, Lea; Schurer, Andrew; Wiles, Greg; Zorita, Eduardo</p> <p>2017-05-01</p> <p>Climate field reconstructions from networks of tree-ring proxy data can be used to characterize regional-scale climate changes, reveal spatial anomaly patterns associated with atmospheric circulation changes, radiative forcing, and large-scale modes of ocean-atmosphere variability, and provide spatiotemporal targets for climate model comparison and evaluation. Here we use a multiproxy network of tree-ring chronologies to reconstruct spatially resolved warm season (May-August) mean temperatures across the extratropical Northern Hemisphere (40-90°N) using Point-by-Point Regression (PPR). The resulting annual maps of temperature anomalies (750-1988 CE) reveal a consistent imprint of volcanism, with 96% of reconstructed grid points experiencing colder conditions following eruptions. Solar influences are detected at the bicentennial (de Vries) frequency, although at other time scales the influence of insolation variability is weak. Approximately 90% of reconstructed grid points show warmer temperatures during the Medieval Climate Anomaly when compared to the Little Ice Age, although the magnitude varies spatially across the hemisphere. Estimates of field reconstruction skill through time and over space can guide future temporal extension and spatial expansion of the proxy network.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFMGC33F..04T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFMGC33F..04T"><span>Energy-Water-Land-Climate Nexus: Modeling Impacts from the Asset to Regional Scale</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Tidwell, V. C.; Bennett, K. E.; Middleton, R. S.; Behery, S.; Macknick, J.; Corning-Padilla, A.; Brinkman, G.; Meng, M.</p> <p>2016-12-01</p> <p>A critical challenge for the energy-water-land nexus is understanding and modeling the connection between the natural system—including changes in climate, land use/cover, and streamflow—and the engineered system including water for energy, agriculture, and society. Equally important is understanding the linkage across scales; that is, how impacts at the asset level aggregate to influence behavior at the local to regional scale. Toward this need, a case study was conducted featuring multi-sector and multi-scale modeling centered on the San Juan River basin (a watershed that accounts for one-tenth of the Colorado River drainage area). Simulations were driven by statistically downscaled climate data from three global climate models (emission scenario RCP 8.5) and planned growth in regional water demand. The Variable Infiltration Capacity (VIC) hydrologic model was fitted with a custom vegetation mortality sub-model and used to estimate tributary inflows to the San Juan River and estimate reservoir evaporation. San Juan River operations, including releases from Navajo Reservoir, were subsequently modeled using RiverWare to estimate impacts on water deliveries out to the year 2100. Major water demands included two large coal-fired power plants, a local electric utility, river-side irrigation, the Navajo Indian Irrigation Project and instream flows managed for endangered aquatic species. Also tracked were basin exports, including water (downstream flows to the Colorado River and interbasin transfers to the Rio Grande) and interstate electric power transmission. Implications for the larger western electric grid were assessed using PLEXOS, a sub-hourly dispatch, electric production-cost model. Results highlight asset-level interactions at the energy-water-land nexus driven by climate and population dynamics; specifically, growing vulnerabilities to shorted water deliveries. Analyses also explored linkages across geographic scales from the San Juan to the larger Colorado River and Rio Grande basins as well as the western power grid.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20020081020&hterms=Influence+clouds+climate&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D50%26Ntt%3DInfluence%2Bclouds%2Bclimate','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20020081020&hterms=Influence+clouds+climate&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D50%26Ntt%3DInfluence%2Bclouds%2Bclimate"><span>Mesoscale Convective Systems During SCSMEX: Simulations with a Regional Climate Model and a Cloud-Resolving Model</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Tao, W.-K.; Wang, Y.; Qian, J.-H.; Shie, C.-L.; Lau, W. K.-M.; Kakar, R.; Starr, David (Technical Monitor)</p> <p>2002-01-01</p> <p>The South China Sea Monsoon Experiment (SCSMEX) was conducted in May-June 1998. One of its major objectives is to better understand the key physical processes for the onset and evolution of the summer monsoon over Southeast Asia and southern China. Multiple observation platforms (e.g., upper-air soundings, Doppler radar, ships, wind profilers, radiometers, etc.) during SCSMEX provided a first attempt at investigating the detailed characteristics of convection and circulation changes associated with monsoons over the South China Sea region. SCSMEX also provided precipitation derived from atmospheric budgets and comparison to those obtained from the Tropical Rainfall Measuring Mission (TRMM). In this paper, a regional scale model (with grid size of 20 km) and Goddard Cumulus Ensemble (GCE) model (with 1 km grid size) are used to perform multi-day integration to understand the precipitation processes associated with the summer monsoon over Southeast Asia and southern China. The regional climate model is used to understand the soil-precipitation interaction and feedback associated with a flood event that occurred in and around China's Yantz River during SCSMEX Sensitivity tests on various land surface models, sea surface temperature (SST) variations, and cloud processes are performed to understand the precipitation processes associated with the onset of the monsoon over the S. China Sea during SCSMEX. These tests have indicated that the land surface model has a major impact on the circulation over the S. China Sea. Cloud processes can effect the precipitation pattern while SST variation can effect the precipitation amounts over both land and ocean. The exact location (region) of the flooding can be effected by the soil-rainfall feedback. The GCE-model results captured many observed precipitation characteristics because it used a fine grid size. For example, the model simulated rainfall temporal variation compared quite well to the sounding-estimated rainfall. The results show there are more latent heat fluxes prior to the onset of the monsoon. However, more rainfall was simulated after the onset of the monsoon. This modeling study indicates the latent heat fluxes (or evaporation) have more of an impact on precipitation processes and rainfall in the regional climate model simulations than in the cloud-resolving model simulations. Research is underway to determine if the difference in the grid sizes or the moist processes used in these two models is responsible for the differing influence of surface fluxes an precipitation processes.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFMGC33F..02Y','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFMGC33F..02Y"><span>Integrated modeling for assessment of energy-water system resilience under changing climate</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Yan, E.; Veselka, T.; Zhou, Z.; Koritarov, V.; Mahalik, M.; Qiu, F.; Mahat, V.; Betrie, G.; Clark, C.</p> <p>2016-12-01</p> <p>Energy and water systems are intrinsically interconnected. Due to an increase in climate variability and extreme weather events, interdependency between these two systems has been recently intensified resulting significant impacts on both systems and energy output. To address this challenge, an Integrated Water-Energy Systems Assessment Framework (IWESAF) is being developed to integrate multiple existing or developed models from various sectors. The IWESAF currently includes an extreme climate event generator to predict future extreme weather events, hydrologic and reservoir models, riverine temperature model, power plant water use simulator, and power grid operation and cost optimization model. The IWESAF can facilitate the interaction among the modeling systems and provide insights of the sustainability and resilience of the energy-water system under extreme climate events and economic consequence. The regional case demonstration in the Midwest region will be presented. The detailed information on some of individual modeling components will also be presented in several other abstracts submitted to AGU this year.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017ClDy...49.4061L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017ClDy...49.4061L"><span>Is there potential added value in COSMO-CLM forced by ERA reanalysis data?</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Lenz, Claus-Jürgen; Früh, Barbara; Adalatpanah, Fatemeh Davary</p> <p>2017-12-01</p> <p>An application of the potential added value (PAV) concept suggested by Di Luca et al. (Clim Dyn 40:443-464, 2013a) is applied to ERA Interim driven runs of the regional climate model COSMO-CLM. They are performed for the time period 1979-2013 for the EURO-CORDEX domain at horizontal grid resolutions 0.11°, 0.22°, and 0.44° such that the higher resolved model grid fits into the next coarser grid. The concept of the potential added value is applied to annual, seasonal, and monthly means of the 2 m air temperature. Results show the highest potential added value at the run with the finest grid and generally increasing PAV with increasing resolution. The potential added value strongly depends on the season as well as the region of consideration. The gain of PAV is higher enhancing the resolution from 0.44° to 0.22° than from 0.22° to 0.11°. At grid aggregations to 0.88° and 1.76° the differences in PAV between the COSMO-CLM runs on the mentioned grid resolutions are maximal. They nearly vanish at aggregations to even coarser grids. In all cases the PAV is dominated by at least 80% by its stationary part.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/1325752','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/1325752"><span>Interactive Correlation Analysis and Visualization of Climate Data</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Ma, Kwan-Liu</p> <p></p> <p>The relationship between our ability to analyze and extract insights from visualization of climate model output and the capability of the available resources to make those visualizations has reached a crisis point. The large volume of data currently produced by climate models is overwhelming the current, decades-old visualization workflow. The traditional methods for visualizing climate output also have not kept pace with changes in the types of grids used, the number of variables involved, and the number of different simulations performed with a climate model or the feature-richness of high-resolution simulations. This project has developed new and faster methods formore » visualization in order to get the most knowledge out of the new generation of high-resolution climate models. While traditional climate images will continue to be useful, there is need for new approaches to visualization and analysis of climate data if we are to gain all the insights available in ultra-large data sets produced by high-resolution model output and ensemble integrations of climate models such as those produced for the Coupled Model Intercomparison Project. Towards that end, we have developed new visualization techniques for performing correlation analysis. We have also introduced highly scalable, parallel rendering methods for visualizing large-scale 3D data. This project was done jointly with climate scientists and visualization researchers at Argonne National Laboratory and NCAR.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/1360674','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/1360674"><span>Xanthos</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p></p> <p>2017-05-30</p> <p>Xanthos is a Python package designed to quantify and analyze global water availability in history and in future at 0.5° × 0.5° spatial resolution and a monthly time step under a changing climate. Its performance was also tested through real applications. It is open-source, extendable and convenient to researchers who work on long-term climate data for studies of global water supply, and Global Change Assessment Model (GCAM). This package integrates inherent global gridded data maps, I/O modules, Water-Balance Model modules and diagnostics modules by user-defined configuration.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014EGUGA..16.5273B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014EGUGA..16.5273B"><span>River salinity on a mega-delta, an unstructured grid model approach.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bricheno, Lucy; Saiful Islam, Akm; Wolf, Judith</p> <p>2014-05-01</p> <p>With an average freshwater discharge of around 40,000 m3/s the BGM (Brahmaputra Ganges and Meghna) river system has the third largest discharge worldwide. The BGM river delta is a low-lying fertile area covering over 100,000 km2 mainly in India and Bangladesh. Approximately two-thirds of the Bangladesh people work in agriculture and these local livelihoods depend on freshwater sources directly linked to river salinity. The finite volume coastal ocean model (FVCOM) has been applied to the BGM delta in order to simulate river salinity under present and future climate conditions. Forced by a combination of regional climate model predictions, and a basin-wide river catchment model, the 3D baroclinic delta model can determine river salinity under the current climate, and make predictions for future wet and dry years. The river salinity demonstrates a strong seasonal and tidal cycle, making it important for the model to be able to capture a wide range of timescales. The unstructured mesh approach used in FVCOM is required to properly represent the delta's structure; a complex network of interconnected river channels. The model extends 250 km inland in order to capture the full extent of the tidal influence and grid resolutions of 10s of metres are required to represent narrow inland river channels. The use of FVCOM to simulate flows so far inland is a novel challenge, which also requires knowledge of the shape and cross-section of the river channels.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://pubs.er.usgs.gov/publication/70187427','USGSPUBS'); return false;" href="https://pubs.er.usgs.gov/publication/70187427"><span>Designing ecological climate change impact assessments to reflect key climatic drivers</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Sofaer, Helen R.; Barsugli, Joseph J.; Jarnevich, Catherine S.; Abatzoglou, John T.; Talbert, Marian; Miller, Brian W.; Morisette, Jeffrey T.</p> <p>2017-01-01</p> <p>Identifying the climatic drivers of an ecological system is a key step in assessing its vulnerability to climate change. The climatic dimensions to which a species or system is most sensitive – such as means or extremes – can guide methodological decisions for projections of ecological impacts and vulnerabilities. However, scientific workflows for combining climate projections with ecological models have received little explicit attention. We review Global Climate Model (GCM) performance along different dimensions of change and compare frameworks for integrating GCM output into ecological models. In systems sensitive to climatological means, it is straightforward to base ecological impact assessments on mean projected changes from several GCMs. Ecological systems sensitive to climatic extremes may benefit from what we term the ‘model space’ approach: a comparison of ecological projections based on simulated climate from historical and future time periods. This approach leverages the experimental framework used in climate modeling, in which historical climate simulations serve as controls for future projections. Moreover, it can capture projected changes in the intensity and frequency of climatic extremes, rather than assuming that future means will determine future extremes. Given the recent emphasis on the ecological impacts of climatic extremes, the strategies we describe will be applicable across species and systems. We also highlight practical considerations for the selection of climate models and data products, emphasizing that the spatial resolution of the climate change signal is generally coarser than the grid cell size of downscaled climate model output. Our review illustrates how an understanding of how climate model outputs are derived and downscaled can improve the selection and application of climatic data used in ecological modeling.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/28173628','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/28173628"><span>Designing ecological climate change impact assessments to reflect key climatic drivers.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Sofaer, Helen R; Barsugli, Joseph J; Jarnevich, Catherine S; Abatzoglou, John T; Talbert, Marian K; Miller, Brian W; Morisette, Jeffrey T</p> <p>2017-07-01</p> <p>Identifying the climatic drivers of an ecological system is a key step in assessing its vulnerability to climate change. The climatic dimensions to which a species or system is most sensitive - such as means or extremes - can guide methodological decisions for projections of ecological impacts and vulnerabilities. However, scientific workflows for combining climate projections with ecological models have received little explicit attention. We review Global Climate Model (GCM) performance along different dimensions of change and compare frameworks for integrating GCM output into ecological models. In systems sensitive to climatological means, it is straightforward to base ecological impact assessments on mean projected changes from several GCMs. Ecological systems sensitive to climatic extremes may benefit from what we term the 'model space' approach: a comparison of ecological projections based on simulated climate from historical and future time periods. This approach leverages the experimental framework used in climate modeling, in which historical climate simulations serve as controls for future projections. Moreover, it can capture projected changes in the intensity and frequency of climatic extremes, rather than assuming that future means will determine future extremes. Given the recent emphasis on the ecological impacts of climatic extremes, the strategies we describe will be applicable across species and systems. We also highlight practical considerations for the selection of climate models and data products, emphasizing that the spatial resolution of the climate change signal is generally coarser than the grid cell size of downscaled climate model output. Our review illustrates how an understanding of how climate model outputs are derived and downscaled can improve the selection and application of climatic data used in ecological modeling. © 2017 John Wiley & Sons Ltd.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013JGRD..11810449Y','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013JGRD..11810449Y"><span>A physically based analytical spatial air temperature and humidity model</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Yang, Yang; Endreny, Theodore A.; Nowak, David J.</p> <p>2013-09-01</p> <p>Spatial variation of urban surface air temperature and humidity influences human thermal comfort, the settling rate of atmospheric pollutants, and plant physiology and growth. Given the lack of observations, we developed a Physically based Analytical Spatial Air Temperature and Humidity (PASATH) model. The PASATH model calculates spatial solar radiation and heat storage based on semiempirical functions and generates spatially distributed estimates based on inputs of topography, land cover, and the weather data measured at a reference site. The model assumes that for all grids under the same mesoscale climate, grid air temperature and humidity are modified by local variation in absorbed solar radiation and the partitioning of sensible and latent heat. The model uses a reference grid site for time series meteorological data and the air temperature and humidity of any other grid can be obtained by solving the heat flux network equations. PASATH was coupled with the USDA iTree-Hydro water balance model to obtain evapotranspiration terms and run from 20 to 29 August 2010 at a 360 m by 360 m grid scale and hourly time step across a 285 km2 watershed including the urban area of Syracuse, NY. PASATH predictions were tested at nine urban weather stations representing variability in urban topography and land cover. The PASATH model predictive efficiency R2 ranged from 0.81 to 0.99 for air temperature and 0.77 to 0.97 for dew point temperature. PASATH is expected to have broad applications on environmental and ecological models.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4618743','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4618743"><span>Mapping Atmospheric Moisture Climatologies across the Conterminous United States</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Daly, Christopher; Smith, Joseph I.; Olson, Keith V.</p> <p>2015-01-01</p> <p>Spatial climate datasets of 1981–2010 long-term mean monthly average dew point and minimum and maximum vapor pressure deficit were developed for the conterminous United States at 30-arcsec (~800m) resolution. Interpolation of long-term averages (twelve monthly values per variable) was performed using PRISM (Parameter-elevation Relationships on Independent Slopes Model). Surface stations available for analysis numbered only 4,000 for dew point and 3,500 for vapor pressure deficit, compared to 16,000 for previously-developed grids of 1981–2010 long-term mean monthly minimum and maximum temperature. Therefore, a form of Climatologically-Aided Interpolation (CAI) was used, in which the 1981–2010 temperature grids were used as predictor grids. For each grid cell, PRISM calculated a local regression function between the interpolated climate variable and the predictor grid. Nearby stations entering the regression were assigned weights based on the physiographic similarity of the station to the grid cell that included the effects of distance, elevation, coastal proximity, vertical atmospheric layer, and topographic position. Interpolation uncertainties were estimated using cross-validation exercises. Given that CAI interpolation was used, a new method was developed to allow uncertainties in predictor grids to be accounted for in estimating the total interpolation error. Local land use/land cover properties had noticeable effects on the spatial patterns of atmospheric moisture content and deficit. An example of this was relatively high dew points and low vapor pressure deficits at stations located in or near irrigated fields. The new grids, in combination with existing temperature grids, enable the user to derive a full suite of atmospheric moisture variables, such as minimum and maximum relative humidity, vapor pressure, and dew point depression, with accompanying assumptions. All of these grids are available online at http://prism.oregonstate.edu, and include 800-m and 4-km resolution data, images, metadata, pedigree information, and station inventory files. PMID:26485026</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFMGC31F..05T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFMGC31F..05T"><span>Effects of Forecasted Climate Change on Stream Temperatures in the Nooksack River Basin</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Truitt, S. E.; Mitchell, R. J.; Yearsley, J. R.; Grah, O. J.</p> <p>2017-12-01</p> <p>The Nooksack River in northwest Washington State provides valuable habitat for endangered salmon species, as such it is critical to understand how stream temperatures will be affected by forecasted climate change. The Middle and North Forks basins of the Nooksack are high-relief and glaciated, whereas the South Fork is a lower relief rain and snow dominated basin. Due to a moderate Pacific maritime climate, snowpack in the basins is sensitive to temperature increases. Previous modeling studies in the upper Nooksack basins indicate a reduction in snowpack and spring runoff, and a recession of glaciers into the 21st century. How stream temperatures will respond to these changes is unknown. We use the Distributed Hydrology Soil Vegetation Model (DHSVM) coupled with a glacier dynamics model and the River Basin Model (RBM) to simulate hydrology and stream temperature from present to the year 2100. We calibrate the DHSVM and RBM to the three forks in the upper 1550 km2 of the Nooksack basin, which contain an estimated 3400 hectares of glacial ice. We employ observed stream-temperature data collected over the past decade and hydrologic data from the four USGS streamflow monitoring sites within the basin and observed gridded climate data developed by Linveh et al. (2013). Field work was conducted in the summer of 2016 to determine stream morphology, discharge, and stream temperatures at a number of stream segments for the RBM calibration. We simulate forecast climate change impacts, using gridded daily downscaled data from global climate models of the CMIP5 with RCP4.5 and RCP8.5 forcing scenarios developed using the multivariate adaptive constructed analogs method (MACA; Abatzoglou and Brown, 2011). Simulation results project a trending increase in stream temperature as a result of lower snowmelt and higher air temperatures into the 21st century, especially in the lower relief, unglaciated South Fork basin.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li class="active"><span>14</span></li> <li><a href="#" onclick='return showDiv("page_15");'>15</a></li> <li><a href="#" onclick='return showDiv("page_16");'>16</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_14 --> <div id="page_15" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li class="active"><span>15</span></li> <li><a href="#" onclick='return showDiv("page_16");'>16</a></li> <li><a href="#" onclick='return showDiv("page_17");'>17</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="281"> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018ThApC.132..219I','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018ThApC.132..219I"><span>Field significance of performance measures in the context of regional climate model evaluation. Part 1: temperature</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ivanov, Martin; Warrach-Sagi, Kirsten; Wulfmeyer, Volker</p> <p>2018-04-01</p> <p>A new approach for rigorous spatial analysis of the downscaling performance of regional climate model (RCM) simulations is introduced. It is based on a multiple comparison of the local tests at the grid cells and is also known as "field" or "global" significance. New performance measures for estimating the added value of downscaled data relative to the large-scale forcing fields are developed. The methodology is exemplarily applied to a standard EURO-CORDEX hindcast simulation with the Weather Research and Forecasting (WRF) model coupled with the land surface model NOAH at 0.11 ∘ grid resolution. Monthly temperature climatology for the 1990-2009 period is analysed for Germany for winter and summer in comparison with high-resolution gridded observations from the German Weather Service. The field significance test controls the proportion of falsely rejected local tests in a meaningful way and is robust to spatial dependence. Hence, the spatial patterns of the statistically significant local tests are also meaningful. We interpret them from a process-oriented perspective. In winter and in most regions in summer, the downscaled distributions are statistically indistinguishable from the observed ones. A systematic cold summer bias occurs in deep river valleys due to overestimated elevations, in coastal areas due probably to enhanced sea breeze circulation, and over large lakes due to the interpolation of water temperatures. Urban areas in concave topography forms have a warm summer bias due to the strong heat islands, not reflected in the observations. WRF-NOAH generates appropriate fine-scale features in the monthly temperature field over regions of complex topography, but over spatially homogeneous areas even small biases can lead to significant deteriorations relative to the driving reanalysis. As the added value of global climate model (GCM)-driven simulations cannot be smaller than this perfect-boundary estimate, this work demonstrates in a rigorous manner the clear additional value of dynamical downscaling over global climate simulations. The evaluation methodology has a broad spectrum of applicability as it is distribution-free, robust to spatial dependence, and accounts for time series structure.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011AGUFM.B34D..02K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011AGUFM.B34D..02K"><span>Evaluating Global Land-use Change Scenario: Carbon Emission in RCP Scenarios and its Effects on Climate Response</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kato, E.; Kawamiya, M.</p> <p>2011-12-01</p> <p>In CMIP5 experiments, new emissions scenarios for GCMs and Earth System Models (ESMs) have been constructed as Representative Concentration Pathways (RCPs) by a community effort of Integrated Assessment Modeling (IAM) groups. In RCP scenarios, regional land-use scenarios have been depicted based on the socio-economic assumption of IAMs, and also downscaled spatially explicit land-use maps from the regional scenarios are prepared. In the land-use harmonization project, integrated gridded land-use transition data for the past and future time period has been developed from the reconstruction based on HYDE 3 agricultural data and FAO wood harvest data, and the future land-use scenarios from IAMs. These gridded land-use dataset are used as a forcing of some ESMs participating to the CMIP5 experiments, to assess the biogeochemical and biogeophysical effects of land-use and land cover change in the climate change simulation. In this study, global net CO2 emissions from land-use change for RCP scenarios are evaluated with an offline terrestrial biogeochemical model, VISIT (Vegetation Integrative SImulation Tool). Also the emissions are evaluated with coupled ESM, MIROC-ESM following the LUCID-CMIP5 protocol to see the effect of land-use and land cover change on climate response. Using the model output, consistency of the land-use change CO2 emission scenarios provided by RCPs are evaluated in terms of effect of CO2 fertilization, climate change, and land-use transition itself including the effect of biomass crops production with CCS. We find that a land-use scenario with decreased agricultural land-use intensity such as RCP 6.0 shows possibility of further absorption of CO2 through the climate-carbon feedback, and cooling effect through both biogeochemical and biogeophysical effects.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFM.A34C..05L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFM.A34C..05L"><span>Using High Resolution Regional Climate Models to Quantify the Snow Albedo Feedback in a Region of Complex Terrain</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Letcher, T.; Minder, J. R.</p> <p>2015-12-01</p> <p>High resolution regional climate models are used to characterize and quantify the snow albedo feedback (SAF) over the complex terrain of the Colorado Headwaters region. Three pairs of 7-year control and pseudo global warming simulations (with horizontal grid spacings of 4, 12, and 36 km) are used to study how the SAF modifies the regional climate response to a large-scale thermodynamic perturbation. The SAF substantially enhances warming within the Headwaters domain, locally as much as 5 °C in regions of snow loss. The SAF also increases the inter-annual variability of the springtime warming within Headwaters domain under the perturbed climate. Linear feedback analysis is used quantify the strength of the SAF. The SAF attains a maximum value of 4 W m-2 K-1 during April when snow loss coincides with strong incoming solar radiation. On sub-seasonal timescales, simulations at 4 km and 12 km horizontal grid-spacing show good agreement in the strength and timing of the SAF, whereas a 36km simulation shows greater discrepancies that are tired to differences in snow accumulation and ablation caused by smoother terrain. An analysis of the regional energy budget shows that transport by atmospheric motion acts as a negative feedback to regional warming, damping the effects of the SAF. On the mesoscale, this transport causes non-local warming in locations with no snow. The methods presented here can be used generally to quantify the role of the SAF in other regional climate modeling experiments.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20140013408','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20140013408"><span>A Long-Term and Reproducible Passive Microwave Sea Ice Concentration Data Record for Climate Studies and Monitoring</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Peng, G.; Meier, W. N.; Scott, D. J.; Savoie, M. H.</p> <p>2013-01-01</p> <p>A long-term, consistent, and reproducible satellite-based passive microwave sea ice concentration climate data record (CDR) is available for climate studies, monitoring, and model validation with an initial operation capability (IOC). The daily and monthly sea ice concentration data are on the National Snow and Ice Data Center (NSIDC) polar stereographic grid with nominal 25 km × 25 km grid cells in both the Southern and Northern Hemisphere polar regions from 9 July 1987 to 31 December 2007. The data files are available in the NetCDF data format at http://nsidc.org/data/g02202.html and archived by the National Climatic Data Center (NCDC) of the National Oceanic and Atmospheric Administration (NOAA) under the satellite climate data record program (http://www.ncdc.noaa.gov/cdr/operationalcdrs.html). The description and basic characteristics of the NOAA/NSIDC passive microwave sea ice concentration CDR are presented here. The CDR provides similar spatial and temporal variability as the heritage products to the user communities with the additional documentation, traceability, and reproducibility that meet current standards and guidelines for climate data records. The data set, along with detailed data processing steps and error source information, can be found at http://dx.doi.org/10.7265/N5B56GN3.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFM.A11N..06N','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFM.A11N..06N"><span>ED(MF)n: Humidity-Convection Feedbacks in a Mass Flux Scheme Based on Resolved Size Densities</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Neggers, R.</p> <p>2014-12-01</p> <p>Cumulus cloud populations remain at least partially unresolved in present-day numerical simulations of global weather and climate, and accordingly their impact on the larger-scale flow has to be represented through parameterization. Various methods have been developed over the years, ranging in complexity from the early bulk models relying on a single plume to more recent approaches that attempt to reconstruct the underlying probability density functions, such as statistical schemes and multiple plume approaches. Most of these "classic" methods capture key aspects of cumulus cloud populations, and have been successfully implemented in operational weather and climate models. However, the ever finer discretizations of operational circulation models, driven by advances in the computational efficiency of supercomputers, is creating new problems for existing sub-grid schemes. Ideally, a sub-grid scheme should automatically adapt its impact on the resolved scales to the dimension of the grid-box within which it is supposed to act. It can be argued that this is only possible when i) the scheme is aware of the range of scales of the processes it represents, and ii) it can distinguish between contributions as a function of size. How to conceptually represent this knowledge of scale in existing parameterization schemes remains an open question that is actively researched. This study considers a relatively new class of models for sub-grid transport in which ideas from the field of population dynamics are merged with the concept of multi plume modelling. More precisely, a multiple mass flux framework for moist convective transport is formulated in which the ensemble of plumes is created in "size-space". It is argued that thus resolving the underlying size-densities creates opportunities for introducing scale-awareness and scale-adaptivity in the scheme. The behavior of an implementation of this framework in the Eddy Diffusivity Mass Flux (EDMF) model, named ED(MF)n, is examined for a standard case of subtropical marine shallow cumulus. We ask if a system of multiple independently resolved plumes is able to automatically create the vertical profile of bulk (mass) flux at which the sub-grid scale transport balances the imposed larger-scale forcings in the cloud layer.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012AGUFM.H21H1278S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012AGUFM.H21H1278S"><span>Satellite-derived potential evapotranspiration for distributed hydrologic runoff modeling</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Spies, R. R.; Franz, K. J.; Bowman, A.; Hogue, T. S.; Kim, J.</p> <p>2012-12-01</p> <p>Distributed models have the ability of incorporating spatially variable data, especially high resolution forcing inputs such as precipitation, temperature and evapotranspiration in hydrologic modeling. Use of distributed hydrologic models for operational streamflow prediction has been partially hindered by a lack of readily available, spatially explicit input observations. Potential evapotranspiration (PET), for example, is currently accounted for through PET input grids that are based on monthly climatological values. The goal of this study is to assess the use of satellite-based PET estimates that represent the temporal and spatial variability, as input to the National Weather Service (NWS) Hydrology Laboratory Research Distributed Hydrologic Model (HL-RDHM). Daily PET grids are generated for six watersheds in the upper Mississippi River basin using a method that applies only MODIS satellite-based observations and the Priestly Taylor formula (MODIS-PET). The use of MODIS-PET grids will be tested against the use of the current climatological PET grids for simulating basin discharge. Gridded surface temperature forcing data are derived by applying the inverse distance weighting spatial prediction method to point-based station observations from the Automated Surface Observing System (ASOS) and Automated Weather Observing System (AWOS). Precipitation data are obtained from the Climate Prediction Center's (CPC) Climatology-Calibrated Precipitation Analysis (CCPA). A-priori gridded parameters for the Sacramento Soil Moisture Accounting Model (SAC-SMA), Snow-17 model, and routing model are initially obtained from the Office of Hydrologic Development and further calibrated using an automated approach. The potential of the MODIS-PET to be used in an operational distributed modeling system will be assessed with the long-term goal of promoting research to operations transfers and advancing the science of hydrologic forecasting.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20140017657','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20140017657"><span>Uncertainties in the Modelled CO2 Threshold for Antarctic Glaciation</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Gasson, E.; Lunt, D. J.; DeConto, R.; Goldner, A.; Heinemann, M.; Huber, M.; LeGrande, A. N.; Pollard, D.; Sagoo, N.; Siddall, M.; <a style="text-decoration: none; " href="javascript:void(0); " onClick="displayelement('author_20140017657'); toggleEditAbsImage('author_20140017657_show'); toggleEditAbsImage('author_20140017657_hide'); "> <img style="display:inline; width:12px; height:12px; " src="images/arrow-up.gif" width="12" height="12" border="0" alt="hide" id="author_20140017657_show"> <img style="width:12px; height:12px; display:none; " src="images/arrow-down.gif" width="12" height="12" border="0" alt="hide" id="author_20140017657_hide"></p> <p>2014-01-01</p> <p>frequently cited atmospheric CO2 threshold for the onset of Antarctic glaciation of approximately780 parts per million by volume is based on the study of DeConto and Pollard (2003) using an ice sheet model and the GENESIS climate model. Proxy records suggest that atmospheric CO2 concentrations passed through this threshold across the Eocene-Oligocene transition approximately 34 million years. However, atmospheric CO2 concentrations may have been close to this threshold earlier than this transition, which is used by some to suggest the possibility of Antarctic ice sheets during the Eocene. Here we investigate the climate model dependency of the threshold for Antarctic glaciation by performing offline ice sheet model simulations using the climate from 7 different climate models with Eocene boundary conditions (HadCM3L, CCSM3, CESM1.0, GENESIS, FAMOUS, ECHAM5 and GISS_ER). These climate simulations are sourced from a number of independent studies, and as such the boundary conditions, which are poorly constrained during the Eocene, are not identical between simulations. The results of this study suggest that the atmospheric CO2 threshold for Antarctic glaciation is highly dependent on the climate model used and the climate model configuration. A large discrepancy between the climate model and ice sheet model grids for some simulations leads to a strong sensitivity to the lapse rate parameter.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016JPS...332..193W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016JPS...332..193W"><span>Quantifying electric vehicle battery degradation from driving vs. vehicle-to-grid services</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wang, Dai; Coignard, Jonathan; Zeng, Teng; Zhang, Cong; Saxena, Samveg</p> <p>2016-11-01</p> <p>The risk of accelerated electric vehicle battery degradation is commonly cited as a concern inhibiting the implementation of vehicle-to-grid (V2G) technology. However, little quantitative evidence exists in prior literature to refute or substantiate these concerns for different grid services that vehicles may offer. In this paper, a methodology is proposed to quantify electric vehicle (EV) battery degradation from driving only vs. driving and several vehicle-grid services, based on a semi-empirical lithium-ion battery capacity fade model. A detailed EV battery pack thermal model and EV powertrain model are utilized to capture the time-varying battery temperature and working parameters including current, internal resistance and state-of-charge (SOC), while an EV is driving and offering various grid services. We use the proposed method to simulate the battery degradation impacts from multiple vehicle-grid services including peak load shaving, frequency regulation and net load shaping. The degradation impact of these grid services is compared against baseline cases for driving and uncontrolled charging only, for several different cases of vehicle itineraries, driving distances, and climate conditions. Over the lifetime of a vehicle, our results show that battery wear is indeed increased when vehicles offer V2G grid services. However, the increased wear from V2G is inconsequential compared with naturally occurring battery wear (i.e. from driving and calendar ageing) when V2G services are offered only on days of the greatest grid need (20 days/year in our study). In the case of frequency regulation and peak load shaving V2G grid services offered 2 hours each day, battery wear remains minimal even if this grid service is offered every day over the vehicle lifetime. Our results suggest that an attractive tradeoff exists where vehicles can offer grid services on the highest value days for the grid with minimal impact on vehicle battery life.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFMNH51A0110B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFMNH51A0110B"><span>Future Changes in Cyclonic Wave Climate in the North Atlantic, with a Focus on the French West Indies</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Belmadani, A.; Palany, P.; Dalphinet, A.; Pilon, R.; Chauvin, F.</p> <p>2017-12-01</p> <p>Tropical cyclones (TCs) are a major environmental hazard in numerous small islands such as the French West Indies (Guadeloupe, Martinique, St-Martin, St-Barthélémy). The intense associated winds, which can reach 300 km/h or more, can cause serious damage in the islands and their coastlines. In particular, the combined action of waves, currents and low atmospheric pressure leads to severe storm surge and coastal flooding. Here we report on future changes in cyclonic wave climate for the North Atlantic basin, as a preliminary step for downscaled projections over the French West Indies at sub-kilometer-scale resolution. A new configuration of the Météo-France ARPEGE atmospheric general circulation model on a stretched grid with increased resolution in the tropical North Atlantic ( 15 km) is able to reproduce the observed distribution of maximum surface winds, including extreme events corresponding to Category 5 hurricanes. Ensemble historical simulations (1985-2014, 5 members) and future projections with the IPCC (Intergovernmental Panel on Climate Change) RCP8.5 scenario (2051-2080, 5 members) are used to drive the MFWAM (Météo-France Wave Action Model) over the North Atlantic basin. A lower 50-km resolution grid is used to propagate distant mid-latitude swells into a higher 10-km resolution grid over the cyclonic basin. Wave model performance is evaluated over a few TC case studies including the Sep-Oct 2016 Category 5 Hurricane Matthew, using an operational version of ARPEGE at similar resolution to force MFWAM together with wave buoy data. The latter are also used to compute multi-year wave statistics, which then allow assessing the realism of the MFWAM historical runs. For each climate scenario and ensemble member, a simulation of the cyclonic season (July to mid-November) is performed every year. The simulated sea states over the North Atlantic cyclonic basin over 150 historical simulations are compared to their counterparts over 150 future simulations. Changes in cyclonic wave climate are discussed in the light of concurrent changes in TC activity, inferred from objective tracking of individual TCs.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010ems..confE.484A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010ems..confE.484A"><span>Regional climate simulations over complex topography using WRF: Andalusian present climate</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Argüeso, D.; Hidalgo-Muñoz, J. M.; Calandria-Hernández, D.; Gámiz-Fortis, S. R.; Esteban-Parra, M. J.; Castro-Díez, Y.</p> <p>2010-09-01</p> <p>In this study three WRF simulations were carried out and analyzed to assess its accuracy to describe the main climate features of Southern Spain in terms of maximum temperature, minimum temperature and precipitation. Present climate was represented by the last 30 year of the 20th Century (1970-1999). The model was evaluated using an observational network distributed throughout Andalusia that comprised both temperatures and precipitation. Since comparison between site-specific measurements and model grid points is definitely troublesome due to differences in spatial-scale, a multi-step regionalization strategy was adopted to upscale observational information. This is of particular importance when studying complex topography regions such as Andalusia, with a wide range of climate conditions in a relative small area. Additionally, WRF outputs were also compared with SPAIN02, a 20-km resolution gridded dataset of precipitation for further validation of the model performance. The model set up consisted in two domains with one-way nesting and spectral nudging. The target domain has a resolution of 10km with 136 by 136 points covering the whole Iberian Peninsula and nested in a coarser domain of 30-km resolution and 130 by 120 grid points. Both domains have 35 vertical levels. Three different driving data were used to provide the boundary conditions, one reanalysis (ERA-40) and two control runs from different General Circulation Models (ECHAM5 and CCSM 3.0). A conservative 7-month spin-up period was added to the 30-year simulation so that dependence on initial conditions can be completely removed. Physics options were chosen on the basis of previous parameterization sensitivity tests over Andalusia that led to a compromise configuration that adequately describes the different subclimates. Probability distributions of daily values as well as monthly statistics were examined to determine the uncertainties associated to each variable and take them into consideration for future regional high-resolution projections of climate change scenarios. These analyses permitted to conclude that WRF is an extremely useful tool due to the significant value-added information produced with respect to the driving data. Nonetheless, according to differences in performance between regions it has also been shown that results must be interpreted carefully depending on the region characteristics. Acknowledgements: The Spanish Ministry of Science and Innovation, with additional support from the European Community Funds (FEDER), project CGL2007-61151/CLI, and the Regional Government of Andalusia project P06-RNM-01622, have financed this study.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017EGUGA..1915739W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017EGUGA..1915739W"><span>SDCLIREF - A sub-daily gridded reference dataset</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wood, Raul R.; Willkofer, Florian; Schmid, Franz-Josef; Trentini, Fabian; Komischke, Holger; Ludwig, Ralf</p> <p>2017-04-01</p> <p>Climate change is expected to impact the intensity and frequency of hydrometeorological extreme events. In order to adequately capture and analyze extreme rainfall events, in particular when assessing flood and flash flood situations, data is required at high spatial and sub-daily resolution which is often not available in sufficient density and over extended time periods. The ClimEx project (Climate Change and Hydrological Extreme Events) addresses the alteration of hydrological extreme events under climate change conditions. In order to differentiate between a clear climate change signal and the limits of natural variability, unique Single-Model Regional Climate Model Ensembles (CRCM5 driven by CanESM2, RCP8.5) were created for a European and North-American domain, each comprising 50 members of 150 years (1951-2100). In combination with the CORDEX-Database, this newly created ClimEx-Ensemble is a one-of-a-kind model dataset to analyze changes of sub-daily extreme events. For the purpose of bias-correcting the regional climate model ensembles as well as for the baseline calibration and validation of hydrological catchment models, a new sub-daily (3h) high-resolution (500m) gridded reference dataset (SDCLIREF) was created for a domain covering the Upper Danube and Main watersheds ( 100.000km2). As the sub-daily observations lack a continuous time series for the reference period 1980-2010, the need for a suitable method to bridge the gap of the discontinuous time series arouse. The Method of Fragments (Sharma and Srikanthan (2006); Westra et al. (2012)) was applied to transform daily observations to sub-daily rainfall events to extend the time series and densify the station network. Prior to applying the Method of Fragments and creating the gridded dataset using rigorous interpolation routines, data collection of observations, operated by several institutions in three countries (Germany, Austria, Switzerland), and the subsequent quality control of the observations was carried out. Among others, the quality control checked for steps, extensive dry seasons, temporal consistency and maximum hourly values. The resulting SDCLIREF dataset provides a robust precipitation reference for hydrometeorological applications in unprecedented high spatio-temporal resolution. References: Sharma, A.; Srikanthan, S. (2006): Continuous Rainfall Simulation: A Nonparametric Alternative. In: 30th Hydrology and Water Resources Symposium 4-7 December 2006, Launceston, Tasmania. Westra, S.; Mehrotra, R.; Sharma, A.; Srikanthan, R. (2012): Continuous rainfall simulation. 1. A regionalized subdaily disaggregation approach. In: Water Resour. Res. 48 (1). DOI: 10.1029/2011WR010489.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017EGUGA..19.8550D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017EGUGA..19.8550D"><span>SPAGETTA, a Gridded Weather Generator: Calibration, Validation and its Use for Future Climate</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Dubrovsky, Martin; Rotach, Mathias W.; Huth, Radan</p> <p>2017-04-01</p> <p>Spagetta is a new (started in 2016) stochastic multi-site multi-variate weather generator (WG). It can produce realistic synthetic daily (or monthly, or annual) weather series representing both present and future climate conditions at multiple sites (grids or stations irregularly distributed in space). The generator, whose model is based on the Wilks' (1999) multi-site extension of the parametric (Richardson's type) single site M&Rfi generator, may be run in two modes: In the first mode, it is run as a classical generator, which is calibrated in the first step using weather data from multiple sites, and only then it may produce arbitrarily long synthetic time series mimicking the spatial and temporal structure of the calibration weather data. To generate the weather series representing the future climate, the WG parameters are modified according to the climate change scenario, typically derived from GCM or RCM simulations. In the second mode, the user provides only basic information (not necessarily to be realistic) on the temporal and spatial auto-correlation structure of the surface weather variables and their mean annual cycle; the generator itself derives the parameters of the underlying autoregressive model, which produces the multi-site weather series. In the latter mode of operation, the user is allowed to prescribe the spatially varying trend, which is superimposed to the values produced by the generator; this feature has been implemented for use in developing the methodology for assessing significance of trends in multi-site weather series (for more details see another EGU-2017 contribution: Huth and Dubrovsky, 2017, Evaluating collective significance of climatic trends: A comparison of methods on synthetic data; EGU2017-4993). This contribution will focus on the first (classical) mode. The poster will present (a) model of the generator, (b) results of the validation tests made in terms of the spatial hot/cold/dry/wet spells, and (c) results of the pilot climate change impact experiment, in which (i) the WG parameters representing the spatial and temporal variability are modified using the climate change scenarios and then (ii) the effect on the above spatial validation indices derived from the synthetic series produced by the modified WG is analysed. In this experiment, the generator is calibrated using the E-OBS gridded daily weather data for several European regions, and the climate change scenarios are derived from the selected RCM simulation (taken from the CORDEX database).</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/biblio/1198008-spectral-element-method-sem-variable-resolution-grids-evaluating-grid-sensitivity-resolution-aware-numerical-viscosity','SCIGOV-STC'); return false;" href="https://www.osti.gov/biblio/1198008-spectral-element-method-sem-variable-resolution-grids-evaluating-grid-sensitivity-resolution-aware-numerical-viscosity"><span>The spectral element method (SEM) on variable-resolution grids: evaluating grid sensitivity and resolution-aware numerical viscosity</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Guba, O.; Taylor, M. A.; Ullrich, P. A.</p> <p>2014-11-27</p> <p>We evaluate the performance of the Community Atmosphere Model's (CAM) spectral element method on variable-resolution grids using the shallow-water equations in spherical geometry. We configure the method as it is used in CAM, with dissipation of grid scale variance, implemented using hyperviscosity. Hyperviscosity is highly scale selective and grid independent, but does require a resolution-dependent coefficient. For the spectral element method with variable-resolution grids and highly distorted elements, we obtain the best results if we introduce a tensor-based hyperviscosity with tensor coefficients tied to the eigenvalues of the local element metric tensor. The tensor hyperviscosity is constructed so that, formore » regions of uniform resolution, it matches the traditional constant-coefficient hyperviscosity. With the tensor hyperviscosity, the large-scale solution is almost completely unaffected by the presence of grid refinement. This later point is important for climate applications in which long term climatological averages can be imprinted by stationary inhomogeneities in the truncation error. We also evaluate the robustness of the approach with respect to grid quality by considering unstructured conforming quadrilateral grids generated with a well-known grid-generating toolkit and grids generated by SQuadGen, a new open source alternative which produces lower valence nodes.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/pages/biblio/1198263-spectral-element-method-variable-resolution-grids-evaluating-grid-sensitivity-resolution-aware-numerical-viscosity','SCIGOV-DOEP'); return false;" href="https://www.osti.gov/pages/biblio/1198263-spectral-element-method-variable-resolution-grids-evaluating-grid-sensitivity-resolution-aware-numerical-viscosity"><span>The spectral element method on variable resolution grids: evaluating grid sensitivity and resolution-aware numerical viscosity</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/pages">DOE PAGES</a></p> <p>Guba, O.; Taylor, M. A.; Ullrich, P. A.; ...</p> <p>2014-06-25</p> <p>We evaluate the performance of the Community Atmosphere Model's (CAM) spectral element method on variable resolution grids using the shallow water equations in spherical geometry. We configure the method as it is used in CAM, with dissipation of grid scale variance implemented using hyperviscosity. Hyperviscosity is highly scale selective and grid independent, but does require a resolution dependent coefficient. For the spectral element method with variable resolution grids and highly distorted elements, we obtain the best results if we introduce a tensor-based hyperviscosity with tensor coefficients tied to the eigenvalues of the local element metric tensor. The tensor hyperviscosity ismore » constructed so that for regions of uniform resolution it matches the traditional constant coefficient hyperviscsosity. With the tensor hyperviscosity the large scale solution is almost completely unaffected by the presence of grid refinement. This later point is important for climate applications where long term climatological averages can be imprinted by stationary inhomogeneities in the truncation error. We also evaluate the robustness of the approach with respect to grid quality by considering unstructured conforming quadrilateral grids generated with a well-known grid-generating toolkit and grids generated by SQuadGen, a new open source alternative which produces lower valence nodes.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFMGC13G0739C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFMGC13G0739C"><span>Long-term hydrometeorological trends in the Midwest region based on a century long gridded hydrometeorological dataset and simulations from a macro-scale hydrology model</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Chiu, C. M.; Hamlet, A. F.</p> <p>2014-12-01</p> <p>Climate change is likely to impact the Great Lakes region and Midwest region via changes in Great Lakes water levels, agricultural impacts, river flooding, urban stormwater impacts, drought, water temperature, and impacts to terrestrial and aquatic ecosystems. Self-consistent and temporally homogeneous long-term data sets of precipitation and temperature over the entire Great Lakes region and Midwest regions are needed to provide inputs to hydrologic models, assess historical trends in hydroclimatic variables, and downscale global and regional-scale climate models. To support these needs a new hybrid gridded meteorological forcing dataset at 1/16 degree resolution based on data from co-op station records, the U. S Historical Climatology Network (HCN) , the Historical Canadian Climate Database (HCCD), and Precipitation Regression on Independent Slopes Method (PRISM) has been assembled over the Great Lakes and Midwest region from 1915-2012 at daily time step. These data were then used as inputs to the macro-scale Variable Infiltration Capacity (VIC) hydrology model, implemented over the Midwest and Great Lakes region at 1/16 degree resolution, to produce simulated hydrologic variables that are amenable to long-term trend analysis. Trends in precipitation and temperature from the new meteorological driving data sets, as well as simulated hydrometeorological variables such as snowpack, soil moisture, runoff, and evaporation over the 20th century are presented and discussed.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014EGUGA..1615262L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014EGUGA..1615262L"><span>The development of an hourly gridded rainfall product for hydrological applications in England and Wales</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Liguori, Sara; O'Loughlin, Fiachra; Souvignet, Maxime; Coxon, Gemma; Freer, Jim; Woods, Ross</p> <p>2014-05-01</p> <p>This research presents a newly developed observed sub-daily gridded precipitation product for England and Wales. Importantly our analysis specifically allows a quantification of rainfall errors from grid to the catchment scale, useful for hydrological model simulation and the evaluation of prediction uncertainties. Our methodology involves the disaggregation of the current one kilometre daily gridded precipitation records available for the United Kingdom[1]. The hourly product is created using information from: 1) 2000 tipping-bucket rain gauges; and 2) the United Kingdom Met-Office weather radar network. These two independent datasets provide rainfall estimates at temporal resolutions much smaller than the current daily gridded rainfall product; thus allowing the disaggregation of the daily rainfall records to an hourly timestep. Our analysis is conducted for the period 2004 to 2008, limited by the current availability of the datasets. We analyse the uncertainty components affecting the accuracy of this product. Specifically we explore how these uncertainties vary spatially, temporally and with climatic regimes. Preliminary results indicate scope for improvement of hydrological model performance by the utilisation of this new hourly gridded rainfall product. Such product will improve our ability to diagnose and identify structural errors in hydrological modelling by including the quantification of input errors. References [1] Keller V, Young AR, Morris D, Davies H (2006) Continuous Estimation of River Flows. Technical Report: Estimation of Precipitation Inputs. in Agency E (ed.). Environmental Agency.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20160012261&hterms=leadership&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D70%26Ntt%3Dleadership','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20160012261&hterms=leadership&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D70%26Ntt%3Dleadership"><span>The Vulnerability, Impacts, Adaptation and Climate Services Advisory Board (VIACS AB V1.0) Contribution to CMIP6</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Ruane, Alex C.; Teichmann, Claas; Arnell, Nigel W.; Carter, Timothy R.; Ebi, Kristie L.; Frieler, Katja; Goodess, Clare M.; Hewitson, Bruce; Horton, Radley; Kovats, R. Sari; <a style="text-decoration: none; " href="javascript:void(0); " onClick="displayelement('author_20160012261'); toggleEditAbsImage('author_20160012261_show'); toggleEditAbsImage('author_20160012261_hide'); "> <img style="display:inline; width:12px; height:12px; " src="images/arrow-up.gif" width="12" height="12" border="0" alt="hide" id="author_20160012261_show"> <img style="width:12px; height:12px; display:none; " src="images/arrow-down.gif" width="12" height="12" border="0" alt="hide" id="author_20160012261_hide"></p> <p>2016-01-01</p> <p>This paper describes the motivation for the creation of the Vulnerability, Impacts, Adaptation and Climate Services (VIACS) Advisory Board for the Sixth Phase of the Coupled Model Intercomparison Project (CMIP6), its initial activities, and its plans to serve as a bridge between climate change applications experts and climate modelers. The climate change application community comprises researchers and other specialists who use climate information (alongside socioeconomic and other environmental information) to analyze vulnerability, impacts, and adaptation of natural systems and society in relation to past, ongoing, and projected future climate change. Much of this activity is directed toward the co-development of information needed by decisionmakers for managing projected risks. CMIP6 provides a unique opportunity to facilitate a two-way dialog between climate modelers and VIACS experts who are looking to apply CMIP6 results for a wide array of research and climate services objectives. The VIACS Advisory Board convenes leaders of major impact sectors, international programs, and climate services to solicit community feedback that increases the applications relevance of the CMIP6-Endorsed Model Intercomparison Projects (MIPs). As an illustration of its potential, the VIACS community provided CMIP6 leadership with a list of prioritized climate model variables and MIP experiments of the greatest interest to the climate model applications community, indicating the applicability and societal relevance of climate model simulation outputs. The VIACS Advisory Board also recommended an impacts version of Obs4MIPs (observational datasets) and indicated user needs for the gridding and processing of model output.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016GMD.....9.3493R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016GMD.....9.3493R"><span>The Vulnerability, Impacts, Adaptation and Climate Services Advisory Board (VIACS AB v1.0) contribution to CMIP6</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ruane, Alex C.; Teichmann, Claas; Arnell, Nigel W.; Carter, Timothy R.; Ebi, Kristie L.; Frieler, Katja; Goodess, Clare M.; Hewitson, Bruce; Horton, Radley; Sari Kovats, R.; Lotze, Heike K.; Mearns, Linda O.; Navarra, Antonio; Ojima, Dennis S.; Riahi, Keywan; Rosenzweig, Cynthia; Themessl, Matthias; Vincent, Katharine</p> <p>2016-09-01</p> <p>This paper describes the motivation for the creation of the Vulnerability, Impacts, Adaptation and Climate Services (VIACS) Advisory Board for the Sixth Phase of the Coupled Model Intercomparison Project (CMIP6), its initial activities, and its plans to serve as a bridge between climate change applications experts and climate modelers. The climate change application community comprises researchers and other specialists who use climate information (alongside socioeconomic and other environmental information) to analyze vulnerability, impacts, and adaptation of natural systems and society in relation to past, ongoing, and projected future climate change. Much of this activity is directed toward the co-development of information needed by decision-makers for managing projected risks. CMIP6 provides a unique opportunity to facilitate a two-way dialog between climate modelers and VIACS experts who are looking to apply CMIP6 results for a wide array of research and climate services objectives. The VIACS Advisory Board convenes leaders of major impact sectors, international programs, and climate services to solicit community feedback that increases the applications relevance of the CMIP6-Endorsed Model Intercomparison Projects (MIPs). As an illustration of its potential, the VIACS community provided CMIP6 leadership with a list of prioritized climate model variables and MIP experiments of the greatest interest to the climate model applications community, indicating the applicability and societal relevance of climate model simulation outputs. The VIACS Advisory Board also recommended an impacts version of Obs4MIPs and indicated user needs for the gridding and processing of model output.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2004JGRD..10913104M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2004JGRD..10913104M"><span>Spectral nudging to eliminate the effects of domain position and geometry in regional climate model simulations</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Miguez-Macho, Gonzalo; Stenchikov, Georgiy L.; Robock, Alan</p> <p>2004-07-01</p> <p>It is well known that regional climate simulations are sensitive to the size and position of the domain chosen for calculations. Here we study the physical mechanisms of this sensitivity. We conducted simulations with the Regional Atmospheric Modeling System (RAMS) for June 2000 over North America at 50 km horizontal resolution using a 7500 km × 5400 km grid and NCEP/NCAR reanalysis as boundary conditions. The position of the domain was displaced in several directions, always maintaining the U.S. in the interior, out of the buffer zone along the lateral boundaries. Circulation biases developed a large scale structure, organized by the Rocky Mountains, resulting from a systematic shifting of the synoptic wave trains that crossed the domain. The distortion of the large-scale circulation was produced by interaction of the modeled flow with the lateral boundaries of the nested domain and varied when the position of the grid was altered. This changed the large-scale environment among the different simulations and translated into diverse conditions for the development of the mesoscale processes that produce most of precipitation for the Great Plains in the summer season. As a consequence, precipitation results varied, sometimes greatly, among the experiments with the different grid positions. To eliminate the dependence of results on the position of the domain, we used spectral nudging of waves longer than 2500 km above the boundary layer. Moisture was not nudged at any level. This constrained the synoptic scales to follow reanalysis while allowing the model to develop the small-scale dynamics responsible for the rainfall. Nudging of the large scales successfully eliminated the variation of precipitation results when the grid was moved. We suggest that this technique is necessary for all downscaling studies with regional models with domain sizes of a few thousand kilometers and larger embedded in global models.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2003EAEJA.....2331A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2003EAEJA.....2331A"><span>CIELO-A GIS integrated model for climatic and water balance simulation in islands environments</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Azevedo, E. B.; Pereira, L. S.</p> <p>2003-04-01</p> <p>The model CIELO (acronym for "Clima Insular à Escala Local") is a physically based model that simulates the climatic variables in an island using data from a single synoptic reference meteorological station. The reference station "knows" its position in the orographic and dynamic regime context. The domain of computation is a GIS raster grid parameterised with a digital elevation model (DEM). The grid is oriented following the direction of the air masses circulation through a specific algorithm named rotational terrain model (RTM). The model consists of two main sub-models. One, relative to the advective component simulation, assumes the Foehn effect to reproduce the dynamic and thermodynamic processes occurring when an air mass moves through the island orographic obstacle. This makes possible to simulate the air temperature, air humidity, cloudiness and precipitation as influenced by the orography along the air displacement. The second concerns the radiative component as affected by the clouds of orographic origin and by the shadow produced by the relief. The initial state parameters are computed starting from the reference meteorological station across the DEM transept until the sea level at the windward side. Then, starting from the sea level, the model computes the local scale meteorological parameters according to the direction of the air displacement, which is adjusted with the RTM. The air pressure, temperature and humidity are directly calculated for each cell in the computational grid, while several algorithms are used to compute the cloudiness, net radiation, evapotranspiration, and precipitation. The model presented in this paper has been calibrated and validated using data from some meteorological stations and a larger number of rainfall stations located at various elevations in the Azores Islands.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li class="active"><span>15</span></li> <li><a href="#" onclick='return showDiv("page_16");'>16</a></li> <li><a href="#" onclick='return showDiv("page_17");'>17</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_15 --> <div id="page_16" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li><a href="#" onclick='return showDiv("page_15");'>15</a></li> <li class="active"><span>16</span></li> <li><a href="#" onclick='return showDiv("page_17");'>17</a></li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="301"> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/pages/biblio/1266397-new-test-statistic-climate-models-includes-field-spatial-dependencies-using-gaussian-markov-random-fields','SCIGOV-DOEP'); return false;" href="https://www.osti.gov/pages/biblio/1266397-new-test-statistic-climate-models-includes-field-spatial-dependencies-using-gaussian-markov-random-fields"><span>A new test statistic for climate models that includes field and spatial dependencies using Gaussian Markov random fields</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/pages">DOE PAGES</a></p> <p>Nosedal-Sanchez, Alvaro; Jackson, Charles S.; Huerta, Gabriel</p> <p>2016-07-20</p> <p>A new test statistic for climate model evaluation has been developed that potentially mitigates some of the limitations that exist for observing and representing field and space dependencies of climate phenomena. Traditionally such dependencies have been ignored when climate models have been evaluated against observational data, which makes it difficult to assess whether any given model is simulating observed climate for the right reasons. The new statistic uses Gaussian Markov random fields for estimating field and space dependencies within a first-order grid point neighborhood structure. We illustrate the ability of Gaussian Markov random fields to represent empirical estimates of fieldmore » and space covariances using "witch hat" graphs. We further use the new statistic to evaluate the tropical response of a climate model (CAM3.1) to changes in two parameters important to its representation of cloud and precipitation physics. Overall, the inclusion of dependency information did not alter significantly the recognition of those regions of parameter space that best approximated observations. However, there were some qualitative differences in the shape of the response surface that suggest how such a measure could affect estimates of model uncertainty.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/biblio/1266397-new-test-statistic-climate-models-includes-field-spatial-dependencies-using-gaussian-markov-random-fields','SCIGOV-STC'); return false;" href="https://www.osti.gov/biblio/1266397-new-test-statistic-climate-models-includes-field-spatial-dependencies-using-gaussian-markov-random-fields"><span>A new test statistic for climate models that includes field and spatial dependencies using Gaussian Markov random fields</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Nosedal-Sanchez, Alvaro; Jackson, Charles S.; Huerta, Gabriel</p> <p></p> <p>A new test statistic for climate model evaluation has been developed that potentially mitigates some of the limitations that exist for observing and representing field and space dependencies of climate phenomena. Traditionally such dependencies have been ignored when climate models have been evaluated against observational data, which makes it difficult to assess whether any given model is simulating observed climate for the right reasons. The new statistic uses Gaussian Markov random fields for estimating field and space dependencies within a first-order grid point neighborhood structure. We illustrate the ability of Gaussian Markov random fields to represent empirical estimates of fieldmore » and space covariances using "witch hat" graphs. We further use the new statistic to evaluate the tropical response of a climate model (CAM3.1) to changes in two parameters important to its representation of cloud and precipitation physics. Overall, the inclusion of dependency information did not alter significantly the recognition of those regions of parameter space that best approximated observations. However, there were some qualitative differences in the shape of the response surface that suggest how such a measure could affect estimates of model uncertainty.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFM.A13L..06K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFM.A13L..06K"><span>A CPT for Improving Turbulence and Cloud Processes in the NCEP Global Models</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Krueger, S. K.; Moorthi, S.; Randall, D. A.; Pincus, R.; Bogenschutz, P.; Belochitski, A.; Chikira, M.; Dazlich, D. A.; Swales, D. J.; Thakur, P. K.; Yang, F.; Cheng, A.</p> <p>2016-12-01</p> <p>Our Climate Process Team (CPT) is based on the premise that the NCEP (National Centers for Environmental Prediction) global models can be improved by installing an integrated, self-consistent description of turbulence, clouds, deep convection, and the interactions between clouds and radiative and microphysical processes. The goal of our CPT is to unify the representation of turbulence and subgrid-scale (SGS) cloud processes and to unify the representation of SGS deep convective precipitation and grid-scale precipitation as the horizontal resolution decreases. We aim to improve the representation of small-scale phenomena by implementing a PDF-based SGS turbulence and cloudiness scheme that replaces the boundary layer turbulence scheme, the shallow convection scheme, and the cloud fraction schemes in the GFS (Global Forecast System) and CFS (Climate Forecast System) global models. We intend to improve the treatment of deep convection by introducing a unified parameterization that scales continuously between the simulation of individual clouds when and where the grid spacing is sufficiently fine and the behavior of a conventional parameterization of deep convection when and where the grid spacing is coarse. We will endeavor to improve the representation of the interactions of clouds, radiation, and microphysics in the GFS/CFS by using the additional information provided by the PDF-based SGS cloud scheme. The team is evaluating the impacts of the model upgrades with metrics used by the NCEP short-range and seasonal forecast operations.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFM.B33E0224B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFM.B33E0224B"><span>Famine Early Warning Systems Network (FEWS NET) Contributions to Strengthening Resilience and Sustainability for the East African Community</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Budde, M. E.; Galu, G.; Funk, C. C.; Verdin, J. P.; Rowland, J.</p> <p>2014-12-01</p> <p>The Planning for Resilience in East Africa through Policy, Adaptation, Research, and Economic Development (PREPARED) is a multi-organizational project aimed at mainstreaming climate-resilient development planning and program implementation into the East African Community (EAC). The Famine Early Warning Systems Network (FEWS NET) has partnered with the PREPARED project to address three key development challenges for the EAC; 1) increasing resiliency to climate change, 2) managing trans-boundary freshwater biodiversity and conservation and 3) improving access to drinking water supply and sanitation services. USGS FEWS NET has been instrumental in the development of gridded climate data sets that are the fundamental building blocks for climate change adaptation studies in the region. Tools such as the Geospatial Climate Tool (GeoCLIM) have been developed to interpolate time-series grids of precipitation and temperature values from station observations and associated satellite imagery, elevation data, and other spatially continuous fields. The GeoCLIM tool also allows the identification of anomalies and assessments of both their frequency of occurrence and directional trends. A major effort has been put forth to build the capacities of local and regional institutions to use GeoCLIM to integrate their station data (which is not typically available to the public) into improved national and regional gridded climate data sets. In addition to the improvements and capacity building activities related to geospatial analysis tools, FEWS NET will assist in two other areas; 1) downscaling of climate change scenarios and 2) vulnerability impact assessments. FEWS NET will provide expertise in statistical downscaling of Global Climate Model output fields and work with regional institutions to assess results of other downscaling methods. Completion of a vulnerability impact assessment (VIA) involves the examination of sectoral consequences in identified climate "hot spots". FEWS NET will lead the VIA for the agriculture and food security sector, but will also provide key geospatial layers needed by multiple sectors in the areas of exposure, sensitivity, and adaptive capacity. Project implementation will strengthen regional coordination in policy-making, planning, and response to climate change issues.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFMIN31F..01K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFMIN31F..01K"><span>The CESM Large Ensemble Project: Inspiring New Ideas and Understanding</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kay, J. E.; Deser, C.</p> <p>2016-12-01</p> <p>While internal climate variability is known to affect climate projections, its influence is often underappreciated and confused with model error. Why? In general, modeling centers contribute a small number of realizations to international climate model assessments [e.g., phase 5 of the Coupled Model Intercomparison Project (CMIP5)]. As a result, model error and internal climate variability are difficult, and at times impossible, to disentangle. In response, the Community Earth System Model (CESM) community designed the CESM Large Ensemble (CESM-LE) with the explicit goal of enabling assessment of climate change in the presence of internal climate variability. All CESM-LE simulations use a single CMIP5 model (CESM with the Community Atmosphere Model, version 5). The core simulations replay the twenty to twenty-first century (1920-2100) 40+ times under historical and representative concentration pathway 8.5 external forcing with small initial condition differences. Two companion 2000+-yr-long preindustrial control simulations (fully coupled, prognostic atmosphere and land only) allow assessment of internal climate variability in the absence of climate change. Comprehensive outputs, including many daily fields, are available as single-variable time series on the Earth System Grid for anyone to use. Examples of scientists and stakeholders that are using the CESM-LE outputs to help interpret the observational record, to understand projection spread and to plan for a range of possible futures influenced by both internal climate variability and forced climate change will be highlighted the presentation.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017EGUGA..1914964Q','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017EGUGA..1914964Q"><span>Coupled ice sheet - climate simulations of the last glacial inception and last glacial maximum with a model of intermediate complexity that includes a dynamical downscaling of heat and moisture</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Quiquet, Aurélien; Roche, Didier M.</p> <p>2017-04-01</p> <p>Comprehensive fully coupled ice sheet - climate models allowing for multi-millenia transient simulations are becoming available. They represent powerful tools to investigate ice sheet - climate interactions during the repeated retreats and advances of continental ice sheets of the Pleistocene. However, in such models, most of the time, the spatial resolution of the ice sheet model is one order of magnitude lower than the one of the atmospheric model. As such, orography-induced precipitation is only poorly represented. In this work, we briefly present the most recent improvements of the ice sheet - climate coupling within the model of intermediate complexity iLOVECLIM. On the one hand, from the native atmospheric resolution (T21), we have included a dynamical downscaling of heat and moisture at the ice sheet model resolution (40 km x 40 km). This downscaling accounts for feedbacks of sub-grid precipitation on large scale energy and water budgets. From the sub-grid atmospheric variables, we compute an ice sheet surface mass balance required by the ice sheet model. On the other hand, we also explicitly use oceanic temperatures to compute sub-shelf melting at a given depth. Based on palaeo evidences for rate of change of eustatic sea level, we discuss the capability of our new model to correctly simulate the last glacial inception ( 116 kaBP) and the ice volume of the last glacial maximum ( 21 kaBP). We show that the model performs well in certain areas (e.g. Canadian archipelago) but some model biases are consistent over time periods (e.g. Kara-Barents sector). We explore various model sensitivities (e.g. initial state, vegetation, albedo) and we discuss the importance of the downscaling of precipitation for ice nucleation over elevated area and for the surface mass balance of larger ice sheets.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018GeoRL..45.2082D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018GeoRL..45.2082D"><span>Resolving Tropical Cyclone Intensity in Models</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Davis, C. A.</p> <p>2018-02-01</p> <p>In recent years, global weather forecast models and global climate models have begun to depict intense tropical cyclones, even up to category 5 on the Saffir-Simpson scale. In light of the limitation of horizontal resolution in such models, the author performs calculations, using the extended Best Track data for Atlantic tropical cyclones, to estimate the ability of models with differing grid spacing to represent Atlantic tropical cyclone intensity statistically. Results indicate that, under optimistic assumptions, models with horizontal grid spacing of one fourth degree or coarser should not produce a realistic number of category 4 and 5 storms unless there are errors in spatial attributes of the wind field. Furthermore, the case of Irma (2017) is used to demonstrate the importance of a realistic depiction of angular momentum and to motivate the use of angular momentum in model evaluation.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..1818205K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..1818205K"><span>Implementation of Web Processing Services (WPS) over IPSL Earth System Grid Federation (ESGF) node</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kadygrov, Nikolay; Denvil, Sebastien; Carenton, Nicolas; Levavasseur, Guillaume; Hempelmann, Nils; Ehbrecht, Carsten</p> <p>2016-04-01</p> <p>The Earth System Grid Federation (ESGF) is aimed to provide access to climate data for the international climate community. ESGF is a system of distributed and federated nodes that dynamically interact with each other. ESGF user may search and download climatic data, geographically distributed over the world, from one common web interface and through standardized API. With the continuous development of the climate models and the beginning of the sixth phase of the Coupled Model Intercomparison Project (CMIP6), the amount of data available from ESGF will continuously increase during the next 5 years. IPSL holds a replication of the different global and regional climate models output, observations and reanalysis data (CMIP5, CORDEX, obs4MIPs, etc) that are available on the IPSL ESGF node. In order to let scientists perform analysis of the models without downloading vast amount of data the Web Processing Services (WPS) were installed at IPSL compute node. The work is part of the CONVERGENCE project founded by French National Research Agency (ANR). PyWPS implementation of the Web processing Service standard from Open Geospatial Consortium (OGC) in the framework of birdhouse software is used. The processes could be run by user remotely through web-based WPS client or by using command-line tool. All the calculations are performed on the server side close to the data. If the models/observations are not available at IPSL it will be downloaded and cached by WPS process from ESGF network using synda tool. The outputs of the WPS processes are available for download as plots, tar-archives or as NetCDF files. We present the architecture of WPS at IPSL along with the processes for evaluation of the model performance, on-site diagnostics and post-analysis processing of the models output, e.g.: - regriding/interpolation/aggregation - ocgis (OpenClimateGIS) based polygon subsetting of the data - average seasonal cycle, multimodel mean, multimodel mean bias - calculation of the climate indices with icclim library (CERFACS) - atmospheric modes of variability In order to evaluate performance of any new model, once it became available in ESGF, we implement WPS with several model diagnostics and performance metrics calculated using ESMValTool (Eyring et al., GMDD 2015). As a further step we are developing new WPS processes and core-functions to be implemented at ISPL ESGF compute node following the scientific community needs.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017EGUGA..19.1930K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017EGUGA..19.1930K"><span>Climatic and landscape controls on travel time distributions across Europe</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kumar, Rohini; Rao, Suresh; Hesse, Falk; Borchardt, Dietrich; Fleckenstein, Jan; Jawitz, James; Musolff, Andreas; Rakovec, Oldrich; Samaniego, Luis; Yang, Soohyun; Zink, Matthias; Attinger, Sabine</p> <p>2017-04-01</p> <p>Travel time distributions (TTDs) are fundamental descriptors to characterize the functioning of storage, mixing and release of water and solutes in a river basin. Identifying the relative importance (and controls) of climate and landscape attributes on TDDs is fundamental to improve our understanding of the underlying mechanism controlling the spatial heterogeneity of TTDs, and their moments (e.g., mean TT). Studies aimed at elucidating such controls have focused on either theoretical developments to gain (physical) insights using mostly synthetic datasets or empirical relationships using limited datasets from experimental sites. A study painting a general picture of emerging controls at a continental scale is still lacking. In this study, we make use of spatially resolved hydrologic fluxes and states generated through an observationally driven, mesoscale Hydrologic Model (mHM; www.ufz.de/mhm) to comprehensively characterize the dominant controls of climate and landscape attributes on TDDs in the vadose zone across the entire European region. mHM uses a novel Multiscale Parameter Regionalization (MPR; Samaniego et al., 2010 and Kumar et al., 2013) scheme that encapsulates fine scale landscape attributes (e.g., topography, soil, and vegetation characteristics) to account for the sub-grid variability in model parameterization. The model was established at 25 km spatial resolution to simulate the daily gridded fluxes and states over Europe for the period 1955-2015. We utilized recent developments in TTDs theory (e.g., Botter et al., 2010, Harman et al., 2011) to characterize the stationary and non-stationary behavior of water particles transported through the vadose zone at every grid cell. Our results suggest a complex set of interactions between climate and landscape properties controlling the spatial heterogeneity of the mean travel time (TT). The spatial variability in the mean TT across the Pan-EU generally follows the climatic gradient with lower values in humid regions and higher in semi-arid or drier regions. The results signifies the role of a landscape attributes like plant available soil-water-storage capacity, when expressed in a dimensionless number that also include climate attributes such as average rain depth and aridity index, forms a potentially useful predictor for explaining the spatial heterogeneity of mean TTs. Finally, the study also highlights the time-varying behavior of TTDs and discusses the seasonal variation in mean TTs across Europe.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFMPP22A..02R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFMPP22A..02R"><span>Impacts of weather versus climate and driver uncertainty on multi-centennial ecosystem model simulations</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Rollinson, C.; Simkins, J.; Fer, I.; Desai, A. R.; Dietze, M.</p> <p>2017-12-01</p> <p>Simulations of ecosystem dynamics and comparisons with empirical data require accurate, continuous, and often sub-daily meteorology records that are spatially aligned to the scale of the empirical data. A wealth of meteorology data for the past, present, and future is available through site-specific observations, modern reanalysis products, and gridded GCM simulations. However, these products are mismatched in spatial and temporal resolution, often with both different means and seasonal patterns. We have designed and implemented a two-step meteorological downscaling and ensemble generation method that combines multiple meteorology data products through debiasing and temporal downscaling protocols. Our methodology is designed to preserve the covariance among seven meteorological variables for use as drivers in ecosystem model simulations: temperature, precipitation, short- and longwave radiation, surface pressure, humidity, and wind. Furthermore, our method propagates uncertainty through the downscaling process and results in ensembles of meteorology that can be compared to paleoclimate reconstructions and used to analyze the effects of both high- and low-frequency climate anomalies on ecosystem dynamics. Using a multiple linear regression approach, we have combined hourly, 0.125-degree gridded data from the NLDAS (1980-present) with CRUNCEP (1901-2010) and CMIP5 historical (1850-2005), past millennium (850-1849), and future (1950-2100) GCM simulations. This has resulted in an ensemble of continuous, hourly-resolved meteorology from from the paleo era into the future with variability in weather events as well as low-frequency climatic changes. We investigate the influence of extreme sub-daily weather phenomena versus long-term climatic changes in an ensemble of ecosystem models that range in atmospheric and biological complexity. Through data assimilation with paleoclimate reconstructions of past climate, we can improve data-model comparisons using observations of vegetation change from the past 1200 years. Accounting for driver uncertainty in model evaluation can help determine the relative influence of structural versus parameterization errors in ecosystem modelings.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=42698&Lab=ORD&keyword=evapotranspiration&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50','EPA-EIMS'); return false;" href="https://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=42698&Lab=ORD&keyword=evapotranspiration&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50"><span>SENSITIVITY OF THE REGIONAL WATER BALANCE IN THE COLUMBIA RIVER BASIN TO CLIMATE VARIABILITY: APPLICATION OF A SPATIALLY DISTRIBUTED WATER BALANCE MODEL</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://oaspub.epa.gov/eims/query.page">EPA Science Inventory</a></p> <p></p> <p></p> <p>A one-dimensional water balance model was developed and used to simulate water balance for the Columbia River Basin. he model was run over a 10 km X 10 km grid for the United State's portion of the basin. he regional water balance was calculated using a monthly time-step for a re...</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016ArtSa..51..107W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016ArtSa..51..107W"><span>Hydrological Excitations of Polar Motion Derived from Different Variables of Fgoals - g2 Climate Model</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Winska, M.</p> <p>2016-12-01</p> <p>The hydrological contribution to decadal, inter-annual and multi-annual suppress polar motion derived from climate model as well as from GRACE (Gravity Recovery and Climate Experiment) data is discussed here for the period 2002.3-2016.0. The data set used here are Earth Orientation Parameters Combined 04 (EOP C04), Flexible Global Ocean-Atmosphere-Land System Model: Grid-point Version 2 (FGOAL-g2) and Global Land Data Assimilation System (GLDAS) climate models and GRACE CSR RL05 data for polar motion, hydrological and gravimetric excitation, respectively. Several Hydrological Angular Momentum (HAM) functions are calculated here from the selected variables: precipitation, evaporation, runoff, soil moisture, accumulated snow of the FGOALS and GLDAS climate models as well as from the global mass change fields from GRACE data provided by the International Earth Rotation and Reference System Service (IERS) Global Geophysical Fluids Center (GGFC). The contribution of different HAM excitation functions to achieve the full agreement between geodetic observations and geophysical excitation functions of polar motion is studied here.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFM.A33C0228G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFM.A33C0228G"><span>Influence of model grid size on the simulation of PM2.5 and the related excess mortality in Japan</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Goto, D.; Ueda, K.; Ng, C. F.; Takami, A.; Ariga, T.; Matsuhashi, K.; Nakajima, T.</p> <p>2016-12-01</p> <p>Aerosols, especially PM2.5, can affect air pollution, climate change, and human health. The estimation of health impacts due to PM2.5 is often performed using global and regional aerosol transport models with various horizontal resolutions. To investigate the dependence of the simulated PM2.5 on model grid sizes, we executed two simulations using a high-resolution model ( 10km; HRM) and a low-resolution model ( 100km; LRM, which is a typical value for general circulation models). In this study, we used a global-to-regional atmospheric transport model to simulate PM2.5 in Japan with a stretched grid system in HRM and a uniform grid system in LRM for the present (the 2000) and the future (the 2030, as proposed by the Representative Concentrations Pathway 4.5, RCP4.5). These calculations were performed by nudging meteorological fields obtained from an atmosphere-ocean coupled model and providing emission inventories used in the coupled model. After correcting for bias, we calculated the excess mortality due to long-term exposure to PM2.5 for the elderly. Results showed the LRM underestimated by approximately 30 % (of PM2.5 concentrations in the 2000 and 2030), approximately 60 % (excess mortality in the 2000) and approximately 90 % (excess mortality in 2030) compared to the HRM results. The estimation of excess mortality therefore performed better with high-resolution grid sizes. In addition, we also found that our nesting method could be a useful tool to obtain better estimation results.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/1325486','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/1325486"><span>Processing and Monthly Summaries of Downscaled Climate Data for Knoxville, Tennessee and Surrounding Region</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Sylvester, Linda; Omitaomu, Olufemi A.; Parish, Esther S.</p> <p>2016-09-01</p> <p>Oak Ridge National Laboratory (ORNL) and the City of Knoxville, Tennessee have partnered to work on a Laboratory Directed Research and Development (LDRD) project towards investigating climate change, mitigation, and adaptation measures in mid-sized cities. ORNL has statistically and dynamically downscaled ten Global Climate Models (GCMs) to both 1 km and 4 km resolutions. The processing and summary of those ten gridded datasets for use in a web-based tool is described. The summaries of each model are shown individually to assist in determining the similarities and differences between the model scenarios. The variables of minimum and maximum daily temperature andmore » total monthly precipitation are summarized for the area of Knoxville, Tennessee for the periods of 1980-2005 and 2025-2050.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013EGUGA..15.8231C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013EGUGA..15.8231C"><span>WRF-Cordex simulations for Europe: mean and extreme precipitation for present and future climates</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Cardoso, Rita M.; Soares, Pedro M. M.; Miranda, Pedro M. A.</p> <p>2013-04-01</p> <p>The Weather Research and Forecast (WRF-ARW) model, version 3.3.1, was used to perform the European domain Cordex simulations, at 50km resolution. A first simulation, forced by ERA-Interim (1989-2009), was carried out to evaluate the models performance to represent the mean and extreme precipitation in present European climate. This evaluation is based in the comparison of WRF results against the ECAD regular gridded dataset of daily precipitation. Results are comparable to recent studies with other models for the European region, at this resolution. For the same domain a control and a future scenario (RCP8.5) simulation was performed to assess the climate change impact on the mean and extreme precipitation. These regional simulations were forced by EC-EARTH model results, and, encompass the periods from 1960-2006 and 2006-2100, respectively.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017EGUGA..1915489M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017EGUGA..1915489M"><span>AI-based (ANN and SVM) statistical downscaling methods for precipitation estimation under climate change scenarios</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Mehrvand, Masoud; Baghanam, Aida Hosseini; Razzaghzadeh, Zahra; Nourani, Vahid</p> <p>2017-04-01</p> <p>Since statistical downscaling methods are the most largely used models to study hydrologic impact studies under climate change scenarios, nonlinear regression models known as Artificial Intelligence (AI)-based models such as Artificial Neural Network (ANN) and Support Vector Machine (SVM) have been used to spatially downscale the precipitation outputs of Global Climate Models (GCMs). The study has been carried out using GCM and station data over GCM grid points located around the Peace-Tampa Bay watershed weather stations. Before downscaling with AI-based model, correlation coefficient values have been computed between a few selected large-scale predictor variables and local scale predictands to select the most effective predictors. The selected predictors are then assessed considering grid location for the site in question. In order to increase AI-based downscaling model accuracy pre-processing has been developed on precipitation time series. In this way, the precipitation data derived from various GCM data analyzed thoroughly to find the highest value of correlation coefficient between GCM-based historical data and station precipitation data. Both GCM and station precipitation time series have been assessed by comparing mean and variances over specific intervals. Results indicated that there is similar trend between GCM and station precipitation data; however station data has non-stationary time series while GCM data does not. Finally AI-based downscaling model have been applied to several GCMs with selected predictors by targeting local precipitation time series as predictand. The consequences of recent step have been used to produce multiple ensembles of downscaled AI-based models.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFMED21A0266R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFMED21A0266R"><span>A computational approach to climate science education with CLIMLAB</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Rose, B. E. J.</p> <p>2017-12-01</p> <p>CLIMLAB is a Python-based software toolkit for interactive, process-oriented climate modeling for use in education and research. It is motivated by the need for simpler tools and more reproducible workflows with which to "fill in the gaps" between blackboard-level theory and the results of comprehensive climate models. With CLIMLAB you can interactively mix and match physical model components, or combine simpler process models together into a more comprehensive model. I use CLIMLAB in the classroom to put models in the hands of students (undergraduate and graduate), and emphasize a hierarchical, process-oriented approach to understanding the key emergent properties of the climate system. CLIMLAB is equally a tool for climate research, where the same needs exist for more robust, process-based understanding and reproducible computational results. I will give an overview of CLIMLAB and an update on recent developments, including: a full-featured, well-documented, interactive implementation of a widely-used radiation model (RRTM) packaging with conda-forge for compiler-free (and hassle-free!) installation on Mac, Windows and Linux interfacing with xarray for i/o and graphics with gridded model data a rich and growing collection of examples and self-computing lecture notes in Jupyter notebook format</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/965467','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/965467"><span>The Earth System Grid Center for Enabling Technologies (ESG-CET): Scaling the Earth System Grid to Petascale Data</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Williams, Dean N.</p> <p>2007-09-27</p> <p>This report, which summarizes work carried out by the ESG-CET during the period April 1, 2007 through September 30, 2007, includes discussion of overall progress, period goals, highlights, collaborations and presentations. To learn more about our project, please visit the Earth System Grid website. In addition, this report will be forwarded to the DOE SciDAC project management, the Office of Biological and Environmental Research (OBER) project management, national and international stakeholders (e.g., the Community Climate System Model (CCSM), the Intergovernmental Panel on Climate Change (IPCC) 5th Assessment Report (AR5), the Climate Science Computational End Station (CCES), etc.), and collaborators. Themore » ESG-CET executive committee consists of David Bernholdt, ORNL; Ian Foster, ANL; Don Middleton, NCAR; and Dean Williams, LLNL. The ESG-CET team is a collective of researchers and scientists with diverse domain knowledge, whose home institutions include seven laboratories (ANL, LANL, LBNL, LLNL, NCAR, ORNL, PMEL) and one university (ISI/USC); all work in close collaboration with the project's stakeholders and domain researchers and scientists. During this semi-annual reporting period, the ESG-CET increased its efforts on completing requirement documents, framework design, and component prototyping. As we strove to complete and expand the overall ESG-CET architectural plans and use-case scenarios to fit our constituency's scope of use, we continued to provide production-level services to the community. These services continued for IPCC AR4, CCES, and CCSM, and were extended to include Cloud Feedback Model Intercomparison Project (CFMIP) data.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://pubs.usgs.gov/of/1994/0281/report.pdf','USGSPUBS'); return false;" href="https://pubs.usgs.gov/of/1994/0281/report.pdf"><span>PRISM 8 degrees X 10 degrees North Hemisphere paleoclimate reconstruction; digital data</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Barron, John A.; Cronin, Thomas M.; Dowsett, Harry J.; Fleming, Farley R.; Holtz, Thomas R.; Ishman, Scott E.; Poore, Richard Z.; Thompson, Robert S.; Willard, Debra A.</p> <p>1994-01-01</p> <p>The PRISM 8?x10? data set represents several years of investigation by PRISM (Pliocene Research, Interpretation, and Synoptic Mapping) Project members. One of the goals of PRISM is to produce time-slice reconstructions of intervals of warmer than modern climate within the Pliocene Epoch. The first of these was chosen to be at 3.0 Ma (time scale of Berggren et al., 1985) and is published in Global and Planetary Change (Dowsett et al., 1994). This document contains the actual data sets and a brief explanation of how they were constructed. For paleoenvironmental interpretations and discussion of each data set, see Dowsett et al., in press. The data sets includes sea level, land ice distribution, vegetation or land cover, sea surface temperature and sea-ice cover matrices. This reconstruction of Middle Pliocene climate is organized as a series of datasets representing different environmental attributes. The data sets are designed for use with the GISS Model II atmospheric general circulation model (GCM) using an 8?x10? resolution (Hansen et al., 1983). The first step in documenting the Pliocene climate involves assigning an appropriate fraction of land versus ocean to each grid box. Following grid cell by grid cell, land versus ocean allocations, winter and summer sea ice coverage of ocean areas are assigned and then winter and summer sea surface temperatures are assigned to open ocean areas. Average land ice cover is recorded for land areas and then land areas not covered by ice are assigned proportions of six vegetation or land cover categories modified from Hansen et al. (1983).</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.B22A..06S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.B22A..06S"><span>Modeling Dynamics of South American Rangelands to Climate Variability and Human Impact</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Stanimirova, R.; Arevalo, P. A.; Kaufmann, R.; Maus, V.; Lesiv, M.; Havlik, P.; Friedl, M. A.</p> <p>2017-12-01</p> <p>The combined pressures of climate change and shifting dietary preferences are creating an urgent need to improve understanding of how climate and land management are jointly affecting the sustainability of rangelands. In particular, our ability to effectively manage rangelands in a manner that satisfies increasing demand for meat and dairy while reducing environmental impact depends on the sensitivity of rangelands to perturbations from both climate (e.g., drought) and land use (e.g., grazing). To characterize the sensitivity of rangeland vegetation to variation in climate, we analyzed gridded time series of satellite and climate data at 0.5-degree spatial resolution from 2003 to 2016 for rangeland ecosystems in South America. We used panel regression and canonical correlation to analyze the relationship between time series of enhanced vegetation index (EVI) derived from NASA's Moderate Spatial Resolution Imaging Spectroradiometer (MODIS) and gridded precipitation and air temperature data from the University of East Anglia's Climate Research Unit. To quantify the degree to which livestock management explains geographic variation of EVI, we used global livestock distribution (FAO) and feed requirements data from the Global Biosphere Management Model (GLOBIOM). Because rangeland ecosystems are sensitive to changes in meteorological variables at different time scales, we evaluated the strength of coupling between anomalies in EVI and anomalies in temperature and standardized precipitation index (SPI) data at 1-6 month lags. Our results show statistically significant relationships between EVI and precipitation during summer, fall, and winter in both tropical and subtropical agroecological zones of South America. Further, lagged precipitation effects, which reflect memory in the system, explain significant variance in winter EVI anomalies. While precipitation emerges as the dominant driver of variability in rangeland greenness, we find evidence of a management-induced signal as well. Our modeling framework integrates satellite observation, meteorological data sets, and land use/cover change information to improve our capability to monitor and manage the long-term sustainability of rangelands.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li><a href="#" onclick='return showDiv("page_15");'>15</a></li> <li class="active"><span>16</span></li> <li><a href="#" onclick='return showDiv("page_17");'>17</a></li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_16 --> <div id="page_17" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_15");'>15</a></li> <li><a href="#" onclick='return showDiv("page_16");'>16</a></li> <li class="active"><span>17</span></li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="321"> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20010068893','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20010068893"><span>A Study of the Response of Deep Tropical Clouds to Mesoscale Processes. Part 1; Modeling Strategies and Simulations of TOGA-COARE Convective Systems</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Johnson, Daniel E.; Tao, W.-K.; Simpson, J.; Sui, C.-H.; Einaudi, Franco (Technical Monitor)</p> <p>2001-01-01</p> <p>Interactions between deep tropical clouds over the western Pacific warm pool and the larger-scale environment are key to understanding climate change. Cloud models are an extremely useful tool in simulating and providing statistical information on heat and moisture transfer processes between cloud systems and the environment, and can therefore be utilized to substantially improve cloud parameterizations in climate models. In this paper, the Goddard Cumulus Ensemble (GCE) cloud-resolving model is used in multi-day simulations of deep tropical convective activity over the Tropical Ocean-Global Atmosphere Coupled Ocean-Atmosphere Response Experiment (TOGA COARE). Large-scale temperature and moisture advective tendencies, and horizontal momentum from the TOGA-COARE Intensive Flux Array (IFA) region, are applied to the GCE version which incorporates cyclical boundary conditions. Sensitivity experiments show that grid domain size produces the largest response to domain-mean temperature and moisture deviations, as well as cloudiness, when compared to grid horizontal or vertical resolution, and advection scheme. It is found that a minimum grid-domain size of 500 km is needed to adequately resolve the convective cloud features. The control experiment shows that the atmospheric heating and moistening is primarily a response to cloud latent processes of condensation/evaporation, and deposition/sublimation, and to a lesser extent, melting of ice particles. Air-sea exchange of heat and moisture is found to be significant, but of secondary importance, while the radiational response is small. The simulated rainfall and atmospheric heating and moistening, agrees well with observations, and performs favorably to other models simulating this case.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017EGUGA..19.2865T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017EGUGA..19.2865T"><span>The impact of mesoscale convective systems on global precipitation: A modeling study</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Tao, Wei-Kuo</p> <p>2017-04-01</p> <p>The importance of precipitating mesoscale convective systems (MCSs) has been quantified from TRMM precipitation radar and microwave imager retrievals. MCSs generate more than 50% of the rainfall in most tropical regions. Typical MCSs have horizontal scales of a few hundred kilometers (km); therefore, a large domain and high resolution are required for realistic simulations of MCSs in cloud-resolving models (CRMs). Almost all traditional global and climate models do not have adequate parameterizations to represent MCSs. Typical multi-scale modeling frameworks (MMFs) with 32 CRM grid points and 4 km grid spacing also might not have sufficient resolution and domain size for realistically simulating MCSs. In this study, the impact of MCSs on precipitation processes is examined by conducting numerical model simulations using the Goddard Cumulus Ensemble model (GCE) and Goddard MMF (GMMF). The results indicate that both models can realistically simulate MCSs with more grid points (i.e., 128 and 256) and higher resolutions (1 or 2 km) compared to those simulations with less grid points (i.e., 32 and 64) and low resolution (4 km). The modeling results also show that the strengths of the Hadley circulations, mean zonal and regional vertical velocities, surface evaporation, and amount of surface rainfall are either weaker or reduced in the GMMF when using more CRM grid points and higher CRM resolution. In addition, the results indicate that large-scale surface evaporation and wind feed back are key processes for determining the surface rainfall amount in the GMMF. A sensitivity test with reduced sea surface temperatures (SSTs) is conducted and results in both reduced surface rainfall and evaporation.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/1036845','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/1036845"><span>Detection and Attribution of Regional Climate Change</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Bala, G; Mirin, A</p> <p>2007-01-19</p> <p>We developed a high resolution global coupled modeling capability to perform breakthrough studies of the regional climate change. The atmospheric component in our simulation uses a 1{sup o} latitude x 1.25{sup o} longitude grid which is the finest resolution ever used for the NCAR coupled climate model CCSM3. Substantial testing and slight retuning was required to get an acceptable control simulation. The major accomplishment is the validation of this new high resolution configuration of CCSM3. There are major improvements in our simulation of the surface wind stress and sea ice thickness distribution in the Arctic. Surface wind stress and oceanmore » circulation in the Antarctic Circumpolar Current are also improved. Our results demonstrate that the FV version of the CCSM coupled model is a state of the art climate model whose simulation capabilities are in the class of those used for IPCC assessments. We have also provided 1000 years of model data to Scripps Institution of Oceanography to estimate the natural variability of stream flow in California. In the future, our global model simulations will provide boundary data to high-resolution mesoscale model that will be used at LLNL. The mesoscale model would dynamically downscale the GCM climate to regional scale on climate time scales.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017GMD....10.4257V','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017GMD....10.4257V"><span>The method ADAMONT v1.0 for statistical adjustment of climate projections applicable to energy balance land surface models</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Verfaillie, Deborah; Déqué, Michel; Morin, Samuel; Lafaysse, Matthieu</p> <p>2017-11-01</p> <p>We introduce the method ADAMONT v1.0 to adjust and disaggregate daily climate projections from a regional climate model (RCM) using an observational dataset at hourly time resolution. The method uses a refined quantile mapping approach for statistical adjustment and an analogous method for sub-daily disaggregation. The method ultimately produces adjusted hourly time series of temperature, precipitation, wind speed, humidity, and short- and longwave radiation, which can in turn be used to force any energy balance land surface model. While the method is generic and can be employed for any appropriate observation time series, here we focus on the description and evaluation of the method in the French mountainous regions. The observational dataset used here is the SAFRAN meteorological reanalysis, which covers the entire French Alps split into 23 massifs, within which meteorological conditions are provided for several 300 m elevation bands. In order to evaluate the skills of the method itself, it is applied to the ALADIN-Climate v5 RCM using the ERA-Interim reanalysis as boundary conditions, for the time period from 1980 to 2010. Results of the ADAMONT method are compared to the SAFRAN reanalysis itself. Various evaluation criteria are used for temperature and precipitation but also snow depth, which is computed by the SURFEX/ISBA-Crocus model using the meteorological driving data from either the adjusted RCM data or the SAFRAN reanalysis itself. The evaluation addresses in particular the time transferability of the method (using various learning/application time periods), the impact of the RCM grid point selection procedure for each massif/altitude band configuration, and the intervariable consistency of the adjusted meteorological data generated by the method. Results show that the performance of the method is satisfactory, with similar or even better evaluation metrics than alternative methods. However, results for air temperature are generally better than for precipitation. Results in terms of snow depth are satisfactory, which can be viewed as indicating a reasonably good intervariable consistency of the meteorological data produced by the method. In terms of temporal transferability (evaluated over time periods of 15 years only), results depend on the learning period. In terms of RCM grid point selection technique, the use of a complex RCM grid points selection technique, taking into account horizontal but also altitudinal proximity to SAFRAN massif centre points/altitude couples, generally degrades evaluation metrics for high altitudes compared to a simpler grid point selection method based on horizontal distance.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016cosp...41E1564P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016cosp...41E1564P"><span>The Copernicus programme and its Climate Change Service (C3S): a European answer to Climate Change</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Pinty, Bernard; Thepaut, Jean-Noel; Dee, Dick</p> <p>2016-07-01</p> <p>In November 2014, The European Centre for Medium-range Weather Forecasts (ECMWF) signed an agreement with the European Commission to deliver two of the Copernicus Earth Observation Programme Services on the Commission's behalf. The ECMWF delivered services - the Copernicus Climate Change Service (C3S) and Atmosphere Monitoring Service (CAMS) - will bring a consistent standard to how we measure and predict atmospheric conditions and climate change. They will maximise the potential of past, current and future earth observations - ground, ocean, airborne, satellite - and analyse these to monitor and predict atmospheric conditions and in the future, climate change. With the wealth of free and open data that the services provide, they will help business users to assess the impact of their business decisions and make informed choices, delivering a more energy efficient and climate aware economy. These sound investment decisions now will not only stimulate growth in the short term, but reduce the impact of climate change on the economy and society in the future. C3S is in its proof of concept phase and through its climate data store will provide global and regional climate data reanalyses; multi-model seasonal forecasts; customisable visual data to enable examination of wide range of scenarios and model the impact of changes; access to all the underlying data, including climate data records from various satellite and in-situ observations. In addition, C3S will provide key indicators on climate change drivers (such as carbon dioxide) and impacts (such as reducing glaciers). The aim of these indicators will be to support European adaptation and mitigation policies in a number of economic sectors. The presentation will provide an overview of this newly created Service, its various components and activities, and a roadmap towards achieving a fully operational European Climate Service at the horizon 2019-2020. It will focus on the requirements for quality-assured Observation Gridded Products to establish an operational delivery of a series of gridded long-term Climate Data Records (CDRs) of Essential Climate Variables (ECVs), along with associated input data and uncertainty estimation.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.H33G1775Y','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.H33G1775Y"><span>Addressing extreme precipitation change under future climates in the Upper Yangtze River Basin</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Yang, Z.; Yuan, Z.; Gao, X.</p> <p>2017-12-01</p> <p>Investigating the impact of climate change on extreme precipitation accurately is of importance for application purposes such as flooding mitigation and urban drainage system design. In this paper, a systematical analysis framework to assess the impact of climate change on extreme precipitation events is developed and practiced in the Upper Yangtze River Basin (UYRB) in China. Firstly, the UYRB is gridded and five extreme precipitation indices (annual maximum 3- 5- 7- 15- and 30-day precipitation) are selected. Secondly, with observed precipitation from China's Ground Precipitation 0.5°×0.5° Gridded Dataset (V2.0) and simulated daily precipitation from ten general circulation models (GCMs) of CMIP5, A regionally efficient GCM is selected for each grid by the skill score (SS) method which maximizes the overlapped area of probability density functions of extreme precipitation indices between observations and simulations during the historical period. Then, simulations of assembled efficient GCMs are bias corrected by Equidistant Cumulative Distribution Function method. Finally, the impact of climate change on extreme precipitation is analyzed. The results show that: (1) the MRI-CGCM3 and MIROC-ESM perform better in the UYRB. There are 19.8 to 20.9% and 14.2 to 18.7% of all grids regard this two GCMs as regionally efficient GCM for the five indices, respectively. Moreover, the regionally efficient GCMs are spatially distributed. (2) The assembled GCM performs much better than any single GCM, with the SS>0.8 and SS>0.6 in more than 65 and 85 percent grids. (3) Under the RCP4.5 scenario, the extreme precipitation of 50-year and 100-year return period is projected to increase in most areas of the UYRB in the future period, with 55.0 to 61.3% of the UYRB increasing larger than 10 percent for the five indices. The changes are spatially and temporal distributed. The upstream region of the UYRB has a relatively significant increase compared to the downstream basin, while the increase for annual maximum 5- and 7-day precipitation are more significant than other indices. The results demonstrate the impact of climate change on extreme precipitation in the UYRB, which provides a support to manage the water resource in this area.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012EGUGA..1414370R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012EGUGA..1414370R"><span>Earth System Grid and EGI interoperability</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Raciazek, J.; Petitdidier, M.; Gemuend, A.; Schwichtenberg, H.</p> <p>2012-04-01</p> <p>The Earth Science data centers have developed a data grid called Earth Science Grid Federation (ESGF) to give the scientific community world wide access to CMIP5 (Coupled Model Inter-comparison Project 5) climate data. The CMIP5 data will permit to evaluate the impact of climate change in various environmental and societal areas, such as regional climate, extreme events, agriculture, insurance… The ESGF grid provides services like searching, browsing and downloading of datasets. At the security level, ESGF data access is protected by an authentication mechanism. An ESGF trusted X509 Short-Lived EEC certificate with the correct roles/attributes is required to get access to the data in a non-interactive way (e.g. from a worker node). To access ESGF from EGI (i.e. by earth science applications running on EGI infrastructure), the security incompatibility between the two grids is the challenge: the EGI proxy certificate is not ESGF trusted nor it contains the correct roles/attributes. To solve this problem, we decided to use a Credential Translation Service (CTS) to translate the EGI X509 proxy certificate into the ESGF Short-Lived EEC certificate (the CTS will issue ESGF certificates based on EGI certificate authentication). From the end user perspective, the main steps to use the CTS are: the user binds his two identities (EGI and ESGF) together in the CTS using the CTS web interface (this steps has to be done only once) and then request an ESGF Short-Lived EEC certificate every time is needed, using a command-line tools. The implementation of the CTS is on-going. It is based on the open source MyProxy software stack, which is used in many grid infrastructures. On the client side, the "myproxy-logon" command-line tools is used to request the certificate translation. A new option has been added to "myproxy-logon" to select the original certificate (in our case, the EGI one). On the server side, MyProxy server operates in Certificate Authority mode, with a new module to store and manage identity pairs. Many European teams are working on the impact of climate change and face the problem of a lack of compute resources in connection with large data sets. This work between the ES VRC in EGI-Inspire and ESGF will be important to facilitate the exploitation of the CMIP5 data on EGI.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4237342','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4237342"><span>Evaluation of Gridded Precipitation Data for Driving SWAT Model in Area Upstream of Three Gorges Reservoir</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Yang, Yan; Wang, Guoqiang; Wang, Lijing; Yu, Jingshan; Xu, Zongxue</p> <p>2014-01-01</p> <p>Gridded precipitation data are becoming an important source for driving hydrologic models to achieve stable and valid simulation results in different regions. Thus, evaluating different sources of precipitation data is important for improving the applicability of gridded data. In this study, we used three gridded rainfall datasets: 1) National Centers for Environmental Prediction - Climate Forecast System Reanalysis (NCEP-CFSR); 2) Asian Precipitation - Highly-Resolved Observational Data Integration Towards Evaluation (APHRODITE); and 3) China trend - surface reanalysis (trend surface) data. These are compared with monitoring precipitation data for driving the Soil and Water Assessment Tool in two basins upstream of Three Gorges Reservoir (TGR) in China. The results of one test basin with significant topographic influence indicates that all the gridded data have poor abilities in reproducing hydrologic processes with the topographic influence on precipitation quantity and distribution. However, in a relatively flat test basin, the APHRODITE and trend surface data can give stable and desirable results. The results of this study suggest that precipitation data for future applications should be considered comprehensively in the TGR area, including the influence of data density and topography. PMID:25409467</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017GeoRL..44.3374B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017GeoRL..44.3374B"><span>Tropical precipitation extremes: Response to SST-induced warming in aquaplanet simulations</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bhattacharya, Ritthik; Bordoni, Simona; Teixeira, João.</p> <p>2017-04-01</p> <p>Scaling of tropical precipitation extremes in response to warming is studied in aquaplanet experiments using the global Weather Research and Forecasting (WRF) model. We show how the scaling of precipitation extremes is highly sensitive to spatial and temporal averaging: while instantaneous grid point extreme precipitation scales more strongly than the percentage increase (˜7% K-1) predicted by the Clausius-Clapeyron (CC) relationship, extremes for zonally and temporally averaged precipitation follow a slight sub-CC scaling, in agreement with results from Climate Model Intercomparison Project (CMIP) models. The scaling depends crucially on the employed convection parameterization. This is particularly true when grid point instantaneous extremes are considered. These results highlight how understanding the response of precipitation extremes to warming requires consideration of dynamic changes in addition to the thermodynamic response. Changes in grid-scale precipitation, unlike those in convective-scale precipitation, scale linearly with the resolved flow. Hence, dynamic changes include changes in both large-scale and convective-scale motions.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://pubs.er.usgs.gov/publication/70099203','USGSPUBS'); return false;" href="https://pubs.er.usgs.gov/publication/70099203"><span>Advances in a distributed approach for ocean model data interoperability</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Signell, Richard P.; Snowden, Derrick P.</p> <p>2014-01-01</p> <p>An infrastructure for earth science data is emerging across the globe based on common data models and web services. As we evolve from custom file formats and web sites to standards-based web services and tools, data is becoming easier to distribute, find and retrieve, leaving more time for science. We describe recent advances that make it easier for ocean model providers to share their data, and for users to search, access, analyze and visualize ocean data using MATLAB® and Python®. These include a technique for modelers to create aggregated, Climate and Forecast (CF) metadata convention datasets from collections of non-standard Network Common Data Form (NetCDF) output files, the capability to remotely access data from CF-1.6-compliant NetCDF files using the Open Geospatial Consortium (OGC) Sensor Observation Service (SOS), a metadata standard for unstructured grid model output (UGRID), and tools that utilize both CF and UGRID standards to allow interoperable data search, browse and access. We use examples from the U.S. Integrated Ocean Observing System (IOOS®) Coastal and Ocean Modeling Testbed, a project in which modelers using both structured and unstructured grid model output needed to share their results, to compare their results with other models, and to compare models with observed data. The same techniques used here for ocean modeling output can be applied to atmospheric and climate model output, remote sensing data, digital terrain and bathymetric data.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010AGUFM.H24F..08H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010AGUFM.H24F..08H"><span>Development of Spatiotemporal Bias-Correction Techniques for Downscaling GCM Predictions</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Hwang, S.; Graham, W. D.; Geurink, J.; Adams, A.; Martinez, C. J.</p> <p>2010-12-01</p> <p>Accurately representing the spatial variability of precipitation is an important factor for predicting watershed response to climatic forcing, particularly in small, low-relief watersheds affected by convective storm systems. Although Global Circulation Models (GCMs) generally preserve spatial relationships between large-scale and local-scale mean precipitation trends, most GCM downscaling techniques focus on preserving only observed temporal variability on point by point basis, not spatial patterns of events. Downscaled GCM results (e.g., CMIP3 ensembles) have been widely used to predict hydrologic implications of climate variability and climate change in large snow-dominated river basins in the western United States (Diffenbaugh et al., 2008; Adam et al., 2009). However fewer applications to smaller rain-driven river basins in the southeastern US (where preserving spatial variability of rainfall patterns may be more important) have been reported. In this study a new method was developed to bias-correct GCMs to preserve both the long term temporal mean and variance of the precipitation data, and the spatial structure of daily precipitation fields. Forty-year retrospective simulations (1960-1999) from 16 GCMs were collected (IPCC, 2007; WCRP CMIP3 multi-model database: https://esg.llnl.gov:8443/), and the daily precipitation data at coarse resolution (i.e., 280km) were interpolated to 12km spatial resolution and bias corrected using gridded observations over the state of Florida (Maurer et al., 2002; Wood et al, 2002; Wood et al, 2004). In this method spatial random fields which preserved the observed spatial correlation structure of the historic gridded observations and the spatial mean corresponding to the coarse scale GCM daily rainfall were generated. The spatiotemporal variability of the spatio-temporally bias-corrected GCMs were evaluated against gridded observations, and compared to the original temporally bias-corrected and downscaled CMIP3 data for the central Florida. The hydrologic response of two southwest Florida watersheds to the gridded observation data, the original bias corrected CMIP3 data, and the new spatiotemporally corrected CMIP3 predictions was compared using an integrated surface-subsurface hydrologic model developed by Tampa Bay Water.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/27066290','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/27066290"><span>Assessing the Role of Climate Change in Malaria Transmission in Africa.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Ngarakana-Gwasira, E T; Bhunu, C P; Masocha, M; Mashonjowa, E</p> <p>2016-01-01</p> <p>The sensitivity of vector borne diseases like malaria to climate continues to raise considerable concern over the implications of climate change on future disease dynamics. The problem of malaria vectors shifting from their traditional locations to invade new zones is of important concern. A mathematical model incorporating rainfall and temperature is constructed to study the transmission dynamics of malaria. The reproduction number obtained is applied to gridded temperature and rainfall datasets for baseline climate and future climate with aid of GIS. As a result of climate change, malaria burden is likely to increase in the tropics, the highland regions, and East Africa and along the northern limit of falciparum malaria. Falciparum malaria will spread into the African highlands; however it is likely to die out at the southern limit of the disease.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/pages/biblio/1104732-accuracy-climate-models-simulated-season-lengths-effectiveness-grid-scale-correction-factors','SCIGOV-DOEP'); return false;" href="https://www.osti.gov/pages/biblio/1104732-accuracy-climate-models-simulated-season-lengths-effectiveness-grid-scale-correction-factors"><span>The accuracy of climate models' simulated season lengths and the effectiveness of grid scale correction factors</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/pages">DOE PAGES</a></p> <p>Winterhalter, Wade E.</p> <p>2011-09-01</p> <p>Global climate change is expected to impact biological populations through a variety of mechanisms including increases in the length of their growing season. Climate models are useful tools for predicting how season length might change in the future. However, the accuracy of these models tends to be rather low at regional geographic scales. Here, I determined the ability of several atmosphere and ocean general circulating models (AOGCMs) to accurately simulate historical season lengths for a temperate ectotherm across the continental United States. I also evaluated the effectiveness of regional-scale correction factors to improve the accuracy of these models. I foundmore » that both the accuracy of simulated season lengths and the effectiveness of the correction factors to improve the model's accuracy varied geographically and across models. These results suggest that regional specific correction factors do not always adequately remove potential discrepancies between simulated and historically observed environmental parameters. As such, an explicit evaluation of the correction factors' effectiveness should be included in future studies of global climate change's impact on biological populations.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018GMD....11.1665F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018GMD....11.1665F"><span>Near-global climate simulation at 1 km resolution: establishing a performance baseline on 4888 GPUs with COSMO 5.0</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Fuhrer, Oliver; Chadha, Tarun; Hoefler, Torsten; Kwasniewski, Grzegorz; Lapillonne, Xavier; Leutwyler, David; Lüthi, Daniel; Osuna, Carlos; Schär, Christoph; Schulthess, Thomas C.; Vogt, Hannes</p> <p>2018-05-01</p> <p>The best hope for reducing long-standing global climate model biases is by increasing resolution to the kilometer scale. Here we present results from an ultrahigh-resolution non-hydrostatic climate model for a near-global setup running on the full Piz Daint supercomputer on 4888 GPUs (graphics processing units). The dynamical core of the model has been completely rewritten using a domain-specific language (DSL) for performance portability across different hardware architectures. Physical parameterizations and diagnostics have been ported using compiler directives. To our knowledge this represents the first complete atmospheric model being run entirely on accelerators on this scale. At a grid spacing of 930 m (1.9 km), we achieve a simulation throughput of 0.043 (0.23) simulated years per day and an energy consumption of 596 MWh per simulated year. Furthermore, we propose a new memory usage efficiency (MUE) metric that considers how efficiently the memory bandwidth - the dominant bottleneck of climate codes - is being used.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018TCry...12..811N','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018TCry...12..811N"><span>Modelling the climate and surface mass balance of polar ice sheets using RACMO2 - Part 1: Greenland (1958-2016)</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Noël, Brice; van de Berg, Willem Jan; Melchior van Wessem, J.; van Meijgaard, Erik; van As, Dirk; Lenaerts, Jan T. M.; Lhermitte, Stef; Kuipers Munneke, Peter; Smeets, C. J. P. Paul; van Ulft, Lambertus H.; van de Wal, Roderik S. W.; van den Broeke, Michiel R.</p> <p>2018-03-01</p> <p>We evaluate modelled Greenland ice sheet (GrIS) near-surface climate, surface energy balance (SEB) and surface mass balance (SMB) from the updated regional climate model RACMO2 (1958-2016). The new model version, referred to as RACMO2.3p2, incorporates updated glacier outlines, topography and ice albedo fields. Parameters in the cloud scheme governing the conversion of cloud condensate into precipitation have been tuned to correct inland snowfall underestimation: snow properties are modified to reduce drifting snow and melt production in the ice sheet percolation zone. The ice albedo prescribed in the updated model is lower at the ice sheet margins, increasing ice melt locally. RACMO2.3p2 shows good agreement compared to in situ meteorological data and point SEB/SMB measurements, and better resolves the spatial patterns and temporal variability of SMB compared with the previous model version, notably in the north-east, south-east and along the K-transect in south-western Greenland. This new model version provides updated, high-resolution gridded fields of the GrIS present-day climate and SMB, and will be used for projections of the GrIS climate and SMB in response to a future climate scenario in a forthcoming study.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017JPhCS.801a2086P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017JPhCS.801a2086P"><span>Harnessing the hybrid power supply systems of utility grid and photovoltaic panels at retrofit residential single family building in Medan</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Pangaribuan, A. B.; Rahmat, R. F.; Lidya, M. S.; Zálešák, M.</p> <p>2017-01-01</p> <p>The paper describes improvisation mode of energy supply source by collaboration between national utility grid as represented by fossil fuels and PV as independent renewable power resource in order to aim the energy consumptions efficiently in retrofit single family house. In this case, one existing single family house model in Medan, Indonesia was observed for the possibility of future refurbishment. The eco-design version of the house model and prediction analyses regarding nearby potential renewable energy resource (solar system) had been made using Autodesk Revit MEP 2015, Climate Consultant 6.0 and Green Building Studio Analysis. Economical evaluation of using hybrid power supply is discussed as well.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/1997WRR....33..711N','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/1997WRR....33..711N"><span>Streamflow simulation for continental-scale river basins</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Nijssen, Bart; Lettenmaier, Dennis P.; Liang, Xu; Wetzel, Suzanne W.; Wood, Eric F.</p> <p>1997-04-01</p> <p>A grid network version of the two-layer variable infiltration capacity (VIC-2L) macroscale hydrologic model is described. VIC-2L is a hydrologically based soil- vegetation-atmosphere transfer scheme designed to represent the land surface in numerical weather prediction and climate models. The grid network scheme allows streamflow to be predicted for large continental rivers. Off-line (observed and estimated surface meteorological and radiative forcings) applications of the model to the Columbia River (1° latitude-longitude spatial resolution) and Delaware River (0.5° resolution) are described. The model performed quite well in both applications, reproducing the seasonal hydrograph and annual flow volumes to within a few percent. Difficulties in reproducing observed streamflow in the arid portion of the Snake River basin are attributed to groundwater-surface water interactions, which are not modeled by VIC-2L.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFMIN14A..05F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFMIN14A..05F"><span>Large-scale, high-performance and cloud-enabled multi-model analytics experiments in the context of the Earth System Grid Federation</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Fiore, S.; Płóciennik, M.; Doutriaux, C.; Blanquer, I.; Barbera, R.; Williams, D. N.; Anantharaj, V. G.; Evans, B. J. K.; Salomoni, D.; Aloisio, G.</p> <p>2017-12-01</p> <p>The increased models resolution in the development of comprehensive Earth System Models is rapidly leading to very large climate simulations output that pose significant scientific data management challenges in terms of data sharing, processing, analysis, visualization, preservation, curation, and archiving.Large scale global experiments for Climate Model Intercomparison Projects (CMIP) have led to the development of the Earth System Grid Federation (ESGF), a federated data infrastructure which has been serving the CMIP5 experiment, providing access to 2PB of data for the IPCC Assessment Reports. In such a context, running a multi-model data analysis experiment is very challenging, as it requires the availability of a large amount of data related to multiple climate models simulations and scientific data management tools for large-scale data analytics. To address these challenges, a case study on climate models intercomparison data analysis has been defined and implemented in the context of the EU H2020 INDIGO-DataCloud project. The case study has been tested and validated on CMIP5 datasets, in the context of a large scale, international testbed involving several ESGF sites (LLNL, ORNL and CMCC), one orchestrator site (PSNC) and one more hosting INDIGO PaaS services (UPV). Additional ESGF sites, such as NCI (Australia) and a couple more in Europe, are also joining the testbed. The added value of the proposed solution is summarized in the following: it implements a server-side paradigm which limits data movement; it relies on a High-Performance Data Analytics (HPDA) stack to address performance; it exploits the INDIGO PaaS layer to support flexible, dynamic and automated deployment of software components; it provides user-friendly web access based on the INDIGO Future Gateway; and finally it integrates, complements and extends the support currently available through ESGF. Overall it provides a new "tool" for climate scientists to run multi-model experiments. At the time this contribution is being written, the proposed testbed represents the first implementation of a distributed large-scale, multi-model experiment in the ESGF/CMIP context, joining together server-side approaches for scientific data analysis, HPDA frameworks, end-to-end workflow management, and cloud computing.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://cfpub.epa.gov/si/si_public_record_report.cfm?direntryid=311886&keyword=ecosystems&subject=ecosystems%20research&showcriteria=2&fed_org_id=111&datebeginpublishedpresented=11/04/2011&dateendpublishedpresented=11/04/2016&sortby=pubdateyear','PESTICIDES'); return false;" href="https://cfpub.epa.gov/si/si_public_record_report.cfm?direntryid=311886&keyword=ecosystems&subject=ecosystems%20research&showcriteria=2&fed_org_id=111&datebeginpublishedpresented=11/04/2011&dateendpublishedpresented=11/04/2016&sortby=pubdateyear"><span>EPIC-Simulated and MODIS-Derived Leaf Area Index (LAI) ...</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.epa.gov/pesticides/search.htm">EPA Pesticide Factsheets</a></p> <p></p> <p></p> <p>Leaf Area Index (LAI) is an important parameter in assessing vegetation structure for characterizing forest canopies over large areas at broad spatial scales using satellite remote sensing data. However, satellite-derived LAI products can be limited by obstructed atmospheric conditions yielding sub-optimal values, or complete non-returns. The United States Environmental Protection Agency’s Exposure Methods and Measurements and Computational Exposure Divisions are investigating the viability of supplemental modelled LAI inputs into satellite-derived data streams to support various regional and local scale air quality models for retrospective and future climate assessments. In this present study, one-year (2002) of plot level stand characteristics at four study sites located in Virginia and North Carolina are used to calibrate species-specific plant parameters in a semi-empirical biogeochemical model. The Environmental Policy Integrated Climate (EPIC) model was designed primarily for managed agricultural field crop ecosystems, but also includes managed woody species that span both xeric and mesic sites (e.g., mesquite, pine, oak, etc.). LAI was simulated using EPIC at a 4 km2 and 12 km2 grid coincident with the regional Community Multiscale Air Quality Model (CMAQ) grid. LAI comparisons were made between model-simulated and MODIS-derived LAI. Field/satellite-upscaled LAI was also compared to the corresponding MODIS LAI value. Preliminary results show field/satel</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/pages/biblio/1237543-changes-concurrent-precipitation-temperature-extremes','SCIGOV-DOEP'); return false;" href="https://www.osti.gov/pages/biblio/1237543-changes-concurrent-precipitation-temperature-extremes"><span>Changes in Concurrent Precipitation and Temperature Extremes</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/pages">DOE PAGES</a></p> <p>Hao, Zengchao; AghaKouchak, Amir; Phillips, Thomas J.</p> <p>2013-08-01</p> <p>While numerous studies have addressed changes in climate extremes, analyses of concurrence of climate extremes are scarce, and climate change effects on joint extremes are rarely considered. This study assesses the occurrence of joint (concurrent) monthly continental precipitation and temperature extremes in Climate Research Unit (CRU) and University of Delaware (UD) observations, and in 13 Coupled Model Intercomparison Project Phase 5 (CMIP5) global climate simulations. Moreover, the joint occurrences of precipitation and temperature extremes simulated by CMIP5 climate models are compared with those derived from the CRU and UD observations for warm/wet, warm/dry, cold/wet, and cold/dry combinations of joint extremes.more » The number of occurrences of these four combinations during the second half of the 20th century (1951–2004) is assessed on a common global grid. CRU and UD observations show substantial increases in the occurrence of joint warm/dry and warm/wet combinations for the period 1978–2004 relative to 1951–1977. The results show that with respect to the sign of change in the concurrent extremes, the CMIP5 climate model simulations are in reasonable overall agreement with observations. The results reveal notable discrepancies between regional patterns and the magnitude of change in individual climate model simulations relative to the observations of precipitation and temperature.« less</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_15");'>15</a></li> <li><a href="#" onclick='return showDiv("page_16");'>16</a></li> <li class="active"><span>17</span></li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_17 --> <div id="page_18" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_16");'>16</a></li> <li><a href="#" onclick='return showDiv("page_17");'>17</a></li> <li class="active"><span>18</span></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="341"> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/23690959','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/23690959"><span>Forecasting the future risk of Barmah Forest virus disease under climate change scenarios in Queensland, Australia.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Naish, Suchithra; Mengersen, Kerrie; Hu, Wenbiao; Tong, Shilu</p> <p>2013-01-01</p> <p>Mosquito-borne diseases are climate sensitive and there has been increasing concern over the impact of climate change on future disease risk. This paper projected the potential future risk of Barmah Forest virus (BFV) disease under climate change scenarios in Queensland, Australia. We obtained data on notified BFV cases, climate (maximum and minimum temperature and rainfall), socio-economic and tidal conditions for current period 2000-2008 for coastal regions in Queensland. Grid-data on future climate projections for 2025, 2050 and 2100 were also obtained. Logistic regression models were built to forecast the otential risk of BFV disease distribution under existing climatic, socio-economic and tidal conditions. The model was applied to estimate the potential geographic distribution of BFV outbreaks under climate change scenarios. The predictive model had good model accuracy, sensitivity and specificity. Maps on potential risk of future BFV disease indicated that disease would vary significantly across coastal regions in Queensland by 2100 due to marked differences in future rainfall and temperature projections. We conclude that the results of this study demonstrate that the future risk of BFV disease would vary across coastal regions in Queensland. These results may be helpful for public health decision making towards developing effective risk management strategies for BFV disease control and prevention programs in Queensland.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..18.6336K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..18.6336K"><span>The role of observational reference data for climate downscaling: Insights from the VALUE COST Action</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kotlarski, Sven; Gutiérrez, José M.; Boberg, Fredrik; Bosshard, Thomas; Cardoso, Rita M.; Herrera, Sixto; Maraun, Douglas; Mezghani, Abdelkader; Pagé, Christian; Räty, Olle; Stepanek, Petr; Soares, Pedro M. M.; Szabo, Peter</p> <p>2016-04-01</p> <p>VALUE is an open European network to validate and compare downscaling methods for climate change research (http://www.value-cost.eu). A key deliverable of VALUE is the development of a systematic validation framework to enable the assessment and comparison of downscaling methods. Such assessments can be expected to crucially depend on the existence of accurate and reliable observational reference data. In dynamical downscaling, observational data can influence model development itself and, later on, model evaluation, parameter calibration and added value assessment. In empirical-statistical downscaling, observations serve as predictand data and directly influence model calibration with corresponding effects on downscaled climate change projections. We here present a comprehensive assessment of the influence of uncertainties in observational reference data and of scale-related issues on several of the above-mentioned aspects. First, temperature and precipitation characteristics as simulated by a set of reanalysis-driven EURO-CORDEX RCM experiments are validated against three different gridded reference data products, namely (1) the EOBS dataset (2) the recently developed EURO4M-MESAN regional re-analysis, and (3) several national high-resolution and quality-controlled gridded datasets that recently became available. The analysis reveals a considerable influence of the choice of the reference data on the evaluation results, especially for precipitation. It is also illustrated how differences between the reference data sets influence the ranking of RCMs according to a comprehensive set of performance measures.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20120013440','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20120013440"><span>Climate Models</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Druyan, Leonard M.</p> <p>2012-01-01</p> <p>Climate models is a very broad topic, so a single volume can only offer a small sampling of relevant research activities. This volume of 14 chapters includes descriptions of a variety of modeling studies for a variety of geographic regions by an international roster of authors. The climate research community generally uses the rubric climate models to refer to organized sets of computer instructions that produce simulations of climate evolution. The code is based on physical relationships that describe the shared variability of meteorological parameters such as temperature, humidity, precipitation rate, circulation, radiation fluxes, etc. Three-dimensional climate models are integrated over time in order to compute the temporal and spatial variations of these parameters. Model domains can be global or regional and the horizontal and vertical resolutions of the computational grid vary from model to model. Considering the entire climate system requires accounting for interactions between solar insolation, atmospheric, oceanic and continental processes, the latter including land hydrology and vegetation. Model simulations may concentrate on one or more of these components, but the most sophisticated models will estimate the mutual interactions of all of these environments. Advances in computer technology have prompted investments in more complex model configurations that consider more phenomena interactions than were possible with yesterday s computers. However, not every attempt to add to the computational layers is rewarded by better model performance. Extensive research is required to test and document any advantages gained by greater sophistication in model formulation. One purpose for publishing climate model research results is to present purported advances for evaluation by the scientific community.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/biblio/1129318-meeting-radiative-forcing-targets-representative-concentration-pathways-world-agricultural-climate-impacts','SCIGOV-STC'); return false;" href="https://www.osti.gov/biblio/1129318-meeting-radiative-forcing-targets-representative-concentration-pathways-world-agricultural-climate-impacts"><span></span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Kyle, G. Page; Mueller, C.; Calvin, Katherine V.</p> <p></p> <p>This study assesses how climate impacts on agriculture may change the evolution of the agricultural and energy systems in meeting the end-of-century radiative forcing targets of the Representative Concentration Pathways (RCPs). We build on the recently completed ISI-MIP exercise that has produced global gridded estimates of future crop yields for major agricultural crops using climate model projections of the RCPs from the Coupled Model Intercomparison Project Phase 5 (CMIP5). For this study we use the bias-corrected outputs of the HadGEM2-ES climate model as inputs to the LPJmL crop growth model, and the outputs of LPJmL to modify inputs to themore » GCAM integrated assessment model. Our results indicate that agricultural climate impacts generally lead to an increase in global cropland, as compared with corresponding emissions scenarios that do not consider climate impacts on agricultural productivity. This is driven mostly by negative impacts on wheat, rice, other grains, and oil crops. Still, including agricultural climate impacts does not significantly increase the costs or change the technological strategies of global, whole-system emissions mitigation. In fact, to meet the most aggressive climate change mitigation target (2.6 W/m2 in 2100), the net mitigation costs are slightly lower when agricultural climate impacts are considered. Key contributing factors to these results are (a) low levels of climate change in the low-forcing scenarios, (b) adaptation to climate impacts, simulated in GCAM through inter-regional shifting in the production of agricultural goods, and (c) positive average climate impacts on bioenergy crop yields.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://pubs.er.usgs.gov/publication/70174012','USGSPUBS'); return false;" href="https://pubs.er.usgs.gov/publication/70174012"><span>Influence of climate drivers on colonization and extinction dynamics of wetland-dependent species</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Ray, Andrew M.; Gould, William R.; Hossack, Blake R.; Sepulveda, Adam; Thoma, David P.; Patla, Debra A.; Daley, Rob; Al-Chokhachy, Robert K.</p> <p>2016-01-01</p> <p>Freshwater wetlands are particularly vulnerable to climate change. Specifically, changes in temperature, precipitation, and evapotranspiration (i.e., climate drivers) are likely to alter flooding regimes of wetlands and affect the vital rates, abundance, and distributions of wetland-dependent species. Amphibians may be among the most climate-sensitive wetland-dependent groups, as many species rely on shallow or intermittently flooded wetland habitats for breeding. Here, we integrated multiple years of high-resolution gridded climate and amphibian monitoring data from Grand Teton and Yellowstone National Parks to explicitly model how variations in climate drivers and habitat conditions affect the occurrence and breeding dynamics (i.e., annual extinction and colonization rates) of amphibians. Our results showed that models incorporating climate drivers outperformed models of amphibian breeding dynamics that were exclusively habitat based. Moreover, climate-driven variation in extinction rates, but not colonization rates, disproportionately influenced amphibian occupancy in monitored wetlands. Long-term monitoring from national parks coupled with high-resolution climate data sets will be crucial to describing population dynamics and characterizing the sensitivity of amphibians and other wetland-dependent species to climate change. Further, long-term monitoring of wetlands in national parks will help reduce uncertainty surrounding wetland resources and strengthen opportunities to make informed, science-based decisions that have far-reaching benefits.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFMGC34D..03T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFMGC34D..03T"><span>Developing Water Resource Security in a Greenhouse Gas Constrained Context - A Case Study in California</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Tarroja, B.; Aghakouchak, A.; Samuelsen, S.</p> <p>2015-12-01</p> <p>The onset of drought conditions in regions such as California due to shortfalls in precipitation has brought refreshed attention to the vulnerability of our water supply paradigm to changes in climate patterns. In the face of a changing climate which can exacerbate drought conditions in already dry areas, building resiliency into our water supply infrastructure requires some decoupling of water supply availability from climate behavior through conservation, efficiency, and alternative water supply measures such as desalination and water reuse. The installation of these measures requires varying degrees of direct energy inputs and/or impacts the energy usage of the water supply infrastructure (conveyance, treatment, distribution, wastewater treatment). These impacts have implications for greenhouse gas emissions from direct fuel usage or impacts on the emissions from the electric grid. At the scale that these measures may need to be deployed to secure water supply availability, especially under climate change impacted hydrology, they can potentially pose obstacles for meeting greenhouse gas emissions reduction and renewable utilization goals. Therefore, the portfolio of these measures must be such that detrimental impacts on greenhouse gas emissions are minimized. This study combines climate data with a water reservoir network model and an electric grid dispatch model for the water-energy system of California to evaluate 1) the different pathways and scale of alternative water resource measures needed to secure water supply availability and 2) the impacts of following these pathways on the ability to meet greenhouse gas and renewable utilization goals. It was discovered that depending on the water supply measure portfolio implemented, impacts on greenhouse gas emissions and renewable utilization can either be beneficial or detrimental, and optimizing the portfolio is more important under climate change conditions due to the scale of measures required.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/biblio/1170463-integrated-assessment-global-water-scarcity-over-century-under-multiple-climate-change-mitigation-policies','SCIGOV-STC'); return false;" href="https://www.osti.gov/biblio/1170463-integrated-assessment-global-water-scarcity-over-century-under-multiple-climate-change-mitigation-policies"><span>Integrated assessment of global water scarcity over the 21st century under multiple climate change mitigation policies</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Hejazi, Mohamad I.; Edmonds, James A.; Clarke, Leon E.</p> <p>2014-08-01</p> <p>Water scarcity conditions over the 21st century both globally and regionally are assessed in the context of climate change and climate mitigation policies, by estimating both water availability and water demand within the Global Change Assessment Model (GCAM), a leading community integrated assessment model of energy, agriculture, climate, and water. To quantify changes in future water availability, a new gridded water-balance global hydrologic model – namely, the Global Water Availability Model (GWAM) – is developed and evaluated. Global water demands for six major demand sectors (irrigation, livestock, domestic, electricity generation, primary energy production, and manufacturing) are modeled in GCAM atmore » the regional scale (14 geopolitical regions, 151 sub-regions) and then spatially downscaled to 0.5 o x 0.5o resolution to match the scale of GWAM. Using a baseline scenario (i.e., no climate change mitigation policy) with radiative forcing reaching 8.8 W/m2 (equivalent to the SRES A1Fi emission scenario) and three climate policy scenarios with increasing mitigation stringency of 7.7, 5.5, and 4.2 W/m2 (equivalent to the SRES A2, B2, and B1 emission scenarios, respectively), we investigate the effects of emission mitigation policies on water scarcity. Two carbon tax regimes (a universal carbon tax (UCT) which includes land use change emissions, and a fossil fuel and industrial emissions carbon tax (FFICT) which excludes land use change emissions) are analyzed. The baseline scenario results in more than half of the world population living under extreme water scarcity by the end of the 21st century. Additionally, in years 2050 and 2095, 36% (28%) and 44% (39%) of the global population, respectively, is projected to live in grid cells (in basins) that will experience greater water demands than the amount of available water in a year (i.e., the water scarcity index (WSI) > 1.0). When comparing the climate policy scenarios to the baseline scenario while maintaining the same baseline socioeconomic assumptions, water scarcity declines under a UCT mitigation policy but increases with a FFICT mitigation scenario by the year 2095 particularly with more stringent climate mitigation targets. Under the FFICT scenario, water scarcity is projected to increase driven by higher water demands for bio-energy crops.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014HESS...18.2859H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014HESS...18.2859H"><span>Integrated assessment of global water scarcity over the 21st century under multiple climate change mitigation policies</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Hejazi, M. I.; Edmonds, J.; Clarke, L.; Kyle, P.; Davies, E.; Chaturvedi, V.; Wise, M.; Patel, P.; Eom, J.; Calvin, K.</p> <p>2014-08-01</p> <p>Water scarcity conditions over the 21st century both globally and regionally are assessed in the context of climate change and climate mitigation policies, by estimating both water availability and water demand within the Global Change Assessment Model (GCAM), a leading community-integrated assessment model of energy, agriculture, climate, and water. To quantify changes in future water availability, a new gridded water-balance global hydrologic model - namely, the Global Water Availability Model (GWAM) - is developed and evaluated. Global water demands for six major demand sectors (irrigation, livestock, domestic, electricity generation, primary energy production, and manufacturing) are modeled in GCAM at the regional scale (14 geopolitical regions, 151 sub-regions) and then spatially downscaled to 0.5° × 0.5° resolution to match the scale of GWAM. Using a baseline scenario (i.e., no climate change mitigation policy) with radiative forcing reaching 8.8 W m-2 (equivalent to the SRES A1Fi emission scenario) and three climate policy scenarios with increasing mitigation stringency of 7.7, 5.5, and 4.2 W m-2 (equivalent to the SRES A2, B2, and B1 emission scenarios, respectively), we investigate the effects of emission mitigation policies on water scarcity. Two carbon tax regimes (a universal carbon tax (UCT) which includes land use change emissions, and a fossil fuel and industrial emissions carbon tax (FFICT) which excludes land use change emissions) are analyzed. The baseline scenario results in more than half of the world population living under extreme water scarcity by the end of the 21st century. Additionally, in years 2050 and 2095, 36% (28%) and 44% (39%) of the global population, respectively, is projected to live in grid cells (in basins) that will experience greater water demands than the amount of available water in a year (i.e., the water scarcity index (WSI) > 1.0). When comparing the climate policy scenarios to the baseline scenario while maintaining the same baseline socioeconomic assumptions, water scarcity declines under a UCT mitigation policy but increases with a FFICT mitigation scenario by the year 2095, particularly with more stringent climate mitigation targets. Under the FFICT scenario, water scarcity is projected to increase, driven by higher water demands for bio-energy crops.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015JAMES...7.1997P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015JAMES...7.1997P"><span>Impact of four-dimensional data assimilation (FDDA) on urban climate analysis</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Pan, Linlin; Liu, Yubao; Liu, Yuewei; Li, Lei; Jiang, Yin; Cheng, Will; Roux, Gregory</p> <p>2015-12-01</p> <p>This study investigates the impact of four-dimensional data assimilation (FDDA) on urban climate analysis, which employs the NCAR (National Center for Atmospheric Research) WRF (the weather research and forecasting model) based on climate FDDA (CFDDA) technology to develop an urban-scale microclimatology database for the Shenzhen area, a rapidly developing metropolitan located along the southern coast of China, where uniquely high-density observations, including ultrahigh-resolution surface AWS (automatic weather station) network, radio sounding, wind profilers, radiometers, and other weather observation platforms, have been installed. CFDDA is an innovative dynamical downscaling regional climate analysis system that assimilates diverse regional observations; and has been employed to produce a 5 year multiscale high-resolution microclimate analysis by assimilating high-density observations at Shenzhen area. The CFDDA system was configured with four nested-grid domains at grid sizes of 27, 9, 3, and 1 km, respectively. This research evaluates the impact of assimilating high-resolution observation data on reproducing the refining features of urban-scale circulations. Two experiments were conducted with a 5 year run using CFSR (climate forecast system reanalysis) as boundary and initial conditions: one with CFDDA and the other without. The comparisons of these two experiments with observations indicate that CFDDA greatly reduces the model analysis error and is able to realistically analyze the microscale features such as urban-rural-coastal circulation, land/sea breezes, and local-hilly terrain thermal circulations. It is demonstrated that the urbanization can produce 2.5 k differences in 2 m temperatures, delays/speeds up the land/sea breeze development, and interacts with local mountain-valley circulations.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFMIN31A1744Z','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFMIN31A1744Z"><span>netCDF Operators for Rapid Analysis of Measured and Modeled Swath-like Data</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Zender, C. S.</p> <p>2015-12-01</p> <p>Swath-like data (hereafter SLD) are defined by non-rectangular and/or time-varying spatial grids in which one or more coordinates are multi-dimensional. It is often challenging and time-consuming to work with SLD, including all Level 2 satellite-retrieved data, non-rectangular subsets of Level 3 data, and model data on curvilinear grids. Researchers and data centers want user-friendly, fast, and powerful methods to specify, extract, serve, manipulate, and thus analyze, SLD. To meet these needs, large research-oriented agencies and modeling center such as NASA, DOE, and NOAA increasingly employ the netCDF Operators (NCO), an open-source scientific data analysis software package applicable to netCDF and HDF data. NCO includes extensive, fast, parallelized regridding features to facilitate analysis and intercomparison of SLD and model data. Remote sensing, weather and climate modeling and analysis communities face similar problems in handling SLD including how to easily: 1. Specify and mask irregular regions such as ocean basins and political boundaries in SLD (and rectangular) grids. 2. Bin, interpolate, average, or re-map SLD to regular grids. 3. Derive secondary data from given quality levels of SLD. These common tasks require a data extraction and analysis toolkit that is SLD-friendly and, like NCO, familiar in all these communities. With NCO users can 1. Quickly project SLD onto the most useful regular grids for intercomparison. 2. Access sophisticated statistical and regridding functions that are robust to missing data and allow easy specification of quality control metrics. These capabilities improve interoperability, software-reuse, and, because they apply to SLD, minimize transmission, storage, and handling of unwanted data. While SLD analysis still poses many challenges compared to regularly gridded, rectangular data, the custom analyses scripts SLD once required are now shorter, more powerful, and user-friendly.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUOSPC24A2125U','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUOSPC24A2125U"><span>Determining the Effect of the Lunar Nodal Cycle on Tidal Mixing and North Pacific Climate Variability</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ullman, D. J.; Schmittner, A.; Danabasoglu, G.; Norton, N. J.; Müller, M.</p> <p>2016-02-01</p> <p>Oscillations in the moon's orbit around the earth modulate regional tidal dissipation with a periodicity of 18.6 years. In regions where the diurnal tidal constituents dominate diapycnal mixing, this Lunar Nodal Cycle (LNC) may be significant enough to influence ocean circulation, sea surface temperature, and climate variability. Such periodicity in the LNC as an external forcing may provide a mechanistic source for Pacific decadal variability (i.e. Pacific Decadal Oscillation, PDO) where diurnal tidal constituents are strong. We have introduced three enhancements to the latest version of the Community Earth System Model (CESM) to better simulate tidal-forced mixing. First, we have produced a sub-grid scale bathymetry scheme that better resolves the vertical distribution of the barotropic energy flux in regions where the native CESM grid does not resolve high spatial-scale bathymetric features. Second, we test a number of alternative barotropic tidal constituent energy flux fields that are derived from various satellite altimeter observations and tidal models. Third, we introduce modulations of the individual diurnal and semi-diurnal tidal constituents, ranging from monthly to decadal periods, as derived from the full lunisolar tidal potential. Using both ocean-only and fully-coupled configurations, we test the influence of these enhancements, particularly the LNC modulations, on ocean mixing and bidecadal climate variability in CESM.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20120015704','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20120015704"><span>Globally Gridded Satellite (GridSat) Observations for Climate Studies</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Knapp, Kenneth R.; Ansari, Steve; Bain, Caroline L.; Bourassa, Mark A.; Dickinson, Michael J.; Funk, Chris; Helms, Chip N.; Hennon, Christopher C.; Holmes, Christopher D.; Huffman, George J.; <a style="text-decoration: none; " href="javascript:void(0); " onClick="displayelement('author_20120015704'); toggleEditAbsImage('author_20120015704_show'); toggleEditAbsImage('author_20120015704_hide'); "> <img style="display:inline; width:12px; height:12px; " src="images/arrow-up.gif" width="12" height="12" border="0" alt="hide" id="author_20120015704_show"> <img style="width:12px; height:12px; display:none; " src="images/arrow-down.gif" width="12" height="12" border="0" alt="hide" id="author_20120015704_hide"></p> <p>2012-01-01</p> <p>Geostationary satellites have provided routine, high temporal resolution Earth observations since the 1970s. Despite the long period of record, use of these data in climate studies has been limited for numerous reasons, among them: there is no central archive of geostationary data for all international satellites, full temporal and spatial resolution data are voluminous, and diverse calibration and navigation formats encumber the uniform processing needed for multi-satellite climate studies. The International Satellite Cloud Climatology Project set the stage for overcoming these issues by archiving a subset of the full resolution geostationary data at approx.10 km resolution at 3 hourly intervals since 1983. Recent efforts at NOAA s National Climatic Data Center to provide convenient access to these data include remapping the data to a standard map projection, recalibrating the data to optimize temporal homogeneity, extending the record of observations back to 1980, and reformatting the data for broad public distribution. The Gridded Satellite (GridSat) dataset includes observations from the visible, infrared window, and infrared water vapor channels. Data are stored in the netCDF format using standards that permit a wide variety of tools and libraries to quickly and easily process the data. A novel data layering approach, together with appropriate satellite and file metadata, allows users to access GridSat data at varying levels of complexity based on their needs. The result is a climate data record already in use by the meteorological community. Examples include reanalysis of tropical cyclones, studies of global precipitation, and detection and tracking of the intertropical convergence zone.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFM.A41L..02C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFM.A41L..02C"><span>Downscaling seasonal to centennial simulations on distributed computing infrastructures using WRF model. The WRF4G project</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Cofino, A. S.; Fernández Quiruelas, V.; Blanco Real, J. C.; García Díez, M.; Fernández, J.</p> <p>2013-12-01</p> <p>Nowadays Grid Computing is powerful computational tool which is ready to be used for scientific community in different areas (such as biomedicine, astrophysics, climate, etc.). However, the use of this distributed computing infrastructures (DCI) is not yet common practice in climate research, and only a few teams and applications in this area take advantage of this infrastructure. Thus, the WRF4G project objective is to popularize the use of this technology in the atmospheric sciences area. In order to achieve this objective, one of the most used applications has been taken (WRF; a limited- area model, successor of the MM5 model), that has a user community formed by more than 8000 researchers worldwide. This community develop its research activity on different areas and could benefit from the advantages of Grid resources (case study simulations, regional hind-cast/forecast, sensitivity studies, etc.). The WRF model is used by many groups, in the climate research community, to carry on downscaling simulations. Therefore this community will also benefit. However, Grid infrastructures have some drawbacks for the execution of applications that make an intensive use of CPU and memory for a long period of time. This makes necessary to develop a specific framework (middleware). This middleware encapsulates the application and provides appropriate services for the monitoring and management of the simulations and the data. Thus,another objective of theWRF4G project consists on the development of a generic adaptation of WRF to DCIs. It should simplify the access to the DCIs for the researchers, and also to free them from the technical and computational aspects of the use of theses DCI. Finally, in order to demonstrate the ability of WRF4G solving actual scientific challenges with interest and relevance on the climate science (implying a high computational cost) we will shown results from different kind of downscaling experiments, like ERA-Interim re-analysis, CMIP5 models, or seasonal. WRF4G is been used to run WRF simulations which are contributing to the CORDEX initiative and others projects like SPECS and EUPORIAS. This work is been partially funded by the European Regional Development Fund (ERDF) and the Spanish National R&D Plan 2008-2011 (CGL2011-28864)</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3792985','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3792985"><span>Future Bloom and Blossom Frost Risk for Malus domestica Considering Climate Model and Impact Model Uncertainties</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Hoffmann, Holger; Rath, Thomas</p> <p>2013-01-01</p> <p>The future bloom and risk of blossom frosts for Malus domestica were projected using regional climate realizations and phenological ( = impact) models. As climate impact projections are susceptible to uncertainties of climate and impact models and model concatenation, the significant horizon of the climate impact signal was analyzed by applying 7 impact models, including two new developments, on 13 climate realizations of the IPCC emission scenario A1B. Advancement of phenophases and a decrease in blossom frost risk for Lower Saxony (Germany) for early and late ripeners was determined by six out of seven phenological models. Single model/single grid point time series of bloom showed significant trends by 2021–2050 compared to 1971–2000, whereas the joint signal of all climate and impact models did not stabilize until 2043. Regarding blossom frost risk, joint projection variability exceeded the projected signal. Thus, blossom frost risk cannot be stated to be lower by the end of the 21st century despite a negative trend. As a consequence it is however unlikely to increase. Uncertainty of temperature, blooming date and blossom frost risk projection reached a minimum at 2078–2087. The projected phenophases advanced by 5.5 d K−1, showing partial compensation of delayed fulfillment of the winter chill requirement and faster completion of the following forcing phase in spring. Finally, phenological model performance was improved by considering the length of day. PMID:24116022</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/24116022','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/24116022"><span>Future bloom and blossom frost risk for Malus domestica considering climate model and impact model uncertainties.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Hoffmann, Holger; Rath, Thomas</p> <p>2013-01-01</p> <p>The future bloom and risk of blossom frosts for Malus domestica were projected using regional climate realizations and phenological ( = impact) models. As climate impact projections are susceptible to uncertainties of climate and impact models and model concatenation, the significant horizon of the climate impact signal was analyzed by applying 7 impact models, including two new developments, on 13 climate realizations of the IPCC emission scenario A1B. Advancement of phenophases and a decrease in blossom frost risk for Lower Saxony (Germany) for early and late ripeners was determined by six out of seven phenological models. Single model/single grid point time series of bloom showed significant trends by 2021-2050 compared to 1971-2000, whereas the joint signal of all climate and impact models did not stabilize until 2043. Regarding blossom frost risk, joint projection variability exceeded the projected signal. Thus, blossom frost risk cannot be stated to be lower by the end of the 21st century despite a negative trend. As a consequence it is however unlikely to increase. Uncertainty of temperature, blooming date and blossom frost risk projection reached a minimum at 2078-2087. The projected phenophases advanced by 5.5 d K(-1), showing partial compensation of delayed fulfillment of the winter chill requirement and faster completion of the following forcing phase in spring. Finally, phenological model performance was improved by considering the length of day.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFM.A24D..04D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFM.A24D..04D"><span>High Definition Clouds and Precipitation for advancing Climate Prediction (HD(CP)2): Large Eddy Simulation Study Over Germany</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Dipankar, A.; Stevens, B. B.; Zängl, G.; Pondkule, M.; Brdar, S.</p> <p>2014-12-01</p> <p>The effect of clouds on large scale dynamics is represented in climate models through parameterization of various processes, of which the parameterization of shallow and deep convection are particularly uncertain. The atmospheric boundary layer, which controls the coupling to the surface, and which defines the scale of shallow convection, is typically 1 km in depth. Thus, simulations on a O(100 m) grid largely obviate the need for such parameterizations. By crossing this threshold of O(100m) grid resolution one can begin thinking of large-eddy simulation (LES), wherein the sub-grid scale parameterization have a sounder theoretical foundation. Substantial initiatives have been taken internationally to approach this threshold. For example, Miura et al., 2007 and Mirakawa et al., 2014 approach this threshold by doing global simulations, with (gradually) decreasing grid resolution, to understand the effect of cloud-resolving scales on the general circulation. Our strategy, on the other hand, is to take a big leap forward by fixing the resolution at O(100 m), and gradually increasing the domain size. We believe that breaking this threshold would greatly help in improving the parameterization schemes and reducing the uncertainty in climate predictions. To take this forward, the German Federal Ministry of Education and Research has initiated a project on HD(CP)2 that aims for a limited area LES at resolution O(100 m) using the new unified modeling system ICON (Zängl et al., 2014). In the talk, results from the HD(CP)2 evaluation simulation will be shown that targets high resolution simulation over a small domain around Jülich, Germany. This site is chosen because high resolution HD(CP)2 Observational Prototype Experiment took place in this region from 1.04.2013 to 31.05.2013, in order to critically evaluate the model. Nesting capabilities of ICON is used to gradually increase the resolution from the outermost domain, which is forced from the COSMO-DE data, to the innermost and finest resolution domain centered around Jülich (see Fig. 1 top panel). Furthermore, detailed analyses of the simulation results against the observation data will be presented. A reprsentative figure showing time series of column integrated water vapor (IWV) for both model and observation on 24.04.2013 is shown in bottom panel of Fig. 1.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012EGUGA..14.8490R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012EGUGA..14.8490R"><span>Olive cultivars adaptability in Southern Italy in present and future climate</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Riccardi, M.; Alfieri, S.; Bonfante, A.; Basile, A.; Di Tommasi, P.; Menenti, M.; De Lorenzi, F.</p> <p>2012-04-01</p> <p>The intra-specific biodiversity of agricultural crops is very significant and likely to provide the single major opportunity to cope with the effects of the changing climate on agricultural ecosystems. Assessment of adaptive capacity must rely on quantitative descriptions of plant responses to environmental factors (e.g. soil water availability, temperature). Moreover climate scenario needs to be downscaled to the spatial scale relevant to crop and farm management. Distributed models of crop response to environmental forcing might be used for this purpose, but severely constrained by the very scarce knowledge on variety-specific values of model parameters, thus limiting the potential exploitation of intra-specific biodiversity towards adaptation. We have developed an approach towards this objective that relies on two complementary elements: a)a distributed model of the soil plant atmosphere system to downscale climate scenarios to landscape units, where generic model parameters for each species are used; b)a data base on climatic requirements of as many varieties as feasible for each species relevant to the agricultural production system of a given region. By means of this approach, the adaptability of some olive cultivars was evaluated in a composite (hills and plains) area of Southern Italy (Valle Telesina, Campania Region, about 20.000 ha). The yearly average temperature is 22.5 °C and rainfall ranges between 600 and 900 mm. Two different climate scenarios were considered: current climate (1961-1990) and future climate (2021-2050). Future climate scenarios at low spatial resolution were generated with general circulation models (AOGCM) and down-scaled by means of a statistical model (Tomozeiu et al., 2007). The climate was represented by daily observations of minimum, maximum temperature and precipitation on a regular grid with a spatial resolution of 35 km; 50 realizations were used for future climate. The soil water regime of 45 soil units was described for the two climate scenarios by using an hydrological distributed model (SWAP). For 11 olive cultivars, the yield response function to soil water regime was determined through the re-analysis of experimental data (unpublished or derived from scientific literature). According to these responses, cultivar-specific threshold values of soil water (or evapotranspiration) deficit were defined. The soil water regime calculated by the distributed model was compared with the threshold values to identify cultivars compatible with present and expected climates. The operation is repeated for a set of realizations of each climate scenario. This analysis is performed in a distributed manner, i.e. using the time series for each model grid to assess possible variations in the extent and spatial distribution of cultivated area of olive cultivars. In the study area future climate scenarios predict an increase of monthly minimum and maximum air temperature of about 2°C during the summer (June, July and August) and a reduction of rainfall in autumn. Spatial pattern of cultivars distribution, according their threshold values and soil water regime, was determined in the present and future climate scenarios, thus assessing variations in cultivars adaptability to future climate with respect to the present. Key words: climate change, biodiversity, water availability, yield response. The work was carried out within the Italian national project AGROSCENARI funded by the Ministry for Agricultural, Food and Forest Policies (MIPAAF, D.M. 8608/7303/2008).</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2002AGUFM.A72B0164W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2002AGUFM.A72B0164W"><span>Regional Atmospheric Modeling of Caribbean Climate</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Winter, A.; Gonzalez, J.; Ramirez, N.; Vásquez, R.</p> <p>2002-12-01</p> <p>We use the Regional Atmospheric Modeling System (RAMS) to simulate climatic pattern on the island of Puerto Rico. We hope our analyses will be used to determine the effects of climate change on other Caribbean and tropical islands. Our first experiments were to simulate the precipitation patterns on the island and the urban heat island effect. The main model configuration consists of two grids. Grid 1 covers the entire Caribbean area and has a horizontal resolution of 20 km; it was used mainly for downscaling the large-scale observational data and for boundary nudging. Grid 2 has a horizontal resolution of 5 km and covers the island of Puerto Rico and surrounding waters with the full microphysical parameterization. RAMS was configured to use a vegetation index based on AVHRR data from NOAA 12 and NOAA 14 satellites. From these images we show that the vegetation for the month of January is more abundant than in March. Mean diameters for cloud droplets and raindrops where specified as 35 micrometers and 100 micrometers, respectively. We minimized errors due to clouds by combining data into a monthly composites. We found that experimentation with the microphysical parameterization had a significant impact in the total precipitation amount over the island. RAMS robustly simulated the total accumulated precipitation for the month of April 1998 as well the dependence of the precipitation pattern on the local topography over the island of Puerto Rico. To test the urban heat island effect RAMS was configured using only infrared emission and absorption of water vapor and carbon dioxide without treating clouds or condensate. A soil model was used with ten layers 5 cm thick. The model clearly shows that because of the urban heat island effect San Juan is 5° warmer than the surrounding area. The model results were validated using an extensive network of environmental monitoring instruments from various agencies covering the island of Puerto Rico. The data was converted to a common format using the Java application and made available over the internet using Java Server Pages. Statistical analysis and neural network techniques were employed to improve resolution of sparse lower atmospheric data.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..17.5291B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..17.5291B"><span>Assessment of extreme value distributions for maximum temperature in the Mediterranean area</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Beck, Alexander; Hertig, Elke; Jacobeit, Jucundus</p> <p>2015-04-01</p> <p>Extreme maximum temperatures highly affect the natural as well as the societal environment Heat stress has great effects on flora, fauna and humans and culminates in heat related morbidity and mortality. Agriculture and different industries are severely affected by extreme air temperatures. Even more under climate change conditions, it is necessary to detect potential hazards which arise from changes in the distributional parameters of extreme values, and this is especially relevant for the Mediterranean region which is characterized as a climate change hot spot. Therefore statistical approaches are developed to estimate these parameters with a focus on non-stationarities emerging in the relationship between regional climate variables and their large-scale predictors like sea level pressure, geopotential heights, atmospheric temperatures and relative humidity. Gridded maximum temperature data from the daily E-OBS dataset (Haylock et al., 2008) with a spatial resolution of 0.25° x 0.25° from January 1950 until December 2012 are the predictands for the present analyses. A s-mode principal component analysis (PCA) has been performed in order to reduce data dimension and to retain different regions of similar maximum temperature variability. The grid box with the highest PC-loading represents the corresponding principal component. A central part of the analyses is the model development for temperature extremes under the use of extreme value statistics. A combined model is derived consisting of a Generalized Pareto Distribution (GPD) model and a quantile regression (QR) model which determines the GPD location parameters. The QR model as well as the scale parameters of the GPD model are conditioned by various large-scale predictor variables. In order to account for potential non-stationarities in the predictors-temperature relationships, a special calibration and validation scheme is applied, respectively. Haylock, M. R., N. Hofstra, A. M. G. Klein Tank, E. J. Klok, P. D. Jones, and M. New (2008), A European daily high-resolution gridded data set of surface temperature and precipitation for 1950 - 2006, J. Geophys. Res., 113, D20119, doi:10.1029/2008JD010201.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20060056386&hterms=customization&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D30%26Ntt%3Dcustomization','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20060056386&hterms=customization&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D30%26Ntt%3Dcustomization"><span>Fibonacci Grids</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Swinbank, Richard; Purser, James</p> <p>2006-01-01</p> <p>Recent years have seen a resurgence of interest in a variety of non-standard computational grids for global numerical prediction. The motivation has been to reduce problems associated with the converging meridians and the polar singularities of conventional regular latitude-longitude grids. A further impetus has come from the adoption of massively parallel computers, for which it is necessary to distribute work equitably across the processors; this is more practicable for some non-standard grids. Desirable attributes of a grid for high-order spatial finite differencing are: (i) geometrical regularity; (ii) a homogeneous and approximately isotropic spatial resolution; (iii) a low proportion of the grid points where the numerical procedures require special customization (such as near coordinate singularities or grid edges). One family of grid arrangements which, to our knowledge, has never before been applied to numerical weather prediction, but which appears to offer several technical advantages, are what we shall refer to as "Fibonacci grids". They can be thought of as mathematically ideal generalizations of the patterns occurring naturally in the spiral arrangements of seeds and fruit found in sunflower heads and pineapples (to give two of the many botanical examples). These grids possess virtually uniform and highly isotropic resolution, with an equal area for each grid point. There are only two compact singular regions on a sphere that require customized numerics. We demonstrate the practicality of these grids in shallow water simulations, and discuss the prospects for efficiently using these frameworks in three-dimensional semi-implicit and semi-Lagrangian weather prediction or climate models.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_16");'>16</a></li> <li><a href="#" onclick='return showDiv("page_17");'>17</a></li> <li class="active"><span>18</span></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_18 --> <div id="page_19" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_17");'>17</a></li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li class="active"><span>19</span></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="361"> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013EGUGA..15..209K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013EGUGA..15..209K"><span>High Resolution Regional Climate Modeling for Lebanon, Eastern Mediterranean Coast</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Katurji, Marwan; Soltanzadeh, Iman; Kuhnlein, Meike; Zawar-Reza, Peyman</p> <p>2013-04-01</p> <p>The Eastern Mediterranean coast consists of Lebanon, Palestine, Syria, Israel and a small part of southern Turkey. The region lies between latitudes 30 degrees S and 40 degrees N, which makes its climate affected by westerly propagating wintertime cyclones spinning off mid-latitude troughs (December, January and February), while during summer (June, July and August) the area is strongly affected by the sub-tropical anti-cyclonic belt as a result of the descending air of the Hadley cell circulation system. The area is considered to be in a transitional zone between tropical to mid-latitude climate regimes, and having a coastal topography up to 3000 m in elevation (like in the Western Ranges of Lebanon), which emphasizes the complexity of climate variability in this area under future predictions of climate change. This research incorporates both regional climate numerical simulations, Tropical Rainfall Measuring Mission (TRMM) satellite derived and surface rain gauge rainfall data to evaluate the Regional Climate Model (RegCM) version 4 ability to represent both the mean and variance of observed precipitation in the Eastern Mediterranean Region, with emphasis on the Lebanese coastal terrain and mountain ranges. The adopted methodology involves dynamically down scaling climate data from reanalysis synoptic files through a double nesting procedure. The retrospective analysis of 13 years with both 50 and 10 km spatial resolution allows for the assessment of the model results on both a climate scale and specific high intensity precipitating events. The spatial averaged mean bias error in precipitation rate for the rainy season predicted by RegCM 50 and 10 km resolution grids was 0.13 and 0.004 mm hr-1 respectively. When correlating RegCM and TRMM precipitation rate for the domain covering Lebanon's coastal mountains, the root mean square error (RMSE) for the mean quantities over the 13-year period was only 0.03, while the RMSE for the standard deviation was higher by one order of magnitude. Initial results showed good spatial variability agreement for precipitation with the satellite-derived data with improved results for the 10 km grid resolution setup. Also, results show a larger uncertainty within RegCM for predicting extreme precipitation events. Future work will investigate the ability of RegCM to simulate these extreme deviations in precipitation. The results from this research can be helpful for the better design of future regional climate down scaling predictions under climate change scenarios.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..1813551M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..1813551M"><span>A user-targeted synthesis of the VALUE perfect predictor experiment</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Maraun, Douglas; Widmann, Martin; Gutierrez, Jose; Kotlarski, Sven; Hertig, Elke; Wibig, Joanna; Rössler, Ole; Huth, Radan</p> <p>2016-04-01</p> <p>VALUE is an open European network to validate and compare downscaling methods for climate change research. A key deliverable of VALUE is the development of a systematic validation framework to enable the assessment and comparison of both dynamical and statistical downscaling methods. VALUE's main approach to validation is user-focused: starting from a specific user problem, a validation tree guides the selection of relevant validation indices and performance measures. We consider different aspects: (1) marginal aspects such as mean, variance and extremes; (2) temporal aspects such as spell length characteristics; (3) spatial aspects such as the de-correlation length of precipitation extremes; and multi-variate aspects such as the interplay of temperature and precipitation or scale-interactions. Several experiments have been designed to isolate specific points in the downscaling procedure where problems may occur. Experiment 1 (perfect predictors): what is the isolated downscaling skill? How do statistical and dynamical methods compare? How do methods perform at different spatial scales? Experiment 2 (Global climate model predictors): how is the overall representation of regional climate, including errors inherited from global climate models? Experiment 3 (pseudo reality): do methods fail in representing regional climate change? Here, we present a user-targeted synthesis of the results of the first VALUE experiment. In this experiment, downscaling methods are driven with ERA-Interim reanalysis data to eliminate global climate model errors, over the period 1979-2008. As reference data we use, depending on the question addressed, (1) observations from 86 meteorological stations distributed across Europe; (2) gridded observations at the corresponding 86 locations or (3) gridded spatially extended observations for selected European regions. With more than 40 contributing methods, this study is the most comprehensive downscaling inter-comparison project so far. The results clearly indicate that for several aspects, the downscaling skill varies considerably between different methods. For specific purposes, some methods can therefore clearly be excluded.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.A31C2183S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.A31C2183S"><span>Insights into the physico-chemical evolution of pyrogenic organic carbon emissions from biomass burning using coupled Lagrangian-Eulerian simulations</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Suciu, L. G.; Griffin, R. J.; Masiello, C. A.</p> <p>2017-12-01</p> <p>Wildfires and prescribed burning are important sources of particulate and gaseous pyrogenic organic carbon (PyOC) emissions to the atmosphere. These emissions impact atmospheric chemistry, air quality and climate, but the spatial and temporal variabilities of these impacts are poorly understood, primarily because small and fresh fire plumes are not well predicted by three-dimensional Eulerian chemical transport models due to their coarser grid size. Generally, this results in underestimation of downwind deposition of PyOC, hydroxyl radical reactivity, secondary organic aerosol formation and ozone (O3) production. However, such models are very good for simulation of multiple atmospheric processes that could affect the lifetimes of PyOC emissions over large spatiotemporal scales. Finer resolution models, such as Lagrangian reactive plumes models (or plume-in-grid), could be used to trace fresh emissions at the sub-grid level of the Eulerian model. Moreover, Lagrangian plume models need background chemistry predicted by the Eulerian models to accurately simulate the interactions of the plume material with the background air during plume aging. Therefore, by coupling the two models, the physico-chemical evolution of the biomass burning plumes can be tracked from local to regional scales. In this study, we focus on the physico-chemical changes of PyOC emissions from sub-grid to grid levels using an existing chemical mechanism. We hypothesize that finer scale Lagrangian-Eulerian simulations of several prescribed burns in the U.S. will allow more accurate downwind predictions (validated by airborne observations from smoke plumes) of PyOC emissions (i.e., submicron particulate matter, organic aerosols, refractory black carbon) as well as O3 and other trace gases. Simulation results could be used to optimize the implementation of additional PyOC speciation in the existing chemical mechanism.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3725040','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3725040"><span>Downscaling CMIP5 climate models shows increased tropical cyclone activity over the 21st century</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Emanuel, Kerry A.</p> <p>2013-01-01</p> <p>A recently developed technique for simulating large [O(104)] numbers of tropical cyclones in climate states described by global gridded data is applied to simulations of historical and future climate states simulated by six Coupled Model Intercomparison Project 5 (CMIP5) global climate models. Tropical cyclones downscaled from the climate of the period 1950–2005 are compared with those of the 21st century in simulations that stipulate that the radiative forcing from greenhouse gases increases by over preindustrial values. In contrast to storms that appear explicitly in most global models, the frequency of downscaled tropical cyclones increases during the 21st century in most locations. The intensity of such storms, as measured by their maximum wind speeds, also increases, in agreement with previous results. Increases in tropical cyclone activity are most prominent in the western North Pacific, but are evident in other regions except for the southwestern Pacific. The increased frequency of events is consistent with increases in a genesis potential index based on monthly mean global model output. These results are compared and contrasted with other inferences concerning the effect of global warming on tropical cyclones. PMID:23836646</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.A24F..05L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.A24F..05L"><span>Scaling a Convection-Resolving RCM to Near-Global Scales</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Leutwyler, D.; Fuhrer, O.; Chadha, T.; Kwasniewski, G.; Hoefler, T.; Lapillonne, X.; Lüthi, D.; Osuna, C.; Schar, C.; Schulthess, T. C.; Vogt, H.</p> <p>2017-12-01</p> <p>In the recent years, first decade-long kilometer-scale resolution RCM simulations have been performed on continental-scale computational domains. However, the size of the planet Earth is still an order of magnitude larger and thus the computational implications of performing global climate simulations at this resolution are challenging. We explore the gap between the currently established RCM simulations and global simulations by scaling the GPU accelerated version of the COSMO model to a near-global computational domain. To this end, the evolution of an idealized moist baroclinic wave has been simulated over the course of 10 days with a grid spacing of up to 930 m. The computational mesh employs 36'000 x 16'001 x 60 grid points and covers 98.4% of the planet's surface. The code shows perfect weak scaling up to 4'888 Nodes of the Piz Daint supercomputer and yields 0.043 simulated years per day (SYPD) which is approximately one seventh of the 0.2-0.3 SYPD required to conduct AMIP-type simulations. However, at half the resolution (1.9 km) we've observed 0.23 SYPD. Besides formation of frontal precipitating systems containing embedded explicitly-resolved convective motions, the simulations reveal a secondary instability that leads to cut-off warm-core cyclonic vortices in the cyclone's core, once the grid spacing is refined to the kilometer scale. The explicit representation of embedded moist convection and the representation of the previously unresolved instabilities exhibit a physically different behavior in comparison to coarser-resolution simulations. The study demonstrates that global climate simulations using kilometer-scale resolution are imminent and serves as a baseline benchmark for global climate model applications and future exascale supercomputing systems.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..1818280F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..1818280F"><span>Toward server-side, high performance climate change data analytics in the Earth System Grid Federation (ESGF) eco-system</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Fiore, Sandro; Williams, Dean; Aloisio, Giovanni</p> <p>2016-04-01</p> <p>In many scientific domains such as climate, data is often n-dimensional and requires tools that support specialized data types and primitives to be properly stored, accessed, analysed and visualized. Moreover, new challenges arise in large-scale scenarios and eco-systems where petabytes (PB) of data can be available and data can be distributed and/or replicated (e.g., the Earth System Grid Federation (ESGF) serving the Coupled Model Intercomparison Project, Phase 5 (CMIP5) experiment, providing access to 2.5PB of data for the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5). Most of the tools currently available for scientific data analysis in the climate domain fail at large scale since they: (1) are desktop based and need the data locally; (2) are sequential, so do not benefit from available multicore/parallel machines; (3) do not provide declarative languages to express scientific data analysis tasks; (4) are domain-specific, which ties their adoption to a specific domain; and (5) do not provide a workflow support, to enable the definition of complex "experiments". The Ophidia project aims at facing most of the challenges highlighted above by providing a big data analytics framework for eScience. Ophidia provides declarative, server-side, and parallel data analysis, jointly with an internal storage model able to efficiently deal with multidimensional data and a hierarchical data organization to manage large data volumes ("datacubes"). The project relies on a strong background of high performance database management and OLAP systems to manage large scientific data sets. It also provides a native workflow management support, to define processing chains and workflows with tens to hundreds of data analytics operators to build real scientific use cases. With regard to interoperability aspects, the talk will present the contribution provided both to the RDA Working Group on Array Databases, and the Earth System Grid Federation (ESGF) Compute Working Team. Also highlighted will be the results of large scale climate model intercomparison data analysis experiments, for example: (1) defined in the context of the EU H2020 INDIGO-DataCloud project; (2) implemented in a real geographically distributed environment involving CMCC (Italy) and LLNL (US) sites; (3) exploiting Ophidia as server-side, parallel analytics engine; and (4) applied on real CMIP5 data sets available through ESGF.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018ClDy..tmp.2378M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018ClDy..tmp.2378M"><span>Mixed precipitation occurrences over southern Québec, Canada, under warmer climate conditions using a regional climate model</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Matte, Dominic; Thériault, Julie M.; Laprise, René</p> <p>2018-05-01</p> <p>Winter weather events with temperatures near 0°C are often associated with freezing rain. They can have major impacts on the society by causing power outages and disruptions to the transportation networks. Despite the catastrophic consequences of freezing rain, very few studies have investigated how their occurrences could evolve under climate change. This study aims to investigate the change of freezing rain and ice pellets over southern Québec using regional climate modeling at high resolution. The fifth-generation Canadian Regional Climate Model with climate scenario RCP 8.5 at 0.11° grid mesh was used. The precipitation types such as freezing rain, ice pellets or their combination are diagnosed using five methods (Cantin and Bachand, Bourgouin, Ramer, Czys and, Baldwin). The occurrences of the diagnosed precipitation types for the recent past (1980-2009) are found to be comparable to observations. The projections for the future scenario (2070-2099) suggested a general decrease in the occurrences of mixed precipitation over southern Québec from October to April. This is mainly due to a decrease in long-duration events (≥6 h ). Overall, this study contributes to better understand how the distribution of freezing rain and ice pellets might change in the future using high-resolution regional climate model.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20110008217','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20110008217"><span>Toward GEOS-6, A Global Cloud System Resolving Atmospheric Model</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Putman, William M.</p> <p>2010-01-01</p> <p>NASA is committed to observing and understanding the weather and climate of our home planet through the use of multi-scale modeling systems and space-based observations. Global climate models have evolved to take advantage of the influx of multi- and many-core computing technologies and the availability of large clusters of multi-core microprocessors. GEOS-6 is a next-generation cloud system resolving atmospheric model that will place NASA at the forefront of scientific exploration of our atmosphere and climate. Model simulations with GEOS-6 will produce a realistic representation of our atmosphere on the scale of typical satellite observations, bringing a visual comprehension of model results to a new level among the climate enthusiasts. In preparation for GEOS-6, the agency's flagship Earth System Modeling Framework [JDl] has been enhanced to support cutting-edge high-resolution global climate and weather simulations. Improvements include a cubed-sphere grid that exposes parallelism; a non-hydrostatic finite volume dynamical core, and algorithm designed for co-processor technologies, among others. GEOS-6 represents a fundamental advancement in the capability of global Earth system models. The ability to directly compare global simulations at the resolution of spaceborne satellite images will lead to algorithm improvements and better utilization of space-based observations within the GOES data assimilation system</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.H53E1504B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.H53E1504B"><span>Applying a regional hydrology model to evaluate locations for groundwater replenishment with hillslope runoff under different climate and land use scenarios in an agricultural basin, central coastal California</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Beganskas, S.; Young, K. S.; Fisher, A. T.; Lozano, S.; Harmon, R. E.; Teo, E. K.</p> <p>2017-12-01</p> <p>We are applying a regional hydrology model, Precipitation-Runoff Modeling System (PRMS), to evaluate locations for groundwater replenishment with hillslope runoff in the Pajaro Valley Groundwater Basin (PVGB), central coastal California. Stormwater managed aquifer recharge (MAR) projects collect hillslope runoff before it reaches a stream and infiltrate it into underlying aquifers, improving groundwater supply. The PVGB is a developed agricultural basin where groundwater provides >85% of water for irrigation and municipal needs; stormwater-MAR projects are being considered to address chronic overdraft and saltwater intrusion. We are applying PRMS to assess on a subwatershed scale (10-100 ha; 25-250 acres) where adequate runoff is generated to supply stormwater-MAR in coincidence with suitable conditions for infiltration and recharge. Data from active stormwater-MAR projects in the PVGB provide ground truth for model results. We are also examining how basinwide hydrology responds to changing land use and climate, and the potential implications for future water management. To prepare extensive input files for PRMS models, we developed ArcGIS and Python tools to delineate a topographic model grid and incorporate high-resolution soil, vegetation, and other physical data into each grid region; we also developed tools to analyze and visualize model output. Using historic climate records, we generated dry, normal, and wet climate scenarios, defined as having approximately 25th, 50th, and 75th percentile annual rainfall, respectively. We also generated multiple land use scenarios by replacing developed areas with native vegetation. Preliminary results indicate that many parts of the PVGB generate significant runoff and have suitable infiltration/recharge conditions. Reducing basinwide overdraft by 10% would require collecting less than 5% of total hillslope runoff, even during the dry scenario; this demonstrates that stormwater-MAR could be an effective water management strategy under a broad range of future climate conditions. The tools we have developed inform the placement and design of stormwater-MAR projects that make effective use of limited resources.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFMIN51A3772W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFMIN51A3772W"><span>SciSpark: Highly Interactive and Scalable Model Evaluation and Climate Metrics</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wilson, B. D.; Mattmann, C. A.; Waliser, D. E.; Kim, J.; Loikith, P.; Lee, H.; McGibbney, L. J.; Whitehall, K. D.</p> <p>2014-12-01</p> <p>Remote sensing data and climate model output are multi-dimensional arrays of massive sizes locked away in heterogeneous file formats (HDF5/4, NetCDF 3/4) and metadata models (HDF-EOS, CF) making it difficult to perform multi-stage, iterative science processing since each stage requires writing and reading data to and from disk. We are developing a lightning fast Big Data technology called SciSpark based on ApacheTM Spark. Spark implements the map-reduce paradigm for parallel computing on a cluster, but emphasizes in-memory computation, "spilling" to disk only as needed, and so outperforms the disk-based ApacheTM Hadoop by 100x in memory and by 10x on disk, and makes iterative algorithms feasible. SciSpark will enable scalable model evaluation by executing large-scale comparisons of A-Train satellite observations to model grids on a cluster of 100 to 1000 compute nodes. This 2nd generation capability for NASA's Regional Climate Model Evaluation System (RCMES) will compute simple climate metrics at interactive speeds, and extend to quite sophisticated iterative algorithms such as machine-learning (ML) based clustering of temperature PDFs, and even graph-based algorithms for searching for Mesocale Convective Complexes. The goals of SciSpark are to: (1) Decrease the time to compute comparison statistics and plots from minutes to seconds; (2) Allow for interactive exploration of time-series properties over seasons and years; (3) Decrease the time for satellite data ingestion into RCMES to hours; (4) Allow for Level-2 comparisons with higher-order statistics or PDF's in minutes to hours; and (5) Move RCMES into a near real time decision-making platform. We will report on: the architecture and design of SciSpark, our efforts to integrate climate science algorithms in Python and Scala, parallel ingest and partitioning (sharding) of A-Train satellite observations from HDF files and model grids from netCDF files, first parallel runs to compute comparison statistics and PDF's, and first metrics quantifying parallel speedups and memory & disk usage.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017EGUGA..1915544V','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017EGUGA..1915544V"><span>Efficient in-situ visualization of unsteady flows in climate simulation</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Vetter, Michael; Olbrich, Stephan</p> <p>2017-04-01</p> <p>The simulation of climate data tends to produce very large data sets, which hardly can be processed in classical post-processing visualization applications. Typically, the visualization pipeline consisting of the processes data generation, visualization mapping and rendering is distributed into two parts over the network or separated via file transfer. Within most traditional post-processing scenarios the simulation is done on a supercomputer whereas the data analysis and visualization is done on a graphics workstation. That way temporary data sets with huge volume have to be transferred over the network, which leads to bandwidth bottlenecks and volume limitations. The solution to this issue is the avoidance of temporary storage, or at least significant reduction of data complexity. Within the Climate Visualization Lab - as part of the Cluster of Excellence "Integrated Climate System Analysis and Prediction" (CliSAP) at the University of Hamburg, in cooperation with the German Climate Computing Center (DKRZ) - we develop and integrate an in-situ approach. Our software framework DSVR is based on the separation of the process chain between the mapping and the rendering processes. It couples the mapping process directly to the simulation by calling methods of a parallelized data extraction library, which create a time-based sequence of geometric 3D scenes. This sequence is stored on a special streaming server with an interactive post-filtering option and then played-out asynchronously in a separate 3D viewer application. Since the rendering is part of this viewer application, the scenes can be navigated interactively. In contrast to other in-situ approaches where 2D images are created as part of the simulation or synchronous co-visualization takes place, our method supports interaction in 3D space and in time, as well as fixed frame rates. To integrate in-situ processing based on our DSVR framework and methods in the ICON climate model, we are continuously evolving the data structures and mapping algorithms of the framework to support the ICON model's native grid structures, since DSVR originally was designed for rectilinear grids only. We now have implemented a new output module to ICON to take advantage of the DSVR visualization. The visualization can be configured as most output modules by using a specific namelist and is exemplarily integrated within the non-hydrostatic atmospheric model time loop. With the integration of a DSVR based in-situ pathline extraction within ICON, a further milestone is reached. The pathline algorithm as well as the grid data structures have been optimized for the domain decomposition used for the parallelization of ICON based on MPI and OpenMP. The software implementation and evaluation is done on the supercomputers at DKRZ. In principle, the data complexity is reduced from O(n3) to O(m), where n is the grid resolution and m the number of supporting point of all pathlines. The stability and scalability evaluation is done using Atmospheric Model Intercomparison Project (AMIP) runs. We will give a short introduction in our software framework, as well as a short overview on the implementation and usage of DSVR within ICON. Furthermore, we will present visualization and evaluation results of sample applications.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/1096281','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/1096281"><span>Preparing the Way for New Policy Regarding Adaptation of US Electricity Infrastructure to Climate Change</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Allen, Melissa R</p> <p>2013-10-01</p> <p>The following pages represent the status of policy regarding adaptation of the electric grid to climate change and proposed directions for new policy development. While strides are being made to understand the current climate and to predict hazards it may present to human systems, both the science and the policy remain at present in an analytical state. The policy proposed in this document involves first continued computational modeling of outcomes which will produce a portfolio of options to be considered in light of specific region-related risks. It is proposed that the modeling continue not only until reasonable policy at variousmore » levels of jurisdiction can be derived from its outcome but also on a continuing basis so that as improvements in the understanding of the state and trajectory of climate science along with advancements in technology arise, they can be incorporated into an appropriate and evolving policy.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFM.A51E0115M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFM.A51E0115M"><span>Atmospheric Rivers in VR-CESM: Historical Comparison and Future Projections</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>McClenny, E. E.; Ullrich, P. A.</p> <p>2016-12-01</p> <p>Atmospheric rivers (ARs) are responsible for most of the horizontal vapor transport from the tropics, and bring upwards of half the annual precipitation to midlatitude west coasts. The difference between a drought year and a wet year can come down to 1-2 ARs. Such few events transform an otherwise arid region into one which supports remarkable biodiversity, productive agriculture, and booming human populations. It follows that such a sensitive hydroclimate feature would demand priority in evaluating end-of-century climate runs, and indeed, the AR subfield has grown significantly over the last decade. However, results tend to vary wildly from study to study, raising questions about how to best approach ARs in models. The disparity may result from any number of issues, including the ability for a model to properly resolve a precipitating AR, to the formulation and application of an AR detection algorithm. ARs pose a unique problem in global climate models (GCMs) computationally and physically, because the GCM horizontal grid must be fine enough to resolve coastal mountain range topography and force orographic precipitation. Thus far, most end-of-century projections on ARs have been performed on models whose grids are too coarse to resolve mountain ranges, causing authors to draw conclusions on AR intensity from water vapor content or transport alone. The use of localized grid refinement in the Variable Resolution version of NCAR's Community Earth System Model (VR-CESM) has succeeded in resolving AR landfall. This study applies an integrated water vapor AR detection algorithm to historical and future projections from VR-CESM, with historical ARs validated against NASA's Modern Era Retrospective-Analysis for Research and Applications. Results on end-of-century precipitating AR frequency, intensity, and landfall location will be discussed.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2000PhDT.......195P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2000PhDT.......195P"><span>A variable resolution nonhydrostatic global atmospheric semi-implicit semi-Lagrangian model</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Pouliot, George Antoine</p> <p>2000-10-01</p> <p>The objective of this project is to develop a variable-resolution finite difference adiabatic global nonhydrostatic semi-implicit semi-Lagrangian (SISL) model based on the fully compressible nonhydrostatic atmospheric equations. To achieve this goal, a three-dimensional variable resolution dynamical core was developed and tested. The main characteristics of the dynamical core can be summarized as follows: Spherical coordinates were used in a global domain. A hydrostatic/nonhydrostatic switch was incorporated into the dynamical equations to use the fully compressible atmospheric equations. A generalized horizontal variable resolution grid was developed and incorporated into the model. For a variable resolution grid, in contrast to a uniform resolution grid, the order of accuracy of finite difference approximations is formally lost but remains close to the order of accuracy associated with the uniform resolution grid provided the grid stretching is not too significant. The SISL numerical scheme was implemented for the fully compressible set of equations. In addition, the generalized minimum residual (GMRES) method with restart and preconditioner was used to solve the three-dimensional elliptic equation derived from the discretized system of equations. The three-dimensional momentum equation was integrated in vector-form to incorporate the metric terms in the calculations of the trajectories. Using global re-analysis data for a specific test case, the model was compared to similar SISL models previously developed. Reasonable agreement between the model and the other independently developed models was obtained. The Held-Suarez test for dynamical cores was used for a long integration and the model was successfully integrated for up to 1200 days. Idealized topography was used to test the variable resolution component of the model. Nonhydrostatic effects were simulated at grid spacings of 400 meters with idealized topography and uniform flow. Using a high-resolution topographic data set and the variable resolution grid, sets of experiments with increasing resolution were performed over specific regions of interest. Using realistic initial conditions derived from re-analysis fields, nonhydrostatic effects were significant for grid spacings on the order of 0.1 degrees with orographic forcing. If the model code was adapted for use in a message passing interface (MPI) on a parallel supercomputer today, it was estimated that a global grid spacing of 0.1 degrees would be achievable for a global model. In this case, nonhydrostatic effects would be significant for most areas. A variable resolution grid in a global model provides a unified and flexible approach to many climate and numerical weather prediction problems. The ability to configure the model from very fine to very coarse resolutions allows for the simulation of atmospheric phenomena at different scales using the same code. We have developed a dynamical core illustrating the feasibility of using a variable resolution in a global model.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20140002897','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20140002897"><span>A Storm Surge and Inundation Model of the Back River Watershed at NASA Langley Research Center</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Loftis, Jon Derek; Wang, Harry V.; DeYoung, Russell J.</p> <p>2013-01-01</p> <p>This report on a Virginia Institute for Marine Science project demonstrates that the sub-grid modeling technology (now as part of Chesapeake Bay Inundation Prediction System, CIPS) can incorporate high-resolution Lidar measurements provided by NASA Langley Research Center into the sub-grid model framework to resolve detailed topographic features for use as a hydrological transport model for run-off simulations within NASA Langley and Langley Air Force Base. The rainfall over land accumulates in the ditches/channels resolved via the model sub-grid was tested to simulate the run-off induced by heavy precipitation. Possessing both the capabilities for storm surge and run-off simulations, the CIPS model was then applied to simulate real storm events starting with Hurricane Isabel in 2003. It will be shown that the model can generate highly accurate on-land inundation maps as demonstrated by excellent comparison of the Langley tidal gauge time series data (CAPABLE.larc.nasa.gov) and spatial patterns of real storm wrack line measurements with the model results simulated during Hurricanes Isabel (2003), Irene (2011), and a 2009 Nor'easter. With confidence built upon the model's performance, sea level rise scenarios from the ICCP (International Climate Change Partnership) were also included in the model scenario runs to simulate future inundation cases.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=280780&Lab=NERL&keyword=budget&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50','EPA-EIMS'); return false;" href="https://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=280780&Lab=NERL&keyword=budget&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50"><span>Evaluation of cumulus cloud – radiation interaction effects on air quality –relevant meteorological variables from WRF, from a regional climate perspective</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://oaspub.epa.gov/eims/query.page">EPA Science Inventory</a></p> <p></p> <p></p> <p>Aware only of the resolved, grid-scale clouds, the Weather Research & Forecasting model (WRF) does not consider the interactions between subgrid-scale convective clouds and radiation. One consequence of this omission may be WRF’s overestimation of surface precipitation during sum...</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/1343540','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/1343540"><span></span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Allen, Melissa R.; Aziz, H. M. Abdul; Coletti, Mark A.</p> <p></p> <p>Changing human activity within a geographical location may have significant influence on the global climate, but that activity must be parameterized in such a way as to allow these high-resolution sub-grid processes to affect global climate within that modeling framework. Additionally, we must have tools that provide decision support and inform local and regional policies regarding mitigation of and adaptation to climate change. The development of next-generation earth system models, that can produce actionable results with minimum uncertainties, depends on understanding global climate change and human activity interactions at policy implementation scales. Unfortunately, at best we currently have only limitedmore » schemes for relating high-resolution sectoral emissions to real-time weather, ultimately to become part of larger regions and well-mixed atmosphere. Moreover, even our understanding of meteorological processes at these scales is imperfect. This workshop addresses these shortcomings by providing a forum for discussion of what we know about these processes, what we can model, where we have gaps in these areas and how we can rise to the challenge to fill these gaps.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/15899975','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/15899975"><span>Human-modified temperatures induce species changes: Joint attribution.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Root, Terry L; MacMynowski, Dena P; Mastrandrea, Michael D; Schneider, Stephen H</p> <p>2005-05-24</p> <p>Average global surface-air temperature is increasing. Contention exists over relative contributions by natural and anthropogenic forcings. Ecological studies attribute plant and animal changes to observed warming. Until now, temperature-species connections have not been statistically attributed directly to anthropogenic climatic change. Using modeled climatic variables and observed species data, which are independent of thermometer records and paleoclimatic proxies, we demonstrate statistically significant "joint attribution," a two-step linkage: human activities contribute significantly to temperature changes and human-changed temperatures are associated with discernible changes in plant and animal traits. Additionally, our analyses provide independent testing of grid-box-scale temperature projections from a general circulation model (HadCM3).</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19820041628&hterms=evapotranspiration&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D50%26Ntt%3Devapotranspiration','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19820041628&hterms=evapotranspiration&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D50%26Ntt%3Devapotranspiration"><span>Influence of land-surface evapotranspiration on the earth's climate</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Shukla, J.; Mintz, Y.</p> <p>1982-01-01</p> <p>Land-surface evapotranspiration is shown to strongly influence global fields of rainfall, temperature and motion by calculations using a numerical model of the atmosphere, confirming the general belief in the importance of evapotranspiration-producing surface vegetation for the earth's climate. The current version of the Goddard Laboratory atmospheric general circulation model is used in the present experiment, in which conservation equations for mass, momentum, moisture and energy are expressed in finite-difference form for a spherical grid to calculate (1) surface pressure field evolution, and (2) the wind, temperature, and water vapor fields at nine levels between the surface and a 20 km height.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=1247621','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=1247621"><span>Assessing Ozone-Related Health Impacts under a Changing Climate</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Knowlton, Kim; Rosenthal, Joyce E.; Hogrefe, Christian; Lynn, Barry; Gaffin, Stuart; Goldberg, Richard; Rosenzweig, Cynthia; Civerolo, Kevin; Ku, Jia-Yeong; Kinney, Patrick L.</p> <p>2004-01-01</p> <p>Climate change may increase the frequency and intensity of ozone episodes in future summers in the United States. However, only recently have models become available that can assess the impact of climate change on O3 concentrations and health effects at regional and local scales that are relevant to adaptive planning. We developed and applied an integrated modeling framework to assess potential O3-related health impacts in future decades under a changing climate. The National Aeronautics and Space Administration–Goddard Institute for Space Studies global climate model at 4° × 5° resolution was linked to the Penn State/National Center for Atmospheric Research Mesoscale Model 5 and the Community Multiscale Air Quality atmospheric chemistry model at 36 km horizontal grid resolution to simulate hourly regional meteorology and O3 in five summers of the 2050s decade across the 31-county New York metropolitan region. We assessed changes in O3-related impacts on summer mortality resulting from climate change alone and with climate change superimposed on changes in O3 precursor emissions and population growth. Considering climate change alone, there was a median 4.5% increase in O3-related acute mortality across the 31 counties. Incorporating O3 precursor emission increases along with climate change yielded similar results. When population growth was factored into the projections, absolute impacts increased substantially. Counties with the highest percent increases in projected O3 mortality spread beyond the urban core into less densely populated suburban counties. This modeling framework provides a potentially useful new tool for assessing the health risks of climate change. PMID:15531442</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_17");'>17</a></li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li class="active"><span>19</span></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_19 --> <div id="page_20" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li class="active"><span>20</span></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="381"> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/1436990','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/1436990"><span></span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Donner, Leo J.; O'Brien, Travis A.; Rieger, Daniel</p> <p></p> <p>Both climate forcing and climate sensitivity persist as stubborn uncertainties limiting the extent to which climate models can provide actionable scientific scenarios for climate change. A key, explicit control on cloud-aerosol interactions, the largest uncertainty in climate forcing, is the vertical velocity of cloud-scale updrafts. Model-based studies of climate sensitivity indicate that convective entrainment, which is closely related to updraft speeds, is an important control on climate sensitivity. Updraft vertical velocities also drive many physical processes essential to numerical weather prediction. Vertical velocities and their role in atmospheric physical processes have been given very limited attention in models for climatemore » and numerical weather prediction. The relevant physical scales range down to tens of meters and are thus frequently sub-grid and require parameterization. Many state-of-science convection parameterizations provide mass fluxes without specifying vertical velocities, and parameterizations which do provide vertical velocities have been subject to limited evaluation against what have until recently been scant observations. Atmospheric observations imply that the distribution of vertical velocities depends on the areas over which the vertical velocities are averaged. Distributions of vertical velocities in climate models may capture this behavior, but it has not been accounted for when parameterizing cloud and precipitation processes in current models. New observations of convective vertical velocities offer a potentially promising path toward developing process-level cloud models and parameterizations for climate and numerical weather prediction. Taking account of scale-dependence of resolved vertical velocities offers a path to matching cloud-scale physical processes and their driving dynamics more realistically, with a prospect of reduced uncertainty in both climate forcing and sensitivity.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20010069265','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20010069265"><span>MODIS Snow-Cover Products</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Hall, Dorothy K.; Riggs, George A.; Salomonson, Vinvent V.; DiGirolamo, Nicolo; Bayr, Klaus J.; Houser, Paul (Technical Monitor)</p> <p>2001-01-01</p> <p>On December 18, 1999, the Terra satellite was launched with a complement of five instruments including the Moderate Resolution Imaging Spectroradiometer (MODIS). Many geophysical products are derived from MODIS data including global snow-cover products. These products have been available through the National Snow and Ice Data Center (NSIDC) Distributed Active Archive Center (DAAC) since September 13, 2000. MODIS snow-cover products represent potential improvement to the currently available operation products mainly because the MODIS products are global and 500-m resolution, and have the capability to separate most snow and clouds. Also the snow-mapping algorithms are automated which means that a consistent data set is generated for long-term climates studies that require snow-cover information. Extensive quality assurance (QA) information is stored with the product. The snow product suite starts with a 500-m resolution swath snow-cover map which is gridded to the Integerized Sinusoidal Grid to produce daily and eight-day composite tile products. The sequence then proceeds to a climate-modeling grid product at 5-km spatial resolution, with both daily and eight-day composite products. A case study from March 6, 2000, involving MODIS data and field and aircraft measurements, is presented. Near-term enhancements include daily snow albedo and fractional snow cover.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..18.3125L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..18.3125L"><span>Two-way against one-way nesting for climate downscaling in Europe and the Mediterranean region using LMDZ4</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Li, Shan; Li, Laurent; Le Treut, Hervé</p> <p>2016-04-01</p> <p>In the 21st century, the estimated surface temperature warming projected by General Circulation Models (GCMs) is between 0.3 and 4.8 °C, depending on the scenario considered. GCMs exhibit a good representation of climate on a global scale, but they are not able to reproduce regional climate processes with the same level of accuracy. Society and policymakers need model projections to define climate change adaptation and mitigation policies on a global, regional and local scale. Climate downscaling is mostly conducted with a regional model nested into the outputs of a global model. This one-way nesting approach is generally used in the climate community without feedbacks from Regional Climate Models (RCMs) to GCMs. This lack of interaction between the two models may affect regional modes of variability, in particular those with a boundary conflict. The objective of this study is to evaluate a two-way nesting configuration that makes an interactive coupling between the RCM and the GCM, an approach against the traditional configuration of one-way nesting system. An additional aim of this work is to examine if the two-way nesting system can improve the RCM performance. The atmospheric component of the IPSL integrated climate model (LMDZ) is configured at both regional (LMDZ-regional) and global (LMDZ-global) scales. The two models have the same configuration for the dynamical framework and the physical forcings. The climatology values of sea surface temperature (SST) are prescribed for the two models. The stretched-grid of LMDZ-global is applied to a region defined by Europe, the Mediterranean, North Africa and Western North Atlantic. To ensure a good statistical significance of results, all simulations last at least 80 years. The nesting process of models is performed by a relaxation procedure of a time scale of 90 minutes. In the case of two-way nesting, the exchange between the two models is every two hours. The relaxation procedure induces a boundary conflict, particularly in the eastern boundary for temperature and geopotential height. A correlation analysis on the synoptic scale evaluates the relationship between the GCM and the RCM. The beginning of the simulations shows a great consistency of the two models. When dominant dynamics apply, RCM inherits most of the GCM signal with a consistent spatial structure. On the contrary, when the atmospheric circulation is weak, there are not that many effects transferred from the GCM to the RCM. When the RCM has its own dynamics, the boundary conflict is more pronounced. Winter season is chosen for the two-way nesting test due to the predominant role of the atmospheric dynamics in winter. The new approach of a two-way nesting system reduces boundary bias, having a influence in some cases in climate model projections. The effect of two-way nesting is enhanced when using a finer grid.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012AGUFM.A41I0106M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012AGUFM.A41I0106M"><span>A Dynamic Evaluation Of A Model And An Estimate Of The Air Quality And Regional Climate Impacts Of Enhanced Solar Power Generation</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Millstein, D.; Brown, N. J.; Zhai, P.; Menon, S.</p> <p>2012-12-01</p> <p>We use the WRF/Chem model (Weather Research and Forecasting model with chemistry) and pollutant emissions based on the EPA National Emission Inventories from 2005 and 2008 to model regional climate and air quality over the continental United States. Additionally, 2030 emission scenarios are developed to investigate the effects of future enhancements to solar power generation. Modeling covered 6 summer and 6 winter weeks each year. We model feedback between aerosols and meteorology and thus capture direct and indirect aerosol effects. The grid resolution is 25 km and includes no nesting. Between 2005 and 2008 significant emission reductions were reported in the National Emission Inventory. The 2008 weekday emissions over the continental U.S. of SO2 and NO were reduced from 2005 values by 28% and 16%, respectively. Emission reductions of this magnitude are similar in scale to the potential emission reductions from various energy policy initiatives. By evaluating modeled and observed air quality changes from 2005 to 2008, we analyze how well the model represents the effects of historical emission changes. We also gain insight into how well the model might predict the effects of future emission changes. In addition to direct comparisons of model outputs to ground and satellite observations, we compare observed differences between 2005 and 2008 to corresponding modeled differences. Modeling was extended to future scenarios (2030) to simulate air quality and regional climate effects of large-scale adoption of solar power. The 2030-year was selected to allow time for development of solar generation infrastructure. The 2030 emission scenario was scaled, with separate factors for different economic sectors, from the 2008 National Emissions Inventory. The changes to emissions caused by the introduction of large-scale solar power (here assumed to be 10% of total energy generation) are based on results from a parallel project that used an electricity grid model applied over multiple regions across the country. The regional climate and air quality effects of future large-scale solar power adoption are analyzed in the context of uncertainty quantified by the dynamic evaluation of the historical (2005 and 2008) WRF/Chem simulations.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016GMD.....9..383E','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016GMD.....9..383E"><span>Distributed visualization of gridded geophysical data: the Carbon Data Explorer, version 0.2.3</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Endsley, K. A.; Billmire, M. G.</p> <p>2016-01-01</p> <p>Due to the proliferation of geophysical models, particularly climate models, the increasing resolution of their spatiotemporal estimates of Earth system processes, and the desire to easily share results with collaborators, there is a genuine need for tools to manage, aggregate, visualize, and share data sets. We present a new, web-based software tool - the Carbon Data Explorer - that provides these capabilities for gridded geophysical data sets. While originally developed for visualizing carbon flux, this tool can accommodate any time-varying, spatially explicit scientific data set, particularly NASA Earth system science level III products. In addition, the tool's open-source licensing and web presence facilitate distributed scientific visualization, comparison with other data sets and uncertainty estimates, and data publishing and distribution.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018ThApC.132.1153L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018ThApC.132.1153L"><span>Towards bridging the gap between climate change projections and maize producers in South Africa</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Landman, Willem A.; Engelbrecht, Francois; Hewitson, Bruce; Malherbe, Johan; van der Merwe, Jacobus</p> <p>2018-05-01</p> <p>Multi-decadal regional projections of future climate change are introduced into a linear statistical model in order to produce an ensemble of austral mid-summer maximum temperature simulations for southern Africa. The statistical model uses atmospheric thickness fields from a high-resolution (0.5° × 0.5°) reanalysis-forced simulation as predictors in order to develop a linear recalibration model which represents the relationship between atmospheric thickness fields and gridded maximum temperatures across the region. The regional climate model, the conformal-cubic atmospheric model (CCAM), projects maximum temperatures increases over southern Africa to be in the order of 4 °C under low mitigation towards the end of the century or even higher. The statistical recalibration model is able to replicate these increasing temperatures, and the atmospheric thickness-maximum temperature relationship is shown to be stable under future climate conditions. Since dry land crop yields are not explicitly simulated by climate models but are sensitive to maximum temperature extremes, the effect of projected maximum temperature change on dry land crops of the Witbank maize production district of South Africa, assuming other factors remain unchanged, is then assessed by employing a statistical approach similar to the one used for maximum temperature projections.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://pubs.er.usgs.gov/publication/70004988','USGSPUBS'); return false;" href="https://pubs.er.usgs.gov/publication/70004988"><span>Projections and downscaling of 21st century temperatures, precipitation, radiative fluxes and winds for the southwestern US, with focus on the Lake Tahoe basin</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Dettinger, Michael D.</p> <p>2013-01-01</p> <p>Recent projections of global climate changes in response to increasing greenhouse-gas concentrations in the atmosphere include warming in the Southwestern US and, especially, in the vicinity of Lake Tahoe of from about +3°C to +6°C by end of century and changes in precipitation on the order of 5-10 % increases or (more commonly) decreases, depending on the climate model considered. Along with these basic changes, other climate variables like solar insolation, downwelling (longwave) radiant heat, and winds may change. Together these climate changes may result in changes in the hydrology of the Tahoe basin and potential changes in lake overturning and ecological regimes. Current climate projections, however, are generally spatially too coarse (with grid cells separated by 1 to 2° latitude and longitude) for direct use in assessments of the vulnerabilities of the much smaller Tahoe basin. Thus, daily temperatures, precipitation, winds, and downward radiation fluxes from selected global projections have been downscaled by a statistical method called the constructed-analogues method onto 10 to 12 km grids over the Southwest and especially over Lake Tahoe. Precipitation, solar insolation and winds over the Tahoe basin change only moderately (and with indeterminate signs) in the downscaled projections, whereas temperatures and downward longwave fluxes increase along with imposed increases in global greenhouse-gas concentrations.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFMGC53F..05M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFMGC53F..05M"><span>The Economic Impacts of Climate Change on Agriculture: New Damage Functions from a Meta-Analsis and the GGCMI</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Moore, F. C.; Baldos, U. L. C.; Hertel, T. W.; Diaz, D.</p> <p>2016-12-01</p> <p>Substantial advances have been made in recent years in understanding the effects of climate change on agriculture, but this is not currently represented in economic models used to quantify the benefits of reducing greenhouse gas emissions. In fact, the science regarding climate change impacts on agriculture in these models dates to the early 1990s or before. In this paper we derive new economic damage functions for the agricultural sector based on two methods for aggregating current scientific understanding of the impacts of warming on yields. We first present a new meta-analysis based on a review of the agronomic literature performed for the IPCC 5th Assessment Report and compare results from this approach with findings from the AgMIP Global Gridded Crop Model Intercomparison (GGCMI). We find yield impacts implied by the meta-analysis are generally more negative than those from the GGCMI, particularly at higher latitudes, but show substantial agreement in many areas. We then use both yield products as input to the Global Trade Analysis Project (GTAP) computable general equilibrium (CGE) model in order to estimate the welfare consequences of these yield shocks and to produce two new economic damage functions. These damage functions are consistently more negative than the current representation of agricultural damages in Integrated Asessment Models (IAMs), in some cases substantially so. Replacing the existing damage functions with those based on more recent science increases the social cost of carbon (SCC) by between 43% (GGCMI) and 143% (Meta-Analysis). In addition to presenting a new mutli-crop, multi-model gridded yield impact prouct that complements the GGCMI, this is also the first end-to-end study that directly links the biophysical impacts of climate change to the SCC, something we believe essential to improving the integrity of IAMs going forward.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUSM.A42A..05L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUSM.A42A..05L"><span>Current and future background ozone simulations for Mexico using a multi-scale regional climate modeling system</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Lamb, B. K.; Gonzalez Abraham, R.; Avise, J. C.; Chung, S. H.; Salathe, E. P.; Zhang, Y.; Guenther, A. B.; Wiedinmyer, C.; Duhl, T.; Streets, D. G.</p> <p>2013-05-01</p> <p>Global change will clearly have a significant impact on the environment. Among the concerns for future air quality in North America, intercontinental transport of pollution has become increasingly important. In this study, we examined the effect of projected changes in Asian emissions and emissions from lightning and wildfires to produce ozone background concentrations within Mexico and the continental US. This provides a basis for developing an understanding of North American background levels and how they may change in the future. Meteorological fields were downscaled from the results of the ECHAM5 global climate model using the Weather Research Forecast (WRF) model. Two nested domains were employed, one covering most of the Northern Hemisphere from eastern Asia to North America using 220 km grid cells (semi-hemispheric domain) and one covering the continental US and northern Mexico using 36 km grid cells. Meteorological results from WRF were used to drive the MEGAN biogenic emissions model, the SMOKE emissions processing tool, and the CMAQ chemical transport model to predict ozone concentrations for current (1995-2004) and future (2045-2054) summertime conditions. The MEGAN model was used to calculate biogenic emissions for all simulations. For the semi-hemispheric domain, year 2000 global emissions of gases (ozone precursors) from anthropogenic (outside of North America), natural, and biomass burning sources from the POET and EDGAR emission inventories were used. The global tabulation for black and organic carbon (BC and OC respectively) was obtained from Bond et al. (2004) For the future decade, the current emissions were projected to the year 2050 following the Intergovernmental Panel for Climate Change (IPCC) A1B emission scenario. Anthropogenic emissions from the US, Canada, and Mexico were omitted so that only global background concentrations, and local biogenic, wildfire, and lightning emissions were treated. In this paper, we focus on background ozone levels in Mexico due to changes in future climate, local biogenic emissions and global emissions.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFMGC33C1088H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFMGC33C1088H"><span>Assessment of Climate Suitability of Maize in South Korea</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Hyun, S.; Choi, D.; Seo, B.</p> <p>2017-12-01</p> <p>Assessing suitable areas for crops would be useful to design alternate cropping systems as an adaptation option to climate change adaptation. Although suitable areas could be identified by using a crop growth model, it would require a number of input parameters including cultivar and soil. Instead, a simple climate suitability model, e.g., EcoCrop model, could be used for an assessment of climate suitability for a major grain crop. The objective of this study was to assess of climate suitability for maize using the EcoCrop model under climate change conditions in Korea. A long term climate data from 2000 - 2100 were compiled from weather data source. The EcoCrop model implemented in R was used to determine climate suitability index at each grid cell. Overall, the EcoCrop model tended to identify suitable areas for maize production near the coastal areas whereas the actual major production areas located in inland areas. It is likely that the discrepancy between assessed and actual crop production areas would result from the socioeconomic aspects of maize production. Because the price of maize is considerably low, maize has been grown in an area where moisture and temperature conditions would be less than optimum. In part, a simple algorithm to predict climate suitability for maize would caused a relatively large error in climate suitability assessment under the present climate conditions. In 2050s, the climate suitability for maize increased in a large areas in southern and western part of Korea. In particular, the plain areas near the coastal region had considerably greater suitability index in the future compared with mountainous areas. The expansion of suitable areas for maize would help crop production policy making such as the allocation of rice production area for other crops due to considerably less demand for the rice in Korea.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018JCoPh.361..331P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018JCoPh.361..331P"><span>Computation at a coordinate singularity</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Prusa, Joseph M.</p> <p>2018-05-01</p> <p>Coordinate singularities are sometimes encountered in computational problems. An important example involves global atmospheric models used for climate and weather prediction. Classical spherical coordinates can be used to parameterize the manifold - that is, generate a grid for the computational spherical shell domain. This particular parameterization offers significant benefits such as orthogonality and exact representation of curvature and connection (Christoffel) coefficients. But it also exhibits two polar singularities and at or near these points typical continuity/integral constraints on dependent fields and their derivatives are generally inadequate and lead to poor model performance and erroneous results. Other parameterizations have been developed that eliminate polar singularities, but problems of weaker singularities and enhanced grid noise compared to spherical coordinates (away from the poles) persist. In this study reparameterization invariance of geometric objects (scalars, vectors and the forms generated by their covariant derivatives) is utilized to generate asymptotic forms for dependent fields of interest valid in the neighborhood of a pole. The central concept is that such objects cannot be altered by the metric structure of a parameterization. The new boundary conditions enforce symmetries that are required for transformations of geometric objects. They are implemented in an implicit polar filter of a structured grid, nonhydrostatic global atmospheric model that is simulating idealized Held-Suarez flows. A series of test simulations using different configurations of the asymptotic boundary conditions are made, along with control simulations that use the default model numerics with no absorber, at three different grid sizes. Typically the test simulations are ∼ 20% faster in wall clock time than the control-resulting from a decrease in noise at the poles in all cases. In the control simulations adverse numerical effects from the polar singularity are observed to increase with grid resolution. In contrast, test simulations demonstrate robust polar behavior independent of grid resolution.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012AGUFM.A41I0103H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012AGUFM.A41I0103H"><span>Effects of Implementing Subgrid-Scale Cloud-Radiation Interactions in a Regional Climate Model</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Herwehe, J. A.; Alapaty, K.; Otte, T.; Nolte, C. G.</p> <p>2012-12-01</p> <p>Interactions between atmospheric radiation, clouds, and aerosols are the most important processes that determine the climate and its variability. In regional scale models, when used at relatively coarse spatial resolutions (e.g., larger than 1 km), convective cumulus clouds need to be parameterized as subgrid-scale clouds. Like many groups, our regional climate modeling group at the EPA uses the Weather Research & Forecasting model (WRF) as a regional climate model (RCM). One of the findings from our RCM studies is that the summertime convective systems simulated by the WRF model are highly energetic, leading to excessive surface precipitation. We also found that the WRF model does not consider the interactions between convective clouds and radiation, thereby omitting an important process that drives the climate. Thus, the subgrid-scale cloudiness associated with convective clouds (from shallow cumuli to thunderstorms) does not exist and radiation passes through the atmosphere nearly unimpeded, potentially leading to overly energetic convection. This also has implications for air quality modeling systems that are dependent upon cloud properties from the WRF model, as the failure to account for subgrid-scale cloudiness can lead to problems such as the underrepresentation of aqueous chemistry processes within clouds and the overprediction of ozone from overactive photolysis. In an effort to advance the climate science of the cloud-aerosol-radiation (CAR) interactions in RCM systems, as a first step we have focused on linking the cumulus clouds with the radiation processes. To this end, our research group has implemented into WRF's Kain-Fritsch (KF) cumulus parameterization a cloudiness formulation that is widely used in global earth system models (e.g., CESM/CAM5). Estimated grid-scale cloudiness and associated condensate are adjusted to account for the subgrid clouds and then passed to WRF's Rapid Radiative Transfer Model - Global (RRTMG) radiation schemes to affect the shortwave and longwave radiative processes. To evaluate the effects of implementing the subgrid-scale cloud-radiation interactions on WRF regional climate simulations, a three-year study period (1988-1990) was simulated over the CONUS using two-way nested domains with 108 km and 36 km horizontal grid spacing, without and with the cumulus feedbacks to radiation, and without and with some form of four dimensional data assimilation (FDDA). Initial and lateral boundary conditions (as well as data for the FDDA, when enabled) were supplied from downscaled NCEP-NCAR Reanalysis II (R2) data sets. Evaluation of the simulation results will be presented comparing regional surface precipitation and temperature statistics with North American Regional Reanalysis (NARR) data and Climate Forecast System Reanalysis (CFSR) data, respectively, as well as comparison with available surface radiation (SURFRAD) and satellite (CERES) observations. This research supports improvements in the EPA's WRF-CMAQ modeling system, leading to better predictions of present and future air quality and climate interactions in order to protect human health and the environment.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFMGC41C1022J','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFMGC41C1022J"><span>Projecting Wind Energy Potential Under Climate Change with Ensemble of Climate Model Simulations</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Jain, A.; Shashikanth, K.; Ghosh, S.; Mukherjee, P. P.</p> <p>2013-12-01</p> <p>Recent years have witnessed an increasing global concern over energy sustainability and security, triggered by a number of issues, such as (though not limited to): fossil fuel depletion, energy resource geopolitics, economic efficiency versus population growth debate, environmental concerns and climate change. Wind energy is a renewable and sustainable form of energy in which wind turbines convert the kinetic energy of wind into electrical energy. Global warming and differential surface heating may significantly impact the wind velocity and hence the wind energy potential. Sustainable design of wind mills requires understanding the impacts of climate change on wind energy potential, which we evaluate here with multiple General Circulation Models (GCMs). GCMs simulate the climate variables globally considering the greenhouse emission scenarios provided as Representation Concentration path ways (RCPs). Here we use new generation climate model outputs obtained from Coupled model Intercomparison Project 5(CMIP5). We first compute the wind energy potential with reanalysis data (NCEP/ NCAR), at a spatial resolution of 2.50, where the gridded data is fitted to Weibull distribution and with the Weibull parameters, the wind energy densities are computed at different grids. The same methodology is then used, to CMIP5 outputs (resultant of U-wind and V-wind) of MRI, CMCC, BCC, CanESM, and INMCM4 for historical runs. This is performed separately for four seasons globally, MAM, JJA, SON and DJF. We observe the muti-model average of wind energy density for historic period has significant bias with respect to that of reanalysis product. Here we develop a quantile based superensemble approach where GCM quantiles corresponding to selected CDF values are regressed to reanalysis data. It is observed that this regression approach takes care of both, bias in GCMs and combination of GCMs. With superensemble, we observe that the historical wind energy density resembles quite well with reanalysis/ observed output. We apply the same for future under RCP scenarios. We observe spatially and temporally varying global change of wind energy density. The underlying assumption is that the regression relationship will also hold good for future. The results highlight the needs to change the design standards of wind mills at different locations, considering climate change and at the same time the requirement of height modifications for existing mills to produce same energy in future.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://cfpub.epa.gov/si/si_public_record_report.cfm?direntryid=336796&keyword=ecosystems&subject=ecosystems%20research&showcriteria=2&fed_org_id=111&datebeginpublishedpresented=08/08/2012&dateendpublishedpresented=08/08/2017&sortby=pubdateyear','PESTICIDES'); return false;" href="https://cfpub.epa.gov/si/si_public_record_report.cfm?direntryid=336796&keyword=ecosystems&subject=ecosystems%20research&showcriteria=2&fed_org_id=111&datebeginpublishedpresented=08/08/2012&dateendpublishedpresented=08/08/2017&sortby=pubdateyear"><span>Continental-Scale Estimates of Runoff Using Future Climate ...</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.epa.gov/pesticides/search.htm">EPA Pesticide Factsheets</a></p> <p></p> <p></p> <p>Recent runoff events have had serious repercussions to both natural ecosystems and human infrastructure. Understanding how shifts in storm event intensities are expected to change runoff responses are valuable for local, regional, and landscape planning. To address this challenge, relative changes in runoff using predicted future climate conditions were estimated over different biophysical areas for the CONterminous U.S. (CONUS). Runoff was estimated using the Curve Number (CN) developed by the USDA Soil Conservation Service (USDA, 1986). A seamless gridded dataset representing a CN for existing land use/land cover (LULC) across the CONUS was used along with two different storm event grids created specifically for this effort. The two storm event grids represent a 2- and a 100-year, 24-hour storm event under current climate conditions. The storm event grids were generated using a compilation of county-scale Texas USGS Intensity-Duration-Frequency (IDF) data (provided by William Asquith, USGS, Lubbock, Texas), and NOAA Atlas-2 and NOAA Atlas-14 gridded data sets. Future CN runoff was predicted using extreme storm events grids created using a method based on Kao and Ganguly (2011) where precipitation extremes reflect changes in saturated water vapor pressure of the atmosphere in response to temperature changes. The Clausius-Clapeyron relationship establishes that the total water vapor mass of fully saturated air increases with increasing temperature, leading to</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3655130','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3655130"><span>Forecasting the Future Risk of Barmah Forest Virus Disease under Climate Change Scenarios in Queensland, Australia</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Naish, Suchithra; Mengersen, Kerrie; Hu, Wenbiao; Tong, Shilu</p> <p>2013-01-01</p> <p>Background Mosquito-borne diseases are climate sensitive and there has been increasing concern over the impact of climate change on future disease risk. This paper projected the potential future risk of Barmah Forest virus (BFV) disease under climate change scenarios in Queensland, Australia. Methods/Principal Findings We obtained data on notified BFV cases, climate (maximum and minimum temperature and rainfall), socio-economic and tidal conditions for current period 2000–2008 for coastal regions in Queensland. Grid-data on future climate projections for 2025, 2050 and 2100 were also obtained. Logistic regression models were built to forecast the otential risk of BFV disease distribution under existing climatic, socio-economic and tidal conditions. The model was applied to estimate the potential geographic distribution of BFV outbreaks under climate change scenarios. The predictive model had good model accuracy, sensitivity and specificity. Maps on potential risk of future BFV disease indicated that disease would vary significantly across coastal regions in Queensland by 2100 due to marked differences in future rainfall and temperature projections. Conclusions/Significance We conclude that the results of this study demonstrate that the future risk of BFV disease would vary across coastal regions in Queensland. These results may be helpful for public health decision making towards developing effective risk management strategies for BFV disease control and prevention programs in Queensland. PMID:23690959</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010EGUGA..12..623K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010EGUGA..12..623K"><span>Estimation of ozone dry deposition over Europe for the period 2071-2100</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Komjáthy, Eszter; Gelybó, Györgyi; László Lagzi, István.; Mészáros, Róbert</p> <p>2010-05-01</p> <p>Ozone in the lower troposphere is a phytotoxic air pollutant which can cause injury to plant tissues, causing reduction in plant growth and productivity. In the last decades, several investigations have been carried out for the purpose to estimate ozone load over different surface types. At the same time, the changes of atmospheric variables as well as surface/vegetation parameters due to the global climate change could also strongly modify both temporal and spatial variations of ozone load over Europe. In this study, the possible effects of climate change on ozone deposition are analyzed. Using a sophisticated deposition model, ozone deposition was estimated on a regular grid over Europe for the period 2071-2100. Our aim is to determine the uncertainties and the possible degree of change in ozone deposition velocity as an important predictor of total ozone load using climate data from multiple climate models and runs. For these model calculations, results of the PRUDENCE (Predicting of Regional Scenarios and Uncertainties for Defining European Climate Change Risks and Effects) climate prediction project were used. As a first step, seasonal variations of ozone deposition over different vegetation types in case of different climate scenarios are presented in this study. Besides model calculations, in the frame of a sensitivity analyses, the effects of surface/vegetation parameters (e.g. leaf area index or stomatal resistance) on ozone deposition under a modified climate regime have also been analyzed.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014EGUGA..1612369D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014EGUGA..1612369D"><span>Convergence in France facing Big Data era and Exascale challenges for Climate Sciences</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Denvil, Sébastien; Dufresne, Jean-Louis; Salas, David; Meurdesoif, Yann; Valcke, Sophie; Caubel, Arnaud; Foujols, Marie-Alice; Servonnat, Jérôme; Sénési, Stéphane; Derouillat, Julien; Voury, Pascal</p> <p>2014-05-01</p> <p>The presentation will introduce a french national project : CONVERGENCE that has been funded for four years. This project will tackle big data and computational challenges faced by climate modeling community in HPC context. Model simulations are central to the study of complex mechanisms and feedbacks in the climate system and to provide estimates of future and past climate changes. Recent trends in climate modelling are to add more physical components in the modelled system, increasing the resolution of each individual component and the more systematic use of large suites of simulations to address many scientific questions. Climate simulations may therefore differ in their initial state, parameter values, representation of physical processes, spatial resolution, model complexity, and degree of realism or degree of idealisation. In addition, there is a strong need for evaluating, improving and monitoring the performance of climate models using a large ensemble of diagnostics and better integration of model outputs and observational data. High performance computing is currently reaching the exascale and has the potential to produce this exponential increase of size and numbers of simulations. However, post-processing, analysis, and exploration of the generated data have stalled and there is a strong need for new tools to cope with the growing size and complexity of the underlying simulations and datasets. Exascale simulations require new scalable software tools to generate, manage and mine those simulations ,and data to extract the relevant information and to take the correct decision. The primary purpose of this project is to develop a platform capable of running large ensembles of simulations with a suite of models, to handle the complex and voluminous datasets generated, to facilitate the evaluation and validation of the models and the use of higher resolution models. We propose to gather interdisciplinary skills to design, using a component-based approach, a specific programming environment for scalable scientific simulations and analytics, integrating new and efficient ways of deploying and analysing the applications on High Performance Computing (HPC) system. CONVERGENCE, gathering HPC and informatics expertise that cuts across the individual partners and the broader HPC community, will allow the national climate community to leverage information technology (IT) innovations to address its specific needs. Our methodology consists in developing an ensemble of generic elements needed to run the French climate models with different grids and different resolution, ensuring efficient and reliable execution of these models, managing large volume and number of data and allowing analysis of the results and precise evaluation of the models. These elements include data structure definition and input-output (IO), code coupling and interpolation, as well as runtime and pre/post-processing environments. A common data and metadata structure will allow transferring consistent information between the various elements. All these generic elements will be open source and publicly available. The IPSL-CM and CNRM-CM climate models will make use of these elements that will constitute a national platform for climate modelling. This platform will be used, in its entirety, to optimise and tune the next version of the IPSL-CM model and to develop a global coupled climate model with a regional grid refinement. It will also be used, at least partially, to run ensembles of the CNRM-CM model at relatively high resolution and to run a very-high resolution prototype of this model. The climate models we developed are already involved in many international projects. For instance we participate to the CMIP (Coupled Model Intercomparison Project) project that is very demanding but has a high visibility: its results are widely used and are in particular synthesised in the IPCC (Intergovernmental Panel on Climate Change) assessment reports. The CONVERGENCE project will constitute an invaluable step for the French climate community to prepare and better contribute to the next phase of the CMIP project.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017TCry...11.1625T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017TCry...11.1625T"><span>Snow water equivalent in the Alps as seen by gridded data sets, CMIP5 and CORDEX climate models</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Terzago, Silvia; von Hardenberg, Jost; Palazzi, Elisa; Provenzale, Antonello</p> <p>2017-07-01</p> <p>The estimate of the current and future conditions of snow resources in mountain areas would require reliable, kilometre-resolution, regional-observation-based gridded data sets and climate models capable of properly representing snow processes and snow-climate interactions. At the moment, the development of such tools is hampered by the sparseness of station-based reference observations. In past decades passive microwave remote sensing and reanalysis products have mainly been used to infer information on the snow water equivalent distribution. However, the investigation has usually been limited to flat terrains as the reliability of these products in mountain areas is poorly characterized.This work considers the available snow water equivalent data sets from remote sensing and from reanalyses for the greater Alpine region (GAR), and explores their ability to provide a coherent view of the snow water equivalent distribution and climatology in this area. Further we analyse the simulations from the latest-generation regional and global climate models (RCMs, GCMs), participating in the Coordinated Regional Climate Downscaling Experiment over the European domain (EURO-CORDEX) and in the Fifth Coupled Model Intercomparison Project (CMIP5) respectively. We evaluate their reliability in reproducing the main drivers of snow processes - near-surface air temperature and precipitation - against the observational data set EOBS, and compare the snow water equivalent climatology with the remote sensing and reanalysis data sets previously considered. We critically discuss the model limitations in the historical period and we explore their potential in providing reliable future projections.The results of the analysis show that the time-averaged spatial distribution of snow water equivalent and the amplitude of its annual cycle are reproduced quite differently by the different remote sensing and reanalysis data sets, which in fact exhibit a large spread around the ensemble mean. We find that GCMs at spatial resolutions equal to or finer than 1.25° longitude are in closer agreement with the ensemble mean of satellite and reanalysis products in terms of root mean square error and standard deviation than lower-resolution GCMs. The set of regional climate models from the EURO-CORDEX ensemble provides estimates of snow water equivalent at 0.11° resolution that are locally much larger than those indicated by the gridded data sets, and only in a few cases are these differences smoothed out when snow water equivalent is spatially averaged over the entire Alpine domain. ERA-Interim-driven RCM simulations show an annual snow cycle that is comparable in amplitude to those provided by the reference data sets, while GCM-driven RCMs present a large positive bias. RCMs and higher-resolution GCM simulations are used to provide an estimate of the snow reduction expected by the mid-21st century (RCP 8.5 scenario) compared to the historical climatology, with the main purpose of highlighting the limits of our current knowledge and the need for developing more reliable snow simulations.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/25985678','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/25985678"><span>[Effect of climate change on rice irrigation water requirement in Songnen Plain, Northeast China].</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Huang, Zhi-gang; Wang, Xiao-li; Xiao, Ye; Yang, Fei; Wang, Chen-xi</p> <p>2015-01-01</p> <p>Based on meteorological data from China national weather stations and climate scenario grid data through regional climate model provided by National Climate Center, rice water requirement was calculated by using McCloud model and Penman-Monteith model combined with crop coefficient approach. Then the rice irrigation water requirement was estimated by water balance model, and the changes of rice water requirement were analyzed. The results indicated that either in historical period or in climate scenario, rice irrigation water requirement contour lines during the whole growth period and Lmid period decreased along southwest to northeast, and the same irrigation water requirement contour line moved north with decade alternation. Rice irrigation water requirement during the whole growth period increased fluctuantly with decade alternation at 44.2 mm . 10 a-1 in historical period and 19.9 mm . 10 a-1 in climate scenario. The increase in rice irrigation water requirement during the Lmid period with decade alternation was significant in historical period, but not significant in climate scenario. Contribution rate of climate change to rice irrigation water requirement would be fluctuantly increased with decade alternation in climate scenario. Compared with 1970s, contribution rates of climate change to rice irrigation water requirement were 23.6% in 2000s and 34.4% in 2040s, which increased 14.8 x 10(8) m3 irrigation water in 2000s and would increase 21.2 x 10(8) m3 irrigation water in 2040s.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFMIN32C..02S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFMIN32C..02S"><span>Producing custom regional climate data sets for impact assessment with xarray</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Simcock, J. G.; Delgado, M.; Greenstone, M.; Hsiang, S. M.; Kopp, R. E.; Carleton, T.; Hultgren, A.; Jina, A.; Nath, I.; Rising, J. A.; Rode, A.; Yuan, J.; Chong, T.; Dobbels, G.; Hussain, A.; Song, Y.; Wang, J.; Mohan, S.; Larsen, K.; Houser, T.</p> <p>2017-12-01</p> <p>Research in the field of climate impact assessment and valuation frequently requires the pairing of economic observations with historical or projected weather variables. Impact assessments with large geographic scope or spatially aggregated data frequently require climate variables to be prepared for use with administrative/political regions, economic districts such as utility service areas, physical regions such as watersheds, or other larger, non-gridded shapes. Approaches to preparing such data in the literature vary from methods developed out of convenience to more complex measures intended to account for spatial heterogeneity. But more sophisticated methods are difficult to implement, from both a theoretical and a technical standpoint. We present a new python package designed to assist researchers in the preparation of historical and projected climate data for arbitrary spatial definitions. Users specify transformations by providing (a) sets of regions in the form of shapefiles, (b) gridded data to be transformed, and, optionally, (c) gridded weights to use in the transformation. By default, aggregation to regions is conducted such that the resulting regional data draws from each grid cell according to the cell's share of total region area. However, researchers can provide alternative weighting schemes, such that the regional data is weighted by, for example, the population or planted agricultural area within each cell. An advantage of this method is that it enables easy preparation of nonlinear transformations of the climate data before aggregation to regions, allowing aggregated variables to more accurately capture the spatial heterogeneity within a region in the transformed data. At this session, we will allow attendees to view transformed climate projections, examining the effect of various weighting schemes and nonlinear transformations on aggregate regional values, highlighting the implications for climate impact assessment work.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li class="active"><span>20</span></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_20 --> <div id="page_21" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li class="active"><span>21</span></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li><a href="#" onclick='return showDiv("page_23");'>23</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="401"> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFMGC33H..01S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFMGC33H..01S"><span>PERSIANN-CDR Daily Precipitation Dataset for Hydrologic Applications and Climate Studies.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Sorooshian, S.; Hsu, K. L.; Ashouri, H.; Braithwaite, D.; Nguyen, P.; Thorstensen, A. R.</p> <p>2015-12-01</p> <p>Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network - Climate Data Record (PERSIANN-CDR) is a newly developed and released dataset which covers more than 3 decades (01/01/1983 - 03/31/2015 to date) of daily precipitation estimations at 0.25° resolution for 60°S-60°N latitude band. PERSIANN-CDR is processed using the archive of the Gridded Satellite IRWIN CDR (GridSat-B1) from the International Satellite Cloud Climatology Project (ISCCP), and the Global Precipitation Climatology Project (GPCP) 2.5° monthly product for bias correction. The dataset has been released and made available for public access through NOAA's National Centers for Environmental Information (NCEI) (http://www1.ncdc.noaa.gov/pub/data/sds/cdr/CDRs/PERSIANN/Overview.pdf). PERSIANN-CDR has already shown its usefulness for a wide range of applications, including climate variability and change monitoring, hydrologic applications, and water resources system planning and management. This precipitation CDR data has also been used in studying the behavior of historical extreme precipitation events. Demonstration of PERSIANN-CDR data in detecting trends and variability of precipitation over the past 30 years, the potential usefulness of the dataset for evaluating climate model performance relevant to precipitation in retrospective mode, will be presented.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/biblio/1339823-what-effects-agro-ecological-zones-land-use-region-boundaries-land-resource-projection-using-global-change-assessment-model','SCIGOV-STC'); return false;" href="https://www.osti.gov/biblio/1339823-what-effects-agro-ecological-zones-land-use-region-boundaries-land-resource-projection-using-global-change-assessment-model"><span>What are the effects of Agro-Ecological Zones and land use region boundaries on land resource projection using the Global Change Assessment Model?</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Di Vittorio, Alan V.; Kyle, Page; Collins, William D.</p> <p></p> <p>Understanding the potential impacts of climate change is complicated by mismatched spatial representations between gridded Earth System Models (ESMs) and Integrated Assessment Models (IAMs), whose regions are typically larger and defined by geopolitical and biophysical criteria. In this study we address uncertainty stemming from the construction of land use regions in an IAM, the Global Change Assessment Model (GCAM), whose regions are currently based on historical climatic conditions (1961-1990). We re-define GCAM’s regions according to projected climatic conditions (2070-2099), and investigate how this changes model outcomes for land use, agriculture, and forestry. By 2100, we find potentially large differences inmore » projected global and regional area of biomass energy crops, fodder crops, harvested forest, and intensive pasture. These land area differences correspond with changes in agricultural commodity prices and production. These results have broader implications for understanding policy scenarios and potential impacts, and for evaluating and comparing IAM and ESM simulations.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19920045134&hterms=Global+warming&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D40%26Ntt%3DGlobal%2Bwarming','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19920045134&hterms=Global+warming&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D40%26Ntt%3DGlobal%2Bwarming"><span>The impact of global warming on river runoff</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Miller, James R.; Russell, Gary L.</p> <p>1992-01-01</p> <p>A global atmospheric model is used to calculate the annual river runoff for 33 of the world's major rivers for the present climate and for a doubled CO2 climate. The model has a horizontal resolution of 4 x 5 deg, but the runoff from each model grid box is quartered and added to the appropriate river drainage basin on a 2 x 2.5 deg resolution. The computed runoff depends on the model's precipitation, evapotranspiration, and soil moisture storage. For the doubled CO2 climate, the runoff increased for 25 of the 33 rivers, and in most cases the increases coincide with increased rainfall within the drainage basins. There were runoff increases in all rivers in high northern latitudes, with a maximum increase of 47 percent. At low latitudes there were both increases and decreases ranging from a 96 increase to a 43 percent decrease. The effect of the simplified model assumptions of land-atmosphere interactions on the results is discussed.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20070030218','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20070030218"><span>Evaluation of a Cloud Resolving Model Using TRMM Observations for Multiscale Modeling Applications</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Posselt, Derek J.; L'Ecuyer, Tristan; Tao, Wei-Kuo; Hou, Arthur Y.; Stephens, Graeme L.</p> <p>2007-01-01</p> <p>The climate change simulation community is moving toward use of global cloud resolving models (CRMs), however, current computational resources are not sufficient to run global CRMs over the hundreds of years necessary to produce climate change estimates. As an intermediate step between conventional general circulation models (GCMs) and global CRMs, many climate analysis centers are embedding a CRM in each grid cell of a conventional GCM. These Multiscale Modeling Frameworks (MMFs) represent a theoretical advance over the use of conventional GCM cloud and convection parameterizations, but have been shown to exhibit an overproduction of precipitation in the tropics during the northern hemisphere summer. In this study, simulations of clouds, precipitation, and radiation over the South China Sea using the CRM component of the NASA Goddard MMF are evaluated using retrievals derived from the instruments aboard the Tropical Rainfall Measuring Mission (TRMM) satellite platform for a 46-day time period that spans 5 May - 20 June 1998. The NASA Goddard Cumulus Ensemble (GCE) model is forced with observed largescale forcing derived from soundings taken during the intensive observing period of the South China Sea Monsoon Experiment. It is found that the GCE configuration used in the NASA Goddard MMF responds too vigorously to the imposed large-scale forcing, accumulating too much moisture and producing too much cloud cover during convective phases, and overdrying the atmosphere and suppressing clouds during monsoon break periods. Sensitivity experiments reveal that changes to ice cloud microphysical parameters have a relatively large effect on simulated clouds, precipitation, and radiation, while changes to grid spacing and domain length have little effect on simulation results. The results motivate a more detailed and quantitative exploration of the sources and magnitude of the uncertainty associated with specified cloud microphysical parameters in the CRM components of MMFs.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010AGUFMGC23C0919W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010AGUFMGC23C0919W"><span>BIOMAP A Daily Time Step, Mechanistic Model for the Study of Ecosystem Dynamics</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wells, J. R.; Neilson, R. P.; Drapek, R. J.; Pitts, B. S.</p> <p>2010-12-01</p> <p>BIOMAP simulates competition between two Plant Functional Types (PFT) at any given point in the conterminous U.S. using a time series of daily temperature (mean, minimum, maximum), precipitation, humidity, light and nutrients, with PFT-specific rooting within a multi-layer soil. The model employs a 2-layer canopy biophysics, Farquhar photosynthesis, the Beer-Lambert Law for light attenuation and a mechanistic soil hydrology. In essence, BIOMAP is a re-built version of the biogeochemistry model, BIOME-BGC, into the form of the MAPSS biogeography model. Specific enhancements are: 1) the 2-layer canopy biophysics of Dolman (1993); 2) the unique MAPSS-based hydrology, which incorporates canopy evaporation, snow dynamics, infiltration and saturated and unsaturated percolation with ‘fast’ flow and base flow and a ‘tunable aquifer’ capacity, a metaphor of D’Arcy’s Law; and, 3) a unique MAPSS-based stomatal conductance algorithm, which simultaneously incorporates vapor pressure and soil water potential constraints, based on physiological information and many other improvements. Over small domains the PFTs can be parameterized as individual species to investigate fundamental vs. potential niche theory; while, at more coarse scales the PFTs can be rendered as more general functional groups. Since all of the model processes are intrinsically leaf to plot scale (physiology to PFT competition), it essentially has no ‘intrinsic’ scale and can be implemented on a grid of any size, taking on the characteristics defined by the homogeneous climate of each grid cell. Currently, the model is implemented on the VEMAP 1/2 degree, daily grid over the conterminous U.S. Although both the thermal and water-limited ecotones are dynamic, following climate variability, the PFT distributions remain fixed. Thus, the model is currently being fitted with a ‘reproduction niche’ to allow full dynamic operation as a Dynamic General Vegetation Model (DGVM). While global simulations of both climate and ecosystems must be done at coarse grid resolutions; smaller domains require higher resolution for the simulation of natural resource processes at the landscape scale and that of on-the-ground management practices. Via a combined multi-agency and private conservation effort we have implemented a Nested Scale Experiment (NeScE) that ranges from 1/2 degree resolution (global, ca. 50 km) to ca. 8km (North America) and 800 m (conterminous U.S.). Our first DGVM, MC1, has been implemented at all 3 scales. We are just beginning to implement BIOMAP into NeScE, with its unique features, and daily time step, as a counterpoint to MC1. We believe it will be more accurate at all resolutions providing better simulations of vegetation distribution, carbon balance, runoff, fire regimes and drought impacts.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.nrel.gov/climate-neutral/smart-grid.html','SCIGOVWS'); return false;" href="https://www.nrel.gov/climate-neutral/smart-grid.html"><span>Smart Grid | Climate Neutral Research Campuses | NREL</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.science.gov/aboutsearch.html">Science.gov Websites</a></p> <p></p> <p></p> <p>begun to build smart grids. Most operate electricity grids that include power generation; <em>load</em> control plant managers use these communications for energy management and <em>load</em> shedding, which are among the top familiar with equipment interoperability, central dispatch, and <em>load</em> shedding. These are common in smart</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFMIN13A1649T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFMIN13A1649T"><span>Extending Climate Analytics as a Service to the Earth System Grid Federation Progress Report on the Reanalysis Ensemble Service</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Tamkin, G.; Schnase, J. L.; Duffy, D.; Li, J.; Strong, S.; Thompson, J. H.</p> <p>2016-12-01</p> <p>We are extending climate analytics-as-a-service, including: (1) A high-performance Virtual Real-Time Analytics Testbed supporting six major reanalysis data sets using advanced technologies like the Cloudera Impala-based SQL and Hadoop-based MapReduce analytics over native NetCDF files. (2) A Reanalysis Ensemble Service (RES) that offers a basic set of commonly used operations over the reanalysis collections that are accessible through NASA's climate data analytics Web services and our client-side Climate Data Services Python library, CDSlib. (3) An Open Geospatial Consortium (OGC) WPS-compliant Web service interface to CDSLib to accommodate ESGF's Web service endpoints. This presentation will report on the overall progress of this effort, with special attention to recent enhancements that have been made to the Reanalysis Ensemble Service, including the following: - An CDSlib Python library that supports full temporal, spatial, and grid-based resolution services - A new reanalysis collections reference model to enable operator design and implementation - An enhanced library of sample queries to demonstrate and develop use case scenarios - Extended operators that enable single- and multiple reanalysis area average, vertical average, re-gridding, and trend, climatology, and anomaly computations - Full support for the MERRA-2 reanalysis and the initial integration of two additional reanalyses - A prototype Jupyter notebook-based distribution mechanism that combines CDSlib documentation with interactive use case scenarios and personalized project management - Prototyped uncertainty quantification services that combine ensemble products with comparative observational products - Convenient, one-stop shopping for commonly used data products from multiple reanalyses, including basic subsetting and arithmetic operations over the data and extractions of trends, climatologies, and anomalies - The ability to compute and visualize multiple reanalysis intercomparisons</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/pages/biblio/1436990-atmospheric-updrafts-key-unlocking-climate-forcing-sensitivity','SCIGOV-DOEP'); return false;" href="https://www.osti.gov/pages/biblio/1436990-atmospheric-updrafts-key-unlocking-climate-forcing-sensitivity"><span>Are Atmospheric Updrafts a Key to Unlocking Climate Forcing and Sensitivity?</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/pages">DOE PAGES</a></p> <p>Donner, Leo J.; O'Brien, Travis A.; Rieger, Daniel; ...</p> <p>2016-06-08</p> <p>Both climate forcing and climate sensitivity persist as stubborn uncertainties limiting the extent to which climate models can provide actionable scientific scenarios for climate change. A key, explicit control on cloud-aerosol interactions, the largest uncertainty in climate forcing, is the vertical velocity of cloud-scale updrafts. Model-based studies of climate sensitivity indicate that convective entrainment, which is closely related to updraft speeds, is an important control on climate sensitivity. Updraft vertical velocities also drive many physical processes essential to numerical weather prediction. Vertical velocities and their role in atmospheric physical processes have been given very limited attention in models for climatemore » and numerical weather prediction. The relevant physical scales range down to tens of meters and are thus frequently sub-grid and require parameterization. Many state-of-science convection parameterizations provide mass fluxes without specifying vertical velocities, and parameterizations which do provide vertical velocities have been subject to limited evaluation against what have until recently been scant observations. Atmospheric observations imply that the distribution of vertical velocities depends on the areas over which the vertical velocities are averaged. Distributions of vertical velocities in climate models may capture this behavior, but it has not been accounted for when parameterizing cloud and precipitation processes in current models. New observations of convective vertical velocities offer a potentially promising path toward developing process-level cloud models and parameterizations for climate and numerical weather prediction. Taking account of scale-dependence of resolved vertical velocities offers a path to matching cloud-scale physical processes and their driving dynamics more realistically, with a prospect of reduced uncertainty in both climate forcing and sensitivity.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2004AGUFMSF31A0714P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2004AGUFMSF31A0714P"><span>Flexible Environments for Grand-Challenge Simulation in Climate Science</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Pierrehumbert, R.; Tobis, M.; Lin, J.; Dieterich, C.; Caballero, R.</p> <p>2004-12-01</p> <p>Current climate models are monolithic codes, generally in Fortran, aimed at high-performance simulation of the modern climate. Though they adequately serve their designated purpose, they present major barriers to application in other problems. Tailoring them to paleoclimate of planetary simulations, for instance, takes months of work. Theoretical studies, where one may want to remove selected processes or break feedback loops, are similarly hindered. Further, current climate models are of little value in education, since the implementation of textbook concepts and equations in the code is obscured by technical detail. The Climate Systems Center at the University of Chicago seeks to overcome these limitations by bringing modern object-oriented design into the business of climate modeling. Our ultimate goal is to produce an end-to-end modeling environment capable of configuring anything from a simple single-column radiative-convective model to a full 3-D coupled climate model using a uniform, flexible interface. Technically, the modeling environment is implemented as a Python-based software component toolkit: key number-crunching procedures are implemented as discrete, compiled-language components 'glued' together and co-ordinated by Python, combining the high performance of compiled languages and the flexibility and extensibility of Python. We are incrementally working towards this final objective following a series of distinct, complementary lines. We will present an overview of these activities, including PyOM, a Python-based finite-difference ocean model allowing run-time selection of different Arakawa grids and physical parameterizations; CliMT, an atmospheric modeling toolkit providing a library of 'legacy' radiative, convective and dynamical modules which can be knitted into dynamical models, and PyCCSM, a version of NCAR's Community Climate System Model in which the coupler and run-control architecture are re-implemented in Python, augmenting its flexibility and adaptability.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/1258593','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/1258593"><span>An evaluation of the variable-resolution CESM for modeling California's climate: Evaluation of VR-CESM for Modeling California's Climate</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Huang, Xingying; Rhoades, Alan M.; Ullrich, Paul A.</p> <p></p> <p>In this paper, the recently developed variable-resolution option within the Community Earth System Model (VR-CESM) is assessed for long-term regional climate modeling of California at 0.25° (~ 28 km) and 0.125° (~ 14 km) horizontal resolutions. The mean climatology of near-surface temperature and precipitation is analyzed and contrasted with reanalysis, gridded observational data sets, and a traditional regional climate model (RCM)—the Weather Research and Forecasting (WRF) model. Statistical metrics for model evaluation and tests for differential significance have been extensively applied. VR-CESM tended to produce a warmer summer (by about 1–3°C) and overestimated overall winter precipitation (about 25%–35%) compared tomore » reference data sets when sea surface temperatures were prescribed. Increasing resolution from 0.25° to 0.125° did not produce a statistically significant improvement in the model results. By comparison, the analogous WRF climatology (constrained laterally and at the sea surface by ERA-Interim reanalysis) was ~1–3°C colder than the reference data sets, underestimated precipitation by ~20%–30% at 27 km resolution, and overestimated precipitation by ~ 65–85% at 9 km. Overall, VR-CESM produced comparable statistical biases to WRF in key climatological quantities. Moreover, this assessment highlights the value of variable-resolution global climate models (VRGCMs) in capturing fine-scale atmospheric processes, projecting future regional climate, and addressing the computational expense of uniform-resolution global climate models.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/pages/biblio/1258593-evaluation-variable-resolution-cesm-modeling-california-climate-evaluation-vr-cesm-modeling-california-climate','SCIGOV-DOEP'); return false;" href="https://www.osti.gov/pages/biblio/1258593-evaluation-variable-resolution-cesm-modeling-california-climate-evaluation-vr-cesm-modeling-california-climate"><span>An evaluation of the variable-resolution CESM for modeling California's climate: Evaluation of VR-CESM for Modeling California's Climate</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/pages">DOE PAGES</a></p> <p>Huang, Xingying; Rhoades, Alan M.; Ullrich, Paul A.; ...</p> <p>2016-03-01</p> <p>In this paper, the recently developed variable-resolution option within the Community Earth System Model (VR-CESM) is assessed for long-term regional climate modeling of California at 0.25° (~ 28 km) and 0.125° (~ 14 km) horizontal resolutions. The mean climatology of near-surface temperature and precipitation is analyzed and contrasted with reanalysis, gridded observational data sets, and a traditional regional climate model (RCM)—the Weather Research and Forecasting (WRF) model. Statistical metrics for model evaluation and tests for differential significance have been extensively applied. VR-CESM tended to produce a warmer summer (by about 1–3°C) and overestimated overall winter precipitation (about 25%–35%) compared tomore » reference data sets when sea surface temperatures were prescribed. Increasing resolution from 0.25° to 0.125° did not produce a statistically significant improvement in the model results. By comparison, the analogous WRF climatology (constrained laterally and at the sea surface by ERA-Interim reanalysis) was ~1–3°C colder than the reference data sets, underestimated precipitation by ~20%–30% at 27 km resolution, and overestimated precipitation by ~ 65–85% at 9 km. Overall, VR-CESM produced comparable statistical biases to WRF in key climatological quantities. Moreover, this assessment highlights the value of variable-resolution global climate models (VRGCMs) in capturing fine-scale atmospheric processes, projecting future regional climate, and addressing the computational expense of uniform-resolution global climate models.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.A23D2382N','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.A23D2382N"><span>Generating High Resolution Climate Scenarios Through Regional Climate Modelling Over Southern Africa</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ndhlovu, G. Z.; Woyessa, Y. E.; Vijayaraghavan, S.</p> <p>2017-12-01</p> <p>limate change has impacted the global environment and the Continent of Africa, especially Southern Africa, regarded as one of the most vulnerable regions in Africa, has not been spared from these impacts. Global Climate Models (GCMs) with coarse horizontal resolutions of 150-300 km do not provide sufficient details at the local basin scale due to mismatch between the size of river basins and the grid cell of the GCM. This makes it difficult to apply the outputs of GCMs directly to impact studies such as hydrological modelling. This necessitates the use of regional climate modelling at high resolutions that provide detailed information at regional and local scales to study both climate change and its impacts. To this end, an experiment was set up and conducted with PRECIS, a regional climate model, to generate climate scenarios at a high resolution of 25km for the local region in Zambezi River basin of Southern Africa. The major input data used included lateral and surface boundary conditions based on the GCMs. The data is processed, analysed and compared with CORDEX climate change project data generated for Africa. This paper, highlights the major differences of the climate scenarios generated by PRECIS Model and CORDEX Project for Africa and further gives recommendations for further research on generation of climate scenarios. The climatic variables such as precipitation and temperatures have been analysed for flood and droughts in the region. The paper also describes the setting up and running of an experiment using a high-resolution PRECIS model. In addition, a description has been made in running the model and generating the output variables on a sub basin scale. Regional climate modelling which provides information on climate change impact may lead to enhanced understanding of adaptive water resources management. Understanding the regional climate modelling results on sub basin scale is the first step in analysing complex hydrological processes and a basis for designing of adaptation and mitigation strategies in the region. Key words: Climate change, regional climate modelling, hydrological processes, extremes, scenarios [1] Corresponding author: Email:gndhlovu@cut.ac.za Tel:+27 (0) 51 507 3072</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFMGC52C..03J','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFMGC52C..03J"><span>Global climate shocks to agriculture from 1950 - 2015</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Jackson, N. D.; Konar, M.; Debaere, P.; Sheffield, J.</p> <p>2016-12-01</p> <p>Climate shocks represent a major disruption to crop yields and agricultural production, yet a consistent and comprehensive database of agriculturally relevant climate shocks does not exist. To this end, we conduct a spatially and temporally disaggregated analysis of climate shocks to agriculture from 1950-2015 using a new gridded dataset. We quantify the occurrence and magnitude of climate shocks for all global agricultural areas during the growing season using a 0.25-degree spatial grid and daily time scale. We include all major crops and both temperature and precipitation extremes in our analysis. Critically, we evaluate climate shocks to all potential agricultural areas to improve projections within our time series. To do this, we use Global Agro-Ecological Zones maps from the Food and Agricultural Organization, the Princeton Global Meteorological Forcing dataset, and crop calendars from Sacks et al. (2010). We trace the dynamic evolution of climate shocks to agriculture, evaluate the spatial heterogeneity in agriculturally relevant climate shocks, and identify the crops and regions that are most prone to climate shocks.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFMGC51A1131K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFMGC51A1131K"><span>High resolution crop growth simulation for identification of potential adaptation strategies under climate change</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kim, K. S.; Yoo, B. H.</p> <p>2016-12-01</p> <p>Impact assessment of climate change on crop production would facilitate planning of adaptation strategies. Because socio-environmental conditions would differ by local areas, it would be advantageous to assess potential adaptation measures at a specific area. The objectives of this study was to develop a crop growth simulation system at a very high spatial resolution, e.g., 30 m, and to assess different adaptation options including shift of planting date and use of different cultivars. The Decision Support System for Agrotechnology Transfer (DSSAT) model was used to predict yields of soybean and maize in Korea. Gridded data for climate and soil were used to prepare input data for the DSSAT model. Weather input data were prepared at the resolution of 30 m using bilinear interpolation from gridded climate scenario data. Those climate data were obtained from Korean Meteorology Administration. Spatial resolution of temperature and precipitation was 1 km whereas that of solar radiation was 12.5 km. Soil series data at the 30 m resolution were obtained from the soil database operated by Rural Development Administration, Korea. The SOL file, which is a soil input file for the DSSAT model was prepared using physical and chemical properties of a given soil series, which were available from the soil database. Crop yields were predicted by potential adaptation options based on planting date and cultivar. For example, 10 planting dates and three cultivars were used to identify ideal management options for climate change adaptation. In prediction of maize yield, combination of 20 planting dates and two cultivars was used as management options. Predicted crop yields differed by site even within a relatively small region. For example, the maximum of average yields for 2001-2010 seasons differed by sites In a county of which areas is 520 km2 (Fig. 1). There was also spatial variation in the ideal management option in the region (Fig. 2). These results suggested that local assessment of climate change impact on crop production would be useful for planning adaptation options.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014EGUGA..1611914L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014EGUGA..1611914L"><span>Towards Cloud-Resolving European-Scale Climate Simulations using a fully GPU-enabled Prototype of the COSMO Regional Model</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Leutwyler, David; Fuhrer, Oliver; Cumming, Benjamin; Lapillonne, Xavier; Gysi, Tobias; Lüthi, Daniel; Osuna, Carlos; Schär, Christoph</p> <p>2014-05-01</p> <p>The representation of moist convection is a major shortcoming of current global and regional climate models. State-of-the-art global models usually operate at grid spacings of 10-300 km, and therefore cannot fully resolve the relevant upscale and downscale energy cascades. Therefore parametrization of the relevant sub-grid scale processes is required. Several studies have shown that this approach entails major uncertainties for precipitation processes, which raises concerns about the model's ability to represent precipitation statistics and associated feedback processes, as well as their sensitivities to large-scale conditions. Further refining the model resolution to the kilometer scale allows representing these processes much closer to first principles and thus should yield an improved representation of the water cycle including the drivers of extreme events. Although cloud-resolving simulations are very useful tools for climate simulations and numerical weather prediction, their high horizontal resolution and consequently the small time steps needed, challenge current supercomputers to model large domains and long time scales. The recent innovations in the domain of hybrid supercomputers have led to mixed node designs with a conventional CPU and an accelerator such as a graphics processing unit (GPU). GPUs relax the necessity for cache coherency and complex memory hierarchies, but have a larger system memory-bandwidth. This is highly beneficial for low compute intensity codes such as atmospheric stencil-based models. However, to efficiently exploit these hybrid architectures, climate models need to be ported and/or redesigned. Within the framework of the Swiss High Performance High Productivity Computing initiative (HP2C) a project to port the COSMO model to hybrid architectures has recently come to and end. The product of these efforts is a version of COSMO with an improved performance on traditional x86-based clusters as well as hybrid architectures with GPUs. We present our redesign and porting approach as well as our experience and lessons learned. Furthermore, we discuss relevant performance benchmarks obtained on the new hybrid Cray XC30 system "Piz Daint" installed at the Swiss National Supercomputing Centre (CSCS), both in terms of time-to-solution as well as energy consumption. We will demonstrate a first set of short cloud-resolving climate simulations at the European-scale using the GPU-enabled COSMO prototype and elaborate our future plans on how to exploit this new model capability.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFMPA13A2160R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFMPA13A2160R"><span>Application of data on climate extremes for the southwestern United States</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Redmond, K. T.; Fleishman, E.; Cayan, D. R.; Daudert, B.; Gershunov, A.</p> <p>2015-12-01</p> <p>We are improving the scientific capacity to evaluate responses of natural resources to climate extremes. We also are enhancing a platform for derivation of and access to customized climate information for the full extent or any subset of the southwestern United States. Extreme climate can have substantial effects on species, ecological and evolutionary processes, and the health of visitors to public lands. We are working with federal and state managers and with researchers who collaborate with decision-makers to use data on climate extremes to inform resource management. Current applications include sudden oak death, estuarine management, and fine-resolution manipulation of montane vegetation. To facilitate practical use of data on climate extremes, we are screening global climate models on the basis of their realism in representing natural regional patterns and extremes of temperature and precipitation, including those driven by El Niño and La Niña. We are assessing how well each model represents different climate elements. We also are delivering point and gridded observations and downscaled model projections, all at daily and 6 km resolution, on past and future climate extremes. Additionally, we are using the downscaled outputs to drive a hydrologic model and derive multiple probabilistic measures of water availability, flood, and drought. Moreover, we are extending the capacity of the Southwest Climate and Environmental Information Collaborative (SCENIC; wrcc.dri.edu/csc/scenic), a product developed by the Western Regional Climate Center, to provide access to diverse observed and simulated data on regional weather and climate, particularly on extremes.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/29313841','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/29313841"><span>TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958-2015.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Abatzoglou, John T; Dobrowski, Solomon Z; Parks, Sean A; Hegewisch, Katherine C</p> <p>2018-01-09</p> <p>We present TerraClimate, a dataset of high-spatial resolution (1/24°, ~4-km) monthly climate and climatic water balance for global terrestrial surfaces from 1958-2015. TerraClimate uses climatically aided interpolation, combining high-spatial resolution climatological normals from the WorldClim dataset, with coarser resolution time varying (i.e., monthly) data from other sources to produce a monthly dataset of precipitation, maximum and minimum temperature, wind speed, vapor pressure, and solar radiation. TerraClimate additionally produces monthly surface water balance datasets using a water balance model that incorporates reference evapotranspiration, precipitation, temperature, and interpolated plant extractable soil water capacity. These data provide important inputs for ecological and hydrological studies at global scales that require high spatial resolution and time varying climate and climatic water balance data. We validated spatiotemporal aspects of TerraClimate using annual temperature, precipitation, and calculated reference evapotranspiration from station data, as well as annual runoff from streamflow gauges. TerraClimate datasets showed noted improvement in overall mean absolute error and increased spatial realism relative to coarser resolution gridded datasets.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018NatSD...570191A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018NatSD...570191A"><span>TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958-2015</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Abatzoglou, John T.; Dobrowski, Solomon Z.; Parks, Sean A.; Hegewisch, Katherine C.</p> <p>2018-01-01</p> <p>We present TerraClimate, a dataset of high-spatial resolution (1/24°, ~4-km) monthly climate and climatic water balance for global terrestrial surfaces from 1958-2015. TerraClimate uses climatically aided interpolation, combining high-spatial resolution climatological normals from the WorldClim dataset, with coarser resolution time varying (i.e., monthly) data from other sources to produce a monthly dataset of precipitation, maximum and minimum temperature, wind speed, vapor pressure, and solar radiation. TerraClimate additionally produces monthly surface water balance datasets using a water balance model that incorporates reference evapotranspiration, precipitation, temperature, and interpolated plant extractable soil water capacity. These data provide important inputs for ecological and hydrological studies at global scales that require high spatial resolution and time varying climate and climatic water balance data. We validated spatiotemporal aspects of TerraClimate using annual temperature, precipitation, and calculated reference evapotranspiration from station data, as well as annual runoff from streamflow gauges. TerraClimate datasets showed noted improvement in overall mean absolute error and increased spatial realism relative to coarser resolution gridded datasets.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.B21F2024S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.B21F2024S"><span>Simulating topographic controls on the abundance of larch forest in eastern Siberia, and its consequences under changing climate</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Sato, H.; Kobayashi, H.</p> <p>2017-12-01</p> <p>In eastern Siberia, larches (Larix spp.) often exist in pure stands, constructing the world's largest coniferous forest, of which changes can significantly affect the earth's albedo and the global carbon balance. Our previous studies tried to reconstruct this vegetation, aiming to forecast its structures and functions under changing climate (1, 2). In previous studies of simulating vegetation at large geographical scales, the examining area is divided into coarse grid cells such as 0.5 × 0.5 degree resolution, and topographical heterogeneities within each grid cell are just ignored. However, in Siberian larch area, which is located on the environmental edge of existence of forest ecosystem, abundance of larch trees largely depends on topographic condition at the scale of tens to hundreds meters. In our preliminary analysis, we found a quantitative pattern that topographic properties controls the abundance of larch forest via both drought and flooding stresses in eastern Siberia. We, therefore, refined the hydrological sub-model of our dynamic vegetation model SEIB-DGVM, and validated whether the modified model can reconstruct the pattern, examined its impact on the estimation of biomass and vegetation productivity under the current and forecasted future climatic conditions. -- References --1. Sato, H., et al. (2010). "Simulation study of the vegetation structure and function in eastern Siberian larch forests using the individual-based vegetation model SEIB-DGVM." Forest Ecology and Management 259(3): 301-311. 2. Sato, H., et al. (2016). "Endurance of larch forest ecosystems in eastern Siberia under warming trends." Ecology and Evolution</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/pages/biblio/1306152-sensitivity-summer-ensembles-fledgling-superparameterized-mesoscale-convective-systems-cloud-resolving-model-microphysics-grid-configuration','SCIGOV-DOEP'); return false;" href="https://www.osti.gov/pages/biblio/1306152-sensitivity-summer-ensembles-fledgling-superparameterized-mesoscale-convective-systems-cloud-resolving-model-microphysics-grid-configuration"><span>Sensitivity of summer ensembles of fledgling superparameterized U.S. mesoscale convective systems to cloud resolving model microphysics and grid configuration</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/pages">DOE PAGES</a></p> <p>Elliott, Elizabeth J.; Yu, Sungduk; Kooperman, Gabriel J.; ...</p> <p>2016-05-01</p> <p>The sensitivities of simulated mesoscale convective systems (MCSs) in the central U.S. to microphysics and grid configuration are evaluated here in a global climate model (GCM) that also permits global-scale feedbacks and variability. Since conventional GCMs do not simulate MCSs, studying their sensitivities in a global framework useful for climate change simulations has not previously been possible. To date, MCS sensitivity experiments have relied on controlled cloud resolving model (CRM) studies with limited domains, which avoid internal variability and neglect feedbacks between local convection and larger-scale dynamics. However, recent work with superparameterized (SP) GCMs has shown that eastward propagating MCS-likemore » events are captured when embedded CRMs replace convective parameterizations. This study uses a SP version of the Community Atmosphere Model version 5 (SP-CAM5) to evaluate MCS sensitivities, applying an objective empirical orthogonal function algorithm to identify MCS-like events, and harmonizing composite storms to account for seasonal and spatial heterogeneity. A five-summer control simulation is used to assess the magnitude of internal and interannual variability relative to 10 sensitivity experiments with varied CRM parameters, including ice fall speed, one-moment and two-moment microphysics, and grid spacing. MCS sensitivities were found to be subtle with respect to internal variability, and indicate that ensembles of over 100 storms may be necessary to detect robust differences in SP-GCMs. Furthermore, these results emphasize that the properties of MCSs can vary widely across individual events, and improving their representation in global simulations with significant internal variability may require comparison to long (multidecadal) time series of observed events rather than single season field campaigns.« less</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li class="active"><span>21</span></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li><a href="#" onclick='return showDiv("page_23");'>23</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_21 --> <div id="page_22" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li class="active"><span>22</span></li> <li><a href="#" onclick='return showDiv("page_23");'>23</a></li> <li><a href="#" onclick='return showDiv("page_24");'>24</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="421"> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://pubs.er.usgs.gov/publication/70020928','USGSPUBS'); return false;" href="https://pubs.er.usgs.gov/publication/70020928"><span>Response of North American freshwater lakes to simulated future climates</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Hostetler, S.W.; Small, E.E.</p> <p>1999-01-01</p> <p>We apply a physically based lake model to assess the response of North American lakes to future climate conditions as portrayed by the transient trace-gas simulations conducted with the Max Planck Institute (ECHAM4) and the Canadian Climate Center (CGCM1) atmosphere-ocean general circulation models (A/OGCMs). To quantify spatial patterns of lake responses (temperature, mixing, ice cover, evaporation) we ran the lake model for theoretical lakes of specified area, depth, and transparency over a uniformly spaced (50 km) grid. The simulations were conducted for two 10-year periods that represent present climatic conditions and those around the time of CO2 doubling. Although the climate model output produces simulated lake responses that differ in specific regional details, there is broad agreement with regard to the direction and area of change. In particular, lake temperatures are generally warmer in the future as a result of warmer climatic conditions and a substantial loss (> 100 days/yr) of winter ice cover. Simulated summer lake temperatures are higher than 30??C ever the Midwest and south, suggesting the potential for future disturbance of existing aquatic ecosystems. Overall increases in lake evaporation combine with disparate changes in A/OGCM precipitation to produce future changes in net moisture (precipitation minus evaporation) that are of less fidelity than those of lake temperature.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.H11E1232K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.H11E1232K"><span>Application of Temperature Index Model to Assess the Future Hydrological Regime of the Glacierized Catchments in Nepal.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kayastha, R.; Kayastha, R. B.</p> <p>2017-12-01</p> <p>Unavailability of hydro meteorological data in the Himalayan regions is challenging on understanding the flow regimes. Temperature index model is simple yet the powerful glacio-hydrological model to simulate the discharge in the glacierized basin. Modified Positive Degree Day (MPDD) Model Version 2.0 is a grid-ded based semi distributed model with baseflow module is a robust melt modelling tools to estimate the discharge. MPDD model uses temperature and precipitation as a forcing datasets to simulate the discharge and also to obtain the snowmelt, icemelt, rain and baseflow contribution on total discharge. In this study two glacierized, Marsyangdi and Langtang catchment were investigated for the future hydrological regimes. Marsyangdi encompasses an area of 4026.19 sq. km with 20% glaciated area, whereas Langtang catchment with area of 354.64 sq. km with 36% glaciated area is studied to examine for the future climatic scenarios. The model simulates discharge well for the observed period; (1992-1998) in Marsyangdi and from (2007-2013) in Langtang catchment. The Nash-Sutcliffe Efficiency (NSE) for the both catchment were above 0.75 with the volume difference less than - 8 %. The snow and ice melts contribution in Marsyangdi were 4.7% and 10.2% whereas in Langtang the contribution is 15.3% and 23.4%, respectively. Rain contribution ( 40%) is higher than the baseflow contribution in total discharge in both basins. The future river discharge is also predicted using the future climate data from the regional climate models (RCMs) of CORDEX South Asia experiments for the medium stabilization scenario RCP4.5 and very high radiative forcing scenario RCP8.5 after bias correction. The projected future discharge of both catchment shows slightly increase in both scenarios with increase of snow and ice melt contribution on discharge. The result generated from the model can be utilized to understand the future hydrological regimes of the glacierized catchment also the impact of climate change on the snow and ice contribution on discharge. The future discharge projection is also helpful for the water resource management and also for the strategic planners.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5226776','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5226776"><span>Downscaling Global Emissions and Its Implications Derived from Climate Model Experiments</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Abe, Manabu; Kinoshita, Tsuguki; Hasegawa, Tomoko; Kawase, Hiroaki; Kushida, Kazuhide; Masui, Toshihiko; Oka, Kazutaka; Shiogama, Hideo; Takahashi, Kiyoshi; Tatebe, Hiroaki; Yoshikawa, Minoru</p> <p>2017-01-01</p> <p>In climate change research, future scenarios of greenhouse gas and air pollutant emissions generated by integrated assessment models (IAMs) are used in climate models (CMs) and earth system models to analyze future interactions and feedback between human activities and climate. However, the spatial resolutions of IAMs and CMs differ. IAMs usually disaggregate the world into 10–30 aggregated regions, whereas CMs require a grid-based spatial resolution. Therefore, downscaling emissions data from IAMs into a finer scale is necessary to input the emissions into CMs. In this study, we examined whether differences in downscaling methods significantly affect climate variables such as temperature and precipitation. We tested two downscaling methods using the same regionally aggregated sulfur emissions scenario obtained from the Asian-Pacific Integrated Model/Computable General Equilibrium (AIM/CGE) model. The downscaled emissions were fed into the Model for Interdisciplinary Research on Climate (MIROC). One of the methods assumed a strong convergence of national emissions intensity (e.g., emissions per gross domestic product), while the other was based on inertia (i.e., the base-year remained unchanged). The emissions intensities in the downscaled spatial emissions generated from the two methods markedly differed, whereas the emissions densities (emissions per area) were similar. We investigated whether the climate change projections of temperature and precipitation would significantly differ between the two methods by applying a field significance test, and found little evidence of a significant difference between the two methods. Moreover, there was no clear evidence of a difference between the climate simulations based on these two downscaling methods. PMID:28076446</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014EGUGA..16.8781L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014EGUGA..16.8781L"><span>Simulations of Tornadoes, Tropical Cyclones, MJOs, and QBOs, using GFDL's multi-scale global climate modeling system</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Lin, Shian-Jiann; Harris, Lucas; Chen, Jan-Huey; Zhao, Ming</p> <p>2014-05-01</p> <p>A multi-scale High-Resolution Atmosphere Model (HiRAM) is being developed at NOAA/Geophysical Fluid Dynamics Laboratory. The model's dynamical framework is the non-hydrostatic extension of the vertically Lagrangian finite-volume dynamical core (Lin 2004, Monthly Wea. Rev.) constructed on a stretchable (via Schmidt transformation) cubed-sphere grid. Physical parametrizations originally designed for IPCC-type climate predictions are in the process of being modified and made more "scale-aware", in an effort to make the model suitable for multi-scale weather-climate applications, with horizontal resolution ranging from 1 km (near the target high-resolution region) to as low as 400 km (near the antipodal point). One of the main goals of this development is to enable simulation of high impact weather phenomena (such as tornadoes, thunderstorms, category-5 hurricanes) within an IPCC-class climate modeling system previously thought impossible. We will present preliminary results, covering a very wide spectrum of temporal-spatial scales, ranging from simulation of tornado genesis (hours), Madden-Julian Oscillations (intra-seasonal), topical cyclones (seasonal), to Quasi Biennial Oscillations (intra-decadal), using the same global multi-scale modeling system.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFM.H11B1310R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFM.H11B1310R"><span>Potential biases in evapotranspiration estimates from Earth system models due to spatial heterogeneity and lateral moisture redistribution</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Rouholahnejad, E.; Kirchner, J. W.</p> <p>2016-12-01</p> <p>Evapotranspiration (ET) is a key process in land-climate interactions and affects the dynamics of the atmosphere at local and regional scales. In estimating ET, most earth system models average over considerable sub-grid heterogeneity in land surface properties, precipitation (P), and potential evapotranspiration (PET). This spatial averaging could potentially bias ET estimates, due to the nonlinearities in the underlying relationships. In addition, most earth system models ignore lateral redistribution of water within and between grid cells, which could potentially alter both local and regional ET. Here we present a first attempt to quantify the effects of spatial heterogeneity and lateral redistribution on grid-cell-averaged ET as seen from the atmosphere over heterogeneous landscapes. Using a Budyko framework to express ET as a function of P and PET, we quantify how sub-grid heterogeneity affects average ET at the scale of typical earth system model grid cells. We show that averaging over sub-grid heterogeneity in P and PET, as typical earth system models do, leads to overestimates of average ET. We use a similar approach to quantify how lateral redistribution of water could affect average ET, as seen from the atmosphere. We show that where the aridity index P/PET increases with altitude, gravitationally driven lateral redistribution will increase average ET, implying that models that neglect lateral moisture redistribution will underestimate average ET. In contrast, where the aridity index P/PET decreases with altitude, gravitationally driven lateral redistribution will decrease average ET. This approach yields a simple conceptual framework and mathematical expressions for determining whether, and how much, spatial heterogeneity and lateral redistribution can affect regional ET fluxes as seen from the atmosphere. This analysis provides the basis for quantifying heterogeneity and redistribution effects on ET at regional and continental scales, which will be the focus of future work.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://pubs.er.usgs.gov/publication/70034093','USGSPUBS'); return false;" href="https://pubs.er.usgs.gov/publication/70034093"><span>Past and ongoing shifts in Joshua tree distribution support future modeled range contraction</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Cole, Kenneth L.; Ironside, Kirsten; Eischeid, Jon K.; Garfin, Gregg; Duffy, Phil; Toney, Chris</p> <p>2011-01-01</p> <p>The future distribution of the Joshua tree (Yucca brevifolia) is projected by combining a geostatistical analysis of 20th-century climates over its current range, future modeled climates, and paleoecological data showing its response to a past similar climate change. As climate rapidly warmed ;11 700 years ago, the range of Joshua tree contracted, leaving only the populations near what had been its northernmost limit. Its ability to spread northward into new suitable habitats after this time may have been inhibited by the somewhat earlier extinction of megafaunal dispersers, especially the Shasta ground sloth. We applied a model of climate suitability for Joshua tree, developed from its 20th-century range and climates, to future climates modeled through a set of six individual general circulation models (GCM) and one suite of 22 models for the late 21st century. All distribution data, observed climate data, and future GCM results were scaled to spatial grids of ;1 km and ;4 km in order to facilitate application within this topographically complex region. All of the models project the future elimination of Joshua tree throughout most of the southern portions of its current range. Although estimates of future monthly precipitation differ between the models, these changes are outweighed by large increases in temperature common to all the models. Only a few populations within the current range are predicted to be sustainable. Several models project significant potential future expansion into new areas beyond the current range, but the species' Historical and current rates of dispersal would seem to prevent natural expansion into these new areas. Several areas are predicted to be potential sites for relocation/ assisted migration. This project demonstrates how information from paleoecology and modern ecology can be integrated in order to understand ongoing processes and future distributions.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/biblio/1177310-integrated-assessment-global-water-scarcity-over-century-under-multiple-climate-change-mitigation-policies','SCIGOV-STC'); return false;" href="https://www.osti.gov/biblio/1177310-integrated-assessment-global-water-scarcity-over-century-under-multiple-climate-change-mitigation-policies"><span>Integrated Assessment of Global Water Scarcity over the 21st Century under Multiple Climate Change Mitigation Policies</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Hejazi, Mohamad I.; Edmonds, James A.; Clarke, Leon E.</p> <p>2014-01-01</p> <p>Water scarcity conditions over the 21st century both globally and regionally are assessed in the context of climate change, by estimating both water availability and water demand within the Global Change Assessment Model (GCAM), a leading community integrated assessment model of energy, agriculture, climate, and water. To quantify changes in future water availability, a new gridded water-balance global hydrologic model – namely, the Global Water Availability Model (GWAM) – is developed and evaluated. Global water demands for six major demand sectors (irrigation, livestock, domestic, electricity generation, primary energy production, and manufacturing) are modeled in GCAM at the regional scale (14more » geopolitical regions, 151 sub-regions) and then spatially downscaled to 0.5 o x 0.5o resolution to match the scale of GWAM. Using a baseline scenario (i.e., no climate change mitigation policy) with radiative forcing reaching 8.8 W/m2 (equivalent to the SRES A1Fi emission scenario) and a global population of 14 billion by 2095, global annual water demand grows from about 9% of total annual renewable freshwater in 2005 to about 32% by 2095. This results in almost half of the world population living under extreme water scarcity by the end of the 21st century. Regionally, the demand for water exceeds the amount of water availability in two GCAM regions, the Middle East and India. Additionally, in years 2050 and 2095, 20% and 27% of the global population, respectively, is projected to live in areas (grid cells) that will experience greater water demands than the amount of available water in a year (i.e., the water scarcity index (WSI) > 1.0). This study implies an increasingly prominent role for water in future human decisions, and highlights the importance of including water in integrated assessment of global change.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFMEP41C0925W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFMEP41C0925W"><span>Modeling the Impact of Soil Conditions on Global Water Balance</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wang, P. L.; Feddema, J. J.</p> <p>2016-12-01</p> <p>The amount of water the soil can hold for plant use, defined as soil water-holding capacity (WHC), has a large influence on the water cycle and climatic variables. Although soil properties vary widely worldwide, many climate modeling applications assume WHC to be spatially invariant. This study explores how a more realistic soil WHC estimate affects the global water balance relative to commonly assumed soil properties. We use a modified Thornthwaite water balance model combined with a newly developed soil WHC and soil thickness data at a 30 arc second resolution. The soil WHC data was obtained by integrating WHCs to a depth of 2 m and modified by the soil thickness data on a grid-by-grid basis, and then resampling to the 0.5 degree climatology data. We observed that down scaling soils data before modifying soil depths greatly increases global soil WHCs. This new dataset is compared to WHC information with a fixed 2-m soil depth, and a constant 150-mm soil WHC. Results indicate higher soil WHC results in increased soil moisture, decreased moisture surplus and deficits, and increased actual evapotranspiration (AE), and vice-versa. However, due to high variability in soil characteristics across climate gradients, this generalization does not hold true for regionally averaged outcomes. Compared to using a constant 150-mm WHC, more realistic soil WHC increases global averaged AE 1%, and decreases deficit 2% and surplus 3%. Most change is observed in areas with pronounced wet and dry seasons; using a constant 2-m soil depth doubles the differences. Regionally, Europe was most affected: AE increases 4%, and the deficit and surplus decrease 20% and 12%. Australia shows that regionally averaged results are not equivocal for moisture surplus and deficit; deficit decreases 0.4%, while surplus decreases 9%. This research highlights the importance of soil condition for climate modeling and how a better representation of soil moisture conditions affects global water balance modeling.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014EGUGA..1616415R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014EGUGA..1616415R"><span>Evolution of extreme temperature events in short term climate projection for Iberian Peninsula.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Rodriguez, Alfredo; Tarquis, Ana M.; Sanchez, Enrique; Dosio, Alessandro; Ruiz-Ramos, Margarita</p> <p>2014-05-01</p> <p>Extreme events of maximum and minimum temperatures are a main hazard for agricultural production in Iberian Peninsula. For this purpose, in this study we analyze projections of their evolution that could be valid for the next decade, represented in this study by the 30-year period 2004-2034 (target period). For this purpose two kinds of data were used in this study: 1) observations from the station network of AEMET (Spanish National Meteorological Agency) for five Spanish locations, and 2) simulated data at a resolution of 50 ×50 km horizontal grid derived from the outputs of twelve Regional Climate Models (RCMs) taken from project ENSEMBLES (van der Linden and Mitchell, 2009), with a bias correction (Dosio and Paruolo, 2011; Dosio et al., 2012) regarding the observational dataset Spain02 (Herrera et al., 2012). To validate the simulated climate, the available period of observations was compared to a baseline period (1964-1994) of simulated climate for all locations. Then, to analyze the changes for the present/very next future, probability of extreme temperature events for 2004-2034 were compared to that of the baseline period. Although only minor changes are expected, small variations in variability may have a significant impact in crop performance. The objective of the work is to evaluate the utility of these short term projections for potential users, as for instance insurance companies. References Dosio A. and Paruolo P., 2011. Bias correction of the ENSEMBLES high-resolution climate change projections for use by impact models: Evaluation on the present climate. Journal of Geophysical Research, VOL. 116,D16106, doi:10.1029/2011JD015934 Dosio A., Paruolo P. and Rojas R., 2012. Bias correction of the ENSEMBLES high resolution climate change projections for use by impact models: Analysis of the climate change signal. Journal of Geophysical Research,Volume 117, D17, doi: 0.1029/2012JD017968 Herrera et. al. (2012) Development and Analysis of a 50 year high-resolution daily gridded precipitation dataset over Spain (Spain02). International Journal of Climatology 32:74-85 DOI: 10.1002/joc.2256. van der Linden, P., and J. F. B. Mitchell (Eds.) (2009), ENSEMBLES: Climate Change and Its Impacts: Summary of Research and Results From the ENSEMBLES Project, Met Off. Hadley Cent, Exeter, U. K.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/pages/biblio/1214753-evaluation-tropical-channel-refinement-using-mpas-aquaplanet-simulations','SCIGOV-DOEP'); return false;" href="https://www.osti.gov/pages/biblio/1214753-evaluation-tropical-channel-refinement-using-mpas-aquaplanet-simulations"><span>Evaluation of tropical channel refinement using MPAS-A aquaplanet simulations</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/pages">DOE PAGES</a></p> <p>Martini, Matus N.; Gustafson, Jr., William I.; O'Brien, Travis A.; ...</p> <p>2015-09-13</p> <p>Climate models with variable-resolution grids offer a computationally less expensive way to provide more detailed information at regional scales and increased accuracy for processes that cannot be resolved by a coarser grid. This study uses the Model for Prediction Across Scales–Atmosphere (MPAS22A), consisting of a nonhydrostatic dynamical core and a subset of Advanced Research Weather Research and Forecasting (ARW-WRF) model atmospheric physics that have been modified to include the Community Atmosphere Model version 5 (CAM5) cloud fraction parameterization, to investigate the potential benefits of using increased resolution in an tropical channel. The simulations are performed with an idealized aquaplanet configurationmore » using two quasi-uniform grids, with 30 km and 240 km grid spacing, and two variable-resolution grids spanning the same grid spacing range; one with a narrow (20°S–20°N) and one with a wide (30°S–30°N) tropical channel refinement. Results show that increasing resolution in the tropics impacts both the tropical and extratropical circulation. Compared to the quasi-uniform coarse grid, the narrow-channel simulation exhibits stronger updrafts in the Ferrel cell as well as in the middle of the upward branch of the Hadley cell. The wider tropical channel has a closer correspondence to the 30 km quasi-uniform simulation. However, the total atmospheric poleward energy transports are similar in all simulations. The largest differences are in the low-level cloudiness. The refined channel simulations show improved tropical and extratropical precipitation relative to the global 240 km simulation when compared to the global 30 km simulation. All simulations have a single ITCZ. Furthermore, the relatively small differences in mean global and tropical precipitation rates among the simulations are a promising result, and the evidence points to the tropical channel being an effective method for avoiding the extraneous numerical artifacts seen in earlier studies that only refined portion of the tropics.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018AtmRe.202..205S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018AtmRe.202..205S"><span>Characterization of the Sahelian-Sudan rainfall based on observations and regional climate models</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Salih, Abubakr A. M.; Elagib, Nadir Ahmed; Tjernström, Michael; Zhang, Qiong</p> <p>2018-04-01</p> <p>The African Sahel region is known to be highly vulnerable to climate variability and change. We analyze rainfall in the Sahelian Sudan in terms of distribution of rain-days and amounts, and examine whether regional climate models can capture these rainfall features. Three regional models namely, Regional Model (REMO), Rossby Center Atmospheric Model (RCA) and Regional Climate Model (RegCM4), are evaluated against gridded observations (Climate Research Unit, Tropical Rainfall Measuring Mission, and ERA-interim reanalysis) and rain-gauge data from six arid and semi-arid weather stations across Sahelian Sudan over the period 1989 to 2008. Most of the observed rain-days are characterized by weak (0.1-1.0 mm/day) to moderate (> 1.0-10.0 mm/day) rainfall, with average frequencies of 18.5% and 48.0% of the total annual rain-days, respectively. Although very strong rainfall events (> 30.0 mm/day) occur rarely, they account for a large fraction of the total annual rainfall (28-42% across the stations). The performance of the models varies both spatially and temporally. RegCM4 most closely reproduces the observed annual rainfall cycle, especially for the more arid locations, but all of the three models fail to capture the strong rainfall events and hence underestimate its contribution to the total annual number of rain-days and rainfall amount. However, excessive moderate rainfall compensates this underestimation in the models in an annual average sense. The present study uncovers some of the models' limitations in skillfully reproducing the observed climate over dry regions, will aid model users in recognizing the uncertainties in the model output and will help climate and hydrological modeling communities in improving models.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017EGUGA..1919082C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017EGUGA..1919082C"><span>Climate simulations and services on HPC, Cloud and Grid infrastructures</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Cofino, Antonio S.; Blanco, Carlos; Minondo Tshuma, Antonio</p> <p>2017-04-01</p> <p>Cloud, Grid and High Performance Computing have changed the accessibility and availability of computing resources for Earth Science research communities, specially for Climate community. These paradigms are modifying the way how climate applications are being executed. By using these technologies the number, variety and complexity of experiments and resources are increasing substantially. But, although computational capacity is increasing, traditional applications and tools used by the community are not good enough to manage this large volume and variety of experiments and computing resources. In this contribution, we evaluate the challenges to run climate simulations and services on Grid, Cloud and HPC infrestructures and how to tackle them. The Grid and Cloud infrastructures provided by EGI's VOs ( esr , earth.vo.ibergrid and fedcloud.egi.eu) will be evaluated, as well as HPC resources from PRACE infrastructure and institutional clusters. To solve those challenges, solutions using DRM4G framework will be shown. DRM4G provides a good framework to manage big volume and variety of computing resources for climate experiments. This work has been supported by the Spanish National R&D Plan under projects WRF4G (CGL2011-28864), INSIGNIA (CGL2016-79210-R) and MULTI-SDM (CGL2015-66583-R) ; the IS-ENES2 project from the 7FP of the European Commission (grant agreement no. 312979); the European Regional Development Fund—ERDF and the Programa de Personal Investigador en Formación Predoctoral from Universidad de Cantabria and Government of Cantabria.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFM.C41B0621G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFM.C41B0621G"><span>Are weather models better than gridded observations for precipitation in the mountains? (Invited)</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Gutmann, E. D.; Rasmussen, R.; Liu, C.; Ikeda, K.; Clark, M. P.; Brekke, L. D.; Arnold, J.; Raff, D. A.</p> <p>2013-12-01</p> <p>Mountain snowpack is a critical storage component in the water cycle, and it provides drinking water for tens of millions of people in the Western US alone. This water store is susceptible to climate change both because warming temperatures are likely to lead to earlier melt and a temporal shift of the hydrograph, and because changing atmospheric conditions are likely to change the precipitation patterns that produce the snowpack. Current measurements of snowfall in complex terrain are limited in number due in part to the logistics of installing equipment in complex terrain. We show that this limitation leads to statistical artifacts in gridded observations of current climate including errors in precipitation season totals of a factor of two or more, increases in wet day fraction, and decreases in storm intensity. In contrast, a high-resolution numerical weather model (WRF) is able to reproduce observed precipitation patterns, leading to confidence in its predictions for areas without measurements and new observations support this. Running WRF for a future climate scenario shows substantial changes in the spatial patterns of precipitation in the mountains related to the physics of hydrometeor production and detrainment that are not captured by statistical downscaling products. The stationarity in statistical downscaling products is likely to lead to important errors in our estimation of future precipitation in complex terrain.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20170011285','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20170011285"><span>The Impact of Simulated Mesoscale Convective Systems on Global Precipitation: A Multiscale Modeling Study</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Tao, Wei-Kuo; Chern, Jiun-Dar</p> <p>2017-01-01</p> <p>The importance of precipitating mesoscale convective systems (MCSs) has been quantified from TRMM precipitation radar and microwave imager retrievals. MCSs generate more than 50% of the rainfall in most tropical regions. MCSs usually have horizontal scales of a few hundred kilometers (km); therefore, a large domain with several hundred km is required for realistic simulations of MCSs in cloud-resolving models (CRMs). Almost all traditional global and climate models do not have adequate parameterizations to represent MCSs. Typical multi-scale modeling frameworks (MMFs) may also lack the resolution (4 km grid spacing) and domain size (128 km) to realistically simulate MCSs. In this study, the impact of MCSs on precipitation is examined by conducting model simulations using the Goddard Cumulus Ensemble (GCE) model and Goddard MMF (GMMF). The results indicate that both models can realistically simulate MCSs with more grid points (i.e., 128 and 256) and higher resolutions (1 or 2 km) compared to those simulations with fewer grid points (i.e., 32 and 64) and low resolution (4 km). The modeling results also show the strengths of the Hadley circulations, mean zonal and regional vertical velocities, surface evaporation, and amount of surface rainfall are weaker or reduced in the GMMF when using more CRM grid points and higher CRM resolution. In addition, the results indicate that large-scale surface evaporation and wind feed back are key processes for determining the surface rainfall amount in the GMMF. A sensitivity test with reduced sea surface temperatures shows both reduced surface rainfall and evaporation.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017JAMES...9..790T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017JAMES...9..790T"><span>The impact of simulated mesoscale convective systems on global precipitation: A multiscale modeling study</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Tao, Wei-Kuo; Chern, Jiun-Dar</p> <p>2017-06-01</p> <p>The importance of precipitating mesoscale convective systems (MCSs) has been quantified from TRMM precipitation radar and microwave imager retrievals. MCSs generate more than 50% of the rainfall in most tropical regions. MCSs usually have horizontal scales of a few hundred kilometers (km); therefore, a large domain with several hundred km is required for realistic simulations of MCSs in cloud-resolving models (CRMs). Almost all traditional global and climate models do not have adequate parameterizations to represent MCSs. Typical multiscale modeling frameworks (MMFs) may also lack the resolution (4 km grid spacing) and domain size (128 km) to realistically simulate MCSs. The impact of MCSs on precipitation is examined by conducting model simulations using the Goddard Cumulus Ensemble (GCE, a CRM) model and Goddard MMF that uses the GCEs as its embedded CRMs. Both models can realistically simulate MCSs with more grid points (i.e., 128 and 256) and higher resolutions (1 or 2 km) compared to those simulations with fewer grid points (i.e., 32 and 64) and low resolution (4 km). The modeling results also show the strengths of the Hadley circulations, mean zonal and regional vertical velocities, surface evaporation, and amount of surface rainfall are weaker or reduced in the Goddard MMF when using more CRM grid points and higher CRM resolution. In addition, the results indicate that large-scale surface evaporation and wind feedback are key processes for determining the surface rainfall amount in the GMMF. A sensitivity test with reduced sea surface temperatures shows both reduced surface rainfall and evaporation.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3348841','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3348841"><span>Fieldservers and Sensor Service Grid as Real-time Monitoring Infrastructure for Ubiquitous Sensor Networks</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Honda, Kiyoshi; Shrestha, Aadit; Witayangkurn, Apichon; Chinnachodteeranun, Rassarin; Shimamura, Hiroshi</p> <p>2009-01-01</p> <p>The fieldserver is an Internet based observation robot that can provide an outdoor solution for monitoring environmental parameters in real-time. The data from its sensors can be collected to a central server infrastructure and published on the Internet. The information from the sensor network will contribute to monitoring and modeling on various environmental issues in Asia, including agriculture, food, pollution, disaster, climate change etc. An initiative called Sensor Asia is developing an infrastructure called Sensor Service Grid (SSG), which integrates fieldservers and Web GIS to realize easy and low cost installation and operation of ubiquitous field sensor networks. PMID:22574018</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/29320501','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/29320501"><span>Uncertainty of future projections of species distributions in mountainous regions.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Tang, Ying; Winkler, Julie A; Viña, Andrés; Liu, Jianguo; Zhang, Yuanbin; Zhang, Xiaofeng; Li, Xiaohong; Wang, Fang; Zhang, Jindong; Zhao, Zhiqiang</p> <p>2018-01-01</p> <p>Multiple factors introduce uncertainty into projections of species distributions under climate change. The uncertainty introduced by the choice of baseline climate information used to calibrate a species distribution model and to downscale global climate model (GCM) simulations to a finer spatial resolution is a particular concern for mountainous regions, as the spatial resolution of climate observing networks is often insufficient to detect the steep climatic gradients in these areas. Using the maximum entropy (MaxEnt) modeling framework together with occurrence data on 21 understory bamboo species distributed across the mountainous geographic range of the Giant Panda, we examined the differences in projected species distributions obtained from two contrasting sources of baseline climate information, one derived from spatial interpolation of coarse-scale station observations and the other derived from fine-spatial resolution satellite measurements. For each bamboo species, the MaxEnt model was calibrated separately for the two datasets and applied to 17 GCM simulations downscaled using the delta method. Greater differences in the projected spatial distributions of the bamboo species were observed for the models calibrated using the different baseline datasets than between the different downscaled GCM simulations for the same calibration. In terms of the projected future climatically-suitable area by species, quantification using a multi-factor analysis of variance suggested that the sum of the variance explained by the baseline climate dataset used for model calibration and the interaction between the baseline climate data and the GCM simulation via downscaling accounted for, on average, 40% of the total variation among the future projections. Our analyses illustrate that the combined use of gridded datasets developed from station observations and satellite measurements can help estimate the uncertainty introduced by the choice of baseline climate information to the projected changes in species distribution.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5761832','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5761832"><span>Uncertainty of future projections of species distributions in mountainous regions</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Tang, Ying; Viña, Andrés; Liu, Jianguo; Zhang, Yuanbin; Zhang, Xiaofeng; Li, Xiaohong; Wang, Fang; Zhang, Jindong; Zhao, Zhiqiang</p> <p>2018-01-01</p> <p>Multiple factors introduce uncertainty into projections of species distributions under climate change. The uncertainty introduced by the choice of baseline climate information used to calibrate a species distribution model and to downscale global climate model (GCM) simulations to a finer spatial resolution is a particular concern for mountainous regions, as the spatial resolution of climate observing networks is often insufficient to detect the steep climatic gradients in these areas. Using the maximum entropy (MaxEnt) modeling framework together with occurrence data on 21 understory bamboo species distributed across the mountainous geographic range of the Giant Panda, we examined the differences in projected species distributions obtained from two contrasting sources of baseline climate information, one derived from spatial interpolation of coarse-scale station observations and the other derived from fine-spatial resolution satellite measurements. For each bamboo species, the MaxEnt model was calibrated separately for the two datasets and applied to 17 GCM simulations downscaled using the delta method. Greater differences in the projected spatial distributions of the bamboo species were observed for the models calibrated using the different baseline datasets than between the different downscaled GCM simulations for the same calibration. In terms of the projected future climatically-suitable area by species, quantification using a multi-factor analysis of variance suggested that the sum of the variance explained by the baseline climate dataset used for model calibration and the interaction between the baseline climate data and the GCM simulation via downscaling accounted for, on average, 40% of the total variation among the future projections. Our analyses illustrate that the combined use of gridded datasets developed from station observations and satellite measurements can help estimate the uncertainty introduced by the choice of baseline climate information to the projected changes in species distribution. PMID:29320501</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://www.osti.gov/sciencecinema/biblio/987926','SCIGOVIMAGE-SCICINEMA'); return false;" href="http://www.osti.gov/sciencecinema/biblio/987926"><span>Faces of the Recovery Act: The Impact of Smart Grid</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/sciencecinema/">ScienceCinema</a></p> <p>President Obama</p> <p>2017-12-09</p> <p>On October 27th, Baltimore Gas & Electric was selected to receive $200 million for Smart Grid innovation projects under the Recovery Act. Watch as members of their team, along with President Obama, explain how building a smarter grid will help consumers cut their utility bills, battle climate change and create jobs.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017MS%26E..273a2010T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017MS%26E..273a2010T"><span>Performance test of a grid-tied PV system to power a split air conditioner system in Surabaya</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Tarigan, E.</p> <p>2017-11-01</p> <p>Air conditioner for cooling air is one of the major needs for those who live in hot climate area such as Indonesia. This work presents the performance test of a grid-tied PV system to power air conditioner under a hot tropical climate in Surabaya, Indonesia. A 800 WP grid-tied photovoltaic (PV) system was used, and its performance was tested to power a 0.5 pk of split air conditioner system. It was found that about 3.5 kWh daily energy was consumed by the tested air conditioner system, and about 80% it could be supplied from the PV system. While the other 20% was supplied by the grid during periods of low solar irradiation, 440 Wh of energy was fed into the grid during operation out of office hours. By using the grid-tied PV system, the energy production by PV system did not need to match the consumption of the air conditioner. However, a larger capacity of PV system would mean that a higher percentage of the load would be covered by PV system.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li class="active"><span>22</span></li> <li><a href="#" onclick='return showDiv("page_23");'>23</a></li> <li><a href="#" onclick='return showDiv("page_24");'>24</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_22 --> <div id="page_23" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li class="active"><span>23</span></li> <li><a href="#" onclick='return showDiv("page_24");'>24</a></li> <li><a href="#" onclick='return showDiv("page_25");'>25</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="441"> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/22743679','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/22743679"><span>Impact of climate change on ozone-related mortality and morbidity in Europe.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Orru, Hans; Andersson, Camilla; Ebi, Kristie L; Langner, Joakim; Aström, Christofer; Forsberg, Bertil</p> <p>2013-02-01</p> <p>Ozone is a highly oxidative pollutant formed from precursors in the presence of sunlight, associated with respiratory morbidity and mortality. All else being equal, concentrations of ground-level ozone are expected to increase due to climate change. Ozone-related health impacts under a changing climate are projected using emission scenarios, models and epidemiological data. European ozone concentrations are modelled with the model of atmospheric transport and chemistry (MATCH)-RCA3 (50×50 km). Projections from two climate models, ECHAM4 and HadCM3, are applied under greenhouse gas emission scenarios A2 and A1B, respectively. We applied a European-wide exposure-response function to gridded population data and country-specific baseline mortality and morbidity. Comparing the current situation (1990-2009) with the baseline period (1961-1990), the largest increase in ozone-associated mortality and morbidity due to climate change (4-5%) have occurred in Belgium, Ireland, the Netherlands and the UK. Comparing the baseline period and the future periods (2021-2050 and 2041-2060), much larger increases in ozone-related mortality and morbidity are projected for Belgium, France, Spain and Portugal, with the impact being stronger using the climate projection from ECHAM4 (A2). However, in Nordic and Baltic countries the same magnitude of decrease is projected. The current study suggests that projected effects of climate change on ozone concentrations could differentially influence mortality and morbidity across Europe.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19990064545&hterms=simulation+processes&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D70%26Ntt%3Dsimulation%2Bprocesses','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19990064545&hterms=simulation+processes&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D70%26Ntt%3Dsimulation%2Bprocesses"><span>Tropical Oceanic Precipitation Processes over Warm Pool: 2D and 3D Cloud Resolving Model Simulations</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Tao, W.- K.; Johnson, D.</p> <p>1998-01-01</p> <p>Rainfall is a key link in the hydrologic cycle as well as the primary heat source for the atmosphere, The vertical distribution of convective latent-heat release modulates the large-scale circulations of the tropics, Furthermore, changes in the moisture distribution at middle and upper levels of the troposphere can affect cloud distributions and cloud liquid water and ice contents. How the incoming solar and outgoing longwave radiation respond to these changes in clouds is a major factor in assessing climate change. Present large-scale weather and climate models simulate cloud processes only crudely, reducing confidence in their predictions on both global and regional scales. One of the most promising methods to test physical parameterizations used in General Circulation Models (GCMS) and climate models is to use field observations together with Cloud Resolving Models (CRMs). The CRMs use more sophisticated and physically realistic parameterizations of cloud microphysical processes, and allow for their complex interactions with solar and infrared radiative transfer processes. The CRMs can reasonably well resolve the evolution, structure, and life cycles of individual clouds and cloud systems, The major objective of this paper is to investigate the latent heating, moisture and momenti,im budgets associated with several convective systems developed during the TOGA COARE IFA - westerly wind burst event (late December, 1992). The tool for this study is the Goddard Cumulus Ensemble (CCE) model which includes a 3-class ice-phase microphysical scheme, The model domain contains 256 x 256 grid points (using 2 km resolution) in the horizontal and 38 grid points (to a depth of 22 km depth) in the vertical, The 2D domain has 1024 grid points. The simulations are performed over a 7 day time period. We will examine (1) the precipitation processes (i.e., condensation/evaporation) and their interaction with warm pool; (2) the heating and moisture budgets in the convective and stratiform regions; (3) the cloud (upward-downward) mass fluxes in convective and stratiform regions; (4) characteristics of clouds (such as cloud size, updraft intensity and cloud lifetime) and the comparison of clouds with Radar observations. Differences and similarities in organization of convection between simulated 2D and 3D cloud systems. Preliminary results indicated that there is major differences between 2D and 3D simulated stratiform rainfall amount and convective updraft and downdraft mass fluxes.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19840014024&hterms=geography&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3Dgeography','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19840014024&hterms=geography&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3Dgeography"><span>Design for and efficient dynamic climate model with realistic geography</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Suarez, M. J.; Abeles, J.</p> <p>1984-01-01</p> <p>The long term climate sensitivity which include realistic atmospheric dynamics are severely restricted by the expense of integrating atmospheric general circulation models are discussed. Taking as an example models used at GSFC for this dynamic model is an alternative which is of much lower horizontal or vertical resolution. The model of Heid and Suarez uses only two levels in the vertical and, although it has conventional grid resolution in the meridional direction, horizontal resolution is reduced by keeping only a few degrees of freedom in the zonal wavenumber spectrum. Without zonally asymmetric forcing this model simulates a day in roughly 1/2 second on a CRAY. The model under discussion is a fully finite differenced, zonally asymmetric version of the Heid-Suarez model. It is anticipated that speeds can be obtained a few seconds a day roughly 50 times faster than moderate resolution, multilayer GCM's.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/pages/biblio/1235593-gridded-global-data-set-soil-intact-regolith-sedimentary-deposit-thicknesses-regional-global-land-surface-modeling','SCIGOV-DOEP'); return false;" href="https://www.osti.gov/pages/biblio/1235593-gridded-global-data-set-soil-intact-regolith-sedimentary-deposit-thicknesses-regional-global-land-surface-modeling"><span>A gridded global data set of soil, intact regolith, and sedimentary deposit thicknesses for regional and global land surface modeling</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/pages">DOE PAGES</a></p> <p>Pelletier, Jon D.; Broxton, Patrick D.; Hazenberg, Pieter; ...</p> <p>2016-01-22</p> <p>Earth’s terrestrial near-subsurface environment can be divided into relatively porous layers of soil, intact regolith, and sedimentary deposits above unweathered bedrock. Variations in the thicknesses of these layers control the hydrologic and biogeochemical responses of landscapes. Currently, Earth System Models approximate the thickness of these relatively permeable layers above bedrock as uniform globally, despite the fact that their thicknesses vary systematically with topography, climate, and geology. To meet the need for more realistic input data for models, we developed a high-resolution gridded global data set of the average thicknesses of soil, intact regolith, and sedimentary deposits within each 30 arcsecmore » (~ 1 km) pixel using the best available data for topography, climate, and geology as input. Our data set partitions the global land surface into upland hillslope, upland valley bottom, and lowland landscape components and uses models optimized for each landform type to estimate the thicknesses of each subsurface layer. On hillslopes, the data set is calibrated and validated using independent data sets of measured soil thicknesses from the U.S. and Europe and on lowlands using depth to bedrock observations from groundwater wells in the U.S. As a result, we anticipate that the data set will prove useful as an input to regional and global hydrological and ecosystems models.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016JAMES...8...41P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016JAMES...8...41P"><span>A gridded global data set of soil, intact regolith, and sedimentary deposit thicknesses for regional and global land surface modeling</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Pelletier, Jon D.; Broxton, Patrick D.; Hazenberg, Pieter; Zeng, Xubin; Troch, Peter A.; Niu, Guo-Yue; Williams, Zachary; Brunke, Michael A.; Gochis, David</p> <p>2016-03-01</p> <p>Earth's terrestrial near-subsurface environment can be divided into relatively porous layers of soil, intact regolith, and sedimentary deposits above unweathered bedrock. Variations in the thicknesses of these layers control the hydrologic and biogeochemical responses of landscapes. Currently, Earth System Models approximate the thickness of these relatively permeable layers above bedrock as uniform globally, despite the fact that their thicknesses vary systematically with topography, climate, and geology. To meet the need for more realistic input data for models, we developed a high-resolution gridded global data set of the average thicknesses of soil, intact regolith, and sedimentary deposits within each 30 arcsec (˜1 km) pixel using the best available data for topography, climate, and geology as input. Our data set partitions the global land surface into upland hillslope, upland valley bottom, and lowland landscape components and uses models optimized for each landform type to estimate the thicknesses of each subsurface layer. On hillslopes, the data set is calibrated and validated using independent data sets of measured soil thicknesses from the U.S. and Europe and on lowlands using depth to bedrock observations from groundwater wells in the U.S. We anticipate that the data set will prove useful as an input to regional and global hydrological and ecosystems models. This article was corrected on 2 FEB 2016. See the end of the full text for details.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2001BAMS...82.2787N','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2001BAMS...82.2787N"><span>Dynamical Downscaling of Seasonal Climate Prediction over Nordeste Brazil with ECHAM3 and NCEP's Regional Spectral Models at IRI.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Nobre, Paulo; Moura, Antonio D.; Sun, Liqiang</p> <p>2001-12-01</p> <p>This study presents an evaluation of a seasonal climate forecast done with the International Research Institute for Climate Prediction (IRI) dynamical forecast system (regional model nested into a general circulation model) over northern South America for January-April 1999, encompassing the rainy season over Brazil's Nordeste. The one-way nesting is one in two tiers: first the NCEP's Regional Spectral Model (RSM) runs with an 80-km grid mesh forced by the ECHAM3 atmospheric general circulation model (AGCM) outputs; then the RSM runs with a finer grid mesh (20 km) forced by the forecasts generated by the RSM-80. An ensemble of three realizations is done. Lower boundary conditions over the oceans for both ECHAM and RSM model runs are sea surface temperature forecasts over the tropical oceans. Soil moisture is initialized by ECHAM's inputs. The rainfall forecasts generated by the regional model are compared with those of the AGCM and observations. It is shown that the regional model at 80-km resolution improves upon the AGCM rainfall forecast, reducing both seasonal bias and root-mean-square error. On the other hand, the RSM-20 forecasts presented larger errors, with spatial patterns that resemble those of local topography. The better forecast of the position and width of the intertropical convergence zone (ITCZ) over the tropical Atlantic by the RSM-80 model is one of the principal reasons for better-forecast scores of the RSM-80 relative to the AGCM. The regional model improved the spatial as well as the temporal details of rainfall distribution, and also presenting the minimum spread among the ensemble members. The statistics of synoptic-scale weather variability on seasonal timescales were best forecast with the regional 80-km model over the Nordeste. The possibility of forecasting the frequency distribution of dry and wet spells within the rainy season is encouraging.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFMGC53F..01R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFMGC53F..01R"><span>AgMIP 1.5°C Assessment: Mitigation and Adaptation at Coordinated Global and Regional Scales</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Rosenzweig, C.</p> <p>2016-12-01</p> <p>The AgMIP 1.5°C Coordinated Global and Regional Integrated Assessments of Climate Change and Food Security (AgMIP 1.5 CGRA) is linking site-based crop and livestock models with similar models run on global grids, and then links these biophysical components with economics models and nutrition metrics at regional and global scales. The AgMIP 1.5 CGRA assessment brings together experts in climate, crop, livestock, economics, nutrition, and food security to define the 1.5°C Protocols and guide the process throughout the assessment. Scenarios are designed to consistently combine elements of intertwined storylines of future society including socioeconomic development (Shared Socioeconomic Pathways), greenhouse gas concentrations (Representative Concentration Pathways), and specific pathways of agricultural sector development (Representative Agricultural Pathways). Shared Climate Policy Assumptions will be extended to provide additional agricultural detail on mitigation and adaptation strategies. The multi-model, multi-disciplinary, multi-scale integrated assessment framework is using scenarios of economic development, adaptation, mitigation, food policy, and food security. These coordinated assessments are grounded in the expertise of AgMIP partners around the world, leading to more consistent results and messages for stakeholders, policymakers, and the scientific community. The early inclusion of nutrition and food security experts has helped to ensure that assessment outputs include important metrics upon which investment and policy decisions may be based. The CGRA builds upon existing AgMIP research groups (e.g., the AgMIP Wheat Team and the AgMIP Global Gridded Crop Modeling Initiative; GGCMI) and regional programs (e.g., AgMIP Regional Teams in Sub-Saharan Africa and South Asia), with new protocols for cross-scale and cross-disciplinary linkages to ensure the propagation of expert judgment and consistent assumptions.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017ThApC.127..533C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017ThApC.127..533C"><span>Surface temperature dataset for North America obtained by application of optimal interpolation algorithm merging tree-ring chronologies and climate model output</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Chen, Xin; Xing, Pei; Luo, Yong; Nie, Suping; Zhao, Zongci; Huang, Jianbin; Wang, Shaowu; Tian, Qinhua</p> <p>2017-02-01</p> <p>A new dataset of surface temperature over North America has been constructed by merging climate model results and empirical tree-ring data through the application of an optimal interpolation algorithm. Errors of both the Community Climate System Model version 4 (CCSM4) simulation and the tree-ring reconstruction were considered to optimize the combination of the two elements. Variance matching was used to reconstruct the surface temperature series. The model simulation provided the background field, and the error covariance matrix was estimated statistically using samples from the simulation results with a running 31-year window for each grid. Thus, the merging process could continue with a time-varying gain matrix. This merging method (MM) was tested using two types of experiment, and the results indicated that the standard deviation of errors was about 0.4 °C lower than the tree-ring reconstructions and about 0.5 °C lower than the model simulation. Because of internal variabilities and uncertainties in the external forcing data, the simulated decadal warm-cool periods were readjusted by the MM such that the decadal variability was more reliable (e.g., the 1940-1960s cooling). During the two centuries (1601-1800 AD) of the preindustrial period, the MM results revealed a compromised spatial pattern of the linear trend of surface temperature, which is in accordance with the phase transition of the Pacific decadal oscillation and Atlantic multidecadal oscillation. Compared with pure CCSM4 simulations, it was demonstrated that the MM brought a significant improvement to the decadal variability of the gridded temperature via the merging of temperature-sensitive tree-ring records.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20020060666&hterms=drought&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D90%26Ntt%3Ddrought','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20020060666&hterms=drought&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D90%26Ntt%3Ddrought"><span>Importance of Winds and Soil Moistures to the US Summertime Drought of 1988: A GCM Simulation Study</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Mocko, David M.; Sud, Y. C.; Lau, William K. M. (Technical Monitor)</p> <p>2001-01-01</p> <p>The climate version of NASA's GEOS 2 GCM did not simulate a realistic 1988 summertime drought in the central United States (Mocko et al., 1999). Despite several new upgrades to the model's parameterizations, as well as finer grid spacing from 4x5 degrees to 2x2.5 degrees, no significant improvements were noted in the model's simulation of the U.S. drought.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017JOUC...16....1Y','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017JOUC...16....1Y"><span>Climate projections of spatial variations in coastal storm surges along the Gulf of Mexico and U.S. east coast</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Yao, Zhigang; Xue, Zuo; He, Ruoying; Bao, Xianwen; Xie, Jun; Ge, Qian</p> <p>2017-02-01</p> <p>Using statistically downscaled atmospheric forcing, we performed a numerical investigation to evaluate future climate's impact on storm surges along the Gulf of Mexico and U.S. east coast. The focus is on the impact of climatic changes in wind pattern and surface pressure while neglecting sea level rise and other factors. We adapted the regional ocean model system (ROMS) to the study region with a mesh grid size of 7-10 km in horizontal and 18 vertical layers. The model was validated by a hindcast of the coastal sea levels in the winter of 2008. Model's robustness was confirmed by the good agreement between model-simulated and observed sea levels at 37 tidal gages. Two 10-year forecasts, one for the IPCC Pre-Industry (PI) and the other for the A1FI scenario, were conducted. The differences in model-simulated surge heights under the two climate scenarios were analyzed. We identified three types of responses in extreme surge heights to future climate: a clear decrease in Middle Atlantic Bight, an increase in the western Gulf of Mexico, and non-significant response for the remaining area. Such spatial pattern is also consistent with previous projections of sea surface winds and ocean wave heights.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFMGC41E0622S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFMGC41E0622S"><span>Simulating the Response of Urban Water Quality to Climate and Land Use Change in Partially Urbanized Basins</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Sun, N.; Yearsley, J. R.; Nijssen, B.; Lettenmaier, D. P.</p> <p>2014-12-01</p> <p>Urban stream quality is particularly susceptible to extreme precipitation events and land use change. Although the projected effects of extreme events and land use change on hydrology have been resonably well studied, the impacts on urban water quality have not been widely examined due in part to the scale mismatch between global climate models and the spatial scales required to represent urban hydrology and water quality signals. Here we describe a grid-based modeling system that integrates the Distributed Hydrology Soil Vegetation Model (DHSVM) and urban water quality module adpated from EPA's Storm Water Management Model (SWMM) and Soil and water assessment tool (SWAT). Using the model system, we evaluate, for four partially urbanized catchments within the Puget Sound basin, urban water quality under current climate conditions, and projected potential changes in urban water quality associated with future changes in climate and land use. We examine in particular total suspended solids, toal nitrogen, total phosphorous, and coliform bacteria, with catchment representations at the 150-meter spatial resolution and the sub-daily timestep. We report long-term streamflow and water quality predictions in response to extreme precipitation events of varying magnitudes in the four partially urbanized catchments. Our simulations show that urban water quality is highly sensitive to both climatic and land use change.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.A23D2398D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.A23D2398D"><span>Assessment of hi-resolution multi-ensemble statistical downscaling regional climate scenarios over Japan</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Dairaku, K.</p> <p>2017-12-01</p> <p>The Asia-Pacific regions are increasingly threatened by large scale natural disasters. Growing concerns that loss and damages of natural disasters are projected to further exacerbate by climate change and socio-economic change. Climate information and services for risk assessments are of great concern. Fundamental regional climate information is indispensable for understanding changing climate and making decisions on when and how to act. To meet with the needs of stakeholders such as National/local governments, spatio-temporal comprehensive and consistent information is necessary and useful for decision making. Multi-model ensemble regional climate scenarios with 1km horizontal grid-spacing over Japan are developed by using CMIP5 37 GCMs (RCP8.5) and a statistical downscaling (Bias Corrected Spatial Disaggregation (BCSD)) to investigate uncertainty of projected change associated with structural differences of the GCMs for the periods of historical climate (1950-2005) and near future climate (2026-2050). Statistical downscaling regional climate scenarios show good performance for annual and seasonal averages for precipitation and temperature. The regional climate scenarios show systematic underestimate of extreme events such as hot days of over 35 Celsius and annual maximum daily precipitation because of the interpolation processes in the BCSD method. Each model projected different responses in near future climate because of structural differences. The most of CMIP5 37 models show qualitatively consistent increase of average and extreme temperature and precipitation. The added values of statistical/dynamical downscaling methods are also investigated for locally forced nonlinear phenomena, extreme events.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20020001033','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20020001033"><span>Snow-Cover Variability in North America in the 2000-2001 Winter as Determined from MODIS Snow Products</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Hall, Dorothy K.; Salomonson, Vincent V.; Riggs, George A.; Chien, Janet Y. L.; Houser, Paul R. (Technical Monitor)</p> <p>2001-01-01</p> <p>Moderate Resolution Imaging Spectroradiometer (MODIS) snow-cover maps have been available since September 13, 2000. These products, at 500 m spatial resolution, are available through the National Snow and Ice Data Center Distributed Active Archive Center in Boulder, Colorado. By the 2001-02 winter, 5 km climate-modeling grid (CMG) products will be available for presentation of global views of snow cover and for use in climate models. All MODIS snow-cover products are produced from automated algorithms that map snow in an objective manner. In this paper, we describe the MODIS snow products, and show snow maps from the fall of 2000 in North America.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20010069504','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20010069504"><span>Snow-Cover Variability in North America in the 2000-2001 Winter as Determined from MODIS Snow Products</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Hall, Dorothy K.; Salomonson, Vincent V.; Riggs, George A.; Chien, Y. L.; Houser, Paul R. (Technical Monitor)</p> <p>2001-01-01</p> <p>Moderate Resolution Imaging Spectroradiometer (MODIS) snow-cover maps have been available since September 13, 2000. These products, at 500-m spatial resolution, are available through the National Snow and Ice Data Center Distributed Active Archive Center in Boulder, Colorado. By the 2001-02 winter, 5-km climate-modeling grid (CMG) products will be available for presentation of global views of snow cover and for use in climate models. All MODIS snow-cover products are produced from automated algorithms that map snow in an objective manner. In this paper, we describe the MODIS snow products, and show snow maps from the fall of 2000 in North America.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFM.H13A1058S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFM.H13A1058S"><span>Evidences of Significant Nonstationarity in Precipitation Extremes over Urbanizing Areas in India</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Singh, J.; H, V.; Karmakar, S.; Ghosh, S.</p> <p>2014-12-01</p> <p>The statistical assumption of stationarity in hydrologic extreme time/event series has been relied heavily in frequency analysis. However, due to the analytically perceivable impacts of climate change, urbanization and concomitant land use pattern, assumption of stationarity in hydrologic time series will draw erroneous results, which inturn effects the policy and decision-making. Past studies provided sufficient evidences on changes in the characteristics of Indian monsoon rainfall extremes and further it has been attributed to climate change and urbanization, which indicates the presence of significant nonstationary in the Indian monsoon extremes. Therefore, a comprehensive nonstationary frequency analysis must be conducted all over India to obtain realistic return periods. The present study aims to conduct a nonstationary frequency analysis of the precipitation extremes over India at 1o resolution for a period of 1901-2004, with the implementation of the Generalized Additive Model for Location, Scale and Shape (GAMLSS) parameters. A cluster of 74 GAMLSS models has been developed by considering nonstationary in different combinations of distribution parameters and regression techniques (families of parametric polynomials and nonparametric/smoothing cubic spline), which overcomes the limitations of the previous studies. Further, for identification of urban, urbanizing and rural grids, an population density data has been utilized. The results showed the significant differences in the stationary and nonstationary return periods for the urbanizing grids, when compared to urbanized and rural grids. The results give implications of presence of nonstationary in the precipitation extremes more prominently in urbanizing areas compare to urbanized and rural areas.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20120003983','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20120003983"><span>AIRS-Observed Interrelationships of Anomaly Time-Series of Moist Process-Related Parameters and Inferred Feedback Values on Various Spatial Scales</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Molnar, Gyula I.; Susskind, Joel; Iredell, Lena</p> <p>2011-01-01</p> <p>In the beginning, a good measure of a GMCs performance was their ability to simulate the observed mean seasonal cycle. That is, a reasonable simulation of the means (i.e., small biases) and standard deviations of TODAY?S climate would suffice. Here, we argue that coupled GCM (CG CM for short) simulations of FUTURE climates should be evaluated in much more detail, both spatially and temporally. Arguably, it is not the bias, but rather the reliability of the model-generated anomaly time-series, even down to the [C]GCM grid-scale, which really matter. This statement is underlined by the social need to address potential REGIONAL climate variability, and climate drifts/changes in a manner suitable for policy decisions.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/19890006875','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19890006875"><span>Equatorial waves simulated by the NCAR community climate model</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Cheng, Xinhua; Chen, Tsing-Chang</p> <p>1988-01-01</p> <p>The equatorial planetary waves simulated by the NCAR CCM1 general circulation model were investigated in terms of space-time spectral analysis (Kao, 1968; Hayashi, 1971, 1973) and energetic analysis (Hayashi, 1980). These analyses are particularly applied to grid-point data on latitude circles. In order to test some physical factors which may affect the generation of tropical transient planetary waves, three different model simulations with the CCM1 (the control, the no-mountain, and the no-cloud experiments) were analyzed.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFM.A51J0198H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFM.A51J0198H"><span>High-resolution regional climate model evaluation using variable-resolution CESM over California</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Huang, X.; Rhoades, A.; Ullrich, P. A.; Zarzycki, C. M.</p> <p>2015-12-01</p> <p>Understanding the effect of climate change at regional scales remains a topic of intensive research. Though computational constraints remain a problem, high horizontal resolution is needed to represent topographic forcing, which is a significant driver of local climate variability. Although regional climate models (RCMs) have traditionally been used at these scales, variable-resolution global climate models (VRGCMs) have recently arisen as an alternative for studying regional weather and climate allowing two-way interaction between these domains without the need for nudging. In this study, the recently developed variable-resolution option within the Community Earth System Model (CESM) is assessed for long-term regional climate modeling over California. Our variable-resolution simulations will focus on relatively high resolutions for climate assessment, namely 28km and 14km regional resolution, which are much more typical for dynamically downscaled studies. For comparison with the more widely used RCM method, the Weather Research and Forecasting (WRF) model will be used for simulations at 27km and 9km. All simulations use the AMIP (Atmospheric Model Intercomparison Project) protocols. The time period is from 1979-01-01 to 2005-12-31 (UTC), and year 1979 was discarded as spin up time. The mean climatology across California's diverse climate zones, including temperature and precipitation, is analyzed and contrasted with the Weather Research and Forcasting (WRF) model (as a traditional RCM), regional reanalysis, gridded observational datasets and uniform high-resolution CESM at 0.25 degree with the finite volume (FV) dynamical core. The results show that variable-resolution CESM is competitive in representing regional climatology on both annual and seasonal time scales. This assessment adds value to the use of VRGCMs for projecting climate change over the coming century and improve our understanding of both past and future regional climate related to fine-scale processes. This assessment is also relevant for addressing the scale limitation of current RCMs or VRGCMs when next-generation model resolution increases to ~10km and beyond.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016cosp...41E1332M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016cosp...41E1332M"><span>Nine martian years of dust optical depth observations: A reference dataset</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Montabone, Luca; Forget, Francois; Kleinboehl, Armin; Kass, David; Wilson, R. John; Millour, Ehouarn; Smith, Michael; Lewis, Stephen; Cantor, Bruce; Lemmon, Mark; Wolff, Michael</p> <p>2016-07-01</p> <p>We present a multi-annual reference dataset of the horizontal distribution of airborne dust from martian year 24 to 32 using observations of the martian atmosphere from April 1999 to June 2015 made by the Thermal Emission Spectrometer (TES) aboard Mars Global Surveyor, the Thermal Emission Imaging System (THEMIS) aboard Mars Odyssey, and the Mars Climate Sounder (MCS) aboard Mars Reconnaissance Orbiter (MRO). Our methodology to build the dataset works by gridding the available retrievals of column dust optical depth (CDOD) from TES and THEMIS nadir observations, as well as the estimates of this quantity from MCS limb observations. The resulting (irregularly) gridded maps (one per sol) were validated with independent observations of CDOD by PanCam cameras and Mini-TES spectrometers aboard the Mars Exploration Rovers "Spirit" and "Opportunity", by the Surface Stereo Imager aboard the Phoenix lander, and by the Compact Reconnaissance Imaging Spectrometer for Mars aboard MRO. Finally, regular maps of CDOD are produced by spatially interpolating the irregularly gridded maps using a kriging method. These latter maps are used as dust scenarios in the Mars Climate Database (MCD) version 5, and are useful in many modelling applications. The two datasets (daily irregularly gridded maps and regularly kriged maps) for the nine available martian years are publicly available as NetCDF files and can be downloaded from the MCD website at the URL: http://www-mars.lmd.jussieu.fr/mars/dust_climatology/index.html</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2008AGUFM.B53B0491M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2008AGUFM.B53B0491M"><span>Application of Ecosystem Models to Assess Environmental Drivers of Mosquito Abundance and Virus Transmission Risk and Associated Public Health Implications of Climate and Land Use Change</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Melton, F.; Barker, C.; Park, B.; Reisen, W.; Michaelis, A.; Wang, W.; Hashimoto, H.; Milesi, C.; Hiatt, S.; Nemani, R.</p> <p>2008-12-01</p> <p>The NASA Terrestrial Observation and Prediction System (TOPS) is a modeling framework that integrates satellite observations, meteorological observations, and ancillary data to support monitoring and modeling of ecosystem and land surface conditions in near real-time. TOPS provides spatially continuous gridded estimates of a suite of measurements describing environmental conditions, and these data products are currently being applied to support the development of new models capable of forecasting estimated mosquito abundance and transmission risk for mosquito-borne diseases such as West Nile virus. We present results from the modeling analyses, describe their incorporation into the California Vectorborne Disease Surveillance System, and describe possible implications of projected climate and land use change for patterns in mosquito abundance and transmission risk for West Nile virus in California.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li class="active"><span>23</span></li> <li><a href="#" onclick='return showDiv("page_24");'>24</a></li> <li><a href="#" onclick='return showDiv("page_25");'>25</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_23 --> <div id="page_24" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li><a href="#" onclick='return showDiv("page_23");'>23</a></li> <li class="active"><span>24</span></li> <li><a href="#" onclick='return showDiv("page_25");'>25</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="461"> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017ESSD....9..471J','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017ESSD....9..471J"><span>Using ERA-Interim reanalysis for creating datasets of energy-relevant climate variables</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Jones, Philip D.; Harpham, Colin; Troccoli, Alberto; Gschwind, Benoit; Ranchin, Thierry; Wald, Lucien; Goodess, Clare M.; Dorling, Stephen</p> <p>2017-07-01</p> <p>The construction of a bias-adjusted dataset of climate variables at the near surface using ERA-Interim reanalysis is presented. A number of different, variable-dependent, bias-adjustment approaches have been proposed. Here we modify the parameters of different distributions (depending on the variable), adjusting ERA-Interim based on gridded station or direct station observations. The variables are air temperature, dewpoint temperature, precipitation (daily only), solar radiation, wind speed, and relative humidity. These are available on either 3 or 6 h timescales over the period 1979-2016. The resulting bias-adjusted dataset is available through the Climate Data Store (CDS) of the Copernicus Climate Change Data Store (C3S) and can be accessed at present from <a href="ftp://ecem.climate.copernicus.eu" target="_blank">ftp://ecem.climate.copernicus.eu</a>. The benefit of performing bias adjustment is demonstrated by comparing initial and bias-adjusted ERA-Interim data against gridded observational fields.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014EGUGA..1610467O','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014EGUGA..1610467O"><span>Some lessons and thoughts from development of an old-fashioned high-resolution atmospheric general circulation model</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ohfuchi, Wataru; Enomoto, Takeshi; Yoshioka, Mayumi K.; Takaya, Koutarou</p> <p>2014-05-01</p> <p>Some high-resolution simulations with a conventional atmospheric general circulation model (AGCM) were conducted right after the first Earth Simulator started operating in the spring of 2002. More simulations with various resolutions followed. The AGCM in this study, AFES (Agcm For the Earth Simulator), is a primitive equation spectral transform method model with a cumulus convection parameterization. In this presentation, some findings from comparisons between high and low-resolution simulations, and some future perspectives of old-fashioned AGCMs will be discussed. One obvious advantage of increasing resolution is capability of resolving the fine structures of topography and atmospheric flow. By increasing resolution from T39 (about 320 km horizontal grid interval) to T79 (160 km), to T159 (80 km) to T319 (40 km), topographic precipitation over Japan becomes increasingly realistic. This feature is necessary for climate and weather studies involving both global and local aspects. In order to resolve submesoscale (about 100 km horizontal scale) atmospheric circulation, about 10-km grid interval is necessary. Comparing T1279 (10 km) simulations with T319 ones, it is found that, for example, the intensity of heavy rain associated with Baiu front and the central pressure of typhoon become more realistic. These realistic submesoscale phenomena should have impact on larger-sale flow through dynamics and thermodynamics. An interesting finding by increasing horizontal resolution of a conventional AGCM is that some cumulus convection parameterizations, such as Arakawa-Schubert type scheme, gradually stop producing precipitation, while some others, such as Emanuel type, do not. With the former, the grid condensation increases with the model resolution to compensate. Which characteristics are more desirable is arguable but it is an important feature one has to consider when developing a high-resolution conventional AGCM. Many may think that conventional primitive equation spectral transform AGCMs, such as AFES, have no future. Developing globally homogeneous nonhydrostatic cloud resolving grid AGCMs is obviously a straightforward direction for the future. However these models will be very expensive for many users for a while, perhaps for the next some decades. On the other hand, old-fashioned AGCMs with a grid interval of 20-100 km will remain to be accurate and efficient tools for many users for many years to come. Also by coupling with a fine-resolution regional nonhydrostatic model, a conventional AGCM may overcome its limitation for use in climate and weather studies in the future.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFMIN53C..01W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFMIN53C..01W"><span>SciSpark: Highly Interactive and Scalable Model Evaluation and Climate Metrics</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wilson, B. D.; Palamuttam, R. S.; Mogrovejo, R. M.; Whitehall, K. D.; Mattmann, C. A.; Verma, R.; Waliser, D. E.; Lee, H.</p> <p>2015-12-01</p> <p>Remote sensing data and climate model output are multi-dimensional arrays of massive sizes locked away in heterogeneous file formats (HDF5/4, NetCDF 3/4) and metadata models (HDF-EOS, CF) making it difficult to perform multi-stage, iterative science processing since each stage requires writing and reading data to and from disk. We are developing a lightning fast Big Data technology called SciSpark based on ApacheTM Spark under a NASA AIST grant (PI Mattmann). Spark implements the map-reduce paradigm for parallel computing on a cluster, but emphasizes in-memory computation, "spilling" to disk only as needed, and so outperforms the disk-based ApacheTM Hadoop by 100x in memory and by 10x on disk. SciSpark will enable scalable model evaluation by executing large-scale comparisons of A-Train satellite observations to model grids on a cluster of 10 to 1000 compute nodes. This 2nd generation capability for NASA's Regional Climate Model Evaluation System (RCMES) will compute simple climate metrics at interactive speeds, and extend to quite sophisticated iterative algorithms such as machine-learning based clustering of temperature PDFs, and even graph-based algorithms for searching for Mesocale Convective Complexes. We have implemented a parallel data ingest capability in which the user specifies desired variables (arrays) as several time-sorted lists of URL's (i.e. using OPeNDAP model.nc?varname, or local files). The specified variables are partitioned by time/space and then each Spark node pulls its bundle of arrays into memory to begin a computation pipeline. We also investigated the performance of several N-dim. array libraries (scala breeze, java jblas & netlib-java, and ND4J). We are currently developing science codes using ND4J and studying memory behavior on the JVM. On the pyspark side, many of our science codes already use the numpy and SciPy ecosystems. The talk will cover: the architecture of SciSpark, the design of the scientific RDD (sRDD) data structure, our efforts to integrate climate science algorithms in Python and Scala, parallel ingest and partitioning of A-Train satellite observations from HDF files and model grids from netCDF files, first parallel runs to compute comparison statistics and PDF's, and first metrics quantifying parallel speedups and memory & disk usage.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013EGUGA..1511535B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013EGUGA..1511535B"><span>WRF4SG: A Scientific Gateway for climate experiment workflows</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Blanco, Carlos; Cofino, Antonio S.; Fernandez-Quiruelas, Valvanuz</p> <p>2013-04-01</p> <p>The Weather Research and Forecasting model (WRF) is a community-driven and public domain model widely used by the weather and climate communities. As opposite to other application-oriented models, WRF provides a flexible and computationally-efficient framework which allows solving a variety of problems for different time-scales, from weather forecast to climate change projection. Furthermore, WRF is also widely used as a research tool in modeling physics, dynamics, and data assimilation by the research community. Climate experiment workflows based on Weather Research and Forecasting (WRF) are nowadays among the one of the most cutting-edge applications. These workflows are complex due to both large storage and the huge number of simulations executed. In order to manage that, we have developed a scientific gateway (SG) called WRF for Scientific Gateway (WRF4SG) based on WS-PGRADE/gUSE and WRF4G frameworks to ease achieve WRF users needs (see [1] and [2]). WRF4SG provides services for different use cases that describe the different interactions between WRF users and the WRF4SG interface in order to show how to run a climate experiment. As WS-PGRADE/gUSE uses portlets (see [1]) to interact with users, its portlets will support these use cases. A typical experiment to be carried on by a WRF user will consist on a high-resolution regional re-forecast. These re-forecasts are common experiments used as input data form wind power energy and natural hazards (wind and precipitation fields). In the cases below, the user is able to access to different resources such as Grid due to the fact that WRF needs a huge amount of computing resources in order to generate useful simulations: * Resource configuration and user authentication: The first step is to authenticate on users' Grid resources by virtual organizations. After login, the user is able to select which virtual organization is going to be used by the experiment. * Data assimilation: In order to assimilate the data sources, the user has to select them browsing through LFC Portlet. * Design Experiment workflow: In order to configure the experiment, the user will define the type of experiment (i.e. re-forecast), and its attributes to simulate. In this case the main attributes are: the field of interest (wind, precipitation, ...), the start and end date simulation and the requirements of the experiment. * Monitor workflow: In order to monitor the experiment the user will receive notification messages based on events and also the gateway will display the progress of the experiment. * Data storage: Like Data assimilation case, the user is able to browse and view the output data simulations using LFC Portlet. The objectives of WRF4SG can be described by considering two goals. The first goal is to show how WRF4SG facilitates to execute, monitor and manage climate workflows based on the WRF4G framework. And the second goal of WRF4SG is to help WRF users to execute their experiment workflows concurrently using heterogeneous computing resources such as HPC and Grid. [1] Kacsuk, P.: P-GRADE portal family for grid infrastructures. Concurrency and Computation: Practice and Experience. 23, 235-245 (2011). [2] http://www.meteo.unican.es/software/wrf4g</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012AGUFMGC41A0945H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012AGUFMGC41A0945H"><span>The relative impact of climate change mitigation policies and socioeconomic drivers on water scarcity - An integrated assessment modeling approach</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Hejazi, M. I.; Edmonds, J. A.; Clarke, L. E.; Kyle, P.; Davies, E. G.; Chaturvedi, V.; Patel, P.; Eom, J.; Wise, M.; Kim, S.; Calvin, K. V.; Moss, R. H.</p> <p>2012-12-01</p> <p>We investigate the relative effects of climate emission mitigation policies and socioeconomic drivers on water scarcity conditions over the 21st century both globally and regionally, by estimating both water availability and demand within a technologically-detailed global integrated assessment model of energy, agriculture, and climate change - the Global Change Assessment Model (GCAM). We first develop a global gridded monthly hydrologic model that reproduces historical streamflow observations and simulates the future availability of freshwater under both a changing climate and an evolving landscape, and incorporate this model into GCAM. We then develop and incorporate technologically oriented representations of water demands for the agricultural (irrigation and livestock), energy (electricity generation, primary energy production and processing), industrial (manufacturing and mining), and municipal sectors. The energy, industrial, and municipal sectors are represented in fourteen geopolitical regions, with the agricultural sector further disaggregated into as many as eighteen agro-ecological zones (AEZs) within each region. To perform the water scarcity analysis at the grid scale, the global water demands for the six demand sectors are spatially downscaled to 0.5 o x 0.5o resolution to match the scale of GWAM. The water scarcity index (WSI) compares total water demand to the total amount of renewable water available, and defines extreme water scarcity in any region as demand greater than 40% of total water availability. Using a reference scenario (i.e., no climate change mitigation policy) with radiative forcing reaching 8.8 W/m2 by 2095 and a global population of 14 billion, global annual water demand grows from about 9% of total annual renewable freshwater in 2005 to about 32% by 2095. This results in almost half of the world population living under extreme water scarcity by the end of the 21st century. Regionally, the demands for water exceed the total renewable freshwater available in two GCAM regions, the Middle East and India. Additionally, 20% and 27% of the global population in years 2050 and 2095, respectively, is projected to live in areas (grid cells) that will experience greater water demands than the amount of renewable water available in a year (i.e., WSI > 1.0). We also investigate the effects of emission mitigation policies on water demand and compare them to the contribution of socioeconomic drivers both globally and regionally. Three climate policy scenarios with increasing mitigation stringency of 7.7, 5.5, and 4.2 W/m2 in year 2095, under two carbon tax regimes (a universal carbon tax (UCT) which includes land use change emissions, and a fossil fuel and industrial emissions carbon tax (FFICT) which excludes land use change emissions) are analyzed. With more stringent climate mitigation targets, water scarcity declines under a UCT mitigation policy while increases with a FFICT mitigation scenario by the year 2095. The decreasing trend with UCT policy stringency is due to substitution from more water-intensive to less water-intensive choices in food, energy, and land use. Under the FFICT scenario, water scarcity is projected to increase driven by higher water demands for bio-energy crops.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFM.B43B0597C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFM.B43B0597C"><span>Documenting Uncertainty and Error in Gridded Growing Degree Day and Spring Onset Maps Generated by the USA National Phenology Network</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Crimmins, T. M.; Switzer, J.; Rosemartin, A.; Marsh, L.; Gerst, K.; Crimmins, M.; Weltzin, J. F.</p> <p>2016-12-01</p> <p>Since 2016 the USA National Phenology Network (USA-NPN; www.usanpn.org) has produced and delivered daily maps and short-term forecasts of accumulated growing degree days and spring onset dates at fine spatial scale for the conterminous United States. Because accumulated temperature is a strong driver of phenological transitions in plants and animals, including leaf-out, flowering, fruit ripening, and migration, these data products have utility for a wide range of natural resource planning and management applications, including scheduling invasive species and pest detection and control activities, determining planting dates, anticipating allergy outbreaks and planning agricultural harvest dates. The USA-NPN is a national-scale program that supports scientific advancement and decision-making by collecting, storing, and sharing phenology data and information. We will be expanding the suite of gridded map products offered by the USA-NPN to include predictive species-specific maps of phenological transitions in plants and animals at fine spatial and temporal resolution in the future. Data products, such as the gridded maps currently produced by the USA-NPN, inherently contain uncertainty and error arising from multiple sources, including error propagated forward from underlying climate data and from the models implemented. As providing high-quality, vetted data in a transparent way is central to the USA-NPN, we aim to identify and report the sources and magnitude of uncertainty and error in gridded maps and forecast products. At present, we compare our real-time gridded products to independent, trustworthy data sources, such as the Climate Reference Network, on a daily basis and report Mean Absolute Error and bias through an interactive online dashboard.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011AGUFM.H11J..05O','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011AGUFM.H11J..05O"><span>Quantifying Key Climate Parameter Uncertainties Using an Earth System Model with a Dynamic 3D Ocean</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Olson, R.; Sriver, R. L.; Goes, M. P.; Urban, N.; Matthews, D.; Haran, M.; Keller, K.</p> <p>2011-12-01</p> <p>Climate projections hinge critically on uncertain climate model parameters such as climate sensitivity, vertical ocean diffusivity and anthropogenic sulfate aerosol forcings. Climate sensitivity is defined as the equilibrium global mean temperature response to a doubling of atmospheric CO2 concentrations. Vertical ocean diffusivity parameterizes sub-grid scale ocean vertical mixing processes. These parameters are typically estimated using Intermediate Complexity Earth System Models (EMICs) that lack a full 3D representation of the oceans, thereby neglecting the effects of mixing on ocean dynamics and meridional overturning. We improve on these studies by employing an EMIC with a dynamic 3D ocean model to estimate these parameters. We carry out historical climate simulations with the University of Victoria Earth System Climate Model (UVic ESCM) varying parameters that affect climate sensitivity, vertical ocean mixing, and effects of anthropogenic sulfate aerosols. We use a Bayesian approach whereby the likelihood of each parameter combination depends on how well the model simulates surface air temperature and upper ocean heat content. We use a Gaussian process emulator to interpolate the model output to an arbitrary parameter setting. We use Markov Chain Monte Carlo method to estimate the posterior probability distribution function (pdf) of these parameters. We explore the sensitivity of the results to prior assumptions about the parameters. In addition, we estimate the relative skill of different observations to constrain the parameters. We quantify the uncertainty in parameter estimates stemming from climate variability, model and observational errors. We explore the sensitivity of key decision-relevant climate projections to these parameters. We find that climate sensitivity and vertical ocean diffusivity estimates are consistent with previously published results. The climate sensitivity pdf is strongly affected by the prior assumptions, and by the scaling parameter for the aerosols. The estimation method is computationally fast and can be used with more complex models where climate sensitivity is diagnosed rather than prescribed. The parameter estimates can be used to create probabilistic climate projections using the UVic ESCM model in future studies.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012AGUFM.B31C0428G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012AGUFM.B31C0428G"><span>How will climate change affect watershed mercury export in a representative Coastal Plain watershed?</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Golden, H. E.; Knightes, C. D.; Conrads, P. A.; Feaster, T.; Davis, G. M.; Benedict, S. T.; Bradley, P. M.</p> <p>2012-12-01</p> <p>Future climate change is expected to drive variations in watershed hydrological processes and water quality across a wide range of physiographic provinces, ecosystems, and spatial scales. How such shifts in climatic conditions will impact watershed mercury (Hg) dynamics and hydrologically-driven Hg transport is a significant concern. We simulate the responses of watershed hydrological and total Hg (HgT) fluxes and concentrations to a unified set of past and future climate change projections in a Coastal Plain basin using multiple watershed models. We use two statistically downscaled global precipitation and temperature models, ECHO, a hybrid of the ECHAM4 and HOPE-G models, and the Community Climate System Model (CCSM3) across two thirty-year simulations (1980 to 2010 and 2040 to 2070). We apply three watershed models to quantify and bracket potential changes in hydrologic and HgT fluxes, including the Visualizing Ecosystems for Land Management Assessment Model for Hg (VELMA-Hg), the Grid Based Mercury Model (GBMM), and TOPLOAD, a water quality constituent model linked to TOPMODEL hydrological simulations. We estimate a decrease in average annual HgT fluxes in response to climate change using the ECHO projections and an increase with the CCSM3 projections in the study watershed. Average monthly HgT fluxes increase using both climate change projections between in the late spring (March through May), when HgT concentrations and flow are high. Results suggest that hydrological transport associated with changes in precipitation and temperature is the primary mechanism driving HgT flux response to climate change. Our multiple model/multiple projection approach allows us to bracket the relative response of HgT fluxes to climate change, thereby illustrating the uncertainty associated with the projections. In addition, our approach allows us to examine potential variations in climate change-driven water and HgT export based on different conceptualizations of watershed HgT dynamics and the representative mathematical structures underpinning existing watershed Hg models.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2006JQS....21..681N','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2006JQS....21..681N"><span>Historical instrumental climate data for Australia - quality and utility for palaeoclimatic studies</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Nicholls, Neville; Collins, Dean; Trewin, Blair; Hope, Pandora</p> <p>2006-10-01</p> <p>The quality and availability of climate data suitable for palaeoclimatic calibration and verification for the Australian region are discussed and documented. Details of the various datasets, including problems with the data, are presented. High-quality datasets, where such problems are reduced or even eliminated, are discussed. Many climate datasets are now analysed onto grids, facilitating the preparation of regional-average time series. Work is under way to produce such high-quality, gridded datasets for a variety of hitherto unavailable climate data, including surface humidity, pan evaporation, wind, and cloud. An experiment suggests that only a relatively small number of palaeoclimatic time series could provide a useful estimate of long-term changes in Australian annual average temperature. Copyright</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..1813442D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..1813442D"><span>Impacts of climate change and internal climate variability on french rivers streamflows</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Dayon, Gildas; Boé, Julien; Martin, Eric</p> <p>2016-04-01</p> <p>The assessment of the impacts of climate change often requires to set up long chains of modeling, from the model to estimate the future concentration of greenhouse gases to the impact model. Throughout the modeling chain, sources of uncertainty accumulate making the exploitation of results for the development of adaptation strategies difficult. It is proposed here to assess the impacts of climate change on the hydrological cycle over France and the associated uncertainties. The contribution of the uncertainties from greenhouse gases emission scenario, climate models and internal variability are addressed in this work. To have a large ensemble of climate simulations, the study is based on Global Climate Models (GCM) simulations from the Coupled Model Intercomparison Phase 5 (CMIP5), including several simulations from the same GCM to properly assess uncertainties from internal climate variability. Simulations from the four Radiative Concentration Pathway (RCP) are downscaled with a statistical method developed in a previous study (Dayon et al. 2015). The hydrological system Isba-Modcou is then driven by the downscaling results on a 8 km grid over France. Isba is a land surface model that calculates the energy and water balance and Modcou a hydrogeological model that routes the surface runoff given by Isba. Based on that framework, uncertainties uncertainties from greenhouse gases emission scenario, climate models and climate internal variability are evaluated. Their relative importance is described for the next decades and the end of this century. In a last part, uncertainties due to internal climate variability on streamflows simulated with downscaled GCM and Isba-Modcou are evaluated against observations and hydrological reconstructions on the whole 20th century. Hydrological reconstructions are based on the downscaling of recent atmospheric reanalyses of the 20th century and observations of temperature and precipitation. We show that the multi-decadal variability of streamflows observed in the 20th century is generally weaker in the hydrological simulations done with the historical simulations from climate models. References: Dayon et al. (2015), Transferability in the future climate of a statistical downscaling mehtod for precipitation in France, J. Geophys. Res. Atmos., 120, 1023-1043, doi:10.1002/2014JD022236</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017EGUGA..19.4466T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017EGUGA..19.4466T"><span>Climate change impact on the establishment and seasonal abundance of Invasive Mosquito Species: current state and future risk maps over southeast Europe</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Tagaris, Efthimios; -Eleni Sotiropoulou, Rafaella; Sotiropoulos, Andreas; Spanos, Ioannis; Milonas, Panayiotis; Michaelakis, Antonios</p> <p>2017-04-01</p> <p>Establishment and seasonal abundance of a region for Invasive Mosquito Species (IMS) are related to climatic parameters such as temperature and precipitation. In this work the current state is assessed using data from the European Climate Assessment and Dataset (ECA&D) project over Greece and Italy for the development of current spatial risk databases of IMS. Results are validated from the installation of a prototype IMS monitoring device that has been designed and developed in the framework of the LIFE CONOPS project at key points across the two countries. Since climate models suggest changes in future temperature and precipitation rates, the future potentiality of IMS establishment and spread over Greece and Italy is assessed using the climatic parameters in 2050's provided by the NASA GISS GCM ModelE under the IPCC-A1B emissions scenarios. The need for regional climate projections in a finer grid size is assessed using the Weather Research and Forecasting (WRF) model to dynamically downscale GCM simulations. The estimated changes in the future meteorological parameters are combined with the observation data in order to estimate the future levels of the climatic parameters of interest. The final product includes spatial distribution maps presenting the future suitability of a region for the establishment and seasonal abundance of the IMS over Greece and Italy. Acknowledgement: LIFE CONOPS project "Development & demonstration of management plans against - the climate change enhanced - invasive mosquitoes in S. Europe" (LIFE12 ENV/GR/000466).</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/1268175','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/1268175"><span>The GEWEX LandFlux project: Evaluation of model evaporation using tower-based and globally gridded forcing data</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>McCabe, M. F.; Ershadi, A.; Jimenez, C.</p> <p></p> <p>Determining the spatial distribution and temporal development of evaporation at regional and global scales is required to improve our understanding of the coupled water and energy cycles and to better monitor any changes in observed trends and variability of linked hydrological processes. With recent international efforts guiding the development of long-term and globally distributed flux estimates, continued product assessments are required to inform upon the selection of suitable model structures and also to establish the appropriateness of these multi-model simulations for global application. In support of the objectives of the Global Energy and Water Cycle Exchanges (GEWEX) LandFlux project, fourmore » commonly used evaporation models are evaluated against data from tower-based eddy-covariance observations, distributed across a range of biomes and climate zones. The selected schemes include the Surface Energy Balance System (SEBS) approach, the Priestley–Taylor Jet Propulsion Laboratory (PT-JPL) model, the Penman–Monteith-based Mu model (PM-Mu) and the Global Land Evaporation Amsterdam Model (GLEAM). Here we seek to examine the fidelity of global evaporation simulations by examining the multi-model response to varying sources of forcing data. To do this, we perform parallel and collocated model simulations using tower-based data together with a global-scale grid-based forcing product. Through quantifying the multi-model response to high-quality tower data, a better understanding of the subsequent model response to the coarse-scale globally gridded data that underlies the LandFlux product can be obtained, while also providing a relative evaluation and assessment of model performance. Using surface flux observations from 45 globally distributed eddy-covariance stations as independent metrics of performance, the tower-based analysis indicated that PT-JPL provided the highest overall statistical performance (0.72; 61 W m –2; 0.65), followed closely by GLEAM (0.68; 64 W m –2; 0.62), with values in parentheses representing the R 2, RMSD and Nash–Sutcliffe efficiency (NSE), respectively. PM-Mu (0.51; 78 W m –2; 0.45) tended to underestimate fluxes, while SEBS (0.72; 101 W m –2; 0.24) overestimated values relative to observations. A focused analysis across specific biome types and climate zones showed considerable variability in the performance of all models, with no single model consistently able to outperform any other. Results also indicated that the global gridded data tended to reduce the performance for all of the studied models when compared to the tower data, likely a response to scale mismatch and issues related to forcing quality. Rather than relying on any single model simulation, the spatial and temporal variability at both the tower- and grid-scale highlighted the potential benefits of developing an ensemble or blended evaporation product for global-scale LandFlux applications. Hence, challenges related to the robust assessment of the LandFlux product are also discussed.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/pages/biblio/1268175-gewex-landflux-project-evaluation-model-evaporation-using-tower-based-globally-gridded-forcing-data','SCIGOV-DOEP'); return false;" href="https://www.osti.gov/pages/biblio/1268175-gewex-landflux-project-evaluation-model-evaporation-using-tower-based-globally-gridded-forcing-data"><span>The GEWEX LandFlux project: Evaluation of model evaporation using tower-based and globally gridded forcing data</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/pages">DOE PAGES</a></p> <p>McCabe, M. F.; Ershadi, A.; Jimenez, C.; ...</p> <p>2016-01-26</p> <p>Determining the spatial distribution and temporal development of evaporation at regional and global scales is required to improve our understanding of the coupled water and energy cycles and to better monitor any changes in observed trends and variability of linked hydrological processes. With recent international efforts guiding the development of long-term and globally distributed flux estimates, continued product assessments are required to inform upon the selection of suitable model structures and also to establish the appropriateness of these multi-model simulations for global application. In support of the objectives of the Global Energy and Water Cycle Exchanges (GEWEX) LandFlux project, fourmore » commonly used evaporation models are evaluated against data from tower-based eddy-covariance observations, distributed across a range of biomes and climate zones. The selected schemes include the Surface Energy Balance System (SEBS) approach, the Priestley–Taylor Jet Propulsion Laboratory (PT-JPL) model, the Penman–Monteith-based Mu model (PM-Mu) and the Global Land Evaporation Amsterdam Model (GLEAM). Here we seek to examine the fidelity of global evaporation simulations by examining the multi-model response to varying sources of forcing data. To do this, we perform parallel and collocated model simulations using tower-based data together with a global-scale grid-based forcing product. Through quantifying the multi-model response to high-quality tower data, a better understanding of the subsequent model response to the coarse-scale globally gridded data that underlies the LandFlux product can be obtained, while also providing a relative evaluation and assessment of model performance. Using surface flux observations from 45 globally distributed eddy-covariance stations as independent metrics of performance, the tower-based analysis indicated that PT-JPL provided the highest overall statistical performance (0.72; 61 W m –2; 0.65), followed closely by GLEAM (0.68; 64 W m –2; 0.62), with values in parentheses representing the R 2, RMSD and Nash–Sutcliffe efficiency (NSE), respectively. PM-Mu (0.51; 78 W m –2; 0.45) tended to underestimate fluxes, while SEBS (0.72; 101 W m –2; 0.24) overestimated values relative to observations. A focused analysis across specific biome types and climate zones showed considerable variability in the performance of all models, with no single model consistently able to outperform any other. Results also indicated that the global gridded data tended to reduce the performance for all of the studied models when compared to the tower data, likely a response to scale mismatch and issues related to forcing quality. Rather than relying on any single model simulation, the spatial and temporal variability at both the tower- and grid-scale highlighted the potential benefits of developing an ensemble or blended evaporation product for global-scale LandFlux applications. Hence, challenges related to the robust assessment of the LandFlux product are also discussed.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018ESSD...10..951T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018ESSD...10..951T"><span>Historical gridded reconstruction of potential evapotranspiration for the UK</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Tanguy, Maliko; Prudhomme, Christel; Smith, Katie; Hannaford, Jamie</p> <p>2018-06-01</p> <p>Potential evapotranspiration (PET) is a necessary input data for most hydrological models and is often needed at a daily time step. An accurate estimation of PET requires many input climate variables which are, in most cases, not available prior to the 1960s for the UK, nor indeed most parts of the world. Therefore, when applying hydrological models to earlier periods, modellers have to rely on PET estimations derived from simplified methods. Given that only monthly observed temperature data is readily available for the late 19th and early 20th century at a national scale for the UK, the objective of this work was to derive the best possible UK-wide gridded PET dataset from the limited data available.To that end, firstly, a combination of (i) seven temperature-based PET equations, (ii) four different calibration approaches and (iii) seven input temperature data were evaluated. For this evaluation, a gridded daily PET product based on the physically based Penman-Monteith equation (the CHESS PET dataset) was used, the rationale being that this provides a reliable <q>ground truth</q> PET dataset for evaluation purposes, given that no directly observed, distributed PET datasets exist. The performance of the models was also compared to a <q>naïve method</q>, which is defined as the simplest possible estimation of PET in the absence of any available climate data. The <q>naïve method</q> used in this study is the CHESS PET daily long-term average (the period from 1961 to 1990 was chosen), or CHESS-PET daily climatology.The analysis revealed that the type of calibration and the input temperature dataset had only a minor effect on the accuracy of the PET estimations at catchment scale. From the seven equations tested, only the calibrated version of the McGuinness-Bordne equation was able to outperform the <q>naïve method</q> and was therefore used to derive the gridded, reconstructed dataset. The equation was calibrated using 43 catchments across Great Britain.The dataset produced is a 5 km gridded PET dataset for the period 1891 to 2015, using the Met Office 5 km monthly gridded temperature data available for that time period as input data for the PET equation. The dataset includes daily and monthly PET grids and is complemented with a suite of mapped performance metrics to help users assess the quality of the data spatially.This dataset is expected to be particularly valuable as input to hydrological models for any catchment in the UK. The data can be accessed at <a href="https://doi.org/10.5285/17b9c4f7-1c30-4b6f-b2fe-f7780159939c" target="_blank">https://doi.org/10.5285/17b9c4f7-1c30-4b6f-b2fe-f7780159939c</a>.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4898765','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4898765"><span>Locally Downscaled and Spatially Customizable Climate Data for Historical and Future Periods for North America</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Wang, Tongli; Hamann, Andreas; Spittlehouse, Dave; Carroll, Carlos</p> <p>2016-01-01</p> <p>Large volumes of gridded climate data have become available in recent years including interpolated historical data from weather stations and future predictions from general circulation models. These datasets, however, are at various spatial resolutions that need to be converted to scales meaningful for applications such as climate change risk and impact assessments or sample-based ecological research. Extracting climate data for specific locations from large datasets is not a trivial task and typically requires advanced GIS and data management skills. In this study, we developed a software package, ClimateNA, that facilitates this task and provides a user-friendly interface suitable for resource managers and decision makers as well as scientists. The software locally downscales historical and future monthly climate data layers into scale-free point estimates of climate values for the entire North American continent. The software also calculates a large number of biologically relevant climate variables that are usually derived from daily weather data. ClimateNA covers 1) 104 years of historical data (1901–2014) in monthly, annual, decadal and 30-year time steps; 2) three paleoclimatic periods (Last Glacial Maximum, Mid Holocene and Last Millennium); 3) three future periods (2020s, 2050s and 2080s); and 4) annual time-series of model projections for 2011–2100. Multiple general circulation models (GCMs) were included for both paleo and future periods, and two representative concentration pathways (RCP4.5 and 8.5) were chosen for future climate data. PMID:27275583</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/27275583','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/27275583"><span>Locally Downscaled and Spatially Customizable Climate Data for Historical and Future Periods for North America.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Wang, Tongli; Hamann, Andreas; Spittlehouse, Dave; Carroll, Carlos</p> <p>2016-01-01</p> <p>Large volumes of gridded climate data have become available in recent years including interpolated historical data from weather stations and future predictions from general circulation models. These datasets, however, are at various spatial resolutions that need to be converted to scales meaningful for applications such as climate change risk and impact assessments or sample-based ecological research. Extracting climate data for specific locations from large datasets is not a trivial task and typically requires advanced GIS and data management skills. In this study, we developed a software package, ClimateNA, that facilitates this task and provides a user-friendly interface suitable for resource managers and decision makers as well as scientists. The software locally downscales historical and future monthly climate data layers into scale-free point estimates of climate values for the entire North American continent. The software also calculates a large number of biologically relevant climate variables that are usually derived from daily weather data. ClimateNA covers 1) 104 years of historical data (1901-2014) in monthly, annual, decadal and 30-year time steps; 2) three paleoclimatic periods (Last Glacial Maximum, Mid Holocene and Last Millennium); 3) three future periods (2020s, 2050s and 2080s); and 4) annual time-series of model projections for 2011-2100. Multiple general circulation models (GCMs) were included for both paleo and future periods, and two representative concentration pathways (RCP4.5 and 8.5) were chosen for future climate data.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFMGC23G..02X','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFMGC23G..02X"><span>Evaluation of the National Solar Radiation Database (NSRDB) Using Ground-Based Measurements</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Xie, Y.; Sengupta, M.; Habte, A.; Lopez, A.</p> <p>2017-12-01</p> <p>Solar resource is essential for a wide spectrum of applications including renewable energy, climate studies, and solar forecasting. Solar resource information can be obtained from ground-based measurement stations and/or from modeled data sets. While measurements provide data for the development and validation of solar resource models and other applications modeled data expands the ability to address the needs for increased accuracy and spatial and temporal resolution. The National Renewable Energy Laboratory (NREL) has developed and regular updates modeled solar resource through the National Solar Radiation Database (NSRDB). The recent NSRDB dataset was developed using the physics-based Physical Solar Model (PSM) and provides gridded solar irradiance (global horizontal irradiance (GHI), direct normal irradiance (DNI), and diffuse horizontal irradiance) at a 4-km by 4-km spatial and half-hourly temporal resolution covering 18 years from 1998-2015. A comprehensive validation of the performance of the NSRDB (1998-2015) was conducted to quantify the accuracy of the spatial and temporal variability of the solar radiation data. Further, the study assessed the ability of NSRDB (1998-2015) to accurately capture inter-annual variability, which is essential information for solar energy conversion projects and grid integration studies. Comparisons of the NSRDB (1998-2015) with nine selected ground-measured data were conducted under both clear- and cloudy-sky conditions. These locations provide a high quality data covering a variety of geographical locations and climates. The comparison of the NSRDB to the ground-based data demonstrated that biases were within +/- 5% for GHI and +/-10% for DNI. A comprehensive uncertainty estimation methodology was established to analyze the performance of the gridded NSRDB and includes all sources of uncertainty at various time-averaged periods, a method that is not often used in model evaluation. Further, the study analyzed the inter-annual and mean-anomaly of the 18 years of solar radiation data. This presentation will outline the validation methodology and provide detailed results of the comparison.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20080023285','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20080023285"><span>Cloud-System Resolving Models: Status and Prospects</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Tao, Wei-Kuo; Moncreiff, Mitch</p> <p>2008-01-01</p> <p>Cloud-system resolving models (CRM), which are based on the nonhydrostatic equations of motion and typically have a grid-spacing of about a kilometer, originated as cloud-process models in the 1970s. This paper reviews the status and prospects of CRMs across a wide range of issues, such as microphysics and precipitation; interaction between clouds and radiation; and the effects of boundary-layer and surface-processes on cloud systems. Since CRMs resolve organized convection, tropical waves and the large-scale circulation, there is the prospect for several advances in both basic knowledge of scale-interaction requisite to parameterizing mesoscale processes in climate models. In superparameterization, CRMs represent convection, explicitly replacing many of the assumptions necessary in contemporary parameterization. Global CRMs have been run on an experimental basis, giving prospect to a new generation of climate weather prediction in a decade, and climate models due course. CRMs play a major role in the retrieval of surface-rain and latent heating from satellite measurements. Finally, enormous wide dynamic ranges of CRM simulations present new challenges for model validation against observations.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://pubs.er.usgs.gov/publication/70027640','USGSPUBS'); return false;" href="https://pubs.er.usgs.gov/publication/70027640"><span>Climate science and famine early warning</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Verdin, James P.; Funk, Chris; Senay, Gabriel B.; Choularton, R.</p> <p>2005-01-01</p> <p>Food security assessment in sub-Saharan Africa requires simultaneous consideration of multiple socio-economic and environmental variables. Early identification of populations at risk enables timely and appropriate action. Since large and widely dispersed populations depend on rainfed agriculture and pastoralism, climate monitoring and forecasting are important inputs to food security analysis. Satellite rainfall estimates (RFE) fill in gaps in station observations, and serve as input to drought index maps and crop water balance models. Gridded rainfall time-series give historical context, and provide a basis for quantitative interpretation of seasonal precipitation forecasts. RFE are also used to characterize flood hazards, in both simple indices and stream flow models. In the future, many African countries are likely to see negative impacts on subsistence agriculture due to the effects of global warming. Increased climate variability is forecast, with more frequent extreme events. Ethiopia requires special attention. Already facing a food security emergency, troubling persistent dryness has been observed in some areas, associated with a positive trend in Indian Ocean sea surface temperatures. Increased African capacity for rainfall observation, forecasting, data management and modelling applications is urgently needed. Managing climate change and increased climate variability require these fundamental technical capacities if creative coping strategies are to be devised.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/16433101','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/16433101"><span>Climate science and famine early warning.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Verdin, James; Funk, Chris; Senay, Gabriel; Choularton, Richard</p> <p>2005-11-29</p> <p>Food security assessment in sub-Saharan Africa requires simultaneous consideration of multiple socio-economic and environmental variables. Early identification of populations at risk enables timely and appropriate action. Since large and widely dispersed populations depend on rainfed agriculture and pastoralism, climate monitoring and forecasting are important inputs to food security analysis. Satellite rainfall estimates (RFE) fill in gaps in station observations, and serve as input to drought index maps and crop water balance models. Gridded rainfall time-series give historical context, and provide a basis for quantitative interpretation of seasonal precipitation forecasts. RFE are also used to characterize flood hazards, in both simple indices and stream flow models. In the future, many African countries are likely to see negative impacts on subsistence agriculture due to the effects of global warming. Increased climate variability is forecast, with more frequent extreme events. Ethiopia requires special attention. Already facing a food security emergency, troubling persistent dryness has been observed in some areas, associated with a positive trend in Indian Ocean sea surface temperatures. Increased African capacity for rainfall observation, forecasting, data management and modelling applications is urgently needed. Managing climate change and increased climate variability require these fundamental technical capacities if creative coping strategies are to be devised.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li><a href="#" onclick='return showDiv("page_23");'>23</a></li> <li class="active"><span>24</span></li> <li><a href="#" onclick='return showDiv("page_25");'>25</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_24 --> <div id="page_25" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li><a href="#" onclick='return showDiv("page_23");'>23</a></li> <li><a href="#" onclick='return showDiv("page_24");'>24</a></li> <li class="active"><span>25</span></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="481"> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=1569579','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=1569579"><span>Climate science and famine early warning</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Verdin, James; Funk, Chris; Senay, Gabriel; Choularton, Richard</p> <p>2005-01-01</p> <p>Food security assessment in sub-Saharan Africa requires simultaneous consideration of multiple socio-economic and environmental variables. Early identification of populations at risk enables timely and appropriate action. Since large and widely dispersed populations depend on rainfed agriculture and pastoralism, climate monitoring and forecasting are important inputs to food security analysis. Satellite rainfall estimates (RFE) fill in gaps in station observations, and serve as input to drought index maps and crop water balance models. Gridded rainfall time-series give historical context, and provide a basis for quantitative interpretation of seasonal precipitation forecasts. RFE are also used to characterize flood hazards, in both simple indices and stream flow models. In the future, many African countries are likely to see negative impacts on subsistence agriculture due to the effects of global warming. Increased climate variability is forecast, with more frequent extreme events. Ethiopia requires special attention. Already facing a food security emergency, troubling persistent dryness has been observed in some areas, associated with a positive trend in Indian Ocean sea surface temperatures. Increased African capacity for rainfall observation, forecasting, data management and modelling applications is urgently needed. Managing climate change and increased climate variability require these fundamental technical capacities if creative coping strategies are to be devised. PMID:16433101</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFM.B22A..06S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFM.B22A..06S"><span>Variability in lateral carbon export from four major tributaries in the Upper Mississippi River Basin</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Stanimirova, R.; Arevalo, P. A.; Kaufmann, R.; Maus, V.; Lesiv, M.; Havlik, P.; Friedl, M. A.</p> <p>2016-12-01</p> <p>The combined pressures of climate change and shifting dietary preferences are creating an urgent need to improve understanding of how climate and land management are jointly affecting the sustainability of rangelands. In particular, our ability to effectively manage rangelands in a manner that satisfies increasing demand for meat and dairy while reducing environmental impact depends on the sensitivity of rangelands to perturbations from both climate (e.g., drought) and land use (e.g., grazing). To characterize the sensitivity of rangeland vegetation to variation in climate, we analyzed gridded time series of satellite and climate data at 0.5-degree spatial resolution from 2003 to 2016 for rangeland ecosystems in South America. We used panel regression and canonical correlation to analyze the relationship between time series of enhanced vegetation index (EVI) derived from NASA's Moderate Spatial Resolution Imaging Spectroradiometer (MODIS) and gridded precipitation and air temperature data from the University of East Anglia's Climate Research Unit. To quantify the degree to which livestock management explains geographic variation of EVI, we used global livestock distribution (FAO) and feed requirements data from the Global Biosphere Management Model (GLOBIOM). Because rangeland ecosystems are sensitive to changes in meteorological variables at different time scales, we evaluated the strength of coupling between anomalies in EVI and anomalies in temperature and standardized precipitation index (SPI) data at 1-6 month lags. Our results show statistically significant relationships between EVI and precipitation during summer, fall, and winter in both tropical and subtropical agroecological zones of South America. Further, lagged precipitation effects, which reflect memory in the system, explain significant variance in winter EVI anomalies. While precipitation emerges as the dominant driver of variability in rangeland greenness, we find evidence of a management-induced signal as well. Our modeling framework integrates satellite observation, meteorological data sets, and land use/cover change information to improve our capability to monitor and manage the long-term sustainability of rangelands.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017EGUGA..1913356J','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017EGUGA..1913356J"><span>Assessing the impact of aerosol-atmosphere interactions in convection-permitting regional climate simulations: the Rolf medicane in 2011</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>José Gómez-Navarro, Juan; María López-Romero, José; Palacios-Peña, Laura; Montávez, Juan Pedro; Jiménez-Guerrero, Pedro</p> <p>2017-04-01</p> <p>A critical challenge for assessing regional climate change projections relies on improving the estimate of atmospheric aerosol impact on clouds and reducing the uncertainty associated with the use of parameterizations. In this sense, the horizontal grid spacing implemented in state-of-the-art regional climate simulations is typically 10-25 kilometers, meaning that very important processes such as convective precipitation are smaller than a grid box, and therefore need to be parameterized. This causes large uncertainties, as closure assumptions and a number of parameters have to be established by model tuning. Convection is a physical process that may be strongly conditioned by atmospheric aerosols, although the solution of aerosol-cloud interactions in warm convective clouds remains nowadays a very important scientific challenge, rendering parametrization of these complex processes an important bottleneck that is responsible from a great part of the uncertainty in current climate change projections. Therefore, the explicit simulation of convective processes might improve the quality and reliability of the simulations of the aerosol-cloud interactions in a wide range of atmospheric phenomena. Particularly over the Mediterranean, the role of aerosol particles is very important, being this a crossroad that fuels the mixing of particles from different sources (sea-salt, biomass burning, anthropogenic, Saharan dust, etc). Still, the role of aerosols in extreme events in this area such as medicanes has been barely addressed. This work aims at assessing the role of aerosol-atmosphere interaction in medicanes with the help of the regional chemistry/climate on-line coupled model WRF-CHEM run at a convection-permitting resolution. The analysis is exemplary based on the "Rolf" medicane (6-8 November 2011). Using this case study as reference, four sets of simulations are run with two spatial resolutions: one at a convection-permitting configuration of 4 km, and other at the lower resolution of 12 km, in whose case the convection has to be parameterized. Each configuration is used to produce two simulations, including and not including aerosol-radiation-cloud interactions. The comparison of the simulated output at different scales allows to evaluate the impact of sub-grid scale mixing of precursors on aerosol production. By focusing on these processes at different resolutions, the differences between convection-permitting models running at resolutions of 4 km to 12 km can be explored. Preliminary results indicate that the inclusion of aerosol effects may indeed impact the severity of this simulated medicane, especially sea salt aerosols, and leads to important spatial shifts and differences in intensity of surface precipitation.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=1569572','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=1569572"><span>Socio-economic and climate change impacts on agriculture: an integrated assessment, 1990–2080</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Fischer, Günther; Shah, Mahendra; N. Tubiello, Francesco; van Velhuizen, Harrij</p> <p>2005-01-01</p> <p>A comprehensive assessment of the impacts of climate change on agro-ecosystems over this century is developed, up to 2080 and at a global level, albeit with significant regional detail. To this end an integrated ecological–economic modelling framework is employed, encompassing climate scenarios, agro-ecological zoning information, socio-economic drivers, as well as world food trade dynamics. Specifically, global simulations are performed using the FAO/IIASA agro-ecological zone model, in conjunction with IIASAs global food system model, using climate variables from five different general circulation models, under four different socio-economic scenarios from the intergovernmental panel on climate change. First, impacts of different scenarios of climate change on bio-physical soil and crop growth determinants of yield are evaluated on a 5′×5′ latitude/longitude global grid; second, the extent of potential agricultural land and related potential crop production is computed. The detailed bio-physical results are then fed into an economic analysis, to assess how climate impacts may interact with alternative development pathways, and key trends expected over this century for food demand and production, and trade, as well as key composite indices such as risk of hunger and malnutrition, are computed. This modelling approach connects the relevant bio-physical and socio-economic variables within a unified and coherent framework to produce a global assessment of food production and security under climate change. The results from the study suggest that critical impact asymmetries due to both climate and socio-economic structures may deepen current production and consumption gaps between developed and developing world; it is suggested that adaptation of agricultural techniques will be central to limit potential damages under climate change. PMID:16433094</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/23625352','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/23625352"><span>Heat wave hazard classification and risk assessment using artificial intelligence fuzzy logic.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Keramitsoglou, Iphigenia; Kiranoudis, Chris T; Maiheu, Bino; De Ridder, Koen; Daglis, Ioannis A; Manunta, Paolo; Paganini, Marc</p> <p>2013-10-01</p> <p>The average summer temperatures as well as the frequency and intensity of hot days and heat waves are expected to increase due to climate change. Motivated by this consequence, we propose a methodology to evaluate the monthly heat wave hazard and risk and its spatial distribution within large cities. A simple urban climate model with assimilated satellite-derived land surface temperature images was used to generate a historic database of urban air temperature fields. Heat wave hazard was then estimated from the analysis of these hourly air temperatures distributed at a 1-km grid over Athens, Greece, by identifying the areas that are more likely to suffer higher temperatures in the case of a heat wave event. Innovation lies in the artificial intelligence fuzzy logic model that was used to classify the heat waves from mild to extreme by taking into consideration their duration, intensity and time of occurrence. The monthly hazard was subsequently estimated as the cumulative effect from the individual heat waves that occurred at each grid cell during a month. Finally, monthly heat wave risk maps were produced integrating geospatial information on the population vulnerability to heat waves calculated from socio-economic variables.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/biblio/993792-high-resolution-climate-simulation-last-glacial-maximum','SCIGOV-STC'); return false;" href="https://www.osti.gov/biblio/993792-high-resolution-climate-simulation-last-glacial-maximum"><span></span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Erickson III, David J</p> <p></p> <p>The climate of the last glacial maximum (LGM) is simulated with a high-resolution atmospheric general circulation model, the NCAR CCM3 at spectral truncation of T170, corresponding to a grid cell size of roughly 75 km. The purpose of the study is to assess whether there are significant benefits from the higher resolution simulation compared to the lower resolution simulation associated with the role of topography. The LGM simulations were forced with modified CLIMAP sea ice distribution and sea surface temperatures (SST) reduced by 1 C, ice sheet topography, reduced CO{sub 2}, and 21,000 BP orbital parameters. The high-resolution model capturesmore » modern climate reasonably well, in particular the distribution of heavy precipitation in the tropical Pacific. For the ice age case, surface temperature simulated by the high-resolution model agrees better with those of proxy estimates than does the low-resolution model. Despite the fact that tropical SSTs were only 2.1 C less than the control run, there are many lowland tropical land areas 4-6 C colder than present. Comparison of T170 model results with the best constrained proxy temperature estimates (noble gas concentrations in groundwater) now yield no significant differences between model and observations. There are also significant upland temperature changes in the best resolved tropical mountain belt (the Andes). We provisionally attribute this result in part as resulting from decreased lateral mixing between ocean and land in a model with more model grid cells. A longstanding model-data discrepancy therefore appears to be resolved without invoking any unusual model physics. The response of the Asian summer monsoon can also be more clearly linked to local geography in the high-resolution model than in the low-resolution model; this distinction should enable more confident validation of climate proxy data with the high-resolution model. Elsewhere, an inferred salinity increase in the subtropical North Atlantic may have significant implications for ocean circulation changes during the LGM. A large part of the Amazon and Congo Basins are simulated to be substantially drier in the ice age - consistent with many (but not all) paleo data. These results suggest that there are considerable benefits derived from high-resolution model regarding regional climate responses, and that observationalists can now compare their results with models that resolve geography at a resolution comparable to that which the proxy data represent.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/861975','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/861975"><span>The impact of SciDAC on US climate change research and the IPCCAR4</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Wehner, Michael</p> <p>2005-07-08</p> <p>SciDAC has invested heavily in climate change research. We offer a candid opinion as to the impact of the DOE laboratories' SciDAC projects on the upcoming Fourth Assessment Report of the Intergovernmental Panel on Climate Change. As a result of the direct importance of climate change to society, climate change research is highly coordinated at the international level. The Intergovernmental Panel on Climate Change (IPCC) is charged with providing regular reports on the state of climate change research to government policymakers. These reports are the product of thousands of scientists efforts. A series of reviews involving both scientists and policymakersmore » make them among the most reviewed documents produced in any scientific field. The high profile of these reports acts a driver to many researchers in the climate sciences. The Fourth Assessment Report (AR4) is scheduled to be released in 2007. SciDAC sponsored research has enabled the United States climate modeling community to make significant contributions to this report. Two large multi-Laboratory SciDAC projects are directly relevant to the activities of the IPCC. The first, entitled ''Collaborative Design and Development of the Community Climate System Model for Terascale Computers'', has made important software contributions to the recently released third version of the Community Climate System Model (CCSM3.0) developed at the National Center for Atmospheric Research. This is a multi-institutional project involving Los Alamos National Laboratory, Oak Ridge National Laboratory, Lawrence Berkeley National Laboratory, Pacific Northwest National Laboratory, Argonne National Laboratory, Lawrence Livermore National Laboratory and the National Center for Atmospheric Research. The original principal investigators were Robert Malone and John B. Drake. The current principal investigators are Phil Jones and John B. Drake. The second project, entitled ''Earth System Grid II: Turning Climate Datasets into Community Resources'' aims to facilitate the distribution of the copious amounts of data produced by coupled climate model integrations to the general scientific community. This is also a multi-institutional project involving Argonne National Laboratory, Oak Ridge National Laboratory, Lawrence Berkeley National Laboratory, Lawrence Livermore National Laboratory and the National Center for Atmospheric Research. The principal investigators are Ian Foster, Don Middleton and Dean Williams. Perhaps most significant among the activities of the ''Collaborative Design'', project was the development of an efficient multi-processor coupling package. CCSM3.0 is an extraordinarily complicated physics code. The fully coupled model consists of separate submodels of the atmosphere, ocean, sea ice and land. In addition, comprehensive biogeochemistry and atmospheric chemistry submodels are under intensive current development. Each of these submodels is a large and sophisticated program in its own right. Furthermore, in the coupled model, each of the submodels, including the coupler, is a separate multiprocessor executable program. The coupler package must efficiently coordinate the communication as well as interpolate or aggregate information between these programs. This regridding function is necessary because each major subsystem (air, water or surface) is allowed to have its own independent grid.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20140010299','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20140010299"><span>Carbon-Temperature-Water Change Analysis for Peanut Production Under Climate Change: A Prototype for the AgMIP Coordinated Climate-Crop Modeling Project (C3MP)</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Ruane, Alex C.; McDermid, Sonali; Rosenzweig, Cynthia; Baigorria, Guillermo A.; Jones, James W.; Romero, Consuelo C.; Cecil, L. DeWayne</p> <p>2014-01-01</p> <p>Climate change is projected to push the limits of cropping systems and has the potential to disrupt the agricultural sector from local to global scales. This article introduces the Coordinated Climate-Crop Modeling Project (C3MP), an initiative of the Agricultural Model Intercomparison and Improvement Project (AgMIP) to engage a global network of crop modelers to explore the impacts of climate change via an investigation of crop responses to changes in carbon dioxide concentration ([CO2]), temperature, and water. As a demonstration of the C3MP protocols and enabled analyses, we apply the Decision Support System for Agrotechnology Transfer (DSSAT) CROPGRO-Peanut crop model for Henry County, Alabama, to evaluate responses to the range of plausible [CO2], temperature changes, and precipitation changes projected by climate models out to the end of the 21st century. These sensitivity tests are used to derive crop model emulators that estimate changes in mean yield and the coefficient of variation for seasonal yields across a broad range of climate conditions, reproducing mean yields from sensitivity test simulations with deviations of ca. 2% for rain-fed conditions. We apply these statistical emulators to investigate how peanuts respond to projections from various global climate models, time periods, and emissions scenarios, finding a robust projection of modest (<10%) median yield losses in the middle of the 21st century accelerating to more severe (>20%) losses and larger uncertainty at the end of the century under the more severe representative concentration pathway (RCP8.5). This projection is not substantially altered by the selection of the AgMERRA global gridded climate dataset rather than the local historical observations, differences between the Third and Fifth Coupled Model Intercomparison Project (CMIP3 and CMIP5), or the use of the delta method of climate impacts analysis rather than the C3MP impacts response surface and emulator approach.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018OcMod.121..117B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018OcMod.121..117B"><span>Simulating the Agulhas system in global ocean models - nesting vs. multi-resolution unstructured meshes</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Biastoch, Arne; Sein, Dmitry; Durgadoo, Jonathan V.; Wang, Qiang; Danilov, Sergey</p> <p>2018-01-01</p> <p>Many questions in ocean and climate modelling require the combined use of high resolution, global coverage and multi-decadal integration length. For this combination, even modern resources limit the use of traditional structured-mesh grids. Here we compare two approaches: A high-resolution grid nested into a global model at coarser resolution (NEMO with AGRIF) and an unstructured-mesh grid (FESOM) which allows to variably enhance resolution where desired. The Agulhas system around South Africa is used as a testcase, providing an energetic interplay of a strong western boundary current and mesoscale dynamics. Its open setting into the horizontal and global overturning circulations also requires global coverage. Both model configurations simulate a reasonable large-scale circulation. Distribution and temporal variability of the wind-driven circulation are quite comparable due to the same atmospheric forcing. However, the overturning circulation differs, owing each model's ability to represent formation and spreading of deep water masses. In terms of regional, high-resolution dynamics, all elements of the Agulhas system are well represented. Owing to the strong nonlinearity in the system, Agulhas Current transports of both configurations and in comparison with observations differ in strength and temporal variability. Similar decadal trends in Agulhas Current transport and Agulhas leakage are linked to the trends in wind forcing.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017ACP....1711541M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017ACP....1711541M"><span>Reanalysis comparisons of upper tropospheric-lower stratospheric jets and multiple tropopauses</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Manney, Gloria L.; Hegglin, Michaela I.; Lawrence, Zachary D.; Wargan, Krzysztof; Millán, Luis F.; Schwartz, Michael J.; Santee, Michelle L.; Lambert, Alyn; Pawson, Steven; Knosp, Brian W.; Fuller, Ryan A.; Daffer, William H.</p> <p>2017-09-01</p> <p>The representation of upper tropospheric-lower stratospheric (UTLS) jet and tropopause characteristics is compared in five modern high-resolution reanalyses for 1980 through 2014. Climatologies of upper tropospheric jet, subvortex jet (the lowermost part of the stratospheric vortex), and multiple tropopause frequency distributions in MERRA (Modern-Era Retrospective analysis for Research and Applications), ERA-I (ERA-Interim; the European Centre for Medium-Range Weather Forecasts, ECMWF, interim reanalysis), JRA-55 (the Japanese 55-year Reanalysis), and CFSR (the Climate Forecast System Reanalysis) are compared with those in MERRA-2. Differences between alternate products from individual reanalysis systems are assessed; in particular, a comparison of CFSR data on model and pressure levels highlights the importance of vertical grid spacing. Most of the differences in distributions of UTLS jets and multiple tropopauses are consistent with the differences in assimilation model grids and resolution - for example, ERA-I (with coarsest native horizontal resolution) typically shows a significant low bias in upper tropospheric jets with respect to MERRA-2, and JRA-55 (the Japanese 55-year Reanalysis) a more modest one, while CFSR (with finest native horizontal resolution) shows a high bias with respect to MERRA-2 in both upper tropospheric jets and multiple tropopauses. Vertical temperature structure and grid spacing are especially important for multiple tropopause characterizations. Substantial differences between MERRA and MERRA-2 are seen in mid- to high-latitude Southern Hemisphere (SH) winter upper tropospheric jets and multiple tropopauses as well as in the upper tropospheric jets associated with tropical circulations during the solstice seasons; some of the largest differences from the other reanalyses are seen in the same times and places. Very good qualitative agreement among the reanalyses is seen between the large-scale climatological features in UTLS jet and multiple tropopause distributions. Quantitative differences may, however, have important consequences for transport and variability studies. Our results highlight the importance of considering reanalyses differences in UTLS studies, especially in relation to resolution and model grids; this is particularly critical when using high-resolution reanalyses as an observational reference for evaluating global chemistry-climate models.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2007AGUSM.U42B..01C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2007AGUSM.U42B..01C"><span>The North American Drought Atlas: Tree-Ring Reconstructions of Drought Variability for Climate Modeling and Assessment</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Cook, E. R.</p> <p>2007-05-01</p> <p>The North American Drought Atlas describes a detailed reconstruction of drought variability from tree rings over most of North America for the past 500-1000 years. The first version of it, produced over three years ago, was based on a network of 835 tree-ring chronologies and a 286-point grid of instrumental Palmer Drought Severity Indices (PDSI). These gridded PDSI reconstructions have been used in numerous published studies now that range from modeling fire in the American West, to the impact of drought on palaeo-Indian societies, and to the determination of the primary causes of drought over North America through climate modeling experiments. Some examples of these applications will be described to illustrate the scientific value of these large-scale reconstructions of drought. Since the development and free public release of Version 1 of the North American Drought Atlas (see http:iridl.ldeo.columbia.edu/SOURCES/.LDEO/.TRL/.NADA2004/.pdsi-atlas.html), great improvements have been made in the critical tree-ring network used to reconstruct PDSI at each grid point. This network has now been enlarged to 1743 annual tree-ring chronologies, which greatly improves the density of tree-ring records in certain parts of the grid, especially in Canada and Mexico. In addition, the number of tree-ring records that extend back before AD 1400 has been substantially increased. These developments justify the creation of Version 2 of the North American Drought Atlas. In this talk I will describe this new version of the drought atlas and some of its properties that make it a significant improvement over the previous version. The new product provides enhanced resolution of the spatial and temporal variability of prolonged drought such as the late 16th century event that impacted regions of both Mexico and the United States. I will also argue for the North American Drought Atlas being used as a template for the development of large-scale drought reconstructions in other land areas of the Northern Hemisphere where sufficient tree-ring data exist. By doing so, the importance of this product to the modeling community will be significantly enhanced.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009EGUGA..11.9688A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009EGUGA..11.9688A"><span>The Climate-G testbed: towards a large scale data sharing environment for climate change</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Aloisio, G.; Fiore, S.; Denvil, S.; Petitdidier, M.; Fox, P.; Schwichtenberg, H.; Blower, J.; Barbera, R.</p> <p>2009-04-01</p> <p>The Climate-G testbed provides an experimental large scale data environment for climate change addressing challenging data and metadata management issues. The main scope of Climate-G is to allow scientists to carry out geographical and cross-institutional climate data discovery, access, visualization and sharing. Climate-G is a multidisciplinary collaboration involving both climate and computer scientists and it currently involves several partners such as: Centro Euro-Mediterraneo per i Cambiamenti Climatici (CMCC), Institut Pierre-Simon Laplace (IPSL), Fraunhofer Institut für Algorithmen und Wissenschaftliches Rechnen (SCAI), National Center for Atmospheric Research (NCAR), University of Reading, University of Catania and University of Salento. To perform distributed metadata search and discovery, we adopted a CMCC metadata solution (which provides a high level of scalability, transparency, fault tolerance and autonomy) leveraging both on P2P and grid technologies (GRelC Data Access and Integration Service). Moreover, data are available through OPeNDAP/THREDDS services, Live Access Server as well as the OGC compliant Web Map Service and they can be downloaded, visualized, accessed into the proposed environment through the Climate-G Data Distribution Centre (DDC), the web gateway to the Climate-G digital library. The DDC is a data-grid portal allowing users to easily, securely and transparently perform search/discovery, metadata management, data access, data visualization, etc. Godiva2 (integrated into the DDC) displays 2D maps (and animations) and also exports maps for display on the Google Earth virtual globe. Presently, Climate-G publishes (through the DDC) about 2TB of data related to the ENSEMBLES project (also including distributed replicas of data) as well as to the IPCC AR4. The main results of the proposed work are: wide data access/sharing environment for climate change; P2P/grid metadata approach; production-level Climate-G DDC; high quality tools for data visualization; metadata search/discovery across several countries/institutions; open environment for climate change data sharing.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.A13E2126F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.A13E2126F"><span>A Novel Observation-Guided Approach for Evaluating Mesoscale Convective Systems Simulated by the DOE ACME Model</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Feng, Z.; Ma, P. L.; Hardin, J. C.; Houze, R.</p> <p>2017-12-01</p> <p>Mesoscale convective systems (MCSs) are the largest type of convective storms that develop when convection aggregates and induces mesoscale circulation features. Over North America, MCSs contribute over 60% of the total warm-season precipitation and over half of the extreme daily precipitation in the central U.S. Our recent study (Feng et al. 2016) found that the observed increases in springtime total and extreme rainfall in this region are dominated by increased frequency and intensity of long-lived MCSs*. To date, global climate models typically do not run at a resolution high enough to explicitly simulate individual convective elements and may not have adequate process representations for MCSs, resulting in a large deficiency in projecting changes of the frequency of extreme precipitation events in future climate. In this study, we developed a novel observation-guided approach specifically designed to evaluate simulated MCSs in the Department of Energy's climate model, Accelerated Climate Modeling for Energy (ACME). The ACME model has advanced treatments for convection and subgrid variability and for this study is run at 25 km and 100 km grid spacings. We constructed a robust MCS database consisting of over 500 MCSs from 3 warm-season observations by applying a feature-tracking algorithm to 4-km resolution merged geostationary satellite and 3-D NEXRAD radar network data over the Continental US. This high-resolution MCS database is then down-sampled to the 25 and 100 km ACME grids to re-characterize key MCS properties. The feature-tracking algorithm is adapted with the adjusted characteristics to identify MCSs from ACME model simulations. We demonstrate that this new analysis framework is useful for evaluating ACME's warm-season precipitation statistics associated with MCSs, and provides insights into the model process representations related to extreme precipitation events for future improvement. *Feng, Z., L. R. Leung, S. Hagos, R. A. Houze, C. D. Burleyson, and K. Balaguru (2016), More frequent intense and long-lived storms dominate the springtime trend in central US rainfall, Nat Commun, 7, 13429, doi: 10.1038/ncomms13429.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016ESSD....8..127B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016ESSD....8..127B"><span>CPLFD-GDPT5: High-resolution gridded daily precipitation and temperature data set for two largest Polish river basins</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Berezowski, Tomasz; Szcześniak, Mateusz; Kardel, Ignacy; Michałowski, Robert; Okruszko, Tomasz; Mezghani, Abdelkader; Piniewski, Mikołaj</p> <p>2016-03-01</p> <p>The CHASE-PL (Climate change impact assessment for selected sectors in Poland) Forcing Data-Gridded Daily Precipitation & Temperature Dataset-5 km (CPLFD-GDPT5) consists of 1951-2013 daily minimum and maximum air temperatures and precipitation totals interpolated onto a 5 km grid based on daily meteorological observations from the Institute of Meteorology and Water Management (IMGW-PIB; Polish stations), Deutscher Wetterdienst (DWD, German and Czech stations), and European Climate Assessment and Dataset (ECAD) and National Oceanic and Atmosphere Administration-National Climatic Data Center (NOAA-NCDC) (Slovak, Ukrainian, and Belarusian stations). The main purpose for constructing this product was the need for long-term aerial precipitation and temperature data for earth-system modelling, especially hydrological modelling. The spatial coverage is the union of the Vistula and Oder basins and Polish territory. The number of available meteorological stations for precipitation and temperature varies in time from about 100 for temperature and 300 for precipitation in the 1950s up to about 180 for temperature and 700 for precipitation in the 1990s. The precipitation data set was corrected for snowfall and rainfall under-catch with the Richter method. The interpolation methods were kriging with elevation as external drift for temperatures and indicator kriging combined with universal kriging for precipitation. The kriging cross validation revealed low root-mean-squared errors expressed as a fraction of standard deviation (SD): 0.54 and 0.47 for minimum and maximum temperature, respectively, and 0.79 for precipitation. The correlation scores were 0.84 for minimum temperatures, 0.88 for maximum temperatures, and 0.65 for precipitation. The CPLFD-GDPT5 product is consistent with 1971-2000 climatic data published by IMGW-PIB. We also confirm good skill of the product for hydrological modelling by performing an application using the Soil and Water Assessment Tool (SWAT) in the Vistula and Oder basins. Link to the data set: <a href="http://dx.doi.org/10.4121/uuid:e939aec0-bdd1-440f-bd1e-c49ff10d0a07" target="_blank">doi:10.4121/uuid:e939aec0-bdd1-440f-bd1e-c49ff10d0a07</a>.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013EGUGA..1511114S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013EGUGA..1511114S"><span>Evaluation of decadal hindcasts using satellite simulators</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Spangehl, Thomas; Mazurkiewicz, Alex; Schröder, Marc</p> <p>2013-04-01</p> <p>The evaluation of dynamical ensemble forecast systems requires a solid validation of basic processes such as the global atmospheric water and energy cycle. The value of any validation approach strongly depends on the quality of the observational data records used. Current approaches utilize in situ measurements, remote sensing data and reanalyses. Related data records are subject to a number of uncertainties and limitations such as representativeness, spatial and temporal resolution and homogeneity. However, recently several climate data records with known and sufficient quality became available. In particular, the satellite data records offer the opportunity to obtain reference information on global scales including the oceans. Here we consider the simulation of satellite radiances from the climate model output enabling an evaluation in the instrument's parameter space to avoid uncertainties stemming from the application of retrieval schemes in order to minimise uncertainties on the reference side. Utilizing the CFMIP Observation Simulator Package (COSP) we develop satellite simulators for the Tropical Rainfall Measuring Mission precipitation radar (TRMM PR) and the Infrared Atmospheric Sounding Interferometer (IASI). The simulators are applied within the MiKlip project funded by BMBF (German Federal Ministry of Education and Research) to evaluate decadal climate predictions performed with the MPI-ESM developed at the Max Planck Institute for Meteorology. While TRMM PR enables the evaluation of the vertical structure of precipitation over tropical and sub-tropical areas, IASI is used to support the global evaluation of clouds and radiation. In a first step the reliability of the developed simulators needs to be explored. The simulation of radiances in the instrument space requires the generation of sub-grid scale variability from the climate model output. Furthermore, assumptions are made to simulate radiances such as, for example, the distribution of different hydrometeor types. Therefore, testing is performed to determine the extent to which the quality of the simulator results depends on the applied methods used to generate sub-grid variability (e.g. sub-grid resolution). Moreover, the sensitivity of results to the choice of different distributions of hydrometeors is explored. The model evaluation is carried out in a statistical manner using histograms of radar reflectivities (TRMM PR) and brightness temperatures (IASI). Finally, methods to deduce data suitable for probabilistic evaluation of decadal hindcasts such as simple indices are discussed.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/19087944','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/19087944"><span>Decadal climate prediction (project GCEP).</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Haines, Keith; Hermanson, Leon; Liu, Chunlei; Putt, Debbie; Sutton, Rowan; Iwi, Alan; Smith, Doug</p> <p>2009-03-13</p> <p>Decadal prediction uses climate models forced by changing greenhouse gases, as in the International Panel for Climate Change, but unlike longer range predictions they also require initialization with observations of the current climate. In particular, the upper-ocean heat content and circulation have a critical influence. Decadal prediction is still in its infancy and there is an urgent need to understand the important processes that determine predictability on these timescales. We have taken the first Hadley Centre Decadal Prediction System (DePreSys) and implemented it on several NERC institute compute clusters in order to study a wider range of initial condition impacts on decadal forecasting, eventually including the state of the land and cryosphere. The eScience methods are used to manage submission and output from the many ensemble model runs required to assess predictive skill. Early results suggest initial condition skill may extend for several years, even over land areas, but this depends sensitively on the definition used to measure skill, and alternatives are presented. The Grid for Coupled Ensemble Prediction (GCEP) system will allow the UK academic community to contribute to international experiments being planned to explore decadal climate predictability.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20040031738&hterms=climate+change+anthropogenic&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D70%26Ntt%3Dclimate%2Bchange%2Banthropogenic','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20040031738&hterms=climate+change+anthropogenic&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D70%26Ntt%3Dclimate%2Bchange%2Banthropogenic"><span>Status of Middle Atmosphere-Climate Models: Results SPARC-GRIPS</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Pawson, Steven; Kodera, Kunihiko</p> <p>2003-01-01</p> <p>The middle atmosphere is an important component of the climate system, primarily because of the radiative forcing of ozone. Middle atmospheric ozone can change, over long times, because of changes in the abundance of anthropogenic pollutants which catalytically destroy it, and because of the temperature sensitivity of kinetic reaction rates. There is thus a complex interaction between ozone, involving chemical and climatic mechanisms. One question of interest is how ozone will change over the next decades , as the "greenhouse-gas cooling" of the middle atmosphere increases but the concentrations of chlorine species decreases (because of policy changes). concerns the climate biases in current middle atmosphere-climate models, especially their ability to simulate the correct seasonal cycle at high latitudes, and the existence of temperature biases in the global mean. A major obstacle when addressing this question This paper will present a summary of recent results from the "GCM-Reality Intercomparison Project for SPARC" (GRIPS) initiative. A set of middle atmosphere-climate models has been compared, identifying common biases. Mechanisms for these biases are being studied in some detail, including off-line assessments of the radiation transfer codes and coordinated studies of the impacts of gravity wave drag due to sub-grid-scale processes. ensemble of models will be presented, along with numerical experiments undertaken with one or more models, designed to investigate the mechanisms at work in the atmosphere. The discussion will focus on dynamical and radiative mechanisms in the current climate, but implications for coupled ozone chemistry and the future climate will be assessed.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..17.4529C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..17.4529C"><span>Decomposing the uncertainty in climate impact projections of Dynamic Vegetation Models: a test with the forest models LANDCLIM and FORCLIM</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Cailleret, Maxime; Snell, Rebecca; von Waldow, Harald; Kotlarski, Sven; Bugmann, Harald</p> <p>2015-04-01</p> <p>Different levels of uncertainty should be considered in climate impact projections by Dynamic Vegetation Models (DVMs), particularly when it comes to managing climate risks. Such information is useful to detect the key processes and uncertainties in the climate model - impact model chain and may be used to support recommendations for future improvements in the simulation of both climate and biological systems. In addition, determining which uncertainty source is dominant is an important aspect to recognize the limitations of climate impact projections by a multi-model ensemble mean approach. However, to date, few studies have clarified how each uncertainty source (baseline climate data, greenhouse gas emission scenario, climate model, and DVM) affects the projection of ecosystem properties. Focusing on one greenhouse gas emission scenario, we assessed the uncertainty in the projections of a forest landscape model (LANDCLIM) and a stand-scale forest gap model (FORCLIM) that is caused by linking climate data with an impact model. LANDCLIM was used to assess the uncertainty in future landscape properties of the Visp valley in Switzerland that is due to (i) the use of different 'baseline' climate data (gridded data vs. data from weather stations), and (ii) differences in climate projections among 10 GCM-RCM chains. This latter point was also considered for the projections of future forest properties by FORCLIM at several sites along an environmental gradient in Switzerland (14 GCM-RCM chains), for which we also quantified the uncertainty caused by (iii) the model chain specific statistical properties of the climate time-series, and (iv) the stochasticity of the demographic processes included in the model, e.g., the annual number of saplings that establish, or tree mortality. Using methods of variance decomposition analysis, we found that (i) The use of different baseline climate data strongly impacts the prediction of forest properties at the lowest and highest, but not so much at medium elevations. (ii) Considering climate change, the variability that is due to the GCM-RCM chains is much greater than the variability induced by the uncertainty in the initial climatic conditions. (iii) The uncertainties caused by the intrinsic stochasticity in the DVMs and by the random generation of the climate time-series are negligible. Overall, our results indicate that DVMs are quite sensitive to the climate data, highlighting particularly (1) the limitations of using one single multi-model average climate change scenario in climate impact studies and (2) the need to better consider the uncertainty in climate model outputs for projecting future vegetation changes.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFM.H43M..07A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFM.H43M..07A"><span>Application of Inverse Modeling to Estimate Groundwater Recharge under Future Climate Scenario</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Akbariyeh, S.; Wang, T.; Bartelt-Hunt, S.; Li, Y.</p> <p>2016-12-01</p> <p>Climate variability and change will impose profound influences on groundwater systems. Accurate estimation of groundwater recharge is extremely important for predicting the flow and contaminant transport in the subsurface, which, however, remains as one of the most challenging tasks in the field of hydrology. Using an inverse modeling technique and HYDRUS 1D software, we predicted the spatial distribution of groundwater recharge across the Upper Platte basin in Nebraska, USA, based on 5-year projected future climate and soil moisture data (2057-2060). The climate data was obtained from Weather Research and Forecasting (WRF) model under RCP 8.5 scenario, which was downscaled from global CCSM4 model to a resolution of 24 by 24 km2. Precipitation, potential evapotranspiration, and soil moisture data were extracted from 76 grids located within the Upper Platte basin to perform the inverse modeling. Hargreaves equation was used to calculate the potential evapotranspiration according to latitude, maximum and minimum temperature, and leaf area index (LAI) data at each node. Van-Genuchten parameters were optimized using the inverse algorithm to minimize the error between input and modeled soil moisture data. The groundwater recharge was calculated as the amount of water that passed the lower boundary of the best fitted model. The year of 2057 was used as a spin-up period to minimize the impact of initial conditions. The model was calibrated for years 2058 to 2059 and validation was performed for 2060. This work demonstrates an efficient approach to estimating groundwater recharge based on climate modeling results, which will aid groundwater resources management under future climate scenarios.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://pubs.er.usgs.gov/publication/70036334','USGSPUBS'); return false;" href="https://pubs.er.usgs.gov/publication/70036334"><span>Globally Gridded Satellite observations for climate studies</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Knapp, K.R.; Ansari, S.; Bain, C.L.; Bourassa, M.A.; Dickinson, M.J.; Funk, Chris; Helms, C.N.; Hennon, C.C.; Holmes, C.D.; Huffman, G.J.; Kossin, J.P.; Lee, H.-T.; Loew, A.; Magnusdottir, G.</p> <p>2011-01-01</p> <p>Geostationary satellites have provided routine, high temporal resolution Earth observations since the 1970s. Despite the long period of record, use of these data in climate studies has been limited for numerous reasons, among them that no central archive of geostationary data for all international satellites exists, full temporal and spatial resolution data are voluminous, and diverse calibration and navigation formats encumber the uniform processing needed for multisatellite climate studies. The International Satellite Cloud Climatology Project (ISCCP) set the stage for overcoming these issues by archiving a subset of the full-resolution geostationary data at ~10-km resolution at 3-hourly intervals since 1983. Recent efforts at NOAA's National Climatic Data Center to provide convenient access to these data include remapping the data to a standard map projection, recalibrating the data to optimize temporal homogeneity, extending the record of observations back to 1980, and reformatting the data for broad public distribution. The Gridded Satellite (GridSat) dataset includes observations from the visible, infrared window, and infrared water vapor channels. Data are stored in Network Common Data Format (netCDF) using standards that permit a wide variety of tools and libraries to process the data quickly and easily. A novel data layering approach, together with appropriate satellite and file metadata, allows users to access GridSat data at varying levels of complexity based on their needs. The result is a climate data record already in use by the meteorological community. Examples include reanalysis of tropical cyclones, studies of global precipitation, and detection and tracking of the intertropical convergence zone.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li><a href="#" onclick='return showDiv("page_23");'>23</a></li> <li><a href="#" onclick='return showDiv("page_24");'>24</a></li> <li class="active"><span>25</span></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_25 --> <div class="footer-extlink text-muted" style="margin-bottom:1rem; text-align:center;">Some links on this page may take you to non-federal websites. Their policies may differ from this site.</div> </div><!-- container --> <a id="backToTop" href="#top"> Top </a> <footer> <nav> <ul class="links"> <li><a href="/sitemap.html">Site Map</a></li> <li><a href="/website-policies.html">Website Policies</a></li> <li><a href="https://www.energy.gov/vulnerability-disclosure-policy" target="_blank">Vulnerability Disclosure Program</a></li> <li><a href="/contact.html">Contact Us</a></li> </ul> </nav> </footer> <script type="text/javascript"><!-- // var lastDiv = ""; function showDiv(divName) { // hide last div if (lastDiv) { document.getElementById(lastDiv).className = "hiddenDiv"; } //if value of the box is not nothing and an object with that name exists, then change the class if (divName && document.getElementById(divName)) { document.getElementById(divName).className = "visibleDiv"; lastDiv = divName; } } //--> </script> <script> /** * Function that tracks a click on an outbound link in Google Analytics. * This function takes a valid URL string as an argument, and uses that URL string * as the event label. */ var trackOutboundLink = function(url,collectionCode) { try { h = window.open(url); setTimeout(function() { ga('send', 'event', 'topic-page-click-through', collectionCode, url); }, 1000); } catch(err){} }; </script> <!-- Google Analytics --> <script> (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){ (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new Date();a=s.createElement(o), m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m) })(window,document,'script','//www.google-analytics.com/analytics.js','ga'); ga('create', 'UA-1122789-34', 'auto'); ga('send', 'pageview'); </script> <!-- End Google Analytics --> <script> showDiv('page_1') </script> </body> </html>