downscaled climate model: Topics by Science.gov

Sample records for downscaled climate model

Statistical Downscaling and Bias Correction of Climate Model Outputs for Climate Change Impact Assessment in the U.S. Northeast

NASA Technical Reports Server (NTRS)

Ahmed, Kazi Farzan; Wang, Guiling; Silander, John; Wilson, Adam M.; Allen, Jenica M.; Horton, Radley; Anyah, Richard

2013-01-01

Statistical downscaling can be used to efficiently downscale a large number of General Circulation Model (GCM) outputs to a fine temporal and spatial scale. To facilitate regional impact assessments, this study statistically downscales (to 1/8deg spatial resolution) and corrects the bias of daily maximum and minimum temperature and daily precipitation data from six GCMs and four Regional Climate Models (RCMs) for the northeast United States (US) using the Statistical Downscaling and Bias Correction (SDBC) approach. Based on these downscaled data from multiple models, five extreme indices were analyzed for the future climate to quantify future changes of climate extremes. For a subset of models and indices, results based on raw and bias corrected model outputs for the present-day climate were compared with observations, which demonstrated that bias correction is important not only for GCM outputs, but also for RCM outputs. For future climate, bias correction led to a higher level of agreements among the models in predicting the magnitude and capturing the spatial pattern of the extreme climate indices. We found that the incorporation of dynamical downscaling as an intermediate step does not lead to considerable differences in the results of statistical downscaling for the study domain.
Climate downscaling effects on predictive ecological models: a case study for threatened and endangered vertebrates in the southeastern United States

USGS Publications Warehouse

Bucklin, David N.; Watling, James I.; Speroterra, Carolina; Brandt, Laura A.; Mazzotti, Frank J.; Romañach, Stephanie S.

2013-01-01

High-resolution (downscaled) projections of future climate conditions are critical inputs to a wide variety of ecological and socioeconomic models and are created using numerous different approaches. Here, we conduct a sensitivity analysis of spatial predictions from climate envelope models for threatened and endangered vertebrates in the southeastern United States to determine whether two different downscaling approaches (with and without the use of a regional climate model) affect climate envelope model predictions when all other sources of variation are held constant. We found that prediction maps differed spatially between downscaling approaches and that the variation attributable to downscaling technique was comparable to variation between maps generated using different general circulation models (GCMs). Precipitation variables tended to show greater discrepancies between downscaling techniques than temperature variables, and for one GCM, there was evidence that more poorly resolved precipitation variables contributed relatively more to model uncertainty than more well-resolved variables. Our work suggests that ecological modelers requiring high-resolution climate projections should carefully consider the type of downscaling applied to the climate projections prior to their use in predictive ecological modeling. The uncertainty associated with alternative downscaling methods may rival that of other, more widely appreciated sources of variation, such as the general circulation model or emissions scenario with which future climate projections are created.
The effects of climate downscaling technique and observational data set on modeled ecological responses

Treesearch

Afshin Pourmokhtarian; Charles T. Driscoll; John L. Campbell; Katharine Hayhoe; Anne M. K. Stoner

2016-01-01

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...
Downscaled and debiased climate simulations for North America from 21,000 years ago to 2100AD

PubMed Central

Lorenz, David J.; Nieto-Lugilde, Diego; Blois, Jessica L.; Fitzpatrick, Matthew C.; Williams, John W.

2016-01-01

Increasingly, ecological modellers are integrating paleodata with future projections to understand climate-driven biodiversity dynamics from the past through the current century. Climate simulations from earth system models are necessary to this effort, but must be debiased and downscaled before they can be used by ecological models. Downscaling methods and observational baselines vary among researchers, which produces confounding biases among downscaled climate simulations. We present unified datasets of debiased and downscaled climate simulations for North America from 21 ka BP to 2100AD, at 0.5° spatial resolution. Temporal resolution is decadal averages of monthly data until 1950AD, average climates for 1950–2005 AD, and monthly data from 2010 to 2100AD, with decadal averages also provided. This downscaling includes two transient paleoclimatic simulations and 12 climate models for the IPCC AR5 (CMIP5) historical (1850–2005), RCP4.5, and RCP8.5 21st-century scenarios. Climate variables include primary variables and derived bioclimatic variables. These datasets provide a common set of climate simulations suitable for seamlessly modelling the effects of past and future climate change on species distributions and diversity. PMID:27377537
Downscaled and debiased climate simulations for North America from 21,000 years ago to 2100AD.

PubMed

Lorenz, David J; Nieto-Lugilde, Diego; Blois, Jessica L; Fitzpatrick, Matthew C; Williams, John W

2016-07-05

Increasingly, ecological modellers are integrating paleodata with future projections to understand climate-driven biodiversity dynamics from the past through the current century. Climate simulations from earth system models are necessary to this effort, but must be debiased and downscaled before they can be used by ecological models. Downscaling methods and observational baselines vary among researchers, which produces confounding biases among downscaled climate simulations. We present unified datasets of debiased and downscaled climate simulations for North America from 21 ka BP to 2100AD, at 0.5° spatial resolution. Temporal resolution is decadal averages of monthly data until 1950AD, average climates for 1950-2005 AD, and monthly data from 2010 to 2100AD, with decadal averages also provided. This downscaling includes two transient paleoclimatic simulations and 12 climate models for the IPCC AR5 (CMIP5) historical (1850-2005), RCP4.5, and RCP8.5 21st-century scenarios. Climate variables include primary variables and derived bioclimatic variables. These datasets provide a common set of climate simulations suitable for seamlessly modelling the effects of past and future climate change on species distributions and diversity.
Downscaled climate projections for the Southeast United States: evaluation and use for ecological applications

USGS Publications Warehouse

Wootten, Adrienne; Smith, Kara; Boyles, Ryan; Terando, Adam; Stefanova, Lydia; Misra, Vasru; Smith, Tom; Blodgett, David L.; Semazzi, Fredrick

2014-01-01

Climate change is likely to have many effects on natural ecosystems in the Southeast U.S. The National Climate Assessment Southeast Technical Report (SETR) indicates that natural ecosystems in the Southeast are likely to be affected by warming temperatures, ocean acidification, sea-level rise, and changes in rainfall and evapotranspiration. To better assess these how climate changes could affect multiple sectors, including ecosystems, climatologists have created several downscaled climate projections (or downscaled datasets) that contain information from the global climate models (GCMs) translated to regional or local scales. The process of creating these downscaled datasets, known as downscaling, can be carried out using a broad range of statistical or numerical modeling techniques. The rapid proliferation of techniques that can be used for downscaling and the number of downscaled datasets produced in recent years present many challenges for scientists and decisionmakers in assessing the impact or vulnerability of a given species or ecosystem to climate change. Given the number of available downscaled datasets, how do these model outputs compare to each other? Which variables are available, and are certain downscaled datasets more appropriate for assessing vulnerability of a particular species? Given the desire to use these datasets for impact and vulnerability assessments and the lack of comparison between these datasets, the goal of this report is to synthesize the information available in these downscaled datasets and provide guidance to scientists and natural resource managers with specific interests in ecological modeling and conservation planning related to climate change in the Southeast U.S. This report enables the Southeast Climate Science Center (SECSC) to address an important strategic goal of providing scientific information and guidance that will enable resource managers and other participants in Landscape Conservation Cooperatives to make science-based climate change adaptation decisions.
Characterizing sources of uncertainty from global climate models and downscaling techniques

USGS Publications Warehouse

Wootten, Adrienne; Terando, Adam; Reich, Brian J.; Boyles, Ryan; Semazzi, Fred

2017-01-01

In recent years climate model experiments have been increasingly oriented towards providing information that can support local and regional adaptation to the expected impacts of anthropogenic climate change. This shift has magnified the importance of downscaling as a means to translate coarse-scale global climate model (GCM) output to a finer scale that more closely matches the scale of interest. Applying this technique, however, introduces a new source of uncertainty into any resulting climate model ensemble. Here we present a method, based on a previously established variance decomposition method, to partition and quantify the uncertainty in climate model ensembles that is attributable to downscaling. We apply the method to the Southeast U.S. using five downscaled datasets that represent both statistical and dynamical downscaling techniques. The combined ensemble is highly fragmented, in that only a small portion of the complete set of downscaled GCMs and emission scenarios are typically available. The results indicate that the uncertainty attributable to downscaling approaches ~20% for large areas of the Southeast U.S. for precipitation and ~30% for extreme heat days (> 35°C) in the Appalachian Mountains. However, attributable quantities are significantly lower for time periods when the full ensemble is considered but only a sub-sample of all models are available, suggesting that overconfidence could be a serious problem in studies that employ a single set of downscaled GCMs. We conclude with recommendations to advance the design of climate model experiments so that the uncertainty that accrues when downscaling is employed is more fully and systematically considered.
The effects of climate downscaling technique and observational data set on modeled ecological responses.

PubMed

Pourmokhtarian, Afshin; Driscoll, Charles T; Campbell, John L; Hayhoe, Katharine; Stoner, Anne M K

2016-07-01

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.
Structural uncertainty of downscaled climate model output in a difficult-to-resolve environment: data sparseness and parameterization error contribution to statistical and dynamical downscaling output in the U.S. Caribbean region

NASA Astrophysics Data System (ADS)

Terando, A. J.; Grade, S.; Bowden, J.; Henareh Khalyani, A.; Wootten, A.; Misra, V.; Collazo, J.; Gould, W. A.; Boyles, R.

2016-12-01

Sub-tropical island nations may be particularly vulnerable to anthropogenic climate change because of predicted changes in the hydrologic cycle that would lead to significant drying in the future. However, decision makers in these regions have seen their adaptation planning efforts frustrated by the lack of island-resolving climate model information. Recently, two investigations have used statistical and dynamical downscaling techniques to develop climate change projections for the U.S. Caribbean region (Puerto Rico and U.S. Virgin Islands). We compare the results from these two studies with respect to three commonly downscaled CMIP5 global climate models (GCMs). The GCMs were dynamically downscaled at a convective-permitting scale using two different regional climate models. The statistical downscaling approach was conducted at locations with long-term climate observations and then further post-processed using climatologically aided interpolation (yielding two sets of projections). Overall, both approaches face unique challenges. The statistical approach suffers from a lack of observations necessary to constrain the model, particularly at the land-ocean boundary and in complex terrain. The dynamically downscaled model output has a systematic dry bias over the island despite ample availability of moisture in the atmospheric column. Notwithstanding these differences, both approaches are consistent in projecting a drier climate that is driven by the strong global-scale anthropogenic forcing.
Quantifying the Value of Downscaled Climate Model Information for Adaptation Decisions: When is Downscaling a Smart Decision?

NASA Astrophysics Data System (ADS)

Terando, A. J.; Wootten, A.; Eaton, M. J.; Runge, M. C.; Littell, J. S.; Bryan, A. M.; Carter, S. L.

2015-12-01

Two types of decisions face society with respect to anthropogenic climate change: (1) whether to enact a global greenhouse gas abatement policy, and (2) how to adapt to the local consequences of current and future climatic changes. The practice of downscaling global climate models (GCMs) is often used to address (2) because GCMs do not resolve key features that will mediate global climate change at the local scale. In response, the development of downscaling techniques and models has accelerated to aid decision makers seeking adaptation guidance. However, quantifiable estimates of the value of information are difficult to obtain, particularly in decision contexts characterized by deep uncertainty and low system-controllability. Here we demonstrate a method to quantify the additional value that decision makers could expect if research investments are directed towards developing new downscaled climate projections. As a proof of concept we focus on a real-world management problem: whether to undertake assisted migration for an endangered tropical avian species. We also take advantage of recently published multivariate methods that account for three vexing issues in climate impacts modeling: maximizing climate model quality information, accounting for model dependence in ensembles of opportunity, and deriving probabilistic projections. We expand on these global methods by including regional (Caribbean Basin) and local (Puerto Rico) domains. In the local domain, we test whether a high resolution (2km) dynamically downscaled GCM reduces the multivariate error estimate compared to the original coarse-scale GCM. Initial tests show little difference between the downscaled and original GCM multivariate error. When propagated through to a species population model, the Value of Information analysis indicates that the expected utility that would accrue to the manager (and species) if this downscaling were completed may not justify the cost compared to alternative actions.
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.
"Going the Extra Mile in Downscaling: Why Downscaling is not jut "Plug-and-Play"

EPA Science Inventory

This presentation provides an example of doing additional work for preprocessing global climate model data for use in regional climate modeling simulations with the Weather Research and Forecasting (WRF) model. In this presentation, results from 15 months of downscaling the Comm...
Multi-site precipitation downscaling using a stochastic weather generator

NASA Astrophysics Data System (ADS)

Chen, Jie; Chen, Hua; Guo, Shenglian

2018-03-01

Statistical downscaling is an efficient way to solve the spatiotemporal mismatch between climate model outputs and the data requirements of hydrological models. However, the most commonly-used downscaling method only produces climate change scenarios for a specific site or watershed average, which is unable to drive distributed hydrological models to study the spatial variability of climate change impacts. By coupling a single-site downscaling method and a multi-site weather generator, this study proposes a multi-site downscaling approach for hydrological climate change impact studies. Multi-site downscaling is done in two stages. The first stage involves spatially downscaling climate model-simulated monthly precipitation from grid scale to a specific site using a quantile mapping method, and the second stage involves the temporal disaggregating of monthly precipitation to daily values by adjusting the parameters of a multi-site weather generator. The inter-station correlation is specifically considered using a distribution-free approach along with an iterative algorithm. The performance of the downscaling approach is illustrated using a 10-station watershed as an example. The precipitation time series derived from the National Centers for Environment Prediction (NCEP) reanalysis dataset is used as the climate model simulation. The precipitation time series of each station is divided into 30 odd years for calibration and 29 even years for validation. Several metrics, including the frequencies of wet and dry spells and statistics of the daily, monthly and annual precipitation are used as criteria to evaluate the multi-site downscaling approach. The results show that the frequencies of wet and dry spells are well reproduced for all stations. In addition, the multi-site downscaling approach performs well with respect to reproducing precipitation statistics, especially at monthly and annual timescales. The remaining biases mainly result from the non-stationarity of NCEP precipitation. Overall, the proposed approach is efficient for generating multi-site climate change scenarios that can be used to investigate the spatial variability of climate change impacts on hydrology.
Downscaling Global Emissions and Its Implications Derived from Climate Model Experiments

PubMed Central

Abe, Manabu; Kinoshita, Tsuguki; Hasegawa, Tomoko; Kawase, Hiroaki; Kushida, Kazuhide; Masui, Toshihiko; Oka, Kazutaka; Shiogama, Hideo; Takahashi, Kiyoshi; Tatebe, Hiroaki; Yoshikawa, Minoru

2017-01-01

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
VALUE - Validating and Integrating Downscaling Methods for Climate Change Research

NASA Astrophysics Data System (ADS)

Maraun, Douglas; Widmann, Martin; Benestad, Rasmus; Kotlarski, Sven; Huth, Radan; Hertig, Elke; Wibig, Joanna; Gutierrez, Jose

2013-04-01

Our understanding of global climate change is mainly based on General Circulation Models (GCMs) with a relatively coarse resolution. Since climate change impacts are mainly experienced on regional scales, high-resolution climate change scenarios need to be derived from GCM simulations by downscaling. Several projects have been carried out over the last years to validate the performance of statistical and dynamical downscaling, yet several aspects have not been systematically addressed: variability on sub-daily, decadal and longer time-scales, extreme events, spatial variability and inter-variable relationships. Different downscaling approaches such as dynamical downscaling, statistical downscaling and bias correction approaches have not been systematically compared. Furthermore, collaboration between different communities, in particular regional climate modellers, statistical downscalers and statisticians has been limited. To address these gaps, the EU Cooperation in Science and Technology (COST) action VALUE (www.value-cost.eu) has been brought into life. VALUE is a research network with participants from currently 23 European countries running from 2012 to 2015. Its main aim is to systematically validate and develop downscaling methods for climate change research in order to improve regional climate change scenarios for use in climate impact studies. Inspired by the co-design idea of the international research initiative "future earth", stakeholders of climate change information have been involved in the definition of research questions to be addressed and are actively participating in the network. The key idea of VALUE is to identify the relevant weather and climate characteristics required as input for a wide range of impact models and to define an open framework to systematically validate these characteristics. Based on a range of benchmark data sets, in principle every downscaling method can be validated and compared with competing methods. The results of this exercise will directly provide end users with important information about the uncertainty of regional climate scenarios, and will furthermore provide the basis for further developing downscaling methods. This presentation will provide background information on VALUE and discuss the identified characteristics and the validation framework.
Climate change effects on extreme flows of water supply area in Istanbul: utility of regional climate models and downscaling method.

PubMed

Kara, Fatih; Yucel, Ismail

2015-09-01

This study investigates the climate change impact on the changes of mean and extreme flows under current and future climate conditions in the Omerli Basin of Istanbul, Turkey. The 15 regional climate model output from the EU-ENSEMBLES project and a downscaling method based on local implications from geophysical variables were used for the comparative analyses. Automated calibration algorithm is used to optimize the parameters of Hydrologiska Byråns Vattenbalansavdel-ning (HBV) model for the study catchment using observed daily temperature and precipitation. The calibrated HBV model was implemented to simulate daily flows using precipitation and temperature data from climate models with and without downscaling method for reference (1960-1990) and scenario (2071-2100) periods. Flood indices were derived from daily flows, and their changes throughout the four seasons and year were evaluated by comparing their values derived from simulations corresponding to the current and future climate. All climate models strongly underestimate precipitation while downscaling improves their underestimation feature particularly for extreme events. Depending on precipitation input from climate models with and without downscaling the HBV also significantly underestimates daily mean and extreme flows through all seasons. However, this underestimation feature is importantly improved for all seasons especially for spring and winter through the use of downscaled inputs. Changes in extreme flows from reference to future increased for the winter and spring and decreased for the fall and summer seasons. These changes were more significant with downscaling inputs. With respect to current time, higher flow magnitudes for given return periods will be experienced in the future and hence, in the planning of the Omerli reservoir, the effective storage and water use should be sustained.
Model Independence in Downscaled Climate Projections: a Case Study in the Southeast United States

NASA Astrophysics Data System (ADS)

Gray, G. M. E.; Boyles, R.

2016-12-01

Downscaled climate projections are used to deduce how the climate will change in future decades at local and regional scales. It is important to use multiple models to characterize part of the future uncertainty given the impact on adaptation decision making. This is traditionally employed through an equally-weighted ensemble of multiple GCMs downscaled using one technique. Newer practices include several downscaling techniques in an effort to increase the ensemble's representation of future uncertainty. However, this practice may be adding statistically dependent models to the ensemble. Previous research has shown a dependence problem in the GCM ensemble in multiple generations, but has not been shown in the downscaled ensemble. In this case study, seven downscaled climate projections on the daily time scale are considered: CLAREnCE10, SERAP, BCCA (CMIP5 and CMIP3 versions), Hostetler, CCR, and MACA-LIVNEH. These data represent 83 ensemble members, 44 GCMs, and two generations of GCMs. Baseline periods are compared against the University of Idaho's METDATA gridded observation dataset. Hierarchical agglomerative clustering is applied to the correlated errors to determine dependent clusters. Redundant GCMs across different downscaling techniques show the most dependence, while smaller dependence signals are detected within downscaling datasets and across generations of GCMs. These results indicate that using additional downscaled projections to increase the ensemble size must be done with care to avoid redundant GCMs and the process of downscaling may increase the dependence of those downscaled GCMs. Climate model generation does not appear dissimilar enough to be treated as two separate statistical populations for ensemble building at the local and regional scales.
Which complexity of regional climate system models is essential for downscaling anthropogenic climate change in the Northwest European Shelf?

NASA Astrophysics Data System (ADS)

Mathis, Moritz; Elizalde, Alberto; Mikolajewicz, Uwe

2018-04-01

Climate change impact studies for the Northwest European Shelf (NWES) make use of various dynamical downscaling strategies in the experimental setup of regional ocean circulation models. Projected change signals from coupled and uncoupled downscalings with different domain sizes and forcing global and regional models show substantial uncertainty. In this paper, we investigate influences of the downscaling strategy on projected changes in the physical and biogeochemical conditions of the NWES. Our results indicate that uncertainties due to different downscaling strategies are similar to uncertainties due to the choice of the parent global model and the downscaling regional model. Downscaled change signals reveal to depend stronger on the downscaling strategy than on the model skills in simulating present-day conditions. Uncoupled downscalings of sea surface temperature (SST) changes are found to be tightly constrained by the atmospheric forcing. The incorporation of coupled air-sea interaction, by contrast, allows the regional model system to develop independently. Changes in salinity show a higher sensitivity to open lateral boundary conditions and river runoff than to coupled or uncoupled atmospheric forcings. Dependencies on the downscaling strategy for changes in SST, salinity, stratification and circulation collectively affect changes in nutrient import and biological primary production.
Verification of GCM-generated regional seasonal precipitation for current climate and of statistical downscaling estimates under changing climate conditions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Busuioc, A.; Storch, H. von; Schnur, R.

Empirical downscaling procedures relate large-scale atmospheric features with local features such as station rainfall in order to facilitate local scenarios of climate change. The purpose of the present paper is twofold: first, a downscaling technique is used as a diagnostic tool to verify the performance of climate models on the regional scale; second, a technique is proposed for verifying the validity of empirical downscaling procedures in climate change applications. The case considered is regional seasonal precipitation in Romania. The downscaling model is a regression based on canonical correlation analysis between observed station precipitation and European-scale sea level pressure (SLP). Themore » climate models considered here are the T21 and T42 versions of the Hamburg ECHAM3 atmospheric GCM run in time-slice mode. The climate change scenario refers to the expected time of doubled carbon dioxide concentrations around the year 2050. Generally, applications of statistical downscaling to climate change scenarios have been based on the assumption that the empirical link between the large-scale and regional parameters remains valid under a changed climate. In this study, a rationale is proposed for this assumption by showing the consistency of the 2 x CO{sub 2} GCM scenarios in winter, derived directly from the gridpoint data, with the regional scenarios obtained through empirical downscaling. Since the skill of the GCMs in regional terms is already established, it is concluded that the downscaling technique is adequate for describing climatically changing regional and local conditions, at least for precipitation in Romania during winter.« less

Projecting future precipitation and temperature at sites with diverse climate through multiple statistical downscaling schemes

NASA Astrophysics Data System (ADS)

Vallam, P.; Qin, X. S.

2017-10-01

Anthropogenic-driven climate change would affect the global ecosystem and is becoming a world-wide concern. Numerous studies have been undertaken to determine the future trends of meteorological variables at different scales. Despite these studies, there remains significant uncertainty in the prediction of future climates. To examine the uncertainty arising from using different schemes to downscale the meteorological variables for the future horizons, projections from different statistical downscaling schemes were examined. These schemes included statistical downscaling method (SDSM), change factor incorporated with LARS-WG, and bias corrected disaggregation (BCD) method. Global circulation models (GCMs) based on CMIP3 (HadCM3) and CMIP5 (CanESM2) were utilized to perturb the changes in the future climate. Five study sites (i.e., Alice Springs, Edmonton, Frankfurt, Miami, and Singapore) with diverse climatic conditions were chosen for examining the spatial variability of applying various statistical downscaling schemes. The study results indicated that the regions experiencing heavy precipitation intensities were most likely to demonstrate the divergence between the predictions from various statistical downscaling methods. Also, the variance computed in projecting the weather extremes indicated the uncertainty derived from selection of downscaling tools and climate models. This study could help gain an improved understanding about the features of different downscaling approaches and the overall downscaling uncertainty.
Assessment of the scale effect on statistical downscaling quality at a station scale using a weather generator-based model

USDA-ARS?s Scientific Manuscript database

The resolution of General Circulation Models (GCMs) is too coarse to assess the fine scale or site-specific impacts of climate change. Downscaling approaches including dynamical and statistical downscaling have been developed to meet this requirement. As the resolution of climate model increases, it...
Comparison of Grid Nudging and Spectral Nudging Techniques for Dynamical Climate Downscaling within the WRF Model

NASA Astrophysics Data System (ADS)

Fan, X.; Chen, L.; Ma, Z.

2010-12-01

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.
Hydrological responses to dynamically and statistically downscaled climate model output

USGS Publications Warehouse

Wilby, R.L.; Hay, L.E.; Gutowski, W.J.; Arritt, R.W.; Takle, E.S.; Pan, Z.; Leavesley, G.H.; Clark, M.P.

2000-01-01

Daily rainfall and surface temperature series were simulated for the Animas River basin, Colorado using dynamically and statistically downscaled output from the National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) re-analysis. A distributed hydrological model was then applied to the downscaled data. Relative to raw NCEP output, downscaled climate variables provided more realistic stimulations of basin scale hydrology. However, the results highlight the sensitivity of modeled processes to the choice of downscaling technique, and point to the need for caution when interpreting future hydrological scenarios.
VALUE - A Framework to Validate Downscaling Approaches for Climate Change Studies

NASA Astrophysics Data System (ADS)

Maraun, Douglas; Widmann, Martin; Gutiérrez, José M.; Kotlarski, Sven; Chandler, Richard E.; Hertig, Elke; Wibig, Joanna; Huth, Radan; Wilke, Renate A. I.

2015-04-01

VALUE is an open European network to validate and compare downscaling methods for climate change research. VALUE aims to foster collaboration and knowledge exchange between climatologists, impact modellers, statisticians, and stakeholders to establish an interdisciplinary downscaling community. 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. Here, we present the key ingredients of this framework. 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. Several experiments have been designed to isolate specific points in the downscaling procedure where problems may occur: what is the isolated downscaling skill? How do statistical and dynamical methods compare? How do methods perform at different spatial scales? Do methods fail in representing regional climate change? How is the overall representation of regional climate, including errors inherited from global climate models? The framework will be the basis for a comprehensive community-open downscaling intercomparison study, but is intended also to provide general guidance for other validation studies.
VALUE: A framework to validate downscaling approaches for climate change studies

NASA Astrophysics Data System (ADS)

Maraun, Douglas; Widmann, Martin; Gutiérrez, José M.; Kotlarski, Sven; Chandler, Richard E.; Hertig, Elke; Wibig, Joanna; Huth, Radan; Wilcke, Renate A. I.

2015-01-01

VALUE is an open European network to validate and compare downscaling methods for climate change research. VALUE aims to foster collaboration and knowledge exchange between climatologists, impact modellers, statisticians, and stakeholders to establish an interdisciplinary downscaling community. 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. In this paper, we present the key ingredients of this framework. 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. Several experiments have been designed to isolate specific points in the downscaling procedure where problems may occur: what is the isolated downscaling skill? How do statistical and dynamical methods compare? How do methods perform at different spatial scales? Do methods fail in representing regional climate change? How is the overall representation of regional climate, including errors inherited from global climate models? The framework will be the basis for a comprehensive community-open downscaling intercomparison study, but is intended also to provide general guidance for other validation studies.
An enhanced archive facilitating climate impacts analysis

USGS Publications Warehouse

Maurer, E.P.; Brekke, L.; Pruitt, T.; Thrasher, B.; Long, J.; Duffy, P.; Dettinger, M.; Cayan, D.; Arnold, J.

2014-01-01

We describe the expansion of a publicly available archive of downscaled climate and hydrology projections for the United States. Those studying or planning to adapt to future climate impacts demand downscaled climate model output for local or regional use. The archive we describe attempts to fulfill this need by providing data in several formats, selectable to meet user needs. Our archive has served as a resource for climate impacts modelers, water managers, educators, and others. Over 1,400 individuals have transferred more than 50 TB of data from the archive. In response to user demands, the archive has expanded from monthly downscaled data to include daily data to facilitate investigations of phenomena sensitive to daily to monthly temperature and precipitation, including extremes in these quantities. New developments include downscaled output from the new Coupled Model Intercomparison Project phase 5 (CMIP5) climate model simulations at both the monthly and daily time scales, as well as simulations of surface hydrologi- cal variables. The web interface allows the extraction of individual projections or ensemble statistics for user-defined regions, promoting the rapid assessment of model consensus and uncertainty for future projections of precipitation, temperature, and hydrology. The archive is accessible online (http://gdo-dcp.ucllnl.org/downscaled_ cmip_projections).
Influences of Regional Climate Change on Air Quality Across the Continental U.S. Projected from Downscaling IPCC AR5 Simulations. Chapter 2

NASA Technical Reports Server (NTRS)

Nolte, Christopher; Otte, Tanya; Pinder, Robert; Bowden, J.; Herwehe, J.; Faluvegi, Gregory; Shindell, Drew

2013-01-01

Projecting climate change scenarios to local scales is important for understanding, mitigating, and adapting to the effects of climate change on society and the environment. Many of the global climate models (GCMs) that are participating in the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5) do not fully resolve regional-scale processes and therefore cannot capture regional-scale changes in temperatures and precipitation. We use a regional climate model (RCM) to dynamically downscale the GCM's large-scale signal to investigate the changes in regional and local extremes of temperature and precipitation that may result from a changing climate. In this paper, we show preliminary results from downscaling the NASA/GISS ModelE IPCC AR5 Representative Concentration Pathway (RCP) 6.0 scenario. We use the Weather Research and Forecasting (WRF) model as the RCM to downscale decadal time slices (1995-2005 and 2025-2035) and illustrate potential changes in regional climate for the continental U.S. that are projected by ModelE and WRF under RCP6.0. The regional climate change scenario is further processed using the Community Multiscale Air Quality modeling system to explore influences of regional climate change on air quality.
Regional Climate Change across the Continental U.S. Projected from Downscaling IPCC AR5 Simulations

NASA Astrophysics Data System (ADS)

Otte, T. L.; Nolte, C. G.; Otte, M. J.; Pinder, R. W.; Faluvegi, G.; Shindell, D. T.

2011-12-01

Projecting climate change scenarios to local scales is important for understanding and mitigating the effects of climate change on society and the environment. Many of the general circulation models (GCMs) that are participating in the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5) do not fully resolve regional-scale processes and therefore cannot capture local changes in temperature and precipitation extremes. We seek to project the GCM's large-scale climate change signal to the local scale using a regional climate model (RCM) by applying dynamical downscaling techniques. The RCM will be used to better understand the local changes of temperature and precipitation extremes that may result from a changing climate. Preliminary results from downscaling NASA/GISS ModelE simulations of the IPCC AR5 Representative Concentration Pathway (RCP) scenario 6.0 will be shown. The Weather Research and Forecasting (WRF) model will be used as the RCM to downscale decadal time slices for ca. 2000 and ca. 2030 and illustrate potential changes in regional climate for the continental U.S. that are projected by ModelE and WRF under RCP6.0.
Application of physical scaling towards downscaling climate model precipitation data

NASA Astrophysics Data System (ADS)

Gaur, Abhishek; Simonovic, Slobodan P.

2018-04-01

Physical scaling (SP) method downscales climate model data to local or regional scales taking into consideration physical characteristics of the area under analysis. In this study, multiple SP method based models are tested for their effectiveness towards downscaling North American regional reanalysis (NARR) daily precipitation data. Model performance is compared with two state-of-the-art downscaling methods: statistical downscaling model (SDSM) and generalized linear modeling (GLM). The downscaled precipitation is evaluated with reference to recorded precipitation at 57 gauging stations located within the study region. The spatial and temporal robustness of the downscaling methods is evaluated using seven precipitation based indices. Results indicate that SP method-based models perform best in downscaling precipitation followed by GLM, followed by the SDSM model. Best performing models are thereafter used to downscale future precipitations made by three global circulation models (GCMs) following two emission scenarios: representative concentration pathway (RCP) 2.6 and RCP 8.5 over the twenty-first century. The downscaled future precipitation projections indicate an increase in mean and maximum precipitation intensity as well as a decrease in the total number of dry days. Further an increase in the frequency of short (1-day), moderately long (2-4 day), and long (more than 5-day) precipitation events is projected.
The Practitioner's Dilemma: How to Assess the Credibility of Downscaled Climate Projections

NASA Technical Reports Server (NTRS)

Barsugli, Joseph J.; Guentchev, Galina; Horton, Radley M.; Wood, Andrew; Mearns, Lindo O.; Liang, Xin-Zhong; Winkler, Julia A.; Dixon, Keith; Hayhoe, Katharine; Rood, Richard B.;

2013-01-01

Suppose you are a city planner, regional water manager, or wildlife conservation specialist who is asked to include the potential impacts of climate variability and change in your risk management and planning efforts. What climate information would you use? The choice is often regional or local climate projections downscaled from global climate models (GCMs; also known as general circulation models) to include detail at spatial and temporal scales that align with those of the decision problem. A few years ago this information was hard to come by. Now there is Web-based access to a proliferation of high-resolution climate projections derived with differing downscaling methods.

Dynamical Downscaling of NASA/GISS ModelE: Continuous, Multi-Year WRF Simulations

NASA Astrophysics Data System (ADS)

Otte, T.; Bowden, J. H.; Nolte, C. G.; Otte, M. J.; Herwehe, J. A.; Faluvegi, G.; Shindell, D. T.

2010-12-01

The WRF Model is being used at the U.S. EPA for dynamical downscaling of the NASA/GISS ModelE fields to assess regional impacts of climate change in the United States. The WRF model has been successfully linked to the ModelE fields in their raw hybrid vertical coordinate, and continuous, multi-year WRF downscaling simulations have been performed. WRF will be used to downscale decadal time slices of ModelE for recent past, current, and future climate as the simulations being conducted for the IPCC Fifth Assessment Report become available. This presentation will focus on the sensitivity to interior nudging within the RCM. The use of interior nudging for downscaled regional climate simulations has been somewhat controversial over the past several years but has been recently attracting attention. Several recent studies that have used reanalysis (i.e., verifiable) fields as a proxy for GCM input have shown that interior nudging can be beneficial toward achieving the desired downscaled fields. In this study, the value of nudging will be shown using fields from ModelE that are downscaled using WRF. Several different methods of nudging are explored, and it will be shown that the method of nudging and the choices made with respect to how nudging is used in WRF are critical to balance the constraint of ModelE against the freedom of WRF to develop its own fields.
Implications of the methodological choices for hydrologic portrayals of climate change over the contiguous United States: Statistically downscaled forcing data and hydrologic models

USGS Publications Warehouse

Mizukami, Naoki; Clark, Martyn P.; Gutmann, Ethan D.; Mendoza, Pablo A.; Newman, Andrew J.; Nijssen, Bart; Livneh, Ben; Hay, Lauren E.; Arnold, Jeffrey R.; Brekke, Levi D.

2016-01-01

Continental-domain assessments of climate change impacts on water resources typically rely on statistically downscaled climate model outputs to force hydrologic models at a finer spatial resolution. This study examines the effects of four statistical downscaling methods [bias-corrected constructed analog (BCCA), bias-corrected spatial disaggregation applied at daily (BCSDd) and monthly scales (BCSDm), and asynchronous regression (AR)] on retrospective hydrologic simulations using three hydrologic models with their default parameters (the Community Land Model, version 4.0; the Variable Infiltration Capacity model, version 4.1.2; and the Precipitation–Runoff Modeling System, version 3.0.4) over the contiguous United States (CONUS). Biases of hydrologic simulations forced by statistically downscaled climate data relative to the simulation with observation-based gridded data are presented. Each statistical downscaling method produces different meteorological portrayals including precipitation amount, wet-day frequency, and the energy input (i.e., shortwave radiation), and their interplay affects estimations of precipitation partitioning between evapotranspiration and runoff, extreme runoff, and hydrologic states (i.e., snow and soil moisture). The analyses show that BCCA underestimates annual precipitation by as much as −250 mm, leading to unreasonable hydrologic portrayals over the CONUS for all models. Although the other three statistical downscaling methods produce a comparable precipitation bias ranging from −10 to 8 mm across the CONUS, BCSDd severely overestimates the wet-day fraction by up to 0.25, leading to different precipitation partitioning compared to the simulations with other downscaled data. Overall, the choice of downscaling method contributes to less spread in runoff estimates (by a factor of 1.5–3) than the choice of hydrologic model with use of the default parameters if BCCA is excluded.
Intercomparison of Downscaling Methods on Hydrological Impact for Earth System Model of NE United States

NASA Astrophysics Data System (ADS)

Yang, P.; Fekete, B. M.; Rosenzweig, B.; Lengyel, F.; Vorosmarty, C. J.

2012-12-01

Atmospheric dynamics are essential inputs to Regional-scale Earth System Models (RESMs). Variables including surface air temperature, total precipitation, solar radiation, wind speed and humidity must be downscaled from coarse-resolution, global General Circulation Models (GCMs) to the high temporal and spatial resolution required for regional modeling. However, this downscaling procedure can be challenging due to the need to correct for bias from the GCM and to capture the spatiotemporal heterogeneity of the regional dynamics. In this study, the results obtained using several downscaling techniques and observational datasets were compared for a RESM of the Northeast Corridor of the United States. Previous efforts have enhanced GCM model outputs through bias correction using novel techniques. For example, the Climate Impact Research at Potsdam Institute developed a series of bias-corrected GCMs towards the next generation climate change scenarios (Schiermeier, 2012; Moss et al., 2010). Techniques to better represent the heterogeneity of climate variables have also been improved using statistical approaches (Maurer, 2008; Abatzoglou, 2011). For this study, four downscaling approaches to transform bias-corrected HADGEM2-ES Model output (daily at .5 x .5 degree) to the 3'*3'(longitude*latitude) daily and monthly resolution required for the Northeast RESM were compared: 1) Bilinear Interpolation, 2) Daily bias-corrected spatial downscaling (D-BCSD) with Gridded Meteorological Datasets (developed by Abazoglou 2011), 3) Monthly bias-corrected spatial disaggregation (M-BCSD) with CRU(Climate Research Unit) and 4) Dynamic Downscaling based on Weather Research and Forecast (WRF) model. Spatio-temporal analysis of the variability in precipitation was conducted over the study domain. Validation of the variables of different downscaling methods against observational datasets was carried out for assessment of the downscaled climate model outputs. The effects of using the different approaches to downscale atmospheric variables (specifically air temperature and precipitation) for use as inputs to the Water Balance Model (WBMPlus, Vorosmarty et al., 1998;Wisser et al., 2008) for simulation of daily discharge and monthly stream flow in the Northeast US for a 100-year period in the 21st century were also assessed. Statistical techniques especially monthly bias-corrected spatial disaggregation (M-BCSD) showed potential advantage among other methods for the daily discharge and monthly stream flow simulation. However, Dynamic Downscaling will provide important complements to the statistical approaches tested.
Using multiple climate projections for assessing hydrological response to climate change in the Thukela River Basin, South Africa

NASA Astrophysics Data System (ADS)

Graham, L. Phil; Andersson, Lotta; Horan, Mark; Kunz, Richard; Lumsden, Trevor; Schulze, Roland; Warburton, Michele; Wilk, Julie; Yang, Wei

This study used climate change projections from different regional approaches to assess hydrological effects on the Thukela River Basin in KwaZulu-Natal, South Africa. Projecting impacts of future climate change onto hydrological systems can be undertaken in different ways and a variety of effects can be expected. Although simulation results from global climate models (GCMs) are typically used to project future climate, different outcomes from these projections may be obtained depending on the GCMs themselves and how they are applied, including different ways of downscaling from global to regional scales. Projections of climate change from different downscaling methods, different global climate models and different future emissions scenarios were used as input to simulations in a hydrological model to assess climate change impacts on hydrology. A total of 10 hydrological change simulations were made, resulting in a matrix of hydrological response results. This matrix included results from dynamically downscaled climate change projections from the same regional climate model (RCM) using an ensemble of three GCMs and three global emissions scenarios, and from statistically downscaled projections using results from five GCMs with the same emissions scenario. Although the matrix of results does not provide complete and consistent coverage of potential uncertainties from the different methods, some robust results were identified. In some regards, the results were in agreement and consistent for the different simulations. For others, particularly rainfall, the simulations showed divergence. For example, all of the statistically downscaled simulations showed an annual increase in precipitation and corresponding increase in river runoff, while the RCM downscaled simulations showed both increases and decreases in runoff. According to the two projections that best represent runoff for the observed climate, increased runoff would generally be expected for this basin in the future. Dealing with such variability in results is not atypical for assessing climate change impacts in Africa and practitioners are faced with how to interpret them. This work highlights the need for additional, well-coordinated regional climate downscaling for the region to further define the range of uncertainties involved.
Climate change and water resources in a tropical island system: propagation of uncertainty from statistically downscaled climate models to hydrologic models

Treesearch

Ashley E. Van Beusekom; William A. Gould; Adam J. Terando; Jaime A. Collazo

2015-01-01

Many tropical islands have limited water resources with historically increasing demand, all potentially affected by a changing climate. The effects of climate change on island hydrology are difficult to model due to steep local precipitation gradients and sparse data. Thiswork uses 10 statistically downscaled general circulationmodels (GCMs) under two greenhouse gas...
Statistical downscaling of mean temperature, maximum temperature, and minimum temperature on the Loess Plateau, China

NASA Astrophysics Data System (ADS)

Jiang, L.

2017-12-01

Climate change is considered to be one of the greatest environmental threats. Global climate models (GCMs) are the primary tool used for studying climate change. However, GCMs are limited because of their coarse spatial resolution and inability to resolve important sub-grid scale features such as terrain and clouds. Statistical downscaling methods can be used to downscale large-scale variables to local-scale. In this study, we assess the applicability of the Statistical Downscaling Model (SDSM) in downscaling the outputs from Beijing Normal University Earth System Model (BNU-ESM). The study focus on the the Loess Plateau, China, and the variables for downscaling include daily mean temperature (TMEAN), maximum temperature (TMAX) and minimum temperature (TMIN). The results show that SDSM performs well for these three climatic variables on the Loess Plateau. After downscaling, the root mean square errors for TMEAN, TMAX, TMIN for BNU-ESM were reduced by 70.9%, 75.1%, and 67.2%, respectively. All the rates of change in TMEAN, TMAX and TMIN during the 21st century decreased after SDSM downscaling. We also show that SDSM can effectively reduce uncertainty, compared with the raw model outputs. TMEAN uncertainty was reduced by 27.1%, 26.8%, and 16.3% for the future scenarios of RCP 2.6, RCP 4.5 and RCP 8.5, respectively. The corresponding reductions in uncertainty were 23.6%, 30.7%, and 18.7% for TMAX; 37.6%, 31.8%, and 23.2% for TMIN.
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.
Regional Climate Change across North America in 2030 Projected from RCP6.0

NASA Astrophysics Data System (ADS)

Otte, T.; Nolte, C. G.; Faluvegi, G.; Shindell, D. T.

2012-12-01

Projecting climate change scenarios to local scales is important for understanding and mitigating the effects of climate change on society and the environment. Many of the general circulation models (GCMs) that are participating in the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5) do not fully resolve regional-scale processes and therefore cannot capture local changes in temperature and precipitation extremes. We seek to project the GCM's large-scale climate change signal to the local scale using a regional climate model (RCM) by applying dynamical downscaling techniques. The RCM will be used to better understand the local changes of temperature and precipitation extremes that may result from a changing climate. In this research, downscaling techniques that we developed with historical data are now applied to GCM fields. Results from downscaling NASA/GISS ModelE2 simulations of the IPCC AR5 Representative Concentration Pathway (RCP) scenario 6.0 will be shown. The Weather Research and Forecasting (WRF) model has been used as the RCM to downscale decadal time slices for ca. 2000 and ca. 2030 over North America and illustrate potential changes in regional climate that are projected by ModelE2 and WRF under RCP6.0. The analysis focuses on regional climate fields that most strongly influence the interactions between climate change and air quality. In particular, an analysis of extreme temperature and precipitation events will be presented.
Evaluation of cool season precipitation event characteristics over the Northeast US in a suite of downscaled climate model hindcasts

NASA Astrophysics Data System (ADS)

Loikith, Paul C.; Waliser, Duane E.; Kim, Jinwon; Ferraro, Robert

2017-08-01

Cool season precipitation event characteristics are evaluated across a suite of downscaled climate models over the northeastern US. Downscaled hindcast simulations are produced by dynamically downscaling the Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA2) using the National Aeronautics and Space Administration (NASA)-Unified Weather Research and Forecasting (WRF) regional climate model (RCM) and the Goddard Earth Observing System Model, Version 5 (GEOS-5) global climate model. NU-WRF RCM simulations are produced at 24, 12, and 4-km horizontal resolutions using a range of spectral nudging schemes while the MERRA2 global downscaled run is provided at 12.5-km. All model runs are evaluated using four metrics designed to capture key features of precipitation events: event frequency, event intensity, even total, and event duration. Overall, the downscaling approaches result in a reasonable representation of many of the key features of precipitation events over the region, however considerable biases exist in the magnitude of each metric. Based on this evaluation there is no clear indication that higher resolution simulations result in more realistic results in general, however many small-scale features such as orographic enhancement of precipitation are only captured at higher resolutions suggesting some added value over coarser resolution. While the differences between simulations produced using nudging and no nudging are small, there is some improvement in model fidelity when nudging is introduced, especially at a cutoff wavelength of 600 km compared to 2000 km. Based on the results of this evaluation, dynamical regional downscaling using NU-WRF results in a more realistic representation of precipitation event climatology than the global downscaling of MERRA2 using GEOS-5.

Report from the workshop on climate downscaling and its application in high Hawaiian Islands, September 16–17, 2015

USGS Publications Warehouse

Helweg, David A.; Keener, Victoria; Burgett, Jeff M.

2016-07-14

In the subtropical and tropical Pacific islands, changing climate is predicted to influence precipitation and freshwater availability, and thus is predicted to impact ecosystems goods and services available to ecosystems and human communities. The small size of high Hawaiian Islands, plus their complex microlandscapes, require downscaling of global climate models to provide future projections of greater skill and spatial resolution. Two different climate modeling approaches (physics-based dynamical downscaling and statistics-based downscaling) have produced dissimilar projections. Because of these disparities, natural resource managers and decision makers have low confidence in using the modeling results and are therefore are unwilling to include climate-related projections in their decisions. In September 2015, the Pacific Islands Climate Science Center (PICSC), the Pacific Islands Climate Change Cooperative (PICCC), and the Pacific Regional Integrated Sciences and Assessments (Pacific RISA) program convened a 2-day facilitated workshop in which the two modeling teams, plus key model users and resource managers, were brought together for a comparison of the two approaches, culminating with a discussion of how to provide predictions that are useable by resource managers. The proceedings, discussions, and outcomes of this Workshop are summarized in this Open-File Report.
GPCC - A weather generator-based statistical downscaling tool for site-specific assessment of climate change impacts

USDA-ARS?s Scientific Manuscript database

Resolution of climate model outputs are too coarse to be used as direct inputs to impact models for assessing climate change impacts on agricultural production, water resources, and eco-system services at local or site-specific scales. Statistical downscaling approaches are usually used to bridge th...
Evaluation of Statistical Downscaling Skill at Reproducing Extreme Events

NASA Astrophysics Data System (ADS)

McGinnis, S. A.; Tye, M. R.; Nychka, D. W.; Mearns, L. O.

2015-12-01

Climate model outputs usually have much coarser spatial resolution than is needed by impacts models. Although higher resolution can be achieved using regional climate models for dynamical downscaling, further downscaling is often required. The final resolution gap is often closed with a combination of spatial interpolation and bias correction, which constitutes a form of statistical downscaling. We use this technique to downscale regional climate model data and evaluate its skill in reproducing extreme events. We downscale output from the North American Regional Climate Change Assessment Program (NARCCAP) dataset from its native 50-km spatial resolution to the 4-km resolution of University of Idaho's METDATA gridded surface meterological dataset, which derives from the PRISM and NLDAS-2 observational datasets. We operate on the major variables used in impacts analysis at a daily timescale: daily minimum and maximum temperature, precipitation, humidity, pressure, solar radiation, and winds. To interpolate the data, we use the patch recovery method from the Earth System Modeling Framework (ESMF) regridding package. We then bias correct the data using Kernel Density Distribution Mapping (KDDM), which has been shown to exhibit superior overall performance across multiple metrics. Finally, we evaluate the skill of this technique in reproducing extreme events by comparing raw and downscaled output with meterological station data in different bioclimatic regions according to the the skill scores defined by Perkins et al in 2013 for evaluation of AR4 climate models. We also investigate techniques for improving bias correction of values in the tails of the distributions. These techniques include binned kernel density estimation, logspline kernel density estimation, and transfer functions constructed by fitting the tails with a generalized pareto distribution.
Evaluating the Appropriateness of Downscaled Climate Information for Projecting Risks of Salmonella.

PubMed

Guentchev, Galina S; Rood, Richard B; Ammann, Caspar M; Barsugli, Joseph J; Ebi, Kristie; Berrocal, Veronica; O'Neill, Marie S; Gronlund, Carina J; Vigh, Jonathan L; Koziol, Ben; Cinquini, Luca

2016-02-29

Foodborne diseases have large economic and societal impacts worldwide. To evaluate how the risks of foodborne diseases might change in response to climate change, credible and usable climate information tailored to the specific application question is needed. Global Climate Model (GCM) data generally need to, both, be downscaled to the scales of the application to be usable, and represent, well, the key characteristics that inflict health impacts. This study presents an evaluation of temperature-based heat indices for the Washington D.C. area derived from statistically downscaled GCM simulations for 1971-2000--a necessary step in establishing the credibility of these data. The indices approximate high weekly mean temperatures linked previously to occurrences of Salmonella infections. Due to bias-correction, included in the Asynchronous Regional Regression Model (ARRM) and the Bias Correction Constructed Analogs (BCCA) downscaling methods, the observed 30-year means of the heat indices were reproduced reasonably well. In April and May, however, some of the statistically downscaled data misrepresent the increase in the number of hot days towards the summer months. This study demonstrates the dependence of the outcomes to the selection of downscaled climate data and the potential for misinterpretation of future estimates of Salmonella infections.
Hydrologic Implications of Dynamical and Statistical Approaches to Downscaling Climate Model Outputs

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wood, Andrew W; Leung, Lai R; Sridhar, V

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
Assessing the Assessment Methods: Climate Change and Hydrologic Impacts

NASA Astrophysics Data System (ADS)

Brekke, L. D.; Clark, M. P.; Gutmann, E. D.; Mizukami, N.; Mendoza, P. A.; Rasmussen, R.; Ikeda, K.; Pruitt, T.; Arnold, J. R.; Rajagopalan, B.

2014-12-01

The Bureau of Reclamation, the U.S. Army Corps of Engineers, and other water management agencies have an interest in developing reliable, science-based methods for incorporating climate change information into longer-term water resources planning. Such assessments must quantify projections of future climate and hydrology, typically relying on some form of spatial downscaling and bias correction to produce watershed-scale weather information that subsequently drives hydrology and other water resource management analyses (e.g., water demands, water quality, and environmental habitat). Water agencies continue to face challenging method decisions in these endeavors: (1) which downscaling method should be applied and at what resolution; (2) what observational dataset should be used to drive downscaling and hydrologic analysis; (3) what hydrologic model(s) should be used and how should these models be configured and calibrated? There is a critical need to understand the ramification of these method decisions, as they affect the signal and uncertainties produced by climate change assessments and, thus, adaptation planning. This presentation summarizes results from a three-year effort to identify strengths and weaknesses of widely applied methods for downscaling climate projections and assessing hydrologic conditions. Methods were evaluated from two perspectives: historical fidelity, and tendency to modulate a global climate model's climate change signal. On downscaling, four methods were applied at multiple resolutions: statistically using Bias Correction Spatial Disaggregation, Bias Correction Constructed Analogs, and Asynchronous Regression; dynamically using the Weather Research and Forecasting model. Downscaling results were then used to drive hydrologic analyses over the contiguous U.S. using multiple models (VIC, CLM, PRMS), with added focus placed on case study basins within the Colorado Headwaters. The presentation will identify which types of climate changes are expressed robustly across methods versus those that are sensitive to method choice; which method choices seem relatively more important; and where strategic investments in research and development can substantially improve guidance on climate change provided to water managers.
Probabilistic projections of 21st century climate change over Northern Eurasia

NASA Astrophysics Data System (ADS)

Monier, E.; Sokolov, A. P.; Schlosser, C. A.; Scott, J. R.; Gao, X.

2013-12-01

We present probabilistic projections of 21st century climate change over Northern Eurasia using the Massachusetts Institute of Technology (MIT) Integrated Global System Model (IGSM), an integrated assessment model that couples an earth system model of intermediate complexity, with a two-dimensional zonal-mean atmosphere, to a human activity model. Regional climate change is obtained by two downscaling methods: a dynamical downscaling, where the IGSM is linked to a three dimensional atmospheric model; and a statistical downscaling, where a pattern scaling algorithm uses climate-change patterns from 17 climate models. This framework allows for key sources of uncertainty in future projections of regional climate change to be accounted for: emissions projections; climate system parameters (climate sensitivity, strength of aerosol forcing and ocean heat uptake rate); natural variability; and structural uncertainty. Results show that the choice of climate policy and the climate parameters are the largest drivers of uncertainty. We also nd that dierent initial conditions lead to dierences in patterns of change as large as when using different climate models. Finally, this analysis reveals the wide range of possible climate change over Northern Eurasia, emphasizing the need to consider all sources of uncertainty when modeling climate impacts over Northern Eurasia.
Probabilistic projections of 21st century climate change over Northern Eurasia

NASA Astrophysics Data System (ADS)

Monier, Erwan; Sokolov, Andrei; Schlosser, Adam; Scott, Jeffery; Gao, Xiang

2013-12-01

We present probabilistic projections of 21st century climate change over Northern Eurasia using the Massachusetts Institute of Technology (MIT) Integrated Global System Model (IGSM), an integrated assessment model that couples an Earth system model of intermediate complexity with a two-dimensional zonal-mean atmosphere to a human activity model. Regional climate change is obtained by two downscaling methods: a dynamical downscaling, where the IGSM is linked to a three-dimensional atmospheric model, and a statistical downscaling, where a pattern scaling algorithm uses climate change patterns from 17 climate models. This framework allows for four major sources of uncertainty in future projections of regional climate change to be accounted for: emissions projections, climate system parameters (climate sensitivity, strength of aerosol forcing and ocean heat uptake rate), natural variability, and structural uncertainty. The results show that the choice of climate policy and the climate parameters are the largest drivers of uncertainty. We also find that different initial conditions lead to differences in patterns of change as large as when using different climate models. Finally, this analysis reveals the wide range of possible climate change over Northern Eurasia, emphasizing the need to consider these sources of uncertainty when modeling climate impacts over Northern Eurasia.
78 FR 13874 - Watershed Modeling To Assess the Sensitivity of Streamflow, Nutrient, and Sediment Loads to...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-03-01

... an improved understanding of methodological challenges associated with integrating existing tools and... methodological challenges associated with integrating existing tools (e.g., climate models, downscaling... sensitivity to methodological choices such as different approaches for downscaling global climate change...
Trend analysis of watershed-scale precipitation over Northern California by means of dynamically-downscaled CMIP5 future climate projections.

PubMed

Ishida, K; Gorguner, M; Ercan, A; Trinh, T; Kavvas, M L

2017-08-15

The impacts of climate change on watershed-scale precipitation through the 21st century were investigated over eight study watersheds in Northern California based on dynamically downscaled CMIP5 future climate projections from three GCMs (CCSM4, HadGEM2-ES, and MIROC5) under the RCP4.5 and RCP8.5 future climate scenarios. After evaluating the modeling capability of the WRF model, the six future climate projections were dynamically downscaled by means of the WRF model over Northern California at 9km grid resolution and hourly temporal resolution during a 94-year period (2006-2100). The biases in the model simulations were corrected, and basin-average precipitation over the eight study watersheds was calculated from the dynamically downscaled precipitation data. Based on the dynamically downscaled basin-average precipitation, trends in annual depth and annual peaks of basin-average precipitation during the 21st century were analyzed over the eight study watersheds. The analyses in this study indicate that there may be differences between trends of annual depths and annual peaks of watershed-scale precipitation during the 21st century. Furthermore, trends in watershed-scale precipitation under future climate conditions may be different for different watersheds depending on their location and topography even if they are in the same region. Copyright © 2017 Elsevier B.V. All rights reserved.
Uncertainty in projected point precipitation extremes for hydrological impact analysis of climate change

NASA Astrophysics Data System (ADS)

Van Uytven, Els; Willems, Patrick

2017-04-01

Current trends in the hydro-meteorological variables indicate the potential impact of climate change on hydrological extremes. Therefore, they trigger an increased importance climate adaptation strategies in water management. The impact of climate change on hydro-meteorological and hydrological extremes is, however, highly uncertain. This is due to uncertainties introduced by the climate models, the internal variability inherent to the climate system, the greenhouse gas scenarios and the statistical downscaling methods. In view of the need to define sustainable climate adaptation strategies, there is a need to assess these uncertainties. This is commonly done by means of ensemble approaches. Because more and more climate models and statistical downscaling methods become available, there is a need to facilitate the climate impact and uncertainty analysis. A Climate Perturbation Tool has been developed for that purpose, which combines a set of statistical downscaling methods including weather typing, weather generator, transfer function and advanced perturbation based approaches. By use of an interactive interface, climate impact modelers can apply these statistical downscaling methods in a semi-automatic way to an ensemble of climate model runs. The tool is applicable to any region, but has been demonstrated so far to cases in Belgium, Suriname, Vietnam and Bangladesh. Time series representing future local-scale precipitation, temperature and potential evapotranspiration (PET) conditions were obtained, starting from time series of historical observations. Uncertainties on the future meteorological conditions are represented in two different ways: through an ensemble of time series, and a reduced set of synthetic scenarios. The both aim to span the full uncertainty range as assessed from the ensemble of climate model runs and downscaling methods. For Belgium, for instance, use was made of 100-year time series of 10-minutes precipitation observations and daily temperature and PET observations at Uccle and a large ensemble of 160 global climate model runs (CMIP5). They cover all four representative concentration pathway based greenhouse gas scenarios. While evaluating the downscaled meteorological series, particular attention was given to the performance of extreme value metrics (e.g. for precipitation, by means of intensity-duration-frequency statistics). Moreover, the total uncertainty was decomposed in the fractional uncertainties for each of the uncertainty sources considered. Research assessing the additional uncertainty due to parameter and structural uncertainties of the hydrological impact model is ongoing.
A New High Resolution Climate Dataset for Climate Change Impacts Assessments in New England

NASA Astrophysics Data System (ADS)

Komurcu, M.; Huber, M.

2016-12-01

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.
Uncertainty Assessment of the NASA Earth Exchange Global Daily Downscaled Climate Projections (NEX-GDDP) Dataset

NASA Technical Reports Server (NTRS)

Wang, Weile; Nemani, Ramakrishna R.; Michaelis, Andrew; Hashimoto, Hirofumi; Dungan, Jennifer L.; Thrasher, Bridget L.; Dixon, Keith W.

2016-01-01

The NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset is comprised of downscaled climate projections that are derived from 21 General Circulation Model (GCM) runs conducted under the Coupled Model Intercomparison Project Phase 5 (CMIP5) and across two of the four greenhouse gas emissions scenarios (RCP4.5 and RCP8.5). Each of the climate projections includes daily maximum temperature, minimum temperature, and precipitation for the periods from 1950 through 2100 and the spatial resolution is 0.25 degrees (approximately 25 km x 25 km). The GDDP dataset has received warm welcome from the science community in conducting studies of climate change impacts at local to regional scales, but a comprehensive evaluation of its uncertainties is still missing. In this study, we apply the Perfect Model Experiment framework (Dixon et al. 2016) to quantify the key sources of uncertainties from the observational baseline dataset, the downscaling algorithm, and some intrinsic assumptions (e.g., the stationary assumption) inherent to the statistical downscaling techniques. We developed a set of metrics to evaluate downscaling errors resulted from bias-correction ("quantile-mapping"), spatial disaggregation, as well as the temporal-spatial non-stationarity of climate variability. Our results highlight the spatial disaggregation (or interpolation) errors, which dominate the overall uncertainties of the GDDP dataset, especially over heterogeneous and complex terrains (e.g., mountains and coastal area). In comparison, the temporal errors in the GDDP dataset tend to be more constrained. Our results also indicate that the downscaled daily precipitation also has relatively larger uncertainties than the temperature fields, reflecting the rather stochastic nature of precipitation in space. Therefore, our results provide insights in improving statistical downscaling algorithms and products in the future.
Dynamically downscaling predictions for deciduous tree leaf emergence in California under current and future climate.

PubMed

Medvigy, David; Kim, Seung Hee; Kim, Jinwon; Kafatos, Menas C

2016-07-01

Models that predict the timing of deciduous tree leaf emergence are typically very sensitive to temperature. However, many temperature data products, including those from climate models, have been developed at a very coarse spatial resolution. Such coarse-resolution temperature products can lead to highly biased predictions of leaf emergence. This study investigates how dynamical downscaling of climate models impacts simulations of deciduous tree leaf emergence in California. Models for leaf emergence are forced with temperatures simulated by a general circulation model (GCM) at ~200-km resolution for 1981-2000 and 2031-2050 conditions. GCM simulations are then dynamically downscaled to 32- and 8-km resolution, and leaf emergence is again simulated. For 1981-2000, the regional average leaf emergence date is 30.8 days earlier in 32-km simulations than in ~200-km simulations. Differences between the 32 and 8 km simulations are small and mostly local. The impact of downscaling from 200 to 8 km is ~15 % smaller in 2031-2050 than in 1981-2000, indicating that the impacts of downscaling are unlikely to be stationary.
Impacts of climate change and internal climate variability on french rivers streamflows

NASA Astrophysics Data System (ADS)

Dayon, Gildas; Boé, Julien; Martin, Eric

2016-04-01

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
Climate Change Projection for the Department of Energy's Savannah River Site

NASA Astrophysics Data System (ADS)

Werth, D. W.

2014-12-01

As per recent Department of Energy (DOE) sustainability requirements, the Savannah River National Laboratory (SRNL) is developing a climate projection for the DOE's Savannah River Site (SRS) near Aiken, SC. This will comprise data from both a statistical and a dynamic downscaling process, each interpolated to the SRS. We require variables most relevant to operational activities at the site (such as the US Forest Service's forest management program), and select temperature, precipitation, wind, and humidity as being most relevant to energy and water resource requirements, fire and forest ecology, and facility and worker safety. We then develop projections of the means and extremes of these variables, estimate the effect on site operations, and develop long-term mitigation strategies. For example, given that outdoor work while wearing protective gear is a daily facet of site operations, heat stress is of primary importance to work planning, and we use the downscaled data to estimate changes in the occurrence of high temperatures. For the statistical downscaling, we use global climate model (GCM) data from the Climate Model Intercomparison Project, version 5 (CMIP-5), which was used in the IPCC Fifth Assessment Report (AR5). GCM data from five research groups was selected, and two climate change scenarios - RCP 4.5 and RCP 8.5 - are used with observed data from site instruments and other databases to produce the downscaled projections. We apply a quantile regression downscaling method, which involves the use of the observed cumulative distribution function to correct that of the GCM. This produces a downscaled projection with an interannual variability closer to that of the observed data and allows for more extreme values in the projections, which are often absent in GCM data. The statistically downscaled data is complemented with dynamically downscaled data from the NARCCAP database, which comprises output from regional climate models forced with GCM output from the CMIP-3 database of GCM simulations. Applications of the downscaled climate projections to some of the unique operational needs of a large DOE weapons complex site are described.
Technical Challenges and Solutions in Representing Lakes when using WRF in Downscaling Applications

EPA Science Inventory

The Weather Research and Forecasting (WRF) model is commonly used to make high resolution future projections of regional climate by downscaling global climate model (GCM) outputs. Because the GCM fields are typically at a much coarser spatial resolution than the target regional ...
Evaluating the Appropriateness of Downscaled Climate Information for Projecting Risks of Salmonella

PubMed Central

Guentchev, Galina S.; Rood, Richard B.; Ammann, Caspar M.; Barsugli, Joseph J.; Ebi, Kristie; Berrocal, Veronica; O’Neill, Marie S.; Gronlund, Carina J.; Vigh, Jonathan L.; Koziol, Ben; Cinquini, Luca

2016-01-01

Foodborne diseases have large economic and societal impacts worldwide. To evaluate how the risks of foodborne diseases might change in response to climate change, credible and usable climate information tailored to the specific application question is needed. Global Climate Model (GCM) data generally need to, both, be downscaled to the scales of the application to be usable, and represent, well, the key characteristics that inflict health impacts. This study presents an evaluation of temperature-based heat indices for the Washington D.C. area derived from statistically downscaled GCM simulations for 1971–2000—a necessary step in establishing the credibility of these data. The indices approximate high weekly mean temperatures linked previously to occurrences of Salmonella infections. Due to bias-correction, included in the Asynchronous Regional Regression Model (ARRM) and the Bias Correction Constructed Analogs (BCCA) downscaling methods, the observed 30-year means of the heat indices were reproduced reasonably well. In April and May, however, some of the statistically downscaled data misrepresent the increase in the number of hot days towards the summer months. This study demonstrates the dependence of the outcomes to the selection of downscaled climate data and the potential for misinterpretation of future estimates of Salmonella infections. PMID:26938544
Evaluation of uncertainty in capturing the spatial variability and magnitudes of extreme hydrological events for the uMngeni catchment, South Africa

NASA Astrophysics Data System (ADS)

Kusangaya, Samuel; Warburton Toucher, Michele L.; van Garderen, Emma Archer

2018-02-01

Downscaled General Circulation Models (GCMs) output are used to forecast climate change and provide information used as input for hydrological modelling. Given that our understanding of climate change points towards an increasing frequency, timing and intensity of extreme hydrological events, there is therefore the need to assess the ability of downscaled GCMs to capture these extreme hydrological events. Extreme hydrological events play a significant role in regulating the structure and function of rivers and associated ecosystems. In this study, the Indicators of Hydrologic Alteration (IHA) method was adapted to assess the ability of simulated streamflow (using downscaled GCMs (dGCMs)) in capturing extreme river dynamics (high and low flows), as compared to streamflow simulated using historical climate data from 1960 to 2000. The ACRU hydrological model was used for simulating streamflow for the 13 water management units of the uMngeni Catchment, South Africa. Statistically downscaled climate models obtained from the Climate System Analysis Group at the University of Cape Town were used as input for the ACRU Model. Results indicated that, high flows and extreme high flows (one in ten year high flows/large flood events) were poorly represented both in terms of timing, frequency and magnitude. Simulated streamflow using dGCMs data also captures more low flows and extreme low flows (one in ten year lowest flows) than that captured in streamflow simulated using historical climate data. The overall conclusion was that although dGCMs output can reasonably be used to simulate overall streamflow, it performs poorly when simulating extreme high and low flows. Streamflow simulation from dGCMs must thus be used with caution in hydrological applications, particularly for design hydrology, as extreme high and low flows are still poorly represented. This, arguably calls for the further improvement of downscaling techniques in order to generate climate data more relevant and useful for hydrological applications such as in design hydrology. Nevertheless, the availability of downscaled climatic output provide the potential of exploring climate model uncertainties in different hydro climatic regions at local scales where forcing data is often less accessible but more accurate at finer spatial scales and with adequate spatial detail.
Projecting climate change impacts on hydrology: the potential role of daily GCM output

NASA Astrophysics Data System (ADS)

Maurer, E. P.; Hidalgo, H. G.; Das, T.; Dettinger, M. D.; Cayan, D.

2008-12-01

A primary challenge facing resource managers in accommodating climate change is determining the range and uncertainty in regional and local climate projections. This is especially important for assessing changes in extreme events, which will drive many of the more severe impacts of a changed climate. Since global climate models (GCMs) produce output at a spatial scale incompatible with local impact assessment, different techniques have evolved to downscale GCM output so locally important climate features are expressed in the projections. We compared skill and hydrologic projections using two statistical downscaling methods and a distributed hydrology model. The downscaling methods are the constructed analogues (CA) and the bias correction and spatial downscaling (BCSD). CA uses daily GCM output, and can thus capture GCM projections for changing extreme event occurrence, while BCSD uses monthly output and statistically generates historical daily sequences. We evaluate the hydrologic impacts projected using downscaled climate (from the NCEP/NCAR reanalysis as a surrogate GCM) for the late 20th century with both methods, comparing skill in projecting soil moisture, snow pack, and streamflow at key locations in the Western United States. We include an assessment of a new method for correcting for GCM biases in a hybrid method combining the most important characteristics of both methods.

An Observation-base investigation of nudging in WRF for downscaling surface climate information to 12-km Grid Spacing

EPA Science Inventory

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...
Multi-Site and Multi-Variables Statistical Downscaling Technique in the Monsoon Dominated Region of Pakistan

NASA Astrophysics Data System (ADS)

Khan, Firdos; Pilz, Jürgen

2016-04-01

South Asia is under the severe impacts of changing climate and global warming. The last two decades showed that climate change or global warming is happening and the first decade of 21st century is considered as the warmest decade over Pakistan ever in history where temperature reached 53 0C in 2010. Consequently, the spatio-temporal distribution and intensity of precipitation is badly effected and causes floods, cyclones and hurricanes in the region which further have impacts on agriculture, water, health etc. To cope with the situation, it is important to conduct impact assessment studies and take adaptation and mitigation remedies. For impact assessment studies, we need climate variables at higher resolution. Downscaling techniques are used to produce climate variables at higher resolution; these techniques are broadly divided into two types, statistical downscaling and dynamical downscaling. The target location of this study is the monsoon dominated region of Pakistan. One reason for choosing this area is because the contribution of monsoon rains in this area is more than 80 % of the total rainfall. This study evaluates a statistical downscaling technique which can be then used for downscaling climatic variables. Two statistical techniques i.e. quantile regression and copula modeling are combined in order to produce realistic results for climate variables in the area under-study. To reduce the dimension of input data and deal with multicollinearity problems, empirical orthogonal functions will be used. Advantages of this new method are: (1) it is more robust to outliers as compared to ordinary least squares estimates and other estimation methods based on central tendency and dispersion measures; (2) it preserves the dependence among variables and among sites and (3) it can be used to combine different types of distributions. This is important in our case because we are dealing with climatic variables having different distributions over different meteorological stations. The proposed model will be validated by using the (National Centers for Environmental Prediction / National Center for Atmospheric Research) NCEP/NCAR predictors for the period of 1960-1990 and validated for 1990-2000. To investigate the efficiency of the proposed model, it will be compared with the multivariate multiple regression model and with dynamical downscaling climate models by using different climate indices that describe the frequency, intensity and duration of the variables of interest. KEY WORDS: Climate change, Copula, Monsoon, Quantile regression, Spatio-temporal distribution.
SDSM-DC: A smarter approach to downscaling for decision-making? (Invited)

NASA Astrophysics Data System (ADS)

Wilby, R. L.; Dawson, C. W.

2011-12-01

General Circulation Model (GCM) output has been used for downscaling and impact assessments for at least 25 years. Downscaling methods raise awareness about risks posed by climate variability and change to human and natural systems. However, there are relatively few instances where these analyses have translated into actionable information for adaptation. One reason is that conventional ';top down' downscaling typically yields very large uncertainty bounds in projected impacts at regional and local scales. Consequently, there are growing calls to use downscaling tools in smarter ways that refocus attention on the decision problem rather than on the climate modelling per se. The talk begins with an overview of various application of the Statistical DownScaling Model (SDSM) over the last decade. This sample offers insights to downscaling practice in terms of regions and sectors of interest, modes of application and adaptation outcomes. The decision-centred rationale and functionality of the latest version of SDSM is then explained. This new downscaling tool does not require GCM input but enables the user to generate plausible daily weather scenarios that may be informed by climate model and/or palaeoenvironmental information. Importantly, the tool is intended for stress-testing adaptation options rather than for exhaustive analysis of uncertainty components. The approach is demonstrated by downscaling multi-basin, multi-elevation temperature and precipitation scenarios for the Upper Colorado River Basin. These scenarios are used alongside other narratives of future conditions that might potential affect the security of water supplies, and for evaluating steps that can be taken to manage these risks.
SDSM-DC: A smarter approach to downscaling for decision-making? (Invited)

NASA Astrophysics Data System (ADS)

Wilby, R. L.; Dawson, C. W.

2013-12-01

General Circulation Model (GCM) output has been used for downscaling and impact assessments for at least 25 years. Downscaling methods raise awareness about risks posed by climate variability and change to human and natural systems. However, there are relatively few instances where these analyses have translated into actionable information for adaptation. One reason is that conventional ';top down' downscaling typically yields very large uncertainty bounds in projected impacts at regional and local scales. Consequently, there are growing calls to use downscaling tools in smarter ways that refocus attention on the decision problem rather than on the climate modelling per se. The talk begins with an overview of various application of the Statistical DownScaling Model (SDSM) over the last decade. This sample offers insights to downscaling practice in terms of regions and sectors of interest, modes of application and adaptation outcomes. The decision-centred rationale and functionality of the latest version of SDSM is then explained. This new downscaling tool does not require GCM input but enables the user to generate plausible daily weather scenarios that may be informed by climate model and/or palaeoenvironmental information. Importantly, the tool is intended for stress-testing adaptation options rather than for exhaustive analysis of uncertainty components. The approach is demonstrated by downscaling multi-basin, multi-elevation temperature and precipitation scenarios for the Upper Colorado River Basin. These scenarios are used alongside other narratives of future conditions that might potential affect the security of water supplies, and for evaluating steps that can be taken to manage these risks.
Examining the Performance of Statistical Downscaling Methods: Toward Matching Applications to Data Products

NASA Astrophysics Data System (ADS)

Dixon, K. W.; Lanzante, J. R.; Adams-Smith, D.

2017-12-01

Several challenges exist when seeking to use future climate model projections in a climate impacts study. A not uncommon approach is to utilize climate projection data sets derived from more than one future emissions scenario and from multiple global climate models (GCMs). The range of future climate responses represented in the set is sometimes taken to be indicative of levels of uncertainty in the projections. Yet, GCM outputs are deemed to be unsuitable for direct use in many climate impacts applications. GCM grids typically are viewed as being too coarse. Additionally, regional or local-scale biases in a GCM's simulation of the contemporary climate that may not be problematic from a global climate modeling perspective may be unacceptably large for a climate impacts application. Statistical downscaling (SD) of climate projections - a type of post-processing that uses observations to inform the refinement of GCM projections - is often used in an attempt to account for GCM biases and to provide additional spatial detail. "What downscaled climate projection is the best one to use" is a frequently asked question, but one that is not always easy to answer, as it can be dependent on stakeholder needs and expectations. Here we present results from a perfect model experimental design illustrating how SD method performance can vary not only by SD method, but how performance can also vary by location, season, climate variable of interest, amount of projected climate change, SD configuration choices, and whether one is interested in central tendencies or the tails of the distribution. Awareness of these factors can be helpful when seeking to determine the suitability of downscaled climate projections for specific climate impacts applications. It also points to the potential value of considering more than one SD data product in a study, so as to acknowledge uncertainties associated with the strengths and weaknesses of different downscaling methods.
The Impact of Spring Subsurface Soil Temperature Anomaly in the Western U.S. on North American Summer Precipitation: A Case Study Using Regional Climate Model Downscaling

DTIC Science & Technology

2012-06-02

regional climate model downscaling , J. Geophys. Res., 117, D11103, doi:10.1029/2012JD017692. 1. Introduction [2] Modeling studies and data analyses...based on ground and satellite data have demonstrated that the land surface state variables, such as soil moisture, snow, vegetation, and soil temperature... downscaling rather than simply applying reanal- ysis data as LBC for both Eta control and sensitivity experiments as done in many RCM sensitivity studies
Identification of robust statistical downscaling methods based on a comprehensive suite of performance metrics for South Korea

NASA Astrophysics Data System (ADS)

Eum, H. I.; Cannon, A. J.

2015-12-01

Climate models are a key provider to investigate impacts of projected future climate conditions on regional hydrologic systems. However, there is a considerable mismatch of spatial resolution between GCMs and regional applications, in particular a region characterized by complex terrain such as Korean peninsula. Therefore, a downscaling procedure is an essential to assess regional impacts of climate change. Numerous statistical downscaling methods have been used mainly due to the computational efficiency and simplicity. In this study, four statistical downscaling methods [Bias-Correction/Spatial Disaggregation (BCSD), Bias-Correction/Constructed Analogue (BCCA), Multivariate Adaptive Constructed Analogs (MACA), and Bias-Correction/Climate Imprint (BCCI)] are applied to downscale the latest Climate Forecast System Reanalysis data to stations for precipitation, maximum temperature, and minimum temperature over South Korea. By split sampling scheme, all methods are calibrated with observational station data for 19 years from 1973 to 1991 are and tested for the recent 19 years from 1992 to 2010. To assess skill of the downscaling methods, we construct a comprehensive suite of performance metrics that measure an ability of reproducing temporal correlation, distribution, spatial correlation, and extreme events. In addition, we employ Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to identify robust statistical downscaling methods based on the performance metrics for each season. The results show that downscaling skill is considerably affected by the skill of CFSR and all methods lead to large improvements in representing all performance metrics. According to seasonal performance metrics evaluated, when TOPSIS is applied, MACA is identified as the most reliable and robust method for all variables and seasons. Note that such result is derived from CFSR output which is recognized as near perfect climate data in climate studies. Therefore, the ranking of this study may be changed when various GCMs are downscaled and evaluated. Nevertheless, it may be informative for end-users (i.e. modelers or water resources managers) to understand and select more suitable downscaling methods corresponding to priorities on regional applications.
Rainfall Downscaling Conditional on Upper-air Atmospheric Predictors: Improved Assessment of Rainfall Statistics in a Changing Climate

NASA Astrophysics Data System (ADS)

Langousis, Andreas; Mamalakis, Antonis; Deidda, Roberto; Marrocu, Marino

2015-04-01

To improve the level skill of Global Climate Models (GCMs) and Regional Climate Models (RCMs) in reproducing the statistics of rainfall at a basin level and at hydrologically relevant temporal scales (e.g. daily), two types of statistical approaches have been suggested. One is the statistical correction of climate model rainfall outputs using historical series of precipitation. The other is the use of stochastic models of rainfall to conditionally simulate precipitation series, based on large-scale atmospheric predictors produced by climate models (e.g. geopotential height, relative vorticity, divergence, mean sea level pressure). The latter approach, usually referred to as statistical rainfall downscaling, aims at reproducing the statistical character of rainfall, while accounting for the effects of large-scale atmospheric circulation (and, therefore, climate forcing) on rainfall statistics. While promising, statistical rainfall downscaling has not attracted much attention in recent years, since the suggested approaches involved complex (i.e. subjective or computationally intense) identification procedures of the local weather, in addition to demonstrating limited success in reproducing several statistical features of rainfall, such as seasonal variations, the distributions of dry and wet spell lengths, the distribution of the mean rainfall intensity inside wet periods, and the distribution of rainfall extremes. In an effort to remedy those shortcomings, Langousis and Kaleris (2014) developed a statistical framework for simulation of daily rainfall intensities conditional on upper air variables, which accurately reproduces the statistical character of rainfall at multiple time-scales. Here, we study the relative performance of: a) quantile-quantile (Q-Q) correction of climate model rainfall products, and b) the statistical downscaling scheme of Langousis and Kaleris (2014), in reproducing the statistical structure of rainfall, as well as rainfall extremes, at a regional level. This is done for an intermediate-sized catchment in Italy, i.e. the Flumendosa catchment, using climate model rainfall and atmospheric data from the ENSEMBLES project (http://ensembleseu.metoffice.com). In doing so, we split the historical rainfall record of mean areal precipitation (MAP) in 15-year calibration and 45-year validation periods, and compare the historical rainfall statistics to those obtained from: a) Q-Q corrected climate model rainfall products, and b) synthetic rainfall series generated by the suggested downscaling scheme. To our knowledge, this is the first time that climate model rainfall and statistically downscaled precipitation are compared to catchment-averaged MAP at a daily resolution. The obtained results are promising, since the proposed downscaling scheme is more accurate and robust in reproducing a number of historical rainfall statistics, independent of the climate model used and the length of the calibration period. This is particularly the case for the yearly rainfall maxima, where direct statistical correction of climate model rainfall outputs shows increased sensitivity to the length of the calibration period and the climate model used. The robustness of the suggested downscaling scheme in modeling rainfall extremes at a daily resolution, is a notable feature that can effectively be used to assess hydrologic risk at a regional level under changing climatic conditions. Acknowledgments The research project is implemented within the framework of the Action «Supporting Postdoctoral Researchers» of the Operational Program "Education and Lifelong Learning" (Action's Beneficiary: General Secretariat for Research and Technology), and is co-financed by the European Social Fund (ESF) and the Greek State. CRS4 highly acknowledges the contribution of the Sardinian regional authorities.
Assessment of climate change downscaling and non-stationarity on the spatial pattern of a mangrove ecosystem in an arid coastal region of southern Iran

NASA Astrophysics Data System (ADS)

Etemadi, Halimeh; Samadi, S. Zahra; Sharifikia, Mohammad; Smoak, Joseph M.

2016-10-01

Mangrove wetlands exist in the transition zone between terrestrial and marine environments and have remarkable ecological and socio-economic value. This study uses climate change downscaling to address the question of non-stationarity influences on mangrove variations (expansion and contraction) within an arid coastal region. Our two-step approach includes downscaling models and uncertainty assessment, followed by a non-stationary and trend procedure using the Extreme Value Analysis (extRemes code). The Long Ashton Research Station Weather Generator (LARS-WG) model along with two different general circulation model (GCMs) (MIRH and HadCM3) were used to downscale climatic variables during current (1968-2011) and future (2011-2030, 2045-2065, and 2080-2099) periods. Parametric and non-parametric bootstrapping uncertainty tests demonstrated that the LARS-WGS model skillfully downscaled climatic variables at the 95 % significance level. Downscaling results using MIHR model show that minimum and maximum temperatures will increase in the future (2011-2030, 2045-2065, and 2080-2099) during winter and summer in a range of +4.21 and +4.7 °C, and +3.62 and +3.55 °C, respectively. HadCM3 analysis also revealed an increase in minimum (˜+3.03 °C) and maximum (˜+3.3 °C) temperatures during wet and dry seasons. In addition, we examined how much mangrove area has changed during the past decades and, thus, if climate change non-stationarity impacts mangrove ecosystems. Our results using remote sensing techniques and the non-parametric Mann-Whitney two-sample test indicated a sharp decline in mangrove area during 1972,1987, and 1997 periods ( p value = 0.002). Non-stationary assessment using the generalized extreme value (GEV) distributions by including mangrove area as a covariate further indicated that the null hypothesis of the stationary climate (no trend) should be rejected due to the very low p values for precipitation ( p value = 0.0027), minimum ( p value = 0.000000029) and maximum ( p value = 0.00016) temperatures. Based on non-stationary analysis and an upward trend in downscaled temperature extremes, climate change may control mangrove development in the future.
Sensitivity of Statistical Downscaling Techniques to Reanalysis Choice and Implications for Regional Climate Change Scenarios

NASA Astrophysics Data System (ADS)

Manzanas, R., Sr.; Brands, S.; San Martin, D., Sr.; Gutiérrez, J. M., Sr.

2014-12-01

This work shows that local-scale climate projections obtained by means of statistical downscaling are sensitive to the choice of reanalysis used for calibration. To this aim, a Generalized Linear Model (GLM) approach is applied to downscale daily precipitation in the Philippines. First, the GLMs are trained and tested -under a cross-validation scheme- separately for two distinct reanalyses (ERA-Interim and JRA-25) for the period 1981-2000. When the observed and downscaled time-series are compared, the attained performance is found to be sensitive to the reanalysis considered if climate change signal bearing variables (temperature and/or specific humidity) are included in the predictor field. Moreover, performance differences are shown to be in correspondence with the disagreement found between the raw predictors from the two reanalyses. Second, the regression coefficients calibrated either with ERA-Interim or JRA-25 are subsequently applied to the output of a Global Climate Model (MPI-ECHAM5) in order to assess the sensitivity of local-scale climate change projections (up to 2100) to reanalysis choice. In this case, the differences detected in present climate conditions are considerably amplified, leading to "delta-change" estimates differing by up to a 35% (on average for the entire country) depending on the reanalysis used for calibration. Therefore, reanalysis choice is shown to importantly contribute to the uncertainty of local-scale climate change projections, and, consequently, should be treated with equal care as other, well-known, sources of uncertainty -e.g., the choice of the GCM and/or downscaling method.- Implications of the results for the entire tropics, as well as for the Model Output Statistics downscaling approach are also briefly discussed.
Development and Evaluation of High-Resolution Climate Simulations Over the Mountainous Northeastern United States

NASA Technical Reports Server (NTRS)

Winter, Jonathan M.; Beckage, Brian; Bucini, Gabriela; Horton, Radley M.; Clemins, Patrick J.

2016-01-01

The mountain regions of the northeastern United States are a critical socioeconomic resource for Vermont, New York State, New Hampshire, Maine, and southern Quebec. While global climate models (GCMs) are important tools for climate change risk assessment at regional scales, even the increased spatial resolution of statistically downscaled GCMs (commonly approximately 1/ 8 deg) is not sufficient for hydrologic, ecologic, and land-use modeling of small watersheds within the mountainous Northeast. To address this limitation, an ensemble of topographically downscaled, high-resolution (30"), daily 2-m maximum air temperature; 2-m minimum air temperature; and precipitation simulations are developed for the mountainous Northeast by applying an additional level of downscaling to intermediately downscaled (1/ 8 deg) data using high-resolution topography and station observations. First, observed relationships between 2-m air temperature and elevation and between precipitation and elevation are derived. Then, these relationships are combined with spatial interpolation to enhance the resolution of intermediately downscaled GCM simulations. The resulting topographically downscaled dataset is analyzed for its ability to reproduce station observations. Topographic downscaling adds value to intermediately downscaled maximum and minimum 2-m air temperature at high-elevation stations, as well as moderately improves domain-averaged maximum and minimum 2-m air temperature. Topographic downscaling also improves mean precipitation but not daily probability distributions of precipitation. Overall, the utility of topographic downscaling is dependent on the initial bias of the intermediately downscaled product and the magnitude of the elevation adjustment. As the initial bias or elevation adjustment increases, more value is added to the topographically downscaled product.
AI-based (ANN and SVM) statistical downscaling methods for precipitation estimation under climate change scenarios

NASA Astrophysics Data System (ADS)

Mehrvand, Masoud; Baghanam, Aida Hosseini; Razzaghzadeh, Zahra; Nourani, Vahid

2017-04-01

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.
Coupling climate and hydrological models to evaluate the impact of climate change on run of the river hydropower schemes from UK study sites

NASA Astrophysics Data System (ADS)

Pasten-Zapata, Ernesto; Jones, Julie; Moggridge, Helen

2015-04-01

As climate change is expected to generate variations on the Earth's precipitation and temperature, the water cycle will also experience changes. Consequently, water users will have to be prepared for possible changes in future water availability. The main objective of this research is to evaluate the impacts of climate change on river regimes and the implications to the operation and feasibility of run of the river hydropower schemes by analyzing four UK study sites. Run of the river schemes are selected for analysis due to their higher dependence to the available river flow volumes when compared to storage hydropower schemes that can rely on previously accumulated water volumes (linked to poster in session HS5.3). Global Climate Models (GCMs) represent the main tool to assess future climate change. In this research, Regional Climate Models (RCMs), which dynamically downscale GCM outputs providing higher resolutions, are used as starting point to evaluate climate change within the study catchments. RCM daily temperature and precipitation will be downscaled to an appropriate scale for impact studies and bias corrected using different statistical methods: linear scaling, local intensity scaling, power transformation, variance scaling and delta change correction. The downscaled variables will then be coupled to hydrological models that have been previously calibrated and validated against observed daily river flow data. The coupled hydrological and climate models will then be used to simulate historic river flows that are compared to daily observed values in order to evaluate the model accuracy. As this research will employ several different RCMs (from the EURO-CORDEX simulations), downscaling and bias correction methodologies, greenhouse emission scenarios and hydrological models, the uncertainty of each element will be estimated. According to their uncertainty magnitude, a prediction of the best downscaling approach (or approaches) is expected to be obtained. The current progress of the project will be presented along with the steps to be followed in the future.
Artificial neural networks and multiple linear regression model using principal components to estimate rainfall over South America

NASA Astrophysics Data System (ADS)

Soares dos Santos, T.; Mendes, D.; Rodrigues Torres, R.

2016-01-01

Several studies have been devoted to dynamic and statistical downscaling for analysis of both climate variability and climate change. This paper introduces an application of artificial neural networks (ANNs) and multiple linear regression (MLR) by principal components to estimate rainfall in South America. This method is proposed for downscaling monthly precipitation time series over South America for three regions: the Amazon; northeastern Brazil; and the La Plata Basin, which is one of the regions of the planet that will be most affected by the climate change projected for the end of the 21st century. The downscaling models were developed and validated using CMIP5 model output and observed monthly precipitation. We used general circulation model (GCM) experiments for the 20th century (RCP historical; 1970-1999) and two scenarios (RCP 2.6 and 8.5; 2070-2100). The model test results indicate that the ANNs significantly outperform the MLR downscaling of monthly precipitation variability.
Development of hi-resolution regional climate scenarios in Japan by statistical downscaling

NASA Astrophysics Data System (ADS)

Dairaku, K.

2016-12-01

Climate information and services for Impacts, Adaptation and Vulnerability (IAV) Assessments are of great concern. To meet with the needs of stakeholders such as local governments, a Japan national project, Social Implementation Program on Climate Change Adaptation Technology (SI-CAT), launched in December 2015. It develops reliable technologies for near-term climate change predictions. Multi-model ensemble regional climate scenarios with 1km horizontal grid-spacing over Japan are developed by using CMIP5 GCMs and a statistical downscaling method to support various municipal adaptation measures appropriate for possible regional climate changes. A statistical downscaling method, Bias Correction Spatial Disaggregation (BCSD), is employed to develop regional climate scenarios based on CMIP5 RCP8.5 five GCMs (MIROC5, MRI-CGCM3, GFDL-CM3, CSIRO-Mk3-6-0, HadGEM2-ES) for the periods of historical climate (1970-2005) and near future climate (2020-2055). Downscaled variables are monthly/daily precipitation and temperature. File format is NetCDF4 (conforming to CF1.6, HDF5 compression). Developed regional climate scenarios will be expanded to meet with needs of stakeholders and interface applications to access and download the data are under developing. Statistical downscaling method is not necessary to well represent locally forced nonlinear phenomena, extreme events such as heavy rain, heavy snow, etc. To complement the statistical method, dynamical downscaling approach is also combined and applied to some specific regions which have needs of stakeholders. The added values of statistical/dynamical downscaling methods compared with parent GCMs are investigated.
The Impact of Incongruous Lake Temperatures on Regional Climate Extremes Downscaled from the CMIP5 Archive Using the WRF Model

EPA Science Inventory

The impact of incongruous lake temperatures is demonstrated using the Weather Research and Forecasting (WRF) Model to downscale global climate fields. Unrealistic lake temperatures prescribed by the default WRF configuration cause obvious biases near the lakes and also affect pre...
Statistical downscaling of mean temperature, maximum temperature, and minimum temperature on the Loess Plateau, China

NASA Astrophysics Data System (ADS)

Lin, Jiang; Miao, Chiyuan

2017-04-01

Climate change is considered to be one of the greatest environmental threats. This has urged scientific communities to focus on the hot topic. Global climate models (GCMs) are the primary tool used for studying climate change. However, GCMs are limited because of their coarse spatial resolution and inability to resolve important sub-grid scale features such as terrain and clouds. Statistical downscaling methods can be used to downscale large-scale variables to local-scale. In this study, we assess the applicability of the widely used Statistical Downscaling Model (SDSM) for the Loess Plateau, China. The observed variables included daily mean temperature (TMEAN), maximum temperature (TMAX) and minimum temperature (TMIN) from 1961 to 2005. The and the daily atmospheric data were taken from reanalysis data from 1961 to 2005, and global climate model outputs from Beijing Normal University Earth System Model (BNU-ESM) from 1961 to 2099 and from observations . The results show that SDSM performs well for these three climatic variables on the Loess Plateau. After downscaling, the root mean square errors for TMEAN, TMAX, TMIN for BNU-ESM were reduced by 70.9%, 75.1%, and 67.2%, respectively. All the rates of change in TMEAN, TMAX and TMIN during the 21st century decreased after SDSM downscaling. We also show that SDSM can effectively reduce uncertainty, compared with the raw model outputs. TMEAN uncertainty was reduced by 27.1%, 26.8%, and 16.3% for the future scenarios of RCP 2.6, RCP 4.5 and RCP 8.5, respectively. The corresponding reductions in uncertainty were 23.6%, 30.7%, and 18.7% for TMAX, ; and 37.6%, 31.8%, and 23.2% for TMIN.
Accounting for downscaling and model uncertainty in fine-resolution seasonal climate projections over the Columbia River Basin

NASA Astrophysics Data System (ADS)

Ahmadalipour, Ali; Moradkhani, Hamid; Rana, Arun

2018-01-01

Climate change is expected to have severe impacts on natural systems as well as various socio-economic aspects of human life. This has urged scientific communities to improve the understanding of future climate and reduce the uncertainties associated with projections. In the present study, ten statistically downscaled CMIP5 GCMs at 1/16th deg. spatial resolution from two different downscaling procedures are utilized over the Columbia River Basin (CRB) to assess the changes in climate variables and characterize the associated uncertainties. Three climate variables, i.e. precipitation, maximum temperature, and minimum temperature, are studied for the historical period of 1970-2000 as well as future period of 2010-2099, simulated with representative concentration pathways of RCP4.5 and RCP8.5. Bayesian Model Averaging (BMA) is employed to reduce the model uncertainty and develop a probabilistic projection for each variable in each scenario. Historical comparison of long-term attributes of GCMs and observation suggests a more accurate representation for BMA than individual models. Furthermore, BMA projections are used to investigate future seasonal to annual changes of climate variables. Projections indicate significant increase in annual precipitation and temperature, with varied degree of change across different sub-basins of CRB. We then characterized uncertainty of future projections for each season over CRB. Results reveal that model uncertainty is the main source of uncertainty, among others. However, downscaling uncertainty considerably contributes to the total uncertainty of future projections, especially in summer. On the contrary, downscaling uncertainty appears to be higher than scenario uncertainty for precipitation.
Spatial, temporal and frequency based climate change assessment in Columbia River Basin using multi downscaled-scenarios

NASA Astrophysics Data System (ADS)

Rana, Arun; Moradkhani, Hamid

2016-07-01

Uncertainties in climate modelling are well documented in literature. Global Climate Models (GCMs) are often used to downscale the climatic parameters on a regional scale. In the present work, we have analyzed the changes in precipitation and temperature for future scenario period of 2070-2099 with respect to historical period of 1970-2000 from statistically downscaled GCM projections in Columbia River Basin (CRB). Analysis is performed using two different statistically downscaled climate projections (with ten GCMs downscaled products each, for RCP 4.5 and RCP 8.5, from CMIP5 dataset) namely, those from the Bias Correction and Spatial Downscaling (BCSD) technique generated at Portland State University and from the Multivariate Adaptive Constructed Analogs (MACA) technique, generated at University of Idaho, totaling to 40 different scenarios. The two datasets for BCSD and MACA are downscaled from observed data for both scenarios projections i.e. RCP4.5 and RCP8.5. Analysis is performed using spatial change (yearly scale), temporal change (monthly scale), percentile change (seasonal scale), quantile change (yearly scale), and wavelet analysis (yearly scale) in the future period from the historical period, respectively, at a scale of 1/16th of degree for entire CRB region. Results have indicated in varied degree of spatial change pattern for the entire Columbia River Basin, especially western part of the basin. At temporal scales, winter precipitation has higher variability than summer and vice versa for temperature. Most of the models have indicated considerate positive change in quantiles and percentiles for both precipitation and temperature. Wavelet analysis provided insights into possible explanation to changes in precipitation.
Climate Change Implications for Tropical Islands: Interpolating and Interpreting Statistically Downscaled GCM Projections for Management and Planning

Treesearch

Azad Henareh Khalyani; William A. Gould; Eric Harmsen; Adam Terando; Maya Quinones; Jaime A. Collazo

2016-01-01

Towards the Goal of Modular Climate Data Services: An Overview of NCPP Applications and Software

NASA Astrophysics Data System (ADS)

Koziol, B. W.; Cinquini, L.; Treshansky, A.; Murphy, S.; DeLuca, C.

2013-12-01

In August 2013, the National Climate Predictions and Projections Platform (NCPP) organized a workshop focusing on the quantitative evaluation of downscaled climate data products (QED-2013). The QED-2013 workshop focused on real-world application problems drawn from several sectors (e.g. hydrology, ecology, environmental health, agriculture), and required that downscaled downscaled data products be dynamically accessed, generated, manipulated, annotated, and evaluated. The cyberinfrastructure elements that were integrated to support the workshop included (1) a wiki-based project hosting environment (Earth System CoG) with an interface to data services provided by an Earth System Grid Federation (ESGF) data node; (2) metadata tools provided by the Earth System Documentation (ES-DOC) collaboration; and (3) a Python-based library OpenClimateGIS (OCGIS) for subsetting and converting NetCDF-based climate data to GIS and tabular formats. Collectively, this toolset represents a first deployment of a 'ClimateTranslator' that enables users to access, interpret, and apply climate information at local and regional scales. This presentation will provide an overview of these components above, how they were used in the workshop, and discussion of current and potential integration. The long-term strategy for this software stack is to offer the suite of services described on a customizable, per-project basis. Additional detail on the three components is below. (1) Earth System CoG is a web-based collaboration environment that integrates data discovery and access services with tools for supporting governance and the organization of information. QED-2013 utilized these capabilities to share with workshop participants a suite of downscaled datasets, associated images derived from those datasets, and metadata files describing the downscaling techniques involved. The collaboration side of CoG was used for workshop organization, discussion, and results. (2) The ES-DOC Questionnaire, Viewer, and Comparator are web-based tools for the creation and use of model and experiment documentation. Workshop participants used the Questionnaire to generate metadata on regional downscaling models and statistical downscaling methods, and the Viewer to display the results. A prototype Comparator was available to compare properties across dynamically downscaled models. (3) OCGIS is a Python (v2.7) package designed for geospatial manipulation, subsetting, computation, and translation of Climate and Forecasting (CF)-compliant climate datasets - either stored in local NetCDF files, or files served through THREDDS data servers.
Framework for Probabilistic Projections of Energy-Relevant Streamflow Indicators under Climate Change Scenarios for the U.S.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wagener, Thorsten; Mann, Michael; Crane, Robert

2014-04-29

This project focuses on uncertainty in streamflow forecasting under climate change conditions. The objective is to develop easy to use methodologies that can be applied across a range of river basins to estimate changes in water availability for realistic projections of climate change. There are three major components to the project: Empirical downscaling of regional climate change projections from a range of Global Climate Models; Developing a methodology to use present day information on the climate controls on the parameterizations in streamflow models to adjust the parameterizations under future climate conditions (a trading-space-for-time approach); and Demonstrating a bottom-up approach tomore » establishing streamflow vulnerabilities to climate change. The results reinforce the need for downscaling of climate data for regional applications, and further demonstrates the challenges of using raw GCM data to make local projections. In addition, it reinforces the need to make projections across a range of global climate models. The project demonstrates the potential for improving streamflow forecasts by using model parameters that are adjusted for future climate conditions, but suggests that even with improved streamflow models and reduced climate uncertainty through the use of downscaled data, there is still large uncertainty is the streamflow projections. The most useful output from the project is the bottom-up vulnerability driven approach to examining possible climate and land use change impacts on streamflow. Here, we demonstrate an inexpensive and easy to apply methodology that uses Classification and Regression Trees (CART) to define the climate and environmental parameters space that can produce vulnerabilities in the system, and then feeds in the downscaled projections to determine the probability top transitioning to a vulnerable sate. Vulnerabilities, in this case, are defined by the end user.« less
Ensemble Downscaling of Winter Seasonal Forecasts: The MRED Project

NASA Astrophysics Data System (ADS)

Arritt, R. W.; Mred Team

2010-12-01

The Multi-Regional climate model Ensemble Downscaling (MRED) project is a multi-institutional project that is producing large ensembles of downscaled winter seasonal forecasts from coupled atmosphere-ocean seasonal prediction models. Eight regional climate models each are downscaling 15-member ensembles from the National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFS) and the new NASA seasonal forecast system based on the GEOS5 atmospheric model coupled with the MOM4 ocean model. This produces 240-member ensembles, i.e., 8 regional models x 15 global ensemble members x 2 global models, for each winter season (December-April) of 1982-2003. Results to date show that combined global-regional downscaled forecasts have greatest skill for seasonal precipitation anomalies during strong El Niño events such as 1982-83 and 1997-98. Ensemble means of area-averaged seasonal precipitation for the regional models generally track the corresponding results for the global model, though there is considerable inter-model variability amongst the regional models. For seasons and regions where area mean precipitation is accurately simulated the regional models bring added value by extracting greater spatial detail from the global forecasts, mainly due to better resolution of terrain in the regional models. Our results also emphasize that an ensemble approach is essential to realizing the added value from the combined global-regional modeling system.
A climate-based multivariate extreme emulator of met-ocean-hydrological events for coastal flooding

NASA Astrophysics Data System (ADS)

Camus, Paula; Rueda, Ana; Mendez, Fernando J.; Tomas, Antonio; Del Jesus, Manuel; Losada, Iñigo J.

2015-04-01

Atmosphere-ocean general circulation models (AOGCMs) are useful to analyze large-scale climate variability (long-term historical periods, future climate projections). However, applications such as coastal flood modeling require climate information at finer scale. Besides, flooding events depend on multiple climate conditions: waves, surge levels from the open-ocean and river discharge caused by precipitation. Therefore, a multivariate statistical downscaling approach is adopted to reproduce relationships between variables and due to its low computational cost. The proposed method can be considered as a hybrid approach which combines a probabilistic weather type downscaling model with a stochastic weather generator component. Predictand distributions are reproduced modeling the relationship with AOGCM predictors based on a physical division in weather types (Camus et al., 2012). The multivariate dependence structure of the predictand (extreme events) is introduced linking the independent marginal distributions of the variables by a probabilistic copula regression (Ben Ayala et al., 2014). This hybrid approach is applied for the downscaling of AOGCM data to daily precipitation and maximum significant wave height and storm-surge in different locations along the Spanish coast. Reanalysis data is used to assess the proposed method. A commonly predictor for the three variables involved is classified using a regression-guided clustering algorithm. The most appropriate statistical model (general extreme value distribution, pareto distribution) for daily conditions is fitted. Stochastic simulation of the present climate is performed obtaining the set of hydraulic boundary conditions needed for high resolution coastal flood modeling. References: Camus, P., Menéndez, M., Méndez, F.J., Izaguirre, C., Espejo, A., Cánovas, V., Pérez, J., Rueda, A., Losada, I.J., Medina, R. (2014b). A weather-type statistical downscaling framework for ocean wave climate. Journal of Geophysical Research, doi: 10.1002/2014JC010141. Ben Ayala, M.A., Chebana, F., Ouarda, T.B.M.J. (2014). Probabilistic Gaussian Copula Regression Model for Multisite and Multivariable Downscaling, Journal of Climate, 27, 3331-3347.
Artificial neural networks and multiple linear regression model using principal components to estimate rainfall over South America

NASA Astrophysics Data System (ADS)

dos Santos, T. S.; Mendes, D.; Torres, R. R.

2015-08-01

Several studies have been devoted to dynamic and statistical downscaling for analysis of both climate variability and climate change. This paper introduces an application of artificial neural networks (ANN) and multiple linear regression (MLR) by principal components to estimate rainfall in South America. This method is proposed for downscaling monthly precipitation time series over South America for three regions: the Amazon, Northeastern Brazil and the La Plata Basin, which is one of the regions of the planet that will be most affected by the climate change projected for the end of the 21st century. The downscaling models were developed and validated using CMIP5 model out- put and observed monthly precipitation. We used GCMs experiments for the 20th century (RCP Historical; 1970-1999) and two scenarios (RCP 2.6 and 8.5; 2070-2100). The model test results indicate that the ANN significantly outperforms the MLR downscaling of monthly precipitation variability.
Projections of the Ganges-Brahmaputra precipitation: downscaled from GCM predictors

USGS Publications Warehouse

Pervez, Md Shahriar; Henebry, Geoffrey M.

2014-01-01

Downscaling Global Climate Model (GCM) projections of future climate is critical for impact studies. Downscaling enables use of GCM experiments for regional scale impact studies by generating regionally specific forecasts connecting global scale predictions and regional scale dynamics. We employed the Statistical Downscaling Model (SDSM) to downscale 21st century precipitation for two data-sparse hydrologically challenging river basins in South Asia—the Ganges and the Brahmaputra. We used CGCM3.1 by Canadian Center for Climate Modeling and Analysis version 3.1 predictors in downscaling the precipitation. Downscaling was performed on the basis of established relationships between historical Global Summary of Day observed precipitation records from 43 stations and National Center for Environmental Prediction re-analysis large scale atmospheric predictors. Although the selection of predictors was challenging during the set-up of SDSM, they were found to be indicative of important physical forcings in the basins. The precipitation of both basins was largely influenced by geopotential height: the Ganges precipitation was modulated by the U component of the wind and specific humidity at 500 and 1000 h Pa pressure levels; whereas, the Brahmaputra precipitation was modulated by the V component of the wind at 850 and 1000 h Pa pressure levels. The evaluation of the SDSM performance indicated that model accuracy for reproducing precipitation at the monthly scale was acceptable, but at the daily scale the model inadequately simulated some daily extreme precipitation events. Therefore, while the downscaled precipitation may not be the suitable input to analyze future extreme flooding or drought events, it could be adequate for analysis of future freshwater availability. Analysis of the CGCM3.1 downscaled precipitation projection with respect to observed precipitation reveals that the precipitation regime in each basin may be significantly impacted by climate change. Precipitation during and after the monsoon is likely to increase in both basins under the A1B and A2 emission scenarios; whereas, the pre-monsoon precipitation is likely to decrease. Peak monsoon precipitation is likely to shift from July to August, and may impact the livelihoods of large rural populations linked to subsistence agriculture in the basins. Uncertainty analysis of the downscaled precipitation indicated that the uncertainty in the downscaled precipitation was less than the uncertainty in the original CGCM3.1 precipitation; hence, the CGCM3.1 downscaled precipitation was a better input for the regional hydrological impact studies. However, downscaled precipitation from multiple GCMs is suggested for comprehensive impact studies.
Improve projections of changes in southern African summer rainfall through comprehensive multi-timescale empirical statistical downscaling

NASA Astrophysics Data System (ADS)

Dieppois, B.; Pohl, B.; Eden, J.; Crétat, J.; Rouault, M.; Keenlyside, N.; New, M. G.

2017-12-01

The water management community has hitherto neglected or underestimated many of the uncertainties in climate impact scenarios, in particular, uncertainties associated with decadal climate variability. Uncertainty in the state-of-the-art global climate models (GCMs) is time-scale-dependant, e.g. stronger at decadal than at interannual timescales, in response to the different parameterizations and to internal climate variability. In addition, non-stationarity in statistical downscaling is widely recognized as a key problem, in which time-scale dependency of predictors plays an important role. As with global climate modelling, therefore, the selection of downscaling methods must proceed with caution to avoid unintended consequences of over-correcting the noise in GCMs (e.g. interpreting internal climate variability as a model bias). GCM outputs from the Coupled Model Intercomparison Project 5 (CMIP5) have therefore first been selected based on their ability to reproduce southern African summer rainfall variability and their teleconnections with Pacific sea-surface temperature across the dominant timescales. In observations, southern African summer rainfall has recently been shown to exhibit significant periodicities at the interannual timescale (2-8 years), quasi-decadal (8-13 years) and inter-decadal (15-28 years) timescales, which can be interpret as the signature of ENSO, the IPO, and the PDO over the region. Most of CMIP5 GCMs underestimate southern African summer rainfall variability and their teleconnections with Pacific SSTs at these three timescales. In addition, according to a more in-depth analysis of historical and pi-control runs, this bias is might result from internal climate variability in some of the CMIP5 GCMs, suggesting potential for bias-corrected prediction based empirical statistical downscaling. A multi-timescale regression based downscaling procedure, which determines the predictors across the different timescales, has thus been used to simulate southern African summer rainfall. This multi-timescale procedure shows much better skills in simulating decadal timescales of variability compared to commonly used statistical downscaling approaches.
Study of Regional Downscaled Climate and Air Quality in the United States

NASA Astrophysics Data System (ADS)

Gao, Y.; Fu, J. S.; Drake, J.; Lamarque, J.; Lam, Y.; Huang, K.

2011-12-01

Due to the increasing anthropogenic greenhouse gas emissions, the global and regional climate patterns have significantly changed. Climate change has exerted strong impact on ecosystem, air quality and human life. The global model Community Earth System Model (CESM v1.0) was used to predict future climate and chemistry under projected emission scenarios. Two new emission scenarios, Representative Community Pathways (RCP) 4.5 and RCP 8.5, were used in this study for climate and chemistry simulations. The projected global mean temperature will increase 1.2 and 1.7 degree Celcius for the RCP 4.5 and RCP 8.5 scenarios in 2050s, respectively. In order to take advantage of local detailed topography, land use data and conduct local climate impact on air quality, we downscaled CESM outputs to 4 km by 4 km Eastern US domain using Weather Research and Forecasting (WRF) Model and Community Multi-scale Air Quality modeling system (CMAQ). The evaluations between regional model outputs and global model outputs, regional model outputs and observational data were conducted to verify the downscaled methodology. Future climate change and air quality impact were also examined on a 4 km by 4 km high resolution scale.
Downscaling future climate scenarios to fine scales for hydrologic and ecological modeling and analysis

USGS Publications Warehouse

Flint, Lorraine E.; Flint, Alan L.

2012-01-01

The methodology, which includes a sequence of rigorous analyses and calculations, is intended to reduce the addition of uncertainty to the climate data as a result of the downscaling while providing the fine-scale climate information necessary for ecological analyses. It results in new but consistent data sets for the US at 4 km, the southwest US at 270 m, and California at 90 m and illustrates the utility of fine-scale downscaling to analyses of ecological processes influenced by topographic complexity.
CWRF performance at downscaling China climate characteristics

NASA Astrophysics Data System (ADS)

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

2018-05-01

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.
A review of downscaling procedures - a contribution to the research on climate change impacts at city scale

NASA Astrophysics Data System (ADS)

Smid, Marek; Costa, Ana; Pebesma, Edzer; Granell, Carlos; Bhattacharya, Devanjan

2016-04-01

Human kind is currently predominantly urban based, and the majority of ever continuing population growth will take place in urban agglomerations. Urban systems are not only major drivers of climate change, but also the impact hot spots. Furthermore, climate change impacts are commonly managed at city scale. Therefore, assessing climate change impacts on urban systems is a very relevant subject of research. Climate and its impacts on all levels (local, meso and global scale) and also the inter-scale dependencies of those processes should be a subject to detail analysis. While global and regional projections of future climate are currently available, local-scale information is lacking. Hence, statistical downscaling methodologies represent a potentially efficient way to help to close this gap. In general, the methodological reviews of downscaling procedures cover the various methods according to their application (e.g. downscaling for the hydrological modelling). Some of the most recent and comprehensive studies, such as the ESSEM COST Action ES1102 (VALUE), use the concept of Perfect Prog and MOS. Other examples of classification schemes of downscaling techniques consider three main categories: linear methods, weather classifications and weather generators. Downscaling and climate modelling represent a multidisciplinary field, where researchers from various backgrounds intersect their efforts, resulting in specific terminology, which may be somewhat confusing. For instance, the Polynomial Regression (also called the Surface Trend Analysis) is a statistical technique. In the context of the spatial interpolation procedures, it is commonly classified as a deterministic technique, and kriging approaches are classified as stochastic. Furthermore, the terms "statistical" and "stochastic" (frequently used as names of sub-classes in downscaling methodological reviews) are not always considered as synonymous, even though both terms could be seen as identical since they are referring to methods handling input modelling factors as variables with certain probability distributions. In addition, the recent development is going towards multi-step methodologies containing deterministic and stochastic components. This evolution leads to the introduction of new terms like hybrid or semi-stochastic approaches, which makes the efforts to systematically classifying downscaling methods to the previously defined categories even more challenging. This work presents a review of statistical downscaling procedures, which classifies the methods in two steps. In the first step, we describe several techniques that produce a single climatic surface based on observations. The methods are classified into two categories using an approximation to the broadest consensual statistical terms: linear and non-linear methods. The second step covers techniques that use simulations to generate alternative surfaces, which correspond to different realizations of the same processes. Those simulations are essential because there is a limited number of real observational data, and such procedures are crucial for modelling extremes. This work emphasises the link between statistical downscaling methods and the research of climate change impacts at city scale.
Assessment of hi-resolution multi-ensemble statistical downscaling regional climate scenarios over Japan

NASA Astrophysics Data System (ADS)

Dairaku, K.

2017-12-01

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.
A user-targeted synthesis of the VALUE perfect predictor experiment

NASA Astrophysics Data System (ADS)

Maraun, Douglas; Widmann, Martin; Gutierrez, Jose; Kotlarski, Sven; Hertig, Elke; Wibig, Joanna; Rössler, Ole; Huth, Radan

2016-04-01

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.
Downscaling CESM1 climate change projections for the MENA-CORDEX domain using WRF

NASA Astrophysics Data System (ADS)

Zittis, George; Hadjinicolaou, Panos; Lelieveld, Jos

2017-04-01

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.
Examining Extreme Events Using Dynamically Downscaled 12-km WRF Simulations

EPA Science Inventory

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...
Reliability of the North America CORDEX and NARCCAP simulations in the context of uncertainty in regional climate change projections

NASA Astrophysics Data System (ADS)

Karmalkar, A.

2017-12-01

Ensembles of dynamically downscaled climate change simulations are routinely used to capture uncertainty in projections at regional scales. I assess the reliability of two such ensembles for North America - NARCCAP and NA-CORDEX - by investigating the impact of model selection on representing uncertainty in regional projections, and the ability of the regional climate models (RCMs) to provide reliable information. These aspects - discussed for the six regions used in the US National Climate Assessment - provide an important perspective on the interpretation of downscaled results. I show that selecting general circulation models for downscaling based on their equilibrium climate sensitivities is a reasonable choice, but the six models chosen for NA-CORDEX do a poor job at representing uncertainty in winter temperature and precipitation projections in many parts of the eastern US, which lead to overconfident projections. The RCM performance is highly variable across models, regions, and seasons and the ability of the RCMs to provide improved seasonal mean performance relative to their parent GCMs seems limited in both RCM ensembles. Additionally, the ability of the RCMs to simulate historical climates is not strongly related to their ability to simulate climate change across the ensemble. This finding suggests limited use of models' historical performance to constrain their projections. Given these challenges in dynamical downscaling, the RCM results should not be used in isolation. Information on how well the RCM ensembles represent known uncertainties in regional climate change projections discussed here needs to be communicated clearly to inform maagement decisions.
Application of statistical downscaling technique for the production of wine grapes (Vitis vinifera L.) in Spain

NASA Astrophysics Data System (ADS)

Gaitán Fernández, E.; García Moreno, R.; Pino Otín, M. R.; Ribalaygua Batalla, J.

2012-04-01

Climate and soil are two of the most important limiting factors for agricultural production. Nowadays climate change has been documented in many geographical locations affecting different cropping systems. The General Circulation Models (GCM) has become important tools to simulate the more relevant aspects of the climate expected for the XXI century in the frame of climatic change. These models are able to reproduce the general features of the atmospheric dynamic but their low resolution (about 200 Km) avoids a proper simulation of lower scale meteorological effects. Downscaling techniques allow overcoming this problem by adapting the model outcomes to local scale. In this context, FIC (Fundación para la Investigación del Clima) has developed a statistical downscaling technique based on a two step analogue methods. This methodology has been broadly tested on national and international environments leading to excellent results on future climate models. In a collaboration project, this statistical downscaling technique was applied to predict future scenarios for the grape growing systems in Spain. The application of such model is very important to predict expected climate for the different growing crops, mainly for grape, where the success of different varieties are highly related to climate and soil. The model allowed the implementation of agricultural conservation practices in the crop production, detecting highly sensible areas to negative impacts produced by any modification of climate in the different regions, mainly those protected with protected designation of origin, and the definition of new production areas with optimal edaphoclimatic conditions for the different varieties.
Uncertainty of future projections of species distributions in mountainous regions.

PubMed

Tang, Ying; Winkler, Julie A; Viña, Andrés; Liu, Jianguo; Zhang, Yuanbin; Zhang, Xiaofeng; Li, Xiaohong; Wang, Fang; Zhang, Jindong; Zhao, Zhiqiang

2018-01-01

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.
Uncertainty of future projections of species distributions in mountainous regions

PubMed Central

Tang, Ying; Viña, Andrés; Liu, Jianguo; Zhang, Yuanbin; Zhang, Xiaofeng; Li, Xiaohong; Wang, Fang; Zhang, Jindong; Zhao, Zhiqiang

2018-01-01

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
A Dynamical Downscaling Approach with GCM Bias Corrections and Spectral Nudging

NASA Astrophysics Data System (ADS)

Xu, Z.; Yang, Z.

2013-12-01

To reduce the biases in the regional climate downscaling simulations, a dynamical downscaling approach with GCM bias corrections and spectral nudging is developed and assessed over North America. Regional climate simulations are performed with the Weather Research and Forecasting (WRF) model embedded in the National Center for Atmospheric Research (NCAR) Community Atmosphere Model (CAM). To reduce the GCM biases, the GCM climatological means and the variances of interannual variations are adjusted based on the National Centers for Environmental Prediction-NCAR global reanalysis products (NNRP) before using them to drive WRF which is the same as our previous method. In this study, we further introduce spectral nudging to reduce the RCM-based biases. Two sets of WRF experiments are performed with and without spectral nudging. All WRF experiments are identical except that the initial and lateral boundary conditions are derived from the NNRP, the original GCM output, and the bias corrected GCM output, respectively. The GCM-driven RCM simulations with bias corrections and spectral nudging (IDDng) are compared with those without spectral nudging (IDD) and North American Regional Reanalysis (NARR) data to assess the additional reduction in RCM biases relative to the IDD approach. The results show that the spectral nudging introduces the effect of GCM bias correction into the RCM domain, thereby minimizing the climate drift resulting from the RCM biases. The GCM bias corrections and spectral nudging significantly improve the downscaled mean climate and extreme temperature simulations. Our results suggest that both GCM bias corrections or spectral nudging are necessary to reduce the error of downscaled climate. Only one of them does not guarantee better downscaling simulation. The new dynamical downscaling method can be applied to regional projection of future climate or downscaling of GCM sensitivity simulations. Annual mean RMSEs. The RMSEs are computed over the verification region by monthly mean data over 1981-2010. Experimental design

Actor groups, related needs, and challenges at the climate downscaling interface

NASA Astrophysics Data System (ADS)

Rössler, Ole; Benestad, Rasmus; Diamando, Vlachogannis; Heike, Hübener; Kanamaru, Hideki; Pagé, Christian; Margarida Cardoso, Rita; Soares, Pedro; Maraun, Douglas; Kreienkamp, Frank; Christodoulides, Paul; Fischer, Andreas; Szabo, Peter

2016-04-01

At the climate downscaling interface, numerous downscaling techniques and different philosophies compete on being the best method in their specific terms. Thereby, it remains unclear to what extent and for which purpose these downscaling techniques are valid or even the most appropriate choice. A common validation framework that compares all the different available methods was missing so far. The initiative VALUE closes this gap with such a common validation framework. An essential part of a validation framework for downscaling techniques is the definition of appropriate validation measures. The selection of validation measures should consider the needs of the stakeholder: some might need a temporal or spatial average of a certain variable, others might need temporal or spatial distributions of some variables, still others might need extremes for the variables of interest or even inter-variable dependencies. Hence, a close interaction of climate data providers and climate data users is necessary. Thus, the challenge in formulating a common validation framework mirrors also the challenges between the climate data providers and the impact assessment community. This poster elaborates the issues and challenges at the downscaling interface as it is seen within the VALUE community. It suggests three different actor groups: one group consisting of the climate data providers, the other two groups being climate data users (impact modellers and societal users). Hence, the downscaling interface faces classical transdisciplinary challenges. We depict a graphical illustration of actors involved and their interactions. In addition, we identified four different types of issues that need to be considered: i.e. data based, knowledge based, communication based, and structural issues. They all may, individually or jointly, hinder an optimal exchange of data and information between the actor groups at the downscaling interface. Finally, some possible ways to tackle these issues are discussed.
Downscaling climate model output for water resources impacts assessment (Invited)

NASA Astrophysics Data System (ADS)

Maurer, E. P.; Pierce, D. W.; Cayan, D. R.

2013-12-01

Water agencies in the U.S. and around the globe are beginning to wrap climate change projections into their planning procedures, recognizing that ongoing human-induced changes to hydrology can affect water management in significant ways. Future hydrology changes are derived using global climate model (GCM) projections, though their output is at a spatial scale that is too coarse to meet the needs of those concerned with local and regional impacts. Those investigating local impacts have employed a range of techniques for downscaling, the process of translating GCM output to a more locally-relevant spatial scale. Recent projects have produced libraries of publicly-available downscaled climate projections, enabling managers, researchers and others to focus on impacts studies, drawing from a shared pool of fine-scale climate data. Besides the obvious advantage to data users, who no longer need to develop expertise in downscaling prior to examining impacts, the use of the downscaled data by hundreds of people has allowed a crowdsourcing approach to examining the data. The wide variety of applications employed by different users has revealed characteristics not discovered during the initial data set production. This has led to a deeper look at the downscaling methods, including the assumptions and effect of bias correction of GCM output. Here new findings are presented related to the assumption of stationarity in the relationships between large- and fine-scale climate, as well as the impact of quantile mapping bias correction on precipitation trends. The validity of these assumptions can influence the interpretations of impacts studies using data derived using these standard statistical methods and help point the way to improved methods.
High-resolution downscaling for hydrological management

NASA Astrophysics Data System (ADS)

Ulbrich, Uwe; Rust, Henning; Meredith, Edmund; Kpogo-Nuwoklo, Komlan; Vagenas, Christos

2017-04-01

Hydrological modellers and water managers require high-resolution climate data to model regional hydrologies and how these may respond to future changes in the large-scale climate. The ability to successfully model such changes and, by extension, critical infrastructure planning is often impeded by a lack of suitable climate data. This typically takes the form of too-coarse data from climate models, which are not sufficiently detailed in either space or time to be able to support water management decisions and hydrological research. BINGO (Bringing INnovation in onGOing water management; ) aims to bridge the gap between the needs of hydrological modellers and planners, and the currently available range of climate data, with the overarching aim of providing adaptation strategies for climate change-related challenges. Producing the kilometre- and sub-daily-scale climate data needed by hydrologists through continuous simulations is generally computationally infeasible. To circumvent this hurdle, we adopt a two-pronged approach involving (1) selective dynamical downscaling and (2) conditional stochastic weather generators, with the former presented here. We take an event-based approach to downscaling in order to achieve the kilometre-scale input needed by hydrological modellers. Computational expenses are minimized by identifying extremal weather patterns for each BINGO research site in lower-resolution simulations and then only downscaling to the kilometre-scale (convection permitting) those events during which such patterns occur. Here we (1) outline the methodology behind the selection of the events, and (2) compare the modelled precipitation distribution and variability (preconditioned on the extremal weather patterns) with that found in observations.
Multi objective climate change impact assessment using multi downscaled climate scenarios

NASA Astrophysics Data System (ADS)

Rana, Arun; Moradkhani, Hamid

2016-04-01

Global Climate Models (GCMs) are often used to downscale the climatic parameters on a regional and global scale. In the present study, we have analyzed the changes in precipitation and temperature for future scenario period of 2070-2099 with respect to historical period of 1970-2000 from a set of statistically downscaled GCM projections for Columbia River Basin (CRB). Analysis is performed using 2 different statistically downscaled climate projections namely the Bias Correction and Spatial Downscaling (BCSD) technique generated at Portland State University and the Multivariate Adaptive Constructed Analogs (MACA) technique, generated at University of Idaho, totaling to 40 different scenarios. Analysis is performed on spatial, temporal and frequency based parameters in the future period at a scale of 1/16th of degree for entire CRB region. Results have indicated in varied degree of spatial change pattern for the entire Columbia River Basin, especially western part of the basin. At temporal scales, winter precipitation has higher variability than summer and vice-versa for temperature. Frequency analysis provided insights into possible explanation to changes in precipitation.
Multi-RCM ensemble downscaling of global seasonal forecasts (MRED)

NASA Astrophysics Data System (ADS)

Arritt, R.

2009-04-01

Regional climate models (RCMs) have long been used to downscale global climate simulations. In contrast the ability of RCMs to downscale seasonal climate forecasts has received little attention. The Multi-RCM Ensemble Downscaling (MRED) project was recently initiated to address the question, Does dynamical downscaling using RCMs provide additional useful information for seasonal forecasts made by global models? MRED is using a suite of RCMs to downscale seasonal forecasts produced by the National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFS) seasonal forecast system and the NASA GEOS5 system. The initial focus is on wintertime forecasts in order to evaluate topographic forcing, snowmelt, and the usefulness of higher resolution for near-surface fields influenced by high resolution orography. Each RCM covers the conterminous U.S. at approximately 32 km resolution, comparable to the scale of the North American Regional Reanalysis (NARR) which will be used to evaluate the models. The forecast ensemble for each RCM is comprised of 15 members over a period of 22+ years (from 1982 to 2003+) for the forecast period 1 December - 30 April. Each RCM will create a 15-member lagged ensemble by starting on different dates in the preceding November. This results in a 120-member ensemble for each projection (8 RCMs by 15 members per RCM). The RCMs will be continually updated at their lateral boundaries using 6-hourly output from CFS or GEOS5. Hydrometeorological output will be produced in a standard netCDF-based format for a common analysis grid, which simplifies both model intercomparison and the generation of ensembles. MRED will compare individual RCM and global forecasts as well as ensemble mean precipitation and temperature forecasts, which are currently being used to drive macroscale land surface models (LSMs). Metrics of ensemble spread will also be evaluated. Extensive process-oriented analysis will be performed to link improvements in downscaled forecast skill to regional forcings and physical mechanisms. Our overarching goal is to determine what additional skill can be provided by a community ensemble of high resolution regional models, which we believe will define a strategy for more skillful and useful regional seasonal climate forecasts.
Satellite-enhanced dynamical downscaling for the analysis of extreme events

NASA Astrophysics Data System (ADS)

Nunes, Ana M. B.

2016-09-01

The use of regional models in the downscaling of general circulation models provides a strategy to generate more detailed climate information. In that case, boundary-forcing techniques can be useful to maintain the large-scale features from the coarse-resolution global models in agreement with the inner modes of the higher-resolution regional models. Although those procedures might improve dynamics, downscaling via regional modeling still aims for better representation of physical processes. With the purpose of improving dynamics and physical processes in regional downscaling of global reanalysis, the Regional Spectral Model—originally developed at the National Centers for Environmental Prediction—employs a newly reformulated scale-selective bias correction, together with the 3-hourly assimilation of the satellite-based precipitation estimates constructed from the Climate Prediction Center morphing technique. The two-scheme technique for the dynamical downscaling of global reanalysis can be applied in analyses of environmental disasters and risk assessment, with hourly outputs, and resolution of about 25 km. Here the satellite-enhanced dynamical downscaling added value is demonstrated in simulations of the first reported hurricane in the western South Atlantic Ocean basin through comparisons with global reanalyses and satellite products available in ocean areas.
Precipitation projections under GCMs perspective and Turkish Water Foundation (TWF) statistical downscaling model procedures

NASA Astrophysics Data System (ADS)

Dabanlı, İsmail; Şen, Zekai

2018-04-01

The statistical climate downscaling model by the Turkish Water Foundation (TWF) is further developed and applied to a set of monthly precipitation records. The model is structured by two phases as spatial (regional) and temporal downscaling of global circulation model (GCM) scenarios. The TWF model takes into consideration the regional dependence function (RDF) for spatial structure and Markov whitening process (MWP) for temporal characteristics of the records to set projections. The impact of climate change on monthly precipitations is studied by downscaling Intergovernmental Panel on Climate Change-Special Report on Emission Scenarios (IPCC-SRES) A2 and B2 emission scenarios from Max Plank Institute (EH40PYC) and Hadley Center (HadCM3). The main purposes are to explain the TWF statistical climate downscaling model procedures and to expose the validation tests, which are rewarded in same specifications as "very good" for all stations except one (Suhut) station in the Akarcay basin that is in the west central part of Turkey. Eventhough, the validation score is just a bit lower at the Suhut station, the results are "satisfactory." It is, therefore, possible to say that the TWF model has reasonably acceptable skill for highly accurate estimation regarding standard deviation ratio (SDR), Nash-Sutcliffe efficiency (NSE), and percent bias (PBIAS) criteria. Based on the validated model, precipitation predictions are generated from 2011 to 2100 by using 30-year reference observation period (1981-2010). Precipitation arithmetic average and standard deviation have less than 5% error for EH40PYC and HadCM3 SRES (A2 and B2) scenarios.
Downscaling GCM Output with Genetic Programming Model

NASA Astrophysics Data System (ADS)

Shi, X.; Dibike, Y. B.; Coulibaly, P.

2004-05-01

Climate change impact studies on watershed hydrology require reliable data at appropriate spatial and temporal resolution. However, the outputs of the current global climate models (GCMs) cannot be used directly because GCM do not provide hourly or daily precipitation and temperature reliable enough for hydrological modeling. Nevertheless, we can get more reliable data corresponding to future climate scenarios derived from GCM outputs using the so called 'downscaling techniques'. This study applies Genetic Programming (GP) based technique to downscale daily precipitation and temperature values at the Chute-du-Diable basin of the Saguenay watershed in Canada. In applying GP downscaling technique, the objective is to find a relationship between the large-scale predictor variables (NCEP data which provide daily information concerning the observed large-scale state of the atmosphere) and the predictand (meteorological data which describes conditions at the site scale). The selection of the most relevant predictor variables is achieved using the Pearson's coefficient of determination ( R2) (between the large-scale predictor variables and the daily meteorological data). In this case, the period (1961 - 2000) is identified to represent the current climate condition. For the forty years of data, the first 30 years (1961-1990) are considered for calibrating the models while the remaining ten years of data (1991-2000) are used to validate those models. In general, the R2 between the predictor variables and each predictand is very low in case of precipitation compared to that of maximum and minimum temperature. Moreover, the strength of individual predictors varies for every month and for each GP grammar. Therefore, the most appropriate combination of predictors has to be chosen by looking at the output analysis of all the twelve months and the different GP grammars. During the calibration of the GP model for precipitation downscaling, in addition to the mean daily precipitation and daily precipitation variability for each month, monthly average dry and wet-spell lengths are also considered as performance criteria. For the cases of Tmax and Tmin, means and variances of these variables corresponding to each month were considered as performance criteria. The GP downscaling results show satisfactory agreement between the observed daily temperature (Tmax and Tmin) and the simulated temperature. However, the downscaling results for the daily precipitation still require some improvement - suggesting further investigation of other grammars. KEY WORDS: Climate change; GP downscaling; GCM.
Influence of climate change on flood magnitude and seasonality in the Arga River catchment in Spain

NASA Astrophysics Data System (ADS)

Garijo, Carlos; Mediero, Luis

2018-04-01

Climate change projections suggest that extremes, such as floods, will modify their behaviour in the future. Detailed catchment-scale studies are needed to implement the European Union Floods Directive and give recommendations for flood management and design of hydraulic infrastructure. In this study, a methodology to quantify changes in future flood magnitude and seasonality due to climate change at a catchment scale is proposed. Projections of 24 global climate models are used, with 10 being downscaled by the Spanish Meteorological Agency (Agencia Estatal de Meteorología, AEMET) and 14 from the EURO-CORDEX project, under two representative concentration pathways (RCPs) 4.5 and 8.5, from the Fifth Assessment Report provided by the Intergovernmental Panel on Climate Change. Downscaled climate models provided by the AEMET were corrected in terms of bias. The HBV rainfall-runoff model was selected to simulate the catchment hydrological behaviour. Simulations were analysed through both annual maximum and peaks-over-threshold (POT) series. The results show a decrease in the magnitude of extreme floods for the climate model projections downscaled by the AEMET. However, results for the climate model projections downscaled by EURO-CORDEX show differing trends, depending on the RCP. A small decrease in the flood magnitude was noticed for the RCP 4.5, while an increase was found for the RCP 8.5. Regarding the monthly seasonality analysis performed by using the POT series, a delay in the flood timing from late-autumn to late-winter is identified supporting the findings of recent studies performed with observed data in recent decades.
Applying downscaled global climate model data to a hydrodynamic surface-water and groundwater model

USGS Publications Warehouse

Swain, Eric; Stefanova, Lydia; Smith, Thomas

2014-01-01

Precipitation data from Global Climate Models have been downscaled to smaller regions. Adapting this downscaled precipitation data to a coupled hydrodynamic surface-water/groundwater model of southern Florida allows an examination of future conditions and their effect on groundwater levels, inundation patterns, surface-water stage and flows, and salinity. The downscaled rainfall data include the 1996-2001 time series from the European Center for Medium-Range Weather Forecasting ERA-40 simulation and both the 1996-1999 and 2038-2057 time series from two global climate models: the Community Climate System Model (CCSM) and the Geophysical Fluid Dynamic Laboratory (GFDL). Synthesized surface-water inflow datasets were developed for the 2038-2057 simulations. The resulting hydrologic simulations, with and without a 30-cm sea-level rise, were compared with each other and field data to analyze a range of projected conditions. Simulations predicted generally higher future stage and groundwater levels and surface-water flows, with sea-level rise inducing higher coastal salinities. A coincident rise in sea level, precipitation and surface-water flows resulted in a narrower inland saline/fresh transition zone. The inland areas were affected more by the rainfall difference than the sea-level rise, and the rainfall differences make little difference in coastal inundation, but a larger difference in coastal salinities.
Designing the Bridge: Perceptions and Use of Downscaled Climate Data by Climate Modelers and Resource Managers in Hawaii

NASA Astrophysics Data System (ADS)

Keener, V. W.; Brewington, L.; Jaspers, K.

2016-12-01

To build an effective bridge from the climate modeling community to natural resource managers, we assessed the existing landscape to see where different groups diverge in their perceptions of climate data and needs. An understanding of a given community's shared knowledge and differences can help design more actionable science. Resource managers in Hawaii are eager to have future climate projections at spatial scales relevant to the islands. National initiatives to downscale climate data often exclude US insular regions, so researchers in Hawaii have generated regional dynamically and statistically downscaled projections. Projections of precipitation diverge, however, leading to difficulties in communication and use. Recently, a two day workshop was held with scientists and managers to evaluate available models and determine a set of best practices for moving forward with decision-relevant downscaling in Hawaii. To seed the discussion, the Pacific Regional Integrated Sciences and Assessments (RISA) program conducted a pre-workshop survey (N=65) of climate modelers and freshwater, ecosystem, and wildfire managers working in Hawaii. Scientists reported spending less than half of their time on operational research, although the majority was eager to partner with managers on specific projects. Resource managers had varying levels of familiarity with downscaled climate projections, but reported needing more information about uncertainty for decision making, and were less interested in the technical model details. There were large differences between groups of managers, with 41.7% of freshwater managers reporting that they used climate projections regularly, while a majority of ecosystem and wildfire managers reported having "no familiarity". Scientists and managers rated which spatial and temporal scales were most relevant to decision making. Finally, when asked to compare how confident they were in projections of specific climate variables between the dynamical and statistical data, 80-90% of managers responded that they had no opinion. Workshop attendees were very interested in the survey results, adding to evidence of a need for sustained engagement between modeler and user groups, as well as different strategies for working with different types of resource managers.
Using a Coupled Lake Model with WRF for Dynamical Downscaling

EPA Science Inventory

The Weather Research and Forecasting (WRF) model is used to downscale a coarse reanalysis (National Centers for Environmental Prediction–Department of Energy Atmospheric Model Intercomparison Project reanalysis, hereafter R2) as a proxy for a global climate model (GCM) to examine...
Simulation of an ensemble of future climate time series with an hourly weather generator

NASA Astrophysics Data System (ADS)

Caporali, E.; Fatichi, S.; Ivanov, V. Y.; Kim, J.

2010-12-01

There is evidence that climate change is occurring in many regions of the world. The necessity of climate change predictions at the local scale and fine temporal resolution is thus warranted for hydrological, ecological, geomorphological, and agricultural applications that can provide thematic insights into the corresponding impacts. Numerous downscaling techniques have been proposed to bridge the gap between the spatial scales adopted in General Circulation Models (GCM) and regional analyses. Nevertheless, the time and spatial resolutions obtained as well as the type of meteorological variables may not be sufficient for detailed studies of climate change effects at the local scales. In this context, this study presents a stochastic downscaling technique that makes use of an hourly weather generator to simulate time series of predicted future climate. Using a Bayesian approach, the downscaling procedure derives distributions of factors of change for several climate statistics from a multi-model ensemble of GCMs. Factors of change are sampled from their distributions using a Monte Carlo technique to entirely account for the probabilistic information obtained with the Bayesian multi-model ensemble. Factors of change are subsequently applied to the statistics derived from observations to re-evaluate the parameters of the weather generator. The weather generator can reproduce a wide set of climate variables and statistics over a range of temporal scales, from extremes, to the low-frequency inter-annual variability. The final result of such a procedure is the generation of an ensemble of hourly time series of meteorological variables that can be considered as representative of future climate, as inferred from GCMs. The generated ensemble of scenarios also accounts for the uncertainty derived from multiple GCMs used in downscaling. Applications of the procedure in reproducing present and future climates are presented for different locations world-wide: Tucson (AZ), Detroit (MI), and Firenze (Italy). The stochastic downscaling is carried out with eight GCMs from the CMIP3 multi-model dataset (IPCC 4AR, A1B scenario).
CMIP5-downscaled projections for the NW European Shelf Seas: initial results and insights into uncertainties

NASA Astrophysics Data System (ADS)

Tinker, Jonathan; Palmer, Matthew; Lowe, Jason; Howard, Tom

2017-04-01

The North Sea, and wider Northwest European Shelf seas (NWS) are economically, environmentally, and culturally important for a number of European countries. They are protected by European legislation, often with specific reference to the potential impacts of climate change. Coastal climate change projections are an important source of information for effective management of European Shelf Seas. For example, potential changes in the marine environment are a key component of the climate change risk assessments (CCRAs) carried out under the UK Climate Change Act We use the NEMO shelf seas model combined with CMIP5 climate model and EURO-CORDEX regional atmospheric model data to generate new simulations of the NWS. Building on previous work using a climate model perturbed physics ensemble and the POLCOMS, this new model setup is used to provide first indication of the uncertainties associated with: (i) the driving climate model; (ii) the atmospheric downscaling model (iii) the shelf seas downscaling model; (iv) the choice of climate change scenario. Our analysis considers a range of physical marine impacts and the drivers of coastal variability and change, including sea level and the propagation of open ocean signals onto the shelf. The simulations are being carried out as part of the UK Climate Projections 2018 (UKCP18) and will feed into the following UK CCRA.
Climate change indices for Greenland applied directly for other arctic regions - Enhanced and utilized climate information from one high resolution RCM downscaling for Greenland evaluated through pattern scaling and CMIP5

NASA Astrophysics Data System (ADS)

Olesen, M.; Christensen, J. H.; Boberg, F.

2016-12-01

Climate change indices for Greenland applied directly for other arctic regions - Enhanced and utilized climate information from one high resolution RCM downscaling for Greenland evaluated through pattern scaling and CMIP5Climate change affects the Greenlandic society both advantageously and disadvantageously. Changes in temperature and precipitation patterns may result in changes in a number of derived society related climate indices, such as the length of growing season or the number of annual dry days or a combination of the two - indices of substantial importance to society in a climate adaptation context.Detailed climate indices require high resolution downscaling. We have carried out a very high resolution (5 km) simulation with the regional climate model HIRHAM5, forced by the global model EC-Earth. Evaluation of RCM output is usually done with an ensemble of downscaled output with multiple RCM's and GCM's. Here we have introduced and tested a new technique; a translation of the robustness of an ensemble of GCM models from CMIP5 into the specific index from the HIRHAM5 downscaling through a correlation between absolute temperatures and its corresponding index values from the HIRHAM5 output.The procedure is basically conducted in two steps: First, the correlation between temperature and a given index for the HIRHAM5 simulation by a best fit to a second order polynomial is identified. Second, the standard deviation from the CMIP5 simulations is introduced to show the corresponding standard deviation of the index from the HIRHAM5 run. The change of specific climate indices due to global warming will then be possible to evaluate elsewhere corresponding to the change in absolute temperature.Results based on selected indices with focus on the future climate in Greenland calculated for the rcp4.5 and rcp8.5 scenarios will be presented.
Assessing the Added Value of Dynamical Downscaling in the Context of Hydrologic Implication

NASA Astrophysics Data System (ADS)

Lu, M.; IM, E. S.; Lee, M. H.

2017-12-01

There is a scientific consensus that high-resolution climate simulations downscaled by Regional Climate Models (RCMs) can provide valuable refined information over the target region. However, a significant body of hydrologic impact assessment has been performing using the climate information provided by Global Climate Models (GCMs) in spite of a fundamental spatial scale gap. It is probably based on the assumption that the substantial biases and spatial scale gap from GCMs raw data can be simply removed by applying the statistical bias correction and spatial disaggregation. Indeed, many previous studies argue that the benefit of dynamical downscaling using RCMs is minimal when linking climate data with the hydrological model, from the comparison of the impact between bias-corrected GCMs and bias-corrected RCMs on hydrologic simulations. It may be true for long-term averaged climatological pattern, but it is not necessarily the case when looking into variability across various temporal spectrum. In this study, we investigate the added value of dynamical downscaling focusing on the performance in capturing climate variability. For doing this, we evaluate the performance of the distributed hydrological model over the Korean river basin using the raw output from GCM and RCM, and bias-corrected output from GCM and RCM. The impacts of climate input data on streamflow simulation are comprehensively analyzed. [Acknowledgements]This research is supported by the Korea Agency for Infrastructure Technology Advancement (KAIA) grant funded by the Ministry of Land, Infrastructure and Transport (Grant 17AWMP-B083066-04).
Downscaling Climate Projections to a Mountainous Landscape: A Climate Impact Assessment for the U.S. Northern Rockies Crown of the Continent Ecosystem

NASA Astrophysics Data System (ADS)

Oyler, J.; Anderson, R.; Running, S. W.

2010-12-01

In topographically complex landscapes, there is often a mismatch in scale between global climate model projections and more local climate-forcing factors and related ecological/hydrological processes. To overcome this limitation, the objective of this study was to downscale climate projections to the rugged Crown of the Continent Ecosystem (CCE) within the U.S. Northern Rockies and assess future impacts on water balances, vegetation dynamics, and carbon fluxes. A 40-year (1970-2009) spatial historical climate dataset (800m resolution, daily timestep) was generated for the CCE and modified for terrain influences. Regional climate projections were downscaled by applying them to the fine-scale historical dataset using a modified delta downscaling method and stochastic weather generator. The downscaled projections were used to drive the Biome-BGC ecosystem model. Overall CCE impacts included decreases in April 1 snow water equivalent, less days with snow on the ground, increased vegetation water stress, and increased growing degree days. The relaxing of temperature constraints increased annual net primary productivity (NPP) throughout most of the CCE landscape. However, an increase in water stress seems to have limited the growth in NPP and, in some areas, NPP actually decreased. Thus, CCE vegetation productivity trends under increasing temperatures will likely be determined by local changes in hydrologic function. Given the greater uncertainty in precipitation projections, future work should concentrate on determining thresholds in water constraints that greatly modify the magnitude and direction of carbon accumulation within the CCE under a warming climate.
Effects of future climate conditions on terrestrial export from coastal southern California

NASA Astrophysics Data System (ADS)

Feng, D.; Zhao, Y.; Raoufi, R.; Beighley, E.; Melack, J. M.

2015-12-01

The Santa Barbara Coastal - Long Term Ecological Research Project (SBC-LTER) is focused on investigating the relative importance of land and ocean processes in structuring giant kelp forest ecosystems. Understanding how current and future climate conditions influence terrestrial export is a central theme for the project. Here we combine the Hillslope River Routing (HRR) model and daily precipitation and temperature downscaled using statistical downscaling based on localized constructed Analogs (LOCA) to estimate recent streamflow dynamics (2000 to 2014) and future conditions (2015 to 2100). The HRR model covers the SBC-LTER watersheds from just west of the Ventura River to Point Conception; a land area of roughly 800 km2 with 179 watersheds ranging from 0.1 to 123 km2. The downscaled climate conditions have a spatial resolution of 6 km by 6 km. Here, we use the Penman-Monteith method with the Food and Agriculture Organization of the United Nations (FAO) limited climate data approximations and land surface conditions (albedo, leaf area index, land cover) measured from NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) on the Terra and Aqua satellites to estimate potential evapotranspiration (PET). The HRR model is calibrated for the period 2000 to 2014 using USGS and LTER streamflow. An automated calibration technique is used. For future climate scenarios, we use mean 8-day land cover conditions. Future streamflow, ET and soil moisture statistics are presented and based on downscaled P and T from ten climate model projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5).
Inter-comparison of precipitable water among reanalyses and its effect on downscaling in the tropics

NASA Astrophysics Data System (ADS)

Takahashi, H. G.; Fujita, M.; Hara, M.

2012-12-01

This paper compared precipitable water (PW) among four major reanalyses. In addition, we also investigated the effect of the boundary conditions on downscaling in the tropics, using a regional climate model. The spatial pattern of PW in the reanalyses agreed closely with observations. However, the absolute amounts of PW in some reanalyses were very small compared to observations. The discrepancies of the 12-year mean PW in July over the Southeast Asian monsoon region exceeded the inter-annual standard deviation of the PW. There was also a discrepancy in tropical PWs throughout the year, an indication that the problem is not regional, but global. The downscaling experiments were conducted, which were forced by the different four reanalyses. The atmospheric circulation, including monsoon westerlies and various disturbances, was very small among the reanalyses. However, simulated precipitation was only 60 % of observed precipitation, although the dry bias in the boundary conditions was only 6 %. This result indicates that dry bias has large effects on precipitation in downscaling over the tropics. This suggests that a simulated regional climate downscaled from ensemble-mean boundary conditions is quite different from an ensemble-mean regional climate averaged over the several regional ones downscaled from boundary conditions of the ensemble members in the tropics. Downscaled models can provide realistic simulations of regional tropical climates only if the boundary conditions include realistic absolute amounts of PW. Use of boundary conditions that include realistic absolute amounts of PW in downscaling in the tropics is imperative at the present time. This work was partly supported by the Global Environment Research Fund (RFa-1101) of the Ministry of the Environment, Japan.
The added value of dynamical downscaling in a climate change scenario simulation:A case study for European Alps and East Asia

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.

NCPP's Use of Standard Metadata to Promote Open and Transparent Climate Modeling

NASA Astrophysics Data System (ADS)

Treshansky, A.; Barsugli, J. J.; Guentchev, G.; Rood, R. B.; DeLuca, C.

2012-12-01

The National Climate Predictions and Projections (NCPP) Platform is developing comprehensive regional and local information about the evolving climate to inform decision making and adaptation planning. This includes both creating and providing tools to create metadata about the models and processes used to create its derived data products. NCPP is using the Common Information Model (CIM), an ontology developed by a broad set of international partners in climate research, as its metadata language. This use of a standard ensures interoperability within the climate community as well as permitting access to the ecosystem of tools and services emerging alongside the CIM. The CIM itself is divided into a general-purpose (UML & XML) schema which structures metadata documents, and a project or community-specific (XML) Controlled Vocabulary (CV) which constraints the content of metadata documents. NCPP has already modified the CIM Schema to accommodate downscaling models, simulations, and experiments. NCPP is currently developing a CV for use by the downscaling community. Incorporating downscaling into the CIM will lead to several benefits: easy access to the existing CIM Documents describing CMIP5 models and simulations that are being downscaled, access to software tools that have been developed in order to search, manipulate, and visualize CIM metadata, and coordination with national and international efforts such as ES-DOC that are working to make climate model descriptions and datasets interoperable. Providing detailed metadata descriptions which include the full provenance of derived data products will contribute to making that data (and, the models and processes which generated that data) more open and transparent to the user community.
Exploring Land Use and Land Cover Change and Feedbacks in the Global Change Assessment Model

NASA Astrophysics Data System (ADS)

Chen, M.; Vernon, C. R.; Huang, M.; Calvin, K. V.; Le Page, Y.; Kraucunas, I.

2017-12-01

Land Use and Land Cover Change (LULCC) is a major driver of global and regional environmental change. Projections of land use change are thus an essential component in Integrated Assessment Models (IAMs) to study feedbacks between transformation of energy systems and land productivity under the context of climate change. However, the spatial scale of IAMs, e.g., the Global Change Assessment Model (GCAM), is typically larger than the scale of terrestrial processes in the human-Earth system, LULCC downscaling therefore becomes a critical linkage among these multi-scale and multi-sector processes. Parametric uncertainties in LULCC downscaling algorithms, however, have been under explored, especially in the context of how such uncertainties could propagate to affect energy systems in a changing climate. In this study, we use a LULCC downscaling model, Demeter, to downscale GCAM-based future land use scenarios into fine spatial scales, and explore the sensitivity of downscaled land allocations to key parameters. Land productivity estimates (e.g., biomass production and crop yield) based on the downscaled LULCC scenarios are then fed to GCAM to evaluate how energy systems might change due to altered water and carbon cycle dynamics and their interactions with the human system, , which would in turn affect future land use projections. We demonstrate that uncertainties in LULCC downscaling can result in significant differences in simulated scenarios, indicating the importance of quantifying parametric uncertainties in LULCC downscaling models for integrated assessment studies.
The role of observational reference data for climate downscaling: Insights from the VALUE COST Action

NASA Astrophysics Data System (ADS)

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

2016-04-01

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.
Accounting for Global Climate Model Projection Uncertainty in Modern Statistical Downscaling

DOE Office of Scientific and Technical Information (OSTI.GOV)

Johannesson, G

2010-03-17

Future climate change has emerged as a national and a global security threat. To carry out the needed adaptation and mitigation steps, a quantification of the expected level of climate change is needed, both at the global and the regional scale; in the end, the impact of climate change is felt at the local/regional level. An important part of such climate change assessment is uncertainty quantification. Decision and policy makers are not only interested in 'best guesses' of expected climate change, but rather probabilistic quantification (e.g., Rougier, 2007). For example, consider the following question: What is the probability that themore » average summer temperature will increase by at least 4 C in region R if global CO{sub 2} emission increases by P% from current levels by time T? It is a simple question, but one that remains very difficult to answer. It is answering these kind of questions that is the focus of this effort. The uncertainty associated with future climate change can be attributed to three major factors: (1) Uncertainty about future emission of green house gasses (GHG). (2) Given a future GHG emission scenario, what is its impact on the global climate? (3) Given a particular evolution of the global climate, what does it mean for a particular location/region? In what follows, we assume a particular GHG emission scenario has been selected. Given the GHG emission scenario, the current batch of the state-of-the-art global climate models (GCMs) is used to simulate future climate under this scenario, yielding an ensemble of future climate projections (which reflect, to some degree our uncertainty of being able to simulate future climate give a particular GHG scenario). Due to the coarse-resolution nature of the GCM projections, they need to be spatially downscaled for regional impact assessments. To downscale a given GCM projection, two methods have emerged: dynamical downscaling and statistical (empirical) downscaling (SDS). Dynamic downscaling involves configuring and running a regional climate model (RCM) nested within a given GCM projection (i.e., the GCM provides bounder conditions for the RCM). On the other hand, statistical downscaling aims at establishing a statistical relationship between observed local/regional climate variables of interest and synoptic (GCM-scale) climate predictors. The resulting empirical relationship is then applied to future GCM projections. A comparison of the pros and cons of dynamical versus statistical downscaling is outside the scope of this effort, but has been extensively studied and the reader is referred to Wilby et al. (1998); Murphy (1999); Wood et al. (2004); Benestad et al. (2007); Fowler et al. (2007), and references within those. The scope of this effort is to study methodology, a statistical framework, to propagate and account for GCM uncertainty in regional statistical downscaling assessment. In particular, we will explore how to leverage an ensemble of GCM projections to quantify the impact of the GCM uncertainty in such an assessment. There are three main component to this effort: (1) gather the necessary climate-related data for a regional SDS study, including multiple GCM projections, (2) carry out SDS, and (3) assess the uncertainty. The first step is carried out using tools written in the Python programming language, while analysis tools were developed in the statistical programming language R; see Figure 1.« less
Quantification of downscaled precipitation uncertainties via Bayesian inference

NASA Astrophysics Data System (ADS)

Nury, A. H.; Sharma, A.; Marshall, L. A.

2017-12-01

Prediction of precipitation from global climate model (GCM) outputs remains critical to decision-making in water-stressed regions. In this regard, downscaling of GCM output has been a useful tool for analysing future hydro-climatological states. Several downscaling approaches have been developed for precipitation downscaling, including those using dynamical or statistical downscaling methods. Frequently, outputs from dynamical downscaling are not readily transferable across regions for significant methodical and computational difficulties. Statistical downscaling approaches provide a flexible and efficient alternative, providing hydro-climatological outputs across multiple temporal and spatial scales in many locations. However these approaches are subject to significant uncertainty, arising due to uncertainty in the downscaled model parameters and in the use of different reanalysis products for inferring appropriate model parameters. Consequently, these will affect the performance of simulation in catchment scale. This study develops a Bayesian framework for modelling downscaled daily precipitation from GCM outputs. This study aims to introduce uncertainties in downscaling evaluating reanalysis datasets against observational rainfall data over Australia. In this research a consistent technique for quantifying downscaling uncertainties by means of Bayesian downscaling frame work has been proposed. The results suggest that there are differences in downscaled precipitation occurrences and extremes.
Assessment of regional climate change and development of climate adaptation decision aids in the Southwestern US

NASA Astrophysics Data System (ADS)

Darmenova, K.; Higgins, G.; Kiley, H.; Apling, D.

2010-12-01

Current General Circulation Models (GCMs) provide a valuable estimate of both natural and anthropogenic climate changes and variability on global scales. At the same time, future climate projections calculated with GCMs are not of sufficient spatial resolution to address regional needs. Many climate impact models require information at scales of 50 km or less, so dynamical downscaling is often used to estimate the smaller-scale information based on larger scale GCM output. To address current deficiencies in local planning and decision making with respect to regional climate change, our research is focused on performing a dynamical downscaling with the Weather Research and Forecasting (WRF) model and developing decision aids that translate the regional climate data into actionable information for users. Our methodology involves development of climatological indices of extreme weather and heating/cooling degree days based on WRF ensemble runs initialized with the NCEP-NCAR reanalysis and the European Center/Hamburg Model (ECHAM5). Results indicate that the downscale simulations provide the necessary detailed output required by state and local governments and the private sector to develop climate adaptation plans. In addition we evaluated the WRF performance in long-term climate simulations over the Southwestern US and validated against observational datasets.
Climate change projections for winter precipitation over Tropical America using statistical downscaling

NASA Astrophysics Data System (ADS)

Palomino-Lemus, Reiner; Córdoba-Machado, Samir; Quishpe-Vásquez, César; García-Valdecasas-Ojeda, Matilde; Raquel Gámiz-Fortis, Sonia; Castro-Díez, Yolanda; Jesús Esteban-Parra, María

2017-04-01

In this study the Principal Component Regression (PCR) method has been used as statistical downscaling technique for simulating boreal winter precipitation in Tropical America during the period 1950-2010, and then for generating climate change projections for 2071-2100 period. The study uses the Global Precipitation Climatology Centre (GPCC, version 6) data set over the Tropical America region [30°N-30°S, 120°W-30°W] as predictand variable in the downscaling model. The mean monthly sea level pressure (SLP) from the National Center for Environmental Prediction - National Center for Atmospheric Research (NCEP-NCAR reanalysis project), has been used as predictor variable, covering a more extended area [30°N-30°S, 180°W-30°W]. Also, the SLP outputs from 20 GCMs, taken from the Coupled Model Intercomparison Project (CMIP5) have been used. The model data include simulations with historical atmospheric concentrations and future projections for the representative concentration pathways RCP2.6, RCP4.5, and RCP8.5. The ability of the different GCMs to simulate the winter precipitation in the study area for present climate (1971-2000) was analyzed by calculating the differences between the simulated and observed precipitation values. Additionally, the statistical significance at 95% confidence level of these differences has been estimated by means of the bilateral rank sum test of Wilcoxon-Mann-Whitney. Finally, to project winter precipitation in the area for the period 2071-2100, the downscaling model, recalibrated for the total period 1950-2010, was applied to the SLP outputs of the GCMs under the RCP2.6, RCP4.5, and RCP8.5 scenarios. The results show that, generally, for present climate the statistical downscaling shows a high ability to faithfully reproduce the precipitation field, while the simulations performed directly by using not downscaled outputs of GCMs strongly distort the precipitation field. For future climate, the projected predictions under the RCP4.5 and RCP8.5 scenarios show large areas with significant changes. For the RCP2.6 scenario, projected results present a predominance of very moderate decreases in rainfall, although significant in some models. Keywords: climate change projections, precipitation, Tropical America, statistical downscaling. Acknowledgements: This work has been financed by the projects P11-RNM-7941 (Junta de Andalucía-Spain) and CGL2013-48539-R (MINECO-Spain, FEDER).
Assessing the Added Value of Dynamical Downscaling Using the Standardized Precipitation Index

EPA Science Inventory

In this study, the Standardized Precipitation Index (SPI) is used to ascertain the added value of dynamical downscaling over the contiguous United States. WRF is used as a regional climate model (RCM) to dynamically downscale reanalysis fields to compare values of SPI over drough...
A framework for evaluating statistical downscaling performance under changing climatic conditions (Invited)

NASA Astrophysics Data System (ADS)

Dixon, K. W.; Balaji, V.; Lanzante, J.; Radhakrishnan, A.; Hayhoe, K.; Stoner, A. K.; Gaitan, C. F.

2013-12-01

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.
An Archive of Downscaled WCRP CMIP3 Climate Projections for Planning Applications in the Contiguous United States

NASA Astrophysics Data System (ADS)

Brekke, L. D.; Pruitt, T.; Maurer, E. P.; Duffy, P. B.

2007-12-01

Incorporating climate change information into long-term evaluations of water and energy resources requires analysts to have access to climate projection data that have been spatially downscaled to "basin-relevant" resolution. This is necessary in order to develop system-specific hydrology and demand scenarios consistent with projected climate scenarios. Analysts currently have access to "climate model" resolution data (e.g., at LLNL PCMDI), but not spatially downscaled translations of these datasets. Motivated by a common interest in supporting regional and local assessments, the U.S. Bureau of Reclamation and LLNL (through support from the DOE National Energy Technology Laboratory) have teamed to develop an archive of downscaled climate projections (temperature and precipitation) with geographic coverage consistent with the North American Land Data Assimilation System domain, encompassing the contiguous United States. A web-based information service, hosted at LLNL Green Data Oasis, has been developed to provide Reclamation, LLNL, and other interested analysts free access to archive content. A contemporary statistical method was used to bias-correct and spatially disaggregate projection datasets, and was applied to 112 projections included in the WCRP CMIP3 multi-model dataset hosted by LLNL PCMDI (i.e. 16 GCMs and their multiple simulations of SRES A2, A1b, and B1 emissions pathways).
Downscaling GISS ModelE Boreal Summer Climate over Africa

NASA Technical Reports Server (NTRS)

Druyan, Leonard M.; Fulakeza, Matthew

2015-01-01

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.
Regional projection of climate impact indices over the Mediterranean region

NASA Astrophysics Data System (ADS)

Casanueva, Ana; Frías, M.; Dolores; Herrera, Sixto; Bedia, Joaquín; San Martín, Daniel; Gutiérrez, José Manuel; Zaninovic, Ksenija

2014-05-01

Climate Impact Indices (CIIs) are being increasingly used in different socioeconomic sectors to transfer information about climate change impacts and risks to stakeholders. CIIs are typically based on different weather variables such as temperature, wind speed, precipitation or humidity and comprise, in a single index, the relevant meteorological information for the particular impact sector (in this study wildfires and tourism). This dependence on several climate variables poses important limitations to the application of statistical downscaling techniques, since physical consistency among variables is required in most cases to obtain reliable local projections. The present study assesses the suitability of the "direct" downscaling approach, in which the downscaling method is directly applied to the CII. In particular, for illustrative purposes, we consider two popular indices used in the wildfire and tourism sectors, the Fire Weather Index (FWI) and the Physiological Equivalent Temperature (PET), respectively. As an example, two case studies are analysed over two representative Mediterranean regions of interest for the EU CLIM-RUN project: continental Spain for the FWI and Croatia for the PET. Results obtained with this "direct" downscaling approach are similar to those found from the application of the statistical downscaling to the individual meteorological drivers prior to the index calculation ("component" downscaling) thus, a wider range of statistical downscaling methods could be used. As an illustration, future changes in both indices are projected by applying two direct statistical downscaling methods, analogs and linear regression, to the ECHAM5 model. Larger differences were found between the two direct statistical downscaling approaches than between the direct and the component approaches with a single downscaling method. While these examples focus on particular indices and Mediterranean regions of interest for CLIM-RUN stakeholders, the same study could be extended to other indices and regions.
Using Four Downscaling Techniques to Characterize Uncertainty in Updating Intensity-Duration-Frequency Curves Under Climate Change

NASA Astrophysics Data System (ADS)

Cook, L. M.; Samaras, C.; McGinnis, S. A.

2017-12-01

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.
High Resolution Simulations of Future Climate in West Africa Using a Variable-Resolution Atmospheric Model

NASA Astrophysics Data System (ADS)

Adegoke, J. O.; Engelbrecht, F.; Vezhapparambu, S.

2013-12-01

In previous work demonstrated the application of a var¬iable-resolution global atmospheric model, the conformal-cubic atmospheric model (CCAM), across a wide range of spatial and time scales to investigate the ability of the model to provide realistic simulations of present-day climate and plausible projections of future climate change over sub-Saharan Africa. By applying the model in stretched-grid mode the versatility of the model dynamics, numerical formulation and physical parameterizations to function across a range of length scales over the region of interest, was also explored. We primarily used CCAM to illustrate the capability of the model to function as a flexible downscaling tool at the climate-change time scale. Here we report on additional long term climate projection studies performed by downscaling at much higher resolutions (8 Km) over an area that stretches from just south of Sahara desert to the southern coast of the Niger Delta and into the Gulf of Guinea. To perform these simulations, CCAM was provided with synoptic-scale forcing of atmospheric circulation from 2.5 deg resolution NCEP reanalysis at 6-hourly interval and SSTs from NCEP reanalysis data uses as lower boundary forcing. CCAM 60 Km resolution downscaled to 8 Km (Schmidt factor 24.75) then 8 Km resolution simulation downscaled to 1 Km (Schmidt factor 200) over an area approximately 50 Km x 50 Km in the southern Lake Chad Basin (LCB). Our intent in conducting these high resolution model runs was to obtain a deeper understanding of linkages between the projected future climate and the hydrological processes that control the surface water regime in this part of sub-Saharan Africa.
Changes in U.S. Regional-Scale Air Quality at 2030 Simulated Using RCP 6.0

NASA Astrophysics Data System (ADS)

Nolte, C. G.; Otte, T.; Pinder, R. W.; Faluvegi, G.; Shindell, D. T.

2012-12-01

Recent improvements in air quality in the United States have been due to significant reductions in emissions of ozone and particulate matter (PM) precursors, and these downward emissions trends are expected to continue in the next few decades. To ensure that planned air quality regulations are robust under a range of possible future climates and to consider possible policy actions to mitigate climate change, it is important to characterize and understand the effects of climate change on air quality. Recent work by several research groups using global and regional models has demonstrated that there is a "climate penalty," in which climate change leads to increases in surface ozone levels in polluted continental regions. One approach to simulating future air quality at the regional scale is via dynamical downscaling, in which fields from a global climate model are used as input for a regional climate model, and these regional climate data are subsequently used for chemical transport modeling. However, recent studies using this approach have encountered problems with the downscaled regional climate fields, including unrealistic surface temperatures and misrepresentation of synoptic pressure patterns such as the Bermuda High. We developed a downscaling methodology and showed that it now reasonably simulates regional climate by evaluating it against historical data. In this work, regional climate simulations created by downscaling the NASA/GISS Model E2 global climate model are used as input for the Community Multiscale Air Quality (CMAQ) model. CMAQ simulations over the continental United States are conducted for two 11-year time slices, one representing current climate (1995-2005) and one following Representative Concentration Pathway 6.0 from 2025-2035. Anthropogenic emissions of ozone and PM precursors are held constant at year 2006 levels for both the current and future periods. In our presentation, we will examine the changes in ozone and PM concentrations, with particular focus on exceedances of the current U.S. air quality standards, and attempt to relate the changes in air quality to the projected changes in regional climate.
Results from the VALUE perfect predictor experiment: process-based evaluation

NASA Astrophysics Data System (ADS)

Maraun, Douglas; Soares, Pedro; Hertig, Elke; Brands, Swen; Huth, Radan; Cardoso, Rita; Kotlarski, Sven; Casado, Maria; Pongracz, Rita; Bartholy, Judit

2016-04-01

Until recently, the evaluation of downscaled climate model simulations has typically been limited to surface climatologies, including long term means, spatial variability and extremes. But these aspects are often, at least partly, tuned in regional climate models to match observed climate. The tuning issue is of course particularly relevant for bias corrected regional climate models. In general, a good performance of a model for these aspects in present climate does therefore not imply a good performance in simulating climate change. It is now widely accepted that, to increase our condidence in climate change simulations, it is necessary to evaluate how climate models simulate relevant underlying processes. In other words, it is important to assess whether downscaling does the right for the right reason. Therefore, VALUE has carried out a broad process-based evaluation study based on its perfect predictor experiment simulations: the downscaling methods are driven by ERA-Interim data over the period 1979-2008, reference observations are given by a network of 85 meteorological stations covering all European climates. More than 30 methods participated in the evaluation. In order to compare statistical and dynamical methods, only variables provided by both types of approaches could be considered. This limited the analysis to conditioning local surface variables on variables from driving processes that are simulated by ERA-Interim. We considered the following types of processes: at the continental scale, we evaluated the performance of downscaling methods for positive and negative North Atlantic Oscillation, Atlantic ridge and blocking situations. At synoptic scales, we considered Lamb weather types for selected European regions such as Scandinavia, the United Kingdom, the Iberian Pensinsula or the Alps. At regional scales we considered phenomena such as the Mistral, the Bora or the Iberian coastal jet. Such process-based evaluation helps to attribute biases in surface variables to underlying processes and ultimately to improve climate models.
Assessment of a stochastic downscaling methodology in generating an ensemble of hourly future climate time series

NASA Astrophysics Data System (ADS)

Fatichi, S.; Ivanov, V. Y.; Caporali, E.

2013-04-01

This study extends a stochastic downscaling methodology to generation of an ensemble of hourly time series of meteorological variables that express possible future climate conditions at a point-scale. The stochastic downscaling uses general circulation model (GCM) realizations and an hourly weather generator, the Advanced WEather GENerator (AWE-GEN). Marginal distributions of factors of change are computed for several climate statistics using a Bayesian methodology that can weight GCM realizations based on the model relative performance with respect to a historical climate and a degree of disagreement in projecting future conditions. A Monte Carlo technique is used to sample the factors of change from their respective marginal distributions. As a comparison with traditional approaches, factors of change are also estimated by averaging GCM realizations. With either approach, the derived factors of change are applied to the climate statistics inferred from historical observations to re-evaluate parameters of the weather generator. The re-parameterized generator yields hourly time series of meteorological variables that can be considered to be representative of future climate conditions. In this study, the time series are generated in an ensemble mode to fully reflect the uncertainty of GCM projections, climate stochasticity, as well as uncertainties of the downscaling procedure. Applications of the methodology in reproducing future climate conditions for the periods of 2000-2009, 2046-2065 and 2081-2100, using the period of 1962-1992 as the historical baseline are discussed for the location of Firenze (Italy). The inferences of the methodology for the period of 2000-2009 are tested against observations to assess reliability of the stochastic downscaling procedure in reproducing statistics of meteorological variables at different time scales.
Downscaled projections of Caribbean coral bleaching that can inform conservation planning.

PubMed

van Hooidonk, Ruben; Maynard, Jeffrey Allen; Liu, Yanyun; Lee, Sang-Ki

2015-09-01

Projections of climate change impacts on coral reefs produced at the coarse resolution (~1°) of Global Climate Models (GCMs) have informed debate but have not helped target local management actions. Here, projections of the onset of annual coral bleaching conditions in the Caribbean under Representative Concentration Pathway (RCP) 8.5 are produced using an ensemble of 33 Coupled Model Intercomparison Project phase-5 models and via dynamical and statistical downscaling. A high-resolution (~11 km) regional ocean model (MOM4.1) is used for the dynamical downscaling. For statistical downscaling, sea surface temperature (SST) means and annual cycles in all the GCMs are replaced with observed data from the ~4-km NOAA Pathfinder SST dataset. Spatial patterns in all three projections are broadly similar; the average year for the onset of annual severe bleaching is 2040-2043 for all projections. However, downscaled projections show many locations where the onset of annual severe bleaching (ASB) varies 10 or more years within a single GCM grid cell. Managers in locations where this applies (e.g., Florida, Turks and Caicos, Puerto Rico, and the Dominican Republic, among others) can identify locations that represent relative albeit temporary refugia. Both downscaled projections are different for the Bahamas compared to the GCM projections. The dynamically downscaled projections suggest an earlier onset of ASB linked to projected changes in regional currents, a feature not resolved in GCMs. This result demonstrates the value of dynamical downscaling for this application and means statistically downscaled projections have to be interpreted with caution. However, aside from west of Andros Island, the projections for the two types of downscaling are mostly aligned; projected onset of ASB is within ±10 years for 72% of the reef locations. © 2015 The Authors. Global Change Biology Published by John Wiley & Sons Ltd.
Examining the Stationarity Assumption for Statistically Downscaled Climate Projections of Precipitation

NASA Astrophysics Data System (ADS)

Wootten, A.; Dixon, K. W.; Lanzante, J. R.; Mcpherson, R. A.

2017-12-01

Empirical statistical downscaling (ESD) approaches attempt to refine global climate model (GCM) information via statistical relationships between observations and GCM simulations. The aim of such downscaling efforts is to create added-value climate projections by adding finer spatial detail and reducing biases. The results of statistical downscaling exercises are often used in impact assessments under the assumption that past performance provides an indicator of future results. Given prior research describing the danger of this assumption with regards to temperature, this study expands the perfect model experimental design from previous case studies to test the stationarity assumption with respect to precipitation. Assuming stationarity implies the performance of ESD methods are similar between the future projections and historical training. Case study results from four quantile-mapping based ESD methods demonstrate violations of the stationarity assumption for both central tendency and extremes of precipitation. These violations vary geographically and seasonally. For the four ESD methods tested the greatest challenges for downscaling of daily total precipitation projections occur in regions with limited precipitation and for extremes of precipitation along Southeast coastal regions. We conclude with a discussion of future expansion of the perfect model experimental design and the implications for improving ESD methods and providing guidance on the use of ESD techniques for impact assessments and decision-support.
Satellite-Enhanced Dynamical Downscaling of Extreme Events

NASA Astrophysics Data System (ADS)

Nunes, A.

2015-12-01

Severe weather events can be the triggers of environmental disasters in regions particularly susceptible to changes in hydrometeorological conditions. In that regard, the reconstruction of past extreme weather events can help in the assessment of vulnerability and risk mitigation actions. Using novel modeling approaches, dynamical downscaling of long-term integrations from global circulation models can be useful for risk analysis, providing more accurate climate information at regional scales. Originally developed at the National Centers for Environmental Prediction (NCEP), the Regional Spectral Model (RSM) is being used in the dynamical downscaling of global reanalysis, within the South American Hydroclimate Reconstruction Project. Here, RSM combines scale-selective bias correction with assimilation of satellite-based precipitation estimates to downscale extreme weather occurrences. Scale-selective bias correction is a method employed in the downscaling, similar to the spectral nudging technique, in which the downscaled solution develops in agreement with its coarse boundaries. Precipitation assimilation acts on modeled deep-convection, drives the land-surface variables, and therefore the hydrological cycle. During the downscaling of extreme events that took place in Brazil in recent years, RSM continuously assimilated NCEP Climate Prediction Center morphing technique precipitation rates. As a result, RSM performed better than its global (reanalysis) forcing, showing more consistent hydrometeorological fields compared with more sophisticated global reanalyses. Ultimately, RSM analyses might provide better-quality initial conditions for high-resolution numerical predictions in metropolitan areas, leading to more reliable short-term forecasting of severe local storms.

Final Technical Report for "Collaborative Research: Regional climate-change projections through next-generation empirical and dynamical models"

DOE Office of Scientific and Technical Information (OSTI.GOV)

Robertson, A.W.; Ghil, M.; Kravtsov, K.

2011-04-08

This project was a continuation of previous work under DOE CCPP funding in which we developed a twin approach of non-homogeneous hidden Markov models (NHMMs) and coupled ocean-atmosphere (O-A) intermediate-complexity models (ICMs) to identify the potentially predictable modes of climate variability, and to investigate their impacts on the regional-scale. We have developed a family of latent-variable NHMMs to simulate historical records of daily rainfall, and used them to downscale seasonal predictions. We have also developed empirical mode reduction (EMR) models for gaining insight into the underlying dynamics in observational data and general circulation model (GCM) simulations. Using coupled O-A ICMs,more » we have identified a new mechanism of interdecadal climate variability, involving the midlatitude oceans mesoscale eddy field and nonlinear, persistent atmospheric response to the oceanic anomalies. A related decadal mode is also identified, associated with the oceans thermohaline circulation. The goal of the continuation was to build on these ICM results and NHMM/EMR model developments and software to strengthen two key pillars of support for the development and application of climate models for climate change projections on time scales of decades to centuries, namely: (a) dynamical and theoretical understanding of decadal-to-interdecadal oscillations and their predictability; and (b) an interface from climate models to applications, in order to inform societal adaptation strategies to climate change at the regional scale, including model calibration, correction, downscaling and, most importantly, assessment and interpretation of spread and uncertainties in multi-model ensembles. Our main results from the grant consist of extensive further development of the hidden Markov models for rainfall simulation and downscaling specifically within the non-stationary climate change context together with the development of parallelized software; application of NHMMs to downscaling of rainfall projections over India; identification and analysis of decadal climate signals in data and models; and, studies of climate variability in terms of the dynamics of atmospheric flow regimes. Each of these project components is elaborated on below, followed by a list of publications resulting from the grant.« less
Final Technical Report for "Collaborative Research. Regional climate-change projections through next-generation empirical and dynamical models"

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kravtsov, S.; Robertson, Andrew W.; Ghil, Michael

2011-04-08

This project was a continuation of previous work under DOE CCPP funding in which we developed a twin approach of non-homogeneous hidden Markov models (NHMMs) and coupled ocean-atmosphere (O-A) intermediate-complexity models (ICMs) to identify the potentially predictable modes of climate variability, and to investigate their impacts on the regional-scale. We have developed a family of latent-variable NHMMs to simulate historical records of daily rainfall, and used them to downscale seasonal predictions. We have also developed empirical mode reduction (EMR) models for gaining insight into the underlying dynamics in observational data and general circulation model (GCM) simulations. Using coupled O-A ICMs,more » we have identified a new mechanism of interdecadal climate variability, involving the midlatitude oceans mesoscale eddy field and nonlinear, persistent atmospheric response to the oceanic anomalies. A related decadal mode is also identified, associated with the oceans thermohaline circulation. The goal of the continuation was to build on these ICM results and NHMM/EMR model developments and software to strengthen two key pillars of support for the development and application of climate models for climate change projections on time scales of decades to centuries, namely: (a) dynamical and theoretical understanding of decadal-to-interdecadal oscillations and their predictability; and (b) an interface from climate models to applications, in order to inform societal adaptation strategies to climate change at the regional scale, including model calibration, correction, downscaling and, most importantly, assessment and interpretation of spread and uncertainties in multi-model ensembles. Our main results from the grant consist of extensive further development of the hidden Markov models for rainfall simulation and downscaling specifically within the non-stationary climate change context together with the development of parallelized software; application of NHMMs to downscaling of rainfall projections over India; identification and analysis of decadal climate signals in data and models; and, studies of climate variability in terms of the dynamics of atmospheric flow regimes. Each of these project components is elaborated on below, followed by a list of publications resulting from the grant.« less
NASA Downscaling Project: Final Report

NASA Technical Reports Server (NTRS)

Ferraro, Robert; Waliser, Duane; Peters-Lidard, Christa

2017-01-01

A team of researchers from NASA Ames Research Center, Goddard Space Flight Center, the Jet Propulsion Laboratory, and Marshall Space Flight Center, along with university partners at UCLA, conducted an investigation to explore whether downscaling coarse resolution global climate model (GCM) predictions might provide valid insights into the regional impacts sought by decision makers. Since the computational cost of running global models at high spatial resolution for any useful climate scale period is prohibitive, the hope for downscaling is that a coarse resolution GCM provides sufficiently accurate synoptic scale information for a regional climate model (RCM) to accurately develop fine scale features that represent the regional impacts of a changing climate. As a proxy for a prognostic climate forecast model, and so that ground truth in the form of satellite and in-situ observations could be used for evaluation, the MERRA and MERRA - 2 reanalyses were used to drive the NU - WRF regional climate model and a GEOS - 5 replay. This was performed at various resolutions that were at factors of 2 to 10 higher than the reanalysis forcing. A number of experiments were conducted that varied resolution, model parameterizations, and intermediate scale nudging, for simulations over the continental US during the period from 2000 - 2010. The results of these experiments were compared to observational datasets to evaluate the output.
NASA Downscaling Project

NASA Technical Reports Server (NTRS)

Ferraro, Robert; Waliser, Duane; Peters-Lidard, Christa

2017-01-01

A team of researchers from NASA Ames Research Center, Goddard Space Flight Center, the Jet Propulsion Laboratory, and Marshall Space Flight Center, along with university partners at UCLA, conducted an investigation to explore whether downscaling coarse resolution global climate model (GCM) predictions might provide valid insights into the regional impacts sought by decision makers. Since the computational cost of running global models at high spatial resolution for any useful climate scale period is prohibitive, the hope for downscaling is that a coarse resolution GCM provides sufficiently accurate synoptic scale information for a regional climate model (RCM) to accurately develop fine scale features that represent the regional impacts of a changing climate. As a proxy for a prognostic climate forecast model, and so that ground truth in the form of satellite and in-situ observations could be used for evaluation, the MERRA and MERRA-2 reanalyses were used to drive the NU-WRF regional climate model and a GEOS-5 replay. This was performed at various resolutions that were at factors of 2 to 10 higher than the reanalysis forcing. A number of experiments were conducted that varied resolution, model parameterizations, and intermediate scale nudging, for simulations over the continental US during the period from 2000-2010. The results of these experiments were compared to observational datasets to evaluate the output.
Creating Dynamically Downscaled Seasonal Climate Forecast and Climate Change Projection Information for the North American Monsoon Region Suitable for Decision Making Purposes

NASA Astrophysics Data System (ADS)

Castro, C. L.; Dominguez, F.; Chang, H.

2010-12-01

Current seasonal climate forecasts and climate change projections of the North American monsoon are based on the use of course-scale information from a general circulation model. The global models, however, have substantial difficulty in resolving the regional scale forcing mechanisms of precipitation. This is especially true during the period of the North American Monsoon in the warm season. Precipitation is driven primarily due to the diurnal cycle of convection, and this process cannot be resolve in coarse-resolution global models that have a relatively poor representation of terrain. Though statistical downscaling may offer a relatively expedient method to generate information more appropriate for the regional scale, and is already being used in the resource decision making processes in the Southwest U.S., its main drawback is that it cannot account for a non-stationary climate. Here we demonstrate the use of a regional climate model, specifically the Weather Research and Forecast (WRF) model, for dynamical downscaling of the North American Monsoon. To drive the WRF simulations, we use retrospective reforecasts from the Climate Forecast System (CFS) model, the operational model used at the U.S. National Center for Environmental Prediction, and three select “well performing” IPCC AR 4 models for the A2 emission scenario. Though relatively computationally expensive, the use of WRF as a regional climate model in this way adds substantial value in the representation of the North American Monsoon. In both cases, the regional climate model captures a fairly realistic and reasonable monsoon, where none exists in the driving global model, and captures the dominant modes of precipitation anomalies associated with ENSO and the Pacific Decadal Oscillation (PDO). Long-term precipitation variability and trends in these simulations is considered via the standardized precipitation index (SPI), a commonly used metric to characterize long-term drought. Dynamically downscaled climate projection data will be integrated into future water resource projections in the state of Arizona, through a cooperative effort involving numerous water resource stakeholders.
Downscaling CMIP5 climate models shows increased tropical cyclone activity over the 21st century

PubMed Central

Emanuel, Kerry A.

2013-01-01

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
Anticipating Future Extreme Climate Events for Alaska Using Dynamical Downscaling and Quantile Mapping

NASA Astrophysics Data System (ADS)

Lader, R.; Walsh, J. E.

2016-12-01

Alaska is projected to experience major changes in extreme climate during the 21st century, due to greenhouse warming and exacerbated by polar amplification, wherein the Arctic is warming at twice the rate compared to the Northern Hemisphere. Given its complex topography, Alaska displays extreme gradients of temperature and precipitation. However, global climate models (GCMs), which typically have a spatial resolution on the order of 100km, struggle to replicate these extremes. To help resolve this issue, this study employs dynamically downscaled regional climate simulations and quantile-mapping methodologies to provide a full suite of daily model variables at 20 km spatial resolution for Alaska, from 1970 to 2100. These data include downscaled products of the: ERA-Interim reanalysis from 1979 to 2015, GFDL-CM3 historical from 1970 to 2005, and GFDL-CM3 RCP 8.5 from 2006 to 2100. Due to the limited nature of long-term observations and high-resolution modeling in Alaska, these data enable a broad expansion of extremes analysis. This study uses these data to highlight a subset of the 27 climate extremes indices, previously defined by the Expert Team on Climate Change Detection and Indices, as they pertain to climate change in Alaska. These indices are based on the statistical distributions of daily surface temperature and precipitation and focus on threshold exceedance, and percentiles. For example, the annual number of days with a daily maximum temperature greater than 25°C is anticipated to triple in many locations in Alaska by the end of the century. Climate extremes can also refer to long duration events, such as the record-setting warmth that defined the 2015-16 cold season in Alaska. The downscaled climate model simulations indicate that this past winter will be considered normal by as early as the mid-2040s, if we continue to warm according to the business-as-usual RCP 8.5 emissions scenario. This represents an accelerated warming as compared to projections form the coarse scale GCMs, and this greater rate of change in the downscaled products is noted with other extremes indices as well.
The climate4impact platform: Providing, tailoring and facilitating climate model data access

NASA Astrophysics Data System (ADS)

Pagé, Christian; Pagani, Andrea; Plieger, Maarten; Som de Cerff, Wim; Mihajlovski, Andrej; de Vreede, Ernst; Spinuso, Alessandro; Hutjes, Ronald; de Jong, Fokke; Bärring, Lars; Vega, Manuel; Cofiño, Antonio; d'Anca, Alessandro; Fiore, Sandro; Kolax, Michael

2017-04-01

One of the main objectives of climate4impact is to provide standardized web services and tools that are reusable in other portals. These services include web processing services, web coverage services and web mapping services (WPS, WCS and WMS). Tailored portals can be targeted to specific communities and/or countries/regions while making use of those services. Easier access to climate data is very important for the climate change impact communities. To fulfill this objective, the climate4impact (http://climate4impact.eu/) web portal and services has been developed, targeting climate change impact modellers, impact and adaptation consultants, as well as other experts using climate change data. It provides to users harmonized access to climate model data through tailored services. It features static and dynamic documentation, Use Cases and best practice examples, an advanced search interface, an integrated authentication and authorization system with the Earth System Grid Federation (ESGF), a visualization interface with ADAGUC web mapping tools. In the latest version, statistical downscaling services, provided by the Santander Meteorology Group Downscaling Portal, were integrated. An innovative interface to integrate statistical downscaling services will be released in the upcoming version. The latter will be a big step in bridging the gap between climate scientists and the climate change impact communities. The climate4impact portal builds on the infrastructure of an international distributed database that has been set to disseminate the results from the global climate model results of the Coupled Model Intercomparison project Phase 5 (CMIP5). This database, the ESGF, is an international collaboration that develops, deploys and maintains software infrastructure for the management, dissemination, and analysis of climate model data. The European FP7 project IS-ENES, Infrastructure for the European Network for Earth System modelling, supports the European contribution to ESGF and contributes to the ESGF open source effort, notably through the development of search, monitoring, quality control, and metadata services. In its second phase, IS-ENES2 supports the implementation of regional climate model results from the international Coordinated Regional Downscaling Experiments (CORDEX). These services were extended within the European FP7 Climate Information Portal for Copernicus (CLIPC) project, and some could be later integrated into the European Copernicus platform.
Precipitation Dynamical Downscaling Over the Great Plains

NASA Astrophysics Data System (ADS)

Hu, Xiao-Ming; Xue, Ming; McPherson, Renee A.; Martin, Elinor; Rosendahl, Derek H.; Qiao, Lei

2018-02-01

Detailed, regional climate projections, particularly for precipitation, are critical for many applications. Accurate precipitation downscaling in the United States Great Plains remains a great challenge for most Regional Climate Models, particularly for warm months. Most previous dynamic downscaling simulations significantly underestimate warm-season precipitation in the region. This study aims to achieve a better precipitation downscaling in the Great Plains with the Weather Research and Forecast (WRF) model. To this end, WRF simulations with different physics schemes and nudging strategies are first conducted for a representative warm season. Results show that different cumulus schemes lead to more pronounced difference in simulated precipitation than other tested physics schemes. Simply choosing different physics schemes is not enough to alleviate the dry bias over the southern Great Plains, which is related to an anticyclonic circulation anomaly over the central and western parts of continental U.S. in the simulations. Spectral nudging emerges as an effective solution for alleviating the precipitation bias. Spectral nudging ensures that large and synoptic-scale circulations are faithfully reproduced while still allowing WRF to develop small-scale dynamics, thus effectively suppressing the large-scale circulation anomaly in the downscaling. As a result, a better precipitation downscaling is achieved. With the carefully validated configurations, WRF downscaling is conducted for 1980-2015. The downscaling captures well the spatial distribution of monthly climatology precipitation and the monthly/yearly variability, showing improvement over at least two previously published precipitation downscaling studies. With the improved precipitation downscaling, a better hydrological simulation over the trans-state Oologah watershed is also achieved.
Climate change impacts in Zhuoshui watershed, Taiwan

NASA Astrophysics Data System (ADS)

Chao, Yi-Chiung; Liu, Pei-Ling; Cheng, Chao-Tzuen; Li, Hsin-Chi; Wu, Tingyeh; Chen, Wei-Bo; Shih, Hung-Ju

2017-04-01

There are 5.3 typhoons hit Taiwan per year on average in last decade. Typhoon Morakot in 2009, the most severe typhoon, causes huge damage in Taiwan, including 677 casualty and roughly NT 110 billion (3.3 billion USD) in economic loss. Some researches documented that typhoon frequency will decrease but increase in intensity in western North Pacific region. It is usually preferred to use high resolution dynamical model to get better projection of extreme events; because coarse resolution models cannot simulate intense extreme events. Under that consideration, dynamical downscaling climate data was chosen to describe typhoon satisfactorily. One of the aims for Taiwan Climate Change Projection and Information Platform (TCCIP) is to demonstrate the linkage between climate change data and watershed impact models. The purpose is to understand relative disasters induced by extreme rainfall (typhoons) under climate change in watersheds including landslides, debris flows, channel erosion and deposition, floods, and economic loss. The study applied dynamic downscaling approach to release climate change projected typhoon events under RCP 8.5, the worst-case scenario. The Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability (TRIGRS) and FLO-2D models, then, were used to simulate hillslope disaster impacts in the upstream of Zhuoshui River. CCHE1D model was used to elevate the sediment erosion or deposition in channel. FVCOM model was used to asses a flood impact in urban area in the downstream. Finally, whole potential loss associate with these typhoon events was evaluated by the Taiwan Typhoon Loss Assessment System (TLAS) under climate change scenario. Results showed that the total loss will increase roughly by NT 49.7 billion (1.6 billion USD) in future in Zhuoshui watershed in Taiwan. The results of this research could help to understand future impact; however model bias still exists. Because typhoon track is a critical factor to consider regional disaster risk and the projection of typhoon is still highly uncertain and typhoon number is very limited in a single model simulation. Since Taiwan is a small island, different typhoon tracks induce different level of disaster impacts in watersheds. Therefore, more samples dynamic downscaled typhoon events are needed for analysis to improve and increase reliability in future. Considering dynamical downscaling methods consume massive computing power, developing a new statistical downscaling approach and new method to release daily climate change data to hourly data could be a short-term solution.
Downscaling Pest Risk Analyses: Identifying Current and Future Potentially Suitable Habitats for Parthenium hysterophorus with Particular Reference to Europe and North Africa

PubMed Central

Kriticos, Darren J.; Brunel, Sarah; Ota, Noboru; Fried, Guillaume; Oude Lansink, Alfons G. J. M.; Panetta, F. Dane; Prasad, T. V. Ramachandra; Shabbir, Asad; Yaacoby, Tuvia

2015-01-01

Pest Risk Assessments (PRAs) routinely employ climatic niche models to identify endangered areas. Typically, these models consider only climatic factors, ignoring the ‘Swiss Cheese’ nature of species ranges due to the interplay of climatic and habitat factors. As part of a PRA conducted for the European and Mediterranean Plant Protection Organization, we developed a climatic niche model for Parthenium hysterophorus, explicitly including the effects of irrigation where it was known to be practiced. We then downscaled the climatic risk model using two different methods to identify the suitable habitat types: expert opinion (following the EPPO PRA guidelines) and inferred from the global spatial distribution. The PRA revealed a substantial risk to the EPPO region and Central and Western Africa, highlighting the desirability of avoiding an invasion by P. hysterophorus. We also consider the effects of climate change on the modelled risks. The climate change scenario indicated the risk of substantial further spread of P. hysterophorus in temperate northern hemisphere regions (North America, Europe and the northern Middle East), and also high elevation equatorial regions (Western Brazil, Central Africa, and South East Asia) if minimum temperatures increase substantially. Downscaling the climate model using habitat factors resulted in substantial (approximately 22–53%) reductions in the areas estimated to be endangered. Applying expert assessments as to suitable habitat classes resulted in the greatest reduction in the estimated endangered area, whereas inferring suitable habitats factors from distribution data identified more land use classes and a larger endangered area. Despite some scaling issues with using a globally conformal Land Use Systems dataset, the inferential downscaling method shows promise as a routine addition to the PRA toolkit, as either a direct model component, or simply as a means of better informing an expert assessment of the suitable habitat types. PMID:26325680
Improved methods for estimating local terrestrial water dynamics from GRACE in the Northern High Plains

NASA Astrophysics Data System (ADS)

Seyoum, Wondwosen M.; Milewski, Adam M.

2017-12-01

Investigating terrestrial water cycle dynamics is vital for understanding the recent climatic variability and human impacts in the hydrologic cycle. In this study, a downscaling approach was developed and tested, to improve the applicability of terrestrial water storage (TWS) anomaly data from the Gravity Recovery and Climate Experiment (GRACE) satellite mission for understanding local terrestrial water cycle dynamics in the Northern High Plains region. A non-parametric, artificial neural network (ANN)-based model, was utilized to downscale GRACE data by integrating it with hydrological variables (e.g. soil moisture) derived from satellite and land surface model data. The downscaling model, constructed through calibration and sensitivity analysis, was used to estimate TWS anomaly for watersheds ranging from 5000 to 20,000 km2 in the study area. The downscaled water storage anomaly data were evaluated using water storage data derived from an (1) integrated hydrologic model, (2) land surface model (e.g. Noah), and (3) storage anomalies calculated from in-situ groundwater level measurements. Results demonstrate the ANN predicts monthly TWS anomaly within the uncertainty (conservative error estimate = 34 mm) for most of the watersheds. Seasonal derived groundwater storage anomaly (GWSA) from the ANN correlated well (r = ∼0.85) with GWSAs calculated from in-situ groundwater level measurements for a watershed size as small as 6000 km2. ANN downscaled TWSA matches closely with Noah-based TWSA compared to standard GRACE extracted TWSA at a local scale. Moreover, the ANN-downscaled change in TWS replicated the water storage variability resulting from the combined effect of climatic and human impacts (e.g. abstraction). The implications of utilizing finer resolution GRACE data for improving local and regional water resources management decisions and applications are clear, particularly in areas lacking in-situ hydrologic monitoring networks.
Validation of the Regional Climate Model ALARO with different dynamical downscaling approaches and different horizontal resolutions

NASA Astrophysics Data System (ADS)

Berckmans, Julie; Hamdi, Rafiq; De Troch, Rozemien; Giot, Olivier

2015-04-01

At the Royal Meteorological Institute of Belgium (RMI), climate simulations are performed with the regional climate model (RCM) ALARO, a version of the ALADIN model with improved physical parameterizations. In order to obtain high-resolution information of the regional climate, lateral bounary conditions (LBC) are prescribed from the global climate model (GCM) ARPEGE. Dynamical downscaling is commonly done in a continuous long-term simulation, with the initialisation of the model at the start and driven by the regularly updated LBCs of the GCM. Recently, more interest exists in the dynamical downscaling approach of frequent reinitializations of the climate simulations. For these experiments, the model is initialised daily and driven for 24 hours by the GCM. However, the surface is either initialised daily together with the atmosphere or free to evolve continuously. The surface scheme implemented in ALARO is SURFEX, which can be either run in coupled mode or in stand-alone mode. The regional climate is simulated on different domains, on a 20km horizontal resolution over Western-Europe and a 4km horizontal resolution over Belgium. Besides, SURFEX allows to perform a stand-alone or offline simulation on 1km horizontal resolution over Belgium. This research is in the framework of the project MASC: "Modelling and Assessing Surface Change Impacts on Belgian and Western European Climate", a 4-year project funded by the Belgian Federal Government. The overall aim of the project is to study the feedbacks between climate changes and land surface changes in order to improve regional climate model projections at the decennial scale over Belgium and Western Europe and thus to provide better climate projections and climate change evaluation tools to policy makers, stakeholders and the scientific community.
Final Technical Report for Collaborative Research: Regional climate-change projections through next-generation empirical and dynamical models, DE-FG02-07ER64429

DOE Office of Scientific and Technical Information (OSTI.GOV)

Smyth, Padhraic

2013-07-22

This is the final report for a DOE-funded research project describing the outcome of research on non-homogeneous hidden Markov models (NHMMs) and coupled ocean-atmosphere (O-A) intermediate-complexity models (ICMs) to identify the potentially predictable modes of climate variability, and to investigate their impacts on the regional-scale. The main results consist of extensive development of the hidden Markov models for rainfall simulation and downscaling specifically within the non-stationary climate change context together with the development of parallelized software; application of NHMMs to downscaling of rainfall projections over India; identification and analysis of decadal climate signals in data and models; and, studies ofmore » climate variability in terms of the dynamics of atmospheric flow regimes.« less
Future Climate Change in the Baltic Sea Area

NASA Astrophysics Data System (ADS)

Bøssing Christensen, Ole; Kjellström, Erik; Zorita, Eduardo; Sonnenborg, Torben; Meier, Markus; Grinsted, Aslak

2015-04-01

Regional climate models have been used extensively since the first assessment of climate change in the Baltic Sea region published in 2008, not the least for studies of Europe (and including the Baltic Sea catchment area). Therefore, conclusions regarding climate model results have a better foundation than was the case for the first BACC report of 2008. This presentation will report model results regarding future climate. What is the state of understanding about future human-driven climate change? We will cover regional models, statistical downscaling, hydrological modelling, ocean modelling and sea-level change as it is projected for the Baltic Sea region. Collections of regional model simulations from the ENSEMBLES project for example, financed through the European 5th Framework Programme and the World Climate Research Programme Coordinated Regional Climate Downscaling Experiment, have made it possible to obtain an increasingly robust estimation of model uncertainty. While the first Baltic Sea assessment mainly used four simulations from the European 5th Framework Programme PRUDENCE project, an ensemble of 13 transient regional simulations with twice the horizontal resolution reaching the end of the 21st century has been available from the ENSEMBLES project; therefore it has been possible to obtain more quantitative assessments of model uncertainty. The literature about future climate change in the Baltic Sea region is largely built upon the ENSEMBLES project. Also within statistical downscaling, a considerable number of papers have been published, encompassing now the application of non-linear statistical models, projected changes in extremes and correction of climate model biases. The uncertainty of hydrological change has received increasing attention since the previous Baltic Sea assessment. Several studies on the propagation of uncertainties originating in GCMs, RCMs, and emission scenarios are presented. The number of studies on uncertainties related to downscaling and impact models is relatively small, but more are emerging. A large number of coupled climate-environmental scenario simulations for the Baltic Sea have been performed within the BONUS+ projects (ECOSUPPORT, INFLOW, AMBER and Baltic-C (2009-2011)), using various combinations of output from GCMs, RCMs, hydrological models and scenarios for load and emission of nutrients as forcing for Baltic Sea models. Such a large ensemble of scenario simulations for the Baltic Sea has never before been produced and enables for the first time an estimation of uncertainties.
Multi-RCM ensemble downscaling of global seasonal forecasts (MRED)

NASA Astrophysics Data System (ADS)

Arritt, R. W.

2008-12-01

The Multi-RCM Ensemble Downscaling (MRED) project was recently initiated to address the question, Can regional climate models provide additional useful information from global seasonal forecasts? MRED will use a suite of regional climate models to downscale seasonal forecasts produced by the new National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFS) seasonal forecast system and the NASA GEOS5 system. The initial focus will be on wintertime forecasts in order to evaluate topographic forcing, snowmelt, and the potential usefulness of higher resolution, especially for near-surface fields influenced by high resolution orography. Each regional model will cover the conterminous US (CONUS) at approximately 32 km resolution, and will perform an ensemble of 15 runs for each year 1982-2003 for the forecast period 1 December - 30 April. MRED will compare individual regional and global forecasts as well as ensemble mean precipitation and temperature forecasts, which are currently being used to drive macroscale land surface models (LSMs), as well as wind, humidity, radiation, turbulent heat fluxes, which are important for more advanced coupled macro-scale hydrologic models. Metrics of ensemble spread will also be evaluated. Extensive analysis will be performed to link improvements in downscaled forecast skill to regional forcings and physical mechanisms. Our overarching goal is to determine what additional skill can be provided by a community ensemble of high resolution regional models, which we believe will eventually define a strategy for more skillful and useful regional seasonal climate forecasts.
Use of dynamical downscaling to improve the simulation of Central U.S. warm season precipitation in CMIP5 models

NASA Astrophysics Data System (ADS)

Harding, Keith J.; Snyder, Peter K.; Liess, Stefan

2013-11-01

supporting exceptionally productive agricultural lands, the Central U.S. is susceptible to severe droughts and floods. Such precipitation extremes are expected to worsen with climate change. However, future projections are highly uncertain as global climate models (GCMs) generally fail to resolve precipitation extremes. In this study, we assess how well models from the Coupled Model Intercomparison Project Phase 5 (CMIP5) simulate summer means, variability, extremes, and the diurnal cycle of Central U.S. summer rainfall. Output from a subset of historical CMIP5 simulations are used to drive the Weather Research and Forecasting model to determine whether dynamical downscaling improves the representation of Central U.S. rainfall. We investigate which boundary conditions influence dynamically downscaled precipitation estimates and identify GCMs that can reasonably simulate precipitation when downscaled. The CMIP5 models simulate the seasonal mean and variability of summer rainfall reasonably well but fail to resolve extremes, the diurnal cycle, and the dynamic forcing of precipitation. Downscaling to 30 km improves these characteristics of precipitation, with the greatest improvement in the representation of extremes. Additionally, sizeable diurnal cycle improvements occur with higher (10 km) resolution and convective parameterization disabled, as the daily rainfall peak shifts 4 h closer to observations than 30 km resolution simulations. This lends greater confidence that the mechanisms responsible for producing rainfall are better simulated. Because dynamical downscaling can more accurately simulate these aspects of Central U.S. summer rainfall, policymakers can have added confidence in dynamically downscaled rainfall projections, allowing for more targeted adaptation and mitigation.
Assessment of regional downscaling simulations for long term mean, excess and deficit Indian Summer Monsoons

NASA Astrophysics Data System (ADS)

Varikoden, Hamza; Mujumdar, M.; Revadekar, J. V.; Sooraj, K. P.; Ramarao, M. V. S.; Sanjay, J.; Krishnan, R.

2018-03-01

This study undertakes a comprehensive assessment of dynamical downscaling of summer monsoon (June-September; JJAS) rainfall over heterogeneous regions namely the Western Ghats (WG), Central India (CI) and North-Eastern Region (NER) for long term mean, excess and deficit episodes for the historical period from 1951 to 2005. This downscaling assessment is based on six Coordinated Regional Climate Downscaling Experiments (CORDEX) for South Asia (SAS) region, their five driving Global Climate Models (GCM) simulations along with observations from India Meteorological Department (IMD) and Asian Precipitation Highly Resolved Observational Integrated Towards Evaluation for Water Resources (APHRODITE). The analysis reveals an overall reduction of dry bias in rainfall across the regions of Indian sub-continent in most of the downscaled CORDEX-SAS models and in their ensemble mean as compared to that of driving GCMs. The interannual variabilities during historical period are reasonably captured by the ensemble means of CORDEX-SAS simulations with an underestimation of 0.43%, 38% and 52% for the WG, CI and NER, respectively. Upon careful examination of the CORDEX-SAS models and their driving GCMs revealed considerable improvement in the regionally downscaled rainfall. The value addition of dynamical downscaling is apparent over the WG in Regional Climate Model (RCM) simulations with an improvement of more than 30% for the long term mean, excess and deficit episodes from their driving GCMs. In the case of NER, the improvement in the downscaled rainfall product is more than 10% for all the episodes. However, the value addition in the CORDEX-SAS simulations for CI region, dominantly influenced by synoptic scale processes, is not clear. Nevertheless, the reduction of dry bias in the complex topographical regions is remarkable. The relative performance of dynamical downscaling of rainfall over complex topography in response to local forcing and orographic lifting depict the value addition (30% over WG and 10% over NER, with a statistical significance of more than 5% level), when compared with the synoptic scale system induced rainfall over the plains of central-India.
Inter-comparison of multiple statistically downscaled climate datasets for the Pacific Northwest, USA

PubMed Central

Jiang, Yueyang; Kim, John B.; Still, Christopher J.; Kerns, Becky K.; Kline, Jeffrey D.; Cunningham, Patrick G.

2018-01-01

Statistically downscaled climate data have been widely used to explore possible impacts of climate change in various fields of study. Although many studies have focused on characterizing differences in the downscaling methods, few studies have evaluated actual downscaled datasets being distributed publicly. Spatially focusing on the Pacific Northwest, we compare five statistically downscaled climate datasets distributed publicly in the US: ClimateNA, NASA NEX-DCP30, MACAv2-METDATA, MACAv2-LIVNEH and WorldClim. We compare the downscaled projections of climate change, and the associated observational data used as training data for downscaling. We map and quantify the variability among the datasets and characterize the spatio-temporal patterns of agreement and disagreement among the datasets. Pair-wise comparisons of datasets identify the coast and high-elevation areas as areas of disagreement for temperature. For precipitation, high-elevation areas, rainshadows and the dry, eastern portion of the study area have high dissimilarity among the datasets. By spatially aggregating the variability measures into watersheds, we develop guidance for selecting datasets within the Pacific Northwest climate change impact studies. PMID:29461513
Inter-comparison of multiple statistically downscaled climate datasets for the Pacific Northwest, USA.

PubMed

Jiang, Yueyang; Kim, John B; Still, Christopher J; Kerns, Becky K; Kline, Jeffrey D; Cunningham, Patrick G

2018-02-20

Statistically downscaled climate data have been widely used to explore possible impacts of climate change in various fields of study. Although many studies have focused on characterizing differences in the downscaling methods, few studies have evaluated actual downscaled datasets being distributed publicly. Spatially focusing on the Pacific Northwest, we compare five statistically downscaled climate datasets distributed publicly in the US: ClimateNA, NASA NEX-DCP30, MACAv2-METDATA, MACAv2-LIVNEH and WorldClim. We compare the downscaled projections of climate change, and the associated observational data used as training data for downscaling. We map and quantify the variability among the datasets and characterize the spatio-temporal patterns of agreement and disagreement among the datasets. Pair-wise comparisons of datasets identify the coast and high-elevation areas as areas of disagreement for temperature. For precipitation, high-elevation areas, rainshadows and the dry, eastern portion of the study area have high dissimilarity among the datasets. By spatially aggregating the variability measures into watersheds, we develop guidance for selecting datasets within the Pacific Northwest climate change impact studies.

Optimal selection of MULTI-model downscaled ensembles for interannual and seasonal climate prediction in the eastern seaboard of Thailand

NASA Astrophysics Data System (ADS)

Bejranonda, W.; Koch, M.

2010-12-01

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.
Multi-scale, multi-model assessment of projected land allocation

NASA Astrophysics Data System (ADS)

Vernon, C. R.; Huang, M.; Chen, M.; Calvin, K. V.; Le Page, Y.; Kraucunas, I.

2017-12-01

Effects of land use and land cover change (LULCC) on climate are generally classified into two scale-dependent processes: biophysical and biogeochemical. An extensive amount of research has been conducted related to the impact of each process under alternative climate change futures. However, these studies are generally focused on the impacts of a single process and fail to bridge the gap between sector-driven scale dependencies and any associated dynamics. Studies have been conducted to better understand the relationship of these processes but their respective scale has not adequately captured overall interdependencies between land surface changes and changes in other human-earth systems (e.g., energy, water, economic, etc.). There has also been considerable uncertainty surrounding land use land cover downscaling approaches due to scale dependencies. Demeter, a land use land cover downscaling and change detection model, was created to address this science gap. Demeter is an open-source model written in Python that downscales zonal land allocation projections to the gridded resolution of a user-selected spatial base layer (e.g., MODIS, NLCD, EIA CCI, etc.). Demeter was designed to be fully extensible to allow for module inheritance and replacement for custom research needs, such as flexible IO design to facilitate the coupling of Earth system models (e.g., the Accelerated Climate Modeling for Energy (ACME) and the Community Earth System Model (CESM)) to integrated assessment models (e.g., the Global Change Assessment Model (GCAM)). In this study, we first assessed the sensitivity of downscaled LULCC scenarios at multiple resolutions from Demeter to its parameters by comparing them to historical LULC change data. "Optimal" values of key parameters for each region were identified and used to downscale GCAM-based future scenarios consistent with those in the Land Use Model Intercomparison Project (LUMIP). Demeter-downscaled land use scenarios were then compared to the default LUMIP scenarios to illustrate the uncertainties in projected LULC as a result of difference in downscaling algorithms. Our results show that such uncertainties could propagate to other components in ACME and CESM and lead to significant differences in simulated water and biogeochemical cycles.
Predicting the Impacts of Climate Change on Runoff and Sediment Processes in Agricultural Watersheds: A Case Study from the Sunflower Watershed in the Lower Mississippi Basin

NASA Astrophysics Data System (ADS)

Elkadiri, R.; Momm, H.; Yasarer, L.; Armour, G. L.

2017-12-01

Climatic conditions play a major role in physical processes impacting soil and agrochemicals detachment and transportation from/in agricultural watersheds. In addition, these climatic conditions are projected to significantly vary spatially and temporally in the 21st century, leading to vast uncertainties about the future of sediment and non-point source pollution transport in agricultural watersheds. In this study, we selected the sunflower basin in the lower Mississippi River basin, USA to contribute in the understanding of how climate change affects watershed processes and the transport of pollutant loads. The climate projections used in this study were retrieved from the archive of World Climate Research Programme's (WCRP) Coupled Model Intercomparison Phase 5 (CMIP5) project. The CMIP5 dataset was selected because it contains the most up-to-date spatially downscaled and bias corrected climate projections. A subset of ten GCMs representing a range in projected climate were spatially downscaled for the sunflower watershed. Statistics derived from downscaled GCM output representing the 2011-2040, 2041-2070 and 2071-2100 time periods were used to generate maximum/minimum temperature and precipitation on a daily time step using the USDA Synthetic Weather Generator, SYNTOR. These downscaled climate data were then utilized as inputs to run in the Annualized Agricultural Non-Point Source (AnnAGNPS) pollution watershed model to estimate time series of runoff, sediment, and nutrient loads produced from the watershed. For baseline conditions a validated simulation of the watershed was created and validated using historical data from 2000 until 2015.
Evaluation of climate change effects on the hydrology of a medium-sized Mediterranean basin affected by data sparseness

NASA Astrophysics Data System (ADS)

Piras, Monica; Mascaro, Giuseppe; Deidda, Roberto; Vivoni, Enrique R.

2014-05-01

Many studies based on global and regional climate models agree on the prediction that the Mediterranean area will be most likely affected by climate changes with consequent reduced water availability and intensified hydrologic extremes. This study evaluates the effects of climate changes on the hydrologic response of a medium-sized Mediterranean basin through downscaling techniques and hydrologic simulations. The watershed is the Rio Mannu at Monastir basin (473 km2), located in an agricultural area of southern Sardinia, Italy, which has suffered drought issues in the last decades. It is one of the seven study cases of a multidisciplinary European research project, CLIMB (Climate Induced Changes on the Hydrology of Mediterranean Basins). In such basins, characterized by strong climate variability and by a complex hydrologic response, process based distributed hydrologic models, DHMs, combined with regional climate models, RCMs, and downscaling techniques can help in the evaluation of the local impacts of climate change on water resources decreasing the uncertainty. Since the Rio Mannu basin is affected by data sparseness (meteorological and streamflow data are collected in non overlapping time periods and at diverse time resolutions), two statistical downscaling strategies for precipitation and potential evapotranspiration have been designed which allow to obtain the high-resolution input data required for the calibration of our hydrologic model, the TIN-based Real time Integrated Basin Simulator (tRIBS). We show how the DHM has been calibrated and validated with reasonable accuracy using the disaggregation tools. Next, the same downscaling algorithms have been used to fill the resolution discrepancy between RCMs and the hydrologic model. The outputs of four RCMs, selected as the best performing and bias corrected within the CLIMB project, have been downscaled and used to force the tRIBS during a reference (1971-2000) and a future (2041-2070) period. Several hydro-climatic indicators have been computed based on the time series and spatial maps produced by the DHM to assess the variation in Rio Mannu water resources budget and hydrologic extremes in the future period as compared to the reference one. Our results confirms what is generally predicted for the Mediterranean area, showing a basin future condition of more water shortages due to both reduced precipitations and increased temperatures.
Application of SDSM and LARS-WG for simulating and downscaling of rainfall and temperature

NASA Astrophysics Data System (ADS)

Hassan, Zulkarnain; Shamsudin, Supiah; Harun, Sobri

2014-04-01

Climate change is believed to have significant impacts on the water basin and region, such as in a runoff and hydrological system. However, impact studies on the water basin and region are difficult, since general circulation models (GCMs), which are widely used to simulate future climate scenarios, do not provide reliable hours of daily series rainfall and temperature for hydrological modeling. There is a technique named as "downscaling techniques", which can derive reliable hour of daily series rainfall and temperature due to climate scenarios from the GCMs output. In this study, statistical downscaling models are used to generate the possible future values of local meteorological variables such as rainfall and temperature in the selected stations in Peninsular of Malaysia. The models are: (1) statistical downscaling model (SDSM) that utilized the regression models and stochastic weather generators and (2) Long Ashton research station weather generator (LARS-WG) that only utilized the stochastic weather generators. The LARS-WG and SDSM models obviously are feasible methods to be used as tools in quantifying effects of climate change condition in a local scale. SDSM yields a better performance compared to LARS-WG, except SDSM is slightly underestimated for the wet and dry spell lengths. Although both models do not provide identical results, the time series generated by both methods indicate a general increasing trend in the mean daily temperature values. Meanwhile, the trend of the daily rainfall is not similar to each other, with SDSM giving a relatively higher change of annual rainfall compared to LARS-WG.
Climate Change Impacts at Department of Defense

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kotamarthi, Rao; Wang, Jiali; Zoebel, Zach

This project is aimed at providing the U.S. Department of Defense (DoD) with a comprehensive analysis of the uncertainty associated with generating climate projections at the regional scale that can be used by stakeholders and decision makers to quantify and plan for the impacts of future climate change at specific locations. The merits and limitations of commonly used downscaling models, ranging from simple to complex, are compared, and their appropriateness for application at installation scales is evaluated. Downscaled climate projections are generated at selected DoD installations using dynamic and statistical methods with an emphasis on generating probability distributions of climatemore » variables and their associated uncertainties. The sites selection and selection of variables and parameters for downscaling was based on a comprehensive understanding of the current and projected roles that weather and climate play in operating, maintaining, and planning DoD facilities and installations.« less
Quantifying the intra-annual uncertainties in climate change assessment over 10 sub-basins across the Pacific Northwest US

NASA Astrophysics Data System (ADS)

Ahmadalipour, Ali; Moradkhani, Hamid; Rana, Arun

2017-04-01

Uncertainty is an inevitable feature of climate change impact assessments. Understanding and quantifying different sources of uncertainty is of high importance, which can help modeling agencies improve the current models and scenarios. In this study, we have assessed the future changes in three climate variables (i.e. precipitation, maximum temperature, and minimum temperature) over 10 sub-basins across the Pacific Northwest US. To conduct the study, 10 statistically downscaled CMIP5 GCMs from two downscaling methods (i.e. BCSD and MACA) were utilized at 1/16 degree spatial resolution for the historical period of 1970-2000 and future period of 2010-2099. For the future projections, two future scenarios of RCP4.5 and RCP8.5 were used. Furthermore, Bayesian Model Averaging (BMA) was employed to develop a probabilistic future projection for each climate variable. Results indicate superiority of BMA simulations compared to individual models. Increasing temperature and precipitation are projected at annual timescale. However, the changes are not uniform among different seasons. Model uncertainty shows to be the major source of uncertainty, while downscaling uncertainty significantly contributes to the total uncertainty, especially in summer.
The Impact of Climate Projection Method on the Analysis of Climate Change in Semi-arid Basins

NASA Astrophysics Data System (ADS)

Halper, E.; Shamir, E.

2016-12-01

In small basins with arid climates, rainfall characteristics are highly variable and stream flow is tightly coupled with the nuances of rainfall events (e.g. hourly precipitation patterns Climate change assessments in these basins typically employ CMIP5 projections downscaled with Bias Corrected Statistical Downscaling and Bias Correction/Constructed Analogs (BCSD-BCCA) methods, but these products have drawbacks. Specifically, BCSD-BCCA these projections do not explicitly account for localized physical precipitation mechanisms (e.g. monsoon and snowfall) that are essential to many hydrological systems in the U. S. Southwest. An investigation of the impact of different types of precipitation projections for two kinds of hydrologic studies is being conducted under the U.S. Bureau of Reclamation's Science and Technology Grant Program. An innovative modeling framework consisting of a weather generator of likely hourly precipitation scenarios, coupled with rainfall-runoff, river routing and groundwater models, has been developed in the Nogales, Arizona area. This framework can simulate the impact of future climate on municipal water operations. This framework allows the rigorous comparison of the BCSD-BCCA methods with alternative approaches including rainfall output from dynamical downscaled Regional Climate Models (RCM), a stochastic rainfall generator forced by either Global Climate Models (GCM) or RCM, and projections using historical records conditioned on either GCM or RCM. The results will provide guide for the use of climate change projections into hydrologic studies of semi-arid areas. The project extends this comparison to analyses of flood control. Large flows on the Bill Williams River are a concern for the operation of dams along the Lower Colorado River. After adapting the weather generator for this region, we will evaluate the model performance for rainfall and stream flow, with emphasis on statistical features important to the specific needs of flood management. The end product of the research is to develop a test to guide selection of a precipitation projection method (including downscaling procedure) for a given region and objective.
Evaluating the utility of dynamical downscaling in agricultural impacts projections

PubMed Central

Glotter, Michael; Elliott, Joshua; McInerney, David; Best, Neil; Foster, Ian; Moyer, Elisabeth J.

2014-01-01

Interest in estimating the potential socioeconomic costs of climate change has led to the increasing use of dynamical downscaling—nested modeling in which regional climate models (RCMs) are driven with general circulation model (GCM) output—to produce fine-spatial-scale climate projections for impacts assessments. We evaluate here whether this computationally intensive approach significantly alters projections of agricultural yield, one of the greatest concerns under climate change. Our results suggest that it does not. We simulate US maize yields under current and future CO2 concentrations with the widely used Decision Support System for Agrotechnology Transfer crop model, driven by a variety of climate inputs including two GCMs, each in turn downscaled by two RCMs. We find that no climate model output can reproduce yields driven by observed climate unless a bias correction is first applied. Once a bias correction is applied, GCM- and RCM-driven US maize yields are essentially indistinguishable in all scenarios (<10% discrepancy, equivalent to error from observations). Although RCMs correct some GCM biases related to fine-scale geographic features, errors in yield are dominated by broad-scale (100s of kilometers) GCM systematic errors that RCMs cannot compensate for. These results support previous suggestions that the benefits for impacts assessments of dynamically downscaling raw GCM output may not be sufficient to justify its computational demands. Progress on fidelity of yield projections may benefit more from continuing efforts to understand and minimize systematic error in underlying climate projections. PMID:24872455
A new dynamical downscaling approach with GCM bias corrections and spectral nudging

NASA Astrophysics Data System (ADS)

Xu, Zhongfeng; Yang, Zong-Liang

2015-04-01

To improve confidence in regional projections of future climate, a new dynamical downscaling (NDD) approach with both general circulation model (GCM) bias corrections and spectral nudging is developed and assessed over North America. GCM biases are corrected by adjusting GCM climatological means and variances based on reanalysis data before the GCM output is used to drive a regional climate model (RCM). Spectral nudging is also applied to constrain RCM-based biases. Three sets of RCM experiments are integrated over a 31 year period. In the first set of experiments, the model configurations are identical except that the initial and lateral boundary conditions are derived from either the original GCM output, the bias-corrected GCM output, or the reanalysis data. The second set of experiments is the same as the first set except spectral nudging is applied. The third set of experiments includes two sensitivity runs with both GCM bias corrections and nudging where the nudging strength is progressively reduced. All RCM simulations are assessed against North American Regional Reanalysis. The results show that NDD significantly improves the downscaled mean climate and climate variability relative to other GCM-driven RCM downscaling approach in terms of climatological mean air temperature, geopotential height, wind vectors, and surface air temperature variability. In the NDD approach, spectral nudging introduces the effects of GCM bias corrections throughout the RCM domain rather than just limiting them to the initial and lateral boundary conditions, thereby minimizing climate drifts resulting from both the GCM and RCM biases.
Climate model biases and statistical downscaling for application in hydrologic model

USDA-ARS?s Scientific Manuscript database

Climate change impact studies use global climate model (GCM) simulations to define future temperature and precipitation. The best available bias-corrected GCM output was obtained from Coupled Model Intercomparison Project phase 5 (CMIP5). CMIP5 data (temperature and precipitation) are available in d...
"Going the Extra Mile in Downscaling: Why Downscaling is not ...

EPA Pesticide Factsheets

This presentation provides an example of doing additional work for preprocessing global climate model data for use in regional climate modeling simulations with the Weather Research and Forecasting (WRF) model. In this presentation, results from 15 months of downscaling the Community Earth System Model (CESM) were shown, both using the out-of-the-box downscaling of CESM and also with a modification to setting the inland lake temperatures. The National Exposure Research Laboratory (NERL) Atmospheric Modeling and Analysis Division (AMAD) conducts research in support of EPA mission to protect human health and the environment. AMAD research program is engaged in developing and evaluating predictive atmospheric models on all spatial and temporal scales for forecasting the air quality and for assessing changes in air quality and air pollutant exposures, as affected by changes in ecosystem management and regulatory decisions. AMAD is responsible for providing a sound scientific and technical basis for regulatory policies based on air quality models to improve ambient air quality. The models developed by AMAD are being used by EPA, NOAA, and the air pollution community in understanding and forecasting not only the magnitude of the air pollution problem, but also in developing emission control policies and regulations for air quality improvements.
Modeling responses of large-river fish populations to global climate change through downscaling and incorporation of predictive uncertainty

USGS Publications Warehouse

Wildhaber, Mark L.; Wikle, Christopher K.; Anderson, Christopher J.; Franz, Kristie J.; Moran, Edward H.; Dey, Rima; Mader, Helmut; Kraml, Julia

2012-01-01

Climate change operates over a broad range of spatial and temporal scales. Understanding its effects on ecosystems requires multi-scale models. For understanding effects on fish populations of riverine ecosystems, climate predicted by coarse-resolution Global Climate Models must be downscaled to Regional Climate Models to watersheds to river hydrology to population response. An additional challenge is quantifying sources of uncertainty given the highly nonlinear nature of interactions between climate variables and community level processes. We present a modeling approach for understanding and accomodating uncertainty by applying multi-scale climate models and a hierarchical Bayesian modeling framework to Midwest fish population dynamics and by linking models for system components together by formal rules of probability. The proposed hierarchical modeling approach will account for sources of uncertainty in forecasts of community or population response. The goal is to evaluate the potential distributional changes in an ecological system, given distributional changes implied by a series of linked climate and system models under various emissions/use scenarios. This understanding will aid evaluation of management options for coping with global climate change. In our initial analyses, we found that predicted pallid sturgeon population responses were dependent on the climate scenario considered.
Climate Change Impact Assessment in Pacific North West Using Copula based Coupling of Temperature and Precipitation variables

NASA Astrophysics Data System (ADS)

Qin, Y.; Rana, A.; Moradkhani, H.

2014-12-01

The multi downscaled-scenario products allow us to better assess the uncertainty of the changes/variations of precipitation and temperature in the current and future periods. Joint Probability distribution functions (PDFs), of both the climatic variables, might help better understand the interdependence of the two, and thus in-turn help in accessing the future with confidence. Using the joint distribution of temperature and precipitation is also of significant importance in hydrological applications and climate change studies. In the present study, we have used multi-modelled statistically downscaled-scenario ensemble of precipitation and temperature variables using 2 different statistically downscaled climate dataset. The datasets used are, 10 Global Climate Models (GCMs) downscaled products from CMIP5 daily dataset, namely, those from the Bias Correction and Spatial Downscaling (BCSD) technique generated at Portland State University and from the Multivariate Adaptive Constructed Analogs (MACA) technique, generated at University of Idaho, leading to 2 ensemble time series from 20 GCM products. Thereafter the ensemble PDFs of both precipitation and temperature is evaluated for summer, winter, and yearly periods for all the 10 sub-basins across Columbia River Basin (CRB). Eventually, Copula is applied to establish the joint distribution of two variables enabling users to model the joint behavior of the variables with any level of correlation and dependency. Moreover, the probabilistic distribution helps remove the limitations on marginal distributions of variables in question. The joint distribution is then used to estimate the change trends of the joint precipitation and temperature in the current and future, along with estimation of the probabilities of the given change. Results have indicated towards varied change trends of the joint distribution of, summer, winter, and yearly time scale, respectively in all 10 sub-basins. Probabilities of changes, as estimated by the joint precipitation and temperature, will provide useful information/insights for hydrological and climate change predictions.
Climatic change on the Gulf of Fonseca (Central America) using two-step statistical downscaling of CMIP5 model outputs

NASA Astrophysics Data System (ADS)

Ribalaygua, Jaime; Gaitán, Emma; Pórtoles, Javier; Monjo, Robert

2018-05-01

A two-step statistical downscaling method has been reviewed and adapted to simulate twenty-first-century climate projections for the Gulf of Fonseca (Central America, Pacific Coast) using Coupled Model Intercomparison Project (CMIP5) climate models. The downscaling methodology is adjusted after looking for good predictor fields for this area (where the geostrophic approximation fails and the real wind fields are the most applicable). The method's performance for daily precipitation and maximum and minimum temperature is analysed and revealed suitable results for all variables. For instance, the method is able to simulate the characteristic cycle of the wet season for this area, which includes a mid-summer drought between two peaks. Future projections show a gradual temperature increase throughout the twenty-first century and a change in the features of the wet season (the first peak and mid-summer rainfall being reduced relative to the second peak, earlier onset of the wet season and a broader second peak).
Importance of Preserving Cross-correlation in developing Statistically Downscaled Climate Forcings and in estimating Land-surface Fluxes and States

NASA Astrophysics Data System (ADS)

Das Bhowmik, R.; Arumugam, S.

2015-12-01

Multivariate downscaling techniques exhibited superiority over univariate regression schemes in terms of preserving cross-correlations between multiple variables- precipitation and temperature - from GCMs. This study focuses on two aspects: (a) develop an analytical solutions on estimating biases in cross-correlations from univariate downscaling approaches and (b) quantify the uncertainty in land-surface states and fluxes due to biases in cross-correlations in downscaled climate forcings. Both these aspects are evaluated using climate forcings available from both historical climate simulations and CMIP5 hindcasts over the entire US. The analytical solution basically relates the univariate regression parameters, co-efficient of determination of regression and the co-variance ratio between GCM and downscaled values. The analytical solutions are compared with the downscaled univariate forcings by choosing the desired p-value (Type-1 error) in preserving the observed cross-correlation. . For quantifying the impacts of biases on cross-correlation on estimating streamflow and groundwater, we corrupt the downscaled climate forcings with different cross-correlation structure.
Examining the Effects of Mosaic Land Cover on Extreme Events in Historical Downscaled WRF Simulations

EPA Science Inventory

The representation of land use and land cover (hereby referred to as “LU”) is a challenging aspect of dynamically downscaled simulations, as a mesoscale model that is utilized as a regional climate model (RCM) may be limited in its ability to represent LU over multi-d...
Very high resolution surface mass balance over Greenland modeled by the regional climate model MAR with a downscaling technique

NASA Astrophysics Data System (ADS)

Kittel, Christoph; Lang, Charlotte; Agosta, Cécile; Prignon, Maxime; Fettweis, Xavier; Erpicum, Michel

2016-04-01

This study presents surface mass balance (SMB) results at 5 km resolution with the regional climate MAR model over the Greenland ice sheet. Here, we use the last MAR version (v3.6) where the land-ice module (SISVAT) using a high resolution grid (5km) for surface variables is fully coupled while the MAR atmospheric module running at a lower resolution of 10km. This online downscaling technique enables to correct near-surface temperature and humidity from MAR by a gradient based on elevation before forcing SISVAT. The 10 km precipitation is not corrected. Corrections are stronger over the ablation zone where topography presents more variations. The model has been force by ERA-Interim between 1979 and 2014. We will show the advantages of using an online SMB downscaling technique in respect to an offline downscaling extrapolation based on local SMB vertical gradients. Results at 5 km show a better agreement with the PROMICE surface mass balance data base than the extrapolated 10 km MAR SMB results.
Streamflow changes in the Sierra Nevada, California, simulated using a statistically downscaled general circulation model scenario of climate change

USGS Publications Warehouse

Wilby, Robert L.; Dettinger, Michael D.

2000-01-01

Simulations of future climate using general circulation models (GCMs) suggest that rising concentrations of greenhouse gases may have significant consequences for the global climate. Of less certainty is the extent to which regional scale (i.e., sub-GCM grid) environmental processes will be affected. In this chapter, a range of downscaling techniques are critiqued. Then a relatively simple (yet robust) statistical downscaling technique and its use in the modelling of future runoff scenarios for three river basins in the Sierra Nevada, California, is described. This region was selected because GCM experiments driven by combined greenhouse-gas and sulphate-aerosol forcings consistently show major changes in the hydro-climate of the southwest United States by the end of the 21st century. The regression-based downscaling method was used to simulate daily rainfall and temperature series for streamflow modelling in three Californian river basins under current-and future-climate conditions. The downscaling involved just three predictor variables (specific humidity, zonal velocity component of airflow, and 500 hPa geopotential heights) supplied by the U.K. Meteorological Office couple ocean-atmosphere model (HadCM2) for the grid point nearest the target basins. When evaluated using independent data, the model showed reasonable skill at reproducing observed area-average precipitation, temperature, and concomitant streamflow variations. Overall, the downscaled data resulted in slight underestimates of mean annual streamflow due to underestimates of precipitation in spring and positive temperature biases in winter. Differences in the skill of simulated streamflows amongst the three basins were attributed to the smoothing effects of snowpack on streamflow responses to climate forcing. The Merced and American River basins drain the western, windward slope of the Sierra Nevada and are snowmelt dominated, whereas the Carson River drains the eastern, leeward slope and is a mix of rainfall runoff and snowmelt runoff. Simulated streamflow in the American River responds rapidly and sensitively to daily-scale temperature and precipitation fluctuations and errors; in the Merced and Carson Rivers, the response to the same short-term influences is much less. Consequently, the skill of simulated flows was significantly lower in the American River model than in the Carson and Merced. The physiography of the three basins also accounts for differences in their sensitivities to future climate change. Increases in winter precipitation exceeding +100% coupled with mean temperature rises greater than +2°C result in increased winter streamflows in all three basins. In the Merced and Carson basins, these streamflow increases reflect large changes in winter snowpack, whereas the streamflow changes in the lower elevation American basin are driven primarily by rainfall runoff. Furthermore, reductions in winter snowpack in the American River basin, owing to less precipitation falling as snow and earlier melting of snow at middle elevations, lead to less spring and summer streamflow. Taken collectively, the downscaling results suggest significant changes to both the timing and magnitude of streamflows in the Sierra Nevada by the end of the 21st Century. In the higher elevation basins, the HadCM2 scenario implies more annual streamflow and more streamflow during the spring and summer months that are critical for water-resources management in California. Depending on the relative significance of rainfall runoff and snowmelt, each basin responds in its own way to regional climate forcing. Generally, then, climate scenarios need to be specified — by whatever means — with sufficient temporal and spatial resolution to capture subtle orographic influences if projections of climate-change responses are to be useful and reproducible.
Using sea surface temperatures to improve performance of single dynamical downscaling model in flood simulation under climate change

NASA Astrophysics Data System (ADS)

Chao, Y.; Cheng, C. T.; Hsiao, Y. H.; Hsu, C. T.; Yeh, K. C.; Liu, P. L.

2017-12-01

There are 5.3 typhoons hit Taiwan per year on average in last decade. Typhoon Morakot in 2009, the most severe typhoon, causes huge damage in Taiwan, including 677 casualties and roughly NT 110 billion (3.3 billion USD) in economic loss. Some researches documented that typhoon frequency will decrease but increase in intensity in western North Pacific region. It is usually preferred to use high resolution dynamical model to get better projection of extreme events; because coarse resolution models cannot simulate intense extreme events. Under that consideration, dynamical downscaling climate data was chosen to describe typhoon satisfactorily, this research used the simulation data from AGCM of Meteorological Research Institute (MRI-AGCM). Considering dynamical downscaling methods consume massive computing power, and typhoon number is very limited in a single model simulation, using dynamical downscaling data could cause uncertainty in disaster risk assessment. In order to improve the problem, this research used four sea surfaces temperatures (SSTs) to increase the climate change scenarios under RCP 8.5. In this way, MRI-AGCMs project 191 extreme typhoons in Taiwan (when typhoon center touches 300 km sea area of Taiwan) in late 21th century. SOBEK, a two dimensions flood simulation model, was used to assess the flood risk under four SSTs climate change scenarios in Tainan, Taiwan. The results show the uncertainty of future flood risk assessment is significantly decreased in Tainan, Taiwan in late 21th century. Four SSTs could efficiently improve the problems of limited typhoon numbers in single model simulation.

Climate change, estuaries and anadromous fish habitat in the northeastern United States: models, downscaling and uncertainty

NASA Astrophysics Data System (ADS)

Muhling, B.; Gaitan, C. F.; Tommasi, D.; Saba, V. S.; Stock, C. A.; Dixon, K. W.

2016-02-01

Estuaries of the northeastern United States provide essential habitat for many anadromous fishes, across a range of life stages. Climate change is likely to impact estuarine environments and habitats through multiple pathways. Increasing air temperatures will result in a warming water column, and potentially increased stratification. In addition, changes to precipitation patterns may alter freshwater inflow dynamics, leading to altered seasonal salinity regimes. However, the spatial resolution of global climate models is generally insufficient to resolve these processes at the scale of individual estuaries. Global models can be downscaled to a regional resolution using a variety of dynamical and statistical methods. In this study, we examined projections of estuarine conditions, and future habitat extent, for several anadromous fishes in the Chesapeake Bay using different statistical downscaling methods. Sources of error from physical and biological models were quantified, and key areas of uncertainty were highlighted. Results suggested that future projections of the distribution and recruitment of species most strongly linked to freshwater flow dynamics had the highest levels of uncertainty. The sensitivity of different life stages to environmental conditions, and the population-level responses of anadromous species to climate change, were identified as important areas for further research.
Linking Global and Regional Models to Simulate U.S. Air Quality in the Year 2050

EPA Science Inventory

The potential impact of global climate change on future air quality in the United States is investigated with global and regional-scale models. Regional climate model scenarios are developed by dynamically downscaling the outputs from a global chemistry and climate model and are...
Development of probabilistic regional climate scenario in East Asia

NASA Astrophysics Data System (ADS)

Dairaku, K.; Ueno, G.; Ishizaki, N. N.

2015-12-01

Climate information and services for Impacts, Adaptation and Vulnerability (IAV) Assessments are of great concern. In order to develop probabilistic regional climate information that represents the uncertainty in climate scenario experiments in East Asia (CORDEX-EA and Japan), the probability distribution of 2m air temperature was estimated by using developed regression model. The method can be easily applicable to other regions and other physical quantities, and also to downscale to finer-scale dependent on availability of observation dataset. Probabilistic climate information in present (1969-1998) and future (2069-2098) climate was developed using CMIP3 SRES A1b scenarios 21 models and the observation data (CRU_TS3.22 & University of Delaware in CORDEX-EA, NIAES AMeDAS mesh data in Japan). The prototype of probabilistic information in CORDEX-EA and Japan represent the quantified structural uncertainties of multi-model ensemble experiments of climate change scenarios. Appropriate combination of statistical methods and optimization of climate ensemble experiments using multi-General Circulation Models (GCMs) and multi-regional climate models (RCMs) ensemble downscaling experiments are investigated.
Application of multi-scale wavelet entropy and multi-resolution Volterra models for climatic downscaling

NASA Astrophysics Data System (ADS)

Sehgal, V.; Lakhanpal, A.; Maheswaran, R.; Khosa, R.; Sridhar, Venkataramana

2018-01-01

This study proposes a wavelet-based multi-resolution modeling approach for statistical downscaling of GCM variables to mean monthly precipitation for five locations at Krishna Basin, India. Climatic dataset from NCEP is used for training the proposed models (Jan.'69 to Dec.'94) and are applied to corresponding CanCM4 GCM variables to simulate precipitation for the validation (Jan.'95-Dec.'05) and forecast (Jan.'06-Dec.'35) periods. The observed precipitation data is obtained from the India Meteorological Department (IMD) gridded precipitation product at 0.25 degree spatial resolution. This paper proposes a novel Multi-Scale Wavelet Entropy (MWE) based approach for clustering climatic variables into suitable clusters using k-means methodology. Principal Component Analysis (PCA) is used to obtain the representative Principal Components (PC) explaining 90-95% variance for each cluster. A multi-resolution non-linear approach combining Discrete Wavelet Transform (DWT) and Second Order Volterra (SoV) is used to model the representative PCs to obtain the downscaled precipitation for each downscaling location (W-P-SoV model). The results establish that wavelet-based multi-resolution SoV models perform significantly better compared to the traditional Multiple Linear Regression (MLR) and Artificial Neural Networks (ANN) based frameworks. It is observed that the proposed MWE-based clustering and subsequent PCA, helps reduce the dimensionality of the input climatic variables, while capturing more variability compared to stand-alone k-means (no MWE). The proposed models perform better in estimating the number of precipitation events during the non-monsoon periods whereas the models with clustering without MWE over-estimate the rainfall during the dry season.
Uncertainty Analysis of Downscaled CMIP5 Precipitation Data for Louisiana, USA

NASA Astrophysics Data System (ADS)

Sumi, S. J.; Tamanna, M.; Chivoiu, B.; Habib, E. H.

2014-12-01

The downscaled CMIP3 and CMIP5 Climate and Hydrology Projections dataset contains fine spatial resolution translations of climate projections over the contiguous United States developed using two downscaling techniques (monthly Bias Correction Spatial Disaggregation (BCSD) and daily Bias Correction Constructed Analogs (BCCA)). The objective of this study is to assess the uncertainty of the CMIP5 downscaled general circulation models (GCM). We performed an analysis of the daily, monthly, seasonal and annual variability of precipitation downloaded from the Downscaled CMIP3 and CMIP5 Climate and Hydrology Projections website for the state of Louisiana, USA at 0.125° x 0.125° resolution. A data set of daily gridded observations of precipitation of a rectangular boundary covering Louisiana is used to assess the validity of 21 downscaled GCMs for the 1950-1999 period. The following statistics are computed using the CMIP5 observed dataset with respect to the 21 models: the correlation coefficient, the bias, the normalized bias, the mean absolute error (MAE), the mean absolute percentage error (MAPE), and the root mean square error (RMSE). A measure of variability simulated by each model is computed as the ratio of its standard deviation, in both space and time, to the corresponding standard deviation of the observation. The correlation and MAPE statistics are also computed for each of the nine climate divisions of Louisiana. Some of the patterns that we observed are: 1) Average annual precipitation rate shows similar spatial distribution for all the models within a range of 3.27 to 4.75 mm/day from Northwest to Southeast. 2) Standard deviation of summer (JJA) precipitation (mm/day) for the models maintains lower value than the observation whereas they have similar spatial patterns and range of values in winter (NDJ). 3) Correlation coefficients of annual precipitation of models against observation have a range of -0.48 to 0.36 with variable spatial distribution by model. 4) Most of the models show negative correlation coefficients in summer and positive in winter. 5) MAE shows similar spatial distribution for all the models within a range of 5.20 to 7.43 mm/day from Northwest to Southeast of Louisiana. 6) Highest values of correlation coefficients are found at seasonal scale within a range of 0.36 to 0.46.
Downscaling Future Climate Change Projections for Water Resource Applications: A Case Study for Mesoamerica (Invited)

NASA Astrophysics Data System (ADS)

Oglesby, R. J.; Rowe, C. M.; Munoz-Arriola, F.

2013-12-01

Mesoamerica is a region that is potentially at severe risk due to future climate change. This is especially true for the water resources required for agriculture, human consumption, and hydroelectric power generation. Yet global climate models cannot properly resolve surface climate in the region, due to it's complex topography and nearness to oceans. Precipitation in particular is poorly handled. Further, Mesoamerica is hardly the only region worldwide for which these issues exist. To address this deficiency, a series of high-resolution (4-12 km) dynamical downscaling simulations of future climate change between now and 2060 have been made for Mesoamerica and the Caribbean. We used the Weather Research and Forecasting (WRF) regional climate model to downscale results from the NCAR CCSM4 CMIP5 RCP8.5 global simulation. The entire region is covered at 12 km horizontal spatial resolution, with as much as possible (especially in mountainous regions) at 4 km. We compare a control period (2006-2010) with 50 years into the future (2056-2060). Basic results for surface climate will be presented, as well as a developing strategy for explicitly employing these results in projecting the implications for water resources in the region. Connections will also be made to other regions around the globe that could benefit from this type of integrated modeling and analysis.
A Statistical Bias Correction Tool for Generating Climate Change Scenarios in Indonesia based on CMIP5 Datasets

NASA Astrophysics Data System (ADS)

Faqih, A.

2017-03-01

Providing information regarding future climate scenarios is very important in climate change study. The climate scenario can be used as basic information to support adaptation and mitigation studies. In order to deliver future climate scenarios over specific region, baseline and projection data from the outputs of global climate models (GCM) is needed. However, due to its coarse resolution, the data have to be downscaled and bias corrected in order to get scenario data with better spatial resolution that match the characteristics of the observed data. Generating this downscaled data is mostly difficult for scientist who do not have specific background, experience and skill in dealing with the complex data from the GCM outputs. In this regards, it is necessary to develop a tool that can be used to simplify the downscaling processes in order to help scientist, especially in Indonesia, for generating future climate scenario data that can be used for their climate change-related studies. In this paper, we introduce a tool called as “Statistical Bias Correction for Climate Scenarios (SiBiaS)”. The tool is specially designed to facilitate the use of CMIP5 GCM data outputs and process their statistical bias corrections relative to the reference data from observations. It is prepared for supporting capacity building in climate modeling in Indonesia as part of the Indonesia 3rd National Communication (TNC) project activities.
Dynamical Downscaling of Global Circulation Models With the Weather Research and Forecast Model in the Northern Great Plains

NASA Astrophysics Data System (ADS)

Burtch, D.; Mullendore, G. L.; Kennedy, A. D.; Simms, M.; Kirilenko, A.; Coburn, J.

2015-12-01

Understanding the impacts of global climate change on regional scales is crucial for accurate decision-making by state and local governments. This is especially true in North Dakota, where climate change can have significant consequences on agriculture, its traditionally strongest economic sector. This region of the country shows a high variability in precipitation, especially in the summer months and so the focus of this study is on warm season processes over decadal time scales. The Weather Research and Forecast (WRF) model is used to dynamically downscale two Global Circulation Models (GCMs) from the CMIP5 ensemble in order to determine the microphysical parameterization and nudging techniques (spectral or analysis) best suited for this region. The downscaled domain includes the entirety of North Dakota at a horizontal resolution of 5 km. In addition, smaller domains of 1 km horizontal resolution are centered over regions of focused hydrological importance. The dynamically downscaled simulations are compared with both gridded observational data and statistically downscaled data to evaluate the performance of the simulations. Preliminary results have shown a marked difference between the two downscaled GCMs in terms of temperature and precipitation bias. Choice of microphysical parameterization has not shown to create any significant differences in the temperature fields. However, the precipitation fields do appear to be most affected by the microphysical parameterization, regardless of the choice of GCM. Implications on the unique water resource challenges faced in this region will also be discussed.
A simple technique for obtaining future climate data inputs for natural resource models

USDA-ARS?s Scientific Manuscript database

Those conducting impact studies using natural resource models need to be able to quickly and easily obtain downscaled future climate data from multiple models, scenarios, and timescales for multiple locations. This paper describes a method of quickly obtaining future climate data over a wide range o...
Application of Climate Assessment Tool (CAT) to estimate climate variability impacts on nutrient loading from local watersheds

Treesearch

Ying Ouyang; Prem B. Parajuli; Gary Feng; Theodor D. Leininger; Yongshan Wan; Padmanava Dash

2018-01-01

A vast amount of future climate scenario datasets, created by climate models such as general circulation models (GCMs), have been used in conjunction with watershed models to project future climate variability impact on hydrological processes and water quality. However, these low spatial-temporal resolution datasets are often difficult to downscale spatially and...
Analyzing the future climate change of Upper Blue Nile River basin using statistical downscaling techniques

NASA Astrophysics Data System (ADS)

Fenta Mekonnen, Dagnenet; Disse, Markus

2018-04-01

Climate change is becoming one of the most threatening issues for the world today in terms of its global context and its response to environmental and socioeconomic drivers. However, large uncertainties between different general circulation models (GCMs) and coarse spatial resolutions make it difficult to use the outputs of GCMs directly, especially for sustainable water management at regional scale, which introduces the need for downscaling techniques using a multimodel approach. This study aims (i) to evaluate the comparative performance of two widely used statistical downscaling techniques, namely the Long Ashton Research Station Weather Generator (LARS-WG) and the Statistical Downscaling Model (SDSM), and (ii) to downscale future climate scenarios of precipitation, maximum temperature (Tmax) and minimum temperature (Tmin) of the Upper Blue Nile River basin at finer spatial and temporal scales to suit further hydrological impact studies. The calibration and validation result illustrates that both downscaling techniques (LARS-WG and SDSM) have shown comparable and good ability to simulate the current local climate variables. Further quantitative and qualitative comparative performance evaluation was done by equally weighted and varying weights of statistical indexes for precipitation only. The evaluation result showed that SDSM using the canESM2 CMIP5 GCM was able to reproduce more accurate long-term mean monthly precipitation but LARS-WG performed best in capturing the extreme events and distribution of daily precipitation in the whole data range. Six selected multimodel CMIP3 GCMs, namely HadCM3, GFDL-CM2.1, ECHAM5-OM, CCSM3, MRI-CGCM2.3.2 and CSIRO-MK3 GCMs, were used for downscaling climate scenarios by the LARS-WG model. The result from the ensemble mean of the six GCM showed an increasing trend for precipitation, Tmax and Tmin. The relative change in precipitation ranged from 1.0 to 14.4 % while the change for mean annual Tmax may increase from 0.4 to 4.3 °C and the change for mean annual Tmin may increase from 0.3 to 4.1 °C. The individual result of the HadCM3 GCM has a good agreement with the ensemble mean result. HadCM3 from CMIP3 using A2a and B2a scenarios and canESM2 from CMIP5 GCMs under RCP2.6, RCP4.5 and RCP8.5 scenarios were downscaled by SDSM. The result from the two GCMs under five different scenarios agrees with the increasing direction of three climate variables (precipitation, Tmax and Tmin). The relative change of the downscaled mean annual precipitation ranges from 2.1 to 43.8 % while the change for mean annual Tmax and Tmin may increase in the range from 0.4 to 2.9 °C and from 0.3 to 1.6 °C respectively.
TopoSCALE v.1.0: downscaling gridded climate data in complex terrain

NASA Astrophysics Data System (ADS)

Fiddes, J.; Gruber, S.

2014-02-01

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).
Mapping past, present, and future climatic suitability for invasive Aedes aegypti and Aedes albopictus in the United States: a process-based modeling approach using CMIP5 downscaled climate scenarios

NASA Astrophysics Data System (ADS)

Donnelly, M. A. P.; Marcantonio, M.; Melton, F. S.; Barker, C. M.

2016-12-01

The ongoing spread of the mosquitoes, Aedes aegypti and Aedes albopictus, in the continental United States leaves new areas at risk for local transmission of dengue, chikungunya, and Zika viruses. All three viruses have caused major disease outbreaks in the Americas with infected travelers returning regularly to the U.S. The expanding range of these mosquitoes raises questions about whether recent spread has been enabled by climate change or other anthropogenic influences. In this analysis, we used downscaled climate scenarios from the NASA Earth Exchange Global Daily Downscaled Projections (NEX GDDP) dataset to model Ae. aegypti and Ae. albopictus population growth rates across the United States. We used a stage-structured matrix population model to understand past and present climatic suitability for these vectors, and to project future suitability under CMIP5 climate change scenarios. Our results indicate that much of the southern U.S. is suitable for both Ae. aegypti and Ae. albopictus year-round. In addition, a large proportion of the U.S. is seasonally suitable for mosquito population growth, creating the potential for periodic incursions into new areas. Changes in climatic suitability in recent decades for Ae. aegypti and Ae. albopictus have occurred already in many regions of the U.S., and model projections of future climate suggest that climate change will continue to reshape the range of Ae. aegypti and Ae. albopictus in the U.S., and potentially the risk of the viruses they transmit.
Mapping Past, Present, and Future Climatic Suitability for Invasive Aedes Aegypti and Aedes Albopictus in the United States: A Process-Based Modeling Approach Using CMIP5 Downscaled Climate Scenarios

NASA Technical Reports Server (NTRS)

Donnelly, Marisa Anne Pella; Marcantonio, Matteo; Melton, Forrest S.; Barker, Christopher M.

2016-01-01

The ongoing spread of the mosquitoes, Aedes aegypti and Aedes albopictus, in the continental United States leaves new areas at risk for local transmission of dengue, chikungunya, and Zika viruses. All three viruses have caused major disease outbreaks in the Americas with infected travelers returning regularly to the U.S. The expanding range of these mosquitoes raises questions about whether recent spread has been enabled by climate change or other anthropogenic influences. In this analysis, we used downscaled climate scenarios from the NASA Earth Exchange Global Daily Downscaled Projections (NEX GDDP) dataset to model Ae. aegypti and Ae. albopictus population growth rates across the United States. We used a stage-structured matrix population model to understand past and present climatic suitability for these vectors, and to project future suitability under CMIP5 climate change scenarios. Our results indicate that much of the southern U.S. is suitable for both Ae. aegypti and Ae. albopictus year-round. In addition, a large proportion of the U.S. is seasonally suitable for mosquito population growth, creating the potential for periodic incursions into new areas. Changes in climatic suitability in recent decades for Ae. aegypti and Ae. albopictus have occurred already in many regions of the U.S., and model projections of future climate suggest that climate change will continue to reshape the range of Ae. aegypti and Ae. albopictus in the U.S., and potentially the risk of the viruses they transmit.
Statistical downscaling of precipitation using long short-term memory recurrent neural networks

NASA Astrophysics Data System (ADS)

Misra, Saptarshi; Sarkar, Sudeshna; Mitra, Pabitra

2017-11-01

Hydrological impacts of global climate change on regional scale are generally assessed by downscaling large-scale climatic variables, simulated by General Circulation Models (GCMs), to regional, small-scale hydrometeorological variables like precipitation, temperature, etc. In this study, we propose a new statistical downscaling model based on Recurrent Neural Network with Long Short-Term Memory which captures the spatio-temporal dependencies in local rainfall. The previous studies have used several other methods such as linear regression, quantile regression, kernel regression, beta regression, and artificial neural networks. Deep neural networks and recurrent neural networks have been shown to be highly promising in modeling complex and highly non-linear relationships between input and output variables in different domains and hence we investigated their performance in the task of statistical downscaling. We have tested this model on two datasets—one on precipitation in Mahanadi basin in India and the second on precipitation in Campbell River basin in Canada. Our autoencoder coupled long short-term memory recurrent neural network model performs the best compared to other existing methods on both the datasets with respect to temporal cross-correlation, mean squared error, and capturing the extremes.
Hydrologic response to multimodel climate output using a physically based model of groundwater/surface water interactions

NASA Astrophysics Data System (ADS)

Sulis, M.; Paniconi, C.; Marrocu, M.; Huard, D.; Chaumont, D.

2012-12-01

General circulation models (GCMs) are the primary instruments for obtaining projections of future global climate change. Outputs from GCMs, aided by dynamical and/or statistical downscaling techniques, have long been used to simulate changes in regional climate systems over wide spatiotemporal scales. Numerous studies have acknowledged the disagreements between the various GCMs and between the different downscaling methods designed to compensate for the mismatch between climate model output and the spatial scale at which hydrological models are applied. Very little is known, however, about the importance of these differences once they have been input or assimilated by a nonlinear hydrological model. This issue is investigated here at the catchment scale using a process-based model of integrated surface and subsurface hydrologic response driven by outputs from 12 members of a multimodel climate ensemble. The data set consists of daily values of precipitation and min/max temperatures obtained by combining four regional climate models and five GCMs. The regional scenarios were downscaled using a quantile scaling bias-correction technique. The hydrologic response was simulated for the 690 km2des Anglais catchment in southwestern Quebec, Canada. The results show that different hydrological components (river discharge, aquifer recharge, and soil moisture storage) respond differently to precipitation and temperature anomalies in the multimodel climate output, with greater variability for annual discharge compared to recharge and soil moisture storage. We also find that runoff generation and extreme event-driven peak hydrograph flows are highly sensitive to any uncertainty in climate data. Finally, the results show the significant impact of changing sequences of rainy days on groundwater recharge fluxes and the influence of longer dry spells in modifying soil moisture spatial variability.
Developing perturbations for Climate Change Impact Assessments

NASA Astrophysics Data System (ADS)

Hewitson, Bruce

Following the 2001 Intergovernmental Panel on Climate Change (IPCC) Third Assessment Report [TAR; IPCC, 2001], and the paucity of climate change impact assessments from developing nations, there has been a significant growth in activities to redress this shortcoming. However, undertaking impact assessments (in relation to malaria, crop stress, regional water supply, etc.) is contingent on available climate-scale scenarios at time and space scales of relevance to the regional issues of importance. These scales are commonly far finer than even the native resolution of the Global Climate Models (GCMs) (the principal tools for climate change research), let alone the skillful resolution (scales of aggregation at which GCM observational error is acceptable for a given application) of GCMs.Consequently, there is a growing demand for regional-scale scenarios, which in turn are reliant on techniques to downscale from GCMs, such as empirical downscaling or nested Regional Climate Models (RCMs). These methods require significant skill, experiential knowledge, and computational infrastructure in order to derive credible regional-scale scenarios. In contrast, it is often the case that impact assessment researchers in developing nations have inadequate resources with limited access to scientists in the broader international scientific community who have the time and expertise to assist. However, where developing effective downscaled scenarios is problematic, it is possible that much useful information can still be obtained for impact assessments by examining the system sensitivity to largerscale climate perturbations. Consequently, one may argue that the early phase of assessing sensitivity and vulnerability should first be characterized by evaluation of the first-order impacts, rather than immediately addressing the finer, secondary factors that are dependant on scenarios derived through downscaling.
High-resolution dynamical downscaling of the future Alpine climate

NASA Astrophysics Data System (ADS)

Bozhinova, Denica; José Gómez-Navarro, Juan; Raible, Christoph

2017-04-01

The Alpine region and Switzerland is a challenging area for simulating and analysing Global Climate Model (GCM) results. This is mostly due to the combination of a very complex topography and the still rather coarse horizontal resolution of current GCMs, in which not all of the many-scale processes that drive the local weather and climate can be resolved. In our study, the Weather Research and Forecasting (WRF) model is used to dynamically downscale a GCM simulation to a resolution as high as 2 km x 2 km. WRF is driven by initial and boundary conditions produced with the Community Earth System Model (CESM) for the recent past (control run) and until 2100 using the RCP8.5 climate scenario (future run). The control run downscaled with WRF covers the period 1976-2005, while the future run investigates a 20-year-slice simulated for the 2080-2099. We compare the control WRF-CESM simulations to an observational product provided by MeteoSwiss and an additional WRF simulation driven by the ERA-Interim reanalysis, to estimate the bias that is introduced by the extra modelling step of our framework. Several bias-correction methods are evaluated, including a quantile mapping technique, to ameliorate the bias in the control WRF-CESM simulation. In the next step of our study these corrections are applied to our future WRF-CESM run. The resulting downscaled and bias-corrected data is analysed for the properties of precipitation and wind speed in the future climate. Our special interest focuses on the absolute quantities simulated for these meteorological variables as these are used to identify extreme events, such as wind storms and situations that can lead to floods.
Climate change impacts on the Lehman-Baker Creek drainage in the Great Basin National Park

NASA Astrophysics Data System (ADS)

Volk, J. M.

2013-12-01

Global climate models (GCMs) forced by increased CO2 emissions forecast anomalously dry and warm trends over the southwestern U.S. for the 21st century. The effect of warmer conditions may result in decreased surface water resources within the Great Basin physiographic region critical for ecology, irrigation and municipal water supply. Here we use downscaled GCM output from the A2 and B1 greenhouse gas emission scenarios to force a Precipitation-Runoff Modeling System (PRMS) watershed model developed for the Lehman and Baker Creeks Drainage (LBCD) in the Great Basin National Park, NV for a century long time period. The goal is to quantify the effects of rising temperature to the water budget in the LBCD at monthly and annual timescales. Dynamically downscaled GCM projections are attained from the NSF EPSCoR Nevada Infrastructure for Climate Change Science, Education, and Outreach project and statistically downscaled output is retrieved from the "U.S. Bias Corrected and Downscaled WCRP CMIP3 Climate Projections". Historical daily climate and streamflow data have been collected simultaneously for periods extending 20 years or longer. Mann-Kendal trend test results showed a statistically significant (α= 0.05) long-term rising trend from 1895 to 2012 in annual and monthly average temperatures for the study area. A grid-based, PRMS watershed model of the LBCD has been created within ArcGIS 10, and physical parameters have been estimated at a spatial resolution of 100m. Simulation results will be available soon. Snow cover is expected to decrease and peak runoff to occur earlier in the spring, resulting in increased runoff, decreased infiltration/recharge, decreased baseflows, and decreased evapo-transpiration.
Downscaling Global Land Cover Projections from an Integrated Assessment Model for Use in Regional Analyses: Results and Evaluation for the US from 2005 to 2095

DOE Office of Scientific and Technical Information (OSTI.GOV)

West, Tristram O.; Le Page, Yannick LB; Huang, Maoyi

2014-06-05

Projections of land cover change generated from Integrated Assessment Models (IAM) and other economic-based models can be applied for analyses of environmental impacts at subregional and landscape scales. For those IAM and economic models that project land use at the sub-continental or regional scale, these projections must be downscaled and spatially distributed prior to use in climate or ecosystem models. Downscaling efforts to date have been conducted at the national extent with relatively high spatial resolution (30m) and at the global extent with relatively coarse spatial resolution (0.5 degree).

EFFECTS OF CLIMATE CHANGE ON WEATHER AND WATER

EPA Science Inventory

Information regarding weather and hydrological processes and how they may change in the future is available from a variety of dynamically downscaled climate models. Current studies are helping to improve the use of such models for regional climate impact studies by testing the s...
Statistical Downscaling of General Circulation Model Outputs to Precipitation Accounting for Non-Stationarities in Predictor-Predictand Relationships

PubMed Central

Sachindra, D. A.; Perera, B. J. C.

2016-01-01

This paper presents a novel approach to incorporate the non-stationarities characterised in the GCM outputs, into the Predictor-Predictand Relationships (PPRs) in statistical downscaling models. In this approach, a series of 42 PPRs based on multi-linear regression (MLR) technique were determined for each calendar month using a 20-year moving window moved at a 1-year time step on the predictor data obtained from the NCEP/NCAR reanalysis data archive and observations of precipitation at 3 stations located in Victoria, Australia, for the period 1950–2010. Then the relationships between the constants and coefficients in the PPRs and the statistics of reanalysis data of predictors were determined for the period 1950–2010, for each calendar month. Thereafter, using these relationships with the statistics of the past data of HadCM3 GCM pertaining to the predictors, new PPRs were derived for the periods 1950–69, 1970–89 and 1990–99 for each station. This process yielded a non-stationary downscaling model consisting of a PPR per calendar month for each of the above three periods for each station. The non-stationarities in the climate are characterised by the long-term changes in the statistics of the climate variables and above process enabled relating the non-stationarities in the climate to the PPRs. These new PPRs were then used with the past data of HadCM3, to reproduce the observed precipitation. It was found that the non-stationary MLR based downscaling model was able to produce more accurate simulations of observed precipitation more often than conventional stationary downscaling models developed with MLR and Genetic Programming (GP). PMID:27997609
Statistical Downscaling of General Circulation Model Outputs to Precipitation Accounting for Non-Stationarities in Predictor-Predictand Relationships.

PubMed

Sachindra, D A; Perera, B J C

2016-01-01

This paper presents a novel approach to incorporate the non-stationarities characterised in the GCM outputs, into the Predictor-Predictand Relationships (PPRs) in statistical downscaling models. In this approach, a series of 42 PPRs based on multi-linear regression (MLR) technique were determined for each calendar month using a 20-year moving window moved at a 1-year time step on the predictor data obtained from the NCEP/NCAR reanalysis data archive and observations of precipitation at 3 stations located in Victoria, Australia, for the period 1950-2010. Then the relationships between the constants and coefficients in the PPRs and the statistics of reanalysis data of predictors were determined for the period 1950-2010, for each calendar month. Thereafter, using these relationships with the statistics of the past data of HadCM3 GCM pertaining to the predictors, new PPRs were derived for the periods 1950-69, 1970-89 and 1990-99 for each station. This process yielded a non-stationary downscaling model consisting of a PPR per calendar month for each of the above three periods for each station. The non-stationarities in the climate are characterised by the long-term changes in the statistics of the climate variables and above process enabled relating the non-stationarities in the climate to the PPRs. These new PPRs were then used with the past data of HadCM3, to reproduce the observed precipitation. It was found that the non-stationary MLR based downscaling model was able to produce more accurate simulations of observed precipitation more often than conventional stationary downscaling models developed with MLR and Genetic Programming (GP).
Projecting water yield and ecosystem productivity across the United States by linking an ecohydrological model to WRF dynamically downscaled climate data

Treesearch

Shanlei Sun; Ge Sun; Erika Cohen Mack; Steve McNulty; Peter V. Caldwell; Kai Duan; Yang Zhang

2016-01-01

Quantifying the potential impacts of climatechange on water yield and ecosystem productivity is essentialÂ to developing sound watershed restoration plans, andecosystem adaptation and mitigation strategies. This studyÂ links an ecohydrological model (Water Supply and StressIndex, WaSSI) with WRF (Weather Research and ForecastingÂ Model) using dynamically downscaled...
Dynamical Downscaling of Meteorology from a Global Model by WRF towards Resolving US PM2.5 Distributions for the Mid 21st Century

NASA Astrophysics Data System (ADS)

Kunwar, S.; Bowden, J.; Milly, G.; Previdi, M. J.; Fiore, A. M.; West, J. J.

2017-12-01

In the coming decades, anthropogenically induced climate change will likely impact PM2.5 through both changing meteorology and feedback in natural emissions. A major goal of our project is to assess changes in PM2.5 levels over the continental US due to climate variability and change for the period 2005-2065. We will achieve this by using regional models to dynamically downscale coarse resolution (20 × 20) meteorology and air chemistry from a global model to finer spatial resolution (12 km), improving air quality projections for regions and subregions of the US (NE, SE, SW, NW, Midwest, Intermountain West). We downscale from GFDL CM3 simulations of the RCP8.5 scenario for the years 2006-2100 with aerosol and ozone precursor emissions fixed at 2005 levels. We carefully select model years from the global simulations that sample the range of PM2.5 distributions for different US regions at mid 21st century (2050-2065). Here we will show results for the meteorological downscaling (using WRF version 3.8.1) for this project, including a performance evaluation for meteorological variables with respect to the global model. In the future, the downscaled meteorology presented here will be used to drive air quality downscaling in CMAQ (version 5.2). Analysis of the resulting PM2.5 statistics for US regions, as well as the drivers for PM2.5 changes, will be important in supporting informed policies for air quality (also health and visibility) planning for different US regions for the next five decades.
Calculating distributed glacier mass balance for the Swiss Alps from RCM output: Development and testing of downscaling and validation methods

NASA Astrophysics Data System (ADS)

Machguth, H.; Paul, F.; Kotlarski, S.; Hoelzle, M.

2009-04-01

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.
Contribution of crop model structure, parameters and climate projections to uncertainty in climate change impact assessments.

PubMed

Tao, Fulu; Rötter, Reimund P; Palosuo, Taru; Gregorio Hernández Díaz-Ambrona, Carlos; Mínguez, M Inés; Semenov, Mikhail A; Kersebaum, Kurt Christian; Nendel, Claas; Specka, Xenia; Hoffmann, Holger; Ewert, Frank; Dambreville, Anaelle; Martre, Pierre; Rodríguez, Lucía; Ruiz-Ramos, Margarita; Gaiser, Thomas; Höhn, Jukka G; Salo, Tapio; Ferrise, Roberto; Bindi, Marco; Cammarano, Davide; Schulman, Alan H

2018-03-01

Climate change impact assessments are plagued with uncertainties from many sources, such as climate projections or the inadequacies in structure and parameters of the impact model. Previous studies tried to account for the uncertainty from one or two of these. Here, we developed a triple-ensemble probabilistic assessment using seven crop models, multiple sets of model parameters and eight contrasting climate projections together to comprehensively account for uncertainties from these three important sources. We demonstrated the approach in assessing climate change impact on barley growth and yield at Jokioinen, Finland in the Boreal climatic zone and Lleida, Spain in the Mediterranean climatic zone, for the 2050s. We further quantified and compared the contribution of crop model structure, crop model parameters and climate projections to the total variance of ensemble output using Analysis of Variance (ANOVA). Based on the triple-ensemble probabilistic assessment, the median of simulated yield change was -4% and +16%, and the probability of decreasing yield was 63% and 31% in the 2050s, at Jokioinen and Lleida, respectively, relative to 1981-2010. The contribution of crop model structure to the total variance of ensemble output was larger than that from downscaled climate projections and model parameters. The relative contribution of crop model parameters and downscaled climate projections to the total variance of ensemble output varied greatly among the seven crop models and between the two sites. The contribution of downscaled climate projections was on average larger than that of crop model parameters. This information on the uncertainty from different sources can be quite useful for model users to decide where to put the most effort when preparing or choosing models or parameters for impact analyses. We concluded that the triple-ensemble probabilistic approach that accounts for the uncertainties from multiple important sources provide more comprehensive information for quantifying uncertainties in climate change impact assessments as compared to the conventional approaches that are deterministic or only account for the uncertainties from one or two of the uncertainty sources. © 2017 John Wiley & Sons Ltd.
Regional climate projections for the MENA-CORDEX domain: analysis of projected temperature and precipitation changes

NASA Astrophysics Data System (ADS)

Hänsler, Andreas; Weber, Torsten; Eggert, Bastian; Saeed, Fahad; Jacob, Daniela

2014-05-01

Within the CORDEX initiative a multi-model suite of regionalized climate change information will be made available for several regions of the world. The German Climate Service Center (CSC) is taking part in this initiative by applying the regional climate model REMO to downscale global climate projections of different coupled general circulation models (GCMs) for several CORDEX domains. Also for the MENA-CORDEX domain, a set of regional climate change projections has been established at the CSC by downscaling CMIP5 projections of the Max-Planck-Institute Earth System Model (MPI-ESM) for the scenarios RCP4.5 and RCP8.5 with the regional model REMO for the time period from 1950 to 2100 to a horizontal resolution of 0.44 degree. In this study we investigate projected changes in future climate conditions over the domain towards the end of the 21st century. Focus in the analysis is given to projected changes in the temperature and rainfall characteristics and their differences for the two scenarios will be highlighted.
On the impact of using downscaled reanalysis data instead of direct measurements for modeling the mass balance of a tropical glacier (Cordillera Blanca, Peru)

NASA Astrophysics Data System (ADS)

Galos, Stephan; Hofer, Marlis; Marzeion, Ben; Mölg, Thomas; Großhauser, Martin

2013-04-01

Due to their setting, tropical glaciers are sensitive indicators of mid-tropospheric meteorological variability and climate change. Furthermore these glaciers are of particular interest because they respond faster to climatic changes than glaciers located in mid- or high-latitudes. As long-term direct meteorological measurements in such remote environments are scarce, reanalysis data (e.g. ERA-Interim) provide a highly valuable source of information. Reanalysis datasets (i) enable a temporal extension of data records gained by direct measurements and (ii) provide information from regions where direct measurements are not available. In order to properly derive the physical exchange processes between glaciers and atmosphere from reanalysis data, downscaling procedures are required. In the present study we investigate if downscaled atmospheric variables (air temperature and relative humidity) from a reanalysis dataset can be used as input for a physically based, high resolution energy and mass balance model. We apply a well validated empirical-statistical downscaling model, fed with ERA-Interim data, to an automated weather station (AWS) on the surface of Glaciar Artesonraju (8.96° S | 77.63° W). The downscaled data is then used to replace measured air temperature and relative humidity in the input for the energy and mass balance model, which was calibrated using ablation data from stakes and a sonic ranger. In order to test the sensitivity of the modeled mass balance to the downscaled data, the results are compared to a reference model run driven solely with AWS data as model input. We finally discuss the results and present future perspectives for further developing this method.
Sensitivity of the atmospheric water cycle to corrections of the sea surface temperature bias over southern Africa in a regional climate model

NASA Astrophysics Data System (ADS)

Weber, Torsten; Haensler, Andreas; Jacob, Daniela

2017-12-01

Regional climate models (RCMs) have been used to dynamically downscale global climate projections at high spatial and temporal resolution in order to analyse the atmospheric water cycle. In southern Africa, precipitation pattern were strongly affected by the moisture transport from the southeast Atlantic and southwest Indian Ocean and, consequently, by their sea surface temperatures (SSTs). However, global ocean models often have deficiencies in resolving regional to local scale ocean currents, e.g. in ocean areas offshore the South African continent. By downscaling global climate projections using RCMs, the biased SSTs from the global forcing data were introduced to the RCMs and affected the results of regional climate projections. In this work, the impact of the SST bias correction on precipitation, evaporation and moisture transport were analysed over southern Africa. For this analysis, several experiments were conducted with the regional climate model REMO using corrected and uncorrected SSTs. In these experiments, a global MPI-ESM-LR historical simulation was downscaled with the regional climate model REMO to a high spatial resolution of 50 × 50 km2 and of 25 × 25 km2 for southern Africa using a double-nesting method. The results showed a distinct impact of the corrected SST on the moisture transport, the meridional vertical circulation and on the precipitation pattern in southern Africa. Furthermore, it was found that the experiment with the corrected SST led to a reduction of the wet bias over southern Africa and to a better agreement with observations as without SST bias corrections.
High-resolution projections of 21st century climate over the Athabasca River Basin through an integrated evaluation-classification-downscaling-based climate projection framework

NASA Astrophysics Data System (ADS)

Cheng, Guanhui; Huang, Guohe; Dong, Cong; Zhu, Jinxin; Zhou, Xiong; Yao, Y.

2017-03-01

An evaluation-classification-downscaling-based climate projection (ECDoCP) framework is developed to fill a methodological gap of general circulation models (GCMs)-driven statistical-downscaling-based climate projections. ECDoCP includes four interconnected modules: GCM evaluation, climate classification, statistical downscaling, and climate projection. Monthly averages of daily minimum (Tmin) and maximum (Tmax) temperature and daily cumulative precipitation (Prec) over the Athabasca River Basin (ARB) at a 10 km resolution in the 21st century under four Representative Concentration Pathways (RCPs) are projected through ECDoCP. At the octodecadal scale, temperature and precipitation would increase; after bias correction, temperature would increase with a decreased increment, while precipitation would increase only under RCP 8.5. Interannual variability of climate anomalies would increase from RCPs 4.5, 2.6, 6.0 to 8.5 for temperature and from RCPs 2.6, 4.5, 6.0 to 8.5 for precipitation. Bidecadal averaged climate anomalies would decrease from December-January-February (DJF), March-April-May (MAM), September-October-November (SON) to June-July-August (JJA) for Tmin, from DJF, SON, MAM to JJA for Tmax, and from JJA, MAM, SON to DJF for Prec. Climate projection uncertainties would decrease in May to September for temperature and in November to April for precipitation. Spatial climatic variability would not obviously change with RCPs; climatic anomalies are highly correlated with climate-variable magnitudes. Climate anomalies would decrease from upstream to downstream for temperature, and precipitation would follow an opposite pattern. The north end and the other zones would have colder and warmer days, respectively; precipitation would decrease in the upstream and increase in the remaining region. Climate changes might lead to issues, e.g., accelerated glacier/snow melting, deserving attentions of researchers and the public.
Projecting Climate Change Impacts on Wildfire Probabilities

NASA Astrophysics Data System (ADS)

Westerling, A. L.; Bryant, B. P.; Preisler, H.

2008-12-01

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.
Understanding the joint behavior of temperature and precipitation for climate change impact studies

NASA Astrophysics Data System (ADS)

Rana, Arun; Moradkhani, Hamid; Qin, Yueyue

2017-07-01

The multiple downscaled scenario products allow us to assess the uncertainty of the variations of precipitation and temperature in the current and future periods. Probabilistic assessments of both climatic variables help better understand the interdependence of the two and thus, in turn, help in assessing the future with confidence. In the present study, we use ensemble of statistically downscaled precipitation and temperature from various models. The dataset used is multi-model ensemble of 10 global climate models (GCMs) downscaled product from CMIP5 daily dataset using the Bias Correction and Spatial Downscaling (BCSD) technique, generated at Portland State University. The multi-model ensemble of both precipitation and temperature is evaluated for dry and wet periods for 10 sub-basins across Columbia River Basin (CRB). Thereafter, copula is applied to establish the joint distribution of two variables on multi-model ensemble data. The joint distribution is then used to estimate the change in trends of said variables in future, along with estimation of the probabilities of the given change. The joint distribution trends vary, but certainly positive, for dry and wet periods in sub-basins of CRB. Dry season, generally, is indicating a higher positive change in precipitation than temperature (as compared to historical) across sub-basins with wet season inferring otherwise. Probabilities of changes in future, as estimated from the joint distribution, indicate varied degrees and forms during dry season whereas the wet season is rather constant across all the sub-basins.
European extra-tropical storm damage risk from a multi-model ensemble of dynamically-downscaled global climate models

NASA Astrophysics Data System (ADS)

Haylock, M. R.

2011-10-01

Uncertainty in the return levels of insured loss from European wind storms was quantified using storms derived from twenty-two 25 km regional climate model runs driven by either the ERA40 reanalyses or one of four coupled atmosphere-ocean global climate models. Storms were identified using a model-dependent storm severity index based on daily maximum 10 m wind speed. The wind speed from each model was calibrated to a set of 7 km historical storm wind fields using the 70 storms with the highest severity index in the period 1961-2000, employing a two stage calibration methodology. First, the 25 km daily maximum wind speed was downscaled to the 7 km historical model grid using the 7 km surface roughness length and orography, also adopting an empirical gust parameterisation. Secondly, downscaled wind gusts were statistically scaled to the historical storms to match the geographically-dependent cumulative distribution function of wind gust speed. The calibrated wind fields were run through an operational catastrophe reinsurance risk model to determine the return level of loss to a European population density-derived property portfolio. The risk model produced a 50-yr return level of loss of between 0.025% and 0.056% of the total insured value of the portfolio.
Sub-daily Statistical Downscaling of Meteorological Variables Using Neural Networks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kumar, Jitendra; Brooks, Bjørn-Gustaf J.; Thornton, Peter E

2012-01-01

A new open source neural network temporal downscaling model is described and tested using CRU-NCEP reanal ysis and CCSM3 climate model output. We downscaled multiple meteorological variables in tandem from monthly to sub-daily time steps while also retaining consistent correlations between variables. We found that our feed forward, error backpropagation approach produced synthetic 6 hourly meteorology with biases no greater than 0.6% across all variables and variance that was accurate within 1% for all variables except atmospheric pressure, wind speed, and precipitation. Correlations between downscaled output and the expected (original) monthly means exceeded 0.99 for all variables, which indicates thatmore » this approach would work well for generating atmospheric forcing data consistent with mass and energy conserved GCM output. Our neural network approach performed well for variables that had correlations to other variables of about 0.3 and better and its skill was increased by downscaling multiple correlated variables together. Poor replication of precipitation intensity however required further post-processing in order to obtain the expected probability distribution. The concurrence of precipitation events with expected changes in sub ordinate variables (e.g., less incident shortwave radiation during precipitation events) were nearly as consistent in the downscaled data as in the training data with probabilities that differed by no more than 6%. Our downscaling approach requires training data at the target time step and relies on a weak assumption that climate variability in the extrapolated data is similar to variability in the training data.« less
Combining Statistics and Physics to Improve Climate Downscaling

NASA Astrophysics Data System (ADS)

Gutmann, E. D.; Eidhammer, T.; Arnold, J.; Nowak, K.; Clark, M. P.

2017-12-01

Getting useful information from climate models is an ongoing problem that has plagued climate science and hydrologic prediction for decades. While it is possible to develop statistical corrections for climate models that mimic current climate almost perfectly, this does not necessarily guarantee that future changes are portrayed correctly. In contrast, convection permitting regional climate models (RCMs) have begun to provide an excellent representation of the regional climate system purely from first principles, providing greater confidence in their change signal. However, the computational cost of such RCMs prohibits the generation of ensembles of simulations or long time periods, thus limiting their applicability for hydrologic applications. Here we discuss a new approach combining statistical corrections with physical relationships for a modest computational cost. We have developed the Intermediate Complexity Atmospheric Research model (ICAR) to provide a climate and weather downscaling option that is based primarily on physics for a fraction of the computational requirements of a traditional regional climate model. ICAR also enables the incorporation of statistical adjustments directly within the model. We demonstrate that applying even simple corrections to precipitation while the model is running can improve the simulation of land atmosphere feedbacks in ICAR. For example, by incorporating statistical corrections earlier in the modeling chain, we permit the model physics to better represent the effect of mountain snowpack on air temperature changes.
Downscale climate change scenarios over the Western Himalayan region of India using multi-generation CMIP experiments

NASA Astrophysics Data System (ADS)

Das, Lalu; Meher, Jitendra K.; Akhter, Javed

2017-04-01

Assessing climate change information over the Western Himalayan Region (WHR) of India is crucial but challenging task due to its limited numbers of station data containing huge missing values. The issues of missing values of station data were replaced the Multiple Imputation Chained Equation (MICE) technique. Finally 22 numbers of rain gauge stations having continuous data during 1901-2005 and 16 numbers stations having continuous temperature data during 1969-2009 were considered as " reference stations for assessing rainfall and temperature trends in addition to evaluation of the GCMs available in the Coupled Model Intercomparison Project, Phase 3 (CMIP3) and phase 5 (CMIP5) over WRH. Station data indicates that the winter warming is higher and rapid (1.05oC) than other seasons and less warming in the post monsoon season in the last 41 years. Area averaged using 22 station data indicates that monsoon and winter rainfall has decreased by -5 mm and -320 mm during 1901-2000 while pre-monsoon and post monsoon showed an increasing trends of 21 mm and 13 mm respectively. Present study is constructed the downscaled climate change information at station locations (22 and 16 stations for rainfall and temperature respectively) over the WHR from the GCMs commonly available in the IPCC's different generations assessment reports namely 2nd, 3rd, 4th and 5th thereafter known as SAR, TAR, AR4 and AR5 respectively. Once the downscaled results are obtained for each generation model outputs, then a comparison of studies is carried out from the results of each generation. Finally an overall model improvement index (OMII) is developed using the downscaling results which is used to investigate the model improvement across generations as well as the improvement of downscaling results obtained from the empirical statistical downscaling (ESD) methods. In general, the results indicate that there is a gradual improvement of GCMs simulations as well as downscaling results across generation. Key words: MICE Techniques, CMIP3, CMIP5, ESD and OMII
Evaluation of statistically downscaled GCM output as input for hydrological and stream temperature simulation in the Apalachicola–Chattahoochee–Flint River Basin (1961–99)

USGS Publications Warehouse

Hay, Lauren E.; LaFontaine, Jacob H.; Markstrom, Steven

2014-01-01

The accuracy of statistically downscaled general circulation model (GCM) simulations of daily surface climate for historical conditions (1961–99) and the implications when they are used to drive hydrologic and stream temperature models were assessed for the Apalachicola–Chattahoochee–Flint River basin (ACFB). The ACFB is a 50 000 km2 basin located in the southeastern United States. Three GCMs were statistically downscaled, using an asynchronous regional regression model (ARRM), to ⅛° grids of daily precipitation and minimum and maximum air temperature. These ARRM-based climate datasets were used as input to the Precipitation-Runoff Modeling System (PRMS), a deterministic, distributed-parameter, physical-process watershed model used to simulate and evaluate the effects of various combinations of climate and land use on watershed response. The ACFB was divided into 258 hydrologic response units (HRUs) in which the components of flow (groundwater, subsurface, and surface) are computed in response to climate, land surface, and subsurface characteristics of the basin. Daily simulations of flow components from PRMS were used with the climate to simulate in-stream water temperatures using the Stream Network Temperature (SNTemp) model, a mechanistic, one-dimensional heat transport model for branched stream networks.The climate, hydrology, and stream temperature for historical conditions were evaluated by comparing model outputs produced from historical climate forcings developed from gridded station data (GSD) versus those produced from the three statistically downscaled GCMs using the ARRM methodology. The PRMS and SNTemp models were forced with the GSD and the outputs produced were treated as “truth.” This allowed for a spatial comparison by HRU of the GSD-based output with ARRM-based output. Distributional similarities between GSD- and ARRM-based model outputs were compared using the two-sample Kolmogorov–Smirnov (KS) test in combination with descriptive metrics such as the mean and variance and an evaluation of rare and sustained events. In general, precipitation and streamflow quantities were negatively biased in the downscaled GCM outputs, and results indicate that the downscaled GCM simulations consistently underestimate the largest precipitation events relative to the GSD. The KS test results indicate that ARRM-based air temperatures are similar to GSD at the daily time step for the majority of the ACFB, with perhaps subweekly averaging for stream temperature. Depending on GCM and spatial location, ARRM-based precipitation and streamflow requires averaging of up to 30 days to become similar to the GSD-based output.Evaluation of the model skill for historical conditions suggests some guidelines for use of future projections; while it seems correct to place greater confidence in evaluation metrics which perform well historically, this does not necessarily mean those metrics will accurately reflect model outputs for future climatic conditions. Results from this study indicate no “best” overall model, but the breadth of analysis can be used to give the product users an indication of the applicability of the results to address their particular problem. Since results for historical conditions indicate that model outputs can have significant biases associated with them, the range in future projections examined in terms of change relative to historical conditions for each individual GCM may be more appropriate.
Modelling uncertainties and possible future trends of precipitation and temperature for 10 sub-basins in Columbia River Basin (CRB)

NASA Astrophysics Data System (ADS)

Ahmadalipour, A.; Rana, A.; Qin, Y.; Moradkhani, H.

2014-12-01

Trends and changes in future climatic parameters, such as, precipitation and temperature have been a central part of climate change studies. In the present work, we have analyzed the seasonal and yearly trends and uncertainties of prediction in all the 10 sub-basins of Columbia River Basin (CRB) for future time period of 2010-2099. The work is carried out using 2 different sets of statistically downscaled Global Climate Model (GCMs) projection datasets i.e. Bias correction and statistical downscaling (BCSD) generated at Portland State University and The Multivariate Adaptive Constructed Analogs (MACA) generated at University of Idaho. The analysis is done for with 10 GCM downscaled products each from CMIP5 daily dataset totaling to 40 different downscaled products for robust analysis. Summer, winter and yearly trend analysis is performed for all the 10 sub-basins using linear regression (significance tested by student t test) and Mann Kendall test (0.05 percent significance level), for precipitation (P), temperature maximum (Tmax) and temperature minimum (Tmin). Thereafter, all the parameters are modelled for uncertainty, across all models, in all the 10 sub-basins and across the CRB for future scenario periods. Results have indicated in varied degree of trends for all the sub-basins, mostly pointing towards a significant increase in all three climatic parameters, for all the seasons and yearly considerations. Uncertainty analysis have reveled very high change in all the parameters across models and sub-basins under consideration. Basin wide uncertainty analysis is performed to corroborate results from smaller, sub-basin scale. Similar trends and uncertainties are reported on the larger scale as well. Interestingly, both trends and uncertainties are higher during winter period than during summer, contributing to large part of the yearly change.
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

NASA Astrophysics Data System (ADS)

Quiquet, Aurélien; Roche, Didier M.

2017-04-01

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.

Impacts of climate change on precipitation and discharge extremes through the use of statistical downscaling approaches in a Mediterranean basin.

PubMed

Piras, Monica; Mascaro, Giuseppe; Deidda, Roberto; Vivoni, Enrique R

2016-02-01

Mediterranean region is characterized by high precipitation variability often enhanced by orography, with strong seasonality and large inter-annual fluctuations, and by high heterogeneity of terrain and land surface properties. As a consequence, catchments in this area are often prone to the occurrence of hydrometeorological extremes, including storms, floods and flash-floods. A number of climate studies focused in the Mediterranean region predict that extreme events will occur with higher intensity and frequency, thus requiring further analyses to assess their effect at the land surface, particularly in small- and medium-sized watersheds. In this study, climate and hydrologic simulations produced within the Climate Induced Changes on the Hydrology of Mediterranean Basins (CLIMB) EU FP7 research project were used to analyze how precipitation extremes propagate into discharge extremes in the Rio Mannu basin (472.5km(2)), located in Sardinia, Italy. The basin hydrologic response to climate forcings in a reference (1971-2000) and a future (2041-2070) period was simulated through the combined use of a set of global and regional climate models, statistical downscaling techniques, and a process based distributed hydrologic model. We analyzed and compared the distribution of annual maxima extracted from hourly and daily precipitation and peak discharge time series, simulated by the hydrologic model under climate forcing. For this aim, yearly maxima were fit by the Generalized Extreme Value (GEV) distribution using a regional approach. Next, we discussed commonality and contrasting behaviors of precipitation and discharge maxima distributions to better understand how hydrological transformations impact propagation of extremes. Finally, we show how rainfall statistical downscaling algorithms produce more reliable forcings for hydrological models than coarse climate model outputs. Copyright © 2015 Elsevier B.V. All rights reserved.
The Use of Statistical Downscaling to Project Regional Climate Changes as they Relate to Future Energy Production

NASA Astrophysics Data System (ADS)

Werth, D. W.; O'Steen, L.; Chen, K.; Altinakar, M. S.; Garrett, A.; Aleman, S.; Ramalingam, V.

2010-12-01

Global climate change has the potential for profound impacts on society, and poses significant challenges to government and industry in the areas of energy security and sustainability. Given that the ability to exploit energy resources often depends on the climate, the possibility of climate change means we cannot simply assume that the untapped potential of today will still exist in the future. Predictions of future climate are generally based on global climate models (GCMs) which, due to computational limitations, are run at spatial resolutions of hundreds of kilometers. While the results from these models can predict climatic trends averaged over large spatial and temporal scales, their ability to describe the effects of atmospheric phenomena that affect weather on regional to local scales is inadequate. We propose the use of several optimized statistical downscaling techniques that can infer climate change at the local scale from coarse resolution GCM predictions, and apply the results to assess future sustainability for two sources of energy production dependent on adequate water resources: nuclear power (through the dissipation of waste heat from cooling towers, ponds, etc.) and hydroelectric power. All methods will be trained with 20th century data, and applied to data from the years 2040-2049 to get the local-scale changes. Models of cooling tower operation and hydropower potential will then use the downscaled data to predict the possible changes in energy production, and the implications of climate change on plant siting, design, and contribution to the future energy grid can then be examined.
Reconstructing the 20th century high-resolution climate of the southeastern United States

NASA Astrophysics Data System (ADS)

Dinapoli, Steven M.; Misra, Vasubandhu

2012-10-01

We dynamically downscale the 20th Century Reanalysis (20CR) to a 10-km grid resolution from 1901 to 2008 over the southeastern United States and the Gulf of Mexico using the Regional Spectral Model. The downscaled data set, which we call theFlorida Climate Institute-Florida State University Land-Atmosphere Reanalysis for theSoutheastern United States at 10-km resolution (FLAReS1.0), will facilitate the study of the effects of low-frequency climate variability and major historical climate events on local hydrology and agriculture. To determine the suitability of the FLAReS1.0 downscaled data set for any subsequent applied climate studies, we compare the annual, seasonal, and diurnal variability of temperature and precipitation in the model to various observation data sets. In addition, we examine the model's depiction of several meteorological phenomena that affect the climate of the region, including extreme cold waves, summer sea breezes and associated convective activity, tropical cyclone landfalls, and midlatitude frontal systems. Our results show that temperature and precipitation variability are well-represented by FLAReS1.0 on most time scales, although systematic biases do exist in the data. FLAReS1.0 accurately portrays some of the major weather phenomena in the region, but the severity of extreme weather events is generally underestimated. The high resolution of FLAReS1.0 makes it more suitable for local climate studies than the coarser 20CR.
Ensemble downscaling in coupled solar wind-magnetosphere modeling for space weather forecasting.

PubMed

Owens, M J; Horbury, T S; Wicks, R T; McGregor, S L; Savani, N P; Xiong, M

2014-06-01

Advanced forecasting of space weather requires simulation of the whole Sun-to-Earth system, which necessitates driving magnetospheric models with the outputs from solar wind models. This presents a fundamental difficulty, as the magnetosphere is sensitive to both large-scale solar wind structures, which can be captured by solar wind models, and small-scale solar wind "noise," which is far below typical solar wind model resolution and results primarily from stochastic processes. Following similar approaches in terrestrial climate modeling, we propose statistical "downscaling" of solar wind model results prior to their use as input to a magnetospheric model. As magnetospheric response can be highly nonlinear, this is preferable to downscaling the results of magnetospheric modeling. To demonstrate the benefit of this approach, we first approximate solar wind model output by smoothing solar wind observations with an 8 h filter, then add small-scale structure back in through the addition of random noise with the observed spectral characteristics. Here we use a very simple parameterization of noise based upon the observed probability distribution functions of solar wind parameters, but more sophisticated methods will be developed in the future. An ensemble of results from the simple downscaling scheme are tested using a model-independent method and shown to add value to the magnetospheric forecast, both improving the best estimate and quantifying the uncertainty. We suggest a number of features desirable in an operational solar wind downscaling scheme. Solar wind models must be downscaled in order to drive magnetospheric models Ensemble downscaling is more effective than deterministic downscaling The magnetosphere responds nonlinearly to small-scale solar wind fluctuations.
What is the importance of climate model bias when projecting the impacts of climate change on land surface processes?

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liu, M. L.; Rajagopalan, K.; Chung, S. H.

2014-05-16

Regional climate change impact (CCI) studies have widely involved downscaling and bias-correcting (BC) Global Climate Model (GCM)-projected climate for driving land surface models. However, BC may cause uncertainties in projecting hydrologic and biogeochemical responses to future climate due to the impaired spatiotemporal covariance of climate variables and a breakdown of physical conservation principles. Here we quantify the impact of BC on simulated climate-driven changes in water variables(evapotranspiration, ET; runoff; snow water equivalent, SWE; and water demand for irrigation), crop yield, biogenic volatile organic compounds (BVOC), nitric oxide (NO) emissions, and dissolved inorganic nitrogen (DIN) export over the Pacific Northwest (PNW)more » Region. We also quantify the impacts on net primary production (NPP) over a small watershed in the region (HJ Andrews). Simulation results from the coupled ECHAM5/MPI-OM model with A1B emission scenario were firstly dynamically downscaled to 12 km resolutions with WRF model. Then a quantile mapping based statistical downscaling model was used to downscale them into 1/16th degree resolution daily climate data over historical and future periods. Two series climate data were generated according to the option of bias-correction (i.e. with bias-correction (BC) and without bias-correction, NBC). Impact models were then applied to estimate hydrologic and biogeochemical responses to both BC and NBC meteorological datasets. These im20 pact models include a macro-scale hydrologic model (VIC), a coupled cropping system model (VIC-CropSyst), an ecohydrologic model (RHESSys), a biogenic emissions model (MEGAN), and a nutrient export model (Global-NEWS). Results demonstrate that the BC and NBC climate data provide consistent estimates of the climate-driven changes in water fluxes (ET, runoff, and water demand), VOCs (isoprene and monoterpenes) and NO emissions, mean crop yield, and river DIN export over the PNW domain. However, significant differences rise from projected SWE, crop yield from dry lands, and HJ Andrews’s ET between BC and NBC data. Even though BC post-processing has no significant impacts on most of the studied variables when taking PNW as a whole, their effects have large spatial variations and some local areas are substantially influenced. In addition, there are months during which BC and NBC post-processing produces significant differences in projected changes, such as summer runoff. Factor-controlled simulations indicate that BC post-processing of precipitation and temperature both substantially contribute to these differences at region scales. We conclude that there are trade-offs between using BC climate data for offline CCI studies vs. direct modeled climate data. These trade-offs should be considered when designing integrated modeling frameworks for specific applications; e.g., BC may be more important when considering impacts on reservoir operations in mountainous watersheds than when investigating impacts on biogenic emissions and air quality (where VOCs are a primary indicator).« less
Overall uncertainty study of the hydrological impacts of climate change for a Canadian watershed

NASA Astrophysics Data System (ADS)

Chen, Jie; Brissette, FrançOis P.; Poulin, Annie; Leconte, Robert

2011-12-01

General circulation models (GCMs) and greenhouse gas emissions scenarios (GGES) are generally considered to be the two major sources of uncertainty in quantifying the climate change impacts on hydrology. Other sources of uncertainty have been given less attention. This study considers overall uncertainty by combining results from an ensemble of two GGES, six GCMs, five GCM initial conditions, four downscaling techniques, three hydrological model structures, and 10 sets of hydrological model parameters. Each climate projection is equally weighted to predict the hydrology on a Canadian watershed for the 2081-2100 horizon. The results show that the choice of GCM is consistently a major contributor to uncertainty. However, other sources of uncertainty, such as the choice of a downscaling method and the GCM initial conditions, also have a comparable or even larger uncertainty for some hydrological variables. Uncertainties linked to GGES and the hydrological model structure are somewhat less than those related to GCMs and downscaling techniques. Uncertainty due to the hydrological model parameter selection has the least important contribution among all the variables considered. Overall, this research underlines the importance of adequately covering all sources of uncertainty. A failure to do so may result in moderately to severely biased climate change impact studies. Results further indicate that the major contributors to uncertainty vary depending on the hydrological variables selected, and that the methodology presented in this paper is successful at identifying the key sources of uncertainty to consider for a climate change impact study.
Processing and Monthly Summaries of Downscaled Climate Data for Knoxville, Tennessee and Surrounding Region

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sylvester, Linda; Omitaomu, Olufemi A.; Parish, Esther S.

2016-09-01

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
Field significance of performance measures in the context of regional climate model evaluation. Part 2: precipitation

NASA Astrophysics Data System (ADS)

Ivanov, Martin; Warrach-Sagi, Kirsten; Wulfmeyer, Volker

2018-04-01

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.
Projection of wave conditions in response to climate change: A community approach to global and regional wave downscaling

USGS Publications Warehouse

Erikson, Li H.; Hemer, M.; Lionello, Piero; Mendez, Fernando J.; Mori, Nobuhito; Semedo, Alvaro; Wang, Xiaolan; Wolf, Judith

2015-01-01

Future changes in wind-wave climate have broad implications for coastal geomorphology and management. General circulation models (GCM) are now routinely used for assessing climatological parameters, but generally do not provide parameterizations of ocean wind-waves. To fill this information gap, a growing number of studies use GCM outputs to independently downscale wave conditions to global and regional levels. To consolidate these efforts and provide a robust picture of projected changes, we present strategies from the community-derived multi-model ensemble of wave climate projections (COWCLIP) and an overview of regional contributions. Results and strategies from one contributing regional study concerning changes along the eastern North Pacific coast are presented.
Mid-21st century projections of hydroclimate in Western Himalayas and Satluj River basin

NASA Astrophysics Data System (ADS)

Tiwari, Sarita; Kar, Sarat C.; Bhatla, R.

2018-02-01

The Himalayan climate system is sensitive to global warming and climate change. Regional hydrology and the downstream water flow in the rivers of Himalayan origin may change due to variations in snow and glacier melt in the region. This study examines the mid-21st century climate projections over western Himalayas from the Coupled Model Intercomparison Project Phase 5 (CMIP5) global climate models under Representative Concentration Pathways (RCP) scenarios (RCP4.5 and RCP8.5). All the global climate models used in the present analysis indicate that the study region would be warmer by mid-century. The temperature trends from all the models studied here are statistically significant at 95% confidence interval. Multi-model ensemble spreads show that there are large differences among the models in their projections of future climate with spread in temperature ranging from about 1.5 °C to 5 °C over various areas of western Himalayas in all the seasons. Spread in precipitation projections lies between 0.3 and 1 mm/day in all the seasons. Major shift in the timing of evaporation maxima and minima is noticed. The GFDL_ESM2G model products have been downscaled to Satluj River basin using the weather research and forecast (WRF) model and impact of climate change on streamflow has been studied. The reduction of precipitation during JJAS is expected to be > 3-6 mm/day in RCP8.5 as compared to present climate. It is expected that precipitation amount shall increase over Satluj basin in future (mid-21st century) The soil and water assessment tool (SWAT) model has been used to simulate the Satluj streamflow for the present and future climate using GFDL_ESM2G precipitation and temperature data as well as the WRF model downscaled data. The computations using the global model data show that total annual discharge from Satluj will be less in future than that in present climate, especially in peak discharge season (JJAS). The SWAT model with downscaled output indicates that during winter and spring, more discharge shall occur in future (RCP8.5) in Satluj River.
RELATIVE EFFECTS OF OBSERVATIONALLY-NUDGED MODEL METEOROLOGY AND DOWN-SCALED GLOBAL CLIMATE MODEL METEOROLOGY ON BIOGENIC EMISSIONS FOR THE UNITED STATES

EPA Science Inventory

The United States Environmental Protection Agency (USEPA) and National Oceanic and Atmospheric Administration (NOAA) participate in a multi-agency examination of the effects of climate change through the U.S. Climate Change Science Program (CCSP, 2003). The EPA Global Change Rese...
A Dynamical Downscaling study over the Great Lakes Region Using WRF-Lake: Historical Simulation

NASA Astrophysics Data System (ADS)

Xiao, C.; Lofgren, B. M.

2014-12-01

As the largest group of fresh water bodies on Earth, the Laurentian Great Lakes have significant influence on local and regional weather and climate through their unique physical features compared with the surrounding land. Due to the limited spatial resolution and computational efficiency of general circulation models (GCMs), the Great Lakes are geometrically ignored or idealized into several grid cells in GCMs. Thus, the nested regional climate modeling (RCM) technique, known as dynamical downscaling, serves as a feasible solution to fill the gap. The latest Weather Research and Forecasting model (WRF) is employed to dynamically downscale the historical simulation produced by the Geophysical Fluid Dynamics Laboratory-Coupled Model (GFDL-CM3) from 1970-2005. An updated lake scheme originated from the Community Land Model is implemented in the latest WRF version 3.6. It is a one-dimensional mass and energy balance scheme with 20-25 model layers, including up to 5 snow layers on the lake ice, 10 water layers, and 10 soil layers on the lake bottom. The lake scheme is used with actual lake points and lake depth. The preliminary results show that WRF-Lake model, with a fine horizontal resolution and realistic lake representation, provides significantly improved hydroclimates, in terms of lake surface temperature, annual cycle of precipitation, ice content, and lake-effect snowfall. Those improvements suggest that better resolution of the lakes and the mesoscale process of lake-atmosphere interaction are crucial to understanding the climate and climate change in the Great Lakes region.
Assessing the Added Value of Dynamical Downscaling Using ...

EPA Pesticide Factsheets

In this study, the Standardized Precipitation Index (SPI) is used to ascertain the added value of dynamical downscaling over the contiguous United States. WRF is used as a regional climate model (RCM) to dynamically downscale reanalysis fields to compare values of SPI over drought timescales that have implications for agriculture and water resources planning. The regional climate generated by WRF has the largest improvement over reanalysis for SPI correlation with observations as the drought timescale increases. This suggests that dynamically downscaled fields may be more reliable than larger-scale fields for water resource applications (e.g., water storage within reservoirs). WRF improves the timing and intensity of moderate to extreme wet and dry periods, even in regions with homogenous terrain. This study also examines changes in SPI from the extreme drought of 1988 and three “drought busting” tropical storms. Each of those events illustrates the importance of using downscaling to resolve the spatial extent of droughts. The analysis of the “drought busting” tropical storms demonstrates that while the impact of these storms on ending prolonged droughts is improved by the RCM relative to the reanalysis, it remains underestimated. These results illustrate the importance and some limitations of using RCMs to project drought. The National Exposure Research Laboratory’s Atmospheric Modeling Division (AMAD) conducts research in support of EPA’s mission t
Risk assessments of regional climate change over Europe: generation of probabilistic ensemble and uncertainty assessment for EURO-CODEX

NASA Astrophysics Data System (ADS)

Yuan, J.; Kopp, R. E.

2017-12-01

Quantitative risk analysis of regional climate change is crucial for risk management and impact assessment of climate change. Two major challenges to assessing the risks of climate change are: CMIP5 model runs, which drive EURO-CODEX downscaling runs, do not cover the full range of uncertainty of future projections; Climate models may underestimate the probability of tail risks (i.e. extreme events). To overcome the difficulties, this study offers a viable avenue, where a set of probabilistic climate ensemble is generated using the Surrogate/Model Mixed Ensemble (SMME) method. The probabilistic ensembles for temperature and precipitation are used to assess the range of uncertainty covered by five bias-corrected simulations from the high-resolution (0.11º) EURO-CODEX database, which are selected by the PESETA (The Projection of Economic impacts of climate change in Sectors of the European Union based on bottom-up Analysis) III project. Results show that the distribution of SMME ensemble is notably wider than both distribution of raw ensemble of GCMs and the spread of the five EURO-CORDEX in RCP8.5. Tail risks are well presented by the SMME ensemble. Both SMME ensemble and EURO-CORDEX projections are aggregated to administrative level, and are integrated into impact functions of PESETA III to assess climate risks in Europe. To further evaluate the uncertainties introduced by the downscaling process, we compare the 5 runs from EURO-CORDEX with runs from the corresponding GCMs. Time series of regional mean, spatial patterns, and climate indices are examined for the future climate (2080-2099) deviating from the present climate (1981-2010). The downscaling processes do not appear to be trend-preserving, e.g. the increase in regional mean temperature from EURO-CORDEX is slower than that from the corresponding GCM. The spatial pattern comparison reveals that the differences between each pair of GCM and EURO-CORDEX are small in winter. In summer, the temperatures of EURO-CORDEX are generally lower than those of GCMs, while the drying trends in precipitation of EURO-CORDEX are smaller than those of GCMs. Climate indices are significantly affected by bias-correction and downscaling process. Our study provides valuable information for selecting climate indices in different regions over Europe.
Climate change beliefs and hazard mitigation behaviors: Homeowners and wildfire risk

Treesearch

Hannah Brenkert-Smith; James R. Meldrum; Patricia A. Champ

2015-01-01

Downscaled climate models provide projections of how climate change may exacerbate the local impacts of natural hazards. The extent to which people facing exacerbated hazard conditions understand or respond to climate-related changes to local hazards has been largely overlooked. In this article, we examine the relationships among climate change beliefs, environmental...
Influences of Regional Climate Change on Air Quality across the Continental U.S. Projected from Downscaling IPCC ARS Simulations

EPA Science Inventory

Projecting climate change scenarios to local scales is important for understanding, mitigating, and adapting to the effects of climate change on society and the environment. Many of the global climate models (GCMs) that are participating in the Intergovernmental Panel on Climate ...
Does Nudging Squelch the Extremes in Regional Climate Modeling?

EPA Science Inventory

An important question in regional climate downscaling is whether to constrain (nudge) the interior of the limited-area domain toward the larger-scale driving ﬁelds. Prior research has demonstrated that interior nudging can increase the skill of regional climate predictions origin...
Evaluating global reanalysis datasets for provision of boundary conditions in regional climate modelling

NASA Astrophysics Data System (ADS)

Moalafhi, Ditiro B.; Evans, Jason P.; Sharma, Ashish

2016-11-01

Regional climate modelling studies often begin by downscaling a reanalysis dataset in order to simulate the observed climate, allowing the investigation of regional climate processes and quantification of the errors associated with the regional model. To date choice of reanalysis to perform such downscaling has been made based either on convenience or on performance of the reanalyses within the regional domain for relevant variables such as near-surface air temperature and precipitation. However, the only information passed from the reanalysis to the regional model are the atmospheric temperature, moisture and winds at the location of the boundaries of the regional domain. Here we present a methodology to evaluate reanalyses derived lateral boundary conditions for an example domain over southern Africa using satellite data. This study focusses on atmospheric temperature and moisture which are easily available. Five commonly used global reanalyses (NCEP1, NCEP2, ERA-I, 20CRv2, and MERRA) are evaluated against the Atmospheric Infrared Sounder satellite temperature and relative humidity over boundaries of two domains centred on southern Africa for the years 2003-2012 inclusive. The study reveals that MERRA is the most suitable for climate mean with NCEP1 the next most suitable. For climate variability, ERA-I is the best followed by MERRA. Overall, MERRA is preferred for generating lateral boundary conditions for this domain, followed by ERA-I. While a "better" LBC specification is not the sole precursor to an improved downscaling outcome, any reduction in uncertainty associated with the specification of LBCs is a step in the right direction.
Comparison Of Downscaled CMIP5 Precipitation Datasets For Projecting Changes In Extreme Precipitation In The San Francisco Bay Area.

NASA Technical Reports Server (NTRS)

Milesi, Cristina; Costa-Cabral, Mariza; Rath, John; Mills, William; Roy, Sujoy; Thrasher, Bridget; Wang, Weile; Chiang, Felicia; Loewenstein, Max; Podolske, James

2014-01-01

Water resource managers planning for the adaptation to future events of extreme precipitation now have access to high resolution downscaled daily projections derived from statistical bias correction and constructed analogs. We also show that along the Pacific Coast the Northern Oscillation Index (NOI) is a reliable predictor of storm likelihood, and therefore a predictor of seasonal precipitation totals and likelihood of extremely intense precipitation. Such time series can be used to project intensity duration curves into the future or input into stormwater models. However, few climate projection studies have explored the impact of the type of downscaling method used on the range and uncertainty of predictions for local flood protection studies. Here we present a study of the future climate flood risk at NASA Ames Research Center, located in South Bay Area, by comparing the range of predictions in extreme precipitation events calculated from three sets of time series downscaled from CMIP5 data: 1) the Bias Correction Constructed Analogs method dataset downscaled to a 1/8 degree grid (12km); 2) the Bias Correction Spatial Disaggregation method downscaled to a 1km grid; 3) a statistical model of extreme daily precipitation events and projected NOI from CMIP5 models. In addition, predicted years of extreme precipitation are used to estimate the risk of overtopping of the retention pond located on the site through simulations of the EPA SWMM hydrologic model. Preliminary results indicate that the intensity of extreme precipitation events is expected to increase and flood the NASA Ames retention pond. The results from these estimations will assist flood protection managers in planning for infrastructure adaptations.
Projecting the potential evapotranspiration by coupling different formulations and input data reliabilities: The possible uncertainty source for climate change impacts on hydrological regime

NASA Astrophysics Data System (ADS)

Wang, Weiguang; Li, Changni; Xing, Wanqiu; Fu, Jianyu

2017-12-01

Representing atmospheric evaporating capability for a hypothetical reference surface, potential evapotranspiration (PET) determines the upper limit of actual evapotranspiration and is an important input to hydrological models. Due that present climate models do not give direct estimates of PET when simulating the hydrological response to future climate change, the PET must be estimated first and is subject to the uncertainty on account of many existing formulae and different input data reliabilities. Using four different PET estimation approaches, i.e., the more physically Penman (PN) equation with less reliable input variables, more empirical radiation-based Priestley-Taylor (PT) equation with relatively dependable downscaled data, the most simply temperature-based Hamon (HM) equation with the most reliable downscaled variable, and downscaling PET directly by the statistical downscaling model, this paper investigated the differences of runoff projection caused by the alternative PET methods by a well calibrated abcd monthly hydrological model. Three catchments, i.e., the Luanhe River Basin, the Source Region of the Yellow River and the Ganjiang River Basin, representing a large climatic diversity were chosen as examples to illustrate this issue. The results indicated that although similar monthly patterns of PET over the period 2021-2050 for each catchment were provided by the four methods, the magnitudes of PET were still slightly different, especially for spring and summer months in the Luanhe River Basin and the Source Region of the Yellow River with relatively dry climate feature. The apparent discrepancy in magnitude of change in future runoff and even the diverse change direction for summer months in the Luanhe River Basin and spring months in the Source Region of the Yellow River indicated that the PET method related uncertainty occurred, especially in the Luanhe River Basin and the Source Region of the Yellow River with smaller aridity index. Moreover, the possible reason of discrepancies in uncertainty between three catchments was quantitatively discussed by the contribution analysis based on climatic elasticity method. This study can provide beneficial reference to comprehensively understand the impacts of climate change on hydrological regime and thus improve the regional strategy for future water resource management.

Downscaling global land-use/land-cover projections for use in region-level state-and-transition simulation modeling

USGS Publications Warehouse

Sherba, Jason T.; Sleeter, Benjamin M.; Davis, Adam W.; Parker, Owen P.

2015-01-01

Global land-use/land-cover (LULC) change projections and historical datasets are typically available at coarse grid resolutions and are often incompatible with modeling applications at local to regional scales. The difficulty of downscaling and reapportioning global gridded LULC change projections to regional boundaries is a barrier to the use of these datasets in a state-and-transition simulation model (STSM) framework. Here we compare three downscaling techniques to transform gridded LULC transitions into spatial scales and thematic LULC classes appropriate for use in a regional STSM. For each downscaling approach, Intergovernmental Panel on Climate Change (IPCC) Representative Concentration Pathway (RCP) LULC projections, at the 0.5 × 0.5 cell resolution, were downscaled to seven Level III ecoregions in the Pacific Northwest, United States. RCP transition values at each cell were downscaled based on the proportional distribution between ecoregions of (1) cell area, (2) land-cover composition derived from remotely-sensed imagery, and (3) historic LULC transition values from a LULC history database. Resulting downscaled LULC transition values were aggregated according to their bounding ecoregion and “cross-walked” to relevant LULC classes. Ecoregion-level LULC transition values were applied in a STSM projecting LULC change between 2005 and 2100. While each downscaling methods had advantages and disadvantages, downscaling using the historical land-use history dataset consistently apportioned RCP LULC transitions in agreement with historical observations. Regardless of the downscaling method, some LULC projections remain improbable and require further investigation.
Multi-year downscaling application of two-way coupled WRF v3.4 and CMAQ v5.0.2 over east Asia for regional climate and air quality modeling: model evaluation and aerosol direct effects

NASA Astrophysics Data System (ADS)

Hong, Chaopeng; Zhang, Qiang; Zhang, Yang; Tang, Youhua; Tong, Daniel; He, Kebin

2017-06-01

In this study, a regional coupled climate-chemistry modeling system using the dynamical downscaling technique was established by linking the global Community Earth System Model (CESM) and the regional two-way coupled Weather Research and Forecasting - Community Multi-scale Air Quality (WRF-CMAQ) model for the purpose of comprehensive assessments of regional climate change and air quality and their interactions within one modeling framework. The modeling system was applied over east Asia for a multi-year climatological application during 2006-2010, driven with CESM downscaling data under Representative Concentration Pathways 4.5 (RCP4.5), along with a short-term air quality application in representative months in 2013 that was driven with a reanalysis dataset. A comprehensive model evaluation was conducted against observations from surface networks and satellite observations to assess the model's performance. This study presents the first application and evaluation of the two-way coupled WRF-CMAQ model for climatological simulations using the dynamical downscaling technique. The model was able to satisfactorily predict major meteorological variables. The improved statistical performance for the 2 m temperature (T2) in this study (with a mean bias of -0.6 °C) compared with the Coupled Model Intercomparison Project Phase 5 (CMIP5) multi-models might be related to the use of the regional model WRF and the bias-correction technique applied for CESM downscaling. The model showed good ability to predict PM2. 5 in winter (with a normalized mean bias (NMB) of 6.4 % in 2013) and O3 in summer (with an NMB of 18.2 % in 2013) in terms of statistical performance and spatial distributions. Compared with global models that tend to underpredict PM2. 5 concentrations in China, WRF-CMAQ was able to capture the high PM2. 5 concentrations in urban areas. In general, the two-way coupled WRF-CMAQ model performed well for both climatological and air quality applications. The coupled modeling system with direct aerosol feedbacks predicted aerosol optical depth relatively well and significantly reduced the overprediction in downward shortwave radiation at the surface (SWDOWN) over polluted regions in China. The performance of cloud variables was not as good as other meteorological variables, and underpredictions of cloud fraction resulted in overpredictions of SWDOWN and underpredictions of shortwave and longwave cloud forcing. The importance of climate-chemistry interactions was demonstrated via the impacts of aerosol direct effects on climate and air quality. The aerosol effects on climate and air quality in east Asia (e.g., SWDOWN and T2 decreased by 21.8 W m-2 and 0.45 °C, respectively, and most pollutant concentrations increased by 4.8-9.5 % in January over China's major cities) were more significant than in other regions because of higher aerosol loadings that resulted from severe regional pollution, which indicates the need for applying online-coupled models over east Asia for regional climate and air quality modeling and to study the important climate-chemistry interactions. This work established a baseline for WRF-CMAQ simulations for a future period under the RCP4.5 climate scenario, which will be presented in a future paper.
A framework for global river flood risk assessments

NASA Astrophysics Data System (ADS)

Winsemius, H. C.; Van Beek, L. P. H.; Jongman, B.; Ward, P. J.; Bouwman, A.

2012-08-01

There is an increasing need for strategic global assessments of flood risks in current and future conditions. In this paper, we propose a framework for global flood risk assessment for river floods, which can be applied in current conditions, as well as in future conditions due to climate and socio-economic changes. The framework's goal is to establish flood hazard and impact estimates at a high enough resolution to allow for their combination into a risk estimate. The framework estimates hazard at high resolution (~1 km2) using global forcing datasets of the current (or in scenario mode, future) climate, a global hydrological model, a global flood routing model, and importantly, a flood extent downscaling routine. The second component of the framework combines hazard with flood impact models at the same resolution (e.g. damage, affected GDP, and affected population) to establish indicators for flood risk (e.g. annual expected damage, affected GDP, and affected population). The framework has been applied using the global hydrological model PCR-GLOBWB, which includes an optional global flood routing model DynRout, combined with scenarios from the Integrated Model to Assess the Global Environment (IMAGE). We performed downscaling of the hazard probability distributions to 1 km2 resolution with a new downscaling algorithm, applied on Bangladesh as a first case-study application area. We demonstrate the risk assessment approach in Bangladesh based on GDP per capita data, population, and land use maps for 2010 and 2050. Validation of the hazard and damage estimates has been performed using the Dartmouth Flood Observatory database and damage estimates from the EM-DAT database and World Bank sources. We discuss and show sensitivities of the estimated risks with regard to the use of different climate input sets, decisions made in the downscaling algorithm, and different approaches to establish impact models.
Hierarchical stochastic modeling of large river ecosystems and fish growth across spatio-temporal scales and climate models: the Missouri River endangered pallid sturgeon example

USGS Publications Warehouse

Wildhaber, Mark L.; Wikle, Christopher K.; Moran, Edward H.; Anderson, Christopher J.; Franz, Kristie J.; Dey, Rima

2017-01-01

We present a hierarchical series of spatially decreasing and temporally increasing models to evaluate the uncertainty in the atmosphere – ocean global climate model (AOGCM) and the regional climate model (RCM) relative to the uncertainty in the somatic growth of the endangered pallid sturgeon (Scaphirhynchus albus). For effects on fish populations of riverine ecosystems, cli- mate output simulated by coarse-resolution AOGCMs and RCMs must be downscaled to basins to river hydrology to population response. One needs to transfer the information from these climate simulations down to the individual scale in a way that minimizes extrapolation and can account for spatio-temporal variability in the intervening stages. The goal is a framework to determine whether, given uncertainties in the climate models and the biological response, meaningful inference can still be made. The non-linear downscaling of climate information to the river scale requires that one realistically account for spatial and temporal variability across scale. Our down- scaling procedure includes the use of fixed/calibrated hydrological flow and temperature models coupled with a stochastically parameterized sturgeon bioenergetics model. We show that, although there is a large amount of uncertainty associated with both the climate model output and the fish growth process, one can establish significant differences in fish growth distributions between models, and between future and current climates for a given model.
Downscaling scheme to drive soil-vegetation-atmosphere transfer models

NASA Astrophysics Data System (ADS)

Schomburg, Annika; Venema, Victor; Lindau, Ralf; Ament, Felix; Simmer, Clemens

2010-05-01

The earth's surface is characterized by heterogeneity at a broad range of scales. Weather forecast models and climate models are not able to resolve this heterogeneity at the smaller scales. Many processes in the soil or at the surface, however, are highly nonlinear. This holds, for example, for evaporation processes, where stomata or aerodynamic resistances are nonlinear functions of the local micro-climate. Other examples are threshold dependent processes, e.g., the generation of runoff or the melting of snow. It has been shown that using averaged parameters in the computation of these processes leads to errors and especially biases, due to the involved nonlinearities. Thus it is necessary to account for the sub-grid scale surface heterogeneities in atmospheric modeling. One approach to take the variability of the earth's surface into account is the mosaic approach. Here the soil-vegetation-atmosphere transfer (SVAT) model is run on an explicit higher resolution than the atmospheric part of a coupled model, which is feasible due to generally lower computational costs of a SVAT model compared to the atmospheric part. The question arises how to deal with the scale differences at the interface between the two resolutions. Usually the assumption of a homogeneous forcing for all sub-pixels is made. However, over a heterogeneous surface, usually the boundary layer is also heterogeneous. Thus, by assuming a constant atmospheric forcing again biases in the turbulent heat fluxes may occur due to neglected atmospheric forcing variability. Therefore we have developed and tested a downscaling scheme to disaggregate the atmospheric variables of the lower atmosphere that are used as input to force a SVAT model. Our downscaling scheme consists of three steps: 1) a bi-quadratic spline interpolation of the coarse-resolution field; 2) a "deterministic" part, where relationships between surface and near-surface variables are exploited; and 3) a noise-generation step, in which the still missing, not explained, variance is added as noise. The scheme has been developed and tested based on high-resolution (400 m) model output of the weather forecast (and regional climate) COSMO model. Downscaling steps 1 and 2 reduce the error made by the homogeneous assumption considerably, whereas the third step leads to close agreement of the sub-grid scale variance with the reference. This is, however, achieved at the cost of higher root mean square errors. Thus, before applying the downscaling system to atmospheric data a decision should be made whether the lowest possible errors (apply only downscaling step 1 and 2) or a most realistic sub-grid scale variability (apply also step 3) is desired. This downscaling scheme is currently being implemented into the COSMO model, where it will be used in combination with the mosaic approach. However, this downscaling scheme can also be applied to drive stand-alone SVAT models or hydrological models, which usually also need high-resolution atmospheric forcing data.
Downscaling wind and wavefields for 21st century coastal flood hazard projections in a region of complex terrain

USGS Publications Warehouse

O'Neill, Andrea; Erikson, Li; Barnard, Patrick

2017-01-01

While global climate models (GCMs) provide useful projections of near-surface wind vectors into the 21st century, resolution is not sufficient enough for use in regional wave modeling. Statistically downscaled GCM projections from Multivariate Adaptive Constructed Analogues provide daily averaged near-surface winds at an appropriate spatial resolution for wave modeling within the orographically complex region of San Francisco Bay, but greater resolution in time is needed to capture the peak of storm events. Short-duration high wind speeds, on the order of hours, are usually excluded in statistically downscaled climate models and are of key importance in wave and subsequent coastal flood modeling. Here we present a temporal downscaling approach, similar to constructed analogues, for near-surface winds suitable for use in local wave models and evaluate changes in wind and wave conditions for the 21st century. Reconstructed hindcast winds (1975–2004) recreate important extreme wind values within San Francisco Bay. A computationally efficient method for simulating wave heights over long time periods was used to screen for extreme events. Wave hindcasts show resultant maximum wave heights of 2.2 m possible within the Bay. Changes in extreme over-water wind speeds suggest contrasting trends within the different regions of San Francisco Bay, but 21th century projections show little change in the overall magnitude of extreme winds and locally generated waves.
A hybrid downscaling procedure for estimating the vertical distribution of ambient temperature in local scale

NASA Astrophysics Data System (ADS)

Yiannikopoulou, I.; Philippopoulos, K.; Deligiorgi, D.

2012-04-01

The vertical thermal structure of the atmosphere is defined by a combination of dynamic and radiation transfer processes and plays an important role in describing the meteorological conditions at local scales. The scope of this work is to develop and quantify the predictive ability of a hybrid dynamic-statistical downscaling procedure to estimate the vertical profile of ambient temperature at finer spatial scales. The study focuses on the warm period of the year (June - August) and the method is applied to an urban coastal site (Hellinikon), located in eastern Mediterranean. The two-step methodology initially involves the dynamic downscaling of coarse resolution climate data via the RegCM4.0 regional climate model and subsequently the statistical downscaling of the modeled outputs by developing and training site-specific artificial neural networks (ANN). The 2.5ox2.5o gridded NCEP-DOE Reanalysis 2 dataset is used as initial and boundary conditions for the dynamic downscaling element of the methodology, which enhances the regional representivity of the dataset to 20km and provides modeled fields in 18 vertical levels. The regional climate modeling results are compared versus the upper-air Hellinikon radiosonde observations and the mean absolute error (MAE) is calculated between the four grid point values nearest to the station and the ambient temperature at the standard and significant pressure levels. The statistical downscaling element of the methodology consists of an ensemble of ANN models, one for each pressure level, which are trained separately and employ the regional scale RegCM4.0 output. The ANN models are theoretically capable of estimating any measurable input-output function to any desired degree of accuracy. In this study they are used as non-linear function approximators for identifying the relationship between a number of predictor variables and the ambient temperature at the various vertical levels. An insight of the statistically derived input-output transfer functions is obtained by utilizing the ANN weights method, which quantifies the relative importance of the predictor variables in the estimation procedure. The overall downscaling performance evaluation incorporates a set of correlation and statistical measures along with appropriate statistical tests. The hybrid downscaling method presented in this work can be extended to various locations by training different site-specific ANN models and the results, depending on the application, can be used for assisting the understanding of the past, present and future climatology. ____________________________ This research has been co-financed by the European Union and Greek national funds through the Operational Program "Education and Lifelong Learning" of the National Strategic Reference Framework (NSRF) - Research Funding Program: Heracleitus II: Investing in knowledge society through the European Social Fund.
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

USDA-ARS?s Scientific Manuscript database

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...
Key Findings from the U.S.-India Partnership for Climate Resilience Workshop on Development and Application of Downscaling Climate Projections

NASA Astrophysics Data System (ADS)

Kunkel, K.; Dissen, J.; Easterling, D. R.; Kulkarni, A.; Akhtar, F. H.; Hayhoe, K.; Stoner, A. M. K.; Swaminathan, R.; Thrasher, B. L.

2017-12-01

s part of the Department of State U.S.-India Partnership for Climate Resilience (PCR), scientists from NOAA NCEI, CICS-NC, Texas Tech University (TTU), Stanford University (SU), and the Indian Institute of Tropical Meteorology (IITM) held a workshop at IITM in Pune, India during 7-9 March 2017 on the development, techniques and applications of downscaled climate projections. Workshop participants from TTU, SU, and IITM presented state-of-the-art climate downscaling techniques using the ARRM method, NASA NEX climate products, CORDEX-South Asia and analysis tools for resilience planning and sustainable development. PCR collaborators in attendance included Indian practitioners, researchers and other NGO including the WRI Partnership for Resilience and Preparedness (PREP), The Energy and Resources Institute (TERI), and NIH. The scientific techniques were provided to workshop participants in a software package written in R by TTU scientists and several sessions were devoted to hands-on experience with the software package. The workshop further examined case studies on the use of downscaled climate data for decision making in a range of sectors, including human health, agriculture, and water resources management as well as to inform the development of the India State Action Plans. This talk will discuss key outcomes including information needs for downscaling climate projections, importance of QA/QC of the data, key findings from select case studies, and the importance of collaborations and partnerships to apply downscaling projections to help inform the development of the India State Action Plans.
Validation of non-stationary precipitation series for site-specific impact assessment: comparison of two statistical downscaling techniques

NASA Astrophysics Data System (ADS)

Mullan, Donal; Chen, Jie; Zhang, Xunchang John

2016-02-01

Statistical downscaling (SD) methods have become a popular, low-cost and accessible means of bridging the gap between the coarse spatial resolution at which climate models output climate scenarios and the finer spatial scale at which impact modellers require these scenarios, with various different SD techniques used for a wide range of applications across the world. This paper compares the Generator for Point Climate Change (GPCC) model and the Statistical DownScaling Model (SDSM)—two contrasting SD methods—in terms of their ability to generate precipitation series under non-stationary conditions across ten contrasting global climates. The mean, maximum and a selection of distribution statistics as well as the cumulative frequencies of dry and wet spells for four different temporal resolutions were compared between the models and the observed series for a validation period. Results indicate that both methods can generate daily precipitation series that generally closely mirror observed series for a wide range of non-stationary climates. However, GPCC tends to overestimate higher precipitation amounts, whilst SDSM tends to underestimate these. This infers that GPCC is more likely to overestimate the effects of precipitation on a given impact sector, whilst SDSM is likely to underestimate the effects. GPCC performs better than SDSM in reproducing wet and dry day frequency, which is a key advantage for many impact sectors. Overall, the mixed performance of the two methods illustrates the importance of users performing a thorough validation in order to determine the influence of simulated precipitation on their chosen impact sector.
Climate Change in Nicaragua: a dynamical downscaling of precipitation and temperature.

NASA Astrophysics Data System (ADS)

Porras, Ignasi; Domingo-Dalmau, Anna; Sole, Josep Maria; Arasa, Raul; Picanyol, Miquel; Ángeles Gonzalez-Serrano, M.°; Masdeu, Marta

2016-04-01

Climate Change affects weather patterns and modifies meteorological extreme events like tropical cyclones, heavy rainfalls, dry events, extreme temperatures, etc. The aim of this study is to show the Climate Change projections over Nicaragua for the period 2010-2040 focused on precipitation and temperature. In order to obtain the climate change signal, the results obtained by modelling a past period (1980-2009) were compared with the ones obtained by modelling a future period (2010-2040). The modelling method was based on a dynamical downscaling, coupling global and regional models. The MPI-ESM-MR global climate model was selected due to the better performance over Nicaragua. Moreover, a detailed sensitivity analysis for different parameterizations and schemes of the Weather Research and Forecast (WRF-ARW) model was made to minimize the model uncertainty. To evaluate and validate the methodology, a comparison between model outputs and satellite measurements data was realized. The results show an expected increment of the temperature and an increment of the number of days per year with temperatures higher than 35°C. Monthly precipitation patterns will change although annual total precipitation will be similar. In addition, number of dry days are expected to increase.
Assessing climate change impacts on water resources in remote mountain regions

NASA Astrophysics Data System (ADS)

Buytaert, Wouter; De Bièvre, Bert

2013-04-01

From a water resources perspective, remote mountain regions are often considered as a basket case. They are often regions where poverty is often interlocked with multiple threats to water supply, data scarcity, and high uncertainties. In these environments, it is paramount to generate locally relevant knowledge about water resources and how they impact local livelihoods. This is often problematic. Existing environmental data collection tends to be geographically biased towards more densely populated regions, and prioritized towards strategic economic activities. Data may also be locked behind institutional and technological barriers. These issues create a "knowledge trap" for data-poor regions, which is especially acute in remote and hard-to-reach mountain regions. We present lessons learned from a decade of water resources research in remote mountain regions of the Andes, Africa and South Asia. We review the entire tool chain of assessing climate change impacts on water resources, including the interrogation and downscaling of global circulation models, translating climate variables in water availability and access, and assessing local vulnerability. In global circulation models, mountain regions often stand out as regions of high uncertainties and lack of agreement of future trends. This is partly a technical artifact because of the different resolution and representation of mountain topography, but it also highlights fundamental uncertainties in climate impacts on mountain climate. This problem also affects downscaling efforts, because regional climate models should be run in very high spatial resolution to resolve local gradients, which is computationally very expensive. At the same time statistical downscaling methods may fail to find significant relations between local climate properties and synoptic processes. Further uncertainties are introduced when downscaled climate variables such as precipitation and temperature are to be translated in hydrologically relevant variables such as streamflow and groundwater recharge. Fundamental limitations in both the understanding of hydrological processes in mountain regions (e.g., glacier melt, wetland attenuation, groundwater flows) and in data availability introduce large uncertainties. Lastly, assessing access to water resources is a major challenge. Topographical gradients and barriers, as well as strong spatiotemporal variations in hydrological processes, makes it particularly difficult to assess which parts of the mountain population is most vulnerable to future perturbations of the water cycle.
Come rain or shine: Multi-model Projections of Climate Hazards affecting Transportation in the South Central United States

NASA Astrophysics Data System (ADS)

Mullens, E.; Mcpherson, R. A.

2016-12-01

This work develops detailed trends in climate hazards affecting the Department of Transportation's Region 6, in the South Central U.S. Firstly, a survey was developed to gather information regarding weather and climate hazards in the region from the transportation community, identifying key phenomena and thresholds to evaluate. Statistically downscaled datasets were obtained from the Multivariate Adaptive Constructed Analogues (MACA) project, and the Asynchronous Regional Regression Model (ARRM), for a total of 21 model projections, two coupled model intercomparisons (CMIP3, and CMIP5), and four emissions pathways (A1Fi, B1, RCP8.5, RCP4.5). Specific hazards investigated include winter weather, freeze-thaw cycles, hot and cold extremes, and heavy precipitation. Projections for each of these variables were calculated for the region, utilizing spatial mapping, and time series analysis at the climate division level. The results indicate that cold-season phenomena such as winter weather, freeze-thaw, and cold extremes, decrease in intensity and frequency, particularly with the higher emissions pathways. Nonetheless, specific model and downscaling method yields variability in magnitudes, with the most notable decreasing trends late in the 21st century. Hot days show a pronounced increase, particularly with greater emissions, producing annual mean 100oF day frequencies by late 21st century analogous to the 2011 heatwave over the central Southern Plains. Heavy precipitation, evidenced by return period estimates and counts-over-thresholds, also show notable increasing trends, particularly between the recent past through mid-21st Century. Conversely, mean precipitation does not show significant trends and is regionally variable. Precipitation hazards (e.g., winter weather, extremes) diverge between downscaling methods and their associated model samples much more substantially than temperature, suggesting that the choice of global model and downscaled data is particularly important when considering region-specific impacts for precipitation. These results are intended to inform region transportation professionals of the susceptibility of the area to climate extremes, and to be a resource for assessing and incorporating changing risk probabilities into their planning processes.
Long-range Prediction of climatic Change in the Eastern Seaboard of Thailand over the 21st Century using various Downscaling Approaches

NASA Astrophysics Data System (ADS)

Bejranonda, Werapol; Koch, Manfred; Koontanakulvong, Sucharit

2010-05-01

Triggered by a long drought, a huge water supply crisis took place at the Eastern Seaboard of Thailand (east of the Gulf of Thailand) in 2005. In that year no rainfall occurred for four months after the beginning of the rainy season which led to the situation that the industrial estates of the Eastern Seaboard were not able to fully operate. Normally, most of the urban and industrial water used in this coastal region along east of the Gulf of Thailand, which is part of the Pacific Ocean, is surface water stored in a large-scale reservoir-network across the main watershed in the region. Thus the three major reservoirs usually gather water from monsoon storms that blow from the South and provide accumulative 80% of the annual rainfall during the 6 months of the rainy season which normally lasts from May-October. During the dry season (November - April) the winds are blowing from northern Indo-China land mass and rain drops only a few days in a month. Because of this typical tropical climate system, surface water resources across most of the southeastern Asia-Pacific region and the Eastern Seaboard of Thailand, in particular, rely on the annual occurrence of the monsoon season. There is now sufficient evidence that the named extreme weather conditions of 2005 occurring in that part of Thailand are not a singularity, but might be another signal of recent ongoing climate change in that country as a whole. Because of this imminent threat to the water resources of the region, and for the set-up of appropriate water management schemes for the near future, a climate impact study is proposed here. More specifically, the water budget of the Khlong Yai basin, which is the main watershed of the Eastern Seaboard, is modeled using the distributed hydrological model SWAT. To that avail the watershed model is coupled sequentially to a global climate model (GCM), whereby the latter provides the input forcing parameters (e.g. precipitation and temperature) to the former. Because of the different scales of the hydrological (local to regional) and of the GCM (global), one is faced with the problem of 'downscaling' the coarse grid resolution output of the GCM to the fine grid of the hydrological model. Although there have been numerous downscaling approaches proposed to that regard over the last decade, the jury is still out about the best method to use in a particular application. The focus here is on the downscaling part of the investigation, i.e. the proper preparation of the GCM's output to serve as input, i.e. the driving force, to the hydrological model (which is not further discussed here). Daily ensembles of climate variables computed by means of the CGCM3 model of the Canadian Climate Center which has a horizontal grid resolution of approximately the size of the whole study basin are used here, indicating clearly the need for downscaling. Daily observations of local climate variables available since 1971 are used as additional input to the various downscaling tools proposed which are, namely, the stochastic weather generator (LARS-WG), the statistical downscaling model (SDSM), and a multiple linear regression model between the observed variables and the CGCM3 predictors. Both the 2D and the 3D versions of the CGCM3 model are employed to predict, 100 years ahead up to year 2100, the monthly rainfall and temperatures, based on the past calibration period (training period) 1971-2000. To investigate the prediction performance, multiple linear regression, autoregressive (AR) and autoregressive integrated moving average (ARIMA) models are applied to the time series of the observation data which are aggregated into monthly time steps to be able compare them with the downscaling results above. Likewise, multiple linear regression and ARIMA models also executed on the CGCM3 predictors and the Pacific / Indian oceans indices as external regressors to predict short-term local climate variations. The results of the various downscaling method are validated for years 2001-2006 at selected meteorological stations in the Khlong Yai basin, assuming the IPCC's A1B and A2 emission scenarios. The performance of the monthly climate prediction has been evaluated by comparison with observed data using the Nash-Sutcliffe model efficiency measure. Among the statistical/stochastical downscaling and the forecasting methods used, the climate prediction by the ARIMA model with ocean indices and CGCM predictor included as external regressors are the most reliable. Thus for the verification period 2001-2006 Nash-Sutcliffe coefficient of 0.84, 0.47 and 0.50 are obtained for the minimum and maximum temperatures and the rainfall, respectively, whereas the corresponding 1-year ahead predictions are 0.77, 0.43 and 0.48, respectively. The best external regressor for the prediction of the minimum temperatures in the basin is, surprisingly, the El Niño 1.2 SST anomaly time series; for the prediction of the maximum temperatures, the minimum surface air temperature predictor in CGCM3 (tasmin); and for the prediction of the rainfall, the 850hPa eastward-wind predictor in CGCM3 (p8_uas). Based on year 2000, the downscaling results show that the average minimum temperature will be higher by 0.4 to 5.9 ° C by year 2100, while the average maximum temperature will be rather stable, with only little change between -0.2 to +0.3° C. As for the rainfall at year 2100, a possible change from +0.2 to 12.0 mm/month is obtained. These climate prediction results mean that, although there will be more rainfall in the future, the much higher temperature will lead to more evapotranspiration, i.e. more agricultural water demand. Besides, the increasing rainfall will most likely lead to unexpected flood events in the future that will require precautionary planning at the watershed-scale. This will be further analyzed during the course of the ongoing study using the SWAT hydrological model mentioned above.
Can bias correction and statistical downscaling methods improve the skill of seasonal precipitation forecasts?

NASA Astrophysics Data System (ADS)

Manzanas, R.; Lucero, A.; Weisheimer, A.; Gutiérrez, J. M.

2018-02-01

Statistical downscaling methods are popular post-processing tools which are widely used in many sectors to adapt the coarse-resolution biased outputs from global climate simulations to the regional-to-local scale typically required by users. They range from simple and pragmatic Bias Correction (BC) methods, which directly adjust the model outputs of interest (e.g. precipitation) according to the available local observations, to more complex Perfect Prognosis (PP) ones, which indirectly derive local predictions (e.g. precipitation) from appropriate upper-air large-scale model variables (predictors). Statistical downscaling methods have been extensively used and critically assessed in climate change applications; however, their advantages and limitations in seasonal forecasting are not well understood yet. In particular, a key problem in this context is whether they serve to improve the forecast quality/skill of raw model outputs beyond the adjustment of their systematic biases. In this paper we analyze this issue by applying two state-of-the-art BC and two PP methods to downscale precipitation from a multimodel seasonal hindcast in a challenging tropical region, the Philippines. To properly assess the potential added value beyond the reduction of model biases, we consider two validation scores which are not sensitive to changes in the mean (correlation and reliability categories). Our results show that, whereas BC methods maintain or worsen the skill of the raw model forecasts, PP methods can yield significant skill improvement (worsening) in cases for which the large-scale predictor variables considered are better (worse) predicted by the model than precipitation. For instance, PP methods are found to increase (decrease) model reliability in nearly 40% of the stations considered in boreal summer (autumn). Therefore, the choice of a convenient downscaling approach (either BC or PP) depends on the region and the season.
Using a Freshwater Lake Model Coupled with WRF for Dynamical Downscaling Applications

EPA Science Inventory

The ability to represent extremes in temperature and precipitation in regional climates (including those affected by inland lakes) has become an area of focus as regional climate models (RCMs) simulate smaller temporal and spatial scales. When using the Weather Research and Fore...
A comparison of dynamical and statistical downscaling methods for regional wave climate projections along French coastlines.

NASA Astrophysics Data System (ADS)

Laugel, Amélie; Menendez, Melisa; Benoit, Michel; Mattarolo, Giovanni; Mendez, Fernando

2013-04-01

Wave climate forecasting is a major issue for numerous marine and coastal related activities, such as offshore industries, flooding risks assessment and wave energy resource evaluation, among others. Generally, there are two main ways to predict the impacts of the climate change on the wave climate at regional scale: the dynamical and the statistical downscaling of GCM (Global Climate Model). In this study, both methods have been applied on the French coast (Atlantic , English Channel and North Sea shoreline) under three climate change scenarios (A1B, A2, B1) simulated with the GCM ARPEGE-CLIMAT, from Météo-France (AR4, IPCC). The aim of the work is to characterise the wave climatology of the 21st century and compare the statistical and dynamical methods pointing out advantages and disadvantages of each approach. The statistical downscaling method proposed by the Environmental Hydraulics Institute of Cantabria (Spain) has been applied (Menendez et al., 2011). At a particular location, the sea-state climate (Predictand Y) is defined as a function, Y=f(X), of several atmospheric circulation patterns (Predictor X). Assuming these climate associations between predictor and predictand are stationary, the statistical approach has been used to project the future wave conditions with reference to the GCM. The statistical relations between predictor and predictand have been established over 31 years, from 1979 to 2009. The predictor is built as the 3-days-averaged squared sea level pressure gradient from the hourly CFSR database (Climate Forecast System Reanalysis, http://cfs.ncep.noaa.gov/cfsr/). The predictand has been extracted from the 31-years hindcast sea-state database ANEMOC-2 performed with the 3G spectral wave model TOMAWAC (Benoit et al., 1996), developed at EDF R&D LNHE and Saint-Venant Laboratory for Hydraulics and forced by the CFSR 10m wind field. Significant wave height, peak period and mean wave direction have been extracted with an hourly-resolution at 110 coastal locations along the French coast. The model, based on the BAJ parameterization of the source terms (Bidlot et al, 2007) was calibrated against ten years of GlobWave altimeter observations (2000-2009) and validated through deep and shallow water buoy observations. The dynamical downscaling method has been performed with the same numerical wave model TOMAWAC used for building ANEMOC-2. Forecast simulations are forced by the 10m wind fields of ARPEGE-CLIMAT (A1B, A2, B1) from 2010 to 2100. The model covers the Atlantic Ocean and uses a spatial resolution along the French and European coast of 10 and 20 km respectively. The results of the model are stored with a time resolution of one hour. References: Benoit M., Marcos F., and F. Becq, (1996). Development of a third generation shallow-water wave model with unstructured spatial meshing. Proc. 25th Int. Conf. on Coastal Eng., (ICCE'1996), Orlando (Florida, USA), pp 465-478. Bidlot J-R, Janssen P. and Adballa S., (2007). A revised formulation of ocean wave dissipation and its model impact, technical memorandum ECMWF n°509. Menendez, M., Mendez, F.J., Izaguirre,C., Camus, P., Espejo, A., Canovas, V., Minguez, R., Losada, I.J., Medina, R. (2011). Statistical Downscaling of Multivariate Wave Climate Using a Weather Type Approach, 12th International Workshop on Wave Hindcasting and Forecasting and 3rd Coastal Hazard Symposium, Kona (Hawaii).
Inevitable end-of-21st-century trends toward earlier surface runoff timing in California's Sierra Nevada Mountains

NASA Astrophysics Data System (ADS)

Schwartz, M. A.; Hall, A. D.; Sun, F.; Walton, D.; Berg, N.

2015-12-01

Hybrid dynamical-statistical downscaling is used to produce surface runoff timing projections for California's Sierra Nevada, a high-elevation mountain range with significant seasonal snow cover. First, future climate change projections (RCP8.5 forcing scenario, 2081-2100 period) from five CMIP5 global climate models (GCMs) are dynamically downscaled. These projections reveal that future warming leads to a shift toward earlier snowmelt and surface runoff timing throughout the Sierra Nevada region. Relationships between warming and surface runoff timing from the dynamical simulations are used to build a simple statistical model that mimics the dynamical model's projected surface runoff timing changes given GCM input or other statistically-downscaled input. This statistical model can be used to produce surface runoff timing projections for other GCMs, periods, and forcing scenarios to quantify ensemble-mean changes, uncertainty due to intermodel variability and consequences stemming from choice of forcing scenario. For all CMIP5 GCMs and forcing scenarios, significant trends toward earlier surface runoff timing occur at elevations below 2500m. Thus, we conclude that trends toward earlier surface runoff timing by the end-of-the-21st century are inevitable. The changes to surface runoff timing diagnosed in this study have implications for many dimensions of climate change, including impacts on surface hydrology, water resources, and ecosystems.
Building hydrologic information systems to promote climate resilience in the Blue Nile/Abay higlands

USDA-ARS?s Scientific Manuscript database

Climate adaptation requires information about climate and land-surface conditions – spatially distributed, and at scales of human influence (the field scale). This article describes a project aimed at combining meteorological data, satellite remote sensing, hydrologic modeling, and downscaled clima...
Century long observation constrained global dynamic downscaling and hydrologic implication

NASA Astrophysics Data System (ADS)

Kim, H.; Yoshimura, K.; Chang, E.; Famiglietti, J. S.; Oki, T.

2012-12-01

It has been suggested that greenhouse gas induced warming climate causes the acceleration of large scale hydrologic cycles, and, indeed, many regions on the Earth have been suffered by hydrologic extremes getting more frequent. However, historical observations are not able to provide enough information in comprehensive manner to understand their long-term variability and/or global distributions. In this study, a century long high resolution global climate data is developed in order to break through existing limitations. 20th Century Reanalysis (20CR) which has relatively low spatial resolution (~2.0°) and longer term availability (140 years) is dynamically downscaled into global T248 (~0.5°) resolution using Experimental Climate Prediction Center (ECPC) Global Spectral Model (GSM) by spectral nudging data assimilation technique. Also, Global Precipitation Climatology Centre (GPCC) and Climate Research Unit (CRU) observational data are adopted to reduce model dependent uncertainty. Downscaled product successfully represents realistic geographical detail keeping low frequency signal in mean state and spatiotemporal variability, while previous bias correction method fails to reproduce high frequency variability. Newly developed data is used to investigate how long-term large scale terrestrial hydrologic cycles have been changed globally and how they have been interacted with various climate modes, such as El-Niño Southern Oscillation (ENSO) and Atlantic Multidecadal Oscillation (AMO). As a further application, it will be used to provide atmospheric boundary condition of multiple land surface models in the Global Soil Wetness Project Phase 3 (GSWP3).

Downscaling to the Climate Near the Ground: Measurements and Modeling Along the Macro-, Meso-, Topo-, and Microclimate Hierarchy

NASA Astrophysics Data System (ADS)

van de Ven, C.; Weiss, S. B.

2009-12-01

Most climate models are expressed at regional scales, with resolutions on the scales of kilometers. When used for ecological modeling, these climate models help explain only broad-scale trends, such as latitudinal and upslope migration of plants. However, more refined ecological models require down-scaled climate data at ecologically relevant spatial scales, and the goal of this presentation is to demonstrate robust downscaling methods. For example, in the White Mountains, eastern California, tree species, including bristlecone pine (Pinus longaeva) are seen moving not just upslope, but also sideways across aspects, and downslope into areas characterized by cold air drainage. Macroclimate in the White Mountains is semi-arid, residing in the rain shadow of the Sierra Nevada. Macroclimate is modified by mesoscale effects of mountain ranges, where climate becomes wetter and colder with elevation, the temperature decreasing according to the regionally and temporally-specific lapse rate. Local topography further modifies climate, where slope angle, aspect, and topographic position further impact the temperature at a given site. Finally, plants experience extremely localized microclimate, where surrounding vegetation provide differing degrees of shade. We measured and modeled topoclimate across the White Mountains using iButton Thermochron temperature data loggers during late summer in 2006 and 2008, and have documented effects of microclimatic temperature differences between sites in the open and shaded by shrubs. Starting with PRISM 800m data, we derived mesoscale lapse rates. Then, we calculated temperature differentials between each Thermochron and a long-term weather station in the middle of the range at Crooked Creek Valley. We modeled month-specific minimum temperature differentials by regressing the Thermochron-weather station minimum temperature differentials with various topographic parameters. Topographic position, the absolute value of topographic position, and slope combined to provide a very close fit (r2>0.9) to measured inversions of >8°C. Although topoclimatic maximum temperature models have been more elusive, regressions with degree hours greater than zero (DH>0) have been modeled with September insolation and slope (r2=0.7). In paired experiments, Thermochrons also recorded the temperature differences between the environment under sagebrush (Artemisia tridentata) and in the open, with an average minimum temperature difference of 2.1°C, and maximum temperature difference of 4.5°C. When we incorporate hourly weather station data, the strength of the inversion is weakened by wind, higher relative humidity, and cloudiness. This hierarchical modeling provides a template for downscaling climate and weather to ecologically relevant scales.
Using LiDAR and remote microclimate loggers to downscale near-surface air temperatures for site-level studies

Treesearch

Andrew D. George; Frank R. Thompson; John. Faaborg

2015-01-01

A spatial mismatch exists between regional climate models and conditions experienced by individual organisms. We demonstrate an approach to downscaling air temperatures for site-level studies using airborne LiDAR data and remote microclimate loggers. In 2012-2013, we established a temperature logger network in the forested region of central Missouri, USA, and obtained...
New statistical downscaling for Canada

NASA Astrophysics Data System (ADS)

Murdock, T. Q.; Cannon, A. J.; Sobie, S.

2013-12-01

This poster will document the production of a set of statistically downscaled future climate projections for Canada based on the latest available RCM and GCM simulations - the North American Regional Climate Change Assessment Program (NARCCAP; Mearns et al. 2007) and the Coupled Model Intercomparison Project Phase 5 (CMIP5). The main stages of the project included (1) downscaling method evaluation, (2) scenarios selection, (3) production of statistically downscaled results, and (4) applications of results. We build upon a previous downscaling evaluation project (Bürger et al. 2012, Bürger et al. 2013) in which a quantile-based method (Bias Correction/Spatial Disaggregation - BCSD; Werner 2011) provided high skill compared with four other methods representing the majority of types of downscaling used in Canada. Additional quantile-based methods (Bias-Correction/Constructed Analogues; Maurer et al. 2010 and Bias-Correction/Climate Imprint ; Hunter and Meentemeyer 2005) were evaluated. A subset of 12 CMIP5 simulations was chosen based on an objective set of selection criteria. This included hemispheric skill assessment based on the CLIMDEX indices (Sillmann et al. 2013), historical criteria used previously at the Pacific Climate Impacts Consortium (Werner 2011), and refinement based on a modified clustering algorithm (Houle et al. 2012; Katsavounidis et al. 1994). Statistical downscaling was carried out on the NARCCAP ensemble and a subset of the CMIP5 ensemble. We produced downscaled scenarios over Canada at a daily time resolution and 300 arc second (~10 km) spatial resolution from historical runs for 1951-2005 and from RCP 2.6, 4.5, and 8.5 projections for 2006-2100. The ANUSPLIN gridded daily dataset (McKenney et al. 2011) was used as a target. It has national coverage, spans the historical period of interest 1951-2005, and has daily time resolution. It uses interpolation of station data based on thin-plate splines. This type of method has been shown to have superior skill in interpolating RCM data over North America (McGinnis et al. 2012). An early application of the new dataset was to provide projections of climate extremes for adaptation planning by the British Columbia Ministry of Transportation and Infrastructure. Recently, certain stretches of highway have experienced extreme precipitation events resulting in substantial damage to infrastructure. As part of the planning process to refurbish or replace components of these highways, information about the magnitude and frequency of future extreme events are needed to inform the infrastructure design. The increased resolution provided by downscaling improves the representation of topographic features, particularly valley temperature and precipitation effects. A range of extreme values, from simple daily maxima and minima to complex multi-day and threshold-based climate indices were computed and analyzed from the downscaled output. Selected results from this process and how the projections of precipitation extremes are being used in the context of highway infrastructure planning in British Columbia will be presented.
Designing ecological climate change impact assessments to reflect key climatic drivers

USGS Publications Warehouse

Sofaer, Helen R.; Barsugli, Joseph J.; Jarnevich, Catherine S.; Abatzoglou, John T.; Talbert, Marian; Miller, Brian W.; Morisette, Jeffrey T.

2017-01-01

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.
Designing ecological climate change impact assessments to reflect key climatic drivers.

PubMed

Sofaer, Helen R; Barsugli, Joseph J; Jarnevich, Catherine S; Abatzoglou, John T; Talbert, Marian K; Miller, Brian W; Morisette, Jeffrey T

2017-07-01

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.
Simulating the impact of the large-scale circulation on the 2-m temperature and precipitation climatology

EPA Science Inventory

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...
Can dynamically downscaled climate model outputs improve pojections of extreme precipitation events?

EPA Science Inventory

Many of the storms that generate damaging floods are caused by locally intense, sub-daily precipitation, yet the spatial and temporal resolution of the most widely available climate model outputs are both too coarse to simulate these events. Thus there is often a disconnect betwe...
What’s Needed from Climate Modeling to Advance Actionable Science for Water Utilities?

NASA Astrophysics Data System (ADS)

Barsugli, J. J.; Anderson, C. J.; Smith, J. B.; Vogel, J. M.

2009-12-01

“…perfect information on climate change is neither available today nor likely to be available in the future, but … over time, as the threats climate change poses to our systems grow more real, predicting those effects with greater certainty is non-discretionary. We’re not yet at a level at which climate change projections can drive climate change adaptation.” (Testimony of WUCA Staff Chair David Behar to the House Committee on Science and Technology, May 5, 2009) To respond to this challenge, the Water Utility Climate Alliance (WUCA) has sponsored a white paper titled “Options for Improving Climate Modeling to Assist Water Utility Planning for Climate Change. ” This report concerns how investments in the science of climate change, and in particular climate modeling and downscaling, can best be directed to help make climate projections more actionable. The meaning of “model improvement” can be very different depending on whether one is talking to a climate model developer or to a water manager trying to incorporate climate projections in to planning. We first surveyed the WUCA members on present and potential uses of climate model projections and on climate inputs to their various system models. Based on those surveys and on subsequent discussions, we identified four dimensions along which improvement in modeling would make the science more “actionable”: improved model agreement on change in key parameters; narrowing the range of model projections; providing projections at spatial and temporal scales that match water utilities system models; providing projections that water utility planning horizons. With these goals in mind we developed four options for improving global-scale climate modeling and three options for improving downscaling that will be discussed. However, there does not seem to be a single investment - the proverbial “magic bullet” -- which will substantially reduce the range of model projections at the scales at which utility planning is conducted. In the near term we feel strongly that water utilities and climate scientists should work together to leverage the upcoming Coupled Model Intercomparison Project, Phase 5 (CMIP5; a coordinated set climate model experiments that will be used to support the upcoming IPCC Fifth Assessment) to better benefit water utilities. In the longer term, even with model and downscaling improvements, it is very likely that substantial uncertainty about future climate change at the desired spatial and temporal scales will remain. Nonetheless, there is no doubt the climate is changing, and the challenge is to work with what we have, or what we can reasonably expect to have in the coming years to make the best decisions we can.
Assessment of gridded observations used for climate model validation in the Mediterranean region: the HyMeX and MED-CORDEX framework

NASA Astrophysics Data System (ADS)

Flaounas, Emmanouil; Drobinski, Philippe; Borga, Marco; Calvet, Jean-Christophe; Delrieu, Guy; Morin, Efrat; Tartari, Gianni; Toffolon, Roberta

2012-06-01

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.
75 FR 54403 - U.S. National Climate Assessment Objectives, Proposed Topics, and Next Steps

Federal Register 2010, 2011, 2012, 2013, 2014

2010-09-07

..., methods and design, tools for assessing climate change and impacts, dealing with uncertainty, sources of..., coordination with other Federal climate-related programs, design of documents and tailored communications with... methodological perspectives related to selecting model and downscaling outputs and approaches for their use in...
Analysis of reference evapotranspiration (ET0) trends under climate change in Bangladesh using observed and CMIP5 data sets

NASA Astrophysics Data System (ADS)

Rahman, Mohammad Atiqur; Yunsheng, Lou; Sultana, Nahid; Ongoma, Victor

2018-03-01

ET0 is an important hydro-meteorological phenomenon, which is influenced by changing climate like other climatic parameters. This study investigates the present and future trends of ET0 in Bangladesh using 39 years' historical and downscaled CMIP5 daily climatic data for the twenty-first century. Statistical Downscaling Model (SDSM) was used to downscale the climate data required to calculate ET0. Penman-Monteith formula was applied in ET0 calculation for both the historical and modelled data. To analyse ET0 trends and trend changing patterns, modified Mann-Kendall and Sequential Mann-Kendall tests were, respectively, done. Spatial variations of ET0 trends are presented by inverse distance weighting interpolation using ArcGIS 10.2.2. Results show that RCP8.5 (2061-2099) will experience the highest amount of ET0 totals in comparison to the historical and all other scenarios in the same time span of 39 years. Though significant positive trends were observed in the mid and last months of year from month-wise trend analysis of representative concentration pathways, significant negative trends were also found for some months using historical data in similar analysis. From long-term annual trend analysis, it was found that major part of the country represents decreasing trends using historical data, but increasing trends were observed for modelled data. Theil-Sen estimations of ET0 trends in the study depict a good consistency with the Mann-Kendall test results. The findings of the study would contribute in irrigation water management and planning of the country and also in furthering the climate change study using modelled data in the context of Bangladesh.
Hydrologic response of Pacific Northwest river to climate change

NASA Astrophysics Data System (ADS)

Su, F.; Cuo, L.; Wu, H.; Mantua, N.; Lettenmaier, D. P.

2009-12-01

The climate of the Pacific Northwest (PNW - which we define as the Columbia River basin and watersheds draining to the Oregon and Washington coasts) is expected to warm by approximately 0.3°C per decade in the next 100 years based on the IPCC the Fourth Assessment Report (AR4) results. PNW hydrology is particularly sensitive to a warming climate because of the dominant role of snowmelt in seasonal streamflow. Timing shifts in seasonality of flows, peak discharge, and base flows will impact water resource management, regional electrical energy production, and freshwater ecosystems. In this work we update previous studies of implications of climate change on PNW hydrology using a macroscale hydrology model driven by simulations of temperature and precipitation downscaled from runs of 20 General Circulation Models (GCMs) under two emissions scenarios (lower B1 and mid-high A1B) in the 21st century. The hydrology model is implemented at 1/16th degree spatial resolution over the entire PNW. A (statistical) bias-correction and spatial disaggregation downscaling approach is used for translating the transient monthly climate model output into continuous daily forcings for the hydrologic analysis. We evaluate projected changes in snow water equivalent, seasonal streamflow, and frequency of peak low flows over a set of case study watersheds in the region. We also compare these hydrologic projections with previous analysis based on delta downscaling method over the PNW. This research is part of a project investigating climate change impacts on the future of wild Pacific salmon, and is a pilot effort to investigate the hydrologic sensitivity of salmon bearing watersheds around the entire North Pacific Rim.
Use of statistically and dynamically downscaled atmospheric model output for hydrologic simulations in three mountainous basins in the western United States

USGS Publications Warehouse

Hay, L.E.; Clark, M.P.

2003-01-01

This paper examines the hydrologic model performance in three snowmelt-dominated basins in the western United States to dynamically- and statistically downscaled output from the National Centers for Environmental Prediction/National Center for Atmospheric Research Reanalysis (NCEP). Runoff produced using a distributed hydrologic model is compared using daily precipitation and maximum and minimum temperature timeseries derived from the following sources: (1) NCEP output (horizontal grid spacing of approximately 210 km); (2) dynamically downscaled (DDS) NCEP output using a Regional Climate Model (RegCM2, horizontal grid spacing of approximately 52 km); (3) statistically downscaled (SDS) NCEP output; (4) spatially averaged measured data used to calibrate the hydrologic model (Best-Sta) and (5) spatially averaged measured data derived from stations located within the area of the RegCM2 model output used for each basin, but excluding Best-Sta set (All-Sta). In all three basins the SDS-based simulations of daily runoff were as good as runoff produced using the Best-Sta timeseries. The NCEP, DDS, and All-Sta timeseries were able to capture the gross aspects of the seasonal cycles of precipitation and temperature. However, in all three basins, the NCEP-, DDS-, and All-Sta-based simulations of runoff showed little skill on a daily basis. When the precipitation and temperature biases were corrected in the NCEP, DDS, and All-Sta timeseries, the accuracy of the daily runoff simulations improved dramatically, but, with the exception of the bias-corrected All-Sta data set, these simulations were never as accurate as the SDS-based simulations. This need for a bias correction may be somewhat troubling, but in the case of the large station-timeseries (All-Sta), the bias correction did indeed 'correct' for the change in scale. It is unknown if bias corrections to model output will be valid in a future climate. Future work is warranted to identify the causes for (and removal of) systematic biases in DDS simulations, and improve DDS simulations of daily variability in local climate. Until then, SDS based simulations of runoff appear to be the safer downscaling choice.
CORDEX Coordinated Output for Regional Evaluation

NASA Astrophysics Data System (ADS)

Gutowski, William; Giorgi, Filippo; Lake, Irene

2017-04-01

The Science Advisory Team for the Coordinated Regional Downscaling Experiment (CORDEX) has developed a baseline framework of specified regions, resolutions and simulation periods intended to provide a foundation for ongoing regional CORDEX activities: the CORDEX Coordinated Output for Regional Evaluation, or CORDEX-CORE. CORDEX-CORE was conceived in part to be responsive to IPCC needs for coordinated simulations that could provide regional climate downscaling (RCD) that yields fine-scale climate information beyond that resolved by GCMs. For each CORDEX region, a matrix of GCM-RCD experiments is designed based on the need to cover as much as possible different dimensions of the uncertainty space (e.g., different emissions and land-use scenarios, GCMs, RCD models and techniques). An appropriate set of driving GCMs can allow a program of simulations that efficiently addresses key scientific issues within CORDEX, while facilitating comparison and transfer of results and lessons learned across different regions. The CORDEX-CORE program seeks to provide, as much as possible, homogeneity across domains, so it is envisioned that a standard set of regional climate models (RCMs) and empirical statistical downscaling (ESD) methods will downscale a standard set of GCMs over all or at least most CORDEX domains for a minimum set of scenarios (high and low end). The focus is on historical climate simulations for the 20th century and projections for 21st century, implying that data would be needed minimally for the period 1950-2100 (but ideally 1900-2100). This foundational ensemble can be regionally enriched with further contributions (additional GCM-RCD pairs) by individual groups over their selected domains of interest. The RCM model resolution for these core experiments will be in the range of 10-20 km, a resolution that has been shown to provide substantial added value for a variety of climate variables and that represents a significant forward step compared in the CORDEX program. This presentation presents the vision and structure of CORDEX-CORE while also soliciting discussion on plans for implementing the program.
Uncertainties in Hydrologic and Climate Change Impact Analysis in Major Watersheds in British Columbia, Canada

NASA Astrophysics Data System (ADS)

Bennett, K. E.; Schnorbus, M.; Werner, A. T.; Music, B.; Caya, D.; Rodenhuis, D. R.

2009-12-01

Uncertainties in the projections of future hydrologic change can be assessed using a suite of tools, thereby allowing researchers to focus on improvement to identifiable sources of uncertainty. A pareto set of optimal hydrologic parameterizations was run for three BC watersheds (Fraser, Peace and Columbia) for a range of downscaled Global Climate Model (GCM) emission scenarios to illustrate the uncertainty in hydrologic response to climate change. Results show varying responses of hydrologic regimes across geographic landscapes. Uncertainties in streamflow and water balance (runoff, evapo-transpiration, snow water equivalent, soil moisture) were analysed by forcing the Variable Infiltration Capacity (VIC) hydrologic model, run under twenty-five optimal parameter solution sets using six Bias-Corrected Statistically Downscaled (BCSD) GCM emission scenario projections for the 2050s and the 2080s. Projected changes by the 2050s include increased winter flows, increases and decreases in freshet magnitude depending on the scenario, and decreases in summer flows persisting until September. Winter runoff had the greatest range between GCM emission scenarios, while the hydrologic parameters within individual GCM emission scenarios had a winter runoff range an order of magnitude smaller. Evapo-transpiration, snow water equivalent and soil moisture exhibited a spread of ~10% or less. Streamflow changes by the 2080s lie outside the natural range of historic variability over the winter and spring. Results indicate that the changes projected between GCM emission scenarios are greater than the differences between the hydrologic model parameterizations. An alternate tool, the Canadian Regional Climate Model (CRCM) has been set up for these watersheds and various runs have been analysed to determine the range and variability present and to examine these results in comparison to the hydrologic model projections. The CRCM range and variability is an improvement over the Canadian GCM and thus requires less bias correction. However, without downscaling the CRCM results are still coarser than what is required to drive macroscale hydrologic models, such as VIC. Applying these tools has illustrated the importance of focusing on improved downscaling efforts, including downscaling CRCM results rather than CGCM data. Tools for decision-making in the face of uncertainty are emerging as a priority for the climate change impacts community, and there is a need to focus on incorporating uncertainty information along with the projection of impacts. Assessing uncertainty across a range of regimes and geographic regions can assist to identify the main sources of uncertainty and allow researchers to focus on improving those sources using more robust methodological approaches and tools.
Process-based evaluation of the ÖKS15 Austrian climate scenarios: First results

NASA Astrophysics Data System (ADS)

Mendlik, Thomas; Truhetz, Heimo; Jury, Martin; Maraun, Douglas

2017-04-01

The climate scenarios for Austria from the ÖKS15 project consists of 13 downscaled and bias-corrected RCMs from the EURO-CORDEX project. This dataset is meant for the broad public and is now available at the central national archive for climate data (CCCA Data Center). Because of this huge public outreach it is absolutely necessary to objectively discuss the limitations of this dataset and to publish these limitations, which should also be understood by a non-scientific audience. Even though systematical climatological biases have been accounted for by the Scaled-Distribution-Mapping (SDM) bias-correction method, it is not guaranteed that the model biases have been removed for the right reasons. If climate scenarios do not get the patterns of synoptic variability right, biases will still prevail in certain weather patterns. Ultimately this will have consequences for the projected climate change signals. In this study we derive typical weather types in the Alpine Region based on patterns from mean sea level pressure from ERA-INTERIM data and check the occurrence of these synoptic phenomena in EURO-CORDEX data and their corresponding driving GCMs. Based on these weather patterns we analyze the remaining biases of the downscaled and bias-corrected scenarios. We argue that such a process-based evaluation is not only necessary from a scientific point of view, but can also help the broader public to understand the limitations of downscaled climate scenarios, as model errors can be interpreted in terms of everyday observable weather.
Multi-model Ensemble Regional Climate Projection of the Maritime Continent using the MIT Regional Climate Model

NASA Astrophysics Data System (ADS)

Kang, S.; IM, E. S.; Eltahir, E. A. B.

2016-12-01

In this study, the future change in precipitation due to global warming is investigated over the Maritime Continent using the MIT Regional Climate Model (MRCM). A total of nine 30-year projections under multi-GCMs (CCSM, MPI, ACCESS) and multi-scenarios of emissions (Control, RCP4.5, RCP8.5) are dynamically downscaled using the MRCM with 12km horizontal resolution. Since downscaled results tend to systematically overestimate the precipitation regardless of GCM used as lateral boundary conditions, the Parametric Quantile Mapping (PQM) is applied to reduce this wet bias. The cross validation for the control simulation shows that the PQM method seems to retain the spatial pattern and temporal variability of raw simulation, however it effectively reduce the wet bias. Based on ensemble projections produced by dynamical downscaling and statistical bias correction, a reduction of future precipitation is discernible, in particular during dry season (June-July-August). For example, intense precipitation in Singapore is expected to be reduced in RCP8.5 projection compared to control simulation. However, the geographical patterns and magnitude of changes still remain uncertain, suffering from statistical insignificance and a lack of model agreement. Acknowledgements This research is supported by the National Research Foundation Singapore under its Campus for Research Excellence and Technological Enterprise programme. The Center for Environmental Sensing and Modeling is an interdisciplinary research group of the Singapore-MIT Alliance for Research and Technology
Assessing Hydrological and Energy Budgets in Amazonia through Regional Downscaling, and Comparisons with Global Reanalysis Products

NASA Astrophysics Data System (ADS)

Nunes, A.; Ivanov, V. Y.

2014-12-01

Although current global reanalyses provide reasonably accurate large-scale features of the atmosphere, systematic errors are still found in the hydrological and energy budgets of such products. In the tropics, precipitation is particularly challenging to model, which is also adversely affected by the scarcity of hydrometeorological datasets in the region. With the goal of producing downscaled analyses that are appropriate for a climate assessment at regional scales, a regional spectral model has used a combination of precipitation assimilation with scale-selective bias correction. The latter is similar to the spectral nudging technique, which prevents the departure of the regional model's internal states from the large-scale forcing. The target area in this study is the Amazon region, where large errors are detected in reanalysis precipitation. To generate the downscaled analysis, the regional climate model used NCEP/DOE R2 global reanalysis as the initial and lateral boundary conditions, and assimilated NOAA's Climate Prediction Center (CPC) MORPHed precipitation (CMORPH), available at 0.25-degree resolution, every 3 hours. The regional model's precipitation was successfully brought closer to the observations, in comparison to the NCEP global reanalysis products, as a result of the impact of a precipitation assimilation scheme on cumulus-convection parameterization, and improved boundary forcing achieved through a new version of scale-selective bias correction. Water and energy budget terms were also evaluated against global reanalyses and other datasets.
Ensemble downscaling in coupled solar wind-magnetosphere modeling for space weather forecasting

PubMed Central

Owens, M J; Horbury, T S; Wicks, R T; McGregor, S L; Savani, N P; Xiong, M

2014-01-01

Advanced forecasting of space weather requires simulation of the whole Sun-to-Earth system, which necessitates driving magnetospheric models with the outputs from solar wind models. This presents a fundamental difficulty, as the magnetosphere is sensitive to both large-scale solar wind structures, which can be captured by solar wind models, and small-scale solar wind “noise,” which is far below typical solar wind model resolution and results primarily from stochastic processes. Following similar approaches in terrestrial climate modeling, we propose statistical “downscaling” of solar wind model results prior to their use as input to a magnetospheric model. As magnetospheric response can be highly nonlinear, this is preferable to downscaling the results of magnetospheric modeling. To demonstrate the benefit of this approach, we first approximate solar wind model output by smoothing solar wind observations with an 8 h filter, then add small-scale structure back in through the addition of random noise with the observed spectral characteristics. Here we use a very simple parameterization of noise based upon the observed probability distribution functions of solar wind parameters, but more sophisticated methods will be developed in the future. An ensemble of results from the simple downscaling scheme are tested using a model-independent method and shown to add value to the magnetospheric forecast, both improving the best estimate and quantifying the uncertainty. We suggest a number of features desirable in an operational solar wind downscaling scheme. Key Points Solar wind models must be downscaled in order to drive magnetospheric models Ensemble downscaling is more effective than deterministic downscaling The magnetosphere responds nonlinearly to small-scale solar wind fluctuations PMID:26213518
Predicting future US water yield and ecosystem productivity by linking an ecohydrological model to WRF dynamically downscaled climate projections

Treesearch

S. Sun; Ge Sun; Erika Cohen Mack; Steve McNulty; Peter Caldwell; K. Duan; Y. Zhang

2015-01-01

Quantifying the potential impacts of climate change on water yield and ecosystem productivityÂ (i.e., carbon balances) is essential to developing sound watershed restorationÂ plans, and climate change adaptation and mitigation strategies. This study links anÂ ecohydrological model (Water Supply and Stress Index, WaSSI) with WRF (WeatherÂ Research and Forecasting Model)...

Regional climate projection of the Maritime Continent using the MIT Regional Climate Model

NASA Astrophysics Data System (ADS)

IM, E. S.; Eltahir, E. A. B.

2014-12-01

Given that warming of the climate system is unequivocal (IPCC AR5), accurate assessment of future climate is essential to understand the impact of climate change due to global warming. Modelling the climate change of the Maritime Continent is particularly challenge, showing a high degree of uncertainty. Compared to other regions, model agreement of future projections in response to anthropogenic emission forcings is much less. Furthermore, the spatial and temporal behaviors of climate projections seem to vary significantly due to a complex geographical condition and a wide range of scale interactions. For the fine-scale climate information (27 km) suitable for representing the complexity of climate change over the Maritime Continent, dynamical downscaling is performed using the MIT regional climate model (MRCM) during two thirty-year period for reference (1970-1999) and future (2070-2099) climate. Initial and boundary conditions are provided by Community Earth System Model (CESM) simulations under the emission scenarios projected by MIT Integrated Global System Model (IGSM). Changes in mean climate as well as the frequency and intensity of extreme climate events are investigated at various temporal and spatial scales. Our analysis is primarily centered on the different behavior of changes in convective and large-scale precipitation over land vs. ocean during dry vs. wet season. In addition, we attempt to find the added value to downscaled results over the Maritime Continent through the comparison between MRCM and CESM projection. Acknowledgements.This research was supported by the National Research Foundation Singapore through the Singapore MIT Alliance for Research and Technology's Center for Environmental Sensing and Modeling interdisciplinary research program.
Testing a Weather Generator for Downscaling Climate Change Projections over Switzerland

NASA Astrophysics Data System (ADS)

Keller, Denise E.; Fischer, Andreas M.; Liniger, Mark A.; Appenzeller, Christof; Knutti, Reto

2016-04-01

Climate information provided by global or regional climate models (RCMs) are often too coarse and prone to substantial biases, making it impossible to directly use daily time-series of the RCMs for local assessments and in climate impact models. Hence, statistical downscaling becomes necessary. For the Swiss National Climate Change Initiative (CH2011), a delta-change approach was used to provide daily climate projections at the local scale. This data have the main limitations that changes in variability, extremes and in the temporal structure, such as changes in the wet day frequency, are not reproduced. The latter is a considerable downside of the delta-change approach for many impact applications. In this regard, stochastic weather generators (WGs) are an appealing technique that allow the simulation of multiple realizations of synthetic weather sequences consistent with the locally observed weather statistics and its future changes. Here, we analyse a Richardson-type weather generator (WG) as an alternative method to downscale daily precipitation, minimum and maximum temperature. The WG is calibrated for 26 Swiss stations and the reference period 1980-2009. It is perturbed with change factors derived from 12 RCMs (ENSEMBLES) to represent the climate of 2070-2099 assuming the SRES A1B emission scenario. The WG can be run in multi-site mode, making it especially attractive for impact-modelers that rely on a realistic spatial structure in downscaled time-series. The results from the WG are benchmarked against the original delta-change approach that applies mean additive or multiplicative adjustments to the observations. According to both downscaling methods, the results reveal area-wide mean temperature increases and a precipitation decrease in summer, consistent with earlier studies. For the summer drying, the WG indicates primarily a decrease in wet-day frequency and correspondingly an increase in mean dry spell length by around 18% - 40% at low-elevation stations. By construction, these potential changes cannot be represented by a delta-change approach. In winter, both methods project a shortening of the frost period (-30 to -60 days) and a decrease of snow days (-20% to -100%). The WG demonstrates though, that almost present-day conditions in snow-days could still occur in the future. As expected, both methods have difficulties in representing extremes. If users focus on changes in temporal sequences and need a large number of future realizations that are spatially consistent, it is recommended to use data from a WG instead of a delta-change approach.
Improvement of downscaled rainfall and temperature across generations over the Western Himalayan region of India

NASA Astrophysics Data System (ADS)

Das, L.; Dutta, M.; Akhter, J.; Meher, J. K.

2016-12-01

It is a challenging task to create station level (local scale) climate change information over the mountainous locations of Western Himalayan Region (WHR) in India because of limited data availability and poor data quality. In the present study, missing values of station data were handled through Multiple Imputation Chained Equation (MICE) technique. Finally 22 numbers of rain gauge and 16 number of temperature station data having continuous record during 19012005 and 19692009 period respectively were considered as reference stations for developing downscaled rainfall and temperature time series from five commonly available GCMs in the IPCC's different generation assessment reports namely 2nd, 3rd, 4th and 5th hereafter known as SAR, TAR, AR4 and AR5 respectively. Downscaled models were developed using the combined data from the ERA-interim reanalysis and GCMs historical runs (in spite of forcing were not identical in different generation) as predictor and station level rainfall and temperature as predictands. Station level downscaled rainfall and temperature time series were constructed for five GCMs available in each generation. Regional averaged downscaled time series comprising of all stations was prepared for each model and generation and the downscaled results were compared with observed time series. Finally an Overall Model Improvement Index (OMII) was developed using the downscaling results, which was used to investigate the model improvement across generations as well as the improvement of downscaling results obtained from the Empirical Statistical Downscaling (ESD) methods. In case of temperature, models have improved from SAR to AR5 over the study area. In all most all the GCMs TAR is showing worst performance over the WHR by considering the different statistical indices used in this study. In case of precipitation, no model has shown gradual improvement from SAR to AR5 both for interpolated and downscaled values.
Climate Change Impact Assessment of Hydro-Climate in Southern Peninsular Malaysia

NASA Astrophysics Data System (ADS)

Ercan, A.; Ishida, K.; Kavvas, M. L.; Chen, Z. R.; Jang, S.; Amin, M. Z. M.; Shaaban, A. J.

2017-12-01

Impacts of climate change on the hydroclimate of the coastal region in the south of Peninsular Malaysia in the 21st century was assessed by means of a regional climate model utilizing an ensemble of 15 different future climate realizations. Coarse resolution Global Climate Models' future projections covering four emission scenarios based on Coupled Model Intercomparison Project phase 3 (CMIP3) datasets were dynamically downscaled to 6 km resolution over the study area. The analyses were made in terms of rainfall, air temperature, evapotranporation, and soil water storage.
Cluster Analysis of Downscaled and Explicitly Simulated North Atlantic Tropical Cyclone Tracks

DOE PAGES

Daloz, Anne S.; Camargo, S. J.; Kossin, J. P.; ...

2015-02-11

A realistic representation of the North Atlantic tropical cyclone tracks is crucial as it allows, for example, explaining potential changes in U.S. landfalling systems. Here, the authors present a tentative study that examines the ability of recent climate models to represent North Atlantic tropical cyclone tracks. Tracks from two types of climate models are evaluated: explicit tracks are obtained from tropical cyclones simulated in regional or global climate models with moderate to high horizontal resolution (1°–0.25°), and downscaled tracks are obtained using a downscaling technique with large-scale environmental fields from a subset of these models. Here, for both configurations, tracksmore » are objectively separated into four groups using a cluster technique, leading to a zonal and a meridional separation of the tracks. The meridional separation largely captures the separation between deep tropical and subtropical, hybrid or baroclinic cyclones, while the zonal separation segregates Gulf of Mexico and Cape Verde storms. The properties of the tracks’ seasonality, intensity, and power dissipation index in each cluster are documented for both configurations. The authors’ results show that, except for the seasonality, the downscaled tracks better capture the observed characteristics of the clusters. The authors also use three different idealized scenarios to examine the possible future changes of tropical cyclone tracks under 1) warming sea surface temperature, 2) increasing carbon dioxide, and 3) a combination of the two. The response to each scenario is highly variable depending on the simulation considered. Lastly, the authors examine the role of each cluster in these future changes and find no preponderant contribution of any single cluster over the others.« less
Coupled Downscaled Climate Models and Ecophysiological Metrics Forecast Habitat Compression for an Endangered Estuarine Fish

PubMed Central

Brown, Larry R.; Komoroske, Lisa M.; Wagner, R. Wayne; Morgan-King, Tara; May, Jason T.; Connon, Richard E.; Fangue, Nann A.

2016-01-01

Climate change is driving rapid changes in environmental conditions and affecting population and species’ persistence across spatial and temporal scales. Integrating climate change assessments into biological resource management, such as conserving endangered species, is a substantial challenge, partly due to a mismatch between global climate forecasts and local or regional conservation planning. Here, we demonstrate how outputs of global climate change models can be downscaled to the watershed scale, and then coupled with ecophysiological metrics to assess climate change effects on organisms of conservation concern. We employed models to estimate future water temperatures (2010–2099) under several climate change scenarios within the large heterogeneous San Francisco Estuary. We then assessed the warming effects on the endangered, endemic Delta Smelt, Hypomesus transpacificus, by integrating localized projected water temperatures with thermal sensitivity metrics (tolerance, spawning and maturation windows, and sublethal stress thresholds) across life stages. Lethal temperatures occurred under several scenarios, but sublethal effects resulting from chronic stressful temperatures were more common across the estuary (median >60 days above threshold for >50% locations by the end of the century). Behavioral avoidance of such stressful temperatures would make a large portion of the potential range of Delta Smelt unavailable during the summer and fall. Since Delta Smelt are not likely to migrate to other estuaries, these changes are likely to result in substantial habitat compression. Additionally, the Delta Smelt maturation window was shortened by 18–85 days, revealing cumulative effects of stressful summer and fall temperatures with early initiation of spring spawning that may negatively impact fitness. Our findings highlight the value of integrating sublethal thresholds, life history, and in situ thermal heterogeneity into global change impact assessments. As downscaled climate models are becoming widely available, we conclude that similar assessments at management-relevant scales will improve the scientific basis for resource management decisions. PMID:26796147
Coupled Downscaled Climate Models and Ecophysiological Metrics Forecast Habitat Compression for an Endangered Estuarine Fish.

PubMed

Brown, Larry R; Komoroske, Lisa M; Wagner, R Wayne; Morgan-King, Tara; May, Jason T; Connon, Richard E; Fangue, Nann A

2016-01-01

Climate change is driving rapid changes in environmental conditions and affecting population and species' persistence across spatial and temporal scales. Integrating climate change assessments into biological resource management, such as conserving endangered species, is a substantial challenge, partly due to a mismatch between global climate forecasts and local or regional conservation planning. Here, we demonstrate how outputs of global climate change models can be downscaled to the watershed scale, and then coupled with ecophysiological metrics to assess climate change effects on organisms of conservation concern. We employed models to estimate future water temperatures (2010-2099) under several climate change scenarios within the large heterogeneous San Francisco Estuary. We then assessed the warming effects on the endangered, endemic Delta Smelt, Hypomesus transpacificus, by integrating localized projected water temperatures with thermal sensitivity metrics (tolerance, spawning and maturation windows, and sublethal stress thresholds) across life stages. Lethal temperatures occurred under several scenarios, but sublethal effects resulting from chronic stressful temperatures were more common across the estuary (median >60 days above threshold for >50% locations by the end of the century). Behavioral avoidance of such stressful temperatures would make a large portion of the potential range of Delta Smelt unavailable during the summer and fall. Since Delta Smelt are not likely to migrate to other estuaries, these changes are likely to result in substantial habitat compression. Additionally, the Delta Smelt maturation window was shortened by 18-85 days, revealing cumulative effects of stressful summer and fall temperatures with early initiation of spring spawning that may negatively impact fitness. Our findings highlight the value of integrating sublethal thresholds, life history, and in situ thermal heterogeneity into global change impact assessments. As downscaled climate models are becoming widely available, we conclude that similar assessments at management-relevant scales will improve the scientific basis for resource management decisions.
Coupled downscaled climate models and ecophysiological metrics forecast habitat compression for an endangered estuarine fish

USGS Publications Warehouse

Brown, Larry R.; Komoroske, Lisa M; Wagner, R Wayne; Morgan-King, Tara; May, Jason T.; Connon, Richard E; Fangue, Nann A.

2016-01-01

Climate change is driving rapid changes in environmental conditions and affecting population and species’ persistence across spatial and temporal scales. Integrating climate change assessments into biological resource management, such as conserving endangered species, is a substantial challenge, partly due to a mismatch between global climate forecasts and local or regional conservation planning. Here, we demonstrate how outputs of global climate change models can be downscaled to the watershed scale, and then coupled with ecophysiological metrics to assess climate change effects on organisms of conservation concern. We employed models to estimate future water temperatures (2010–2099) under several climate change scenarios within the large heterogeneous San Francisco Estuary. We then assessed the warming effects on the endangered, endemic Delta Smelt, Hypomesus transpacificus, by integrating localized projected water temperatures with thermal sensitivity metrics (tolerance, spawning and maturation windows, and sublethal stress thresholds) across life stages. Lethal temperatures occurred under several scenarios, but sublethal effects resulting from chronic stressful temperatures were more common across the estuary (median >60 days above threshold for >50% locations by the end of the century). Behavioral avoidance of such stressful temperatures would make a large portion of the potential range of Delta Smelt unavailable during the summer and fall. Since Delta Smelt are not likely to migrate to other estuaries, these changes are likely to result in substantial habitat compression. Additionally, the Delta Smelt maturation window was shortened by 18–85 days, revealing cumulative effects of stressful summer and fall temperatures with early initiation of spring spawning that may negatively impact fitness. Our findings highlight the value of integrating sublethal thresholds, life history, and in situ thermal heterogeneity into global change impact assessments. As downscaled climate models are becoming widely available, we conclude that similar assessments at management-relevant scales will improve the scientific basis for resource management decisions.
Regional reanalysis without local data: Exploiting the downscaling paradigm

NASA Astrophysics Data System (ADS)

von Storch, Hans; Feser, Frauke; Geyer, Beate; Klehmet, Katharina; Li, Delei; Rockel, Burkhardt; Schubert-Frisius, Martina; Tim, Nele; Zorita, Eduardo

2017-08-01

This paper demonstrates two important aspects of regional dynamical downscaling of multidecadal atmospheric reanalysis. First, that in this way skillful regional descriptions of multidecadal climate variability may be constructed in regions with little or no local data. Second, that the concept of large-scale constraining allows global downscaling, so that global reanalyses may be completed by additions of consistent detail in all regions of the world. Global reanalyses suffer from inhomogeneities. However, their large-scale componenst are mostly homogeneous; Therefore, the concept of downscaling may be applied to homogeneously complement the large-scale state of the reanalyses with regional detail—wherever the condition of homogeneity of the description of large scales is fulfilled. Technically, this can be done by dynamical downscaling using a regional or global climate model, which's large scales are constrained by spectral nudging. This approach has been developed and tested for the region of Europe, and a skillful representation of regional weather risks—in particular marine risks—was identified. We have run this system in regions with reduced or absent local data coverage, such as Central Siberia, the Bohai and Yellow Sea, Southwestern Africa, and the South Atlantic. Also, a global simulation was computed, which adds regional features to prescribed global dynamics. Our cases demonstrate that spatially detailed reconstructions of the climate state and its change in the recent three to six decades add useful supplementary information to existing observational data for midlatitude and subtropical regions of the world.
Projection of Summer Climate on Tokyo Metropolitan Area using Pseudo Global Warming Method

NASA Astrophysics Data System (ADS)

Adachi, S. A.; Kimura, F.; Kusaka, H.; Hara, M.

2010-12-01

Recent surface air temperature observations in most of urban areas show the remarkable increasing trend affected by the global warming and the heat island effects. There are many populous areas in Japan. In such areas, the effects of land-use change and urbanization on the local climate are not negligible (Fujibe, 2010). The heat stress for citizen there is concerned to swell moreover in the future. Therefore, spatially detailed climate projection is required for making adaptation and mitigation plans. This study focuses on the Tokyo metropolitan area (TMA) in summer and aims to estimate the local climate change over the TMA in 2070s using a regional climate model. The Regional Atmospheric Modeling System (RAMS) was used for downscaling. A single layer urban canopy model (Kusaka et al., 2001) is built into RAMS as a parameterization expressing the features of urban surface. We performed two experiments for estimating present and future climate. In the present climate simulation, the initial and boundary conditions for RAMS are provided from the JRA-25/JCDAS. On the other hand, the Pseudo Global Warming (PGW) method (Sato et al., 2007) is applied to estimate the future climate, instead of the conventional dynamical downscaling method. The PGW method is expected to reduce the model biases in the future projection estimated by Atmosphere-Ocean General Circulation Models (AOGCM). The boundary conditions used in the PGW method is given by the PGW data, which are obtained by adding the climate monthly difference between 1990s and 2070s estimated by AOGCMs to the 6-hourly reanalysis data. In addition, the uncertainty in the regional climate projection depending on the AOGCM projections is estimated from additional downscaling experiments using the different PGW data obtained from five AOGCMs. Acknowledgment: This work was supported by the Global Environment Research Fund (S-5-3) of the Ministry of the Environment, Japan. References: 1. Fujibe, F., Int. J. Climatol., doi:10.1002/joc.2142 (2010). 2. Kusaka, H., H. Kondo, Y. Kikegawa, and F. Kimura, Bound.-Layer Meteor., 101, 329-358 (2001). 3. Sato, T., F. Kimura, and A. Kitoh, J. Hydrology, 144-154 (2007).
Future Temperatures and Precipitations in the Arid Northern-Central Chile: A Multi-Model Downscaling Approach

NASA Astrophysics Data System (ADS)

Souvignet, M.; Heinrich, J.

2010-03-01

Downscaling of global climate outputs is necessary to transfer projections of potential climate change scenarios to local levels. This is of special interest to dry mountainous areas, which are particularly vulnerable to climate change due to risks of reduced freshwater availability. These areas play a key role for hydrology since they usually receive the highest local precipitation rates stored in form of snow and glaciers. In the central-northern Chile (Norte Chico, 26-33ºS), where agriculture still serves as a backbone of the economy as well as ensures the well being of people, the knowledge of water resources availability is essential. The region is characterised by a semiarid climate with a mean annual precipitation inferior to 100mm. Moreover, the local climate is also highly influenced by the ENSO phenomenon, which accounts for the strong inter-annual variability in precipitation patterns. Although historical and spatially extensive precipitation data in the headwaters of the basins in this region are not readily available, records at coastal stations show worrisome trends. For instance, the average precipitation in La Serena, the most important city located in the Coquimbo Region, has decreased dramatically in the past 100 years. The 30-year monthly average has decreased from 170 mm in the early 20th century to values less than 80 mm nowadays. Climate Change is expected to strengthen this pattern in the region, and therefore strongly influence local hydrological patterns. The objectives of this study are i) to develop climate change scenarios (2046-2099) for the Norte Chico using multi-model predictions in terms of temperatures and precipitations, and ii) to compare the efficiency of two downscaling techniques in arid mountainous regions. In addition, this study aims at iii) providing decision makers with sound analysis of potential impact of Climate Change on streamflow in the region. For the present study, future local climate scenarios were developed for maximum, minimum temperature and precipitation in the research area based on four different General Circulation Models (GCMs). On the first hand, the Statistical Downscaling Model (SDSM) was used. This model is based on a multiple linear regression method and is best described as a hybrid of the stochastic weather generator and transfer function methods. One common advantage of statistical downscaling is that it ensures the maintenance of local spatial and temporal variability in generating realistic data time series. On the other hand and for comparison purposes, the Change Factor method was used. This methodology is relatively straightforward and ideal for rapid climate change assessment. The outputs of the HadCM3, CGCM3.1, GDFL-CM2 and MRI-CGCM2.3.2 A1 and B2 scenarios were downscaled with both methodologies and thereafter compared by means of several hydro-meteorological indices for a 55-years period (2045-2099). Preliminary results indicate that local temperatures are expected to rise in the region, whereas precipitations may decrease. However, minimum and maximum temperatures might increase at a faster rate at higher altitude areas. In addition, the Cordillera mountain range may encounter and longer winters with a dramatic decrease of icing days (Tmax<0°C). As for precipitation, both SRES scenarios for all models return a diminishing tendency, though the A2 scenario results show a faster decrease rate. Results indicate potential strong inter-seasonal and inter-annual perturbations in Rainfall in the region. Consequently, the Norte Chico will possibly see its streamflow strongly impacted with a resulting high variability at the seasonal and inter-annual level. A probabilistic analysis of the projections of the four GCMs provided a better representation of uncertainties linked with downscaled scenarios. Whereas maximum and minimum temperatures were accurately simulated by both downscaling methods, precipitation simulations returned weaker results. SDSM proved to have a poor ability to simulate extreme rainfall events and few conclusions could be drawn with respect to future occurrences of ENSO phenomena. On the other hand, the change factor method reproduced comparatively better historical precipitations. Despite all sources of error and uncertainties, which must be taken into account when handling the projections, this study addresses an issue that goes beyond local concerns and aims at developing a better understanding of impacts of climate change in fragile environments such as the arid and semiarid transition zone of north-central Chile. Its additional applied component goes therefore beyond the classical comparative study and aims at supporting stakeholders in their processes of decision making.
Downscaling climate information for local disease mapping.

PubMed

Bernardi, M; Gommes, R; Grieser, J

2006-06-01

The study of the impacts of climate on human health requires the interdisciplinary efforts of health professionals, climatologists, biologists, and social scientists to analyze the relationships among physical, biological, ecological, and social systems. As the disease dynamics respond to variations in regional and local climate, climate variability affects every region of the world and the diseases are not necessarily limited to specific regions, so that vectors may become endemic in other regions. Climate data at local level are thus essential to evaluate the dynamics of vector-borne disease through health-climate models and most of the times the climatological databases are not adequate. Climate data at high spatial resolution can be derived by statistical downscaling using historical observations but the method is limited by the availability of historical data at local level. Since the 90s', the statistical interpolation of climate data has been an important priority of the Agrometeorology Group of the Food and Agriculture Organization of the United Nations (FAO), as they are required for agricultural planning and operational activities at the local level. Since 1995, date of the first FAO spatial interpolation software for climate data, more advanced applications have been developed such as SEDI (Satellite Enhanced Data Interpolation) for the downscaling of climate data, LOCCLIM (Local Climate Estimator) and the NEW_LOCCLIM in collaboration with the Deutscher Wetterdienst (German Weather Service) to estimate climatic conditions at locations for which no observations are available. In parallel, an important effort has been made to improve the FAO climate database including at present more than 30,000 stations worldwide and expanding the database from developing countries coverage to global coverage.
Tempo-spatial downscaling of multiple GCMs projections for soil erosion risk analysis at El Reno, Oklahoma, USA

USDA-ARS?s Scientific Manuscript database

Proper spatial and temporal treatments of climate change scenarios projected by General Circulation Models (GCMs) are critical to accurate assessment of climatic impacts on natural resources and ecosystems. For accurate prediction of soil erosion risk at a particular farm or field under climate cha...
Future intensification of hydro-meteorological extremes: downscaling using the weather research and forecasting model

NASA Astrophysics Data System (ADS)

El-Samra, R.; Bou-Zeid, E.; Bangalath, H. K.; Stenchikov, G.; El-Fadel, M.

2017-12-01

A set of ten downscaling simulations at high spatial resolution (3 km horizontally) were performed using the Weather Research and Forecasting (WRF) model to generate future climate projections of annual and seasonal temperature and precipitation changes over the Eastern Mediterranean (with a focus on Lebanon). The model was driven with the High Resolution Atmospheric Model (HiRAM), running over the whole globe at a resolution of 25 km, under the conditions of two Representative Concentration Pathways (RCP) (4.5 and 8.5). Each downscaling simulation spanned one year. Two past years (2003 and 2008), also forced by HiRAM without data assimilation, were simulated to evaluate the model's ability to capture the cold and wet (2003) and hot and dry (2008) extremes. The downscaled data were in the range of recent observed climatic variability, and therefore corrected for the cold bias of HiRAM. Eight future years were then selected based on an anomaly score that relies on the mean annual temperature and accumulated precipitation to identify the worst year per decade from a water resources perspective. One hot and dry year per decade, from 2011 to 2050, and per scenario was simulated and compared to the historic 2008 reference. The results indicate that hot and dry future extreme years will be exacerbated and the study area might be exposed to a significant decrease in annual precipitation (rain and snow), reaching up to 30% relative to the current extreme conditions.
Fundamental statistical relationships between monthly and daily meteorological variables: Temporal downscaling of weather based on a global observational dataset

NASA Astrophysics Data System (ADS)

Sommer, Philipp; Kaplan, Jed

2016-04-01

Accurate modelling of large-scale vegetation dynamics, hydrology, and other environmental processes requires meteorological forcing on daily timescales. While meteorological data with high temporal resolution is becoming increasingly available, simulations for the future or distant past are limited by lack of data and poor performance of climate models, e.g., in simulating daily precipitation. To overcome these limitations, we may temporally downscale monthly summary data to a daily time step using a weather generator. Parameterization of such statistical models has traditionally been based on a limited number of observations. Recent developments in the archiving, distribution, and analysis of "big data" datasets provide new opportunities for the parameterization of a temporal downscaling model that is applicable over a wide range of climates. Here we parameterize a WGEN-type weather generator using more than 50 million individual daily meteorological observations, from over 10'000 stations covering all continents, based on the Global Historical Climatology Network (GHCN) and Synoptic Cloud Reports (EECRA) databases. Using the resulting "universal" parameterization and driven by monthly summaries, we downscale mean temperature (minimum and maximum), cloud cover, and total precipitation, to daily estimates. We apply a hybrid gamma-generalized Pareto distribution to calculate daily precipitation amounts, which overcomes much of the inability of earlier weather generators to simulate high amounts of daily precipitation. Our globally parameterized weather generator has numerous applications, including vegetation and crop modelling for paleoenvironmental studies.
Regional climate model downscaling may improve the prediction of alien plant species distributions

NASA Astrophysics Data System (ADS)

Liu, Shuyan; Liang, Xin-Zhong; Gao, Wei; Stohlgren, Thomas J.

2014-12-01

Distributions of invasive species are commonly predicted with species distribution models that build upon the statistical relationships between observed species presence data and climate data. We used field observations, climate station data, and Maximum Entropy species distribution models for 13 invasive plant species in the United States, and then compared the models with inputs from a General Circulation Model (hereafter GCM-based models) and a downscaled Regional Climate Model (hereafter, RCM-based models).We also compared species distributions based on either GCM-based or RCM-based models for the present (1990-1999) to the future (2046-2055). RCM-based species distribution models replicated observed distributions remarkably better than GCM-based models for all invasive species under the current climate. This was shown for the presence locations of the species, and by using four common statistical metrics to compare modeled distributions. For two widespread invasive taxa ( Bromus tectorum or cheatgrass, and Tamarix spp. or tamarisk), GCM-based models failed miserably to reproduce observed species distributions. In contrast, RCM-based species distribution models closely matched observations. Future species distributions may be significantly affected by using GCM-based inputs. Because invasive plants species often show high resilience and low rates of local extinction, RCM-based species distribution models may perform better than GCM-based species distribution models for planning containment programs for invasive species.
Evaluating climate change impacts on streamflow variability based on a multisite multivariate GCM downscaling method in the Jing River of China

NASA Astrophysics Data System (ADS)

Li, Zhi; Jin, Jiming

2017-11-01

Projected hydrological variability is important for future resource and hazard management of water supplies because changes in hydrological variability can cause more disasters than changes in the mean state. However, climate change scenarios downscaled from Earth System Models (ESMs) at single sites cannot meet the requirements of distributed hydrologic models for simulating hydrological variability. This study developed multisite multivariate climate change scenarios via three steps: (i) spatial downscaling of ESMs using a transfer function method, (ii) temporal downscaling of ESMs using a single-site weather generator, and (iii) reconstruction of spatiotemporal correlations using a distribution-free shuffle procedure. Multisite precipitation and temperature change scenarios for 2011-2040 were generated from five ESMs under four representative concentration pathways to project changes in streamflow variability using the Soil and Water Assessment Tool (SWAT) for the Jing River, China. The correlation reconstruction method performed realistically for intersite and intervariable correlation reproduction and hydrological modeling. The SWAT model was found to be well calibrated with monthly streamflow with a model efficiency coefficient of 0.78. It was projected that the annual mean precipitation would not change, while the mean maximum and minimum temperatures would increase significantly by 1.6 ± 0.3 and 1.3 ± 0.2 °C; the variance ratios of 2011-2040 to 1961-2005 were 1.15 ± 0.13 for precipitation, 1.15 ± 0.14 for mean maximum temperature, and 1.04 ± 0.10 for mean minimum temperature. A warmer climate was predicted for the flood season, while the dry season was projected to become wetter and warmer; the findings indicated that the intra-annual and interannual variations in the future climate would be greater than in the current climate. The total annual streamflow was found to change insignificantly but its variance ratios of 2011-2040 to 1961-2005 increased by 1.25 ± 0.55. Streamflow variability was predicted to become greater over most months on the seasonal scale because of the increased monthly maximum streamflow and decreased monthly minimum streamflow. The increase in streamflow variability was attributed mainly to larger positive contributions from increased precipitation variances rather than negative contributions from increased mean temperatures.
Projecting the Influence of Climate Change on Extreme Ground-level Ozone Events in Selected Ontario Cities =

NASA Astrophysics Data System (ADS)

Leung, Kinson He Yin

Ground-level ozone (O3) is perhaps one of the most familiar pollutants in Ontario, Canada because it is associated with most smog alerts in the province. O3 varies on a number of spatial and temporal scales, primarily due to meteorological variability and the impact of long-range transport of its precursors on the photochemical processes. The goal of this thesis is to project the change in the probability of occurrence of future Extreme Ground-level Ozone Events (EGLOEs) due to changes in atmospheric conditions as a result of climate change for cities located in the southern, eastern and northern parts of Ontario, Canada by using a combination of General Circulation / Global Climate Models (GCMs) and statistical downscaling. These Ontario cities are Toronto, Windsor, London, Kingston, Ottawa, Thunder Bay, Sudbury and North Bay. The successful downscaling method used in this research to generate city-specific climate change scenarios was the Statistical DownScaling Model (SDSM) version 4.2.2, which is a hybrid of regression-based and stochastic weather-generator downscaling methods. The results indicate that the mean values of the daily maximum ground-level ozone concentrations could increase by up to 12-17% in Southern Ontario, 8-16% in Eastern Ontario and 1.5-9% in Northern Ontario by the end of the century due largely to changes in long-range transport. Three important themes emerge from the results: 1) the research successfully model O3 concentration in a region where long-range transport plays a substantial role. 2) The clear confirmation regarding the role of long-range transport in determining O 3 concentration in most areas of Ontario. 3) The projected increase of ozone in Ontario, due largely to an increase of long-range transport, caused by shifting atmospheric dynamics rather than a direct temperature effect on ozone production. Moreover, the results indicate that the future Southern, Eastern and Northern Ontario's EGLOEs with the O3 concentration ≥ 80 ppb (the current Ontario 1-hour Ambient Air Quality criterion for extreme ozone concentration) will have an increase of over 60%, 50% and 62% respectively by the year of 2100 under the different future scenarios in the third version of the Coupled Global Climate Model (CGCM3) and the Hadley Centre's Climate Model (HadCM3).
Climate Modeling at the Austrian Weather Service (ZAMG)

NASA Astrophysics Data System (ADS)

Matulla, C.; Anders, I.; Auer, I.

2009-05-01

In later 2007 the Austrian Weather Service (ZAMG) established a group that shall deal with climate change modeling. Two of the group's main goals are to provide climate change scenarios for the assessment of the impact on ecosystems and to reconstruct past climate states along with their change. The former aim is to derive estimates of might happen to our ecosystems under different emission-pathways, whilst the latter goal is to better understand what has caused characteristical changes, which are to be found in proxies. Both aims can be achieved by empirical or dynamical downscaling models, which are ultimately based on the reliability of the driving GCMs results. It is well known that empirical and dynamical downscaling models do have advantages and disadvantages, which are different. As such it appears reasonable to use the approach which is better adapted to the considered question. It may be meaningful to apply empirical downscaling if long periods of time (such as substantial parts of the Holocene) are in the center of attention, whereas dynamical downscaling may be better suited to address questions that are related to decades. Up to now we were more involved with empirical downscaling that helped us to work together with scientists assessing the impact on ecosystems, as for instance, fish in a river (Matulla et al. 2007), forests (Lexer et al. 2002) or phenological phases (Scheifinger et al. 2007). After catching a glimpse of those results, we will turn to dynamical modeling. Here we would like to present findings from case studies, which are related to the more recent past. Our next target is the modelling of possible future climate conditions within the Greater Alpine Region (GAR, see e.g. Auer et al. 2007) as well as some characteristical periods throughout the Holocene as for instance the 8.2k event. This event is to be found in a variety of proxies within and also outside GAR. Auer I., Boehm R., Jurkovic A., Lipa W., Orlik A., Potzmann R., Schoener W., Ungersboeck M., Matulla C., Briffa K., Jones P. D., Efthymiadis D., Brunetti M., Nanni T., Maugeri M., Mercalli L., Mestre O., Moisselin J.-M., Begert M., Müller-Westermeier G., Kveton V., Bochnicek O., Stastny P., Lapin M., Szalai S., Szentimrey T., Cegnar T., Dolinar M., Gajic-Capka M., Zaninovic K., Majstorovic Z., Nieplova E., 2007. HISTALP - Historical instrumental climatological surface time series of the greater Alpine region 1760-2003. International Journal of Climatology, 27, 17-46 Lexer M.J., Hoenninger K., Scheifinger H., Matulla C., Groll N., Kromp-Kolb H., Schaudauer K., Starlinger F., Englisch M. (2002): The sensitivity of Austrian forests to scenarios of climate change: a large-scale risk assessment based on a modified gap model and forest inventory data. Forest Ecology and Management, 162, 53-72 Matulla, C., S. Schmutz, A. Melcher, T. Gerersdorfer and P. Haas, 2007: Climatic Change impact on fish fauna for an Inner-Alpine River based on a transient AOGCM simulation, International Journal of Biometeorology, 52(2), 127-137 Scheifinger H., C. Matulla, P. Cate, A. Kahrer, E. Koch, 2007: Climate impact on plant and insect phenology in Austria (http://epub.oeaw.ac.at/3966-9inhalt).
Downscaling a Global Climate Model to Simulate Climate Change Impacts on U.S. Regional and Urban Air Quality

NASA Technical Reports Server (NTRS)

Trail, M.; Tsimpidi, A. P.; Liu, P.; Tsigaridis, K.; Hu, Y.; Nenes, A.; Russell, A. G.

2013-01-01

Climate change can exacerbate future regional air pollution events by making conditions more favorable to form high levels of ozone. In this study, we use spectral nudging with WRF to downscale NASA earth system GISS modelE2 results during the years 2006 to 2010 and 2048 to 2052 over the continental United States in order to compare the resulting meteorological fields from the air quality perspective during the four seasons of five-year historic and future climatological periods. GISS results are used as initial and boundary conditions by the WRF RCM to produce hourly meteorological fields. The downscaling technique and choice of physics parameterizations used are evaluated by comparing them with in situ observations. This study investigates changes of similar regional climate conditions down to a 12km by 12km resolution, as well as the effect of evolving climate conditions on the air quality at major U.S. cities. The high resolution simulations produce somewhat different results than the coarse resolution simulations in some regions. Also, through the analysis of the meteorological variables that most strongly influence air quality, we find consistent changes in regional climate that would enhance ozone levels in four regions of the U.S. during fall (Western U.S., Texas, Northeastern, and Southeastern U.S), one region during summer (Texas), and one region where changes potentially would lead to better air quality during spring (Northeast). We also find that daily peak temperatures tend to increase in most major cities in the U.S. which would increase the risk of health problems associated with heat stress. Future work will address a more comprehensive assessment of emissions and chemistry involved in the formation and removal of air pollutants.

Hydrological response to climate change for Gilgel Abay River, in the Lake Tana Basin -Upper Blue Nile Basin of Ethiopia.

PubMed

Dile, Yihun Taddele; Berndtsson, Ronny; Setegn, Shimelis G

2013-01-01

Climate change is likely to have severe effects on water availability in Ethiopia. The aim of the present study was to assess the impact of climate change on the Gilgel Abay River, Upper Blue Nile Basin. The Statistical Downscaling Tool (SDSM) was used to downscale the HadCM3 (Hadley centre Climate Model 3) Global Circulation Model (GCM) scenario data into finer scale resolution. The Soil and Water Assessment Tool (SWAT) was set up, calibrated, and validated. SDSM downscaled climate outputs were used as an input to the SWAT model. The climate projection analysis was done by dividing the period 2010-2100 into three time windows with each 30 years of data. The period 1990-2001 was taken as the baseline period against which comparison was made. Results showed that annual mean precipitation may decrease in the first 30-year period but increase in the following two 30-year periods. The decrease in mean monthly precipitation may be as much as about -30% during 2010-2040 but the increase may be more than +30% in 2070-2100. The impact of climate change may cause a decrease in mean monthly flow volume between -40% to -50% during 2010-2040 but may increase by more than the double during 2070-2100. Climate change appears to have negligible effect on low flow conditions of the river. Seasonal mean flow volume, however, may increase by more than the double and +30% to +40% for the Belg (small rainy season) and Kiremit (main rainy season) periods, respectively. Overall, it appears that climate change will result in an annual increase in flow volume for the Gilgel Abay River. The increase in flow is likely to have considerable importance for local small scale irrigation activities. Moreover, it will help harnessing a significant amount of water for ongoing dam projects in the Gilgel Abay River Basin.
Hydrological Response to Climate Change for Gilgel Abay River, in the Lake Tana Basin - Upper Blue Nile Basin of Ethiopia

PubMed Central

Dile, Yihun Taddele; Berndtsson, Ronny; Setegn, Shimelis G.

2013-01-01

Climate change is likely to have severe effects on water availability in Ethiopia. The aim of the present study was to assess the impact of climate change on the Gilgel Abay River, Upper Blue Nile Basin. The Statistical Downscaling Tool (SDSM) was used to downscale the HadCM3 (Hadley centre Climate Model 3) Global Circulation Model (GCM) scenario data into finer scale resolution. The Soil and Water Assessment Tool (SWAT) was set up, calibrated, and validated. SDSM downscaled climate outputs were used as an input to the SWAT model. The climate projection analysis was done by dividing the period 2010-2100 into three time windows with each 30 years of data. The period 1990-2001 was taken as the baseline period against which comparison was made. Results showed that annual mean precipitation may decrease in the first 30-year period but increase in the following two 30-year periods. The decrease in mean monthly precipitation may be as much as about -30% during 2010-2040 but the increase may be more than +30% in 2070-2100. The impact of climate change may cause a decrease in mean monthly flow volume between -40% to -50% during 2010-2040 but may increase by more than the double during 2070-2100. Climate change appears to have negligible effect on low flow conditions of the river. Seasonal mean flow volume, however, may increase by more than the double and +30% to +40% for the Belg (small rainy season) and Kiremit (main rainy season) periods, respectively. Overall, it appears that climate change will result in an annual increase in flow volume for the Gilgel Abay River. The increase in flow is likely to have considerable importance for local small scale irrigation activities. Moreover, it will help harnessing a significant amount of water for ongoing dam projects in the Gilgel Abay River Basin. PMID:24250755
Statistical downscaling of daily precipitation over Llobregat river basin in Catalonia (Spain) using three downscaling methods.

NASA Astrophysics Data System (ADS)

Ballinas, R.; Versini, P.-A.; Sempere, D.; Escaler, I.

2009-09-01

Any long-term change in the patterns of average weather in a global or regional scale is called climate change. It may cause a progressive increase of atmospheric temperature and consequently may change the amount, frequency and intensity of precipitation. All these changes of meteorological parameters may modify the water cycle: run-off, infiltration, aquifer recharge, etc. Recent studies in Catalonia foresee changes in hydrological systems caused by climate change. This will lead to alterations in the hydrological cycle that could impact in land use, in the regimen of water extractions, in the hydrological characteristics of the territory and reduced groundwater recharge. Besides, can expect a loss of flow in rivers. In addition to possible increases in the frequency of extreme rainfall, being necessary to modify the design of infrastructure. Because this, it work focuses on studying the impacts of climate change in one of the most important basins in Catalonia, the Llobregat River Basin. The basin is the hub of the province of Barcelona. It is a highly populated and urbanized catchment, where water resources are used for different purposes, as drinking water production, agricultural irrigation, industry and hydro-electrical energy production. In consequence, many companies and communities depend on these resources. To study the impact of climate change in the Llobregat basin, storms (frequency, intensity) mainly, we will need regional climate change information. A regional climate is determined by interactions at large, regional and local scales. The general circulation models (GCMs) are run at too coarse resolution to permit accurate description of these regional and local interactions. So far, they have been unable to provide consistent estimates of climate change on a local scale. Several regionalization techniques have been developed to bridge the gap between the large-scale information provided by GCMs and fine spatial scales required for regional and environmental impact studies. Downscaling methods to assess the effect of large-scale circulations on local parameters have. Statistical downscaling methods are based on the view that regional climate can be conditioned by two factors: large-scale climatic state and regional/local features. Local climate information is derived by first developing a statistical model which relates large-scale variables or "predictors" for which GCMs are trustable to regional or local surface "predictands" for which models are less skilful. The main advantage of these methods is that they are computationally inexpensive, and can be applied to outputs from different GCM experiments. Three statistical downscaling methods are applied: Analogue method, Delta Change and Direct Forcing. These methods have been used to determine daily precipitation projections at rain gauge location to study the intensity, frequency and variability of storms in a context of climate change in the Llobregat River Basin in Catalonia, Spain. This work is part of the European project "Water Change" (included in the LIFE + Environment Policy and Governance program). It deals with Medium and long term water resources modelling as a tool for planning and global change adaptation. Two stakeholders involved in the project provided the historical time series: Catalan Water Agency (ACA) and the State Meteorological Agency (AEMET).
Projecting 21st century snowpack trends in western USA mountains using variable-resolution CESM

NASA Astrophysics Data System (ADS)

Rhoades, Alan M.; Ullrich, Paul A.; Zarzycki, Colin M.

2018-01-01

Climate change will impact western USA water supplies by shifting precipitation from snow to rain and driving snowmelt earlier in the season. However, changes at the regional-to-mountain scale is still a major topic of interest. This study addresses the impacts of climate change on mountain snowpack by assessing historical and projected variable-resolution (VR) climate simulations in the community earth system model (VR-CESM) forced by prescribed sea-surface temperatures along with widely used regional downscaling techniques, the coupled model intercomparison projects phase 5 bias corrected and statistically downscaled (CMIP5-BCSD) and the North American regional climate change assessment program (NARCCAP). The multi-model RCP8.5 scenario analysis of winter season SWE for western USA mountains indicates by 2040-2065 mean SWE could decrease -19% (NARCCAP) to -38% (VR-CESM), with an ensemble median change of -27%. Contrary to CMIP5-BCSD and NARCCAP, VR-CESM highlights a more pessimistic outcome for western USA mountain snowpack in latter-parts of the 21st century. This is related to temperature changes altering the snow-albedo feedback, snowpack storage, and precipitation phase, but may indicate that VR-CESM resolves more physically consistent elevational effects lacking in statistically downscaled datasets and teleconnections that are not captured in limited area models. Overall, VR-CESM projects by 2075-2100 that average western USA mountain snowfall decreases by -30%, snow cover by -44%, SWE by -69%, and average surface temperature increase of +5.0°C. This places pressure on western USA states to preemptively invest in climate adaptation measures such as alternative water storage, water use efficiency, and reassess reservoir storage operations.
Climate change impacts on rainfall extremes and urban drainage: state-of-the-art review

NASA Astrophysics Data System (ADS)

Willems, Patrick; Olsson, Jonas; Arnbjerg-Nielsen, Karsten; Beecham, Simon; Pathirana, Assela; Bülow Gregersen, Ida; Madsen, Henrik; Nguyen, Van-Thanh-Van

2013-04-01

Under the umbrella of the IWA/IAHR Joint Committee on Urban Drainage, the International Working Group on Urban Rainfall (IGUR) has reviewed existing methodologies for the analysis of long-term historical and future trends in urban rainfall extremes and their effects on urban drainage systems, due to anthropogenic climate change. Current practises have several limitations and pitfalls, which are important to be considered by trend or climate change impact modellers and users of trend/impact results. The review considers the following aspects: Analysis of long-term historical trends due to anthropogenic climate change: influence of data limitation, instrumental or environmental changes, interannual variations and longer term climate oscillations on trend testing results. Analysis of long-term future trends due to anthropogenic climate change: by complementing empirical historical data with the results from physically-based climate models, dynamic downscaling to the urban scale by means of Limited Area Models (LAMs) including explicitly small-scale cloud processes; validation of RCM/GCM results for local conditions accounting for natural variability, limited length of the available time series, difference in spatial scales, and influence of climate oscillations; statistical downscaling methods combined with bias correction; uncertainties associated with the climate forcing scenarios, the climate models, the initial states and the statistical downscaling step; uncertainties in the impact models (e.g. runoff peak flows, flood or surcharge frequencies, and CSO frequencies and volumes), including the impacts of more extreme conditions than considered during impact model calibration and validation. Implications for urban drainage infrastructure design and management: upgrading of the urban drainage system as part of a program of routine and scheduled replacement and renewal of aging infrastructure; how to account for the uncertainties; flexible and sustainable solutions; adaptive approach that provides inherent flexibility and reversibility and avoids closing off options; importance of active learning. References: Willems, P., Olsson, J., Arnbjerg-Nielsen, K., Beecham, S., Pathirana, A., Bülow Gregersen, I., Madsen, H., Nguyen, V-T-V. (2012). Impacts of climate change on rainfall extremes and urban drainage. IWA Publishing, 252 p., Paperback Print ISBN 9781780401256; Ebook ISBN 9781780401263 Willems, P., Arnbjerg-Nielsen, K., Olsson, J., Nguyen, V.T.V. (2012), 'Climate change impact assessment on urban rainfall extremes and urban drainage: methods and shortcomings', Atmospheric Research, 103, 106-118
The Added Value to Global Model Projections of Climate Change by Dynamical Downscaling: A Case Study over the Continental U.S. using the GISS-ModelE2 and WRF Models

NASA Technical Reports Server (NTRS)

Racherla, P. N.; Shindell, D. T.; Faluvegi, G. S.

2012-01-01

Dynamical downscaling is being increasingly used for climate change studies, wherein the climates simulated by a coupled atmosphere-ocean general circulation model (AOGCM) for a historical and a future (projected) decade are used to drive a regional climate model (RCM) over a specific area. While previous studies have demonstrated that RCMs can add value to AOGCM-simulated climatologies over different world regions, it is unclear as to whether or not this translates to a better reproduction of the observed climate change therein. We address this issue over the continental U.S. using the GISS-ModelE2 and WRF models, a state-of-the-science AOGCM and RCM, respectively. As configured here, the RCM does not effect holistic improvement in the seasonally and regionally averaged surface air temperature or precipitation for the individual historical decades. Insofar as the climate change between the two decades is concerned, the RCM does improve upon the AOGCM when nudged in the domain proper, but only modestly so. Further, the analysis indicates that there is not a strong relationship between skill in capturing climatological means and skill in capturing climate change. Though additional research would be needed to demonstrate the robustness of this finding in AOGCM/RCM models generally, the evidence indicates that, for climate change studies, the most important factor is the skill of the driving global model itself, suggesting that highest priority should be given to improving the long-range climate skill of AOGCMs.
Statistical downscaling and future scenario generation of temperatures for Pakistan Region

NASA Astrophysics Data System (ADS)

Kazmi, Dildar Hussain; Li, Jianping; Rasul, Ghulam; Tong, Jiang; Ali, Gohar; Cheema, Sohail Babar; Liu, Luliu; Gemmer, Marco; Fischer, Thomas

2015-04-01

Finer climate change information on spatial scale is required for impact studies than that presently provided by global or regional climate models. It is especially true for regions like South Asia with complex topography, coastal or island locations, and the areas of highly heterogeneous land-cover. To deal with the situation, an inexpensive method (statistical downscaling) has been adopted. Statistical DownScaling Model (SDSM) employed for downscaling of daily minimum and maximum temperature data of 44 national stations for base time (1961-1990) and then the future scenarios generated up to 2099. Observed as well as Predictors (product of National Oceanic and Atmospheric Administration) data were calibrated and tested on individual/multiple basis through linear regression. Future scenario was generated based on HadCM3 daily data for A2 and B2 story lines. The downscaled data has been tested, and it has shown a relatively strong relationship with the observed in comparison to ECHAM5 data. Generally, the southern half of the country is considered vulnerable in terms of increasing temperatures, but the results of this study projects that in future, the northern belt in particular would have a possible threat of increasing tendency in air temperature. Especially, the northern areas (hosting the third largest ice reserves after the Polar Regions), an important feeding source for Indus River, are projected to be vulnerable in terms of increasing temperatures. Consequently, not only the hydro-agricultural sector but also the environmental conditions in the area may be at risk, in future.
Effects on Storm-Water Management for Three Major US Cities Using Location Specific Extreme Precipitation Dynamical Downscaling

NASA Astrophysics Data System (ADS)

Pelle, A.; Allen, M.; Fu, J. S.

2013-12-01

With rising population and increasing urban density, it is of pivotal importance for urban planners to plan for increasing extreme precipitation events. Climate models indicate that an increase in global mean temperature will lead to increased frequency and intensity of storms of a variety of types. Analysis of results from the Coupled Model Intercomparison Project, Phase 5 (CMIP5) has demonstrated that global climate models severely underestimate precipitation, however. Preliminary results from dynamical downscaling indicate that Philadelphia, Pennsylvania is expected to experience the greatest increase of precipitation due to an increase in annual extreme events in the US. New York City, New York and Chicago, Illinois are anticipated to have similarly large increases in annual extreme precipitation events. In order to produce more accurate results, we downscale Philadelphia, Chicago, and New York City using the Weather Research and Forecasting model (WRF). We analyze historical precipitation data and WRF output utilizing a Log Pearson Type III (LP3) distribution for frequency of extreme precipitation events. This study aims to determine the likelihood of extreme precipitation in future years and its effect on the of cost of stormwater management for these three cities.
An effective drift correction for dynamical downscaling of decadal global climate predictions

NASA Astrophysics Data System (ADS)

Paeth, Heiko; Li, Jingmin; Pollinger, Felix; Müller, Wolfgang A.; Pohlmann, Holger; Feldmann, Hendrik; Panitz, Hans-Jürgen

2018-04-01

Initialized decadal climate predictions with coupled climate models are often marked by substantial climate drifts that emanate from a mismatch between the climatology of the coupled model system and the data set used for initialization. While such drifts may be easily removed from the prediction system when analyzing individual variables, a major problem prevails for multivariate issues and, especially, when the output of the global prediction system shall be used for dynamical downscaling. In this study, we present a statistical approach to remove climate drifts in a multivariate context and demonstrate the effect of this drift correction on regional climate model simulations over the Euro-Atlantic sector. The statistical approach is based on an empirical orthogonal function (EOF) analysis adapted to a very large data matrix. The climate drift emerges as a dramatic cooling trend in North Atlantic sea surface temperatures (SSTs) and is captured by the leading EOF of the multivariate output from the global prediction system, accounting for 7.7% of total variability. The SST cooling pattern also imposes drifts in various atmospheric variables and levels. The removal of the first EOF effectuates the drift correction while retaining other components of intra-annual, inter-annual and decadal variability. In the regional climate model, the multivariate drift correction of the input data removes the cooling trends in most western European land regions and systematically reduces the discrepancy between the output of the regional climate model and observational data. In contrast, removing the drift only in the SST field from the global model has hardly any positive effect on the regional climate model.
Downscaling U.S. public opinion about climate change and the 'Six Americas' to states, cities, and counties

NASA Astrophysics Data System (ADS)

Marlon, J. R.; Howe, P. D.; Leiserowitz, A.

2013-12-01

For climate change communication to be most effective, messages should be targeted to the characteristics of local audiences. In the U.S., 'Six Americas' have been identified among the public based on their response to the climate change issue. The distribution of these different 'publics' varies between states and communities, yet data about public opinion at the sub-national scale remains scarce. In this presentation, we describe a methodology to statistically downscale results from national-level surveys about the Six Americas, climate literacy, and other aspects of public opinion to smaller areas, including states, metropolitan areas, and counties. The method utilizes multilevel regression with poststratification (MRP) to model public opinion at various scales using a large national-level survey dataset. We present state and county-level estimates of two key beliefs about climate change: belief that climate change is happening, and belief in the scientific consensus about climate change. We further present estimates of how the Six Americas vary across the U.S.
Climate change impact assessment on Veneto and Friuli Plain groundwater. Part I: an integrated modeling approach for hazard scenario construction.

PubMed

Baruffi, F; Cisotto, A; Cimolino, A; Ferri, M; Monego, M; Norbiato, D; Cappelletto, M; Bisaglia, M; Pretner, A; Galli, A; Scarinci, A; Marsala, V; Panelli, C; Gualdi, S; Bucchignani, E; Torresan, S; Pasini, S; Critto, A; Marcomini, A

2012-12-01

Climate change impacts on water resources, particularly groundwater, is a highly debated topic worldwide, triggering international attention and interest from both researchers and policy makers due to its relevant link with European water policy directives (e.g. 2000/60/EC and 2007/118/EC) and related environmental objectives. The understanding of long-term impacts of climate variability and change is therefore a key challenge in order to address effective protection measures and to implement sustainable management of water resources. This paper presents the modeling approach adopted within the Life+ project TRUST (Tool for Regional-scale assessment of groUndwater Storage improvement in adaptation to climaTe change) in order to provide climate change hazard scenarios for the shallow groundwater of high Veneto and Friuli Plain, Northern Italy. Given the aim to evaluate potential impacts on water quantity and quality (e.g. groundwater level variation, decrease of water availability for irrigation, variations of nitrate infiltration processes), the modeling approach integrated an ensemble of climate, hydrologic and hydrogeologic models running from the global to the regional scale. Global and regional climate models and downscaling techniques were used to make climate simulations for the reference period 1961-1990 and the projection period 2010-2100. The simulation of the recent climate was performed using observed radiative forcings, whereas the projections have been done prescribing the radiative forcings according to the IPCC A1B emission scenario. The climate simulations and the downscaling, then, provided the precipitation, temperatures and evapo-transpiration fields used for the impact analysis. Based on downscaled climate projections, 3 reference scenarios for the period 2071-2100 (i.e. the driest, the wettest and the mild year) were selected and used to run a regional geomorphoclimatic and hydrogeological model. The final output of the model ensemble produced information about the potential variations of the water balance components (e.g. river discharge, groundwater level and volume) due to climate change. Such projections were used to develop potential hazard scenarios for the case study area, to be further applied within climate change risk assessment studies for groundwater resources and associated ecosystems. This paper describes the models' chain and the methodological approach adopted in the TRUST project and analyzes the hazard scenarios produced in order to investigate climate change risks for the case study area. Copyright © 2012 Elsevier B.V. All rights reserved.
Assessing the link between Atlantic Niño 1 and drought over West Africa using CORDEX regional climate models

NASA Astrophysics Data System (ADS)

Adeniyi, Mojisola Oluwayemisi; Dilau, Kabiru Alabi

2018-02-01

The skill of Coordinated Regional Climate Downscaling Experiment (CORDEX) models (ARPEGE, CCLM, HIRHAM, RACMO, REMO, PRECIS, RegCM3, RCA, WRF and CRCM) in simulating the climate (precipitation, temperature and drought) of West Africa is determined using a process-based metric. This is done by comparing the CORDEX models' simulated and observed correlation coefficients between Atlantic Niño Index 1 (ATLN1) and the climate over West Africa. Strong positive correlation is observed between ATLN1 and the climate parameters at the Guinea Coast (GC). The Atlantic Ocean has Niño behaviours through the ATLN indices which influence the climate of the tropics. Drought has distinct dipole structure of correlation with ATLN1 (negative at the Sahel); precipitation does not have distinct dipole structure of correlation, while temperature has almost a monopole correlation structure with ATLN1 over West Africa. The magnitude of the correlation increases with closeness to the equatorial eastern Atlantic. Correlations between ATLN1 and temperature are mostly stronger than those between ATLN1 and precipitation over the region. Most models have good performance over the GC, but ARPEGE has the highest skill at GC. The PRECIS is the most skilful over Savannah and RCA over Sahel. These models can be used to downscale the projected climate at the region of their highest skill.
Do downscaled general circulation models reliably simulate historical climatic conditions?

USGS Publications Warehouse

Bock, Andrew R.; Hay, Lauren E.; McCabe, Gregory J.; Markstrom, Steven L.; Atkinson, R. Dwight

2018-01-01

The accuracy of statistically downscaled (SD) general circulation model (GCM) simulations of monthly surface climate for historical conditions (1950–2005) was assessed for the conterminous United States (CONUS). The SD monthly precipitation (PPT) and temperature (TAVE) from 95 GCMs from phases 3 and 5 of the Coupled Model Intercomparison Project (CMIP3 and CMIP5) were used as inputs to a monthly water balance model (MWBM). Distributions of MWBM input (PPT and TAVE) and output [runoff (RUN)] variables derived from gridded station data (GSD) and historical SD climate were compared using the Kolmogorov–Smirnov (KS) test For all three variables considered, the KS test results showed that variables simulated using CMIP5 generally are more reliable than those derived from CMIP3, likely due to improvements in PPT simulations. At most locations across the CONUS, the largest differences between GSD and SD PPT and RUN occurred in the lowest part of the distributions (i.e., low-flow RUN and low-magnitude PPT). Results indicate that for the majority of the CONUS, there are downscaled GCMs that can reliably simulate historical climatic conditions. But, in some geographic locations, none of the SD GCMs replicated historical conditions for two of the three variables (PPT and RUN) based on the KS test, with a significance level of 0.05. In these locations, improved GCM simulations of PPT are needed to more reliably estimate components of the hydrologic cycle. Simple metrics and statistical tests, such as those described here, can provide an initial set of criteria to help simplify GCM selection.
Climate change presents increased potential for very large fires in the contiguous United States

Treesearch

R. Barbero; J. T. Abatzoglou; Sim Larkin; C. A. Kolden; B. Stocks

2015-01-01

Very large fires (VLFs) have important implications for communities, ecosystems, air quality and fire suppression expenditures. VLFs over the contiguous US have been strongly linked with meteorological and climatological variability. Building on prior modelling of VLFs (>5000 ha), an ensemble of 17 global climate models were statistically downscaled over the US...
Inundation downscaling for the development of a long-term and global inundation database compatible to SWOT mission

NASA Astrophysics Data System (ADS)

Aires, Filipe; Prigent, Catherine; Papa, Fabrice

2014-05-01

The Global Inundation Extent from Multi-Satellite (GIEMS) provides multi-year monthly variations of the global surface water extent at about 25 kmx25 km resolution, from 1993 to 2007. It is derived from multiple satellite observations. Its spatial resolution is usually compatible with climate model outputs and with global land surface model grids but is clearly not adequate for local applications that require the characterization of small individual water bodies. There is today a strong demand for high-resolution inundation extent datasets, for a large variety of applications such as water management, regional hydrological modeling, or for the analysis of mosquitos-related diseases. Even for climate applications, the GIEMS resolution might be limited given recent results on the key importance of the smallest ponds in the emission of CH4, as compared to the largest ones. If the inundation extent is combined to altimetry measurements to obtain water volume changes, and finally river discharge to the ocean (Frappart et al. 2011), then a better resolved inundation extent will also improve the accuracy of these estimates. In the context of the SWOT mission, the downscaling of GIEMS has multiple applications uses but a major one will be to use the SWOT retrievals to develop a downscaling of GIEMS. This SWOT-compatible downscaling could then be used to built a SWOT-compatible high-resolution database back in time from 1993 to the SWOT launch date. This extension of SWOT record is necessary to perform climate studies related to climate change. This paper present three approaches to do downscale GIEMS. Two basins will be considered for illustrative purpose, Amazon, Niger and Mekhong. - Aires, F., F. Papa, C. Prigent, J.-F. Cretaux and M. Berge-Nguyen, Characterization and downscaling of the inundation extent over the Inner Niger delta using a multi-wavelength retrievals and Modis data, J. of Hydrometeorology, in press, 2014. - Aires, F., F. Papa and C. Prigent, A long-term, high-resolution wetland dataset over the Amazon basin, downscaled from a multi-wavelength retrieval using SAR, J. of Hydrometeorology, 14, 594-6007, 2013. - Prigent, C., F. Papa, F. Aires, C. Jimenez, W.B. Rossow, and E. Matthews. Changes in land surface water dynamics since the 1990s and relation to population pressure. Geophys. Res. Lett., 39(L08403), 2012. - Frappart, F.; F. Papa, A. Guntner, S. Werth, J. Santos da Silva, J. Tomasella, F. Seyler, C. Prigent, W.B. Rossow, S. Calmant, and M.-P. Bonnet. Satellite-based estimates of groundwater storage variations in large drainage basins with extensive floodplains. Remote Sens. Environ., 115 :1588-1594, 2011.
Backcasting long-term climate data: evaluation of hypothesis

NASA Astrophysics Data System (ADS)

Saghafian, Bahram; Aghbalaghi, Sara Ghasemi; Nasseri, Mohsen

2018-05-01

Most often than not, incomplete datasets or short-term recorded data in vast regions impedes reliable climate and water studies. Various methods, such as simple correlation with stations having long-term time series, are practiced to infill or extend the period of observation at stations with missing or short-term data. In the current paper and for the first time, the hypothesis on the feasibility of extending the downscaling concept to backcast local observation records using large-scale atmospheric predictors is examined. Backcasting is coined here to contrast forecasting/projection; the former is implied to reconstruct in the past, while the latter represents projection in the future. To assess our hypotheses, daily and monthly statistical downscaling models were employed to reconstruct past precipitation data and lengthen the data period. Urmia and Tabriz synoptic stations, located in northwestern Iran, constituted two case study stations. SDSM and data-mining downscaling model (DMDM) daily as well as the group method of data handling (GMDH) and model tree (Mp5) monthly downscaling models were trained with National Center for Environmental Prediction (NCEP) data. After training, reconstructed precipitation data of the past was validated against observed data. Then, the data was fully extended to the 1948 to 2009 period corresponding to available NCEP data period. The results showed that DMDM performed superior in generation of monthly average precipitation compared with the SDSM, Mp5, and GMDH models, although none of the models could preserve the monthly variance. This overall confirms practical value of the proposed approach in extension of the past historic data, particularly for long-term climatological and water budget studies.
Potential impact of climate change to the future streamflow of Yellow River Basin based on CMIP5 data

NASA Astrophysics Data System (ADS)

Yang, Xiaoli; Zheng, Weifei; Ren, Liliang; Zhang, Mengru; Wang, Yuqian; Liu, Yi; Yuan, Fei; Jiang, Shanhu

2018-02-01

The Yellow River Basin (YRB) is the largest river basin in northern China, which has suffering water scarcity and drought hazard for many years. Therefore, assessments the potential impacts of climate change on the future streamflow in this basin is very important for local policy and planning on food security. In this study, based on the observations of 101 meteorological stations in YRB, equidistant CDF matching (EDCDFm) statistical downscaling approach was applied to eight climate models under two emissions scenarios (RCP4.5 and RCP8.5) from phase five of the Coupled Model Intercomparison Project (CMIP5). Variable infiltration capacity (VIC) model with 0.25° × 0.25° spatial resolution was developed based on downscaled fields for simulating streamflow in the future period over YRB. The results show that with the global warming trend, the annual streamflow will reduced about 10 % during the period of 2021-2050, compared to the base period of 1961-1990 in YRB. There should be suitable water resources planning to meet the demands of growing populations and future climate changing in this region.
Regional projections of the likelihood of very large wildland fires under a changing climate in the contiguous Western United States

Treesearch

Donald McKenzie; John T. Abatzoglou; E. Natasha Stavros; Narasimhan K. Larkin

2014-01-01

Seasonal changes in the climatic potential for very large wildfires (VLWF >= 50,000 ac~20,234 ha) across the western contiguous United States are projected over the 21st century using generalized linear models and downscaled climate projections for two representative concentration pathways (RCPs). Significant (p
Spoilt for choice - A comparison of downscaling approaches for hydrological impact studies

NASA Astrophysics Data System (ADS)

Rössler, Ole; Fischer, Andreas; Kotlarski, Sven; Keller, Denise; Liniger, Mark; Weingartner, Rolf

2017-04-01

With the increasing number of available climate downscaling approaches, users are often faced with the luxury problem of which downscaling method to apply in a climate change impact assessment study. In Switzerland, for instance, the new generation of local scale climate scenarios CH2018 will be based on quantile mapping (QM), replacing the previous delta change (DC) method. Parallel to those two methods, a multi-site weather generator (WG) was developed to meet specific user needs. The question poses which downscaling method is the most suitable for a given application. Here, we analyze the differences of the three approaches in terms of hydro-meteorological responses in the Swiss pre-Alps in terms of mean values as well as indices of extremes. The comparison of the three different approaches was carried out in the frame of a hydrological impact assessment study that focused on different runoff characteristics and their related meteorological indices in the meso-scale catchment of the river Thur ( 1700 km2), Switzerland. For this purpose, we set up the hydrological model WaSiM-ETH under present (1980-2009) and under future conditions (2070-2099), assuming the SRES A1B emission scenario. Input to the three downscaling approaches were 10 GCM-RCM simulations of the ENSEMBLES project, while eight meteorological station observations served as the reference. All station data, observed and downscaled, were interpolated to obtain meteorological fields of temperature and precipitation required by the hydrological model. For the present-day reference period we evaluated the ability of each downscaling method to reproduce today's hydro-meteorological patterns. In the scenario runs, we focused on the comparison of change signals for each hydro-meteorological parameter generated by the three downscaling techniques. The evaluation exercise reveals that QM and WG perform equally well in representing present day average conditions, but that QM outperforms WG in reproducing indices related to extreme conditions like the number of drought events or multi-day rain sums. In terms of mean monthly discharge changes, the three downscaling methods reveal notable differences: DC shows the strongest (in summer) and less pronounced (in winter) change signal. Regarding some extreme features of runoff like frequency of droughts and the low flow level, DC shows similar change signals compared to QM and WG. This was unexpected as DC is commonly reported to fail in terms of projecting extreme changes. In contrast, QM mostly shows the strongest change signals for the 10 different extreme related indices, due to its ability to pick up more features of the climate change signals from the RCM. This indicates that DC and also WG miss some aspects, especially for flood related indices. Hence, depending on the target variable of interest, DC and QM typically provide the full range of change signals, while WG mostly lies in between both method. However, it offers the great advantage of multiple realizations combined with inter-variable consistency.
Observational uncertainty and regional climate model evaluation: A pan-European perspective

NASA Astrophysics Data System (ADS)

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

2017-04-01

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.

Statistical downscaling of GCM simulations to streamflow using relevance vector machine

NASA Astrophysics Data System (ADS)

Ghosh, Subimal; Mujumdar, P. P.

2008-01-01

General circulation models (GCMs), the climate models often used in assessing the impact of climate change, operate on a coarse scale and thus the simulation results obtained from GCMs are not particularly useful in a comparatively smaller river basin scale hydrology. The article presents a methodology of statistical downscaling based on sparse Bayesian learning and Relevance Vector Machine (RVM) to model streamflow at river basin scale for monsoon period (June, July, August, September) using GCM simulated climatic variables. NCEP/NCAR reanalysis data have been used for training the model to establish a statistical relationship between streamflow and climatic variables. The relationship thus obtained is used to project the future streamflow from GCM simulations. The statistical methodology involves principal component analysis, fuzzy clustering and RVM. Different kernel functions are used for comparison purpose. The model is applied to Mahanadi river basin in India. The results obtained using RVM are compared with those of state-of-the-art Support Vector Machine (SVM) to present the advantages of RVMs over SVMs. A decreasing trend is observed for monsoon streamflow of Mahanadi due to high surface warming in future, with the CCSR/NIES GCM and B2 scenario.
Aspect of ECMWF downscaled Regional Climate Modeling in simulating Indian summer monsoon rainfall and dependencies on lateral boundary conditions

NASA Astrophysics Data System (ADS)

Ghosh, Soumik; Bhatla, R.; Mall, R. K.; Srivastava, Prashant K.; Sahai, A. K.

2018-03-01

Climate model faces considerable difficulties in simulating the rainfall characteristics of southwest summer monsoon. In this study, the dynamical downscaling of European Centre for Medium-Range Weather Forecast's (ECMWF's) ERA-Interim (EIN15) has been utilized for the simulation of Indian summer monsoon (ISM) through the Regional Climate Model version 4.3 (RegCM-4.3) over the South Asia Co-Ordinated Regional Climate Downscaling EXperiment (CORDEX) domain. The complexities of model simulation over a particular terrain are generally influenced by factors such as complex topography, coastal boundary, and lack of unbiased initial and lateral boundary conditions. In order to overcome some of these limitations, the RegCM-4.3 is employed for simulating the rainfall characteristics over the complex topographical conditions. For reliable rainfall simulation, implementations of numerous lower boundary conditions are forced in the RegCM-4.3 with specific horizontal grid resolution of 50 km over South Asia CORDEX domain. The analysis is considered for 30 years of climatological simulation of rainfall, outgoing longwave radiation (OLR), mean sea level pressure (MSLP), and wind with different vertical levels over the specified region. The dependency of model simulation with the forcing of EIN15 initial and lateral boundary conditions is used to understand the impact of simulated rainfall characteristics during different phases of summer monsoon. The results obtained from this study are used to evaluate the activity of initial conditions of zonal wind circulation speed, which causes an increase in the uncertainty of regional model output over the region under investigation. Further, the results showed that the EIN15 zonal wind circulation lacks sufficient speed over the specified region in a particular time, which was carried forward by the RegCM output and leads to a disrupted regional simulation in the climate model.
Potential impact of climate change on the Intra-Americas Sea: Part 2. Implications for Atlantic bluefin tuna and skipjack tuna adult and larval habitats

NASA Astrophysics Data System (ADS)

Muhling, Barbara A.; Liu, Yanyun; Lee, Sang-Ki; Lamkin, John T.; Roffer, Mitchell A.; Muller-Karger, Frank; Walter, John F., III

2015-08-01

Increasing water temperatures due to climate change will likely have significant impacts on distributions and life histories of Atlantic tunas. In this study, we combined predictive habitat models with a downscaled climate model to examine potential impacts on adults and larvae of Atlantic bluefin tuna (Thunnus thynnus) and skipjack tuna (Katsuwonus pelamis) in the Intra-Americas Sea (IAS). An additional downscaled model covering the 20th century was used to compare habitat fluctuations from natural variability to predicted future changes under two climate change scenarios: Representative Concentration Pathway (RCP) 4.5 (medium-low) and RCP 8.5 (high). Results showed marked temperature-induced habitat losses for both adult and larval bluefin tuna on their northern Gulf of Mexico spawning grounds. In contrast, habitat suitability for skipjack tuna increased as temperatures warmed. Model error was highest for the two skipjack tuna models, particularly at higher temperatures. This work suggests that influences of climate change on highly migratory Atlantic tuna species are likely to be substantial, but strongly species-specific. While impacts on fish populations remain uncertain, these changes in habitat suitability will likely alter the spatial and temporal availability of species to fishing fleets, and challenge equilibrium assumptions of environmental stability, upon which fisheries management benchmarks are based.
Downscaling RCP8.5 daily temperatures and precipitation in Ontario using localized ensemble optimal interpolation (EnOI) and bias correction

NASA Astrophysics Data System (ADS)

Deng, Ziwang; Liu, Jinliang; Qiu, Xin; Zhou, Xiaolan; Zhu, Huaiping

2017-10-01

A novel method for daily temperature and precipitation downscaling is proposed in this study which combines the Ensemble Optimal Interpolation (EnOI) and bias correction techniques. For downscaling temperature, the day to day seasonal cycle of high resolution temperature of the NCEP climate forecast system reanalysis (CFSR) is used as background state. An enlarged ensemble of daily temperature anomaly relative to this seasonal cycle and information from global climate models (GCMs) are used to construct a gain matrix for each calendar day. Consequently, the relationship between large and local-scale processes represented by the gain matrix will change accordingly. The gain matrix contains information of realistic spatial correlation of temperature between different CFSR grid points, between CFSR grid points and GCM grid points, and between different GCM grid points. Therefore, this downscaling method keeps spatial consistency and reflects the interaction between local geographic and atmospheric conditions. Maximum and minimum temperatures are downscaled using the same method. For precipitation, because of the non-Gaussianity issue, a logarithmic transformation is used to daily total precipitation prior to conducting downscaling. Cross validation and independent data validation are used to evaluate this algorithm. Finally, data from a 29-member ensemble of phase 5 of the Coupled Model Intercomparison Project (CMIP5) GCMs are downscaled to CFSR grid points in Ontario for the period from 1981 to 2100. The results show that this method is capable of generating high resolution details without changing large scale characteristics. It results in much lower absolute errors in local scale details at most grid points than simple spatial downscaling methods. Biases in the downscaled data inherited from GCMs are corrected with a linear method for temperatures and distribution mapping for precipitation. The downscaled ensemble projects significant warming with amplitudes of 3.9 and 6.5 °C for 2050s and 2080s relative to 1990s in Ontario, respectively; Cooling degree days and hot days will significantly increase over southern Ontario and heating degree days and cold days will significantly decrease in northern Ontario. Annual total precipitation will increase over Ontario and heavy precipitation events will increase as well. These results are consistent with conclusions in many other studies in the literature.
Prognostic Aspects of Sub-seasonal Rainfall Characteristics using the Outputs of General Circulation Model: An Application of Statistical Downscaling and Temporal Disaggregation

NASA Astrophysics Data System (ADS)

Singh, A.; Mohanty, U. C.; Ghosh, K.

2015-12-01

Most regions of India experience varied rainfall duration during the southwest monsoon, changes in which exhibit major impact not only agriculture, but also other sectors like hydrology, agriculture, food and fodder storage etc. In addition, changes in sub-seasonal rainfall characteristics highly impact the rice production. As part of the endeavor seasonal climate outlook, as well as information for weather within climate may be helpful for advance planning and risk management in agriculture. The General Circulation Model (GCM) provide an alternative to gather information for weather within climate but variability is very low in comparison to observation. On the other hand, the spatial resolution of GCM predicted rainfall is not found at the observed station/grid point. To tackle the problem, initially a statistical downscaling over 19 station of Odisha state is undertaken using the atmospheric parameters predicted by a GCM (NCEP-CFSv2). For the purpose, an extended domain is taken for analyzing the significant zone for the atmospheric parameters like zonal wind at 850hPa, Sea Surface Temperature (SST), geopotential height. A statistical model using the pattern projection method is further developed based on empirical orthogonal function. The downscaled rainfall is found better in association with station observation in comparison to raw GCM prediction in view of deterministic and probabilistic skill measure. Further, the sub-seasonal and seasonal forecast from the GCMs can be used at different time steps for risk management. Therefore, downscaled seasonal/monthly rainfall is further converted to sub-seasonal/daily time scale using a non-homogeneous markov model. The simulated weather sequences are further compared with the observed sequence in view of categorical rainfall events. The outcomes suggest that the rainfall amount are overestimated for excess rainfall and henceforth larger excess rainfall events can be realized. The skill for prediction of rainfall events corresponding to lower thresholds is found higher. A detail discussion regarding skill of spatial downscale rainfall at observed stations and its further representation of sub-seasonal characteristics (spells, less rainfall, heavy rainfall, and moderate rainfall events) of rainfall for disaggregated outputs will be presented.
Field significance of performance measures in the context of regional climate model evaluation. Part 1: temperature

NASA Astrophysics Data System (ADS)

Ivanov, Martin; Warrach-Sagi, Kirsten; Wulfmeyer, Volker

2018-04-01

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.
Investigating the Sensitivity of Streamflow and Water Quality to Climate Change and Urbanization in 20 U.S. Watersheds

NASA Astrophysics Data System (ADS)

Johnson, T. E.; Weaver, C. P.; Butcher, J.; Parker, A.

2011-12-01

Watershed modeling was conducted in 20 large (15,000-60,000 km2), U.S. watersheds to address gaps in our knowledge of the sensitivity of U.S. streamflow, nutrient (N and P) and sediment loading to potential future climate change, and methodological challenges associated with integrating existing tools (e.g., climate models, watershed models) and datasets to address these questions. Climate change scenarios are based on dynamically downscaled (50x50 km2) output from four of the GCMs used in the Intergovernmental Panel on Climate Change (IPCC) 4th Assessment Report for the period 2041-2070 archived by the North American Regional Climate Change Assessment Program (NARCCAP). To explore the potential interaction of climate change and urbanization, model simulations also include urban and residential development scenarios for each of the 20 study watersheds. Urban and residential development scenarios were acquired from EPA's national-scale Integrated Climate and Land Use Scenarios (ICLUS) project. Watershed modeling was conducted using the Hydrologic Simulation Program-FORTRAN (HSPF) and Soil and Water Assessment Tool (SWAT) models. Here we present a summary of results for 5 of the study watersheds; the Minnesota River, the Susquehanna River, the Apalachicola-Chattahoochee-Flint, the Salt/Verde/San Pedro, and the Willamette River Basins. This set of results provide an overview of the response to climate change in different regions of the U.S., the different sensitivities of different streamflow and water quality endpoints, and illustrate a number of methodological issues including the sensitivities and uncertainties associated with use of different watershed models, approaches for downscaling climate change projections, and interaction between climate change and other forcing factors, specifically urbanization and changes in atmospheric CO2 concentration.
Seasonal Atmospheric and Oceanic Predictions

NASA Technical Reports Server (NTRS)

Roads, John; Rienecker, Michele (Technical Monitor)

2003-01-01

Several projects associated with dynamical, statistical, single column, and ocean models are presented. The projects include: 1) Regional Climate Modeling; 2) Statistical Downscaling; 3) Evaluation of SCM and NSIPP AGCM Results at the ARM Program Sites; and 4) Ocean Forecasts.
Dynamical downscaling inter-comparison for high resolution climate reconstruction

NASA Astrophysics Data System (ADS)

Ferreira, J.; Rocha, A.; Castanheira, J. M.; Carvalho, A. C.

2012-04-01

In the scope of the project: "High-resolution Rainfall EroSivity analysis and fORecasTing - RESORT", an evaluation of various methods of dynamic downscaling is presented. The methods evaluated range from the classic method of nesting a regional model results in a global model, in this case the ECMWF reanalysis, to more recently proposed methods, which consist in using Newtonian relaxation methods in order to nudge the results of the regional model to the reanalysis. The method with better results involves using a system of variational data assimilation to incorporate observational data with results from the regional model. The climatology of a simulation of 5 years using this method is tested against observations on mainland Portugal and the ocean in the area of the Portuguese Continental Shelf, which shows that the method developed is suitable for the reconstruction of high resolution climate over continental Portugal.
Online dynamical downscaling of temperature and precipitation within the iLOVECLIM model (version 1.1)

NASA Astrophysics Data System (ADS)

Quiquet, Aurélien; Roche, Didier M.; Dumas, Christophe; Paillard, Didier

2018-02-01

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.
The foundation for climate services in Belgium: CORDEX.be

NASA Astrophysics Data System (ADS)

Van Schaeybroeck, Bert; Termonia, Piet; De Ridder, Koen; Fettweis, Xavier; Gobin, Anne; Luyten, Patrick; Marbaix, Philippe; Pottiaux, Eric; Stavrakou, Trissevgeni; Van Lipzig, Nicole; van Ypersele, Jean-Pascal; Willems, Patrick

2017-04-01

According to the Global Framework for Climate Services (GFCS) there are four pillars required to build climate services. As the first step towards the realization of a climate center in Belgium, the national project CORDEX.be focused on one pillar: research modelling and projection. By bringing together the Belgian climate and impact modeling research of nine groups a data-driven capacity development and community building in Belgium based on interactions with users. The project is based on the international CORDEX ("COordinated Regional Climate Downscaling Experiment") project where ".be" indicates it will go beyond for Belgium. Our national effort links to the regional climate initiatives through the contribution of multiple high-resolution climate simulations over Europe following the EURO-CORDEX guidelines. Additionally the same climate simulations were repeated at convection-permitting resolutions over Belgium (3 to 5 km). These were used to drive different local impact models to investigate the impact of climate change on urban effects, storm surges and waves, crop production and changes in emissions from vegetation. Akin to international frameworks such as CMIP and CORDEX a multi-model approach is adopted allowing for uncertainty estimation, a crucial aspect of climate projections for policy-making purposes. However, due to the lack of a large set of high resolution model runs, a combination of all available climate information is supplemented with the statistical downscaling approach. The organization of the project, together with its main results will be outlined. The proposed coordination framework could serve as a demonstration case for regions or countries where the climate-research capacity is present but a structure is required to assemble it coherently. Based on interactions and feedback with stakeholders different applications are planned, demonstrating the use of the climate data.
Projected changes over western Canada using convection-permitting regional climate model and the pseudo-global warming method

NASA Astrophysics Data System (ADS)

Li, Y.; Kurkute, S.; Chen, L.

2017-12-01

Results from the General Circulation Models (GCMs) suggest more frequent and more severe extreme rain events in a climate warmer than the present. However, current GCMs cannot accurately simulate extreme rainfall events of short duration due to their coarse model resolutions and parameterizations. This limitation makes it difficult to provide the detailed quantitative information for the development of regional adaptation and mitigation strategies. Dynamical downscaling using nested Regional Climate Models (RCMs) are able to capture key regional and local climate processes with an affordable computational cost. Recent studies have demonstrated that the downscaling of GCM results with weather-permitting mesoscale models, such as the pseudo-global warming (PGW) technique, could be a viable and economical approach of obtaining valuable climate change information on regional scales. We have conducted a regional climate 4-km Weather Research and Forecast Model (WRF) simulation with one domain covering the whole western Canada, for a historic run (2000-2015) and a 15-year future run to 2100 and beyond with the PGW forcing. The 4-km resolution allows direct use of microphysics and resolves the convection explicitly, thus providing very convincing spatial detail. With this high-resolution simulation, we are able to study the convective mechanisms, specifically the control of convections over the Prairies, the projected changes of rainfall regimes, and the shift of the convective mechanisms in a warming climate, which has never been examined before numerically at such large scale with such high resolution.
Impacts of weather versus climate and driver uncertainty on multi-centennial ecosystem model simulations

NASA Astrophysics Data System (ADS)

Rollinson, C.; Simkins, J.; Fer, I.; Desai, A. R.; Dietze, M.

2017-12-01

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.
Application of data on climate extremes for the southwestern United States

NASA Astrophysics Data System (ADS)

Redmond, K. T.; Fleishman, E.; Cayan, D. R.; Daudert, B.; Gershunov, A.

2015-12-01

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.
Assessing drought risk under climate change in the US Great Plains via evaporative demand from downscaled GCM projections

NASA Astrophysics Data System (ADS)

Dewes, C.; Rangwala, I.; Hobbins, M.; Barsugli, J. J.

2016-12-01

Drought conditions in the US Great Plains occur primarily in response to periods of low precipitation, but they can be exacerbated by enhanced evaporative demand (E0) during periods of elevated temperatures, radiation, advection, and/or decreased humidity. A number of studies project severe to unprecedented drought conditions for this region later in the 21st century. Yet, we have found that methodological choices in the estimation of E0 and the selection of global climate model (GCM) output account for large uncertainties in projections of drought risk. Furthermore, the coarse resolution of GCMs offers little usability for drought risk assessments applied to socio-ecological systems, and users of climate data for that purpose tend to prefer existing downscaled products. Here we derive a physically based estimation of E0 - the FAO56 Penman-Monteith reference evapotranspiration - using driving variables from the Multivariate Adaptive Constructed Analogs (MACA) dataset, which have a spatial resolution of approximately 4 km. We select downscaled outputs from five CMIP5 GCMs, whereby we aim to represent different scenarios for the future of the Great Plains region (e.g. warm/wet, hot/dry, etc.). While this downscaling methodology removes GCM bias relative to a gridded product for historical data (METDATA), we first examine the remaining bias relative to ground (point) estimates of E0. Next we assess whether the downscaled products preserve the variability of their parent GCMs, in both historical and future (RCP8.5) projections. We then use the E0 estimates to compute multi-scale time series of drought indices such as the Evaporative Demand Drought Index (EDDI) and the Standardized Precipitation-Evaporation Index (SPEI) over the Great Plains region. We also attribute variability and drought anomalies to each of the driving parameters, to tease out the influence of specific model biases and evaluate geographical nuances of E0 drivers. Aside from improved understanding of plausible future drought conditions at higher spatial resolutions, our findings should offer insights on the reliability of downscaled projections for drought risk assessment in socio-ecological applications.
Statistical downscaling rainfall using artificial neural network: significantly wetter Bangkok?

NASA Astrophysics Data System (ADS)

Vu, Minh Tue; Aribarg, Thannob; Supratid, Siriporn; Raghavan, Srivatsan V.; Liong, Shie-Yui

2016-11-01

Artificial neural network (ANN) is an established technique with a flexible mathematical structure that is capable of identifying complex nonlinear relationships between input and output data. The present study utilizes ANN as a method of statistically downscaling global climate models (GCMs) during the rainy season at meteorological site locations in Bangkok, Thailand. The study illustrates the applications of the feed forward back propagation using large-scale predictor variables derived from both the ERA-Interim reanalyses data and present day/future GCM data. The predictors are first selected over different grid boxes surrounding Bangkok region and then screened by using principal component analysis (PCA) to filter the best correlated predictors for ANN training. The reanalyses downscaled results of the present day climate show good agreement against station precipitation with a correlation coefficient of 0.8 and a Nash-Sutcliffe efficiency of 0.65. The final downscaled results for four GCMs show an increasing trend of precipitation for rainy season over Bangkok by the end of the twenty-first century. The extreme values of precipitation determined using statistical indices show strong increases of wetness. These findings will be useful for policy makers in pondering adaptation measures due to flooding such as whether the current drainage network system is sufficient to meet the changing climate and to plan for a range of related adaptation/mitigation measures.
Projecting Future Water Levels of the Laurentian Great Lakes

NASA Astrophysics Data System (ADS)

Bennington, V.; Notaro, M.; Holman, K.

2013-12-01

The Laurentian Great Lakes are the largest freshwater system on Earth, containing 84% of North America's freshwater. The lakes are a valuable economic and recreational resource, valued at over 62 billion in annual wages and supporting a 7 billion fishery. Shipping, recreation, and coastal property values are significantly impacted by water level variability, with large economic consequences. Great Lakes water levels fluctuate both seasonally and long-term, responding to natural and anthropogenic climate changes. Due to the integrated nature of water levels, a prolonged small change in any one of the net basin supply components: over-lake precipitation, watershed runoff, or evaporation from the lake surface, may result in important trends in water levels. We utilize the Abdus Salam International Centre for Theoretical Physics's Regional Climate Model Version 4.5.6 to dynamically downscale three global global climate models that represent a spread of potential future climate change for the region to determine whether the climate models suggest a robust response of the Laurentian Great Lakes to anthropogenic climate change. The Model for Interdisciplinary Research on Climate Version 5 (MIROC5), the National Centre for Meteorological Research Earth system model (CNRM-CM5), and the Community Climate System Model Version 4 (CCSM4) project different regional temperature increases and precipitation change over the next century and are used as lateral boundary conditions. We simulate the historical (1980-2000) and late-century periods (2080-2100). Upon model evaluation we will present dynamically downscaled projections of net basin supply changes for each of the Laurentian Great Lakes.
Statistical downscaling of sub-daily (6-hour) temperature in Romania, by means of artificial neural networks

NASA Astrophysics Data System (ADS)

Birsan, Marius-Victor; Dumitrescu, Alexandru; Cǎrbunaru, Felicia

2016-04-01

The role of statistical downscaling is to model the relationship between large-scale atmospheric circulation and climatic variables on a regional and sub-regional scale, making use of the predictions of future circulation generated by General Circulation Models (GCMs) in order to capture the effects of climate change on smaller areas. The study presents a statistical downscaling model based on a neural network-based approach, by means of multi-layer perceptron networks. Sub-daily temperature data series from 81 meteorological stations over Romania, with full data records are used as predictands. As large-scale predictor, the NCEP/NCAD air temperature data at 850 hPa over the domain 20-30E / 40-50N was used, at a spatial resolution of 2.5×2.5 degrees. The period 1961-1990 was used for calibration, while the validation was realized over the 1991-2010 interval. Further, in order to estimate future changes in air temperature for 2021-2050 and 2071-2100, air temperature data at 850 hPa corresponding to the IPCC A1B scenario was extracted from the CNCM33 model (Meteo-France) and used as predictor. This work has been realized within the research project "Changes in climate extremes and associated impact in hydrological events in Romania" (CLIMHYDEX), code PN II-ID-2011-2-0073, financed by the Romanian Executive Agency for Higher Education Research, Development and Innovation Funding (UEFISCDI).
Future Climate Change Impact Assessment of River Flows at Two Watersheds of Peninsular Malaysia

NASA Astrophysics Data System (ADS)

Ercan, A.; Ishida, K.; Kavvas, M. L.; Chen, Z. R.; Jang, S.; Amin, M. Z. M.; Shaaban, A. J.

2016-12-01

Impacts of climate change on the river flows under future climate change conditions were assessed over Muda and Dungun watersheds of Peninsular Malaysia by means of a coupled regional climate model and a physically-based hydrology model utilizing an ensemble of 15 different future climate realizations. Coarse resolution GCMs' future projections covering a wide range of emission scenarios were dynamically downscaled to 6 km resolution over the study area. Hydrologic simulations of the two selected watersheds were carried out at hillslope-scale and at hourly increments.
CORDEX.be: COmbining Regional climate Downscaling EXpertise in Belgium

NASA Astrophysics Data System (ADS)

Termonia, P.

2015-12-01

The main objective of the ongoing project CORDEX.be, "COmbining Regional Downscaling EXpertise in Belgium: CORDEX and Beyond", is to gather existing and ongoing Belgian research activities in the domain of climate modelling to create a coherent scientific basis for future climate services in Belgium. The project regroups 8 Belgian Institutes under a single research program of the Belgian Science Policy (BELSPO). The project involves three regional climate models: the ALARO model, the COSMO-CLM model and the MAR model running according to the guidelines of the CORDEX project and at convection permitting resolution on small domains over Belgium. The project creates a framework to address four objectives/challenges. First, this projects aims to contribute to the EURO-CORDEX project. Secondly, RCP simulations are executed at convection-permitting resolutions (3 to 5 km) on small domains. Thirdly, the output of the atmospheric models is used to drive land surface models (the SURFEX model and the Urbclim model) with urban modules, a crop model (REGCROP), a tides and storm model (COHERENS) and the MEGAN-MOHYCAN model that simulates the fluxes emitted by vegetation. Finally, one work package will translate the uncertainty present in the CORDEX database to the high-resolution output of the CORDEX.be project. The organization of the project will be presented and first results will be shown, demonstrating that convection-permitting models can add extra skill to the mesoscale version of the regional climate models, in particular regarding the extreme value statistics and the diurnal cycle.

CORDEX.be: COmbining Regional climate Downscaling EXpertise in Belgium

NASA Astrophysics Data System (ADS)

Termonia, Piet; Van Schaeybroeck, Bert; De Ridder, Koen; Fettweis, Xavier; Gobin, Anne; Luyten, Patrick; Marbaix, Philippe; Pottiaux, Eric; Stavrakou, Trissevgeni; Van Lipzig, Nicole; van Ypersele, Jean-Pascal; Willems, Patrick

2016-04-01

The main objective of the ongoing project CORDEX.be, "COmbining Regional Downscaling EXpertise in Belgium: CORDEX and Beyond" is to gather existing and ongoing Belgian research activities in the domain of climate modelling to create a coherent scientific basis for future climate services in Belgium. The project regroups eight Belgian Institutes under a single research program of the Belgian Science Policy (BELSPO). The project involves three regional climate models: the ALARO model, the COSMO-CLM model and the MAR model running according to the guidelines of the CORDEX project and at convection permitting resolution on small domains over Belgium. The project creates a framework to address four objectives/challenges. First, this projects aims to contribute to the EURO-CORDEX project. Secondly, RCP simulations are executed at convection-permitting resolutions (3 to 5 km) on small domains. Thirdly, the output of the atmospheric models is used to drive land surface models (the SURFEX model and the Urbclim model) with urban modules, a crop model (REGCROP), a tides and storm model (COHERENS) and the MEGAN-MOHYCAN model that simulates the fluxes emitted by vegetation. Finally, one work package will translate the uncertainty present in the CORDEX database to the high-resolution output of the CORDEX.be project. The organization of the project will be presented and first results will be shown, demonstrating that convection-permitting models can add extra skill to the mesoscale version of the regional climate models, in particular regarding the extreme value statistics and the diurnal cycle.
Optimizing dynamic downscaling in one-way nesting using a regional ocean model

NASA Astrophysics Data System (ADS)

Pham, Van Sy; Hwang, Jin Hwan; Ku, Hyeyun

2016-10-01

Dynamical downscaling with nested regional oceanographic models has been demonstrated to be an effective approach for both operationally forecasted sea weather on regional scales and projections of future climate change and its impact on the ocean. However, when nesting procedures are carried out in dynamic downscaling from a larger-scale model or set of observations to a smaller scale, errors are unavoidable due to the differences in grid sizes and updating intervals. The present work assesses the impact of errors produced by nesting procedures on the downscaled results from Ocean Regional Circulation Models (ORCMs). Errors are identified and evaluated based on their sources and characteristics by employing the Big-Brother Experiment (BBE). The BBE uses the same model to produce both nesting and nested simulations; so it addresses those error sources separately (i.e., without combining the contributions of errors from different sources). Here, we focus on discussing errors resulting from the spatial grids' differences, the updating times and the domain sizes. After the BBE was separately run for diverse cases, a Taylor diagram was used to analyze the results and recommend an optimal combination of grid size, updating period and domain sizes. Finally, suggested setups for the downscaling were evaluated by examining the spatial correlations of variables and the relative magnitudes of variances between the nested model and the original data.
Quantifying Information Gain from Dynamic Downscaling Experiments

NASA Astrophysics Data System (ADS)

Tian, Y.; Peters-Lidard, C. D.

2015-12-01

Dynamic climate downscaling experiments are designed to produce information at higher spatial and temporal resolutions. Such additional information is generated from the low-resolution initial and boundary conditions via the predictive power of the physical laws. However, errors and uncertainties in the initial and boundary conditions can be propagated and even amplified to the downscaled simulations. Additionally, the limit of predictability in nonlinear dynamical systems will also damper the information gain, even if the initial and boundary conditions were error-free. Thus it is critical to quantitatively define and measure the amount of information increase from dynamic downscaling experiments, to better understand and appreciate their potentials and limitations. We present a scheme to objectively measure the information gain from such experiments. The scheme is based on information theory, and we argue that if a downscaling experiment is to exhibit value, it has to produce more information than what can be simply inferred from information sources already available. These information sources include the initial and boundary conditions, the coarse resolution model in which the higher-resolution models are embedded, and the same set of physical laws. These existing information sources define an "information threshold" as a function of the spatial and temporal resolution, and this threshold serves as a benchmark to quantify the information gain from the downscaling experiments, or any other approaches. For a downscaling experiment to shown any value, the information has to be above this threshold. A recent NASA-supported downscaling experiment is used as an example to illustrate the application of this scheme.
Multi-scale modeling of relationships between forest health and climatic factors

Treesearch

Michael K. Crosby; Zhaofei Fan; Xingang Fan; Martin A. Spetich; Theodor D. Leininger

2015-01-01

Forest health and mortality trends are impacted by changes in climate. These trends can vary by species, plot location, forest type, and/or ecoregion. To assess the variation among these groups, Forest Inventory and Analysis (FIA) data were obtained for 10 states in the southeastern United States and combined with downscaled climate data from the Weather Research and...
Climate Projections from the NARCliM Project: Bayesian Model Averaging of Maximum Temperature Projections

NASA Astrophysics Data System (ADS)

Olson, R.; Evans, J. P.; Fan, Y.

2015-12-01

NARCliM (NSW/ACT Regional Climate Modelling Project) is a regional climate project for Australia and the surrounding region. It dynamically downscales 4 General Circulation Models (GCMs) using three Regional Climate Models (RCMs) to provide climate projections for the CORDEX-AustralAsia region at 50 km resolution, and for south-east Australia at 10 km resolution. The project differs from previous work in the level of sophistication of model selection. Specifically, the selection process for GCMs included (i) conducting literature review to evaluate model performance, (ii) analysing model independence, and (iii) selecting models that span future temperature and precipitation change space. RCMs for downscaling the GCMs were chosen based on their performance for several precipitation events over South-East Australia, and on model independence.Bayesian Model Averaging (BMA) provides a statistically consistent framework for weighing the models based on their likelihood given the available observations. These weights are used to provide probability distribution functions (pdfs) for model projections. We develop a BMA framework for constructing probabilistic climate projections for spatially-averaged variables from the NARCliM project. The first step in the procedure is smoothing model output in order to exclude the influence of internal climate variability. Our statistical model for model-observations residuals is a homoskedastic iid process. Comparing RCMs with Australian Water Availability Project (AWAP) observations is used to determine model weights through Monte Carlo integration. Posterior pdfs of statistical parameters of model-data residuals are obtained using Markov Chain Monte Carlo. The uncertainty in the properties of the model-data residuals is fully accounted for when constructing the projections. We present the preliminary results of the BMA analysis for yearly maximum temperature for New South Wales state planning regions for the period 2060-2079.
Evaluation of downscaled, gridded climate data for the conterminous United States

USGS Publications Warehouse

Robert J. Behnke,; Stephen J. Vavrus,; Andrew Allstadt,; Thomas P. Albright,; Thogmartin, Wayne E.; Volker C. Radeloff,

2016-01-01

Weather and climate affect many ecological processes, making spatially continuous yet fine-resolution weather data desirable for ecological research and predictions. Numerous downscaled weather data sets exist, but little attempt has been made to evaluate them systematically. Here we address this shortcoming by focusing on four major questions: (1) How accurate are downscaled, gridded climate data sets in terms of temperature and precipitation estimates?, (2) Are there significant regional differences in accuracy among data sets?, (3) How accurate are their mean values compared with extremes?, and (4) Does their accuracy depend on spatial resolution? We compared eight widely used downscaled data sets that provide gridded daily weather data for recent decades across the United States. We found considerable differences among data sets and between downscaled and weather station data. Temperature is represented more accurately than precipitation, and climate averages are more accurate than weather extremes. The data set exhibiting the best agreement with station data varies among ecoregions. Surprisingly, the accuracy of the data sets does not depend on spatial resolution. Although some inherent differences among data sets and weather station data are to be expected, our findings highlight how much different interpolation methods affect downscaled weather data, even for local comparisons with nearby weather stations located inside a grid cell. More broadly, our results highlight the need for careful consideration among different available data sets in terms of which variables they describe best, where they perform best, and their resolution, when selecting a downscaled weather data set for a given ecological application.
Can Regional Climate Models Improve Warm Season Forecasts in the North American Monsoon Region?

NASA Astrophysics Data System (ADS)

Dominguez, F.; Castro, C. L.

2009-12-01

The goal of this work is to improve warm season forecasts in the North American Monsoon Region. To do this, we are dynamically downscaling warm season CFS (Climate Forecast System) reforecasts from 1982-2005 for the contiguous U.S. using the Weather Research and Forecasting (WRF) regional climate model. CFS is the global coupled ocean-atmosphere model used by the Climate Prediction Center (CPC), a branch of the National Center for Environmental Prediction (NCEP), to provide official U.S. seasonal climate forecasts. Recently, NCEP has produced a comprehensive long-term retrospective ensemble CFS reforecasts for the years 1980-2005. These reforecasts show that CFS model 1) has an ability to forecast tropical Pacific SSTs and large-scale teleconnection patterns, at least as evaluated for the winter season; 2) has greater skill in forecasting winter than summer climate; and 3) demonstrates an increase in skill when a greater number of ensembles members are used. The decrease in CFS skill during the warm season is due to the fact that the physical mechanisms of rainfall at this time are more related to mesoscale processes, such as the diurnal cycle of convection, low-level moisture transport, propagation and organization of convection, and surface moisture recycling. In general, these are poorly represented in global atmospheric models. Preliminary simulations for years with extreme summer climate conditions in the western and central U.S. (specifically 1988 and 1993) show that CFS-WRF simulations can provide a more realistic representation of convective rainfall processes. Thus a RCM can potentially add significant value in climate forecasting of the warm season provided the downscaling methodology incorporates the following: 1) spectral nudging to preserve the variability in the large scale circulation while still permitting the development of smaller-scale variability in the RCM; and 2) use of realistic soil moisture initial condition, in this case provided by the North American Regional Reanalysis. With these conditions, downscaled CFS-WRF reforecast simulations can produce realistic continental-scale patterns of warm season precipitation. This includes a reasonable representation of the North American monsoon in the southwest U.S. and northwest Mexico, which is notoriously difficult to represent in a global atmospheric model. We anticipate that this research will help lead the way toward substantially improved real time operational forecasts of North American summer climate with a RCM.
Evaluating meteo marine climatic model inputs for the investigation of coastal hydrodynamics

NASA Astrophysics Data System (ADS)

Bellafiore, D.; Bucchignani, E.; Umgiesser, G.

2010-09-01

One of the major aspects discussed in the recent works on climate change is how to provide information from the global scale to the local one. In fact the influence of sea level rise and changes in the meteorological conditions due to climate change in strategic areas like the coastal zone is at the base of the well known mitigation and risk assessment plans. The investigation of the coastal zone hydrodynamics, from a modeling point of view, has been the field for the connection between hydraulic models and ocean models and, in terms of process studies, finite element models have demonstrated their suitability in the reproduction of complex coastal morphology and in the capability to reproduce different spatial scale hydrodynamic processes. In this work the connection between two different model families, the climate models and the hydrodynamic models usually implemented for process studies, is tested. Together, they can be the most suitable tool for the investigation of climate change on coastal systems. A finite element model, SHYFEM (Shallow water Hydrodynamic Finite Element Model), is implemented on the Adriatic Sea, to investigate the effect of wind forcing datasets produced by different downscaling from global climate models in terms of surge and its coastal effects. The wind datasets are produced by the regional climate model COSMO-CLM (CIRA), and by EBU-POM model (Belgrade University), both downscaling from ECHAM4. As a first step the downscaled wind datasets, that have different spatial resolutions, has been analyzed for the period 1960-1990 to compare what is their capability to reproduce the measured wind statistics in the coastal zone in front of the Venice Lagoon. The particularity of the Adriatic Sea meteo climate is connected with the influence of the orography in the strengthening of winds like Bora, from North-East. The increase in spatial resolution permits the more resolved wind dataset to better reproduce meteorology and to provide a more realistic forcing for hydrodynamic simulations. After this analysis, effects on water level variations, under different wind forcing, has been analyzed to define what is the local effect on sea level changes in the coastal area of the North Adriatic. Surge statistics produced from different climate model forcings for the IPCC A1B scenario have been studied to provide local information on climate change effects on coastal hydrodynamics due to meteorological effect. This typology of application has been considered a suitable tool for coastal management and can be considered a study field that will increase its importance in the more general investigation on scale interaction processes as the effects of global scale climate phenomena on local areas.
A multimodal wave spectrum-based approach for statistical downscaling of local wave climate

USGS Publications Warehouse

Hegermiller, Christie; Antolinez, Jose A A; Rueda, Ana C.; Camus, Paula; Perez, Jorge; Erikson, Li; Barnard, Patrick; Mendez, Fernando J.

2017-01-01

Characterization of wave climate by bulk wave parameters is insufficient for many coastal studies, including those focused on assessing coastal hazards and long-term wave climate influences on coastal evolution. This issue is particularly relevant for studies using statistical downscaling of atmospheric fields to local wave conditions, which are often multimodal in large ocean basins (e.g. the Pacific). Swell may be generated in vastly different wave generation regions, yielding complex wave spectra that are inadequately represented by a single set of bulk wave parameters. Furthermore, the relationship between atmospheric systems and local wave conditions is complicated by variations in arrival time of wave groups from different parts of the basin. Here, we address these two challenges by improving upon the spatiotemporal definition of the atmospheric predictor used in statistical downscaling of local wave climate. The improved methodology separates the local wave spectrum into “wave families,” defined by spectral peaks and discrete generation regions, and relates atmospheric conditions in distant regions of the ocean basin to local wave conditions by incorporating travel times computed from effective energy flux across the ocean basin. When applied to locations with multimodal wave spectra, including Southern California and Trujillo, Peru, the new methodology improves the ability of the statistical model to project significant wave height, peak period, and direction for each wave family, retaining more information from the full wave spectrum. This work is the base of statistical downscaling by weather types, which has recently been applied to coastal flooding and morphodynamic applications.
High Resolution Climate Modeling of the Water Cycle over the Contiguous United States Including Potential Climate Change Scenarios

NASA Astrophysics Data System (ADS)

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.

2015-12-01

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.
Dynamic Rainfall Patterns and the Simulation of Changing Scenarios: A behavioral watershed response

NASA Astrophysics Data System (ADS)

Chu, M.; Guzman, J.; Steiner, J. L.; Hou, C.; Moriasi, D.

2015-12-01

Rainfall is one of the fundamental drivers that control hydrologic responses including runoff production and transport phenomena that consequently drive changes in aquatic ecosystems. Quantifying the hydrologic responses to changing scenarios (e.g., climate, land use, and management) using environmental models requires a realistic representation of probable rainfall in its most sensible spatio-temporal dimensions matching that of the phenomenon under investigation. Downscaling projected rainfall from global circulation models (GCMs) is the most common practice in deriving rainfall datasets to be used as main inputs to hydrologic models which in turn are used to assess the impacts of climate changes on ecosystems. Downscaling assumes that local climate is a combination of large-scale climatic/atmospheric conditions and local conditions. However, the representation of the latter is generally beyond the capacity of current GCMs. The main objective of this study was to develop and implement a synthetic rainfall generator to downscale expected rainfall trends to 1 x 1 km rainfall daily patterns that mimic the dynamic propagation of probability distribution functions (pdf) derived from historic rainfall data (rain-gauge or radar estimated). Future projections were determined based on actual and expected changes in the pdf and stochastic processes to account for variability. Watershed responses in terms of streamflow and nutrients loads were evaluated using synthetically generated rainfall patterns and actual data. The framework developed in this study will allow practitioners to generate rainfall datasets that mimic the temporal and spatial patterns exclusive to their study area under full disclosure of the uncertainties involved. This is expected to provide significantly more accurate environmental models than is currently available and would provide practitioners with ways to evaluate the spectrum of systemic responses to changing scenarios.
Locally Downscaled and Spatially Customizable Climate Data for Historical and Future Periods for North America

PubMed Central

Wang, Tongli; Hamann, Andreas; Spittlehouse, Dave; Carroll, Carlos

2016-01-01

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
Locally Downscaled and Spatially Customizable Climate Data for Historical and Future Periods for North America.

PubMed

Wang, Tongli; Hamann, Andreas; Spittlehouse, Dave; Carroll, Carlos

2016-01-01

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.
Influence of reanalysis datasets on dynamically downscaling the recent past

NASA Astrophysics Data System (ADS)

Moalafhi, Ditiro B.; Evans, Jason P.; Sharma, Ashish

2017-08-01

Multiple reanalysis datasets currently exist that can provide boundary conditions for dynamic downscaling and simulating local hydro-climatic processes at finer spatial and temporal resolutions. Previous work has suggested that there are two reanalyses alternatives that provide the best lateral boundary conditions for downscaling over southern Africa. This study dynamically downscales these reanalyses (ERA-I and MERRA) over southern Africa to a high resolution (10 km) grid using the WRF model. Simulations cover the period 1981-2010. Multiple observation datasets were used for both surface temperature and precipitation to account for observational uncertainty when assessing results. Generally, temperature is simulated quite well, except over the Namibian coastal plain where the simulations show anomalous warm temperature related to the failure to propagate the influence of the cold Benguela current inland. Precipitation tends to be overestimated in high altitude areas, and most of southern Mozambique. This could be attributed to challenges in handling complex topography and capturing large-scale circulation patterns. While MERRA driven WRF exhibits slightly less bias in temperature especially for La Nina years, ERA-I driven simulations are on average superior in terms of RMSE. When considering multiple variables and metrics, ERA-I is found to produce the best simulation of the climate over the domain. The influence of the regional model appears to be large enough to overcome the small difference in relative errors present in the lateral boundary conditions derived from these two reanalyses.
Hydrological regime modifications induced by climate change in Mediterranean area

NASA Astrophysics Data System (ADS)

Pumo, Dario; Caracciolo, Domenico; Viola, Francesco; Valerio Noto, Leonardo

2015-04-01

The knowledge of river flow regimes has a capital importance for a variety of practical applications, in water resource management, including optimal and sustainable use. Hydrological regime is highly dependent on climatic factors, among which the most important is surely the precipitation, in terms of frequency, seasonal distribution and intensity of rainfall events. The streamflow frequency regime of river basins are often summarized by flow duration curves (FDCs), that offer a simple and comprehensive graphical view of the overall historical variability associated with streamflow, and characterize the ability of the basin to provide flows of various magnitudes. Climate change is likely to lead shifts in the hydrological regime, and, consequently, in the FDCs. Staring from this premise, the primary objective of the present study is to explore the effects of potential climate changes on the hydrological regime of some small Mediterranean basins. To this aim it is here used a recent hydrological model, the ModABa model (MODel for Annual flow duration curves assessment in ephemeral small BAsins), for the probabilistic characterization of the daily streamflows in small catchments. The model has been calibrated and successively validated in a unique small catchment, where it has shown a satisfactory accuracy in reproducing the empirical FDC starting from easily derivable parameters arising from basic ecohydrological knowledge of the basin and commonly available climatic data such as daily precipitation and temperatures. Thus, this work also represents a first attempt to apply the ModABa to basins different from that used for its preliminary design in order to testing its generality. Different case studies are selected within the Sicily region; the model is first calibrated at the sites and then forced by future climatic scenarios, highlighting the principal differences emerging from the current scenario and future FDCs. The future climate scenarios are generated using a stochastic downscaling technique based on the weather generator, AWE-GEN. This methodology allows for the downscaling of an ensemble of climate model outputs deriving the frequency distribution functions of factors of change for several statistics of temperature and precipitation from outputs of General Circulation Models (GCMs). The stochastic downscaling is carried out using simulations of GCMs adopted in the IPCC 5AR, for the future periods of 2046-2065 and 2081-2100.
Downscaling of RCM outputs for representative catchments in the Mediterranean region, for the 1951-2100 time-frame

NASA Astrophysics Data System (ADS)

Deidda, Roberto; Marrocu, Marino; Pusceddu, Gabriella; Langousis, Andreas; Mascaro, Giuseppe; Caroletti, Giulio

2013-04-01

Within the activities of the EU FP7 CLIMB project (www.climb-fp7.eu), we developed downscaling procedures to reliably assess climate forcing at hydrologically relevant scales, and applied them to six representative hydrological basins located in the Mediterranean region: Riu Mannu and Noce in Italy, Chiba in Tunisia, Kocaeli in Turkey, Thau in France, and Gaza in Palestine. As a first step towards this aim, we used daily precipitation and temperature data from the gridded E-OBS project (www.ecad.eu/dailydata), as reference fields, to rank 14 Regional Climate Model (RCM) outputs from the ENSEMBLES project (http://ensembles-eu.metoffice.com). The four best performing model outputs were selected, with the additional constraint of maintaining 2 outputs obtained from running different RCMs driven by the same GCM, and 2 runs from the same RCM driven by different GCMs. For these four RCM-GCM model combinations, a set of downscaling techniques were developed and applied, for the period 1951-2100, to variables used in hydrological modeling (i.e. precipitation; mean, maximum and minimum daily temperatures; direct solar radiation, relative humidity, magnitude and direction of surface winds). The quality of the final products is discussed, together with the results obtained after applying a bias reduction procedure to daily temperature and precipitation fields.
Projections and downscaling of 21st century temperatures, precipitation, radiative fluxes and winds for the southwestern US, with focus on the Lake Tahoe basin

USGS Publications Warehouse

Dettinger, Michael D.

2013-01-01

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.
Use of a Weather Generator for analysis of projections of future daily temperature and its validation with climate change indices

NASA Astrophysics Data System (ADS)

Di Piazza, A.; Cordano, E.; Eccel, E.

2012-04-01

The issue of climate change detection is considered a major challenge. In particular, high temporal resolution climate change scenarios are required in the evaluation of the effects of climate change on agricultural management (crop suitability, yields, risk assessment, etc.) energy production and water management. In this work, a "Weather Generator" technique was used for downscaling climate change scenarios for temperature. An R package (RMAWGEN, Cordano and Eccel, 2011 - available on http://cran.r-project.org) was developed aiming to generate synthetic daily weather conditions by using the theory of vectorial auto-regressive models (VAR). The VAR model was chosen for its ability in maintaining the temporal and spatial correlations among variables. In particular, observed time series of daily maximum and minimum temperature are transformed into "new" normally-distributed variable time series which are used to calibrate the parameters of a VAR model by using ordinary least square methods. Therefore the implemented algorithm, applied to monthly mean climatic values downscaled by Global Climate Model predictions, can generate several stochastic daily scenarios where the statistical consistency among series is saved. Further details are present in RMAWGEN documentation. An application is presented here by using a dataset with daily temperature time series recorded in 41 different sites of Trentino region for the period 1958-2010. Temperature time series were pre-processed to fill missing values (by a site-specific calibrated Inverse Distance Weighting algorithm, corrected with elevation) and to remove inhomogeneities. Several climatic indices were taken into account, useful for several impact assessment applications, and their time trends within the time series were analyzed. The indices go from the more classical ones, as annual mean temperatures, seasonal mean temperatures and their anomalies (from the reference period 1961-1990) to the climate change indices selected from the list recommended by the World Meteorological Organization Commission for Climatology (WMO-CCL) and the Research Programme on Climate Variability and Predictability (CLIVAR) project's Expert Team on Climate Change Detection, Monitoring and Indices (ETCCDMI). Each index was applied to both observed (and processed) data and to synthetic time series produced by the Weather Generator, over the thirty year reference period 1981-2010, in order to validate the procedure. Climate projections were statistically downscaled for a selection of sites for the two 30-year periods 2021-2050 and 2071-2099 of the European project "Ensembles" multi-model output (scenario A1B). The use of several climatic indices strengthens the trend analysis of both the generated synthetic series and future climate projections.
Use of NARCCAP data to characterize regional climate uncertainty in the impact of global climate change on large river fish population: Missouri River sturgeon example

NASA Astrophysics Data System (ADS)

Anderson, C. J.; Wildhaber, M. L.; Wikle, C. K.; Moran, E. H.; Franz, K. J.; Dey, R.

2012-12-01

Climate change operates over a broad range of spatial and temporal scales. Understanding the effects of change on ecosystems requires accounting for the propagation of information and uncertainty across these scales. For example, to understand potential climate change effects on fish populations in riverine ecosystems, climate conditions predicted by course-resolution atmosphere-ocean global climate models must first be translated to the regional climate scale. In turn, this regional information is used to force watershed models, which are used to force river condition models, which impact the population response. A critical challenge in such a multiscale modeling environment is to quantify sources of uncertainty given the highly nonlinear nature of interactions between climate variables and the individual organism. We use a hierarchical modeling approach for accommodating uncertainty in multiscale ecological impact studies. This framework allows for uncertainty due to system models, model parameter settings, and stochastic parameterizations. This approach is a hybrid between physical (deterministic) downscaling and statistical downscaling, recognizing that there is uncertainty in both. We use NARCCAP data to determine confidence the capability of climate models to simulate relevant processes and to quantify regional climate variability within the context of the hierarchical model of uncertainty quantification. By confidence, we mean the ability of the regional climate model to replicate observed mechanisms. We use the NCEP-driven simulations for this analysis. This provides a base from which regional change can be categorized as either a modification of previously observed mechanisms or emergence of new processes. The management implications for these categories of change are significantly different in that procedures to address impacts from existing processes may already be known and need adjustment; whereas, an emergent processes may require new management strategies. The results from hierarchical analysis of uncertainty are used to study the relative change in weights of the endangered Missouri River pallid sturgeon (Scaphirhynchus albus) under a 21st century climate scenario.
A framework for global river flood risk assessments

NASA Astrophysics Data System (ADS)

Winsemius, H. C.; Van Beek, L. P. H.; Jongman, B.; Ward, P. J.; Bouwman, A.

2013-05-01

There is an increasing need for strategic global assessments of flood risks in current and future conditions. In this paper, we propose a framework for global flood risk assessment for river floods, which can be applied in current conditions, as well as in future conditions due to climate and socio-economic changes. The framework's goal is to establish flood hazard and impact estimates at a high enough resolution to allow for their combination into a risk estimate, which can be used for strategic global flood risk assessments. The framework estimates hazard at a resolution of ~ 1 km2 using global forcing datasets of the current (or in scenario mode, future) climate, a global hydrological model, a global flood-routing model, and more importantly, an inundation downscaling routine. The second component of the framework combines hazard with flood impact models at the same resolution (e.g. damage, affected GDP, and affected population) to establish indicators for flood risk (e.g. annual expected damage, affected GDP, and affected population). The framework has been applied using the global hydrological model PCR-GLOBWB, which includes an optional global flood routing model DynRout, combined with scenarios from the Integrated Model to Assess the Global Environment (IMAGE). We performed downscaling of the hazard probability distributions to 1 km2 resolution with a new downscaling algorithm, applied on Bangladesh as a first case study application area. We demonstrate the risk assessment approach in Bangladesh based on GDP per capita data, population, and land use maps for 2010 and 2050. Validation of the hazard estimates has been performed using the Dartmouth Flood Observatory database. This was done by comparing a high return period flood with the maximum observed extent, as well as by comparing a time series of a single event with Dartmouth imagery of the event. Validation of modelled damage estimates was performed using observed damage estimates from the EM-DAT database and World Bank sources. We discuss and show sensitivities of the estimated risks with regard to the use of different climate input sets, decisions made in the downscaling algorithm, and different approaches to establish impact models.

Dynamical downscaling of historical climate over CORDEX East Asia domain: A comparison of regional ocean-atmosphere coupled model to stand-alone RCM simulations

NASA Astrophysics Data System (ADS)

Zou, Liwei; Zhou, Tianjun; Peng, Dongdong

2016-02-01

The FROALS (flexible regional ocean-atmosphere-land system) model, a regional ocean-atmosphere coupled model, has been applied to the Coordinated Regional Downscaling Experiment (CORDEX) East Asia domain. Driven by historical simulations from a global climate system model, dynamical downscaling for the period from 1980 to 2005 has been conducted at a uniform horizontal resolution of 50 km. The impacts of regional air-sea couplings on the simulations of East Asian summer monsoon rainfall have been investigated, and comparisons have been made to corresponding simulations performed using a stand-alone regional climate model (RCM). The added value of the FROALS model with respect to the driving global climate model was evident in terms of both climatology and the interannual variability of summer rainfall over East China by the contributions of both the high horizontal resolution and the reasonably simulated convergence of the moisture fluxes. Compared with the stand-alone RCM simulations, the spatial pattern of the simulated low-level monsoon flow over East Asia and the western North Pacific was improved in the FROALS model due to its inclusion of regional air-sea coupling. The results indicated that the simulated sea surface temperature (SSTs) resulting from the regional air-sea coupling were lower than those derived directly from the driving global model over the western North Pacific north of 15°N. These colder SSTs had both positive and negative effects. On the one hand, they strengthened the western Pacific subtropical high, which improved the simulation of the summer monsoon circulation over East Asia. On the other hand, the colder SSTs suppressed surface evaporation and favored weaker local interannual variability in the SST, which led to less summer rainfall and weaker interannual rainfall variability over the Korean Peninsula and Japan. Overall, the reference simulation performed using the FROALS model is reasonable in terms of rainfall over the land area of East Asia and will become the basis for the generation of climate change scenarios for the CORDEX East Asia domain that will be described in future reports.
Using Dynamically Downscaled Climate Model Outputs to Inform Projections of Extreme Precipitation Events

NASA Technical Reports Server (NTRS)

Wobus, Cameron; Reynolds, Lara; Jones, Russell; Horton, Radley; Smith, Joel; Fries, J. Stephen; Tryby, Michael; Spero, Tanya; Nolte, Chris

2015-01-01

Many of the storms that generate damaging floods are caused by locally intense, sub-daily precipitation, yet the spatial and temporal resolution of the most widely available climate model outputs are both too coarse to simulate these events. Thus there is often a disconnect between the nature of the events that cause damaging floods and the models used to project how climate change might influence their magnitude. This could be a particular problem when developing scenarios to inform future storm water management options under future climate scenarios. In this study we sought to close this gap, using sub-daily outputs from the Weather Research and Forecasting model (WRF) from each of the nine climate regions in the United States. Specifically, we asked 1) whether WRF outputs projected consistent patterns of change for sub-daily and daily precipitation extremes; and 2) whether this dynamically downscaled model projected different magnitudes of change for 3-hourly vs 24-hourly extreme events. We extracted annual maximum values for 3-hour through 24-hour precipitation totals from an 11-year time series of hindcast (1995-2005) and mid-century (2045-2055) climate, and calculated the direction and magnitude of change for 3-hour and 24-hour extreme events over this timeframe. The model results project that the magnitude of both 3-hour and 24-hour events will increase over most regions of the United States, but there was no clear or consistent difference in the relative magnitudes of change for sub-daily vs daily events.
A large-scale simulation of climate change effects on flood regime - A case study for the Alabama-Coosa-Tallapoosa River Basin

NASA Astrophysics Data System (ADS)

Dullo, T. T.; Gangrade, S.; Marshall, R.; Islam, S. R.; Ghafoor, S. K.; Kao, S. C.; Kalyanapu, A. J.

2017-12-01

The damage and cost of flooding are continuously increasing due to climate change and variability, which compels the development and advance of global flood hazard models. However, due to computational expensiveness, evaluation of large-scale and high-resolution flood regime remains a challenge. The objective of this research is to use a coupled modeling framework that consists of a dynamically downscaled suite of eleven Coupled Model Intercomparison Project Phase 5 (CMIP5) climate models, a distributed hydrologic model called DHSVM, and a computational-efficient 2-dimensional hydraulic model called Flood2D-GPU to study the impacts of climate change on flood regime in the Alabama-Coosa-Tallapoosa (ACT) River Basin. Downscaled meteorologic forcings for 40 years in the historical period (1966-2005) and 40 years in the future period (2011-2050) were used as inputs to drive the calibrated DHSVM to generate annual maximum flood hydrographs. These flood hydrographs along with 30-m resolution digital elevation and estimated surface roughness were then used by Flood2D-GPU to estimate high-resolution flood depth, velocities, duration, and regime. Preliminary results for the Conasauga river basin (an upper subbasin within ACT) indicate that seven of the eleven climate projections show an average increase of 25 km2 in flooded area (between historic and future projections). Future work will focus on illustrating the effects of climate change on flood duration and area for the entire ACT basin.
Impacts of climate change under CMIP5 RCP scenarios on the streamflow in the Dinder River and ecosystem habitats in Dinder National Park, Sudan

NASA Astrophysics Data System (ADS)

Basheer, A. K.; Lu, H.; Omer, A.; Ali, A. B.; Abdelgader, A. M. S.

2015-10-01

The fate of seasonal rivers ecosystem habitats under climate change essentially depends on the changes in annual recharge, which related to alterations in precipitation and evaporation over the river basin. Therefore the change in climate conditions is expected to significantly affect hydrological and ecological components, particularly in fragmented ecosystems. This study aims to assess the impacts of climate change on the streamflow in Dinder River Basin (DRB), and infer its relative possible effects on the Dinder National Park (DNP) ecosystem habitats in the Sudan. Two global circulation models (GCMs) from Coupled Model Intercomparison Project Phase 5 and two statistical downscaling approaches combined with hydrological model (SWAT) were used to project the climate change conditions over the study periods 2020s, 2050s and 2080s. The results indicated that the climate over the DRB will become warmer and wetter under the most scenarios. The projected precipitation variability mainly depends on the selected GCM and downscaling approach. Moreover, the projected streamflow was more sensitive to rainfall and temperature variation, and will likely increase in this century. In contrast to drought periods during (1960s, 1970s and 1980s), the predicted climate change is likely to affect ecosystems in DNP positively and promote the ecological restoration of the flora and fauna habitats'.
Flow regime, temperature, and biotic interactions drive differential declines of trout species under climate change [includes Supporting Information

Treesearch

Seth J. Wenger; Daniel J. Isaak; Charlie Luce; Helen M. Neville; Kurt D. Fausch; Jason B. Dunham; Daniel C. Dauwalter; Michael K. Young; Marketa M. Elsner; Bruce E. Rieman; Alan F. Hamlet; Jack E. Williams

2011-01-01

Broad-scale studies of climate change effects on freshwater species have focused mainly on temperature, ignoring critical drivers such as flow regime and biotic interactions. We use downscaled outputs from general circulation models coupled with a hydrologic model to forecast the effects of altered flows and increased temperatures on four interacting species of trout...
Validation of non-stationary precipitation series for site-specific impact assessment: Comparison of two statistical downscaling techniques

USDA-ARS?s Scientific Manuscript database

The generation of realistic future precipitation scenarios is crucial for assessing their impacts on a range of environmental and socio-economic impact sectors. A scale mismatch exists, however, between the coarse spatial resolution at which global climate models (GCMs) output future climate scenari...
Development of incremental dynamical downscaling and analysis system for regional scale climate change projections

NASA Astrophysics Data System (ADS)

Wakazuki, Yasutaka; Hara, Masayuki; Fujita, Mikiko; Ma, Xieyao; Kimura, Fujio

2013-04-01

Regional scale climate change projections play an important role in assessments of influences of global warming and include statistical (SD) and dynamical downscaling (DD) approaches. One of DD methods is developed basing on the pseudo-global-warming (PGW) method developed by Kimura and Kitoh (2007) in this study. In general, DD uses regional climate model (RCM) with lateral boundary data. In PGW method, the climatological mean difference estimated by GCMs are added to the objective analysis data (ANAL), and the data are used as the lateral boundary data in the future climate simulations. The ANAL is also used as the lateral boundary conditions of the present climate simulation. One of merits of the PGW method is that influences of biases of GCMs in RCM simulations are reduced. However, the PGW method does not treat climate changes in relative humidity, year-to-year variation, and short-term disturbances. The developing new downscaling method is named as the incremental dynamical downscaling and analysis system (InDDAS). The InDDAS treat climate changes in relative humidity and year-to-year variations. On the other hand, uncertainties of climate change projections estimated by many GCMs are large and are not negligible. Thus, stochastic regional scale climate change projections are expected for assessments of influences of global warming. Many RCM runs must be performed to make stochastic information. However, the computational costs are huge because grid size of RCM runs should be small to resolve heavy rainfall phenomena. Therefore, the number of runs to make stochastic information must be reduced. In InDDAS, climatological differences added to ANAL become statistically pre-analyzed information. The climatological differences of many GCMs are divided into mean climatological difference (MD) and departures from MD. The departures are analyzed by principal component analysis, and positive and negative perturbations (positive and negative standard deviations multiplied by departure patterns (eigenvectors)) with multi modes are added to MD. Consequently, the most likely future states are calculated with climatological difference of MD. For example, future states in cases that temperature increase is large and small are calculated with MD plus positive and negative perturbations of the first mode.
Hydrologic drought prediction under climate change: Uncertainty modeling with Dempster-Shafer and Bayesian approaches

NASA Astrophysics Data System (ADS)

Raje, Deepashree; Mujumdar, P. P.

2010-09-01

Representation and quantification of uncertainty in climate change impact studies are a difficult task. Several sources of uncertainty arise in studies of hydrologic impacts of climate change, such as those due to choice of general circulation models (GCMs), scenarios and downscaling methods. Recently, much work has focused on uncertainty quantification and modeling in regional climate change impacts. In this paper, an uncertainty modeling framework is evaluated, which uses a generalized uncertainty measure to combine GCM, scenario and downscaling uncertainties. The Dempster-Shafer (D-S) evidence theory is used for representing and combining uncertainty from various sources. A significant advantage of the D-S framework over the traditional probabilistic approach is that it allows for the allocation of a probability mass to sets or intervals, and can hence handle both aleatory or stochastic uncertainty, and epistemic or subjective uncertainty. This paper shows how the D-S theory can be used to represent beliefs in some hypotheses such as hydrologic drought or wet conditions, describe uncertainty and ignorance in the system, and give a quantitative measurement of belief and plausibility in results. The D-S approach has been used in this work for information synthesis using various evidence combination rules having different conflict modeling approaches. A case study is presented for hydrologic drought prediction using downscaled streamflow in the Mahanadi River at Hirakud in Orissa, India. Projections of n most likely monsoon streamflow sequences are obtained from a conditional random field (CRF) downscaling model, using an ensemble of three GCMs for three scenarios, which are converted to monsoon standardized streamflow index (SSFI-4) series. This range is used to specify the basic probability assignment (bpa) for a Dempster-Shafer structure, which represents uncertainty associated with each of the SSFI-4 classifications. These uncertainties are then combined across GCMs and scenarios using various evidence combination rules given by the D-S theory. A Bayesian approach is also presented for this case study, which models the uncertainty in projected frequencies of SSFI-4 classifications by deriving a posterior distribution for the frequency of each classification, using an ensemble of GCMs and scenarios. Results from the D-S and Bayesian approaches are compared, and relative merits of each approach are discussed. Both approaches show an increasing probability of extreme, severe and moderate droughts and decreasing probability of normal and wet conditions in Orissa as a result of climate change.
Downscaling with a nested regional climate model in near-surface fields over the contiguous United States: WRF dynamical downscaling

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Jiali; Kotamarthi, Veerabhadra R.

The Weather Research and Forecasting (WRF) model is used for dynamic downscaling of 2.5 degree National Centers for Environmental Prediction-U.S. Department of Energy Reanalysis II (NCEP-R2) data for 1980-2010 at 12 km resolution over most of North America. The model's performance for surface air temperature and precipitation is evaluated by comparison with high-resolution observational data sets. The model's ability to add value is investigated by comparison with NCEP-R2 data and a 50 km regional climate simulation. The causes for major model bias are studied through additional sensitivity experiments with various model setup/integration approaches and physics representations. The WRF captures themore » main features of the spatial patterns and annual cycles of air temperature and precipitation over most of the contiguous United States. However, simulated air temperatures over the south central region and precipitation over the Great Plains and the Southwest have significant biases. Allowing longer spin-up time, reducing the nudging strength, or replacing the WRF Single-Moment 6-class microphysics with Morrison microphysics reduces the bias over some subregions. However, replacing the Grell-Devenyi cumulus parameterization with Kain-Fritsch shows no improvement. The 12 km simulation does add value above the NCEP-R2 data and the 50 km simulation over mountainous and coastal zones.« less
Choice of baseline climate data impacts projected species' responses to climate change.

PubMed

Baker, David J; Hartley, Andrew J; Butchart, Stuart H M; Willis, Stephen G

2016-07-01

Climate data created from historic climate observations are integral to most assessments of potential climate change impacts, and frequently comprise the baseline period used to infer species-climate relationships. They are often also central to downscaling coarse resolution climate simulations from General Circulation Models (GCMs) to project future climate scenarios at ecologically relevant spatial scales. Uncertainty in these baseline data can be large, particularly where weather observations are sparse and climate dynamics are complex (e.g. over mountainous or coastal regions). Yet, importantly, this uncertainty is almost universally overlooked when assessing potential responses of species to climate change. Here, we assessed the importance of historic baseline climate uncertainty for projections of species' responses to future climate change. We built species distribution models (SDMs) for 895 African bird species of conservation concern, using six different climate baselines. We projected these models to two future periods (2040-2069, 2070-2099), using downscaled climate projections, and calculated species turnover and changes in species-specific climate suitability. We found that the choice of baseline climate data constituted an important source of uncertainty in projections of both species turnover and species-specific climate suitability, often comparable with, or more important than, uncertainty arising from the choice of GCM. Importantly, the relative contribution of these factors to projection uncertainty varied spatially. Moreover, when projecting SDMs to sites of biodiversity importance (Important Bird and Biodiversity Areas), these uncertainties altered site-level impacts, which could affect conservation prioritization. Our results highlight that projections of species' responses to climate change are sensitive to uncertainty in the baseline climatology. We recommend that this should be considered routinely in such analyses. © 2016 John Wiley & Sons Ltd.
Paddy rice productivity under climate and land-use change in northern Japan

NASA Astrophysics Data System (ADS)

Yoshida, R.; Fukui, S.; Shimada, T.; Hasegawa, T.; Iwasaki, T.

2013-12-01

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.
Modeling Future Fire danger over North America in a Changing Climate

NASA Astrophysics Data System (ADS)

Jain, P.; Paimazumder, D.; Done, J.; Flannigan, M.

2016-12-01

Fire danger ratings are used to determine wildfire potential due to weather and climate factors. The Fire Weather Index (FWI), part of the Canadian Forest Fire Danger Rating System (CFFDRS), incorporates temperature, relative humidity, windspeed and precipitation to give a daily fire danger rating that is used by wildfire management agencies in an operational context. Studies using GCM output have shown that future wildfire danger will increase in a warming climate. However, these studies are somewhat limited by the coarse spatial resolution (typically 100-400km) and temporal resolution (typically 6-hourly to monthly) of the model output. Future wildfire potential over North America based on FWI is calculated using output from the Weather, Research and Forecasting (WRF) model, which is used to downscale future climate scenarios from the bias-corrected Community Climate System Model (CCSM) under RCP8.5 scenarios at a spatial resolution of 36km. We consider five eleven year time slices: 1990-2000, 2020-2030, 2030-2040, 2050-2060 and 2080-2090. The dynamically downscaled simulation improves determination of future extreme weather by improving both spatial and temporal resolution over most GCM models. To characterize extreme fire weather we calculate annual numbers of spread days (days for which FWI > 19) and annual 99th percentile of FWI. Additionally, an extreme value analysis based on the peaks-over-threshold method allows us to calculate the return values for extreme FWI values.
Effect of climate data on simulated carbon and nitrogen balances for Europe

NASA Astrophysics Data System (ADS)

Blanke, Jan Hendrik; Lindeskog, Mats; Lindström, Johan; Lehsten, Veiko

2016-05-01

In this study, we systematically assess the spatial variability in carbon and nitrogen balance simulations related to the choice of global circulation models (GCMs), representative concentration pathways (RCPs), spatial resolutions, and the downscaling methods used as calculated with LPJ-GUESS. We employed a complete factorial design and performed 24 simulations for Europe with different climate input data sets and different combinations of these four factors. Our results reveal that the variability in simulated output in Europe is moderate with 35.6%-93.5% of the total variability being common among all combinations of factors. The spatial resolution is the most important factor among the examined factors, explaining 1.5%-10.7% of the total variability followed by GCMs (0.3%-7.6%), RCPs (0%-6.3%), and downscaling methods (0.1%-4.6%). The higher-order interactions effect that captures nonlinear relations between the factors and random effects is pronounced and accounts for 1.6%-45.8% to the total variability. The most distinct hot spots of variability include the mountain ranges in North Scandinavia and the Alps, and the Iberian Peninsula. Based on our findings, we advise to conduct the application of models such as LPJ-GUESS at a reasonably high spatial resolution which is supported by the model structure. There is no notable gain in simulations of ecosystem carbon and nitrogen stocks and fluxes from using regionally downscaled climate in preference to bias-corrected, bilinearly interpolated CMIP5 projections.
Statistical downscaling of summer precipitation over northwestern South America

NASA Astrophysics Data System (ADS)

Palomino Lemus, Reiner; Córdoba Machado, Samir; Raquel Gámiz Fortis, Sonia; Castro Díez, Yolanda; Jesús Esteban Parra, María

2015-04-01

In this study a statistical downscaling (SD) model using Principal Component Regression (PCR) for simulating summer precipitation in Colombia during the period 1950-2005, has been developed, and climate projections during the 2071-2100 period by applying the obtained SD model have been obtained. For these ends the Principal Components (PCs) of the SLP reanalysis data from NCEP were used as predictor variables, while the observed gridded summer precipitation was the predictand variable. Period 1950-1993 was utilized for calibration and 1994-2010 for validation. The Bootstrap with replacement was applied to provide estimations of the statistical errors. All models perform reasonably well at regional scales, and the spatial distribution of the correlation coefficients between predicted and observed gridded precipitation values show high values (between 0.5 and 0.93) along Andes range, north and north Pacific of Colombia. Additionally, the ability of the MIROC5 GCM to simulate the summer precipitation in Colombia, for present climate (1971-2005), has been analyzed by calculating the differences between the simulated and observed precipitation values. The simulation obtained by this GCM strongly overestimates the precipitation along a horizontal sector through the center of Colombia, especially important at the east and west of this country. However, the SD model applied to the SLP of the GCM shows its ability to faithfully reproduce the rainfall field. Finally, in order to get summer precipitation projections in Colombia for the period 1971-2100, the downscaled model, recalibrated for the total period 1950-2010, has been applied to the SLP output from MIROC5 model under the RCP2.6, RCP4.5 and RCP8.5 scenarios. The changes estimated by the SD models are not significant under the RCP2.6 scenario, while for the RCP4.5 and RCP8.5 scenarios a significant increase of precipitation appears regard to the present values in all the regions, reaching around the 27% in northern Colombia region under the RCP8.5 scenario. Keywords: Statistical downscaling, precipitation, Principal Component Regression, climate change, Colombia. ACKNOWLEDGEMENTS This work has been financed by the projects P11-RNM-7941 (Junta de Andalucía-Spain) and CGL2013-48539-R (MINECO-Spain, FEDER).
Evaluating Changes in Extreme Weather During the North American Monsoon in the Southwest U.S. Using High Resolution, Convective-Permitting Regional Atmospheric Modeling

NASA Astrophysics Data System (ADS)

Castro, C. L.; Chang, H. I.; Luong, T. M.; Lahmers, T.; Jares, M.; Mazon, J.; Carrillo, C. M.; Adams, D. K.

2015-12-01

The North American monsoon (NAM) is the principal driver of summer severe weather in the Southwest U.S. Monsoon convection typically initiates during daytime over the mountains and may organize into mesoscale convective systems (MCSs). Most monsoon-related severe weather occurs in association with organized convection, including microbursts, dust storms, flash flooding and lightning. A convective resolving grid spacing (on the kilometer scale) model is required to explicitly represent the physical characteristics of organized convection, for example the presence of leading convective lines and trailing stratiform precipitation regions. Our objective is to analyze how monsoon severe weather is changing in relation to anthropogenic climate change. We first consider a dynamically downscaled reanalysis during a historical period 1948-2010. Individual severe weather event days, identified by favorable thermodynamic conditions, are then simulated for short-term, numerical weather prediction-type simulations of 30h at a convective-permitting scale. Changes in modeled severe weather events indicate increases in precipitation intensity in association with long-term increases in atmospheric instability and moisture, particularly with organized convection downwind of mountain ranges. However, because the frequency of synoptic transients is decreasing during the monsoon, organized convection is less frequent and convective precipitation tends to be more phased locked to terrain. These types of modeled changes also similarly appear in observed CPC precipitation, when the severe weather event days are selected using historical radiosonde data. Next, we apply the identical model simulation and analysis procedures to several dynamically downscaled CMIP3 and CMIP5 models for the period 1950-2100, to assess how monsoon severe weather may change in the future with respect to occurrence and intensity and if these changes correspond with what is already occurring in the historical record. The CMIP5 models we are downscaling (HadGEM2-ES and MPI-ESM-LR) will be included as part of North American COordinated Regional climate Downscaling EXperiment (CORDEX). Results from this project will be used for climate change impacts assessment for U.S. military installations in the region.
Importance of anthropogenic climate impact, sampling error and urban development in sewer system design.

PubMed

Egger, C; Maurer, M

2015-04-15

Urban drainage design relying on observed precipitation series neglects the uncertainties associated with current and indeed future climate variability. Urban drainage design is further affected by the large stochastic variability of precipitation extremes and sampling errors arising from the short observation periods of extreme precipitation. Stochastic downscaling addresses anthropogenic climate impact by allowing relevant precipitation characteristics to be derived from local observations and an ensemble of climate models. This multi-climate model approach seeks to reflect the uncertainties in the data due to structural errors of the climate models. An ensemble of outcomes from stochastic downscaling allows for addressing the sampling uncertainty. These uncertainties are clearly reflected in the precipitation-runoff predictions of three urban drainage systems. They were mostly due to the sampling uncertainty. The contribution of climate model uncertainty was found to be of minor importance. Under the applied greenhouse gas emission scenario (A1B) and within the period 2036-2065, the potential for urban flooding in our Swiss case study is slightly reduced on average compared to the reference period 1981-2010. Scenario planning was applied to consider urban development associated with future socio-economic factors affecting urban drainage. The impact of scenario uncertainty was to a large extent found to be case-specific, thus emphasizing the need for scenario planning in every individual case. The results represent a valuable basis for discussions of new drainage design standards aiming specifically to include considerations of uncertainty. Copyright © 2015 Elsevier Ltd. All rights reserved.
Are We Telling Decision-makers the Wrong Things - and with Too Much Confidence?

NASA Astrophysics Data System (ADS)

Arnold, J.; Nowak, K. C.; Vano, J. A.; Newman, A. J.; Mizukami, N.; Mendoza, P. A.; Nijssen, B.; Wood, A.; Gutmann, E. D.; Clark, M. P.; Rasmussen, R.

2016-12-01

Water-resource management relies on decision-making over a wide range of space-time scales, nearly none of which maps cleanly onto the scales of current hydroclimatic scenarios of anthropogenic change. Myriad choices are made during vulnerability and impact assessments to quantify the changed-climate sensitivities of models used in that decision-making, including choices of hydrologic models, parameters, and parameterizations; their input forcings determined with various climate downscaling approaches; selected GCMs and output variables to be downscaled; and the forcing emissions scenarios, to name a few. Choosing alternative methods for producing gridded meteorological fields, for examples, can produce very different effects on the projected hydrologic outcomes they drive, with uncertainties across those methods revealed to be as large or larger than the climate change signal itself in some cases. Additionally, many popular climate downscaling methods simply rescale GCM precipitation, producing hydroclimatic projections with too much drizzle, incorrect representations of extreme events, and improper spatial scaling of variables crucial to water-resource vulnerability assessments and, importantly, the decisions they seek to inform. Real-world water-resource vulnerability and impacts assessments can be highly time-sensitive and resource limited, though, so they typically do not confront or even fully represent uncertainties associated with all choices. That deficiency results in assessments built on only partially revealed uncertainties which can misrepresent significant sensitivities and impacts in the final assessments of climate threats and hydrologic vulnerabilities. This talk will describe recent work by the U.S. Army Corps of Engineers, Bureau of Reclamation, University of Washington, and National Center for Atmospheric Research to develop and test methods to characterize more fully the uncertainties in the modeling chain for real-world uses. Examples will illustrate new implementations for communicating that fuller characterization in the ways most useful to inform water-resource management across multiple space-time scales under climate-changed futures.
South Asia river flow projections and their implications for water resources

NASA Astrophysics Data System (ADS)

Mathison, C.; Wiltshire, A. J.; Falloon, P.; Challinor, A. J.

2015-06-01

South Asia is a region with a large and rising population and a high dependance on industries sensitive to water resource such as agriculture. The climate is hugely variable with the region relying on both the Asian Summer Monsoon (ASM) and glaciers for its supply of fresh water. In recent years, changes in the ASM, fears over the rapid retreat of glaciers and the increasing demand for water resources for domestic and industrial use, have caused concern over the reliability of water resources both in the present day and future for this region. The climate of South Asia means it is one of the most irrigated agricultural regions in the world, therefore pressures on water resource affecting the availability of water for irrigation could adversely affect crop yields and therefore food production. In this paper we present the first 25 km resolution regional climate projections of river flow for the South Asia region. ERA-Interim, together with two global climate models (GCMs), which represent the present day processes, particularly the monsoon, reasonably well are downscaled using a regional climate model (RCM) for the periods; 1990-2006 for ERA-Interim and 1960-2100 for the two GCMs. The RCM river flow is routed using a river-routing model to allow analysis of present day and future river flows through comparison with river gauge observations, where available. In this analysis we compare the river flow rate for 12 gauges selected to represent the largest river basins for this region; Ganges, Indus and Brahmaputra basins and characterize the changing conditions from east to west across the Himalayan arc. Observations of precipitation and runoff in this region have large or unknown uncertainties, are short in length or are outside the simulation period, hindering model development and validation designed to improve understanding of the water cycle for this region. In the absence of robust observations for South Asia, a downscaled ERA-Interim RCM simulation provides a benchmark for comparison against the downscaled GCMs. On the basis that these simulations are among the highest resolution climate simulations available we examine how useful they are for understanding the changes in water resources for the South Asia region. In general the downscaled GCMs capture the seasonality of the river flows, with timing of maximum river flows broadly matching the available observations and the downscaled ERA-Interim simulation. Typically the RCM simulations over-estimate the maximum river flows compared to the observations probably due to a positive rainfall bias and a lack of abstraction in the model although comparison with the downscaled ERA-Interim simulation is more mixed with only a couple of the gauges showing a bias compared with the downscaled GCM runs. The simulations suggest an increasing trend in annual mean river flows for some of the river gauges in this analysis, in some cases almost doubling by the end of the century; this trend is generally masked by the large annual variability of river flows for this region. The future seasonality of river flows does not change with the future maximum river flow rates still occuring during the ASM period, with a magnitude in some cases, greater than the present day natural variability. Increases in river flow during peak flow periods means additional water resource for irrigation, the largest usage of water in this region, but also has implications in terms of inundation risk. Low flow rates also increase which is likely to be important at times of the year when water is historically more scarce. However these projected increases in resource from rivers could be more than countered by changes in demand due to reductions in the quantity and quality of water available from groundwater, increases in domestic use due to a rising population or expansion of other industries such as hydro-electric power generation.
Enabling the use of climate model data in the Dutch climate effect community

NASA Astrophysics Data System (ADS)

Som de Cerff, Wim; Plieger, Maarten

2010-05-01

Within the climate effect community the usage of climate model data is emerging. Where mostly climate time series and weather generators were used, there is a shift to incorporate climate model data into climate effect models. The use of climate model data within the climate effect models is difficult, due to missing metadata, resolution and projection issues, data formats and availability of the parameters of interest. Often the climate effect modelers are not aware of available climate model data or are not aware of how they can use it. Together with seven other partners (CERFACS, CNR-IPSL, SMHI, INHGA, CMCC, WUR, MF-CNRM), KNMI is involved in the FP7 IS ENES (http://www.enes.org) project work package 10/JRA5 ‘Bridging Climate Research Data and the Needs of the Impact Community. The aims of this work package are to enhance the use of Climate Research Data and to enhance the interaction with climate effect/impact communities. Phase one is to define use cases together with the Dutch climate effect community, which describe the intended use of climate model data in climate effect models. We defined four use cases: 1) FEWS hydrological Framework (Deltares) 2) METAPHOR, a plants and species dispersion model (Wageningen University) 3) Natuurplanner, an Ecological model suite (Wageningen University) 4) Land use models (Free University/JRC). Also the other partners in JRA5 have defined use cases, which are representative for the climate effect and impact communities in their country. Goal is to find commonalities between all defined use cases. The common functionality will be implemented as e-tools and incorporated in the IS-ENES data portal. Common issues relate to e.g., need for high resolution: downscaling from GCM to local scale (also involves interpolation); parameter selection; finding extremes; averaging methods. At the conference we will describe the FEWS case in more detail: Delft FEWS is an open shell system (in development since 1995) for performing hydrological predictions and the handling of time series data. The most important capabilities of FEWS are importing of meteorological and hydrological data and organizing the workflows of the different models which can be used within FEWS, like the Netherlands Hydrological Instrumentarium (NHI). Besides predictions, the system is currently being used for hydrological climate effects studies. Currently regionally downscaled data are used, but using model data will be the next step. This coupling of climate model data to FEWS will open a wider rage of climate impact and effect research, but it is a difficult task to accomplish. Issues to be dealt with are: regridding, downscaling, format conversion, extraction of required data and addition of descriptive metadata, including quality and uncertainty parameters. Finding an appropriate solution involves several iterations: first, the use case was defined, then we just provided a single data file containing some data of interest provided via FTP, next this data was offered through OGC services. Currently we are working on providing larger datasets and improving on the parameters and metadata. We will present the results (e-tools/data) and experiences gained on implementing the described use cases. Note that we are currently using experimental data, as the official climate model runs are not available yet.
Intersections of downscaling, the ethics of climate services, and regional research grand challenges.

NASA Astrophysics Data System (ADS)

Hewitson, B.; Jack, C. D.; Gutowski, W. J., Jr.

2014-12-01

Possibly the leading complication for users of climate information for policy and adaptation is the confusing mix of contrasting data sets that offer widely differing (and often times fundamentally contradictory) indications of the magnitude and direction of past and future regional climate change. In this light, the most pressing scientific-societal challenge is the need to find new ways to understand the sources of conflicting messages from multi-model, multi-method and multi-scale disparities, to develop and implement new analytical methodologies to address this difficulty and so to advance the interpretation and communication of robust climate information to decision makers. Compounding this challenge is the growth of climate services which, in view of the confusing mix of climate change messages, raises serious concerns as to the ethics of communication and dissemination of regional climate change data.The Working Group on Regional Climate (WGRC) of the World Climate Research Program (WCRP) oversees the CORDEX downscaling program which offers a systematic approach to compare the CMIP5 GCMs alongside RCMs and Empirical-statistical (ESD) downscaling within a common experimental design, and which facilitates the evaluation and assessment of the relative information content and sources of error. Using results from the CORDEX RCM and ESD evaluation experiment, and set against the regional messages from the CMIP5 GCMs, we examine the differing messages that arise from each data source. These are then considered in terms of the implications of consequence if each data source were to be independently adopted in a real world use-case scenario. This is then cast in the context of the emerging developments on the distillation dilemma - where the pressing need is for multi-method integration - and how this relates to the WCRP regional research grand challenges.

Simulating Climate Change in Ireland

NASA Astrophysics Data System (ADS)

Nolan, P.; Lynch, P.

2012-04-01

At the Meteorology & Climate Centre at University College Dublin, we are using the CLM-Community's COSMO-CLM Regional Climate Model (RCM) and the WRF RCM (developed at NCAR) to simulate the climate of Ireland at high spatial resolution. To address the issue of model uncertainty, a Multi-Model Ensemble (MME) approach is used. The ensemble method uses different RCMs, driven by several Global Climate Models (GCMs), to simulate climate change. Through the MME approach, the uncertainty in the RCM projections is quantified, enabling us to estimate the probability density function of predicted changes, and providing a measure of confidence in the predictions. The RCMs were validated by performing a 20-year simulation of the Irish climate (1981-2000), driven by ECMWF ERA-40 global re-analysis data, and comparing the output to observations. Results confirm that the output of the RCMs exhibit reasonable and realistic features as documented in the historical data record. Projections for the future Irish climate were generated by downscaling the Max Planck Institute's ECHAM5 GCM, the UK Met Office HadGEM2-ES GCM and the CGCM3.1 GCM from the Canadian Centre for Climate Modelling. Simulations were run for a reference period 1961-2000 and future period 2021-2060. The future climate was simulated using the A1B, A2, B1, RCP 4.5 & RCP 8.5 greenhouse gas emission scenarios. Results for the downscaled simulations show a substantial overall increase in precipitation and wind speed for the future winter months and a decrease during the summer months. The predicted annual change in temperature is approximately 1.1°C over Ireland. To date, all RCM projections are in general agreement, thus increasing our confidence in the robustness of the results.
The trend of the multi-scale temporal variability of precipitation in Colorado River Basin

NASA Astrophysics Data System (ADS)

Jiang, P.; Yu, Z.

2011-12-01

Hydrological problems like estimation of flood and drought frequencies under future climate change are not well addressed as a result of the disability of current climate models to provide reliable prediction (especially for precipitation) shorter than 1 month. In order to assess the possible impacts that multi-scale temporal distribution of precipitation may have on the hydrological processes in Colorado River Basin (CRB), a comparative analysis of multi-scale temporal variability of precipitation as well as the trend of extreme precipitation is conducted in four regions controlled by different climate systems. Multi-scale precipitation variability including within-storm patterns and intra-annual, inter-annual and decadal variabilities will be analyzed to explore the possible trends of storm durations, inter-storm periods, average storm precipitation intensities and extremes under both long-term natural climate variability and human-induced warming. Further more, we will examine the ability of current climate models to simulate the multi-scale temporal variability and extremes of precipitation. On the basis of these analyses, a statistical downscaling method will be developed to disaggregate the future precipitation scenarios which will provide a more reliable and finer temporal scale precipitation time series for hydrological modeling. Analysis results and downscaling results will be presented.
Downscaling SMAP Radiometer Soil Moisture over the CONUS using Soil-Climate Information and Ensemble Learning

NASA Astrophysics Data System (ADS)

Abbaszadeh, P.; Moradkhani, H.

2017-12-01

Soil moisture contributes significantly towards the improvement of weather and climate forecast and understanding terrestrial ecosystem processes. It is known as a key hydrologic variable in the agricultural drought monitoring, flood modeling and irrigation management. While satellite retrievals can provide an unprecedented information on soil moisture at global-scale, the products are generally at coarse spatial resolutions (25-50 km2). This often hampers their use in regional or local studies, which normally require a finer resolution of the data set. This work presents a new framework based on an ensemble learning method while using soil-climate information derived from remote-sensing and ground-based observations to downscale the level 3 daily composite version (L3_SM_P) of SMAP radiometer soil moisture over the Continental U.S. (CONUS) at 1 km spatial resolution. In the proposed method, a suite of remotely sensed and in situ data sets in addition to soil texture information and topography data among others were used. The downscaled product was validated against in situ soil moisture measurements collected from a limited number of core validation sites and several hundred sparse soil moisture networks throughout the CONUS. The obtained results indicated a great potential of the proposed methodology to derive the fine resolution soil moisture information applicable for fine resolution hydrologic modeling, data assimilation and other regional studies.
Forest-stressing climate factors on the US West Coast as simulated by CMIP5

NASA Astrophysics Data System (ADS)

Rupp, D. E.; Buotte, P.; Hicke, J. A.; Law, B. E.; Mote, P.; Sharp, D.; Zhenlin, Y.

2013-12-01

The rate of forest mortality has increased significantly in western North America since the 1970s. Causes include insect attacks, fire, and soil water deficit, all of which are interdependent. We first identify climate factors that stress forests by reducing photosynthesis and hydraulic conductance, and by promoting bark beetle infestation and wildfire. Examples of such factors may be two consecutive years of extreme summer precipitation deficit, or prolonged vapor pressure deficit exceeding some threshold. Second, we quantify the frequency and magnitude of these climate factors in 20th and 21st century climates, as simulated by global climate models (GCMs) in Coupled Model Intercomparison Project phase 5 (CMIP5), of Washington, Oregon, and California in the western US. Both ';raw' (i.e., original spatial resolution) and statistically downscaled simulations are considered, the latter generated using the Multivariate Adaptive Constructed Analogs (MACA) method. CMIP5 models that most faithfully reproduce the observed historical statistics of these climate factors are identified. Furthermore, significant changes in the statistics between the 20th and 21st centuries are reported. A subsequent task will be to use a selected subset of MACA-downscaled CMIP5 simulations to force the Community Land Model, version 4.5 (CLM 4.5). CLM 4.5 will be modified to better simulate forest mortality and to couple CLM with an economic model. The ultimate goal of this study is to understand the interactions and the feedbacks by which the market and the forest ecosystem influence each other.
Examining South Atlantic Subtropical Cyclone Anita using the Satellite-Enhanced Regional Downscaling for Applied Studies Hourly Outputs

NASA Astrophysics Data System (ADS)

Vaicberg, H.; Palmeira, A. C. P. A.; Nunes, A.

2017-12-01

Studies on South Atlantic cyclones are mainly compromised by scarcity of observations. Therefore, remote sensing and global (re) analysis products are usually employed in investigations of their evolution. However, the frequent use of global reanalysis might difficult the assessment of the characteristics of the cyclones found in South Atlantic. In that regard, studies on "subtropical" cyclones have been performed using the 25-km resolution, Satellite-enhanced Regional Downscaling for Applied Studies (SRDAS), a product developed at the Federal University of Rio de Janeiro in Brazil. In SRDAS, the Regional Spectral Model assimilates precipitation estimates from environmental satellites, while dynamically downscaling a global reanalysis using the spectral nudging technique to maintain the large-scale features in agreement with the regional model solution. The use of regional models in the downscaling of general circulation models provides more detailed information on weather and climate. As a way of illustrating the usefulness of SRDAS in the study of the subtropical South Atlantic cyclones, the subtropical cyclone Anita was selected because of its intensity. Anita developed near Brazilian south/southeast coast, with damages to local communities. Comparisons with available observations demonstrated the skill of SRDAS in simulating such an extreme event.
Climate change impact on soil erosion in the Mandakini River Basin, North India

NASA Astrophysics Data System (ADS)

Khare, Deepak; Mondal, Arun; Kundu, Sananda; Mishra, Prabhash Kumar

2017-09-01

Correct estimation of soil loss at catchment level helps the land and water resources planners to identify priority areas for soil conservation measures. Soil erosion is one of the major hazards affected by the climate change, particularly the increasing intensity of rainfall resulted in increasing erosion, apart from other factors like landuse change. Changes in climate have an adverse effect with increasing rainfall. It has caused increasing concern for modeling the future rainfall and projecting future soil erosion. In the present study, future rainfall has been generated with the downscaling of GCM (Global Circulation Model) data of Mandakini river basin, a hilly catchment in the state of Uttarakhand, India, to obtain future impact on soil erosion within the basin. The USLE is an erosion prediction model designed to predict the long-term average annual soil loss from specific field slopes in specified landuse and management systems (i.e., crops, rangeland, and recreational areas) using remote sensing and GIS technologies. Future soil erosion has shown increasing trend due to increasing rainfall which has been generated from the statistical-based downscaling method.
Regional climate modeling of heat stress, frost, and water stress events in the agricultural region of Southwest Western Australia under the current climate and future climate scenarios.

NASA Astrophysics Data System (ADS)

Kala, Jatin; Lyons, Tom J.; Abbs, Deborah J.; Foster, Ian J.

2010-05-01

Heat stress, frost, and water stress events have significant impacts on grain quality and production within the agricultural region (wheat-belt) of Southwest Western Australia (SWWA) (Cramb, 2000) and understanding how the frequency and intensity of these events will change in the future is crucial for management purposes. Hence, the Regional Atmospheric Modeling System (Pielke et al, 1992) (RAMS Version 6.0) is used to simulate the past 10 years of the climate of SWWA at a 20 km grid resolution by down-scaling the 6-hourly 1.0 by 1.0 degree National Center for Environmental Prediction Final Analyses from December 1999 to Present. Daily minimum and maximum temperatures, as well as daily rainfall are validated against observations. Simulations of future climate are carried out by down-scaling the Commonwealth Scientific and Industrial Research Organization (CSIRO) Mark 3.5 General Circulation Model (Gordon et al, 2002) for 10 years (2046-2055) under the SRES A2 scenario using the Cubic Conformal Atmospheric Model (CCAM) (McGregor and Dix, 2008). The 6-hourly CCAM output is then downscaled to a 20 km resolution using RAMS. Changes in extreme events are discussed within the context of the continued viability of agriculture in SWWA. Cramb, J. (2000) Climate in relation to agriculture in south-western Australia. In: The Wheat Book (Eds W. K. Anderson and J. R. Garlinge). Bulletin 4443. Department of Agriculture, Western Australia. Gordon, H. B., Rotstayn, L. D., McGregor, J. L., Dix, M. R., Kowalczyk, E. A., O'Farrell, S. P., Waterman, L. J., Hirst, A. C., Wilson, S. G., Collier, M. A., Watterson, I. G., and Elliott, T. I. (2002). The CSIRO Mk3 Climate System Model [Electronic publication]. Aspendale: CSIRO Atmospheric Research. (CSIRO Atmospheric Research technical paper; no. 60). 130 p McGregor, J. L., and Dix, M. R., (2008) An updated description of the conformal-cubic atmospheric model. High Resolution Simulation of the Atmosphere and Ocean, Hamilton, K. and Ohfuchi, W., Eds., Springer, 51-76. Pielke, R. A., Cotton, W. R., Walko, R. L., Tremback, C. J., Lyons, W. A., Grasso, L. D., Nicholls, M. E., Moran, M. D., Wesley, D. A., Lee, T. J., Copeland, J. H., (1992) A comprehensive meteorological modeling system - RAMS. Meteorol. Atmos. Phys., 49, 69-91.
Projecting Future Changes in Extreme Weather During the North American Monsoon in the Southwest with High Resolution, Convective-Permitting Regional Atmospheric Modeling

NASA Astrophysics Data System (ADS)

Chang, H. I.; Castro, C. L.; Luong, T. M.; Lahmers, T.; Jares, M.; Carrillo, C. M.

2014-12-01

Most severe weather during the North American monsoon in the Southwest U.S. occurs in association with organized convection, including microbursts, dust storms, flash flooding and lightning. Our objective is to project how monsoon severe weather is changing due to anthropogenic global warming. We first consider a dynamically downscaled reanalysis (35 km grid spacing), generated with the Weather Research and Forecasting (WRF) model during the period 1948-2010. Individual severe weather events, identified by favorable thermodynamic conditions of instability and precipitable water, are then simulated for short-term, numerical weather prediction-type simulations of 24h at a convective-permitting scale (2 km grid spacing). Changes in the character of severe weather events within this period likely reflect long-term climate change driven by anthropogenic forcing. Next, we apply the identical model simulation and analysis procedures to several dynamically downscaled CMIP3 and CMIP5 models for the period 1950-2100, to assess how monsoon severe weather may change in the future and if these changes correspond with what is already occurring per the downscaled renalaysis and available observational data. The CMIP5 models we are downscaling (HadGEM and MPI-ECHAM6) will be included as part of North American CORDEX. The regional model experimental design for severe weather event projection reasonably accounts for the known operational forecast prerequisites for severe monsoon weather. The convective-permitting simulations show that monsoon convection appears to be reasonably well captured with the use of the dynamically downscaled reanalysis, in comparison to Stage IV precipitation data. The regional model tends to initiate convection too early, though correctly simulates the diurnal maximum in convection in the afternoon and subsequent westward propagation of thunderstorms. Projected changes in extreme event precipitation will be described in relation to the long-term changes in thermodynamic and dynamic forcing mechanisms for severe weather. Results from this project will be used for climate change impacts assessment for U.S. military installations in the region.
Making CORDEX accessible to users: case studies in the Middle East

NASA Astrophysics Data System (ADS)

Dubois, Ghislain

2017-04-01

The current demand of long term climate projections corresponds to more applied requests from users: climate data and services are supposed to enable robust decision making in very diversified environments…Issues like uncertainty management (elaborating probabilistic projections based on full ensembles analysis) or tailoring of indicators should be central. However, an assessment of a sample of local, regional and national climate change adaptation strategies, in Europe and in the Med (Stoverinck, Dubois and Amelung 2013) highlighted the frequent insufficient robustness of climate information used to inform policy making. Some initiatives only refer to past climate data, use only one SRES or RCP scenario, one model or a too limited set of downscaling techniques. The CORDEX program (Coordinated regional climate downscaling experiment, coordinated by WCRP) forms the largest effort of climate downscaling so far. Its datasets, initially developed for scientific purposes have strong potential to improve regional and local adaptation policies. They can be considered as reference for the coming years, not only reflecting the improvement of our knowledge of climate, but also offering data in a much more harmonized and accessible way. The PROCLIM initiative (www.pro-clim.org) aims at developing a European climate service, proposing territorialized climate projections, supporting local adaptation frameworks, derived from CORDEX. This encompasses several methodological challenges: understanding users' needs at the European level, specifying indices, selecting relevant geographical domains, correcting systematic biases, selecting sub-ensembles of the CORDEX datasets so as to provide a sound uncertainty analysis, representing results in an user-friendly manner. The presentation will detail some features of PROCLIM, based on two recent experiments: the elaboration of long term climate projections, based on AFRICA-CORDEX, supporting the elaboration of the third national communication on climate change of Jordan; and the provision of high resolution hydro-climatic projections for Israel, Palestine and Jordan, which combined post-processing of CORDEX, and some dedicated runs of WRF, configured in climate mode.
Simulating climate change impact on soil erosion using RUSLE model - A case study in a watershed of mid-Himalayan landscape

NASA Astrophysics Data System (ADS)

Gupta, Surya; Kumar, Suresh

2017-06-01

Climate change, particularly due to the changed precipitation trend, can have a severe impact on soil erosion. The effect is more pronounced on the higher slopes of the Himalayan region. The goal of this study was to estimate the impact of climate change on soil erosion in a watershed of the Himalayan region using RUSLE model. The GCM (general circulation model) derived emission scenarios (HadCM3 A2a and B2a SRES) were used for climate projection. The statistical downscaling model (SDSM) was used to downscale the precipitation for three future periods, 2011-2040, 2041-2070, and 2071-2099, at large scale. Rainfall erosivity ( R) was calculated for future periods using the SDSM downscaled precipitation data. ASTER digital elevation model (DEM) and Indian Remote Sensing data - IRS LISS IV satellite data were used to generate the spatial input parameters required by RUSLE model. A digital soil-landscape map was prepared to generate spatially distributed soil erodibility ( K) factor map of the watershed. Topographic factors, slope length ( L) and steepness ( S) were derived from DEM. Normalised difference vegetation index (NDVI) derived from the satellite data was used to represent spatial variation vegetation density and condition under various land use/land cover. This variation was used to represent spatial vegetation cover factor. Analysis revealed that the average annual soil loss may increase by 28.38, 25.64 and 20.33% in the 2020s, 2050s and 2080s, respectively under A2 scenario, while under B2 scenario, it may increase by 27.06, 25.31 and 23.38% in the 2020s, 2050s and 2080s, respectively, from the base period (1985-2013). The study provides a comprehensive understanding of the possible future scenario of soil erosion in the mid-Himalaya for scientists and policy makers.
Evaluating Downscaling Methods for Seasonal Climate Forecasts over East Africa

NASA Technical Reports Server (NTRS)

Roberts, J. Brent; Robertson, Franklin R.; Bosilovich, Michael; Lyon, Bradfield; Funk, Chris

2013-01-01

The U.S. National Multi-Model Ensemble seasonal forecasting system is providing hindcast and real-time data streams to be used in assessing and improving seasonal predictive capacity. The NASA / USAID SERVIR project, which leverages satellite and modeling-based resources for environmental decision making in developing nations, is focusing on the evaluation of NMME forecasts specifically for use in impact modeling within hub regions including East Africa, the Hindu Kush-Himalayan (HKH) region and Mesoamerica. One of the participating models in NMME is the NASA Goddard Earth Observing System (GEOS5). This work will present an intercomparison of downscaling methods using the GEOS5 seasonal forecasts of temperature and precipitation over East Africa. The current seasonal forecasting system provides monthly averaged forecast anomalies. These anomalies must be spatially downscaled and temporally disaggregated for use in application modeling (e.g. hydrology, agriculture). There are several available downscaling methodologies that can be implemented to accomplish this goal. Selected methods include both a non-homogenous hidden Markov model and an analogue based approach. A particular emphasis will be placed on quantifying the ability of different methods to capture the intermittency of precipitation within both the short and long rain seasons. Further, the ability to capture spatial covariances will be assessed. Both probabilistic and deterministic skill measures will be evaluated over the hindcast period
Evaluating Downscaling Methods for Seasonal Climate Forecasts over East Africa

NASA Technical Reports Server (NTRS)

Robertson, Franklin R.; Roberts, J. Brent; Bosilovich, Michael; Lyon, Bradfield

2013-01-01

The U.S. National Multi-Model Ensemble seasonal forecasting system is providing hindcast and real-time data streams to be used in assessing and improving seasonal predictive capacity. The NASA / USAID SERVIR project, which leverages satellite and modeling-based resources for environmental decision making in developing nations, is focusing on the evaluation of NMME forecasts specifically for use in impact modeling within hub regions including East Africa, the Hindu Kush-Himalayan (HKH) region and Mesoamerica. One of the participating models in NMME is the NASA Goddard Earth Observing System (GEOS5). This work will present an intercomparison of downscaling methods using the GEOS5 seasonal forecasts of temperature and precipitation over East Africa. The current seasonal forecasting system provides monthly averaged forecast anomalies. These anomalies must be spatially downscaled and temporally disaggregated for use in application modeling (e.g. hydrology, agriculture). There are several available downscaling methodologies that can be implemented to accomplish this goal. Selected methods include both a non-homogenous hidden Markov model and an analogue based approach. A particular emphasis will be placed on quantifying the ability of different methods to capture the intermittency of precipitation within both the short and long rain seasons. Further, the ability to capture spatial covariances will be assessed. Both probabilistic and deterministic skill measures will be evaluated over the hindcast period.
Climate impacts on agricultural biomass production in the CORDEX.be project context

NASA Astrophysics Data System (ADS)

Gobin, Anne; Van Schaeybroeck, Bert; Termonia, Piet; Willems, Patrick; Van Lipzig, Nicole; Marbaix, Philippe; van Ypersele, Jean-Pascal; Fettweis, Xavier; De Ridder, Koen; Stavrakou, Trissevgeni; Luyten, Patrick; Pottiaux, Eric

2016-04-01

The most important coordinated international effort to translate the IPCC-AR5 outcomes to regional climate modelling is the so-called "COordinated Regional climate Downscaling EXperiment" (CORDEX, http://wcrp-cordex.ipsl.jussieu.fr/). CORDEX.be is a national initiative that aims at combining the Belgian climate and impact modelling research into a single network. The climate network structure is naturally imposed by the top-down data flow, from the four participating upper-air Regional Climate Modelling groups towards seven Local Impact Models (LIMs). In addition to the production of regional climate projections following the CORDEX guidelines, very high-resolution results are provided at convection-permitting resolutions of about 4 km across Belgium. These results are coupled to seven local-impact models with severity indices as output. A multi-model approach is taken that allows uncertainty estimation, a crucial aspect of climate projections for policy-making purposes. The down-scaled scenarios at 4 km resolution allow for impact assessment in different Belgian agro-ecological zones. Climate impacts on arable agriculture are quantified using REGCROP which is a regional dynamic agri-meteorological model geared towards modelling climate impact on biomass production of arable crops (Gobin, 2010, 2012). Results from previous work show that heat stress and water shortages lead to reduced crop growth, whereas increased CO2-concentrations and a prolonged growing season have a positive effect on crop yields. The interaction between these effects depend on the crop type and the field conditions. Root crops such as potato will experience increased drought stress particularly when the probability rises that sensitive crop stages coincide with dry spells. This may be aggravated when wet springs cause water logging in the field and delay planting dates. Despite lower summer precipitation projections for future climate in Belgium, winter cereal yield reductions due to drought stress will be smaller due to earlier maturity. Preliminary results will be presented using the new scenario runs for Belgium.
Theoretical electron scattering amplitudes and spin polarizations. Electron energies 100 to 1500 eV Part II. Be, N, O, Al, Cl, V, Co, Cu, As, Nb, Ag, Sn, Sb, I, and Ta targets

NASA Astrophysics Data System (ADS)

Wildhaber, M. L.; Wikle, C. K.; Anderson, C. J.; Franz, K. J.; Moran, E. H.; Dey, R.

2012-12-01

Recent decades have brought substantive changes in land use and climate across the earth, prompting a need to think of population and community ecology not as a static entity, but as a dynamic process. Increasingly there is evidence of ecological changes due to climate change. Although much of this evidence comes from ground-truth observations of biogeographic data, there is increasing reliance on models that relate climate variables to biological systems. Such models can then be used to explore potential changes to population and community level ecological systems in response to climate scenarios as obtained from global climate models (GCMs). A key issue associated with modeling ecosystem response to climate is GCM downscaling to regional and local ecological/biological response models that can be used in vulnerability and risk assessments of the potential effects of climate change. The need is for an explicit means for scaling results up or down multiple hierarchical levels and an effective assessment of the level of uncertainty surrounding current knowledge, data, and data collection methods with these goals identified as in need of acceleration in the U.S. Climate Change Science Program FY2009 Implementation Priorities. In the end, such work should provide the information needed to develop adaptation and mitigation methodologies to minimize the effects of directional and nonlinear climate change on the Nation's land, water, ecosystems, and biological populations. We are working to develop an approach that includes multi-scale and hierarchical Bayesian modeling of Missouri River sturgeon population dynamics. Statistical linkages are defined to quantify implications of climate on fish populations of the Missouri River ecosystem. This approach is a hybrid between physical (deterministic) downscaling and statistical downscaling, recognizing that there is uncertainty in both. The model must include linkages between climate and habitat, and between habitat and population. A key advantage of the hierarchical approach used in this study is that it incorporates various sources of observations and includes established scientific knowledge, and associated uncertainties. The goal is to evaluate the potential distributional changes in an ecological system, given distributional changes implied by a series of linked climate and system models under various emissions/use scenarios. The predictive modeling system being developed will be a powerful tool for evaluating management options for coping with global change consequences and assessing uncertainty of those evaluations. Specifically for the endangered pallid sturgeon (Scaphirhynchus albus), we are already able to assess potential effects of any climate scenario on growth and population size distribution. Future models will incorporate survival and reproduction. Ultimately, these models provide guidance for successful recovery and conservation of the pallid sturgeon. Here we present a basic outline of the approach we are developing and a simple pallid sturgeon example to demonstrate how multiple scales and parameter uncertainty are incorporated.
Evaluating the uncertainty of predicting future climate time series at the hourly time scale

NASA Astrophysics Data System (ADS)

Caporali, E.; Fatichi, S.; Ivanov, V. Y.

2011-12-01

A stochastic downscaling methodology is developed to generate hourly, point-scale time series for several meteorological variables, such as precipitation, cloud cover, shortwave radiation, air temperature, relative humidity, wind speed, and atmospheric pressure. The methodology uses multi-model General Circulation Model (GCM) realizations and an hourly weather generator, AWE-GEN. Probabilistic descriptions of factors of change (a measure of climate change with respect to historic conditions) are computed for several climate statistics and different aggregation times using a Bayesian approach that weights the individual GCM contributions. The Monte Carlo method is applied to sample the factors of change from their respective distributions thereby permitting the generation of time series in an ensemble fashion, which reflects the uncertainty of climate projections of future as well as the uncertainty of the downscaling procedure. Applications of the methodology and probabilistic expressions of certainty in reproducing future climates for the periods, 2000 - 2009, 2046 - 2065 and 2081 - 2100, using the 1962 - 1992 period as the baseline, are discussed for the location of Firenze (Italy). The climate predictions for the period of 2000 - 2009 are tested against observations permitting to assess the reliability and uncertainties of the methodology in reproducing statistics of meteorological variables at different time scales.
Meeting the Regional Climate Information Needs of Decision Makers: The CORDEX Framework

NASA Astrophysics Data System (ADS)

Asrar, G. R.; Jones, C.; Giorgi, F.

2011-12-01

Regional Climate Downscaling (RCD), both dynamical (e.g. regional climate modeling) and statistical, is an important approach to produce fine scale climate information for use in impact assessment and adaptation/mitigation studies and practices. RCD techniques have evolved significantly over the last decade, however a coherent and wide picture of regional climate change based on RCD products is still not available and the potentials, limitations and uncertainties of RCD methods need to be better understood by the user community. In order to address these issues a new initiative has been launched under the WCRP auspices, referred to as Coordinated Regional climate Downscaling EXperiment, or CORDEX. The aim of CORDEX is to bring together the international RCD community to assess different RCD techniques, recommend best practices and produce a next generation set of RCD-based projections of climate change for regions world-wide. This will involve close interactions between the RCD, global climate modeling, and end users communities. This paper will describe the motivations and design of the first phase of the CORDEX framework, which has a priority focus on Africa, along with the steps that are envisioned to achieve the CORDEX goals within the time framework of the Fifth IPCC assessment report. Some early results for Africa will be presented, together with a short summary of the CORDEX activities in Asia, Americas and other regions of the world.
Spatial downscaling and correction of precipitation and temperature time series to high resolution hydrological response units in the Canadian Rocky Mountains

NASA Astrophysics Data System (ADS)

Kienzle, Stefan

2015-04-01

Precipitation is the central driving force of most hydrological processes, and is also the most variable element of the hydrological cycle. As the precipitation to runoff ratio is non-linear, errors in precipitation estimations are amplified in streamflow simulations. Therefore, the accurate estimate of areal precipitation is essential for watershed models and relevant impacts studies. A procedure is presented to demonstrate the spatial distribution of daily precipitation and temperature estimates across the Rocky Mountains within the framework of the ACRU agro-hydrological modelling system (ACRU). ACRU (Schulze, 1995) is a physical-conceptual, semi-distributed hydrological modelling system designed to be responsive to changes in land use and climate. The model has been updated to include specific high-mountain and cold climate routines and is applied to simulate impacts of land cover and climate change on the hydrological behaviour of numerous Rocky Mountain watersheds in Alberta, Canada. Both air temperature and precipitation time series need to be downscaled to hydrological response units (HRUs), as they are the spatial modelling units for the model. The estimation of accurate daily air temperatures is critical for the separation of rain and snow. The precipitation estimation procedure integrates a spatially distributed daily precipitation database for the period 1950 to 2010 at a scale of 10 by 10 km with a 1971-2000 climate normal database available at 2 by 2 km (PRISM). Resulting daily precipitation time series are further downscaled to the spatial resolution of hydrological response units, defined by 100 m elevation bands, land cover, and solar radiation, which have an average size of about 15 km2. As snow measurements are known to have a potential under-catch of up to 40%, further adjustment of snowfall may need to be increased using a procedure by Richter (1995). Finally, precipitation input to HRUs with slopes steeper than 10% need to be further corrected, because the true, sloped area, has a larger area than the planimetric area derived from a GIS. The omission of correcting for sloped areas would result in incorrect calculations of interception volumes, soil moisture storages, groundwater recharge rates, actual evapotranspiration volumes, and runoff coefficients. Daily minimum and maximum air temperatures are estimated for each HRU by downscaling the 10km time series to the HRUs by (a) applying monthly mean lapse rates, estimated either from surrounding climate stations or from the PRISM climate normal dataset in combination with a digital elevation model, (b) adjusting further for aspect of the HRU based on monthly mean incoming solar radiation, and (c) adjusting for canopy cover using the monthly mean leaf area indices. Precipitation estimates can be verified using independent snow water equivalent measurements derived from snow pillow or snow course observations, while temperature estimates are verified against either independent temperature measurements from climate stations, or from fire observation towers.
REMO poor man's reanalysis

NASA Astrophysics Data System (ADS)

Ries, H.; Moseley, C.; Haensler, A.

2012-04-01

Reanalyses depict the state of the atmosphere as a best fit in space and time of many atmospheric observations in a physically consistent way. By essentially solving the data assimilation problem in a very accurate manner, reanalysis results can be used as reference for model evaluation procedures and as forcing data sets for different model applications. However, the spatial resolution of the most common and accepted reanalysis data sets (e.g. JRA25, ERA-Interim) ranges from approximately 124 km to 80 km. This resolution is too coarse to simulate certain small scale processes often associated with extreme events. In addition, many models need higher resolved forcing data ( e.g. land-surface models, tools for identifying and assessing hydrological extremes). Therefore we downscaled the ERA-Interim reanalysis over the EURO-CORDEX-Domain for the time period 1989 to 2008 to a horizontal resolution of approximately 12 km. The downscaling is performed by nudging REMO-simulations to lower and lateral boundary conditions of the reanalysis, and by re-initializing the model every 24 hours ("REMO in forecast mode"). In this study the three following questions will be addressed: 1.) Does the REMO poor man's reanalysis meet the needs (accuracy, extreme value distribution) in validation and forcing? 2.) What lessons can be learned about the model used for downscaling? As REMO is used as a pure downscaling procedure, any systematic deviations from ERA-Interim result from poor process modelling but not from predictability limitations. 3.) How much small scale information generated by the downscaling model is lost with frequent initializations? A comparison to a simulation that is performed in climate mode will be presented.
Regional downscaling of temporal resolution in near-surface wind from statistically downscaled Global Climate Models (GCMs) for use in San Francisco Bay coastal flood modeling

NASA Astrophysics Data System (ADS)

O'Neill, A.; Erikson, L. H.; Barnard, P.

2013-12-01

While Global Climate Models (GCMs) provide useful projections of near-surface wind vectors into the 21st century, resolution is not sufficient enough for use in regional wave modeling. Statistically downscaled GCM projections from Multivariate Adaptive Constructed Analogues (MACA) provide daily near-surface winds at an appropriate spatial resolution for wave modeling within San Francisco Bay. Using 30 years (1975-2004) of climatological data from four representative stations around San Francisco Bay, a library of example daily wind conditions for four corresponding over-water sub-regions is constructed. Empirical cumulative distribution functions (ECDFs) of station conditions are compared to MACA GFDL hindcasts to create correction factors, which are then applied to 21st century MACA wind projections. For each projection day, a best match example is identified via least squares error among all stations from the library. The best match's daily variation in velocity components (u/v) is used as an analogue of representative wind variation and is applied at 3-hour increments about the corresponding sub-region's projected u/v values. High temporal resolution reconstructions using this methodology on hindcast MACA fields from 1975-2004 accurately recreate extreme wind values within the San Francisco Bay, and because these extremes in wind forcing are of key importance in wave and subsequent coastal flood modeling, this represents a valuable method of generating near-surface wind vectors for use in coastal flood modeling.
Influence of spatial temperature estimation method in ecohydrologic modeling in the western Oregon Cascades

Treesearch

E. Garcia; C.L. Tague; J. Choate

2013-01-01

Most spatially explicit hydrologic models require estimates of air temperature patterns. For these models, empirical relationships between elevation and air temperature are frequently used to upscale point measurements or downscale regional and global climate model estimates of air temperature. Mountainous environments are particularly sensitive to air temperature...

From Global Climate Model Projections to Local Impacts Assessments: Analyses in Support of Planning for Climate Change

NASA Astrophysics Data System (ADS)

Snover, A. K.; Littell, J. S.; Mantua, N. J.; Salathe, E. P.; Hamlet, A. F.; McGuire Elsner, M.; Tohver, I.; Lee, S.

2010-12-01

Assessing and planning for the impacts of climate change require regionally-specific information. Information is required not only about projected changes in climate but also the resultant changes in natural and human systems at the temporal and spatial scales of management and decision making. Therefore, climate impacts assessment typically results in a series of analyses, in which relatively coarse-resolution global climate model projections of changes in regional climate are downscaled to provide appropriate input to local impacts models. This talk will describe recent examples in which coarse-resolution (~150 to 300km) GCM output was “translated” into information requested by decision makers at relatively small (watershed) and large (multi-state) scales using regional climate modeling, statistical downscaling, hydrologic modeling, and sector-specific impacts modeling. Projected changes in local air temperature, precipitation, streamflow, and stream temperature were developed to support Seattle City Light’s assessment of climate change impacts on hydroelectric operations, future electricity load, and resident fish populations. A state-wide assessment of climate impacts on eight sectors (agriculture, coasts, energy, forests, human health, hydrology and water resources, salmon, and urban stormwater infrastructure) was developed for Washington State to aid adaptation planning. Hydro-climate change scenarios for approximately 300 streamflow locations in the Columbia River basin and selected coastal drainages west of the Cascades were developed in partnership with major water management agencies in the Pacific Northwest to allow planners to consider how hydrologic changes may affect management objectives. Treatment of uncertainty in these assessments included: using “bracketing” scenarios to describe a range of impacts, using ensemble averages to characterize the central estimate of future conditions (given an emissions scenario), and explicitly assessing the impacts of multiple GCM ensemble members. The implications of various approaches to dealing with uncertainty, such as these, must be carefully communicated to decision makers in order for projected climate impacts to be viewed as credible and used appropriately.
Recent Advances in High-Resolution Regional Climate Modeling at the U.S. Environmental Protection Agency

NASA Astrophysics Data System (ADS)

Alapaty, Kiran; Bullock, O. Russell; Herwehe, Jerold; Spero, Tanya; Nolte, Christopher; Mallard, Megan

2014-05-01

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.
Impact of spectral nudging on the downscaling of tropical cyclones in regional climate simulations

NASA Astrophysics Data System (ADS)

Choi, Suk-Jin; Lee, Dong-Kyou

2016-06-01

This study investigated the simulations of three months of seasonal tropical cyclone (TC) activity over the western North Pacific using the Advanced Research WRF Model. In the control experiment (CTL), the TC frequency was considerably overestimated. Additionally, the tracks of some TCs tended to have larger radii of curvature and were shifted eastward. The large-scale environments of westerly monsoon flows and subtropical Pacific highs were unreasonably simulated. The overestimated frequency of TC formation was attributed to a strengthened westerly wind field in the southern quadrants of the TC center. In comparison with the experiment with the spectral nudging method, the strengthened wind speed was mainly modulated by large-scale flow that was greater than approximately 1000 km in the model domain. The spurious formation and undesirable tracks of TCs in the CTL were considerably improved by reproducing realistic large-scale atmospheric monsoon circulation with substantial adjustment between large-scale flow in the model domain and large-scale boundary forcing modified by the spectral nudging method. The realistic monsoon circulation took a vital role in simulating realistic TCs. It revealed that, in the downscaling from large-scale fields for regional climate simulations, scale interaction between model-generated regional features and forced large-scale fields should be considered, and spectral nudging is a desirable method in the downscaling method.
Back to the future: using historical climate variation to project near-term shifts in habitat suitable for coast redwood.

PubMed

Fernández, Miguel; Hamilton, Healy H; Kueppers, Lara M

2015-11-01

Studies that model the effect of climate change on terrestrial ecosystems often use climate projections from downscaled global climate models (GCMs). These simulations are generally too coarse to capture patterns of fine-scale climate variation, such as the sharp coastal energy and moisture gradients associated with wind-driven upwelling of cold water. Coastal upwelling may limit future increases in coastal temperatures, compromising GCMs' ability to provide realistic scenarios of future climate in these coastal ecosystems. Taking advantage of naturally occurring variability in the high-resolution historic climatic record, we developed multiple fine-scale scenarios of California climate that maintain coherent relationships between regional climate and coastal upwelling. We compared these scenarios against coarse resolution GCM projections at a regional scale to evaluate their temporal equivalency. We used these historically based scenarios to estimate potential suitable habitat for coast redwood (Sequoia sempervirens D. Don) under 'normal' combinations of temperature and precipitation, and under anomalous combinations representative of potential future climates. We found that a scenario of warmer temperature with historically normal precipitation is equivalent to climate projected by GCMs for California by 2020-2030 and that under these conditions, climatically suitable habitat for coast redwood significantly contracts at the southern end of its current range. Our results suggest that historical climate data provide a high-resolution alternative to downscaled GCM outputs for near-term ecological forecasts. This method may be particularly useful in other regions where local climate is strongly influenced by ocean-atmosphere dynamics that are not represented by coarse-scale GCMs. © 2015 John Wiley & Sons Ltd.
Impacts of climate change under CMIP5 RCP scenarios on the streamflow in the Dinder River and ecosystem habitats in Dinder National Park, Sudan

NASA Astrophysics Data System (ADS)

Basheer, Amir K.; Lu, Haishen; Omer, Abubaker; Ali, Abubaker B.; Abdelgader, Abdeldime M. S.

2016-04-01

The fate of seasonal river ecosystem habitats under climate change essentially depends on the changes in annual recharge of the river, which are related to alterations in precipitation and evaporation over the river basin. Therefore, the change in climate conditions is expected to significantly affect hydrological and ecological components, particularly in fragmented ecosystems. This study aims to assess the impacts of climate change on the streamflow in the Dinder River basin (DRB) and to infer its relative possible effects on the Dinder National Park (DNP) ecosystem habitats in Sudan. Four global circulation models (GCMs) from Coupled Model Intercomparison Project Phase 5 and two statistical downscaling approaches combined with a hydrological model (SWAT - the Soil and Water Assessment Tool) were used to project the climate change conditions over the study periods 2020s, 2050s, and 2080s. The results indicated that the climate over the DRB will become warmer and wetter under most scenarios. The projected precipitation variability mainly depends on the selected GCM and downscaling approach. Moreover, the projected streamflow is quite sensitive to rainfall and temperature variation, and will likely increase in this century. In contrast to drought periods during the 1960s, 1970s, and 1980s, the predicted climate change is likely to affect ecosystems in DNP positively and promote the ecological restoration for the habitats of flora and fauna.
Statistical Downscaling Of Local Climate In The Alpine Region

NASA Astrophysics Data System (ADS)

Kaspar, Severin; Philipp, Andreas; Jacobeit, Jucundus

2016-04-01

The impact of climate change on the alpine region was disproportional strong in the past decades compared to the surrounding areas, which becomes manifest in a higher increase in surface air temperature. Beside the thermal changes also implications for the hydrological cycle may be expected, acting as a very important factor not only for the ecosystem but also for mankind, in the form of water security or considering economical aspects like winter tourism etc. Therefore, in climate impact studies, it is necessary to focus on variables with high influence on the hydrological cycle, for example temperature, precipitation, wind, humidity and radiation. The aim of this study is to build statistical downscaling models which are able to reproduce temperature and precipitation at the mountainous alpine weather stations Zugspitze and Sonnblick and to further project these models into the future to identify possible changes in the behavior of these climate variables and with that in the hydrological cycle. Beside facing a in general very complex terrain in this high elevated regions, we have the advantage of a more direct atmospheric influence on the meteorology of the exposed weather stations from the large scale circulation. Two nonlinear statistical methods are developed to model the station-data series on a daily basis: On the one hand a conditional classification approach was used and on the other hand a model based on artificial neural networks (ANNs) was built. The latter is in focus of this presentation. One of the important steps of developing a new model approach is to find a reliable predictor setup with e.g. informative predictor variables or adequate location and size of the spatial domain. The question is: Can we include synoptic background knowledge to identify an optimal domain for an ANN approach? The yet developed ANN setups and configurations show promising results in downscaling both, temperature (up to 80 % of explained variance) and precipitation (up to 60 % of explained variance).
The response of future projections of the North American monsoon when combining dynamical downscaling and bias correction of CCSM4 output

NASA Astrophysics Data System (ADS)

Meyer, Jonathan D. D.; Jin, Jiming

2017-07-01

A 20-km regional climate model (RCM) dynamically downscaled the Community Climate System Model version 4 (CCSM4) to compare 32-year historical and future "end-of-the-century" climatologies of the North American Monsoon (NAM). CCSM4 and other phase 5 Coupled Model Intercomparison Project models have indicated a delayed NAM and overall general drying trend. Here, we test the suggested mechanism for this drier NAM where increasing atmospheric static stability and reduced early-season evapotranspiration under global warming will limit early-season convection and compress the mature-season of the NAM. Through our higher resolution RCM, we found the role of accelerated evaporation under a warmer climate is likely understated in coarse resolution models such as CCSM4. Improving the representation of mesoscale interactions associated with the Gulf of California and surrounding topography produced additional surface evaporation, which overwhelmed the convection-suppressing effects of a warmer troposphere. Furthermore, the improved land-sea temperature gradient helped drive stronger southerly winds and greater moisture transport. Finally, we addressed limitations from inherent CCSM4 biases through a form of mean bias correction, which resulted in a more accurate seasonality of the atmospheric thermodynamic profile. After bias correction, greater surface evaporation from average peak GoC SSTs of 32 °C compared to 29 °C from the original CCSM4 led to roughly 50 % larger changes to low-level moist static energy compared to that produced by the downscaled original CCSM4. The increasing destabilization of the NAM environment produced onset dates that were one to 2 weeks earlier in the core of the NAM and northern extent, respectively. Furthermore, a significantly more vigorous NAM signal was produced after bias correction, with >50 mm month-1 increases to the June-September precipitation found along east and west coasts of Mexico and into parts of Texas. A shift towards more extreme daily precipitation was found in both downscaled climatologies, with the bias-corrected climatology containing a much more apparent and extreme shift.
Studies of regional-scale climate variability and change. Hidden Markov models and coupled ocean-atmosphere modes

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ghil, M.; Kravtsov, S.; Robertson, A. W.

2008-10-14

This project was a continuation of previous work under DOE CCPP funding, in which we had developed a twin approach of probabilistic network (PN) models (sometimes called dynamic Bayesian networks) and intermediate-complexity coupled ocean-atmosphere models (ICMs) to identify the predictable modes of climate variability and to investigate their impacts on the regional scale. We had developed a family of PNs (similar to Hidden Markov Models) to simulate historical records of daily rainfall, and used them to downscale GCM seasonal predictions. Using an idealized atmospheric model, we had established a novel mechanism through which ocean-induced sea-surface temperature (SST) anomalies might influencemore » large-scale atmospheric circulation patterns on interannual and longer time scales; we had found similar patterns in a hybrid coupled ocean-atmosphere-sea-ice model. The goal of the this continuation project was to build on these ICM results and PN model development to address prediction of rainfall and temperature statistics at the local scale, associated with global climate variability and change, and to investigate the impact of the latter on coupled ocean-atmosphere modes. Our main results from the grant consist of extensive further development of the hidden Markov models for rainfall simulation and downscaling together with the development of associated software; new intermediate coupled models; a new methodology of inverse modeling for linking ICMs with observations and GCM results; and, observational studies of decadal and multi-decadal natural climate results, informed by ICM results.« less
Blending Pan-European and local hydrological models for water resource assessment in Mediterranean areas: lessons learnt from a mountainous catchment

NASA Astrophysics Data System (ADS)

José Polo, María; José Pérez-Palazón, María; Saénz de Rodrigáñez, Marta; Pimentel, Rafael; Arheimer, Berit

2017-04-01

Global hydrological models provide scientists and technicians with distributed data over medium to large areas from which assessment of water resource planning and use can be easily performed. However, scale conflicts between global models' spatial resolution and the local significant spatial scales in heterogeneous areas usually pose a constraint for the direct use and application of these models' results. The SWICCA (Service for Water Indicators in Climate Change Adaptation) Platform developed under the Copernicus Climate Change Service (C3S) offers a wide range of both climate and hydrological indicators obtained on a global scale with different time and spatial resolutions. Among the different study cases supporting the SWICCA demonstration of local impact assessment, the Sierra Nevada study case (South Spain) is a representative example of mountainous coastal catchments in the Mediterranean region. This work shows the lessons learnt during the study case development to derive local impact indicator tailored to suit the local end-users of water resource in this snow-dominated area. Different approaches were followed to select the most accurate method to downscale the global data and variables to the local level in a highly abrupt topography, in a sequential step approach. 1) SWICCA global climate variable downscaling followed by river flow simulation from a local hydrological model in selected control points in the catchment, together with 2) SWICCA global river flow values downscaling to the control points followed by corrections with local transfer functions were both tested against the available local river flow series of observations during the reference period. This test was performed for the different models and the available spatial resolutions included in the SWICCA platform. From the results, the second option, that is, the use of SWICCA river flow variables, performed the best approximations, once the local transfer functions were applied to the global values and an additional correction was performed based on the relative anomalies obtained instead of the absolute values. This approach was used to derive the future projections of selected local indicators for each end-user in the area under different climate change scenarios. Despite the spatial scale conflicts, the SWICCA river flow indicators (simulated by the E-HYPEv3.1.2 model) succeeded in approximating the observations during the reference period 1970-2000 when provided on a catchment scale, once local transfer functions and further anomaly correction were performed. Satisfactory results were obtained on a monthly scale for river flow in the main stream of the watershed, and on a daily scale for the headwater streams. The accessibility to the hydrological model WiMMed, which includes a snow module, locally validated in the study area has been crucial to downscale the SWICCA results and prove their usefulness.
Dynamical downscaling with the fifth-generation Canadian regional climate model (CRCM5) over the CORDEX Arctic domain: effect of large-scale spectral nudging and of empirical correction of sea-surface temperature

NASA Astrophysics Data System (ADS)

Takhsha, Maryam; Nikiéma, Oumarou; Lucas-Picher, Philippe; Laprise, René; Hernández-Díaz, Leticia; Winger, Katja

2017-10-01

As part of the CORDEX project, the fifth-generation Canadian Regional Climate Model (CRCM5) is used over the Arctic for climate simulations driven by reanalyses and by the MPI-ESM-MR coupled global climate model (CGCM) under the RCP8.5 scenario. The CRCM5 shows adequate skills capturing general features of mean sea level pressure (MSLP) for all seasons. Evaluating 2-m temperature (T2m) and precipitation is more problematic, because of inconsistencies between observational reference datasets over the Arctic that suffer of a sparse distribution of weather stations. In our study, we additionally investigated the effect of large-scale spectral nudging (SN) on the hindcast simulation driven by reanalyses. The analysis shows that SN is effective in reducing the spring MSLP bias, but otherwise it has little impact. We have also conducted another experiment in which the CGCM-simulated sea-surface temperature (SST) is empirically corrected and used as lower boundary conditions over the ocean for an atmosphere-only global simulation (AGCM), which in turn provides the atmospheric lateral boundary conditions to drive the CRCM5 simulation. This approach, so-called 3-step approach of dynamical downscaling (CGCM-AGCM-RCM), which had considerably improved the CRCM5 historical simulations over Africa, exhibits reduced impact over the Arctic domain. The most notable positive effect over the Arctic is a reduction of the T2m bias over the North Pacific Ocean and the North Atlantic Ocean in all seasons. Future projections using this method are compared with the results obtained with the traditional 2-step dynamical downscaling (CGCM-RCM) to assess the impact of correcting systematic biases of SST upon future-climate projections. The future projections are mostly similar for the two methods, except for precipitation.
Implications of climate change on landslide hazard in Central Italy.

PubMed

Alvioli, Massimiliano; Melillo, Massimo; Guzzetti, Fausto; Rossi, Mauro; Palazzi, Elisa; von Hardenberg, Jost; Brunetti, Maria Teresa; Peruccacci, Silvia

2018-07-15

The relation between climate change and its potential effects on the stability of slopes remains an open issue. For rainfall induced landslides, the point consists in determining the effects of the projected changes in the duration and amounts of rainfall that can initiate slope failures. We investigated the relationship between fine-scale climate projections obtained by downscaling and the expected modifications in landslide occurrence in Central Italy. We used rainfall measurements taken by 56 rain gauges in the 9-year period 2003-2011, and the RainFARM technique to generate downscaled synthetic rainfall fields from regional climate model projections for the 14-year calibration period 2002-2015, and for the 40-year projection period 2010-2049. Using a specific algorithm, we extracted a number of rainfall events, i.e. rainfall periods separated by dry periods of no or negligible amount of rain, from the measured and the synthetic rainfall series. Then, we used the selected rainfall events to forcethe Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability Model TRIGRS v. 2.1. We analyzed the results in terms of variations (or lack of variations) in the rainfall thresholds for the possible initiation of landslides, in the probability distribution of landslide size (area), and in landslide hazard. Results showed that the downscaled rainfall fields obtained by RainFARM can be used to single out rainfall events, and to force the slope stability model. Results further showed that while the rainfall thresholds for landslide occurrence are expected to change in future scenarios, the probability distribution of landslide areas are not. We infer that landslide hazard in the study area is expected to change in response to the projected variations in the rainfall conditions. We expect our results to contribute to regional investigations of the expected impact of projected climate variations on slope stability conditions and on landslide hazards. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.
The polar WRF downscaled historical and projected 21st century climate for the coast and foothills of Arctic Alaska

NASA Astrophysics Data System (ADS)

Cai, Lei; Alexeev, Vladimir A.; Arp, Christopher D.; Jones, Benjamin M.; Liljedahl, Anna K.; Gädeke, Anne

2018-01-01

Climate change is most pronounced in the northern high latitude region. Yet, climate observations are unable to fully capture regional-scale dynamics due to the sparse weather station coverage, which limits our ability to make reliable climate-based assessments. A set of simulated data products was therefore developed for the North Slope of Alaska through a dynamical downscaling approach. The polar-optimized Weather Research & Forecast (Polar WRF) model was forced by three sources: The ERA-interim reanalysis data (for 1979-2014), the Community Earth System Model 1.0 (CESM1.0) historical simulation (for 1950-2005), and the CESM1.0 projected (for 2006-2100) simulations in two Representative Concentration Pathways (RCP4.5 and RCP8.5) scenarios. Climatic variables were produced in a 10-km grid spacing and a 3-hour interval. The ERA-interim forced WRF (ERA-WRF) proves the value of dynamical downscaling, which yields more realistic topographical-induced precipitation and air temperature, as well as corrects underestimations in observed precipitation. In summary, dry and cold biases to the north of the Brooks Range are presented in ERA-WRF, while CESM forced WRF (CESM-WRF) holds wet and warm biases in its historical period. A linear scaling method allowed for an adjustment of the biases, while keeping the majority of the variability and extreme values of modeled precipitation and air temperature. CESM-WRF under RCP 4.5 scenario projects smaller increase in precipitation and air temperature than observed in the historical CESM-WRF product, while the CESM-WRF under RCP8.5 scenario shows larger changes. The fine spatial and temporal resolution, long temporal coverage, and multi-scenario projections jointly make the dataset appropriate to address a myriad of physical and biological changes occurring on the North Slope of Alaska.
Climate-Agriculture-Modeling and Decision Tool for Disease (CAMDT-Disease) for seasonal climate forecast-based crop disease risk management in agriculture

NASA Astrophysics Data System (ADS)

Kim, K. H.; Lee, S.; Han, E.; Ines, A. V. M.

2017-12-01

Climate-Agriculture-Modeling and Decision Tool (CAMDT) is a decision support system (DSS) tool that aims to facilitate translations of probabilistic seasonal climate forecasts (SCF) to crop responses such as yield and water stress. Since CAMDT is a software framework connecting different models and algorithms with SCF information, it can be easily customized for different types of agriculture models. In this study, we replaced the DSSAT-CSM-Rice model originally incorporated in CAMDT with a generic epidemiological model, EPIRICE, to generate a seasonal pest outlook. The resulting CAMDT-Disease generates potential risks for selected fungal, viral, and bacterial diseases of rice over the next months by translating SCFs into agriculturally-relevant risk information. The integrated modeling procedure of CAMDT-Disease first disaggregates a given SCF using temporal downscaling methods (predictWTD or FResampler1), runs EPIRICE with the downscaled weather inputs, and finally visualizes the EPIRICE outputs as disease risk compared to that of the previous year and the 30-year-climatological average. In addition, the easy-to-use graphical user interface adopted from CAMDT allows users to simulate "what-if" scenarios of disease risks over different planting dates with given SCFs. Our future work includes the simulation of the effect of crop disease on yields through the disease simulation models with the DSSAT-CSM-Rice model, as disease remains one of the most critical yield-reducing factors in the field.
Islands Climatology at Local Scale. Downscaling with CIELO model

NASA Astrophysics Data System (ADS)

Azevedo, Eduardo; Reis, Francisco; Tomé, Ricardo; Rodrigues, Conceição

2016-04-01

Islands with horizontal scales of the order of tens of km, as is the case of the Atlantic Islands of Macaronesia, are subscale orographic features for Global Climate Models (GCMs) since the horizontal scales of these models are too coarse to give a detailed representation of the islands' topography. Even the Regional Climate Models (RCMs) reveals limitations when they are forced to reproduce the climate of small islands mainly by the way they flat and lowers the elevation of the islands, reducing the capacity of the model to reproduce important local mechanisms that lead to a very deep local climate differentiation. Important local thermodynamics mechanisms like Foehn effect, or the influence of topography on radiation balance, have a prominent role in the climatic spatial differentiation. Advective transport of air - and the consequent induced adiabatic cooling due to orography - lead to transformations of the state parameters of the air that leads to the spatial configuration of the fields of pressure, temperature and humidity. The same mechanism is in the origin of the orographic clouds cover that, besides the direct role as water source by the reinforcement of precipitation, act like a filter to direct solar radiation and as a source of long-wave radiation that affect the local balance of energy. Also, the saturation (or near saturation) conditions that they provide constitute a barrier to water vapour diffusion in the mechanisms of evapotranspiration. Topographic factors like slope, aspect and orographic mask have also significant importance in the local energy balance. Therefore, the simulation of the local scale climate (past, present and future) in these archipelagos requires the use of downscaling techniques to adjust locally outputs obtained at upper scales. This presentation will discuss and analyse the evolution of the CIELO model (acronym for Clima Insular à Escala LOcal) a statistical/dynamical technique developed at the University of the Azores, which has been improved since its original version, constituting currently a downscaling tool widely applied with success in different islands of Macaronesia. Recently the CIELO model has been tested against data from the Eastern North Atlantic (ENA), Graciosa Island ARM facility programme (established and supported by the U.S. Department of Energy with the collaboration of the local government and the University of the Azores).
Some Advances in Downscaling Probabilistic Climate Forecasts for Agricultural Decision Support

NASA Astrophysics Data System (ADS)

Han, E.; Ines, A.

2015-12-01

Seasonal climate forecasts, commonly provided in tercile-probabilities format (below-, near- and above-normal), need to be translated into more meaningful information for decision support of practitioners in agriculture. In this paper, we will present two new novel approaches to temporally downscale probabilistic seasonal climate forecasts: one non-parametric and another parametric method. First, the non-parametric downscaling approach called FResampler1 uses the concept of 'conditional block sampling' of weather data to create daily weather realizations of a tercile-based seasonal climate forecasts. FResampler1 randomly draws time series of daily weather parameters (e.g., rainfall, maximum and minimum temperature and solar radiation) from historical records, for the season of interest from years that belong to a certain rainfall tercile category (e.g., being below-, near- and above-normal). In this way, FResampler1 preserves the covariance between rainfall and other weather parameters as if conditionally sampling maximum and minimum temperature and solar radiation if that day is wet or dry. The second approach called predictWTD is a parametric method based on a conditional stochastic weather generator. The tercile-based seasonal climate forecast is converted into a theoretical forecast cumulative probability curve. Then the deviates for each percentile is converted into rainfall amount or frequency or intensity to downscale the 'full' distribution of probabilistic seasonal climate forecasts. Those seasonal deviates are then disaggregated on a monthly basis and used to constrain the downscaling of forecast realizations at different percentile values of the theoretical forecast curve. As well as the theoretical basis of the approaches we will discuss sensitivity analysis (length of data and size of samples) of them. In addition their potential applications for managing climate-related risks in agriculture will be shown through a couple of case studies based on actual seasonal climate forecasts for: rice cropping in the Philippines and maize cropping in India and Kenya.
Evaluation of CMIP5 models in the context of food security assessments in Sahel and Eastern Africa

NASA Astrophysics Data System (ADS)

Shukla, S.; Funk, C. C.; Dettinger, M. D.; Robertson, F. R.

2012-12-01

Global climate change will adversely impact agricultural production in many African countries, mainly in the Sahel region and Eastern Africa that are already considered food insecure regions. The impacts of climate change will be particularly severe in these food insecure countries due to their high dependence on domestic agriculture production, rapid population growth, and lack of technological advances. Early planning and the targeted use of resources will therefore be critical to informing and motivating climate change adaptation actions that can save lives and mitigate economic losses. We seek to use Climate Model Intercomparison Project Phase-5 (CMIP5) global climate model projections to assess and attribute food and water security conditions in the above mentioned regions over next two decades or so. As a first order of business, however, we need to understand how the different models represent the tropical ocean response to anthropogenic warming. We pursue this question through an evaluation of the performance of eight different coupled ocean-atmosphere models under the conditions of the 'historical' experiment. The historical experiment forces the simulations with observed 1850-2005 greenhouse gas, aerosol and land cover. While all the models show substantial warming of the tropical oceans, the pattern and atmospheric response to that warming varies substantially. This analysis suggests that the Community Climate System Model (CCSM4) provides the most realistic 1850-2005 changes over the Indo-Pacific. We then present initial downscaling results, based on large scale forcing from the CCSM4, combined with statistical downscaling based on a combination of monthly simulations from Community Atmopsheric Model 4 (CAM4) and observed gridded time series of African rainfall and air temperatures.
Downscaling Aerosols and the Impact of Neglected Subgrid Processes on Direct Aerosol Radiative Forcing for a Representative Global Climate Model Grid Spacing

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
Impact of bias-corrected reanalysis-derived lateral boundary conditions on WRF simulations

NASA Astrophysics Data System (ADS)

Moalafhi, Ditiro Benson; Sharma, Ashish; Evans, Jason Peter; Mehrotra, Rajeshwar; Rocheta, Eytan

2017-08-01

Lateral and lower boundary conditions derived from a suitable global reanalysis data set form the basis for deriving a dynamically consistent finer resolution downscaled product for climate and hydrological assessment studies. A problem with this, however, is that systematic biases have been noted to be present in the global reanalysis data sets that form these boundaries, biases which can be carried into the downscaled simulations thereby reducing their accuracy or efficacy. In this work, three Weather Research and Forecasting (WRF) model downscaling experiments are undertaken to investigate the impact of bias correcting European Centre for Medium range Weather Forecasting Reanalysis ERA-Interim (ERA-I) atmospheric temperature and relative humidity using Atmospheric Infrared Sounder (AIRS) satellite data. The downscaling is performed over a domain centered over southern Africa between the years 2003 and 2012. The sample mean and the mean as well as standard deviation at each grid cell for each variable are used for bias correction. The resultant WRF simulations of near-surface temperature and precipitation are evaluated seasonally and annually against global gridded observational data sets and compared with ERA-I reanalysis driving field. The study reveals inconsistencies between the impact of the bias correction prior to downscaling and the resultant model simulations after downscaling. Mean and standard deviation bias-corrected WRF simulations are, however, found to be marginally better than mean only bias-corrected WRF simulations and raw ERA-I reanalysis-driven WRF simulations. Performances, however, differ when assessing different attributes in the downscaled field. This raises questions about the efficacy of the correction procedures adopted.
Eco-hydrological Modeling in the Framework of Climate Change

NASA Astrophysics Data System (ADS)

Fatichi, Simone; Ivanov, Valeriy Y.; Caporali, Enrica

2010-05-01

A blueprint methodology for studying climate change impacts, as inferred from climate models, on eco-hydrological dynamics at the plot and small catchment scale is presented. Input hydro-meteorological variables for hydrological and eco-hydrological models for present and future climates are reproduced using a stochastic downscaling technique and a weather generator, "AWE-GEN". The generated time series of meteorological variables for the present climate and an ensemble of possible future climates serve as input to a newly developed physically-based eco-hydrological model "Tethys-Chloris". An application of the proposed methodology is realized reproducing the current (1961-2000) and multiple future (2081-2100) climates for the location of Tucson (Arizona). A general reduction of precipitation and a significant increase of air temperature are inferred. The eco-hydrological model is successively applied to detect changes in water recharge and vegetation dynamics for a desert shrub ecosystem, typical of the semi-arid climate of south Arizona. Results for the future climate account for uncertainties in the downscaling and are produced in terms of probability density functions. A comparison of control and future scenarios is discussed in terms of changes in the hydrological balance components, energy fluxes, and indices of vegetation productivity. An appreciable effect of climate change can be observed in metrics of vegetation performance. The negative impact on vegetation due to amplification of water stress in a warmer and dryer climate is offset by a positive effect of carbon dioxide augment. This implies a positive shift in plant capabilities to exploit water. Consequently, the plant water use efficiency and rain use efficiency are expected to increase. Interesting differences in the long-term vegetation productivity are also observed for the ensemble of future climates. The reduction of precipitation and the substantial maintenance of vegetation cover ultimately leads to the depletion of soil moisture and recharge to deeper layers. Such an outcome can affect the long-tem water availability in semi-arid systems and expose plants to more severe and frequent periods of stress.
Improving the representation of clouds, radiation, and precipitation using spectral nudging in the Weather Research and Forecasting model

EPA Science Inventory

Spectral nudging – a scale-selective interior constraint technique – is commonly used in regional climate models to maintain consistency with large-scale forcing while permitting mesoscale features to develop in the downscaled simulations. Several studies have demonst...

Scenarios of land use and land cover change in the conterminous United States: Utilizing the special report on emission scenarios at ecoregional scales

USGS Publications Warehouse

Sleeter, Benjamin M.; Sohl, Terry L.; Bouchard, Michelle A.; Reker, Ryan R.; Soulard, Christopher E.; Acevedo, William; Griffith, Glenn E.; Sleeter, Rachel R.; Auch, Roger F.; Sayler, Kristi L.; Prisley, Stephen; Zhu, Zhi-Liang

2012-01-01

Global environmental change scenarios have typically provided projections of land use and land cover for a relatively small number of regions or using a relatively coarse resolution spatial grid, and for only a few major sectors. The coarseness of global projections, in both spatial and thematic dimensions, often limits their direct utility at scales useful for environmental management. This paper describes methods to downscale projections of land-use and land-cover change from the Intergovernmental Panel on Climate Change's Special Report on Emission Scenarios to ecological regions of the conterminous United States, using an integrated assessment model, land-use histories, and expert knowledge. Downscaled projections span a wide range of future potential conditions across sixteen land use/land cover sectors and 84 ecological regions, and are logically consistent with both historical measurements and SRES characteristics. Results appear to provide a credible solution for connecting regionalized projections of land use and land cover with existing downscaled climate scenarios, under a common set of scenario-based socioeconomic assumptions.
Current and future pluvial flood hazard analysis for the city of Antwerp

NASA Astrophysics Data System (ADS)

Willems, Patrick; Tabari, Hossein; De Niel, Jan; Van Uytven, Els; Lambrechts, Griet; Wellens, Geert

2016-04-01

For the city of Antwerp in Belgium, higher rainfall extremes were observed in comparison with surrounding areas. The differences were found statistically significant for some areas and may be the result of the heat island effect in combination with the higher concentrations of aerosols. A network of 19 rain gauges but with varying records length (the longest since the 1960s) and continuous radar data for 10 years were combined to map the spatial variability of rainfall extremes over the city at various durations from 15 minutes to 1 day together with the uncertainty. The improved spatial rainfall information was used as input in the sewer system model of the city to analyze the frequency of urban pluvial floods. Comparison with historical flood observations from various sources (fire brigade and media) confirmed that the improved spatial rainfall information also improved sewer impact results on both the magnitude and frequency of the sewer floods. Next to these improved urban flood impact results for recent and current climatological conditions, the new insights on the local rainfall microclimate were also helpful to enhance future projections on rainfall extremes and pluvial floods in the city. This was done by improved statistical downscaling of all available CMIP5 global climate model runs (160 runs) for the 4 RCP scenarios, as well as the available EURO-CORDEX regional climate model runs. Two types of statistical downscaling methods were applied for that purpose (a weather typing based method, and a quantile perturbation approach), making use of the microclimate results and its dependency on specific weather types. Changes in extreme rainfall intensities were analyzed and mapped as a function of the RCP scenario, together with the uncertainty, decomposed in the uncertainties related to the climate models, the climate model initialization or limited length of the 30-year time series (natural climate variability) and the statistical downscaling (albeit limited to two types of methods). These were finally transferred into future pluvial flash flood hazard maps for the city together with the uncertainties, and are considered as basis for spatial planning and adaptation.
Climate downscaling over South America for 1971-2000: application in SMAP rainfall-runoff model for Grande River Basin

NASA Astrophysics Data System (ADS)

da Silva, Felipe das Neves Roque; Alves, José Luis Drummond; Cataldi, Marcio

2018-03-01

This paper aims to validate inflow simulations concerning the present-day climate at Água Vermelha Hydroelectric Plant (AVHP—located on the Grande River Basin) based on the Soil Moisture Accounting Procedure (SMAP) hydrological model. In order to provide rainfall data to the SMAP model, the RegCM regional climate model was also used working with boundary conditions from the MIROC model. Initially, present-day climate simulation performed by RegCM model was analyzed. It was found that, in terms of rainfall, the model was able to simulate the main patterns observed over South America. A bias correction technique was also used and it was essential to reduce mistakes related to rainfall simulation. Comparison between rainfall simulations from RegCM and MIROC showed improvements when the dynamical downscaling was performed. Then, SMAP, a rainfall-runoff hydrological model, was used to simulate inflows at Água Vermelha Hydroelectric Plant. After calibration with observed rainfall, SMAP simulations were evaluated in two different periods from the one used in calibration. During calibration, SMAP captures the inflow variability observed at AVHP. During validation periods, the hydrological model obtained better results and statistics with observed rainfall. However, in spite of some discrepancies, the use of simulated rainfall without bias correction captured the interannual flow variability. However, the use of bias removal in the simulated rainfall performed by RegCM brought significant improvements to the simulation of natural inflows performed by SMAP. Not only the curve of simulated inflow became more similar to the observed inflow, but also the statistics improved their values. Improvements were also noticed in the inflow simulation when the rainfall was provided by the regional climate model compared to the global model. In general, results obtained so far prove that there was an added value in rainfall when regional climate model was compared to global climate model and that data from regional models must be bias-corrected so as to improve their results.
Participatory Scenario Planning for Climate Change Adaptation: the Maui Groundwater Project

NASA Astrophysics Data System (ADS)

Keener, V. W.; Brewington, L.; Finucane, M.

2015-12-01

For the last century, the island of Maui in Hawai'i has been the center of environmental, agricultural, and legal conflict with respect to both surface and groundwater allocation. Planning for sustainable future freshwater supply in Hawai'i requires adaptive policies and decision-making that emphasizes private and public partnerships and knowledge transfer between scientists and non-scientists. We have downscaled dynamical climate models to 1 km resolution in Maui and coupled them with a USGS Water Budget model and a participatory scenario building process to quantify future changes in island-scale climate and groundwater recharge under different land uses. Although these projections are uncertain, the integrated nature of the Pacific RISA research program has allowed us to take a multi-pronged approach to facilitate the uptake of climate information into policy and management. This presentation details the ongoing work to support the development of Hawai'i's first island-wide water use plan under the new climate adaptation directive. Participatory scenario planning began in 2012 to bring together a diverse group of ~100 decision-makers in state and local government, watershed restoration, agriculture, and conservation to 1) determine the type of information (climate variables, land use and development, agricultural practices) they would find helpful in planning for climate change, and 2) develop a set of nested scenarios that represent alternative climate and management futures. This integration of knowledge is an iterative process, resulting in flexible and transparent narratives of complex futures comprised of information at multiple scales. We will present an overview of the downscaling, scenario building, hydrological modeling processes, and stakeholder response.
Gridded climate data from 5 GCMs of the Last Glacial Maximum downscaled to 30 arc s for Europe

NASA Astrophysics Data System (ADS)

Schmatz, D. R.; Luterbacher, J.; Zimmermann, N. E.; Pearman, P. B.

2015-06-01

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.
Improving medium-range and seasonal hydroclimate forecasts in the southeast USA

NASA Astrophysics Data System (ADS)

Tian, Di

Accurate hydro-climate forecasts are important for decision making by water managers, agricultural producers, and other stake holders. Numerical weather prediction models and general circulation models may have potential for improving hydro-climate forecasts at different scales. In this study, forecast analogs of the Global Forecast System (GFS) and Global Ensemble Forecast System (GEFS) based on different approaches were evaluated for medium-range reference evapotranspiration (ETo), irrigation scheduling, and urban water demand forecasts in the southeast United States; the Climate Forecast System version 2 (CFSv2) and the North American national multi-model ensemble (NMME) were statistically downscaled for seasonal forecasts of ETo, precipitation (P) and 2-m temperature (T2M) at the regional level. The GFS mean temperature (Tmean), relative humidity, and wind speed (Wind) reforecasts combined with the climatology of Reanalysis 2 solar radiation (Rs) produced higher skill than using the direct GFS output only. Constructed analogs showed slightly higher skill than natural analogs for deterministic forecasts. Both irrigation scheduling driven by the GEFS-based ETo forecasts and GEFS-based ETo forecast skill were generally positive up to one week throughout the year. The GEFS improved ETo forecast skill compared to the GFS. The GEFS-based analog forecasts for the input variables of an operational urban water demand model were skillful when applied in the Tampa Bay area. The modified operational models driven by GEFS analog forecasts showed higher forecast skill than the operational model based on persistence. The results for CFSv2 seasonal forecasts showed maximum temperature (Tmax) and Rs had the greatest influence on ETo. The downscaled Tmax showed the highest predictability, followed by Tmean, Tmin, Rs, and Wind. The CFSv2 model could better predict ETo in cold seasons during El Nino Southern Oscillation (ENSO) events only when the forecast initial condition was in ENSO. Downscaled P and T2M forecasts were produced by directly downscaling the NMME P and T2M output or indirectly using the NMME forecasts of Nino3.4 sea surface temperatures to predict local-scale P and T2M. The indirect method generally showed the highest forecast skill which occurs in cold seasons. The bias-corrected NMME ensemble forecast skill did not outperform the best single model.
Climatologies at high resolution for the earth’s land surface areas

PubMed Central

Karger, Dirk Nikolaus; Conrad, Olaf; Böhner, Jürgen; Kawohl, Tobias; Kreft, Holger; Soria-Auza, Rodrigo Wilber; Zimmermann, Niklaus E.; Linder, H. Peter; Kessler, Michael

2017-01-01

High-resolution information on climatic conditions is essential to many applications in environmental and ecological sciences. Here we present the CHELSA (Climatologies at high resolution for the earth’s land surface areas) data of downscaled model output temperature and precipitation estimates of the ERA-Interim climatic reanalysis to a high resolution of 30 arc sec. The temperature algorithm is based on statistical downscaling of atmospheric temperatures. The precipitation algorithm incorporates orographic predictors including wind fields, valley exposition, and boundary layer height, with a subsequent bias correction. The resulting data consist of a monthly temperature and precipitation climatology for the years 1979–2013. We compare the data derived from the CHELSA algorithm with other standard gridded products and station data from the Global Historical Climate Network. We compare the performance of the new climatologies in species distribution modelling and show that we can increase the accuracy of species range predictions. We further show that CHELSA climatological data has a similar accuracy as other products for temperature, but that its predictions of precipitation patterns are better. PMID:28872642
Climatologies at high resolution for the earth's land surface areas

NASA Astrophysics Data System (ADS)

Karger, Dirk Nikolaus; Conrad, Olaf; Böhner, Jürgen; Kawohl, Tobias; Kreft, Holger; Soria-Auza, Rodrigo Wilber; Zimmermann, Niklaus E.; Linder, H. Peter; Kessler, Michael

2017-09-01

High-resolution information on climatic conditions is essential to many applications in environmental and ecological sciences. Here we present the CHELSA (Climatologies at high resolution for the earth's land surface areas) data of downscaled model output temperature and precipitation estimates of the ERA-Interim climatic reanalysis to a high resolution of 30 arc sec. The temperature algorithm is based on statistical downscaling of atmospheric temperatures. The precipitation algorithm incorporates orographic predictors including wind fields, valley exposition, and boundary layer height, with a subsequent bias correction. The resulting data consist of a monthly temperature and precipitation climatology for the years 1979-2013. We compare the data derived from the CHELSA algorithm with other standard gridded products and station data from the Global Historical Climate Network. We compare the performance of the new climatologies in species distribution modelling and show that we can increase the accuracy of species range predictions. We further show that CHELSA climatological data has a similar accuracy as other products for temperature, but that its predictions of precipitation patterns are better.
An application of a hydraulic model simulator in flood risk assessment under changing climatic conditions

NASA Astrophysics Data System (ADS)

Doroszkiewicz, J. M.; Romanowicz, R. J.

2016-12-01

The standard procedure of climate change impact assessment on future hydrological extremes consists of a chain of consecutive actions, starting from the choice of GCM driven by an assumed CO2 scenario, through downscaling of climatic forcing to a catchment scale, estimation of hydrological extreme indices using hydrological modelling tools and subsequent derivation of flood risk maps with the help of a hydraulic model. Among many possible sources of uncertainty, the main are the uncertainties related to future climate scenarios, climate models, downscaling techniques and hydrological and hydraulic models. Unfortunately, we cannot directly assess the impact of these different sources of uncertainties on flood risk in future due to lack of observations of future climate realizations. The aim of this study is an assessment of a relative impact of different sources of uncertainty on the uncertainty of flood risk maps. Due to the complexity of the processes involved, an assessment of total uncertainty of maps of inundation probability might be very computer time consuming. As a way forward we present an application of a hydraulic model simulator based on a nonlinear transfer function model for the chosen locations along the river reach. The transfer function model parameters are estimated based on the simulations of the hydraulic model at each of the model cross-sections. The study shows that the application of a simulator substantially reduces the computer requirements related to the derivation of flood risk maps under future climatic conditions. Biala Tarnowska catchment, situated in southern Poland is used as a case study. Future discharges at the input to a hydraulic model are obtained using the HBV model and climate projections obtained from the EUROCORDEX project. The study describes a cascade of uncertainty related to different stages of the process of derivation of flood risk maps under changing climate conditions. In this context it takes into account the uncertainty of future climate projections, an uncertainty of flow routing model, the propagation of that uncertainty through the hydraulic model, and finally, the uncertainty related to the derivation of flood risk maps.
Prediction of climate change in Brunei Darussalam using statistical downscaling model

NASA Astrophysics Data System (ADS)

Hasan, Dk. Siti Nurul Ain binti Pg. Ali; Ratnayake, Uditha; Shams, Shahriar; Nayan, Zuliana Binti Hj; Rahman, Ena Kartina Abdul

2017-06-01

Climate is changing and evidence suggests that the impact of climate change would influence our everyday lives, including agriculture, built environment, energy management, food security and water resources. Brunei Darussalam located within the heart of Borneo will be affected both in terms of precipitation and temperature. Therefore, it is crucial to comprehend and assess how important climate indicators like temperature and precipitation are expected to vary in the future in order to minimise its impact. This study assesses the application of a statistical downscaling model (SDSM) for downscaling General Circulation Model (GCM) results for maximum and minimum temperatures along with precipitation in Brunei Darussalam. It investigates future climate changes based on numerous scenarios using Hadley Centre Coupled Model, version 3 (HadCM3), Canadian Earth System Model (CanESM2) and third-generation Coupled Global Climate Model (CGCM3) outputs. The SDSM outputs were improved with the implementation of bias correction and also using a monthly sub-model instead of an annual sub-model. The outcomes of this assessment show that monthly sub-model performed better than the annual sub-model. This study indicates a satisfactory applicability for generation of maximum temperatures, minimum temperatures and precipitation for future periods of 2017-2046 and 2047-2076. All considered models and the scenarios were consistent in predicting increasing trend of maximum temperature, increasing trend of minimum temperature and decreasing trend of precipitations. Maximum overall trend of Tmax was also observed for CanESM2 with Representative Concentration Pathways (RCP) 8.5 scenario. The increasing trend is 0.014 °C per year. Accordingly, by 2076, the highest prediction of average maximum temperatures is that it will increase by 1.4 °C. The same model predicts an increasing trend of Tmin of 0.004 °C per year, while the highest trend is seen under CGCM3-A2 scenario which is 0.009 °C per year. The highest change predicted for the Tmin is therefore 0.9 °C by 2076. The precipitation showed a maximum trend of decrease of 12.7 mm year. It is also seen in the output using CanESM2 data that precipitation will be more chaotic with some reaching 4800 mm per year and also producing low rainfall about 1800 mm per year. All GCMs considered are consistent in predicting it is very likely that Brunei is expected to experience more warming as well as less frequent precipitation events but with a possibility of intensified and drastically high rainfalls in the future.
How does dynamical downscaling affect model biases and future projections of explosive extratropical cyclones along North America's Atlantic coast?

NASA Astrophysics Data System (ADS)

Seiler, C.; Zwiers, F. W.; Hodges, K. I.; Scinocca, J. F.

2018-01-01

Explosive extratropical cyclones (EETCs) are rapidly intensifying low pressure systems that generate severe weather along North America's Atlantic coast. Global climate models (GCMs) tend to simulate too few EETCs, perhaps partly due to their coarse horizontal resolution and poorly resolved moist diabatic processes. This study explores whether dynamical downscaling can reduce EETC frequency biases, and whether this affects future projections of storms along North America's Atlantic coast. A regional climate model (CanRCM4) is forced with the CanESM2 GCM for the periods 1981 to 2000 and 2081 to 2100. EETCs are tracked from relative vorticity using an objective feature tracking algorithm. CanESM2 simulates 38% fewer EETC tracks compared to reanalysis data, which is consistent with a negative Eady growth rate bias (-0.1 day^{-1}). Downscaling CanESM2 with CanRCM4 increases EETC frequency by one third, which reduces the frequency bias to -22%, and increases maximum EETC precipitation by 22%. Anthropogenic greenhouse gas forcing is projected to decrease EETC frequency (-15%, -18%) and Eady growth rate (-0.2 day^{-1}, -0.2 day^{-1}), and increase maximum EETC precipitation (46%, 52%) in CanESM2 and CanRCM4, respectively. The limited effect of dynamical downscaling on EETC frequency projections is consistent with the lack of impact on the maximum Eady growth rate. The coarse spatial resolution of GCMs presents an important limitation for simulating extreme ETCs, but Eady growth rate biases are likely just as relevant. Further bias reductions could be achieved by addressing processes that lead to an underestimation of lower tropospheric meridional temperature gradients.
Future projections of extreme precipitation using Advanced Weather Generator (AWE-GEN) over Peninsular Malaysia

NASA Astrophysics Data System (ADS)

Syafrina, A. H.; Zalina, M. D.; Juneng, L.

2014-09-01

A stochastic downscaling methodology known as the Advanced Weather Generator, AWE-GEN, has been tested at four stations in Peninsular Malaysia using observations available from 1975 to 2005. The methodology involves a stochastic downscaling procedure based on a Bayesian approach. Climate statistics from a multi-model ensemble of General Circulation Model (GCM) outputs were calculated and factors of change were derived to produce the probability distribution functions (PDF). New parameters were obtained to project future climate time series. A multi-model ensemble was used in this study. The projections of extreme precipitation were based on the RCP 6.0 scenario (2081-2100). The model was able to simulate both hourly and 24-h extreme precipitation, as well as wet spell durations quite well for almost all regions. However, the performance of GCM models varies significantly in all regions showing high variability of monthly precipitation for both observed and future periods. The extreme precipitation for both hourly and 24-h seems to increase in future, while extreme of wet spells remain unchanged, up to the return periods of 10-40 years.
Downscaled ice-ocean simulations for the Chukchi and Eastern Siberian Seas from an oceanic re-analysis product

NASA Astrophysics Data System (ADS)

Fujisaki-Manome, A.; Wang, J.

2016-12-01

Arctic summer sea ice has been declining at the rate that is much faster than any climate models predict. While the accelerated sea ice melting in the recent few decades could be attributed to several mechanisms such as the Arctic temperature amplification and the ice-albedo feedback, this does not necessarily explain why climate models underestimate the observed rate of summer sea ice loss. Clearly, an improved understanding is needed in what processes could be missed in climate models and could play roles in unprecedented loss of sea ice. This study evaluates contributions of sub-mesoscale processes in the ice edge (i.e. the boundary region between open water and ice covered area), which include eddies, ice bands, and the vertical mixing associated with ice bands, to the melting of sea ice and how they explain the underestimation of sea ice loss in the current state-of-art climate models. The focus area is in the pacific side of the Arctic Ocean. First, several oceanic re-analysis products including NCEP-Climate Forecast System Reanalysis (CFSR) and Modern-Era Retrospective Analysis for Research and Applications (MERRA) are evaluated in comparison with the in-situ observations from the Russian-American Long-term Census of the Arctic (RUSALCA) project. Second, the downscaled ice-ocean simulations are conducted for the Chukchi and East Siberian Seas with initial and open boundary conditions provided from a selected oceanic re-analysis product.
Projection of spatial and temporal changes of rainfall in Sarawak of Borneo Island using statistical downscaling of CMIP5 models

NASA Astrophysics Data System (ADS)

Sa'adi, Zulfaqar; Shahid, Shamsuddin; Chung, Eun-Sung; Ismail, Tarmizi bin

2017-11-01

This study assesses the possible changes in rainfall patterns of Sarawak in Borneo Island due to climate change through statistical downscaling of General Circulation Models (GCM) projections. Available in-situ observed rainfall data were used to downscale the future rainfall from ensembles of 20 GCMs of Coupled Model Intercomparison Project phase 5 (CMIP5) for four Representative Concentration Pathways (RCP) scenarios, namely, RCP2.6, RCP4.5, RCP6.0 and RCP8.5. Model Output Statistics (MOS) based downscaling models were developed using two data mining approaches known as Random Forest (RF) and Support Vector Machine (SVM). The SVM was found to downscale all GCMs with normalized mean square error (NMSE) of 48.2-75.2 and skill score (SS) of 0.94-0.98 during validation. The results show that the future projection of the annual rainfalls is increasing and decreasing on the region-based and catchment-based basis due to the influence of the monsoon season affecting the coast of Sarawak. The ensemble mean of GCMs projections reveals the increased and decreased mean of annual precipitations at 33 stations with the rate of 0.1% to 19.6% and one station with the rate of - 7.9% to - 3.1%, respectively under all RCP scenarios. The remaining 15 stations showed inconsistency neither increasing nor decreasing at the rate of - 5.6% to 5.2%, but mainly showing a trend of decreasing rainfall during the first period (2010-2039) followed by increasing rainfall for the period of 2070-2099.
Estimating future flood frequency and magnitude in basins affected by glacier wastage.

DOT National Transportation Integrated Search

2015-03-01

We present field measurements of meteorology, hydrology and glaciers and long-term modeled projections of glacier mass balance and : stream flow informed by downscaled climate simulations. The study basins include Valdez Glacier Stream (342 km2 : ), ...
Effects of climate change on hydrology and hydraulics of Qu River Basin, East China.

NASA Astrophysics Data System (ADS)

Gao, C.; Zhu, Q.; Zhao, Z.; Pan, S.; Xu, Y. P.

2015-12-01

The impacts of climate change on regional hydrological extreme events have attracted much attention in recent years. This paper aims to provide a general overview of changes on future runoffs and water levels in the Qu River Basin, upper reaches of Qiantang River, East China by combining future climate scenarios, hydrological model and 1D hydraulic model. The outputs of four GCMs BCC, BNU, CanESM and CSIRO under two scenarios RCP4.5 and RCP8.5 for 2021-2050 are chosen to represent future climate change projections. The LARS-WG statistical downscaling method is used to downscale the coarse GCM outputs and generate 50 years of synthetic precipitation and maximum and minimum temperatures to drive the GR4J hydrological model and the 1D hydraulic model for the baseline period 1971-2000 and the future period 2021-2050. Finally the POT (Peaks Over Threshold) method is applied to analyze the change of extreme events in the study area. The results show that design runoffs and water levels all indicate an increasing trend in the future period for Changshangang River, Jiangshangang River and Qu River at most cases, especially for small return periods(≤20), and for Qu River the increase becomes larger, which suggests that the risk of flooding will probably become greater and appropriate adaptation measures need to be taken.
Examining Dynamical Processes of Tropical Mountain Hydroclimate, Particularly During the Wet Season, Through Integration of Autonomous Sensor Observations and Climate Modeling

NASA Astrophysics Data System (ADS)

Hellstrom, R. A.; Fernandez, A.; Mark, B. G.; Covert, J. M.

2016-12-01

Peru is facing imminent water resource issues as glaciers retreat and demand increases, yet limited observations and model resolution hamper understanding of hydrometerological processes on local to regional scales. Much of current global and regional climate studies neglect the meteorological forcing of lapse rates (LRs) and valley and slope wind dynamics on critical components of the Peruvian Andes' water-cycle, and herein we emphasize the wet season. In 2004 and 2005 we installed an autonomous sensor network (ASN) within the glacierized Llanganuco Valley, Cordillera Blanca (9°S), consisting of discrete, cost-effective, automatic temperature loggers located along the valley axis and anchored by two automatic weather stations. Comparisons of these embedded hydrometeorological measurements from the ASN and climate modeling by dynamical downscaling using the Weather Research and Forecasting model (WRF) elucidate distinct diurnal and seasonal characteristics of the mountain wind regime and LRs. Wind, temperature, humidity, and cloud simulations suggest that thermally driven up-valley and slope winds converging with easterly flow aloft enhance late afternoon and evening cloud development which helps explain nocturnal wet season precipitation maxima measured by the ASN. Furthermore, the extreme diurnal variability of along-valley-axis LR, and valley wind detected from ground observations and confirmed by dynamical downscaling demonstrate the importance of realistic scale parameterizations of the atmospheric boundary layer to improve regional climate model projections in mountainous regions. We are currently considering to use intermediate climate models such as ICAR to reduce computing cost and we continue to maintain the ASN in the Cordillera Blanca.
Recent Regional Climate State and Change - Derived through Downscaling Homogeneous Large-scale Components of Re-analyses

NASA Astrophysics Data System (ADS)

Von Storch, H.; Klehmet, K.; Geyer, B.; Li, D.; Schubert-Frisius, M.; Tim, N.; Zorita, E.

2015-12-01

Global re-analyses suffer from inhomogeneities, as they process data from networks under development. However, the large-scale component of such re-analyses is mostly homogeneous; additional observational data add in most cases to a better description of regional details and less so on large-scale states. Therefore, the concept of downscaling may be applied to homogeneously complementing the large-scale state of the re-analyses with regional detail - wherever the condition of homogeneity of the large-scales is fulfilled. Technically this can be done by using a regional climate model, or a global climate model, which is constrained on the large scale by spectral nudging. This approach has been developed and tested for the region of Europe, and a skillful representation of regional risks - in particular marine risks - was identified. While the data density in Europe is considerably better than in most other regions of the world, even here insufficient spatial and temporal coverage is limiting risk assessments. Therefore, downscaled data-sets are frequently used by off-shore industries. We have run this system also in regions with reduced or absent data coverage, such as the Lena catchment in Siberia, in the Yellow Sea/Bo Hai region in East Asia, in Namibia and the adjacent Atlantic Ocean. Also a global (large scale constrained) simulation has been. It turns out that spatially detailed reconstruction of the state and change of climate in the three to six decades is doable for any region of the world.The different data sets are archived and may freely by used for scientific purposes. Of course, before application, a careful analysis of the quality for the intended application is needed, as sometimes unexpected changes in the quality of the description of large-scale driving states prevail.
Decomposition of Sources of Errors in Seasonal Streamflow Forecasting over the U.S. Sunbelt

NASA Technical Reports Server (NTRS)

Mazrooei, Amirhossein; Sinah, Tusshar; Sankarasubramanian, A.; Kumar, Sujay V.; Peters-Lidard, Christa D.

2015-01-01

Seasonal streamflow forecasts, contingent on climate information, can be utilized to ensure water supply for multiple uses including municipal demands, hydroelectric power generation, and for planning agricultural operations. However, uncertainties in the streamflow forecasts pose significant challenges in their utilization in real-time operations. In this study, we systematically decompose various sources of errors in developing seasonal streamflow forecasts from two Land Surface Models (LSMs) (Noah3.2 and CLM2), which are forced with downscaled and disaggregated climate forecasts. In particular, the study quantifies the relative contributions of the sources of errors from LSMs, climate forecasts, and downscaling/disaggregation techniques in developing seasonal streamflow forecast. For this purpose, three month ahead seasonal precipitation forecasts from the ECHAM4.5 general circulation model (GCM) were statistically downscaled from 2.8deg to 1/8deg spatial resolution using principal component regression (PCR) and then temporally disaggregated from monthly to daily time step using kernel-nearest neighbor (K-NN) approach. For other climatic forcings, excluding precipitation, we considered the North American Land Data Assimilation System version 2 (NLDAS-2) hourly climatology over the years 1979 to 2010. Then the selected LSMs were forced with precipitation forecasts and NLDAS-2 hourly climatology to develop retrospective seasonal streamflow forecasts over a period of 20 years (1991-2010). Finally, the performance of LSMs in forecasting streamflow under different schemes was analyzed to quantify the relative contribution of various sources of errors in developing seasonal streamflow forecast. Our results indicate that the most dominant source of errors during winter and fall seasons is the errors due to ECHAM4.5 precipitation forecasts, while temporal disaggregation scheme contributes to maximum errors during summer season.
Use of NARCCAP Model Projections to Develop a Future Typical Meteorological Year and Estimate the Impact of a Changing Climate on Building Energy Consumption

NASA Astrophysics Data System (ADS)

Patton, S. L.; Takle, E. S.; Passe, U.; Kalvelage, K.

2013-12-01

Current simulations of building energy consumption use weather input files based on the past thirty years of climate observations. These 20th century climate conditions may be inadequate when designing buildings meant to function well into the 21st century. An alternative is using model projections of climate change to estimate future risk to the built environment. In this study, model-projected changes in climate were combined with existing typical meteorological year data to create future typical meteorological year data. These data were then formatted for use in EnergyPlus simulation software to evaluate their potential impact on commercial building energy consumption. The modeled climate data were taken from the North American Regional Climate Change Assessment Program (NARCCAP). NARCCAP uses results of global climate models to drive regional climate models, also known as dynamical downscaling. This downscaling gives higher resolution results over specific locations, and the multiple global/regional climate model combinations provide a unique opportunity to quantify the uncertainty of climate change projections and their impacts. Our results show a projected decrease in heating energy consumption and a projected increase in cooling energy consumption for nine locations across the United States for all model combinations. Warmer locations may expect a decrease in heating load of around 30% to 45% and an increase in cooling load of around 25% to 35%. Colder locations may expect a decrease in heating load of around 15% to 25% and an increase in cooling load of around 40% to 70%. The change in net energy consumption is determined by the balance between the magnitudes of heating change and cooling change. Net energy consumption is projected to increase by an average of 5% for lower-latitude locations and decrease by an average of 5% for higher-latitude locations. With these projected annual and seasonal changes presenting strong evidence for the unsuitable nature of current building practices holding up under future climate change, we recommend using our methods and results to make modifications and adaptations to existing buildings and to aid in the design of future buildings.

A multi-scale methodology for comparing GCM and RCM results over the Eastern Mediterranean

NASA Astrophysics Data System (ADS)

Samuels, Rana; Krichak, Simon; Breitgand, Joseph; Alpert, Pinhas

2010-05-01

The importance of skillful climate modeling is increasingly being realized as results are being incorporated into environmental, economic, and even business planning. Global circulation models (GCMs) employed by the IPCC provide results at spatial scales of hundreds of kilometers, which is useful for understanding global trends but not appropriate for use as input into regional and local impacts models used to inform policy and development. To address this shortcoming, regional climate models (RCMs) which dynamically downscale the results of the GCMs are used. In this study we present first results of a dynamically downscaled RCM focusing on the Eastern Mediterranean region. For the historical 1960-2000 time period, results at a spatial scale of both 25 km and 50 km are compared with historical station data from 5 locations across Israel as well as with the results of 3 GCM models (ECHAM5, NOAA GFDL, and CCCMA) at annual, monthly and daily time scales. Results from a recently completed Japanese GCM at a spatial scale of 20 km are also included. For the historical validation period, we show that as spatial scale increases the skill in capturing annual and inter-annual temperature and rainfall also increases. However, for intra-seasonal rainfall characteristics important for hydrological and agricultural planning (eg. dry and wet spells, number of rain days) the GCM results (including the 20 km Japanese model) capture the historical trends better than the dynamically downscaled RegCM. For future scenarios of temperature and precipitation changes, we compare results across the models for the available time periods, generating a range of future trends.
Hazardous Convective Weather in the Central United States: Present and Future

NASA Astrophysics Data System (ADS)

Liu, C.; Ikeda, K.; Rasmussen, R.

2017-12-01

Two sets of 13-year continental-scale convection-permitting simulations were performed using the 4-km-resolution WRF model. They consist of a retrospective simulation, which downscales the ERA-Interim reanalysis during the period October 2000 - September 2013, and a future climate sensitivity simulation for the same period based on the perturbed reanalysis-derived boundary conditions with the CMIP5 ensemble-mean high-end emission scenario climate change. The evaluation of the retrospective simulation indicates that the model is able to realistically reproduce the main characteristics of deep precipitating convection observed in the current climate such as the spectra of convective population and propagating mesoscale convective systems (MCSs). It is also shown that severe convection and associated MCS will increase in frequency and intensity, implying a potential increase in high impact convective weather in a future warmer climate. In this study, the warm-season hazardous convective weather (i.e., tonadoes, hails and damaging gusty wind) in the central United states is examined using these 4-km downscaling simulations. First, a model-based proxy for hazardous convective weather is derived on the basis of a set of characteristic meteorological variables such as the model composite radar reflectivity, updraft helicity, vertical wind shear, and low-level wind. Second, the developed proxy is applied to the retrospective simulation for estimate of the model hazardous weather events during the historical period. Third, the simulated hazardous weather statistics are evaluated against the NOAA severe weather reports. Lastly, the proxy is applied to the future climate simulation for the projected change of hazardous convective weather in response to global warming. Preliminary results will be reported at the 2017 AGU session "High Resolution Climate Modeling".
What is the benefit of driving a hydrological model with data from a multi-site weather generator compared to data from a simple delta change approach?"

NASA Astrophysics Data System (ADS)

Rössler, Ole; Keller, Denise; Fischer, Andreas

2016-04-01

In 2011 the Swiss national consortium C2SM providednew climate change scenarios were released in Switzerland that came with a comprehensive data set of temperature and precipitation changes under climate change conditions for every a large network of meteorological stations, and for aggregated as well as regions in across Switzerland. These climate change signals were generated for three emission scenarios and three different future time-periods and designed to be used asbased on a delta change factors approach. This data set proved to be very successful in Switzerland as many different users, researchers, private companies, and societal users were able to use and interpret the climate data set. Thus, a range of applications that are all based on the same climate data set enabled a comparable view on climate change impact in several disciplines. The main limitation and criticism to this data set was the usage of the delta change approach for downscaling as it comes with severe limitations such as underestimatinges changes in extreme values and neglecting changes in variability and changes in temporal sequencesneglecting changes in variability, be it year-to-year or day-to-day, and changes in temporal sequences . lacks a change in the day-to-day-variability. One way to overcome this the latter limitation is the usage of stochastic weather generators in a downscaling context. Weather generators are known to be one suitable downscaling technique, but A common limitation of most weather generators is the absence of spatial consistency rrelation in the generated daily time-series, resulting in an underestimation of areal means over several stations that are often low-biased. refer to one point scale (single-site) and lacks the spatial representation of weather. The latter A realistic representation of the inter-station correlation in the downscaled time-series This is of high particular importance in some impact studies, especially infor any hydrological impact studiesy. Recently, a multi-site weather generator was developed and tested for downscaling purposes over Switzerland. The weather generator is of type Richardson, that is run with spatially correlated random number streams to ensure spatial consistency. As a downside, multi-site weather generators are much more complex to develop, but they are a very promising alternative downscaling technique. A new multi-site-weather generator was developed for Switzerland in a previous study (Keller et al. 2014). In this study, we tested this new multi-site-weather generator against the "standard" delta change derived data in a hydrological impact assessment study that focused on runoff in the meso-scale catchment of the river Thur catchment. Two hydrological models of different complexity were run with the data sets under present (1980-2009) and under future conditions (2070-2099), assuming the SRES A1B emission2070-2100 scenario conditions. Eight meteorological stations were used to interpolate a meteorological field that served as input to calibrate and validate the two hydrological models against runoff. The downscaling intercomparison was done for We applied 10 GCM-RCM combinations simulations of the ENSEMBLES. In case of the weather generator, that allows for multiple synthetic realizations, we generated for which change factors for each station (delta change approach) were available and generated 25 realizations of multi-site weather. with each climate model projection. Results show that the delta change driven data constitutes only one appropriate representation compared to theof a bandwidth of runoff projections yielded by the multi-site weather generator data. Especially oOn average, differences between both the two approaches are small. Low and high runoff Runoff values to both extremes are however better reproduced with the weather generator driven data set. The stochastic representation of multiday rainfall events are considered as the main reason. Hence, tThere is a clear yet small added value to the delta change approach that in turn performs rather well. Although these small but considerable differences might questioning the need to construct a multi-site-weather generator with a huge effort, the potential and possibilities to further develop the multi-site weather generator is undoubted.
Towards process-informed bias correction of climate change simulations

NASA Astrophysics Data System (ADS)

Maraun, Douglas; Shepherd, Theodore G.; Widmann, Martin; Zappa, Giuseppe; Walton, Daniel; Gutiérrez, José M.; Hagemann, Stefan; Richter, Ingo; Soares, Pedro M. M.; Hall, Alex; Mearns, Linda O.

2017-11-01

Biases in climate model simulations introduce biases in subsequent impact simulations. Therefore, bias correction methods are operationally used to post-process regional climate projections. However, many problems have been identified, and some researchers question the very basis of the approach. Here we demonstrate that a typical cross-validation is unable to identify improper use of bias correction. Several examples show the limited ability of bias correction to correct and to downscale variability, and demonstrate that bias correction can cause implausible climate change signals. Bias correction cannot overcome major model errors, and naive application might result in ill-informed adaptation decisions. We conclude with a list of recommendations and suggestions for future research to reduce, post-process, and cope with climate model biases.
Regional Climate Simulation with a Variable Resolution Stretched Grid GCM: The Regional Down-Scaling Effects

NASA Technical Reports Server (NTRS)

Fox-Rabinovitz, Michael S.; Takacs, Lawrence L.; Suarez, Max; Sawyer, William; Govindaraju, Ravi C.

1999-01-01

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.
Marine downscaling of a future climate scenario in the North Sea and possible effects on dinoflagellate harmful algal blooms.

PubMed

Friocourt, Y F; Skogen, M; Stolte, W; Albretsen, J

2012-01-01

Two hydrodynamic and ecological models were used to investigate the effects of climate change-according to the IPCC A1b emission scenario - on the primary productivity of the North Sea and on harmful algal blooms. Both models were forced with atmospheric fields from a regional downscaling of General Circulation Models to compare two sets of 20-year simulations representative of present climate (1984-2004) conditions and of the 2040s. Both models indicated a general warming of the North Sea by up to 0.8°C and a slight freshening by the 2040s. The models suggested that the eastern North Sea would be subjected to more temperature and salinity changes than the western part. In addition, the ecological modules of the models indicated that the warming up of the sea would result in a slightly earlier spring bloom. The one model that also computes the distribution of four different phytoplankton groups suggests an increase in the abundance of dinoflagellates, whereas the abundance of diatoms, flagellates and Phaeocystis sp. remains comparable to current levels, or decrease. Assuming that Dinophysis spp. would experience a similar increase in abundance as the modelled group of dinoflagellates, it is hypothesised that blooms of Dinophysis spp. may occur more frequently in the North Sea by 2040. However, implications for shellfish toxicity remain unclear.
Robust changes in the socio-climate risk over CONUS by mid 21st century

NASA Astrophysics Data System (ADS)

Ashfaq, M.; Rastogi, D.; Batibeniz, F.; Alifa, M.; Pagán, B. R.; Bonds, B. W.; Pal, J. S.; Diffenbaugh, N. S.; Preston, B. L.

2017-12-01

Using high-resolution near-term ensemble projections of hydro-climatic changes, we investigate impacts of climate change on natural and human systems across the CONUS. Climate projections are based a hybrid downscaling approach where a combination of regional and hydrological models are used to downscales 11 Global Climate Models from the 5th phase of Coupled Model Inter-comparison Project to 4km horizontal grid spacing for 41 years in the historical period (1965-2005) and 41 years in the near-term future period (2010-2050) under Representative Concentration Pathway 8.5. Should emissions continue to rise, climatic changes will likely intensify the regional hydrological cycle over CONUS through the acceleration of the historical trends in cold, warm and wet extremes. Our results show robust changes in the occurrence of severe weather conditions and in the likelihood of ice, freezing rain and snowstorms that may have disruptive impact on large human population across the U.S. More summer like conditions will also drive increase in cooling demands and a net increase in the energy consumption over many regions. We further use an integrated vulnerability index that combines human exposure to different climate extremes (hot, cold, wet and dry) and changes in socioeconomic pathways (due to changes in population and income levels), to reveal that future exposure to potentially damaging climatic conditions will likely increase manifold for population living in major urban centers in California, Texas, Florida, Michigan, Illinois and Northeast. With the current trajectory of emissions, these results warrant that a large human population across the U.S. may feel the impacts of climate change within its lifespan.
The use of EuroCordex in marine climate projections

NASA Astrophysics Data System (ADS)

Tinker, Jonathan; Palmer, Matthew; Lowe, Jason; Howard, Tom

2017-04-01

The Northwest European Shelf seas (NWS, including the North Sea, Irish Sea and Celtic Sea) are of economic, environmental and cultural importance to a number of European countries. However, their representation by global climate models (GCMs) is very crude, due to their inability to represent the complex geometry and the absence of tides. Therefore, there is a need to employ dynamical downscaling methods when considering the potential impacts of climate change on the European (and other) shelf seas. Using a shelf seas model to dynamically downscale of the ocean component of the GCM is a well established method. While taking open ocean lateral boundary conditions from the GCM ocean is acceptable, using surface flux forcings from the GCM atmosphere is often problematic. The CORDEX project provides an important dataset of high spatial and temporal resolution atmospheric forcings, derived from 'parent' CMIP5 GCM simulations. We drive the NEMO shelf seas model with data from CMIP5 models and EURO-CORDEX Regional Climate Model (RCM) data to produce a set of NWS climate projections. We require relatively high temporal resolution output, and run-off (for the river forcings), and so are limited to a subset of the available EURO-CORDEX RCMs. From these we select two CMIP5 GCMs with the same RCM with two emissions scenarios to give a minimum estimate of GCM model structural and emission scenario uncertainty. Other experiments allow an initial estimate of the uncertainty associated with the model structure of both the shelf seas and the RCM. Our analysis is focused on the uncertainty associated with the mean change in a number of physical marine impacts and the drivers of coastal variability and change, including sea level and the propagation of open ocean signals onto the shelf. Our work is part of the UK Climate Projections (UKCP18) and will inform the following UK Climate Change Risk Assessments, required as part of the Climate Change Act.
The End-to-end Demonstrator for improved decision making in the water sector in Europe (EDgE)

NASA Astrophysics Data System (ADS)

Wood, Eric; Wanders, Niko; Pan, Ming; Sheffield, Justin; Samaniego, Luis; Thober, Stephan; Kumar, Rohinni; Prudhomme, Christel; Houghton-Carr, Helen

2017-04-01

High-resolution simulations of water resources from hydrological models are vital to supporting important climate services. Apart from a high level of detail, both spatially and temporally, it is important to provide simulations that consistently cover a range of timescales, from historical reanalysis to seasonal forecast and future projections. In the new EDgE project commissioned by the ECMWF (C3S) we try to fulfill these requirements. EDgE is a proof-of-concept project which combines climate data and state-of-the-art hydrological modelling to demonstrate a water-oriented information system implemented through a web application. EDgE is working with key European stakeholders representative of private and public sectors to jointly develop and tailor approaches and techniques. With these tools, stakeholders are assisted in using improved climate information in decision-making, and supported in the development of climate change adaptation and mitigation policies. Here, we present the first results of the EDgE modelling chain, which is divided into three main processes: 1) pre-processing and downscaling; 2) hydrological modelling; 3) post-processing. Consistent downscaling and bias corrections for historical simulations, seasonal forecasts and climate projections ensure that the results across scales are robust. The daily temporal resolution and 5km spatial resolution ensure locally relevant simulations. With the use of four hydrological models (PCR-GLOBWB, VIC, mHM, Noah-MP), uncertainty between models is properly addressed, while consistency is guaranteed by using identical input data for static land surface parameterizations. The forecast results are communicated to stakeholders via Sectoral Climate Impact Indicators (SCIIs) that have been created in collaboration with the end-user community of the EDgE project. The final product of this project is composed of 15 years of seasonal forecast and 10 climate change projections, all combined with four hydrological models. These unique high-resolution climate information simulations in the EDgE project provide an unprecedented information system for decision-making over Europe.
Mass Balance Modelling of Saskatchewan Glacier, Canada Using Empirically Downscaled Reanalysis Data

NASA Astrophysics Data System (ADS)

Larouche, O.; Kinnard, C.; Demuth, M. N.

2017-12-01

Observations show that glaciers around the world are retreating. As sites with long-term mass balance observations are scarce, models are needed to reconstruct glacier mass balance and assess its sensitivity to climate. In regions with discontinuous and/or sparse meteorological data, high-resolution climate reanalysis data provide a convenient alternative to in situ weather observations, but can also suffer from strong bias due to the spatial and temporal scale mismatch. In this study we used data from the North American Regional Reanalysis (NARR) project with a 30 x 30 km spatial resolution and 3-hour temporal resolution to produce the meteorological forcings needed to drive a physically-based, distributed glacier mass balance model (DEBAM, Hock and Holmgren 2005) for the historical period 1979-2016. A two-year record from an automatic weather station (AWS) operated on Saskatchewan Glacier (2014-2016) was used to downscale air temperature, relative humidity, wind speed and incoming solar radiation from the nearest NARR gridpoint to the glacier AWS site. An homogenized historical precipitation record was produced using data from two nearby, low-elevation weather stations and used to downscale the NARR precipitation data. Three bias correction methods were applied (scaling, delta and empirical quantile mapping - EQM) and evaluated using split sample cross-validation. The EQM method gave better results for precipitation and for air temperature. Only a slight improvement in the relative humidity was obtained using the scaling method, while none of the methods improved the wind speed. The later correlates poorly with AWS observations, probably because the local glacier wind is decoupled from the larger scale NARR wind field. The downscaled data was used to drive the DEBAM model in order to reconstruct the mass balance of Saskatchewan Glacier over the past 30 years. The model was validated using recent snow thickness measurements and previously published geodetic mass balance estimates.
Changes in Seasonal and Extreme Hydrologic Conditions of the Georgia Basin/Puget Sound in an Ensemble Regional Climate Simulation for the Mid-Century

DOE Office of Scientific and Technical Information (OSTI.GOV)

Leung, Lai R.; Qian, Yun

This study examines an ensemble of climate change projections simulated by a global climate model (GCM) and downscaled with a region climate model (RCM) to 40 km spatial resolution for the western North America. One control and three ensemble future climate simulations were produced by the GCM following a business as usual scenario for greenhouse gases and aerosols emissions from 1995 to 2100. The RCM was used to downscale the GCM control simulation (1995-2015) and each ensemble future GCM climate (2040-2060) simulation. Analyses of the regional climate simulations for the Georgia Basin/Puget Sound showed a warming of 1.5-2oC and statisticallymore » insignificant changes in precipitation by the mid-century. Climate change has large impacts on snowpack (about 50% reduction) but relatively smaller impacts on the total runoff for the basin as a whole. However, climate change can strongly affect small watersheds such as those located in the transient snow zone, causing a higher likelihood of winter flooding as a higher percentage of precipitation falls in the form of rain rather than snow, and reduced streamflow in early summer. In addition, there are large changes in the monthly total runoff above the upper 1% threshold (or flood volume) from October through May, and the December flood volume of the future climate is 60% above the maximum monthly flood volume of the control climate. Uncertainty of the climate change projections, as characterized by the spread among the ensemble future climate simulations, is relatively small for the basin mean snowpack and runoff, but increases in smaller watersheds, especially in the transient snow zone, and associated with extreme events. This emphasizes the importance of characterizing uncertainty through ensemble simulations.« less
Olive cultivars adaptability in Southern Italy in present and future climate

NASA Astrophysics Data System (ADS)

Riccardi, M.; Alfieri, S.; Bonfante, A.; Basile, A.; Di Tommasi, P.; Menenti, M.; De Lorenzi, F.

2012-04-01

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).
The Use of Climate Projections in the Modelling of Bud Burst

NASA Astrophysics Data System (ADS)

O'Neill, Bridget F.; Caffara, Amelia; Gleeson, Emily; Semmler, Tido; McGrath, Ray; Donnelly, Alison

2010-05-01

Recent changes in global climate, such as increasing temperature, have had notable effects on the phenology (timing of biological events) of plants. The effects are variable across habitats and between species, but increasing temperatures have been shown to advance certain key phenophases of trees, such as bud burst (beginning of leaf unfolding). This project considered climate change impacts on phenology of plants at a local scale in Ireland. The output from the ENSEMBLES climate simulations were down-scaled to Ireland and utilised by a phenological model to project changes over the next 50-100 years. This project helps to showcase the potential use of climate simulations in phenological research.
Seltzer_et_al_2016

EPA Pesticide Factsheets

This dataset supports the modeling study of Seltzer et al. (2016) published in Atmospheric Environment. In this study, techniques typically used for future air quality projections are applied to a historical 11-year period to assess the performance of the modeling system when the driving meteorological conditions are obtained using dynamical downscaling of coarse-scale fields without correcting toward higher resolution observations. The Weather Research and Forecasting model and the Community Multiscale Air Quality model are used to simulate regional climate and air quality over the contiguous United States for 2000-2010. The air quality simulations for that historical period are then compared to observations from four national networks. Comparisons are drawn between defined performance metrics and other published modeling results for predicted ozone, fine particulate matter, and speciated fine particulate matter. The results indicate that the historical air quality simulations driven by dynamically downscaled meteorology are typically within defined modeling performance benchmarks and are consistent with results from other published modeling studies using finer-resolution meteorology. This indicates that the regional climate and air quality modeling framework utilized here does not introduce substantial bias, which provides confidence in the method??s use for future air quality projections.This dataset is associated with the following publication:Seltzer, K., C
A downscaled 1 km dataset of daily Greenland ice sheet surface mass balance components (1958-2014)

NASA Astrophysics Data System (ADS)

Noel, B.; Van De Berg, W. J.; Fettweis, X.; Machguth, H.; Howat, I. M.; van den Broeke, M. R.

2015-12-01

The current spatial resolution in regional climate models (RCMs), typically around 5 to 20 km, remains too coarse to accurately reproduce the spatial variability in surface mass balance (SMB) components over the narrow ablation zones, marginal outlet glaciers and neighbouring ice caps of the Greenland ice sheet (GrIS). In these topographically rough terrains, the SMB components are highly dependent on local variations in topography. However, the relatively low-resolution elevation and ice mask prescribed in RCMs contribute to significantly underestimate melt and runoff in these regions due to unresolved valley glaciers and fjords. Therefore, near-km resolution topography is essential to better capture SMB variability in these spatially restricted regions. We present a 1 km resolution dataset of daily GrIS SMB covering the period 1958-2014, which is statistically downscaled from data of the polar regional climate model RACMO2.3 at 11 km, using an elevation dependence. The dataset includes all individual SMB components projected on the elevation and ice mask from the GIMP DEM, down-sampled to 1 km. Daily runoff and sublimation are interpolated to the 1 km topography using a local regression to elevation valid for each day specifically; daily precipitation is bi-linearly downscaled without elevation corrections. The daily SMB dataset is then reconstructed by summing downscaled precipitation, sublimation and runoff. High-resolution elevation and ice mask allow for properly resolving the narrow ablation zones and valley glaciers at the GrIS margins, leading to significant increase in runoff estimate. In these regions, and especially over narrow glaciers tongues, the downscaled products improve on the original RACMO2.3 outputs by better representing local SMB patterns through a gradual ablation increase towards the GrIS margins. We discuss the impact of downscaling on the SMB components in a case study for a spatially restricted region, where large elevation discrepancies are observed between both resolutions. Owing to generally enhanced runoff in the GrIS ablation zone, the evaluation of daily downscaled SMB against ablation measurements, collected at in-situ measuring sites derived from a newly compiled ablation dataset, shows a better agreement with observations relative to native RACMO2.3 SMB at 11 km.
Prediction of hydrological responds to climate changes in the Upper Yangtze River Basin, China

NASA Astrophysics Data System (ADS)

Yang, X.; Ren, L.; Wang, Y.; Zhang, M.; Liu, Y.; Jiang, S.; Yuan, F.

2017-12-01

Climate changes have direct effects on hydrological cycle, with the increasing temperature and seasonal shift of precipitation. Therefore, understanding of how climate change may affect the population and water resources and economic development is critical to the water and food security for China. This study aims to evaluate the potential impacts of future climate changes on water resources of the upper basin of Yangtze River (the area controlled by the Yichang hydrological station) using the variable infiltration capacity (VIC) model driven by composite observations (1961-2005) and projections of eight CMIP5 models under scenarios RCP4.5 and RCP8.5 from 2006 to 2099. The raw eight CMIP5 models have been downscaled by the equidistant cumulative distribution functions (EDCDF) statistical downscaling approach from 1961 to 2099. The assessment of the performance of model simulated precipitation and temperature were calculated by comparing to the observations during the historical period (1961-2005). For the same variables, eight CMIP5 models for RCP 4.5 and RCP 8.5 downscaled by EDCDF method were generated during the future period (2006-2099). Overall, the VIC model performed well in monthly streamflow simulation, with the Nash-Sutcliffe coefficient of efficiency (NSCE) 0.92 and 0.97 for calibration and validation, respectively. The annual precipitation is projected to increase by 6.3mm and 8.6mm per decade and the annual temperature will increase by 0.22 °C and 0.53°C per decade (2006-2099) for RCP4.5 and RCP8.5, respectively. In the future period, The total runoff of the study basins would either remain stable or moderately increase by 2.7% and 22.4% per decade, the evapotranspiration increase by 2mm and 13mm per decade, and the soil moisture will reduce by -0.1% and -7.4% per decade under RCP4.5 and RCP8.5, respectively. The changes of model-simulated soil moisture, runoff, and evapotranspiration suggest that there probably be an increasing risk of drought in the twenty-first century in UYRB and the policy maker and managers need to pay more attention to the adaption actions of implement robust water management in UYRB. Keywords: EDCDF; Bias correction; Climate changes; Water and food security; Upper Yangtze River Basin
A parametric approach for simultaneous bias correction and high-resolution downscaling of climate model rainfall

NASA Astrophysics Data System (ADS)

Mamalakis, Antonios; Langousis, Andreas; Deidda, Roberto; Marrocu, Marino

2017-03-01

Distribution mapping has been identified as the most efficient approach to bias-correct climate model rainfall, while reproducing its statistics at spatial and temporal resolutions suitable to run hydrologic models. Yet its implementation based on empirical distributions derived from control samples (referred to as nonparametric distribution mapping) makes the method's performance sensitive to sample length variations, the presence of outliers, the spatial resolution of climate model results, and may lead to biases, especially in extreme rainfall estimation. To address these shortcomings, we propose a methodology for simultaneous bias correction and high-resolution downscaling of climate model rainfall products that uses: (a) a two-component theoretical distribution model (i.e., a generalized Pareto (GP) model for rainfall intensities above a specified threshold u*, and an exponential model for lower rainrates), and (b) proper interpolation of the corresponding distribution parameters on a user-defined high-resolution grid, using kriging for uncertain data. We assess the performance of the suggested parametric approach relative to the nonparametric one, using daily raingauge measurements from a dense network in the island of Sardinia (Italy), and rainfall data from four GCM/RCM model chains of the ENSEMBLES project. The obtained results shed light on the competitive advantages of the parametric approach, which is proved more accurate and considerably less sensitive to the characteristics of the calibration period, independent of the GCM/RCM combination used. This is especially the case for extreme rainfall estimation, where the GP assumption allows for more accurate and robust estimates, also beyond the range of the available data.
Application of a stochastic weather generator to assess climate change impacts in a semi-arid climate: The Upper Indus Basin

NASA Astrophysics Data System (ADS)

Forsythe, N.; Fowler, H. J.; Blenkinsop, S.; Burton, A.; Kilsby, C. G.; Archer, D. R.; Harpham, C.; Hashmi, M. Z.

2014-09-01

Assessing local climate change impacts requires downscaling from Global Climate Model simulations. Here, a stochastic rainfall model (RainSim) combined with a rainfall conditioned weather generator (CRU WG) have been successfully applied in a semi-arid mountain climate, for part of the Upper Indus Basin (UIB), for point stations at a daily time-step to explore climate change impacts. Validation of the simulated time-series against observations (1961-1990) demonstrated the models' skill in reproducing climatological means of core variables with monthly RMSE of <2.0 mm for precipitation and ⩽0.4 °C for mean temperature and daily temperature range. This level of performance is impressive given complexity of climate processes operating in this mountainous context at the boundary between monsoonal and mid-latitude (westerly) weather systems. Of equal importance the model captures well the observed interannual variability as quantified by the first and last decile of 30-year climatic periods. Differences between a control (1961-1990) and future (2071-2100) regional climate model (RCM) time-slice experiment were then used to provide change factors which could be applied within the rainfall and weather models to produce perturbed ‘future' weather time-series. These project year-round increases in precipitation (maximum seasonal mean change:+27%, annual mean change: +18%) with increased intensity in the wettest months (February, March, April) and year-round increases in mean temperature (annual mean +4.8 °C). Climatic constraints on the productivity of natural resource-dependent systems were also assessed using relevant indices from the European Climate Assessment (ECA) and indicate potential future risk to water resources and local agriculture. However, the uniformity of projected temperature increases is in stark contrast to recent seasonally asymmetrical trends in observations, so an alternative scenario of extrapolated trends was also explored. We conclude that interannual variability in climate will continue to have the dominant impact on water resources management whichever trajectory is followed. This demonstrates the need for sophisticated downscaling methods which can evaluate changes in variability and sequencing of events to explore climate change impacts in this region.
Effects of Climate Change on Flood Frequency in the Pacific Northwest

NASA Astrophysics Data System (ADS)

Gergel, D. R.; Stumbaugh, M. R.; Lee, S. Y.; Nijssen, B.; Lettenmaier, D. P.

2014-12-01

A key concern about climate change as related to water resources is the potential for changes in hydrologic extremes, including flooding. We explore changes in flood frequency in the Pacific Northwest using downscaled output from ten Global Climate Models (GCMs) from the Coupled Model Inter-Comparison Project 5 (CMIP5) for historical forcings (1950-2005) and future Representative Concentration Pathways (RCPs) 4.5 and 8.5 (2006-2100). We use archived output from the Integrated Scenarios Project (ISP) (http://maca.northwestknowledge.net/), which uses the Multivariate Adaptive Constructed Analogs (MACA) method for statistical downscaling. The MACA-downscaled GCM output was then used to force the Variable Infiltration Capacity (VIC) hydrology model with a 1/16th degree spatial resolution and a daily time step. For each of the 238 HUC-08 areas within the Pacific Northwest (USGS Hydrologic Region 15), we computed, from the ISP archive, the series of maximum daily runoff values (surrogate for the annual maximum flood), and then the mean annual flood. Finally, we computed the ratios of the RCP4.5 and RCP8.5 mean annual floods to their corresponding values for the historical period. We evaluate spatial patterns in the results. For snow-dominated watersheds, the changes are dominated by reductions in flood frequency in basins that currently have spring-dominant floods, and increases in snow affected basins with fall-dominant floods. In low elevation basins west of the Cascades, changes in flooding are more directly related to changes in precipitation extremes. We further explore the nature of these effects by evaluating the mean Julian day of the annual maximum flood for each HUC-08 and how this changes between the historical and RCP4.5 and RCP8.5 scenarios.
Risk of spring frost to apple production under future climate scenarios: the role of phenological acclimation.

PubMed

Eccel, Emanuele; Rea, Roberto; Caffarra, Amelia; Crisci, Alfonso

2009-05-01

In the context of global warming, the general trend towards earlier flowering dates of many temperate tree species is likely to result in an increased risk of damage from exposure to frost. To test this hypothesis, a phenological model of apple flowering was applied to a temperature series from two locations in an important area for apple production in Europe (Trentino, Italy). Two simulated 50-year climatic projections (A2 and B2 of the Intergovernmental Panel on Climate Change--Special Report on Emission Scenarios) from the HadCM3 general circulation model were statistically downscaled to the two sites. Hourly temperature records over a 40-year period were used as the reference for past climate. In the phenological model, the heat requirement (degree hours) for flowering was parameterized using two approaches; static (constant over time) and dynamic (climate dependent). Parameterisation took into account the trees' adaptation to changing temperatures based on either past instrumental records or the downscaled outputs from the climatic simulations. Flowering dates for the past 40 years and simulated flowering dates for the next 50 years were used in the model. A significant trend towards earlier flowering was clearly detected in the past. This negative trend was also apparent in the simulated data. However, the significance was less apparent when the "dynamic" setting for the degree hours requirement was used in the model. The number of frost episodes and flowering dates, on an annual basis, were graphed to assess the risk of spring frost. Risk analysis confirmed a lower risk of exposure to frost at present than in the past, and probably either constant or a slightly lower risk in future, especially given that physiological processes are expected to acclimate to higher temperatures.

Statistical Downscaling in Multi-dimensional Wave Climate Forecast

NASA Astrophysics Data System (ADS)

Camus, P.; Méndez, F. J.; Medina, R.; Losada, I. J.; Cofiño, A. S.; Gutiérrez, J. M.

2009-04-01

Wave climate at a particular site is defined by the statistical distribution of sea state parameters, such as significant wave height, mean wave period, mean wave direction, wind velocity, wind direction and storm surge. Nowadays, long-term time series of these parameters are available from reanalysis databases obtained by numerical models. The Self-Organizing Map (SOM) technique is applied to characterize multi-dimensional wave climate, obtaining the relevant "wave types" spanning the historical variability. This technique summarizes multi-dimension of wave climate in terms of a set of clusters projected in low-dimensional lattice with a spatial organization, providing Probability Density Functions (PDFs) on the lattice. On the other hand, wind and storm surge depend on instantaneous local large-scale sea level pressure (SLP) fields while waves depend on the recent history of these fields (say, 1 to 5 days). Thus, these variables are associated with large-scale atmospheric circulation patterns. In this work, a nearest-neighbors analog method is used to predict monthly multi-dimensional wave climate. This method establishes relationships between the large-scale atmospheric circulation patterns from numerical models (SLP fields as predictors) with local wave databases of observations (monthly wave climate SOM PDFs as predictand) to set up statistical models. A wave reanalysis database, developed by Puertos del Estado (Ministerio de Fomento), is considered as historical time series of local variables. The simultaneous SLP fields calculated by NCEP atmospheric reanalysis are used as predictors. Several applications with different size of sea level pressure grid and with different temporal domain resolution are compared to obtain the optimal statistical model that better represents the monthly wave climate at a particular site. In this work we examine the potential skill of this downscaling approach considering perfect-model conditions, but we will also analyze the suitability of this methodology to be used for seasonal forecast and for long-term climate change scenario projection of wave climate.
WRF added value to capture the spatio-temporal drought variability

NASA Astrophysics Data System (ADS)

García-Valdecasas Ojeda, Matilde; Quishpe-Vásquez, César; Raquel Gámiz-Fortis, Sonia; Castro-Díez, Yolanda; Jesús Esteban-Parra, María

2017-04-01

Regional Climate Models (RCM) has been widely used as a tool to perform high resolution climate fields in areas with high climate variability such as Spain. However, the outputs provided by downscaling techniques have many sources of uncertainty associated at different aspects. In this study, the ability of the Weather Research and Forecasting (WRF) model to capture drought conditions has been analyzed. The WRF simulation was carried out for a period that spanned from 1980 to 2010 over a domain centered in the Iberian Peninsula with a spatial resolution of 0.088°, and nested in the coarser EURO-CORDEX domain (0.44° spatial resolution). To investigate the spatiotemporal drought variability, the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI) has been computed at two different timescales: 3- and 12-months due to its suitability to study agricultural and hydrological droughts. The drought indices computed from WRF outputs were compared with those obtained from the observational (MOTEDAS and MOPREDAS) datasets. In order to assess the added value provided by downscaled fields, these indices were also computed from the ERA-Interim Re-Analysis database, which provides the lateral and boundary conditions of the WRF simulations. Results from this study indicate that WRF provides a noticeable benefit with respect to ERA-Interim for many regions in Spain in terms of drought indices, greater for SPI than for SPEI. The improvement offered by WRF depends on the region, index and timescale analyzed, being greater at longer timescales. These findings prove the reliability of the downscaled fields to detect drought events and, therefore, it is a remarkable source of knowledge for a suitable decision making related to water-resource management. Keywords: Drought, added value, Regional Climate Models, WRF, SPEI, SPI. Acknowledgements: This work has been financed by the projects P11-RNM-7941 (Junta de Andalucía-Spain) and CGL2013-48539-R (MINECO-Spain, FEDER).
Effect of climate change on marine ecosystems

NASA Astrophysics Data System (ADS)

Vikebo, F. B.; Sundby, S.; Aadlandsvik, B.; Fiksen, O.

2003-04-01

As a part of the INTEGRATION project, headed by Potsdam Institute for Climate Impact Research, funded by the German Research Council, the impact of climate change scenarios on marine fish populations will be addressed on a spesific population basis and will focus on fish populations in the northern North Atlantic with special emphasis on cod. The approach taken will mainly be a modelling study supported by analysis of existing data on fish stocks and climate. Through down-scaling and nesting techniques, various climate change scenarios with reduced THC in the North Atlantic will be investigated with higher spatial resolution for selected shelf areas. The hydrodynamical model used for the regional ocean modeling is ROMS (http://marine.rutgers.edu/po/models/roms/). An individual based model will be implemented into the larval drift module to simulate growth of the larvae along the drift paths.
A framework for global river flood risk assessment

NASA Astrophysics Data System (ADS)

Winsemius, H. C.; Van Beek, L. P. H.; Bouwman, A.; Ward, P. J.; Jongman, B.

2012-04-01

There is an increasing need for strategic global assessments of flood risks. Such assessments may be required by: (a) International Financing Institutes and Disaster Management Agencies to evaluate where, when, and which investments in flood risk mitigation are most required; (b) (re-)insurers, who need to determine their required coverage capital; and (c) large companies to account for risks of regional investments. In this contribution, we propose a framework for global river flood risk assessment. The framework combines coarse scale resolution hazard probability distributions, derived from global hydrological model runs (typical scale about 0.5 degree resolution) with high resolution estimates of exposure indicators. The high resolution is required because floods typically occur at a much smaller scale than the typical resolution of global hydrological models, and exposure indicators such as population, land use and economic value generally are strongly variable in space and time. The framework therefore estimates hazard at a high resolution ( 1 km2) by using a) global forcing data sets of the current (or in scenario mode, future) climate; b) a global hydrological model; c) a global flood routing model, and d) importantly, a flood spatial downscaling routine. This results in probability distributions of annual flood extremes as an indicator of flood hazard, at the appropriate resolution. A second component of the framework combines the hazard probability distribution with classical flood impact models (e.g. damage, affected GDP, affected population) to establish indicators for flood risk. The framework can be applied with a large number of datasets and models and sensitivities of such choices can be evaluated by the user. The framework is applied using the global hydrological model PCR-GLOBWB, combined with a global flood routing model. Downscaling of the hazard probability distributions to 1 km2 resolution is performed with a new downscaling algorithm, applied on a number of target regions. We demonstrate the use of impact models in these regions based on global GDP, population, and land use maps. In this application, we show sensitivities of the estimated risks with regard to the use of different climate input datasets, decisions made in the downscaling algorithm, and different approaches to establish distributed estimates of GDP and asset exposure to flooding.
Development of Spatiotemporal Bias-Correction Techniques for Downscaling GCM Predictions

NASA Astrophysics Data System (ADS)

Hwang, S.; Graham, W. D.; Geurink, J.; Adams, A.; Martinez, C. J.

2010-12-01

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.
Added value of dynamical downscaling of winter seasonal forecasts over North America

NASA Astrophysics Data System (ADS)

Tefera Diro, Gulilat; Sushama, Laxmi

2017-04-01

Skillful seasonal forecasts have enormous potential benefits for socio-economic sectors that are sensitive to weather and climate conditions, as the early warning routines could reduce the vulnerability of such sectors. In this study, individual ensemble members of the ECMWF global ensemble seasonal forecasts are dynamically downscaled to produce ensemble of regional seasonal forecasts over North America using the fifth generation Canadian Regional Climate Model (CRCM5). CRCM5 forecasts are initialized on November 1st of each year and are integrated for four months for the 1991-2001 period at 0.22 degree resolution to produce a one-month lead-time forecast. The initial conditions for atmospheric variables are obtained from ERA-Interim reanalysis, whereas the initial conditions for land surface are obtained from a separate ERA-interim driven CRCM5 simulation with spectral nudging applied to the interior domain. The global and regional ensemble forecasts were then verified to investigate the skill and economic benefits of dynamical downscaling. Results indicate that both the global and regional climate models produce skillful precipitation forecast over the southern Great Plains and eastern coasts of the U.S and skillful temperature forecasts over the northern U.S. and most of Canada. In comparison to ECMWF forecasts, CRCM5 forecasts improved the temperature forecast skill over most part of the domain, but the improvements for precipitation is limited to regions with complex topography, where it improves the frequency of intense daily precipitation. CRCM5 forecast also yields a better economic value compared to ECMWF precipitation forecasts, for users whose cost to loss ratio is smaller than 0.5.
Dynamical Downscaling of Climate Change over the Hawaiian Islands

NASA Astrophysics Data System (ADS)

Wang, Y.; Zhang, C.; Hamilton, K. P.; Lauer, A.

2015-12-01

The pseudo-global-warming (PGW) method was applied to the Hawaii Regional Climate Model (HRCM) to dynamically downscale the projected climate in the late 21st century over the Hawaiian Islands. The initial and boundary conditions were adopted from MERRA reanalysis and NOAA SST data for the present-day simulations. The global warming increments constructed from the CMIP3 multi-model ensemble mean were added to the reanalysis and SST data to perform the future climate simulations. We found that the Hawaiian Islands are vulnerable to global warming effects and the changes are diverse due to the varied topography. The windward side will have more clouds and receive more rainfall. The increase of the moisture in the boundary layer makes the major contribution. On the contrary, the leeward side will have less clouds and rainfall. The clouds and rain can slightly slow down the warming trend over the windward side. The temperature increases almost linearly with the terrain height. Cloud base and top heights will slightly decline in response to the slightly lower trade wind inversion base height, while the trade wind occurrence frequency will increase by about 8% in the future. More extreme rainfall events will occur in the warming climate over the Hawaiian Islands. And the snow cover on the top of Mauna Kea and Mauna Loa will nearly disappear in the future winter.
Comparing the urbanization and global warming impacts on extreme rainfall characteristics in Southern China Pearl River Delta megacity based on dynamical downscaling

NASA Astrophysics Data System (ADS)

Fung, K. Y.; Tam, C. Y.; Wang, Z.

2017-12-01

It is well known that urban land use can significantly influence the local temperature, precipitation and meteorology through altering land-atmosphere exchange of momentum, moisture and heat in urban areas. In recent decades, there has been a substantial increase ( 5-10%) on the intensity of extreme rainfall over Southeast China; it is projected to increase further according to the latest IPCC reports. In this study, we assess how urbanization and global warming together might impact on heavy precipitation characteristics over the highly urbanized Pearl River Delta (PRD) megacity, located in southern China. This is done by dynamically downscaling GFDL-ESM2M simulations for the present and future (RCP8.5) climate scenarios, using the Weather Research and Forecasting (WRF) model coupled with a single-layer urban canopy model (UCM). Over the PRD area, the WRF model is integrated at a resolution of 2km x 2km. To focus on extreme events, episodes covering daily rainfall intensity above the 99th percentile in Southeast China in the GFDL-ESM2M daily precipitation datasets were first identified. These extreme episodes were then dynamically downscaled in two parallel experiments with the following model designs: one with anthropogenic heat flux (AH) = 0 Wm-2 and the other with peak AH = 300 Wm-2 in the AH diurnal cycle over the urban domain. Results show that, with AH in urban area, the urban 2m-temperature can rise by about 2oC. This in turn leads to an increase of the mean as well as the extreme rain rates by 10-15% in urban domain. The latter is comparable to the impact of global warming alone, according to downscaling experiments for the RCP8.5 scenario. Implications of our results on urban effects on extreme rainfall under a warming background climate will be discussed.
Translating Extreme Precipitation Data from Climate Change Projections into Resilient Engineering Applications

NASA Astrophysics Data System (ADS)

Cook, L. M.; Samaras, C.; Anderson, C.

2016-12-01

Engineers generally use historical precipitation trends to inform assumptions and parameters for long-lived infrastructure designs. However, resilient design calls for the adjustment of current engineering practice to incorporate a range of future climate conditions that are likely to be different than the past. Despite the availability of future projections from downscaled climate models, there remains a considerable mismatch between climate model outputs and the inputs needed in the engineering community to incorporate climate resiliency. These factors include differences in temporal and spatial scales, model uncertainties, and a lack of criteria for selection of an ensemble of models. This research addresses the limitations to working with climate data by providing a framework for the use of publicly available downscaled climate projections to inform engineering resiliency. The framework consists of five steps: 1) selecting the data source based on the engineering application, 2) extracting the data at a specific location, 3) validating for performance against observed data, 4) post-processing for bias or scale, and 5) selecting the ensemble and calculating statistics. The framework is illustrated with an example application to extreme precipitation-frequency statistics, the 25-year daily precipitation depth, using four publically available climate data sources: NARCCAP, USGS, Reclamation, and MACA. The attached figure presents the results for step 5 from the framework, analyzing how the 24H25Y depth changes when the model ensemble is culled based on model performance against observed data, for both post-processing techniques: bias-correction and change factor. Culling the model ensemble increases both the mean and median values for all data sources, and reduces range for NARCCAP and MACA ensembles due to elimination of poorer performing models, and in some cases, those that predict a decrease in future 24H25Y precipitation volumes. This result is especially relevant to engineers who wish to reduce the range of the ensemble and remove contradicting models; however, this result is not generalizable for all cases. Finally, this research highlights the need for the formation of an intermediate entity that is able to translate climate projections into relevant engineering information.
A Study of the Climate Change during 21st Century over Peninsular Malaysia Watersheds

NASA Astrophysics Data System (ADS)

Kavvas, M. L.; Ercan, A.; Ishida, K.; Chen, Z. R.; Jang, S.; Amin, M. Z. M.; Shaaban, A. J.

2016-12-01

15 coarse-resolution (150 - 300 km) climate projections for the 21st century by 3 different coupled land-atmosphere-ocean GCMs (ECHAM5 of the Max Planck Institute of Meteorology of Germany, CCSM3 of the National Center for Atmospheric Research (NCAR) of the United States, and MRI-CGCM2.3.2 of the Meteorological Research Institute of Japan) under 4 different greenhouse gas emission scenarios (B1, A1B, A2, A1FI) were dynamically downscaled at hourly intervals by a regional hydro-climate model of Peninsular Malaysia (RegHCM-PM) that consisted of Regional Atmospheric Model MM5 that was coupled with WEHY watershed hydrology model over Peninsular Malaysia (PM), at the scale of the hillslopes of 13 selected watersheds (Batu Pahat, Johor, Muda, Kelang, Kelantan, Linggi, Muar, Pahang, Perak, Selangor, Dungun, Kemaman and Kuantan) and 12 selected intervening coastal regions in order to assess the impact of climate change on the climate conditions at the selected watersheds and coastal regions of PM. From the downscaled climate projections it can be concluded that the mean annual precipitation gradually increases toward the end of the 21st century over each of the 13 watersheds and the 12 coastal regions. The basin-average mean annual temperature increases in the range of 2.50C - 2.950C over PM during the 2010 -2100 period when compared to the 1970-2000 historical period. The ensemble average basin-average annual potential evapotranspiration increases gradually throughout the 21st century over all watersheds.
Impact of the choice of the precipitation reference data set on climate model selection and the resulting climate change signal

NASA Astrophysics Data System (ADS)

Gampe, D.; Ludwig, R.

2017-12-01

Regional Climate Models (RCMs) that downscale General Circulation Models (GCMs) are the primary tool to project future climate and serve as input to many impact models to assess the related changes and impacts under such climate conditions. Such RCMs are made available through the Coordinated Regional climate Downscaling Experiment (CORDEX). The ensemble of models provides a range of possible future climate changes around the ensemble mean climate change signal. The model outputs however are prone to biases compared to regional observations. A bias correction of these deviations is a crucial step in the impact modelling chain to allow the reproduction of historic conditions of i.e. river discharge. However, the detection and quantification of model biases are highly dependent on the selected regional reference data set. Additionally, in practice due to computational constraints it is usually not feasible to consider the entire ensembles of climate simulations with all members as input for impact models which provide information to support decision-making. Although more and more studies focus on model selection based on the preservation of the climate model spread, a selection based on validity, i.e. the representation of the historic conditions is still a widely applied approach. In this study, several available reference data sets for precipitation are selected to detect the model bias for the reference period 1989 - 2008 over the alpine catchment of the Adige River located in Northern Italy. The reference data sets originate from various sources, such as station data or reanalysis. These data sets are remapped to the common RCM grid at 0.11° resolution and several indicators, such as dry and wet spells, extreme precipitation and general climatology, are calculate to evaluate the capability of the RCMs to produce the historical conditions. The resulting RCM spread is compared against the spread of the reference data set to determine the related uncertainties and detect potential model biases with respect to each reference data set. The RCMs are then ranked based on various statistical measures for each indicator and a score matrix is derived to select a subset of RCMs. We show the impact and importance of the reference data set with respect to the resulting climate change signal on the catchment scale.
Climate Downscaling over Nordeste, Brazil, Using the NCEP RSM97.

NASA Astrophysics Data System (ADS)

Sun, Liqiang; Ferran Moncunill, David; Li, Huilan; Divino Moura, Antonio; de Assis de Souza Filho, Francisco

2005-02-01

The NCEP Regional Spectral Model (RSM), with horizontal resolution of 60 km, was used to downscale the ECHAM4.5 AGCM (T42) simulations forced with observed SSTs over northeast Brazil. An ensemble of 10 runs for the period January-June 1971-2000 was used in this study. The RSM can resolve the spatial patterns of observed seasonal precipitation and capture the interannual variability of observed seasonal precipitation as well. The AGCM bias in displacement of the Atlantic ITCZ is partially corrected in the RSM. The RSM probability distribution function of seasonal precipitation anomalies is in better agreement with observations than that of the driving AGCM. Good potential prediction skills are demonstrated by the RSM in predicting the interannual variability of regional seasonal precipitation. The RSM can also capture the interannual variability of observed precipitation at intraseasonal time scales, such as precipitation intensity distribution and dry spells. A drought index and a flooding index were adopted to indicate the severity of drought and flooding conditions, and their interannual variability was reproduced by the RSM. The overall RSM performance in the downscaled climate of the ECHAM4.5 AGCM is satisfactory over Nordeste. The primary deficiency is a systematic dry bias for precipitation simulation.
Downscaling of daily precipitation using a hybrid model of Artificial Neural Network, Wavelet, and Quantile Mapping in Gharehsoo River Basin, Iran

NASA Astrophysics Data System (ADS)

Taie Semiromi, M.; Koch, M.

2017-12-01

Although linear/regression statistical downscaling methods are very straightforward and widely used, and they can be applied to a single predictor-predictand pair or spatial fields of predictors-predictands, the greatest constraint is the requirement of a normal distribution of the predictor and the predictand values, which means that it cannot be used to predict the distribution of daily rainfall because it is typically non-normal. To tacked with such a limitation, the current study aims to introduce a new developed hybrid technique taking advantages from Artificial Neural Networks (ANNs), Wavelet and Quantile Mapping (QM) for downscaling of daily precipitation for 10 rain-gauge stations located in Gharehsoo River Basin, Iran. With the purpose of daily precipitation downscaling, the study makes use of Second Generation Canadian Earth System Model (CanESM2) developed by Canadian Centre for Climate Modeling and Analysis (CCCma). Climate projections are available for three representative concentration pathways (RCPs) namely RCP 2.6, RCP 4.5 and RCP 8.5 for up to 2100. In this regard, 26 National Centers for Environmental Prediction (NCEP) reanalysis large-scale variables which have potential physical relationships with precipitation, were selected as candidate predictors. Afterwards, predictor screening was conducted using correlation, partial correlation and explained variance between predictors and predictand (precipitation). Depending on each rain-gauge station between two and three predictors were selected which their decomposed details (D) and approximation (A) obtained from discrete wavelet analysis were fed as inputs to the neural networks. After downscaling of daily precipitation, bias correction was conducted using quantile mapping. Out of the complete time series available, i.e. 1978-2005, two third of which namely 1978-1996 was used for calibration of QM and the reminder, i.e. 1997-2005 was considered for the validation. Result showed that the proposed hybrid method supported by QM for bias-correction could quite satisfactorily simulate daily precipitation. Also, results indicated that under all RCPs, precipitation will be more or less than 12% decreased by 2100. However, precipitation will be less decreased under RCP 8.5 compared with RCP 4.5.
Incremental dynamical downscaling for probabilistic analysis based on multiple GCM projections

NASA Astrophysics Data System (ADS)

Wakazuki, Y.

2015-12-01

A dynamical downscaling method for probabilistic regional scale climate change projections was developed to cover an uncertainty of multiple general circulation model (GCM) climate simulations. The climatological increments (future minus present climate states) estimated by GCM simulation results were statistically analyzed using the singular vector decomposition. Both positive and negative perturbations from the ensemble mean with the magnitudes of their standard deviations were extracted and were added to the ensemble mean of the climatological increments. The analyzed multiple modal increments were utilized to create multiple modal lateral boundary conditions for the future climate regional climate model (RCM) simulations by adding to an objective analysis data. This data handling is regarded to be an advanced method of the pseudo-global-warming (PGW) method previously developed by Kimura and Kitoh (2007). The incremental handling for GCM simulations realized approximated probabilistic climate change projections with the smaller number of RCM simulations. Three values of a climatological variable simulated by RCMs for a mode were used to estimate the response to the perturbation of the mode. For the probabilistic analysis, climatological variables of RCMs were assumed to show linear response to the multiple modal perturbations, although the non-linearity was seen for local scale rainfall. Probability of temperature was able to be estimated within two modes perturbation simulations, where the number of RCM simulations for the future climate is five. On the other hand, local scale rainfalls needed four modes simulations, where the number of the RCM simulations is nine. The probabilistic method is expected to be used for regional scale climate change impact assessment in the future.
High-resolution stochastic downscaling of climate models: simulating wind advection, cloud cover and precipitation

NASA Astrophysics Data System (ADS)

Peleg, Nadav; Fatichi, Simone; Burlando, Paolo

2015-04-01

A new stochastic approach to generate wind advection, cloud cover and precipitation fields is presented with the aim of formulating a space-time weather generator characterized by fields with high spatial and temporal resolution (e.g., 1 km x 1 km and 5 min). Its use is suitable for stochastic downscaling of climate scenarios in the context of hydrological, ecological and geomorphological applications. The approach is based on concepts from the Advanced WEather GENerator (AWE-GEN) presented by Fatichi et al. (2011, Adv. Water Resour.), the Space-Time Realizations of Areal Precipitation model (STREAP) introduced by Paschalis et al. (2013, Water Resour. Res.), and the High-Resolution Synoptically conditioned Weather Generator (HiReS-WG) presented by Peleg and Morin (2014, Water Resour. Res.). Advection fields are generated on the basis of the 500 hPa u and v wind direction variables derived from global or regional climate models. The advection velocity and direction are parameterized using Kappa and von Mises distributions respectively. A random Gaussian fields is generated using a fast Fourier transform to preserve the spatial correlation of advection. The cloud cover area, total precipitation area and mean advection of the field are coupled using a multi-autoregressive model. The approach is relatively parsimonious in terms of computational demand and, in the context of climate change, allows generating many stochastic realizations of current and projected climate in a fast and efficient way. A preliminary test of the approach is presented with reference to a case study in a complex orography terrain in the Swiss Alps.
Dynamically downscaled climate simulations over North America: Methods, evaluation, and supporting documentation for users

USGS Publications Warehouse

Hostetler, S.W.; Alder, J.R.; Allan, A.M.

2011-01-01

We have completed an array of high-resolution simulations of present and future climate over Western North America (WNA) and Eastern North America (ENA) by dynamically downscaling global climate simulations using a regional climate model, RegCM3. The simulations are intended to provide long time series of internally consistent surface and atmospheric variables for use in climate-related research. In addition to providing high-resolution weather and climate data for the past, present, and future, we have developed an integrated data flow and methodology for processing, summarizing, viewing, and delivering the climate datasets to a wide range of potential users. Our simulations were run over 50- and 15-kilometer model grids in an attempt to capture more of the climatic detail associated with processes such as topographic forcing than can be captured by general circulation models (GCMs). The simulations were run using output from four GCMs. All simulations span the present (for example, 1968-1999), common periods of the future (2040-2069), and two simulations continuously cover 2010-2099. The trace gas concentrations in our simulations were the same as those of the GCMs: the IPCC 20th century time series for 1968-1999 and the A2 time series for simulations of the future. We demonstrate that RegCM3 is capable of producing present day annual and seasonal climatologies of air temperature and precipitation that are in good agreement with observations. Important features of the high-resolution climatology of temperature, precipitation, snow water equivalent (SWE), and soil moisture are consistently reproduced in all model runs over WNA and ENA. The simulations provide a potential range of future climate change for selected decades and display common patterns of the direction and magnitude of changes. As expected, there are some model to model differences that limit interpretability and give rise to uncertainties. Here, we provide background information about the GCMs and the RegCM3, a basic evaluation of the model output and examples of simulated future climate. We also provide information needed to access the web applications for visualizing and downloading the data, and give complete metadata that describe the variables in the datasets.
Regional Climate and Streamflow Projections in North America Under IPCC CMIP5 Scenarios

NASA Astrophysics Data System (ADS)

Chang, H. I.; Castro, C. L.; Troch, P. A. A.; Mukherjee, R.

2014-12-01

The Colorado River system is the predominant source of water supply for the Southwest U.S. and is already fully allocated, making the region's environmental and economic health particularly sensitive to annual and multi-year streamflow variability. Observed streamflow declines in the Colorado Basin in recent years are likely due to synergistic combination of anthropogenic global warming and natural climate variability, which are creating an overall warmer and more extreme climate. IPCC assessment reports have projected warmer and drier conditions in arid to semi-arid regions (e.g. Solomon et al. 2007). The NAM-related precipitation contributes to substantial Colorado streamflows. Recent climate change studies for the Southwest U.S. region project a dire future, with chronic drought, and substantially reduced Colorado River flows. These regional effects reflect the general observation that climate is being more extreme globally, with areas climatologically favored to be wet getting wetter and areas favored to be dry getting drier (Wang et al. 2012). Multi-scale downscaling modeling experiments are designed using recent IPCC AR5 global climate projections, which incorporate regional climate and hydrologic modeling components. The Weather Research and Forecasting model (WRF) has been selected as the main regional modeling tool; the Variable Infiltration Capacity model (VIC) will be used to generate streamflow projections for the Colorado River Basin. The WRF domain is set up to follow the CORDEX-North America guideline with 25km grid spacing, and VIC model is individually calibrated for upper and lower Colorado River basins in 1/8° resolution. The multi-scale climate and hydrology study aims to characterize how the combination of climate change and natural climate variability is changing cool and warm season precipitation. Further, to preserve the downscaled RCM sensitivity and maintain a reasonable climatology mean based on observed record, a new bias correction technique is applied when using the RCM climatology to the streamflow model. Of specific interest is how major droughts associated with La Niña-like conditions may worsen in the future, as these are the times when the Colorado River system is most critically stressed and would define the "worst case" scenario for water resource planning.
A daily, 1 km resolution data set of downscaled Greenland ice sheet surface mass balance (1958-2015)

NASA Astrophysics Data System (ADS)

Noël, Brice; van de Berg, Willem Jan; Machguth, Horst; Lhermitte, Stef; Howat, Ian; Fettweis, Xavier; van den Broeke, Michiel R.

2016-10-01

This study presents a data set of daily, 1 km resolution Greenland ice sheet (GrIS) surface mass balance (SMB) covering the period 1958-2015. Applying corrections for elevation, bare ice albedo and accumulation bias, the high-resolution product is statistically downscaled from the native daily output of the polar regional climate model RACMO2.3 at 11 km. The data set includes all individual SMB components projected to a down-sampled version of the Greenland Ice Mapping Project (GIMP) digital elevation model and ice mask. The 1 km mask better resolves narrow ablation zones, valley glaciers, fjords and disconnected ice caps. Relative to the 11 km product, the more detailed representation of isolated glaciated areas leads to increased precipitation over the southeastern GrIS. In addition, the downscaled product shows a significant increase in runoff owing to better resolved low-lying marginal glaciated regions. The combined corrections for elevation and bare ice albedo markedly improve model agreement with a newly compiled data set of ablation measurements.
Future climate scenarios and rainfall--runoff modelling in the Upper Gallego catchment (Spain).

PubMed

Bürger, C M; Kolditz, O; Fowler, H J; Blenkinsop, S

2007-08-01

Global climate change may have large impacts on water supplies, drought or flood frequencies and magnitudes in local and regional hydrologic systems. Water authorities therefore rely on computer models for quantitative impact prediction. In this study we present kernel-based learning machine river flow models for the Upper Gallego catchment of the Ebro basin. Different learning machines were calibrated using daily gauge data. The models posed two major challenges: (1) estimation of the rainfall-runoff transfer function from the available time series is complicated by anthropogenic regulation and mountainous terrain and (2) the river flow model is weak when only climate data are used, but additional antecedent flow data seemed to lead to delayed peak flow estimation. These types of models, together with the presented downscaled climate scenarios, can be used for climate change impact assessment in the Gallego, which is important for the future management of the system.
Changing precipitation in western Europe, climate change or natural variability?

NASA Astrophysics Data System (ADS)

Aalbers, Emma; Lenderink, Geert; van Meijgaard, Erik; van den Hurk, Bart

2017-04-01

Multi-model RCM-GCM ensembles provide high resolution climate projections, valuable for among others climate impact assessment studies. While the application of multiple models (both GCMs and RCMs) provides a certain robustness with respect to model uncertainty, the interpretation of differences between ensemble members - the combined result of model uncertainty and natural variability of the climate system - is not straightforward. Natural variability is intrinsic to the climate system, and a potentially large source of uncertainty in climate change projections, especially for projections on the local to regional scale. To quantify the natural variability and get a robust estimate of the forced climate change response (given a certain model and forcing scenario), large ensembles of climate model simulations of the same model provide essential information. While for global climate models (GCMs) a number of such large single model ensembles exists and have been analyzed, for regional climate models (RCMs) the number and size of single model ensembles is limited, and the predictability of the forced climate response at the local to regional scale is still rather uncertain. We present a regional downscaling of a 16-member single model ensemble over western Europe and the Alps at a resolution of 0.11 degrees (˜12km), similar to the highest resolution EURO-CORDEX simulations. This 16-member ensemble was generated by the GCM EC-EARTH, which was downscaled with the RCM RACMO for the period 1951-2100. This single model ensemble has been investigated in terms of the ensemble mean response (our estimate of the forced climate response), as well as the difference between the ensemble members, which measures natural variability. We focus on the response in seasonal mean and extreme precipitation (seasonal maxima and extremes with a return period up to 20 years) for the near to far future. For most precipitation indices we can reliably determine the climate change signal, given the applied model chain and forcing scenario. However, the analysis also shows how limited the information in single ensemble members is on the local scale forced climate response, even for high levels of global warming when the forced response has emerged from natural variability. Analysis and application of multi-model ensembles like EURO-CORDEX should go hand-in-hand with single model ensembles, like the one presented here, to be able to correctly interpret the fine-scale information in terms of a forced signal and random noise due to natural variability.

Analysis of convection-permitting simulations for capturing heavy rainfall events over Myanmar Region

NASA Astrophysics Data System (ADS)

Acierto, R. A. E.; Kawasaki, A.

2017-12-01

Perennial flooding due to heavy rainfall events causes strong impacts on the society and economy. With increasing pressures of rapid development and potential for climate change impacts, Myanmar experiences a rapid increase in disaster risk. Heavy rainfall hazard assessment is key on quantifying such disaster risk in both current and future conditions. Downscaling using Regional Climate Models (RCM) such as Weather Research and Forecast model have been used extensively for assessing such heavy rainfall events. However, usage of convective parameterizations can introduce large errors in simulating rainfall. Convective-permitting simulations have been used to deal with this problem by increasing the resolution of RCMs to 4km. This study focuses on the heavy rainfall events during the six-year (2010-2015) wet period season from May to September in Myanmar. The investigation primarily utilizes rain gauge observation for comparing downscaled heavy rainfall events in 4km resolution using ERA-Interim as boundary conditions using 12km-4km one-way nesting method. The study aims to provide basis for production of high-resolution climate projections over Myanmar in order to contribute for flood hazard and risk assessment.
An intercomparison of a large ensemble of statistical downscaling methods for Europe: Overall results from the VALUE perfect predictor cross-validation experiment

NASA Astrophysics Data System (ADS)

Gutiérrez, Jose Manuel; Maraun, Douglas; Widmann, Martin; Huth, Radan; Hertig, Elke; Benestad, Rasmus; Roessler, Ole; Wibig, Joanna; Wilcke, Renate; Kotlarski, Sven

2016-04-01

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 both dynamical and statistical downscaling methods. This framework is based on a user-focused validation tree, guiding the selection of relevant validation indices and performance measures for different aspects of the validation (marginal, temporal, spatial, multi-variable). Moreover, several experiments have been designed to isolate specific points in the downscaling procedure where problems may occur (assessment of intrinsic performance, effect of errors inherited from the global models, effect of non-stationarity, etc.). The list of downscaling experiments includes 1) cross-validation with perfect predictors, 2) GCM predictors -aligned with EURO-CORDEX experiment- and 3) pseudo reality predictors (see Maraun et al. 2015, Earth's Future, 3, doi:10.1002/2014EF000259, for more details). The results of these experiments are gathered, validated and publicly distributed through the VALUE validation portal, allowing for a comprehensive community-open downscaling intercomparison study. In this contribution we describe the overall results from Experiment 1), consisting of a European wide 5-fold cross-validation (with consecutive 6-year periods from 1979 to 2008) using predictors from ERA-Interim to downscale precipitation and temperatures (minimum and maximum) over a set of 86 ECA&D stations representative of the main geographical and climatic regions in Europe. As a result of the open call for contribution to this experiment (closed in Dec. 2015), over 40 methods representative of the main approaches (MOS and Perfect Prognosis, PP) and techniques (linear scaling, quantile mapping, analogs, weather typing, linear and generalized regression, weather generators, etc.) were submitted, including information both data (downscaled values) and metadata (characterizing different aspects of the downscaling methods). This constitutes the largest and most comprehensive to date intercomparison of statistical downscaling methods. Here, we present an overall validation, analyzing marginal and temporal aspects to assess the intrinsic performance and added value of statistical downscaling methods at both annual and seasonal levels. This validation takes into account the different properties/limitations of different approaches and techniques (as reported in the provided metadata) in order to perform a fair comparison. It is pointed out that this experiment alone is not sufficient to evaluate the limitations of (MOS) bias correction techniques. Moreover, it also does not fully validate PP since we don't learn whether we have the right predictors and whether the PP assumption is valid. These problems will be analyzed in the subsequent community-open VALUE experiments 2) and 3), which will be open for participation along the present year.
Ecological contingency in the effects of climatic warming on forest herb communities.

PubMed

Harrison, Susan; Damschen, Ellen I; Grace, James B

2010-11-09

Downscaling from the predictions of general climate models is critical to current strategies for mitigating species loss caused by climate change. A key impediment to this downscaling is that we lack a fully developed understanding of how variation in physical, biological, or land-use characteristics mediates the effects of climate change on ecological communities within regions. We analyzed change in understory herb communities over a 60-y period (1949/1951-2007/2009) in a complex montane landscape (the Siskiyou Mountains, Oregon) where mean temperatures have increased 2 °C since 1948, similar to projections for other terrestrial communities. Our 185 sites included primary and secondary-growth lower montane forests (500-1.200 m above sea level) and primary upper montane to subalpine forests (1,500-2,100 m above sea level). In lower montane forests, regardless of land-use history, we found multiple herb-community changes consistent with an effectively drier climate, including lower mean specific leaf area, lower relative cover by species of northern biogeographic affinity, and greater compositional resemblance to communities in southerly topographic positions. At higher elevations we found qualitatively different and more modest changes, including increases in herbs of northern biogeographic affinity and in forest canopy cover. Our results provide community-level validation of predicted nonlinearities in climate change effects.
Ecological contingency in the effects of climatic warming on forest herb communities

USGS Publications Warehouse

Harrison, Susan; Damschen, Ellen Ingman; Grace, James B.

2010-01-01

Downscaling from the predictions of general climate models is critical to current strategies for mitigating species loss caused by climate change. A key impediment to this downscaling is that we lack a fully developed understanding of how variation in physical, biological, or land-use characteristics mediates the effects of climate change on ecological communities within regions. We analyzed change in understory herb communities over a 60-y period (1949/1951–2007/2009) in a complex montane landscape (the Siskiyou Mountains, Oregon) where mean temperatures have increased 2 °C since 1948, similar to projections for other terrestrial communities. Our 185 sites included primary and secondary-growth lower montane forests (500–1.200 m above sea level) and primary upper montane to subalpine forests (1,500–2,100 m above sea level). In lower montane forests, regardless of land-use history, we found multiple herb-community changes consistent with an effectively drier climate, including lower mean specific leaf area, lower relative cover by species of northern biogeographic affinity, and greater compositional resemblance to communities in southerly topographic positions. At higher elevations we found qualitatively different and more modest changes, including increases in herbs of northern biogeographic affinity and in forest canopy cover. Our results provide community-level validation of predicted nonlinearities in climate change effects.
A statistical adjustment approach for climate projections of snow conditions in mountain regions using energy balance land surface models

NASA Astrophysics Data System (ADS)

Verfaillie, Deborah; Déqué, Michel; Morin, Samuel; Lafaysse, Matthieu

2017-04-01

Projections of future climate change have been increasingly called for lately, as the reality of climate change has been gradually accepted and societies and governments have started to plan upcoming mitigation and adaptation policies. In mountain regions such as the Alps or the Pyrenees, where winter tourism and hydropower production are large contributors to the regional revenue, particular attention is brought to current and future snow availability. The question of the vulnerability of mountain ecosystems as well as the occurrence of climate-related hazards such as avalanches and debris-flows is also under consideration. In order to generate projections of snow conditions, however, downscaling global climate models (GCMs) by using regional climate models (RCMs) is not sufficient to capture the fine-scale processes and thresholds at play. In particular, the altitudinal resolution matters, since the phase of precipitation is mainly controlled by the temperature which is altitude-dependent. Simulations from GCMs and RCMs moreover suffer from biases compared to local observations, due to their rather coarse spatial and altitudinal resolution, and often provide outputs at too coarse time resolution to drive impact models. RCM simulations must therefore be adjusted using empirical-statistical downscaling and error correction methods, before they can be used to drive specific models such as energy balance land surface models. In this study, time series of hourly temperature, precipitation, wind speed, humidity, and short- and longwave radiation were generated over the Pyrenees and the French Alps for the period 1950-2100, by using a new approach (named ADAMONT for ADjustment of RCM outputs to MOuNTain regions) based on quantile mapping applied to daily data, followed by time disaggregation accounting for weather patterns selection. We first introduce a thorough evaluation of the method using using model runs from the ALADIN RCM driven by a global reanalysis over the French Alps. We then illustrate the potential of this method by processing outputs from EURO-CORDEX simulations spanning 6 different RCMs forced by 6 different GCMs under 3 representative concentration pathways scenarios (RCP 2.6, 4.5 and 8.5) over Europe, downscaled at the massif scale and for 300 m elevation bands and statistically adjusted against the extensive SAFRAN reanalysis (1958-2015). These corrected fields were then used to force the SURFEX/ISBA-Crocus land surface model over the Pyrenees and the French Alps. We show the wealth of information, which can be obtained through the systematic application of such a method to a large ensemble of climate projections, in order to capture upcoming trends with an explicit representation of their uncertainty.
Downscaling the Local Weather Above Glaciers in Complex Topography

NASA Astrophysics Data System (ADS)

Horak, Johannes; Hofer, Marlis; Gutmann, Ethan; Gohm, Alexander; Rotach, Mathias

2017-04-01

Glaciers have experienced a substantial ice-volume loss during the 20th century. To study their response to climate change, process-based glacier mass-balance models (PBGMs) are employed, which require a faithful representation of the state of the atmosphere above the glacier at high spatial and temporal resolution. Glaciers are usually located in complex topography where weather stations are scarce or not existent at all due to the remoteness of such sites and the associated high cost of maintenance. Furthermore. the effective resolution of global circulation models is too large to adequately capture the local topography and represent local weather, which is prerequisite for atmospheric input used by PBGMs. Dynamical downscaling is a physically consistent but computationally expensive approach to bridge the scale gap between GCM output and input needed by PBGMs, while statistical downscaling is faster but requires measurements for training. Both methods have their merits, however, a computationally frugal approach that does not rely on measurements is desirable, especially for long term studies of glacier response to future climate. In this study the intermediate complexity atmospheric research model (ICAR) is employed (Gutmann et al., 2016). It simplifies the wind field physics by relying on analytical solutions derived with linear theory. ICAR then advects atmospheric quantities within this wind field. This allows for computationally fast downscaling and yields a physically consistent set of atmospheric variables. First results obtained from downscaling air temperature, precipitation amount, relative humidity and wind speed to 4 × 4 km2 are presented. Preliminary ICAR is applied for a six month simulation period during five years and evaluated for three domains located in very distinct climates, namely the Southern Alps of New Zealand, the Cordillera Blanca in Peru and the European Alps using ERA Interim reanalysis data (ERAI) as forcing data set. The evaluation is based on determining the added value of the ICAR simulations - with ERAI output as a reference - in representing the local-scale weather measured at several automatic weather stations. For precipitation amount in particular, data by the Global Precipitation Measurement project are used in a fuzzy verification approach. The results indicate that ICAR provides added value for the Southern Alps of New Zealand in the case of precipitation and relative humidity, for the Cordillera Blanca and the European Alps for wind speed and, at certain locations in the European Alps, for precipitation. In order to more comprehensively investigate the physical plausibility of skill obtained for specific weather situations, the spatio-temporal evolution of the wind field resulting from the ICAR dynamics is analysed for individual case studies. To the authors knowledge this is the first study that specifically investigates the multi-variable consistency of ICAR for different climates, an important prerequisite for all applications which require multi-variable or multi-site input. References: Gutmann, E., Barstad, I., Clark, M., Arnold, J., and Rasmussen, R. (2016). The Intermediate Complexity Atmospheric Research Model (ICAR). Journal of Hydrometeorology, 17(3), 957-973.
Montane ecosystem productivity responds more to global circulation patterns than climatic trends.

PubMed

Desai, A R; Wohlfahrt, G; Zeeman, M J; Katata, G; Eugster, W; Montagnani, L; Gianelle, D; Mauder, M; Schmid, H-P

2016-02-01

Regional ecosystem productivity is highly sensitive to inter-annual climate variability, both within and outside the primary carbon uptake period. However, Earth system models lack sufficient spatial scales and ecosystem processes to resolve how these processes may change in a warming climate. Here, we show, how for the European Alps, mid-latitude Atlantic ocean winter circulation anomalies drive high-altitude summer forest and grassland productivity, through feedbacks among orographic wind circulation patterns, snowfall, winter and spring temperatures, and vegetation activity. Therefore, to understand future global climate change influence to regional ecosystem productivity, Earth systems models need to focus on improvements towards topographic downscaling of changes in regional atmospheric circulation patterns and to lagged responses in vegetation dynamics to non-growing season climate anomalies.
Montane ecosystem productivity responds more to global circulation patterns than climatic trends

NASA Astrophysics Data System (ADS)

Desai, A. R.; Wohlfahrt, G.; Zeeman, M. J.; Katata, G.; Eugster, W.; Montagnani, L.; Gianelle, D.; Mauder, M.; Schmid, H.-P.

2016-02-01

Regional ecosystem productivity is highly sensitive to inter-annual climate variability, both within and outside the primary carbon uptake period. However, Earth system models lack sufficient spatial scales and ecosystem processes to resolve how these processes may change in a warming climate. Here, we show, how for the European Alps, mid-latitude Atlantic ocean winter circulation anomalies drive high-altitude summer forest and grassland productivity, through feedbacks among orographic wind circulation patterns, snowfall, winter and spring temperatures, and vegetation activity. Therefore, to understand future global climate change influence to regional ecosystem productivity, Earth systems models need to focus on improvements towards topographic downscaling of changes in regional atmospheric circulation patterns and to lagged responses in vegetation dynamics to non-growing season climate anomalies.
Post-processing Seasonal Precipitation Forecasts via Integrating Climate Indices and the Analog Approach

NASA Astrophysics Data System (ADS)

Liu, Y.; Zhang, Y.; Wood, A.; Lee, H. S.; Wu, L.; Schaake, J. C.

2016-12-01

Seasonal precipitation forecasts are a primary driver for seasonal streamflow prediction that is critical for a range of water resources applications, such as reservoir operations and drought management. However, it is well known that seasonal precipitation forecasts from climate models are often biased and also too coarse in spatial resolution for hydrologic applications. Therefore, post-processing procedures such as downscaling and bias correction are often needed. In this presentation, we discuss results from a recent study that applies a two-step methodology to downscale and correct the ensemble mean precipitation forecasts from the Climate Forecast System (CFS). First, CFS forecasts are downscaled and bias corrected using monthly reforecast analogs: we identify past precipitation forecasts that are similar to the current forecast, and then use the finer-scale observational analysis fields from the corresponding dates to represent the post-processed ensemble forecasts. Second, we construct the posterior distribution of forecast precipitation from the post-processed ensemble by integrating climate indices: a correlation analysis is performed to identify dominant climate indices for the study region, which are then used to weight the analysis analogs selected in the first step using a Bayesian approach. The methodology is applied to the California Nevada River Forecast Center (CNRFC) and the Middle Atlantic River Forecast Center (MARFC) regions for 1982-2015, using the North American Land Data Assimilation System (NLDAS-2) precipitation as the analysis. The results from cross validation show that the post-processed CFS precipitation forecast are considerably more skillful than the raw CFS with the analog approach only. Integrating climate indices can further improve the skill if the number of ensemble members considered is large enough; however, the improvement is generally limited to the first couple of months when compared against climatology. Impacts of various factors such as ensemble size, lead time, and choice of climate indices will also be discussed.
MiKlip-PRODEF: Probabilistic Decadal Forecast for Central and Western Europe

NASA Astrophysics Data System (ADS)

Reyers, Mark; Haas, Rabea; Ludwig, Patrick; Pinto, Joaquim

2013-04-01

The demand for skilful climate predictions on time-scales of several years to decades has increased in recent years, in particular for economic, societal and political terms. Within the BMBF MiKlip consortium, a decadal prediction system on the global to local scale is currently being developed. The subproject PRODEF is part of the MiKlip-Module C, which aims at the regionalisation of decadal predictability for Central and Western Europe. In PRODEF, a combined statistical-dynamical downscaling (SDD) and a probabilistic forecast tool are developed and applied to the new Earth system model of the Max-Planck Institute Hamburg (MPI-ESM), which is part of the CMIP5 experiment. Focus is given on the decadal predictability of windstorms, related wind gusts as well as wind energy potentials. SDD combines the benefits of both high resolution dynamical downscaling and purely statistical downscaling of GCM output. Hence, the SDD approach is used to obtain a very large ensemble of highly resolved decadal forecasts. With respect to the focal points of PRODEF, a clustering of temporal evolving atmospheric fields, a circulation weather type (CWT) analysis, and a storm damage indices analysis is applied to the full ensemble of the decadal hindcast experiments of the MPI-ESM in its lower resolution (MPI-ESM-LR). The ensemble consists of up to ten realisations per yearly initialised decadal hindcast experiments for the period 1960-2010 (altogether 287 realisations). Representatives of CWTs / clusters and single storm episodes are dynamical downscaled with the regional climate model COSMO-CLM with a horizontal resolution of 0.22°. For each model grid point, the distributions of the local climate parameters (e.g. surface wind gusts) are determined for different periods (e.g. each decades) by recombining dynamical downscaled episodes weighted with the respective weather type frequencies. The applicability of the SDD approach is illustrated with examples of decadal forecasts of the MPI-ESM. We are able to perform a bias correction of the frequencies of large scale weather types and to quantify the uncertainties of decadal predictability on large and local scale arising from different initial conditions. Further, probability density functions of local parameters like e.g. wind gusts for different periods and decades derived from the SDD approach is compared to observations and reanalysis data. Skill scores are used to quantify the decadal predictability for different leading time periods and to analyse whether the SDD approach shows systematic errors for some regions.
Uncertainty in simulating wheat yields under climate change

NASA Astrophysics Data System (ADS)

Asseng, S.; Ewert, F.; Rosenzweig, C.; Jones, J. W.; Hatfield, J. L.; Ruane, A. C.; Boote, K. J.; Thorburn, P. J.; Rötter, R. P.; Cammarano, D.; Brisson, N.; Basso, B.; Martre, P.; Aggarwal, P. K.; Angulo, C.; Bertuzzi, P.; Biernath, C.; Challinor, A. J.; Doltra, J.; Gayler, S.; Goldberg, R.; Grant, R.; Heng, L.; Hooker, J.; Hunt, L. A.; Ingwersen, J.; Izaurralde, R. C.; Kersebaum, K. C.; Müller, C.; Naresh Kumar, S.; Nendel, C.; O'Leary, G.; Olesen, J. E.; Osborne, T. M.; Palosuo, T.; Priesack, E.; Ripoche, D.; Semenov, M. A.; Shcherbak, I.; Steduto, P.; Stöckle, C.; Stratonovitch, P.; Streck, T.; Supit, I.; Tao, F.; Travasso, M.; Waha, K.; Wallach, D.; White, J. W.; Williams, J. R.; Wolf, J.

2013-09-01

Projections of climate change impacts on crop yields are inherently uncertain. Uncertainty is often quantified when projecting future greenhouse gas emissions and their influence on climate. However, multi-model uncertainty analysis of crop responses to climate change is rare because systematic and objective comparisons among process-based crop simulation models are difficult. Here we present the largest standardized model intercomparison for climate change impacts so far. We found that individual crop models are able to simulate measured wheat grain yields accurately under a range of environments, particularly if the input information is sufficient. However, simulated climate change impacts vary across models owing to differences in model structures and parameter values. A greater proportion of the uncertainty in climate change impact projections was due to variations among crop models than to variations among downscaled general circulation models. Uncertainties in simulated impacts increased with CO2 concentrations and associated warming. These impact uncertainties can be reduced by improving temperature and CO2 relationships in models and better quantified through use of multi-model ensembles. Less uncertainty in describing how climate change may affect agricultural productivity will aid adaptation strategy development andpolicymaking.
Hydrological climate change projections for Central America

NASA Astrophysics Data System (ADS)

Hidalgo, Hugo G.; Amador, Jorge A.; Alfaro, Eric J.; Quesada, Beatriz

2013-07-01

Runoff climate change projections for the 21st century were calculated from a suite of 30 General Circulation Model (GCM) simulations for the A1B emission scenario in a 0.5° × 0.5° grid over Central America. The GCM data were downscaled using a version of the Bias Correction and Spatial Downscaling (BCSD) method and then used in the Variable Infiltration Capacity (VIC) macroscale hydrological model. The VIC model showed calibration skill in Honduras, Nicaragua, Costa Rica and Panama, but the results for some of the northern countries (Guatemala, El Salvador and Belize) and for the Caribbean coast of Central America was not satisfactory. Bias correction showed to remove effectively the biases in the GCMs. Results of the projected climate in the 2050-2099 period showed median significant reductions in precipitation (as much as 5-10%) and runoff (as much as 10-30%) in northern Central America. Therefore in this sub-region the prevalence of severe drought may increase significantly in the future under this emissions scenario. Northern Central America could warm as much as 3 °C during 2050-2099 and southern Central America could reach increases as much as 4 °C during the same period. The projected dry pattern over Central America is consistent with a southward displacement of the Intertropical Convergence Zone (ITCZ). In addition, downscaling of the NCEP/NCAR Reanalysis data from 1948 to 2012 and posterior run in VIC, for two locations in the northern and southern sub-regions of Central America, suggested that the annual runoff has been decreasing since ca. 1980, which is consistent with the sign of the runoff changes of the GCM projections. However, the Reanalysis 1980-2012 drying trends are generally much stronger than the corresponding GCM trends. Among the possible reasons for that discrepancy are model deficiencies, amplification of the trends due to constructive interference with natural modes of variability in the Reanalysis data, errors in the Reanalysis (modeled) precipitation data, and that the drying signal is more pronounced than predicted by the emissions scenario used. A few studies show that extrapolations of future climate from paleoclimatic indicators project a wetter climate in northern Central America, which is inconsistent with the modeling results presented here. However, these types of extrapolations should be done with caution, as the future climate responds to an extra forcing mechanism (anthropogenic) that was not present prehistorically and therefore the response could also be different than in the past.
Downscaling large-scale circulation to local winter climate using neural network techniques

NASA Astrophysics Data System (ADS)

Cavazos Perez, Maria Tereza

1998-12-01

The severe impacts of climate variability on society reveal the increasing need for improving regional-scale climate diagnosis. A new downscaling approach for climate diagnosis is developed here. It is based on neural network techniques that derive transfer functions from the large-scale atmospheric controls to the local winter climate in northeastern Mexico and southeastern Texas during the 1985-93 period. A first neural network (NN) model employs time-lagged component scores from a rotated principal component analysis of SLP, 500-hPa heights, and 1000-500 hPa thickness as predictors of daily precipitation. The model is able to reproduce the phase and, to some decree, the amplitude of large rainfall events, reflecting the influence of the large-scale circulation. Large errors are found over the Sierra Madre, over the Gulf of Mexico, and during El Nino events, suggesting an increase in the importance of meso-scale rainfall processes. However, errors are also due to the lack of randomization of the input data and the absence of local atmospheric predictors such as moisture. Thus, a second NN model uses time-lagged specific humidity at the Earth's surface and at the 700 hPa level, SLP tendency, and 700-500 hPa thickness as input to a self-organizing map (SOM) that pre-classifies the atmospheric fields into different patterns. The results from the SOM classification document that negative (positive) anomalies of winter precipitation over the region are associated with: (1) weaker (stronger) Aleutian low; (2) stronger (weaker) North Pacific high; (3) negative (positive) phase of the Pacific North American pattern; and (4) La Nina (El Nino) events. The SOM atmospheric patterns are then used as input to a feed-forward NN that captures over 60% of the daily rainfall variance and 94% of the daily minimum temperature variance over the region. This demonstrates the ability of artificial neural network models to simulate realistic relationships on daily time scales. The results of this research also reveal that the SOM pre-classification of days with similar atmospheric conditions succeeded in emphasizing the differences of the atmospheric variance conducive to extreme events. This resulted in a downscaling NN model that is highly sensitive to local-scale weather anomalies associated with El Nino and extreme cold events.
South Asia river-flow projections and their implications for water resources

NASA Astrophysics Data System (ADS)

Mathison, C.; Wiltshire, A. J.; Falloon, P.; Challinor, A. J.

2015-12-01

South Asia is a region with a large and rising population, a high dependence on water intense industries, such as agriculture and a highly variable climate. In recent years, fears over the changing Asian summer monsoon (ASM) and rapidly retreating glaciers together with increasing demands for water resources have caused concern over the reliability of water resources and the potential impact on intensely irrigated crops in this region. Despite these concerns, there is a lack of climate simulations with a high enough resolution to capture the complex orography, and water resource analysis is limited by a lack of observations of the water cycle for the region. In this paper we present the first 25 km resolution regional climate projections of river flow for the South Asia region. Two global climate models (GCMs), which represent the ASM reasonably well are downscaled (1960-2100) using a regional climate model (RCM). In the absence of robust observations, ERA-Interim reanalysis is also downscaled providing a constrained estimate of the water balance for the region for comparison against the GCMs (1990-2006). The RCM river flow is routed using a river-routing model to allow analysis of present-day and future river flows through comparison with available river gauge observations. We examine how useful these simulations are for understanding potential changes in water resources for the South Asia region. In general the downscaled GCMs capture the seasonality of the river flows but overestimate the maximum river flows compared to the observations probably due to a positive rainfall bias and a lack of abstraction in the model. The simulations suggest an increasing trend in annual mean river flows for some of the river gauges in this analysis, in some cases almost doubling by the end of the century. The future maximum river-flow rates still occur during the ASM period, with a magnitude in some cases, greater than the present-day natural variability. Increases in river flow could mean additional water resources for irrigation, the largest usage of water in this region, but has implications in terms of inundation risk. These projected increases could be more than countered by changes in demand due to depleted groundwater, increases in domestic use or expansion of water intense industries. Including missing hydrological processes in the model would make these projections more robust but could also change the sign of the projections.
Downscaling of inundation extents

NASA Astrophysics Data System (ADS)

Aires, Filipe; Prigent, Catherine; Papa, Fabrice

2014-05-01

The Global Inundation Extent from Multi-Satellite (GIEMS) provides multi-year monthly variations of the global surface water extent at about 25 kmx25 km resolution, from 1993 to 2007. It is derived from multiple satellite observations. Its spatial resolution is usually compatible with climate model outputs and with global land surface model grids but is clearly not adequate for local applications that require the characterization of small individual water bodies. There is today a strong demand for high-resolution inundation extent datasets, for a large variety of applications such as water management, regional hydrological modeling, or for the analysis of mosquitos-related diseases. This paper present three approaches to do downscale GIEMS: The first one is based on a image-processing technique using neighborhood constraints. The third approach uses a PCA representation to perform an algebraic inversion. The PCA-representation is also very convenient to perform temporal and spatial interpolation of complexe inundation fields. The third downscaling method uses topography information from Hydroshed Digital Elevation Model (DEM). Information such as the elevation, distance to river and flow accumulation are used to define a ``flood ability index'' that is used by the downscaling. Three basins will be considered for illustrative purposes: Amazon, Niger and Mekong. Aires, F., F. Papa, C. Prigent, J.-F. Cretaux and M. Berge-Nguyen, Characterization and downscaling of the inundation extent over the Inner Niger delta using a multi-wavelength retrievals and Modis data, J. of Hydrometeorology, in press, 2014. Aires, F., F. Papa and C. Prigent, A long-term, high-resolution wetland dataset over the Amazon basin, downscaled from a multi-wavelength retrieval using SAR, J. of Hydrometeorology, 14, 594-6007, 2013. Prigent, C., F. Papa, F. Aires, C. Jimenez, W.B. Rossow, and E. Matthews. Changes in land surface water dynamics since the 1990s and relation to population pressure. Geophys. Res. Lett., 39(L08403), 2012.
Quantifying uncertainty in future floods and drought conditions in the Northeastern United States using regionally downscaled climate projections

NASA Astrophysics Data System (ADS)

Siddique, R.; Wu, C.; Karmalkar, A.; Bradley, R. S.; Palmer, R. N.

2017-12-01

Northeastern region (NER) of the United States (US) has been projected to be a place where climate change can have the most severe impacts. These impacts include, but are not limited to, increases in the following: extreme precipitation events, temperature, flood magnitudes, flood frequencies, droughts, and sea level rise. In this study, we estimate the frequency of hydrological extremes under different climate change scenarios using regionally downscaled climate projections from a limited number of selected models from the fifth phase of Coupled Model Intercomparison Project (CMIP5). The models are chosen to minimize the loss of key climate information relevant to the NER. Precipitation and temperature from the selected models are forced into a distributed hydrological model called Hydrology Laboratory - Research Distributed Hydrological Model (HL-RDHM) to obtain streamflows for two different time regimes, near-term (20-50 years out) and long-term (50-80 years out). For this, two climate emission scenarios will be considered: RCP 4.5 and RCP 8.5. The impacts of the climate projections on the streamflows are then evaluated across different watershed scales in the NER. Among different metrics, we employ: 1) Flood Events - return period of 1 year, 10 year, 20 year, 50 year, and 100 year flood events and 2) Drought Events -low flow events associated with the 7-day 10 year low flow, number of days per month that will be below the historic monthly average, number of days per month that will be below the 25 percentile monthly historic average, changes in the 30-day and 60-day cumulative summer flows, and the timing and magnitude of spring run-off. For estimates of the climate impacts on low and high flows, only the unregulated watersheds are taken into consideration. Ensembles of streamflows obtained by forcing different climate projections are used to quantify and account for the associated uncertainties. Thus, the outcomes of this study are expected to guide regional decision makers on potential impacts of climate change on hydrological extreme events and water resources across different spatial scales within NER of the US.
Integrated Modeling and Participatory Scenario Planning for Climate Adaptation: the Maui Groundwater Project

NASA Astrophysics Data System (ADS)

Keener, V. W.; Finucane, M.; Brewington, L.

2014-12-01

For the last century, the island of Maui, Hawaii, has been the center of environmental, agricultural, and legal conflict with respect to surface and groundwater allocation. Planning for adequate future freshwater resources requires flexible and adaptive policies that emphasize partnerships and knowledge transfer between scientists and non-scientists. In 2012 the Hawai'i state legislature passed the Climate Change Adaptation Priority Guidelines (Act 286) law requiring county and state policy makers to include island-wide climate change scenarios in their planning processes. This research details the ongoing work by researchers in the NOAA funded Pacific RISA to support the development of Hawaii's first island-wide water use plan under the new climate adaptation directive. This integrated project combines several models with participatory future scenario planning. The dynamically downscaled triply nested Hawaii Regional Climate Model (HRCM) was modified from the WRF community model and calibrated to simulate the many microclimates on the Hawaiian archipelago. For the island of Maui, the HRCM was validated using 20 years of hindcast data, and daily projections were created at a 1 km scale to capture the steep topography and diverse rainfall regimes. Downscaled climate data are input into a USGS hydrological model to quantify groundwater recharge. This model was previously used for groundwater management, and is being expanded utilizing future climate projections, current land use maps and future scenario maps informed by stakeholder input. Participatory scenario planning began in 2012 to bring together a diverse group of over 50 decision-makers in government, conservation, and agriculture to 1) determine the type of information they would find helpful in planning for climate change, and 2) develop a set of scenarios that represent alternative climate/management futures. This is an iterative process, resulting in flexible and transparent narratives at multiple scales. The resulting climate, land use, and groundwater recharge maps give stakeholders a common set of future scenarios that they understand through the participatory scenario process, and identify the vulnerabilities, trade-offs, and adaptive priorities for different groundwater management and land uses in an uncertain future.
Famine Early Warning Systems Network (FEWS NET) Contributions to Strengthening Resilience and Sustainability for the East African Community

NASA Astrophysics Data System (ADS)

Budde, M. E.; Galu, G.; Funk, C. C.; Verdin, J. P.; Rowland, J.

2014-12-01

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.
Modeling of the Assiniboine Delta Aquifer (ADA) of Manitoba using the Groundwater Storage from GRACE

NASA Astrophysics Data System (ADS)

Yirdaw-Zeleke, S.; Snelgrove, K.

2007-12-01

This paper investigates the use of GRACE (Gravity Recovery and Climate Experiment) moisture storages for modeling of the Assiniboine Delta Aquifer (ADA) of Manitoba, Canada. There are great promises from GRACE in capturing regional groundwater storages that are potentially used for modeling application. However, it is well known that these storages are difficult to measure over the scales needed for hydrological model applications. Therefore, prior to modeling the aquifer using GRACE moisture storages, the storages need to be downscaled in to regional groundwater storages using the measured groundwater head data available in the area. Previous studies in the ADA have shown that the downscaled moisture storage estimates compared favorably with the measured groundwater storage over the area. This study focuses on the modeling of the ADA aquifer using the downscaled GRACE moisture storages. These storages will be used to initialize, calibration and potentially steer the hydrologic simulation. The calibrated model then will be validated independently using the measured data. These validations will hopefully provide better explanations for the underlying reasons for the differences in model predictions and measurements. This will identify some of the key assumptions and uncertainties in predicting moisture storage, and so highlight topics for further discussion and research.
Current and future carbon budget at Takayama site, Japan, evaluated by a regional climate model and a process-based terrestrial ecosystem model.

PubMed

Kuribayashi, Masatoshi; Noh, Nam-Jin; Saitoh, Taku M; Ito, Akihiko; Wakazuki, Yasutaka; Muraoka, Hiroyuki

2017-06-01

Accurate projection of carbon budget in forest ecosystems under future climate and atmospheric carbon dioxide (CO 2 ) concentration is important to evaluate the function of terrestrial ecosystems, which serve as a major sink of atmospheric CO 2 . In this study, we examined the effects of spatial resolution of meteorological data on the accuracies of ecosystem model simulation for canopy phenology and carbon budget such as gross primary production (GPP), ecosystem respiration (ER), and net ecosystem production (NEP) of a deciduous forest in Japan. Then, we simulated the future (around 2085) changes in canopy phenology and carbon budget of the forest by incorporating high-resolution meteorological data downscaled by a regional climate model. The ecosystem model overestimated GPP and ER when we inputted low-resolution data, which have warming biases over mountainous landscape. But, it reproduced canopy phenology and carbon budget well, when we inputted high-resolution data. Under the future climate, earlier leaf expansion and delayed leaf fall by about 10 days compared with the present state was simulated, and also, GPP, ER and NEP were estimated to increase by 25.2%, 23.7% and 35.4%, respectively. Sensitivity analysis showed that the increase of NEP in June and October would be mainly caused by rising temperature, whereas that in July and August would be largely attributable to CO 2 fertilization. This study suggests that the downscaling of future climate data enable us to project more reliable carbon budget of forest ecosystem in mountainous landscape than the low-resolution simulation due to the better predictions of leaf expansion and shedding.

Some links on this page may take you to non-federal websites. Their policies may differ from this site.