Evaluation of Historical and Projected Agricultural Climate Risk Over the Continental US

NASA Astrophysics Data System (ADS)

Zhu, X.; Troy, T. J.; Devineni, N.

2016-12-01

Food demands are rising due to an increasing population with changing food preferences, which places pressure on agricultural systems. In addition, in the past decade climate extremes have highlighted the vulnerability of our agricultural production to climate variability. Quantitative analyses in the climate-agriculture research field have been performed in many studies. However, climate risk still remains difficult to evaluate at large scales yet shows great potential of help us better understand historical climate change impacts and evaluate the future risk given climate projections. In this study, we developed a framework to evaluate climate risk quantitatively by applying statistical methods such as Bayesian regression, distribution fitting, and Monte Carlo simulation. We applied the framework over different climate regions in the continental US both historically and for modeled climate projections. The relative importance of any major growing season climate index, such as maximum dry period or heavy precipitation, was evaluated to determine what climate indices play a role in affecting crop yields. The statistical modeling framework was applied using county yields, with irrigated and rainfed yields separated to evaluate the different risk. This framework provides estimates of the climate risk facing agricultural production in the near-term that account for the full uncertainty of climate occurrences, range of crop response, and spatial correlation in climate. In particular, the method provides robust estimates of importance of irrigation in mitigating agricultural climate risk. The results of this study can contribute to decision making about crop choice and water use in an uncertain climate.

Evaluation of regional climate simulations for air quality modelling purposes

NASA Astrophysics Data System (ADS)

Menut, Laurent; Tripathi, Om P.; Colette, Augustin; Vautard, Robert; Flaounas, Emmanouil; Bessagnet, Bertrand

2013-05-01

In order to evaluate the future potential benefits of emission regulation on regional air quality, while taking into account the effects of climate change, off-line air quality projection simulations are driven using weather forcing taken from regional climate models. These regional models are themselves driven by simulations carried out using global climate models (GCM) and economical scenarios. Uncertainties and biases in climate models introduce an additional "climate modeling" source of uncertainty that is to be added to all other types of uncertainties in air quality modeling for policy evaluation. In this article we evaluate the changes in air quality-related weather variables induced by replacing reanalyses-forced by GCM-forced regional climate simulations. As an example we use GCM simulations carried out in the framework of the ERA-interim programme and of the CMIP5 project using the Institut Pierre-Simon Laplace climate model (IPSLcm), driving regional simulations performed in the framework of the EURO-CORDEX programme. In summer, we found compensating deficiencies acting on photochemistry: an overestimation by GCM-driven weather due to a positive bias in short-wave radiation, a negative bias in wind speed, too many stagnant episodes, and a negative temperature bias. In winter, air quality is mostly driven by dispersion, and we could not identify significant differences in either wind or planetary boundary layer height statistics between GCM-driven and reanalyses-driven regional simulations. However, precipitation appears largely overestimated in GCM-driven simulations, which could significantly affect the simulation of aerosol concentrations. The identification of these biases will help interpreting results of future air quality simulations using these data. Despite these, we conclude that the identified differences should not lead to major difficulties in using GCM-driven regional climate simulations for air quality projections.

From Past to future: the Paleoclimate Modelling Intercomparison Project's contribution to CMIP6

NASA Astrophysics Data System (ADS)

Kageyama, Masa; Braconnot, Pascale; Harrison, Sandy; Haywood, Alan; Jungclaus, Johann; Otto-Bliesner, Bette; Abe-Ouchi, Ayako

2016-04-01

Since the 1990s, PMIP has developed with the following objectives: 1/to evaluate the ability of climate models used for climate prediction in simulating well-documented past climates outside the range of present and recent climate variability; 2/to understand the mechanisms of these climate changes, in particular the role of the different climate feedbacks. To achieve these goals, PMIP has actively fostered paleo-data syntheses, multi-model analyses, including analyses of relationships between model results from past and future simulations, and model-data comparisons. For CMIP6, PMIP will focus on five past periods: - the Last Millennium (850 CE - present), to analyse natural climate variability on multidecadal or longer time-scales - the mid-Holocene, 6000 years ago, to compare model runs with paleodata for a period of warmer climate in the Northern Hemisphere, with an enhanced hydrological cycle - the Last Glacial Maximum, 21000 years ago, to evaluate the ability of climate models to represent a cold climate extreme and examine whether paleoinformation about this period can help and constrain climate sensitivity - the Last InterGlacial (~127,000 year ago), which provides a benchmark for a period of high sea-level stand - the mid-Pliocene warm period (~3.2 million years ago), which allows for the evaluation of the model's long-term response to a CO2 level analogous to the modern one. This poster will present the rationale of these "PMIP4-CMIP6" experiments. Participants are invited to come and discuss about the experimental set-up and the model output to be distributed via CMIP6. For more information and discussion of the PMIP4-CMIP6 experimental design, please visit: https://wiki.lsce.ipsl.fr/pmip3/doku.php/pmip3:cmip6:design:index

Using historical and projected future climate model simulations as drivers of agricultural and biological models (Invited)

NASA Astrophysics Data System (ADS)

Stefanova, L. B.

2013-12-01

Climate model evaluation is frequently performed as a first step in analyzing climate change simulations. Atmospheric scientists are accustomed to evaluating climate models through the assessment of model climatology and biases, the models' representation of large-scale modes of variability (such as ENSO, PDO, AMO, etc) and the relationship between these modes and local variability (e.g. the connection between ENSO and the wintertime precipitation in the Southeast US). While these provide valuable information about the fidelity of historical and projected climate model simulations from an atmospheric scientist's point of view, the application of climate model data to fields such as agriculture, ecology and biology may require additional analyses focused on the particular application's requirements and sensitivities. Typically, historical climate simulations are used to determine a mapping between the model and observed climate, either through a simple (additive for temperature or multiplicative for precipitation) or a more sophisticated (such as quantile matching) bias correction on a monthly or seasonal time scale. Plants, animals and humans however are not directly affected by monthly or seasonal means. To assess the impact of projected climate change on living organisms and related industries (e.g. agriculture, forestry, conservation, utilities, etc.), derivative measures such as the heating degree-days (HDD), cooling degree-days (CDD), growing degree-days (GDD), accumulated chill hours (ACH), wet season onset (WSO) and duration (WSD), among others, are frequently useful. We will present a comparison of the projected changes in such derivative measures calculated by applying: (a) the traditional temperature/precipitation bias correction described above versus (b) a bias correction based on the mapping between the historical model and observed derivative measures themselves. In addition, we will present and discuss examples of various application-based climate model evaluations, such as: (a) agricultural crop yield estimates and (b) species population viability estimates modeled using observed climate data vs. historical climate simulations.

Evaluation of CMIP5 Ability to Reproduce 20th Century Regional Trends in Surface Air Temperature and Precipitation over CONUS

NASA Astrophysics Data System (ADS)

Lee, J.; Waliser, D. E.; Lee, H.; Loikith, P. C.; Kunkel, K.

2017-12-01

Monitoring temporal changes in key climate variables, such as surface air temperature and precipitation, is an integral part of the ongoing efforts of the United States National Climate Assessment (NCA). Climate models participating in CMIP5 provide future trends for four different emissions scenarios. In order to have confidence in the future projections of surface air temperature and precipitation, it is crucial to evaluate the ability of CMIP5 models to reproduce observed trends for three different time periods (1895-1939, 1940-1979, and 1980-2005). Towards this goal, trends in surface air temperature and precipitation obtained from the NOAA nClimGrid 5 km gridded station observation-based product are compared during all three time periods to the 206 CMIP5 historical simulations from 48 unique GCMs and their multi-model ensemble (MME) for NCA-defined climate regions during summer (JJA) and winter (DJF). This evaluation quantitatively examines the biases of simulated trends of the spatially averaged temperature and precipitation in the NCA climate regions. The CMIP5 MME reproduces historical surface air temperature trends for JJA for all time period and all regions, except the Northern Great Plains from 1895-1939 and Southeast during 1980-2005. Likewise, for DJF, the MME reproduces historical surface air temperature trends across all time periods over all regions except the Southeast from 1895-1939 and the Midwest during 1940-1979. The Regional Climate Model Evaluation System (RCMES), an analysis tool which supports the NCA by providing access to data and tools for regional climate model validation, facilitates the comparisons between the models and observation. The RCMES Toolkit is designed to assist in the analysis of climate variables and the procedure of the evaluation of climate projection models to support the decision-making processes. This tool is used in conjunction with the above analysis and results will be presented to demonstrate its capability to access observation and model datasets, calculate evaluation metrics, and visualize the results. Several other examples of the RCMES capabilities can be found at https://rcmes.jpl.nasa.gov.

Evaluation of Projected Agricultural Climate Risk over the Contiguous US

NASA Astrophysics Data System (ADS)

Zhu, X.; Troy, T. J.; Devineni, N.

2017-12-01

Food demands are rising due to an increasing population with changing food preferences, which places pressure on agricultural production. Additionally, climate extremes have recently highlighted the vulnerability of our agricultural system to climate variability. This study seeks to fill two important gaps in current knowledge: how does the widespread response of irrigated crops differ from rainfed and how can we best account for uncertainty in yield responses. We developed a stochastic approach to evaluate climate risk quantitatively to better understand the historical impacts of climate change and estimate the future impacts it may bring about to agricultural system. Our model consists of Bayesian regression, distribution fitting, and Monte Carlo simulation to simulate rainfed and irrigated crop yields at the US county level. The model was fit using historical data for 1970-2010 and was then applied over different climate regions in the contiguous US using the CMIP5 climate projections. The relative importance of many major growing season climate indices, such as consecutive dry days without rainfall or heavy precipitation, was evaluated to determine what climate indices play a role in affecting future crop yields. The statistical modeling framework also evaluated the impact of irrigation by using county-level irrigated and rainfed yields separately. Furthermore, the projected years with negative yield anomalies were specifically evaluated in terms of magnitude, trend and potential climate drivers. This framework provides estimates of the agricultural climate risk for the 21st century that account for the full uncertainty of climate occurrences, range of crop response, and spatial correlation in climate. The results of this study can contribute to decision making about crop choice and water use in an uncertain future climate.

Validating predictions from climate envelope models

USGS Publications Warehouse

Watling, J.; Bucklin, D.; Speroterra, C.; Brandt, L.; Cabal, C.; Romañach, Stephanie S.; Mazzotti, Frank J.

2013-01-01

Climate envelope models are a potentially important conservation tool, but their ability to accurately forecast species’ distributional shifts using independent survey data has not been fully evaluated. We created climate envelope models for 12 species of North American breeding birds previously shown to have experienced poleward range shifts. For each species, we evaluated three different approaches to climate envelope modeling that differed in the way they treated climate-induced range expansion and contraction, using random forests and maximum entropy modeling algorithms. All models were calibrated using occurrence data from 1967–1971 (t1) and evaluated using occurrence data from 1998–2002 (t2). Model sensitivity (the ability to correctly classify species presences) was greater using the maximum entropy algorithm than the random forest algorithm. Although sensitivity did not differ significantly among approaches, for many species, sensitivity was maximized using a hybrid approach that assumed range expansion, but not contraction, in t2. Species for which the hybrid approach resulted in the greatest improvement in sensitivity have been reported from more land cover types than species for which there was little difference in sensitivity between hybrid and dynamic approaches, suggesting that habitat generalists may be buffered somewhat against climate-induced range contractions. Specificity (the ability to correctly classify species absences) was maximized using the random forest algorithm and was lowest using the hybrid approach. Overall, our results suggest cautious optimism for the use of climate envelope models to forecast range shifts, but also underscore the importance of considering non-climate drivers of species range limits. The use of alternative climate envelope models that make different assumptions about range expansion and contraction is a new and potentially useful way to help inform our understanding of climate change effects on species.

A potato model intercomparison across varying climates and productivity levels

USDA-ARS?s Scientific Manuscript database

A potato crop multi-model assessment was conducted to quantify variation among models and evaluate responses to climate change. Nine modeling groups simulated agronomic and climatic responses at low- (Chinoli, Bolivia and Gisozi, Burundi) and high- (Jyndevad, Denmark and Washington, United States) ...

Multi-objective optimization for generating a weighted multi-model ensemble

NASA Astrophysics Data System (ADS)

Lee, H.

2017-12-01

Many studies have demonstrated that multi-model ensembles generally show better skill than each ensemble member. When generating weighted multi-model ensembles, the first step is measuring the performance of individual model simulations using observations. There is a consensus on the assignment of weighting factors based on a single evaluation metric. When considering only one evaluation metric, the weighting factor for each model is proportional to a performance score or inversely proportional to an error for the model. While this conventional approach can provide appropriate combinations of multiple models, the approach confronts a big challenge when there are multiple metrics under consideration. When considering multiple evaluation metrics, it is obvious that a simple averaging of multiple performance scores or model ranks does not address the trade-off problem between conflicting metrics. So far, there seems to be no best method to generate weighted multi-model ensembles based on multiple performance metrics. The current study applies the multi-objective optimization, a mathematical process that provides a set of optimal trade-off solutions based on a range of evaluation metrics, to combining multiple performance metrics for the global climate models and their dynamically downscaled regional climate simulations over North America and generating a weighted multi-model ensemble. NASA satellite data and the Regional Climate Model Evaluation System (RCMES) software toolkit are used for assessment of the climate simulations. Overall, the performance of each model differs markedly with strong seasonal dependence. Because of the considerable variability across the climate simulations, it is important to evaluate models systematically and make future projections by assigning optimized weighting factors to the models with relatively good performance. Our results indicate that the optimally weighted multi-model ensemble always shows better performance than an arithmetic ensemble mean and may provide reliable future projections.

Modifying climate change habitat models using tree species-specific assessments of model uncertainty and life history-factors

Treesearch

Stephen N. Matthews; Louis R. Iverson; Anantha M. Prasad; Matthew P. Peters; Paul G. Rodewald

2011-01-01

Species distribution models (SDMs) to evaluate trees' potential responses to climate change are essential for developing appropriate forest management strategies. However, there is a great need to better understand these models' limitations and evaluate their uncertainties. We have previously developed statistical models of suitable habitat, based on both...

Hydrological modeling as an evaluation tool of EURO-CORDEX climate projections and bias correction methods

NASA Astrophysics Data System (ADS)

Hakala, Kirsti; Addor, Nans; Seibert, Jan

2017-04-01

Streamflow stemming from Switzerland's mountainous landscape will be influenced by climate change, which will pose significant challenges to the water management and policy sector. In climate change impact research, the determination of future streamflow is impeded by different sources of uncertainty, which propagate through the model chain. In this research, we explicitly considered the following sources of uncertainty: (1) climate models, (2) downscaling of the climate projections to the catchment scale, (3) bias correction method and (4) parameterization of the hydrological model. We utilize climate projections at the 0.11 degree 12.5 km resolution from the EURO-CORDEX project, which are the most recent climate projections for the European domain. EURO-CORDEX is comprised of regional climate model (RCM) simulations, which have been downscaled from global climate models (GCMs) from the CMIP5 archive, using both dynamical and statistical techniques. Uncertainties are explored by applying a modeling chain involving 14 GCM-RCMs to ten Swiss catchments. We utilize the rainfall-runoff model HBV Light, which has been widely used in operational hydrological forecasting. The Lindström measure, a combination of model efficiency and volume error, was used as an objective function to calibrate HBV Light. Ten best sets of parameters are then achieved by calibrating using the genetic algorithm and Powell optimization (GAP) method. The GAP optimization method is based on the evolution of parameter sets, which works by selecting and recombining high performing parameter sets with each other. Once HBV is calibrated, we then perform a quantitative comparison of the influence of biases inherited from climate model simulations to the biases stemming from the hydrological model. The evaluation is conducted over two time periods: i) 1980-2009 to characterize the simulation realism under the current climate and ii) 2070-2099 to identify the magnitude of the projected change of streamflow under the climate scenarios RCP4.5 and RCP8.5. We utilize two techniques for correcting biases in the climate model output: quantile mapping and a new method, frequency bias correction. The FBC method matches the frequencies between observed and GCM-RCM data. In this way, it can be used to correct for all time scales, which is a known limitation of quantile mapping. A novel approach for the evaluation of the climate simulations and bias correction methods was then applied. Streamflow can be thought of as the "great integrator" of uncertainties. The ability, or the lack thereof, to correctly simulate streamflow is a way to assess the realism of the bias-corrected climate simulations. Long-term monthly mean as well as high and low flow metrics are used to evaluate the realism of the simulations under current climate and to gauge the impacts of climate change on streamflow. Preliminary results show that under present climate, calibration of the hydrological model comprises of a much smaller band of uncertainty in the modeling chain as compared to the bias correction of the GCM-RCMs. Therefore, for future time periods, we expect the bias correction of climate model data to have a greater influence on projected changes in streamflow than the calibration of the hydrological model.

Objective spatiotemporal proxy-model comparisons of the Asian monsoon for the last millennium

NASA Astrophysics Data System (ADS)

Anchukaitis, K. J.; Cook, E. R.; Ammann, C. M.; Buckley, B. M.; D'Arrigo, R. D.; Jacoby, G.; Wright, W. E.; Davi, N.; Li, J.

2008-12-01

The Asian monsoon system can be studied using a complementary proxy/simulation approach which evaluates climate models using estimates of past precipitation and temperature, and which subsequently applies the best understanding of the physics of the climate system as captured in general circulation models to evaluate the broad-scale dynamics behind regional paleoclimate reconstructions. Here, we use a millennial-length climate field reconstruction of monsoon season summer (JJA) drought, developed from tree- ring proxies, with coupled climate simulations from NCAR CSM1.4 and CCSM3 to evaluate the cause of large- scale persistent droughts over the last one thousand years. Direct comparisons are made between the external forced response within the climate model and the spatiotemporal field reconstruction. In order to identify patterns of drought associated with internal variability in the climate system, we use a model/proxy analog technique which objectively selects epochs in the model that most closely reproduce those observed in the reconstructions. The concomitant ocean-atmosphere dynamics are then interpreted in order to identify and understand the internal climate system forcing of low frequency monsoon variability. We examine specific periods of extensive or intensive regional drought in the 15th, 17th, and 18th centuries, many of which are coincident with major cultural changes in the region.

Probabilistic Evaluation of Competing Climate Models

NASA Astrophysics Data System (ADS)

Braverman, A. J.; Chatterjee, S.; Heyman, M.; Cressie, N.

2017-12-01

A standard paradigm for assessing the quality of climate model simulations is to compare what these models produce for past and present time periods, to observations of the past and present. Many of these comparisons are based on simple summary statistics called metrics. Here, we propose an alternative: evaluation of competing climate models through probabilities derived from tests of the hypothesis that climate-model-simulated and observed time sequences share common climate-scale signals. The probabilities are based on the behavior of summary statistics of climate model output and observational data, over ensembles of pseudo-realizations. These are obtained by partitioning the original time sequences into signal and noise components, and using a parametric bootstrap to create pseudo-realizations of the noise sequences. The statistics we choose come from working in the space of decorrelated and dimension-reduced wavelet coefficients. We compare monthly sequences of CMIP5 model output of average global near-surface temperature anomalies to similar sequences obtained from the well-known HadCRUT4 data set, as an illustration.

Local Difference Measures between Complex Networks for Dynamical System Model Evaluation

PubMed Central

Lange, Stefan; Donges, Jonathan F.; Volkholz, Jan; Kurths, Jürgen

2015-01-01

A faithful modeling of real-world dynamical systems necessitates model evaluation. A recent promising methodological approach to this problem has been based on complex networks, which in turn have proven useful for the characterization of dynamical systems. In this context, we introduce three local network difference measures and demonstrate their capabilities in the field of climate modeling, where these measures facilitate a spatially explicit model evaluation. Building on a recent study by Feldhoff et al. [1] we comparatively analyze statistical and dynamical regional climate simulations of the South American monsoon system. Three types of climate networks representing different aspects of rainfall dynamics are constructed from the modeled precipitation space-time series. Specifically, we define simple graphs based on positive as well as negative rank correlations between rainfall anomaly time series at different locations, and such based on spatial synchronizations of extreme rain events. An evaluation against respective networks built from daily satellite data provided by the Tropical Rainfall Measuring Mission 3B42 V7 reveals far greater differences in model performance between network types for a fixed but arbitrary climate model than between climate models for a fixed but arbitrary network type. We identify two sources of uncertainty in this respect. Firstly, climate variability limits fidelity, particularly in the case of the extreme event network; and secondly, larger geographical link lengths render link misplacements more likely, most notably in the case of the anticorrelation network; both contributions are quantified using suitable ensembles of surrogate networks. Our model evaluation approach is applicable to any multidimensional dynamical system and especially our simple graph difference measures are highly versatile as the graphs to be compared may be constructed in whatever way required. Generalizations to directed as well as edge- and node-weighted graphs are discussed. PMID:25856374

Local difference measures between complex networks for dynamical system model evaluation.

PubMed

Lange, Stefan; Donges, Jonathan F; Volkholz, Jan; Kurths, Jürgen

2015-01-01

A faithful modeling of real-world dynamical systems necessitates model evaluation. A recent promising methodological approach to this problem has been based on complex networks, which in turn have proven useful for the characterization of dynamical systems. In this context, we introduce three local network difference measures and demonstrate their capabilities in the field of climate modeling, where these measures facilitate a spatially explicit model evaluation.Building on a recent study by Feldhoff et al. [8] we comparatively analyze statistical and dynamical regional climate simulations of the South American monsoon system [corrected]. types of climate networks representing different aspects of rainfall dynamics are constructed from the modeled precipitation space-time series. Specifically, we define simple graphs based on positive as well as negative rank correlations between rainfall anomaly time series at different locations, and such based on spatial synchronizations of extreme rain events. An evaluation against respective networks built from daily satellite data provided by the Tropical Rainfall Measuring Mission 3B42 V7 reveals far greater differences in model performance between network types for a fixed but arbitrary climate model than between climate models for a fixed but arbitrary network type. We identify two sources of uncertainty in this respect. Firstly, climate variability limits fidelity, particularly in the case of the extreme event network; and secondly, larger geographical link lengths render link misplacements more likely, most notably in the case of the anticorrelation network; both contributions are quantified using suitable ensembles of surrogate networks. Our model evaluation approach is applicable to any multidimensional dynamical system and especially our simple graph difference measures are highly versatile as the graphs to be compared may be constructed in whatever way required. Generalizations to directed as well as edge- and node-weighted graphs are discussed.

A new test statistic for climate models that includes field and spatial dependencies using Gaussian Markov random fields

DOE PAGES

Nosedal-Sanchez, Alvaro; Jackson, Charles S.; Huerta, Gabriel

2016-07-20

A new test statistic for climate model evaluation has been developed that potentially mitigates some of the limitations that exist for observing and representing field and space dependencies of climate phenomena. Traditionally such dependencies have been ignored when climate models have been evaluated against observational data, which makes it difficult to assess whether any given model is simulating observed climate for the right reasons. The new statistic uses Gaussian Markov random fields for estimating field and space dependencies within a first-order grid point neighborhood structure. We illustrate the ability of Gaussian Markov random fields to represent empirical estimates of fieldmore » and space covariances using "witch hat" graphs. We further use the new statistic to evaluate the tropical response of a climate model (CAM3.1) to changes in two parameters important to its representation of cloud and precipitation physics. Overall, the inclusion of dependency information did not alter significantly the recognition of those regions of parameter space that best approximated observations. However, there were some qualitative differences in the shape of the response surface that suggest how such a measure could affect estimates of model uncertainty.« less

A new test statistic for climate models that includes field and spatial dependencies using Gaussian Markov random fields

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

Nosedal-Sanchez, Alvaro; Jackson, Charles S.; Huerta, Gabriel

A new test statistic for climate model evaluation has been developed that potentially mitigates some of the limitations that exist for observing and representing field and space dependencies of climate phenomena. Traditionally such dependencies have been ignored when climate models have been evaluated against observational data, which makes it difficult to assess whether any given model is simulating observed climate for the right reasons. The new statistic uses Gaussian Markov random fields for estimating field and space dependencies within a first-order grid point neighborhood structure. We illustrate the ability of Gaussian Markov random fields to represent empirical estimates of fieldmore » and space covariances using "witch hat" graphs. We further use the new statistic to evaluate the tropical response of a climate model (CAM3.1) to changes in two parameters important to its representation of cloud and precipitation physics. Overall, the inclusion of dependency information did not alter significantly the recognition of those regions of parameter space that best approximated observations. However, there were some qualitative differences in the shape of the response surface that suggest how such a measure could affect estimates of model uncertainty.« less

Climate Modeling and Analysis with Decision Makers in Mind

NASA Astrophysics Data System (ADS)

Jones, A. D.; Jagannathan, K.; Calvin, K. V.; Lamarque, J. F.; Ullrich, P. A.

2016-12-01

There is a growing need for information about future climate conditions to support adaptation planning across a wide range of sectors and stakeholder communities. However, our principal tools for understanding future climate - global Earth system models - were not developed with these user needs in mind, nor have we developed transparent methods for evaluating and communicating the credibility of various climate information products with respect to the climate characteristics that matter most to decision-makers. Several recent community engagements have identified a need for "co-production" of knowledge among stakeholders and scientists. Here we highlight some of the barriers to communication and collaboration that must be overcome to improve the dialogue among researchers and climate adaptation practitioners in a meaningful way. Solutions to this challenge are two-fold: 1) new institutional arrangements and collaborative mechanisms designed to improve coordination and understanding among communities, and 2) a research agenda that explicitly incorporates stakeholder needs into model evaluation, development, and experimental design. We contrast the information content in global-scale model evaluation exercises with that required for in specific decision contexts, such as long-term agricultural management decisions. Finally, we present a vision for advancing the science of model evaluation in the context of predicting decision-relevant hydroclimate regime shifts in North America.

Constructing optimal ensemble projections for predictive environmental modelling in Northern Eurasia

NASA Astrophysics Data System (ADS)

Anisimov, Oleg; Kokorev, Vasily

2013-04-01

Large uncertainties in climate impact modelling are associated with the forcing climate data. This study is targeted at the evaluation of the quality of GCM-based climatic projections in the specific context of predictive environmental modelling in Northern Eurasia. To accomplish this task, we used the output from 36 CMIP5 GCMs from the IPCC AR-5 data base for the control period 1975-2005 and calculated several climatic characteristics and indexes that are most often used in the impact models, i.e. the summer warmth index, duration of the vegetation growth period, precipitation sums, dryness index, thawing degree-day sums, and the annual temperature amplitude. We used data from 744 weather stations in Russia and neighbouring countries to analyze the spatial patterns of modern climatic change and to delineate 17 large regions with coherent temperature changes in the past few decades. GSM results and observational data were averaged over the coherent regions and compared with each other. Ultimately, we evaluated the skills of individual models, ranked them in the context of regional impact modelling and identified top-end GCMs that "better than average" reproduce modern regional changes of the selected meteorological parameters and climatic indexes. Selected top-end GCMs were used to compose several ensembles, each combining results from the different number of models. Ensembles were ranked using the same algorithm and outliers eliminated. We then used data from top-end ensembles for the 2000-2100 period to construct the climatic projections that are likely to be "better than average" in predicting climatic parameters that govern the state of environment in Northern Eurasia. The ultimate conclusions of our study are the following. • High-end GCMs that demonstrate excellent skills in conventional atmospheric model intercomparison experiments are not necessarily the best in replicating climatic characteristics that govern the state of environment in Northern Eurasia, and independent model evaluation on regional level is necessary to identify "better than average" GCMs. • Each of the ensembles combining results from several "better than average" models replicate selected meteorological parameters and climatic indexes better than any single GCM. The ensemble skills are parameter-specific and depend on models it consists of. The best results are not necessarily those based on the ensemble comprised by all "better than average" models. • Comprehensive evaluation of climatic scenarios using specific criteria narrows the range of uncertainties in environmental projections.

Evaluating the Contribution of Natural Variability and Climate Model Response to Uncertainty in Projections of Climate Change Impacts on U.S. Air Quality

EPA Science Inventory

We examine the effects of internal variability and model response in projections of climate impacts on U.S. ground-level ozone across the 21st century using integrated global system modeling and global atmospheric chemistry simulations. The impact of climate change on air polluti...

A comparison of metrics for assessing state-of-the-art climate models and implications for probabilistic projections of climate change

NASA Astrophysics Data System (ADS)

Ring, Christoph; Pollinger, Felix; Kaspar-Ott, Irena; Hertig, Elke; Jacobeit, Jucundus; Paeth, Heiko

2018-03-01

A major task of climate science are reliable projections of climate change for the future. To enable more solid statements and to decrease the range of uncertainty, global general circulation models and regional climate models are evaluated based on a 2 × 2 contingency table approach to generate model weights. These weights are compared among different methodologies and their impact on probabilistic projections of temperature and precipitation changes is investigated. Simulated seasonal precipitation and temperature for both 50-year trends and climatological means are assessed at two spatial scales: in seven study regions around the globe and in eight sub-regions of the Mediterranean area. Overall, 24 models of phase 3 and 38 models of phase 5 of the Coupled Model Intercomparison Project altogether 159 transient simulations of precipitation and 119 of temperature from four emissions scenarios are evaluated against the ERA-20C reanalysis over the 20th century. The results show high conformity with previous model evaluation studies. The metrics reveal that mean of precipitation and both temperature mean and trend agree well with the reference dataset and indicate improvement for the more recent ensemble mean, especially for temperature. The method is highly transferrable to a variety of further applications in climate science. Overall, there are regional differences of simulation quality, however, these are less pronounced than those between the results for 50-year mean and trend. The trend results are suitable for assigning weighting factors to climate models. Yet, the implications for probabilistic climate projections is strictly dependent on the region and season.

Developing a Toolkit for Model Evaluation Using Speleothem Isotope Data

NASA Astrophysics Data System (ADS)

Comas-Bru, L.; Deininger, M.; Harrison, S.

2017-12-01

Speleothems can provide high-resolution records of changes in both climate and atmospheric composition. These records have the potential to be used to document regional changes in mean climate and climate variability on annual to centennial timescales. They can also be used to refine our understanding of regional changes in climate forcings, such as dust and volcanic aerosols, through time. Many climate models now explicitly include isotopic tracers, and thus the isotopic records from speleothems can be used for model evaluation. Previous attempts to compile speleothem data have not provided a globally-comprehensive synthesis, nor have they provided assessments of measurement, chronological or interpretation uncertainties. SISAL (Speleothem Isotopes Synthesis and Analysis) is a new community-based working groupsponsored by Past Global Changes (PAGES) to synthesise the 500+speleothem isotopic records available globallyand develop a public-accessdatabase, that can be used both to explore past climate changes and in model evaluation. This presentation will showcase preliminary syntheses for the Last Glacial Maximum (21 ka), the mid-Holocene (6 ka) and the Last Millennium (850-1850 CE).

Mathematics applied to the climate system: outstanding challenges and recent progress

PubMed Central

Williams, Paul D.; Cullen, Michael J. P.; Davey, Michael K.; Huthnance, John M.

2013-01-01

The societal need for reliable climate predictions and a proper assessment of their uncertainties is pressing. Uncertainties arise not only from initial conditions and forcing scenarios, but also from model formulation. Here, we identify and document three broad classes of problems, each representing what we regard to be an outstanding challenge in the area of mathematics applied to the climate system. First, there is the problem of the development and evaluation of simple physically based models of the global climate. Second, there is the problem of the development and evaluation of the components of complex models such as general circulation models. Third, there is the problem of the development and evaluation of appropriate statistical frameworks. We discuss these problems in turn, emphasizing the recent progress made by the papers presented in this Theme Issue. Many pressing challenges in climate science require closer collaboration between climate scientists, mathematicians and statisticians. We hope the papers contained in this Theme Issue will act as inspiration for such collaborations and for setting future research directions. PMID:23588054

Intercomparison of the capabilities of simplified climate models to project the effects of aviation CO2 on climate

NASA Astrophysics Data System (ADS)

Khodayari, Arezoo; Wuebbles, Donald J.; Olsen, Seth C.; Fuglestvedt, Jan S.; Berntsen, Terje; Lund, Marianne T.; Waitz, Ian; Wolfe, Philip; Forster, Piers M.; Meinshausen, Malte; Lee, David S.; Lim, Ling L.

2013-08-01

This study evaluates the capabilities of the carbon cycle and energy balance treatments relative to the effect of aviation CO2 emissions on climate in several existing simplified climate models (SCMs) that are either being used or could be used for evaluating the effects of aviation on climate. Since these models are used in policy-related analyses, it is important that the capabilities of such models represent the state of understanding of the science. We compare the Aviation Environmental Portfolio Management Tool (APMT) Impacts climate model, two models used at the Center for International Climate and Environmental Research-Oslo (CICERO-1 and CICERO-2), the Integrated Science Assessment Model (ISAM) model as described in Jain et al. (1994), the simple Linear Climate response model (LinClim) and the Model for the Assessment of Greenhouse-gas Induced Climate Change version 6 (MAGICC6). In this paper we select scenarios to illustrate the behavior of the carbon cycle and energy balance models in these SCMs. This study is not intended to determine the absolute and likely range of the expected climate response in these models but to highlight specific features in model representations of the carbon cycle and energy balance models that need to be carefully considered in studies of aviation effects on climate. These results suggest that carbon cycle models that use linear impulse-response-functions (IRF) in combination with separate equations describing air-sea and air-biosphere exchange of CO2 can account for the dominant nonlinearities in the climate system that would otherwise not have been captured with an IRF alone, and hence, produce a close representation of more complex carbon cycle models. Moreover, results suggest that an energy balance model with a 2-box ocean sub-model and IRF tuned to reproduce the response of coupled Earth system models produces a close representation of the globally-averaged temperature response of more complex energy balance models.

Characterization of the Dynamics of Climate Systems and Identification of Missing Mechanisms Impacting the Long Term Predictive Capabilities of Global Climate Models Utilizing Dynamical Systems Approaches to the Analysis of Observed and Modeled Climate

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

Bhatt, Uma S.; Wackerbauer, Renate; Polyakov, Igor V.

The goal of this research was to apply fractional and non-linear analysis techniques in order to develop a more complete characterization of climate change and variability for the oceanic, sea ice and atmospheric components of the Earth System. This research applied two measures of dynamical characteristics of time series, the R/S method of calculating the Hurst exponent and Renyi entropy, to observational and modeled climate data in order to evaluate how well climate models capture the long-term dynamics evident in observations. Fractional diffusion analysis was applied to ARGO ocean buoy data to quantify ocean transport. Self organized maps were appliedmore » to North Pacific sea level pressure and analyzed in ways to improve seasonal predictability for Alaska fire weather. This body of research shows that these methods can be used to evaluate climate models and shed light on climate mechanisms (i.e., understanding why something happens). With further research, these methods show promise for improving seasonal to longer time scale forecasts of climate.« less

GCSS/WGNE Pacific Cross-section Intercomparison: Tropical and Subtropical Cloud Transitions

NASA Astrophysics Data System (ADS)

Teixeira, J.

2008-12-01

In this presentation I will discuss the role of the GEWEX Cloud Systems Study (GCSS) working groups in paving the way for substantial improvements in cloud parameterization in weather and climate models. The GCSS/WGNE Pacific Cross-section Intercomparison (GPCI) is an extension of GCSS and is a different type of model evaluation where climate models are analyzed along a Pacific Ocean transect from California to the equator. This approach aims at complementing the more traditional efforts in GCSS by providing a simple framework for the evaluation of models that encompasses several fundamental cloud regimes such as stratocumulus, shallow cumulus and deep cumulus, as well as the transitions between them. Currently twenty four climate and weather prediction models are participating in GPCI. We will present results of the comparison between models and recent satellite data. In particular, we will explore in detail the potential of the Atmospheric Infrared Sounder (AIRS) and CloudSat data for the evaluation of the representation of clouds and convection in climate models.

Evaluation of high-resolution climate simulations for West Africa using COSMO-CLM

NASA Astrophysics Data System (ADS)

Dieng, Diarra; Smiatek, Gerhard; Bliefernicht, Jan; Laux, Patrick; Heinzeller, Dominikus; Kunstmann, Harald; Sarr, Abdoulaye; Thierno Gaye, Amadou

2017-04-01

The climate change modeling activities within the WASCAL program (West African Science Service Center on Climate Change and Adapted Land Use) concentrate on the provisioning of future climate change scenario data at high spatial and temporal resolution and quality in West Africa. Such information is highly required for impact studies in water resources and agriculture for the development of reliable climate change adaptation and mitigation strategies. In this study, we present a detailed evaluation of high simulation runs based on the regional climate model, COSMO model in CLimate Mode (COSMO-CLM). The model is applied over West Africa in a nested approach with two simulation domains at 0.44° and 0.11° resolution using reanalysis data from ERA-Interim (1979-2013). The models runs are compared to several state-of-the-art observational references (e.g., CRU, CHIRPS) including daily precipitation data provided by national meteorological services in West Africa. Special attention is paid to the reproduction of the dynamics of the West African Monsoon (WMA), its associated precipitation patterns and crucial agro-climatological indices such as the onset of the rainy season. In addition, first outcomes of the regional climate change simulations driven by MPI-ESM-LR are presented for a historical period (1980 to 2010) and two future periods (2020 to 2050, 2070 to 2100). The evaluation of the reanalysis runs shows that COSMO-CLM is able to reproduce the observed major climate characteristics including the West African Monsoon within the range of comparable RCM evaluations studies. However, substantial uncertainties remain, especially in the Sahel zone. The added value of the higher resolution of the nested run is reflected in a smaller bias in extreme precipitation statistics with respect to the reference data.

Simulating forage crop production in a northern climate with the Integrated Farm System Model

USDA-ARS?s Scientific Manuscript database

Whole-farm simulation models are useful tools for evaluating the effect of management practices and climate variability on the agro-environmental and economic performance of farms. A few process-based farm-scale models have been developed, but none have been evaluated in a northern region with a sho...

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.

An evaluation of the variable-resolution CESM for modeling California's climate: Evaluation of VR-CESM for Modeling California's Climate

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

Huang, Xingying; Rhoades, Alan M.; Ullrich, Paul A.

In this paper, the recently developed variable-resolution option within the Community Earth System Model (VR-CESM) is assessed for long-term regional climate modeling of California at 0.25° (~ 28 km) and 0.125° (~ 14 km) horizontal resolutions. The mean climatology of near-surface temperature and precipitation is analyzed and contrasted with reanalysis, gridded observational data sets, and a traditional regional climate model (RCM)—the Weather Research and Forecasting (WRF) model. Statistical metrics for model evaluation and tests for differential significance have been extensively applied. VR-CESM tended to produce a warmer summer (by about 1–3°C) and overestimated overall winter precipitation (about 25%–35%) compared tomore » reference data sets when sea surface temperatures were prescribed. Increasing resolution from 0.25° to 0.125° did not produce a statistically significant improvement in the model results. By comparison, the analogous WRF climatology (constrained laterally and at the sea surface by ERA-Interim reanalysis) was ~1–3°C colder than the reference data sets, underestimated precipitation by ~20%–30% at 27 km resolution, and overestimated precipitation by ~ 65–85% at 9 km. Overall, VR-CESM produced comparable statistical biases to WRF in key climatological quantities. Moreover, this assessment highlights the value of variable-resolution global climate models (VRGCMs) in capturing fine-scale atmospheric processes, projecting future regional climate, and addressing the computational expense of uniform-resolution global climate models.« less

An evaluation of the variable-resolution CESM for modeling California's climate: Evaluation of VR-CESM for Modeling California's Climate

DOE PAGES

Huang, Xingying; Rhoades, Alan M.; Ullrich, Paul A.; ...

2016-03-01

In this paper, the recently developed variable-resolution option within the Community Earth System Model (VR-CESM) is assessed for long-term regional climate modeling of California at 0.25° (~ 28 km) and 0.125° (~ 14 km) horizontal resolutions. The mean climatology of near-surface temperature and precipitation is analyzed and contrasted with reanalysis, gridded observational data sets, and a traditional regional climate model (RCM)—the Weather Research and Forecasting (WRF) model. Statistical metrics for model evaluation and tests for differential significance have been extensively applied. VR-CESM tended to produce a warmer summer (by about 1–3°C) and overestimated overall winter precipitation (about 25%–35%) compared tomore » reference data sets when sea surface temperatures were prescribed. Increasing resolution from 0.25° to 0.125° did not produce a statistically significant improvement in the model results. By comparison, the analogous WRF climatology (constrained laterally and at the sea surface by ERA-Interim reanalysis) was ~1–3°C colder than the reference data sets, underestimated precipitation by ~20%–30% at 27 km resolution, and overestimated precipitation by ~ 65–85% at 9 km. Overall, VR-CESM produced comparable statistical biases to WRF in key climatological quantities. Moreover, this assessment highlights the value of variable-resolution global climate models (VRGCMs) in capturing fine-scale atmospheric processes, projecting future regional climate, and addressing the computational expense of uniform-resolution global climate models.« less

Simulation of Climate Change Impacts on Wheat-Fallow Cropping Systems

USDA-ARS?s Scientific Manuscript database

Agricultural system simulation models are predictive tools for assessing climate change impacts on crop production. In this study, RZWQM2 that contains the DSSAT 4.0-CERES model was evaluated for simulating climate change impacts on wheat growth. The model was calibrated and validated using data fro...

Investigating Downscaling Methods and Evaluating Climate Models for Use in Estimating Regional Water Resources in Mountainous Regions under Changing Climatic Conditions

NASA Technical Reports Server (NTRS)

Frei, Allan; Nolin, Anne W.; Serreze, Mark C.; Armstrong, Richard L.; McGinnis, David L.; Robinson, David A.

2004-01-01

The purpose of this three-year study is to develop and evaluate techniques to estimate the range of potential hydrological impacts of climate change in mountainous areas. Three main objectives are set out in the proposal. (1) To develop and evaluate transfer functions to link tropospheric circulation to regional snowfall. (2) To evaluate a suite of General Circulation Models (GCMs) for use in estimating synoptic scale circulation and the resultant regional snowfall. And (3) to estimate the range of potential hydrological impacts of changing climate in the two case study areas: the Upper Colorado River basin, and the Catskill Mountains of southeastern New York State. Both regions provide water to large populations.

Challenges in Quantifying Pliocene Terrestrial Warming Revealed by Data-Model Discord

NASA Technical Reports Server (NTRS)

Salzmann, Ulrich; Dolan, Aisling M.; Haywood, Alan M.; Chan, Wing-Le; Voss, Jochen; Hill, Daniel J.; Abe-Ouchi, Ayako; Otto-Bliesner, Bette; Bragg, Frances J.; Chandler, Mark A.;

Emergent climate and CO2 sensitivities of net primary productivity in ecosystem models do not agree with empirical data in temperate forests of eastern North America.

PubMed

Rollinson, Christine R; Liu, Yao; Raiho, Ann; Moore, David J P; McLachlan, Jason; Bishop, Daniel A; Dye, Alex; Matthes, Jaclyn H; Hessl, Amy; Hickler, Thomas; Pederson, Neil; Poulter, Benjamin; Quaife, Tristan; Schaefer, Kevin; Steinkamp, Jörg; Dietze, Michael C

2017-07-01

Ecosystem models show divergent responses of the terrestrial carbon cycle to global change over the next century. Individual model evaluation and multimodel comparisons with data have largely focused on individual processes at subannual to decadal scales. Thus far, data-based evaluations of emergent ecosystem responses to climate and CO 2 at multidecadal and centennial timescales have been rare. We compared the sensitivity of net primary productivity (NPP) to temperature, precipitation, and CO 2 in ten ecosystem models with the sensitivities found in tree-ring reconstructions of NPP and raw ring-width series at six temperate forest sites. These model-data comparisons were evaluated at three temporal extents to determine whether the rapid, directional changes in temperature and CO 2 in the recent past skew our observed responses to multiple drivers of change. All models tested here were more sensitive to low growing season precipitation than tree-ring NPP and ring widths in the past 30 years, although some model precipitation responses were more consistent with tree rings when evaluated over a full century. Similarly, all models had negative or no response to warm-growing season temperatures, while tree-ring data showed consistently positive effects of temperature. Although precipitation responses were least consistent among models, differences among models to CO 2 drive divergence and ensemble uncertainty in relative change in NPP over the past century. Changes in forest composition within models had no effect on climate or CO 2 sensitivity. Fire in model simulations reduced model sensitivity to climate and CO 2 , but only over the course of multiple centuries. Formal evaluation of emergent model behavior at multidecadal and multicentennial timescales is essential to reconciling model projections with observed ecosystem responses to past climate change. Future evaluation should focus on improved representation of disturbance and biomass change as well as the feedbacks with moisture balance and CO 2 in individual models. © 2017 John Wiley & Sons Ltd.

Evaluation of regional climate simulations over the Great Lakes region driven by three global data sets

Treesearch

Shiyuan Zhong; Xiuping Li; Xindi Bian; Warren E. Heilman; L. Ruby Leung; William I. Jr. Gustafson

2012-01-01

The performance of regional climate simulations is evaluated for the Great Lakes region. Three 10-year (1990-1999) current-climate simulations are performed using the MM5 regional climate model (RCM) with 36-km horizontal resolution. The simulations employed identical configuration and physical parameterizations, but different lateral boundary conditions and sea-...

Geoengineering as a design problem

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

Kravitz, Ben; MacMartin, Douglas G.; Wang, Hailong

2016-01-01

Understanding the climate impacts of solar geoengineering is essential for evaluating its benefits and risks. Most previous simulations have prescribed a particular strategy and evaluated its modeled effects. Here we turn this approach around by first choosing example climate objectives and then designing a strategy to meet those objectives in climate models. There are four essential criteria for designing a strategy: (i) an explicit specification of the objectives, (ii) defining what climate forcing agents to modify so the objectives are met, (iii) a method for managing uncertainties, and (iv) independent verification of the strategy in an evaluation model. We demonstrate this design perspective throughmore » two multi-objective examples. First, changes in Arctic temperature and the position of tropical precipitation due to CO 2 increases are offset by adjusting high-latitude insolation in each hemisphere independently. Second, three different latitude-dependent patterns of insolation are modified to offset CO 2-induced changes in global mean temperature, interhemispheric temperature asymmetry, and the Equator-to-pole temperature gradient. In both examples, the "design" and "evaluation" models are state-of-the-art fully coupled atmosphere–ocean general circulation models.« less

Evaluating the performance of infectious disease forecasts: A comparison of climate-driven and seasonal dengue forecasts for Mexico.

PubMed

Johansson, Michael A; Reich, Nicholas G; Hota, Aditi; Brownstein, John S; Santillana, Mauricio

2016-09-26

Dengue viruses, which infect millions of people per year worldwide, cause large epidemics that strain healthcare systems. Despite diverse efforts to develop forecasting tools including autoregressive time series, climate-driven statistical, and mechanistic biological models, little work has been done to understand the contribution of different components to improved prediction. We developed a framework to assess and compare dengue forecasts produced from different types of models and evaluated the performance of seasonal autoregressive models with and without climate variables for forecasting dengue incidence in Mexico. Climate data did not significantly improve the predictive power of seasonal autoregressive models. Short-term and seasonal autocorrelation were key to improving short-term and long-term forecasts, respectively. Seasonal autoregressive models captured a substantial amount of dengue variability, but better models are needed to improve dengue forecasting. This framework contributes to the sparse literature of infectious disease prediction model evaluation, using state-of-the-art validation techniques such as out-of-sample testing and comparison to an appropriate reference model.

Evaluating the performance of infectious disease forecasts: A comparison of climate-driven and seasonal dengue forecasts for Mexico

PubMed Central

Johansson, Michael A.; Reich, Nicholas G.; Hota, Aditi; Brownstein, John S.; Santillana, Mauricio

2016-01-01

Dengue viruses, which infect millions of people per year worldwide, cause large epidemics that strain healthcare systems. Despite diverse efforts to develop forecasting tools including autoregressive time series, climate-driven statistical, and mechanistic biological models, little work has been done to understand the contribution of different components to improved prediction. We developed a framework to assess and compare dengue forecasts produced from different types of models and evaluated the performance of seasonal autoregressive models with and without climate variables for forecasting dengue incidence in Mexico. Climate data did not significantly improve the predictive power of seasonal autoregressive models. Short-term and seasonal autocorrelation were key to improving short-term and long-term forecasts, respectively. Seasonal autoregressive models captured a substantial amount of dengue variability, but better models are needed to improve dengue forecasting. This framework contributes to the sparse literature of infectious disease prediction model evaluation, using state-of-the-art validation techniques such as out-of-sample testing and comparison to an appropriate reference model. PMID:27665707

Goals and Values in School: A Model Developed for Describing, Evaluating and Changing the Social Climate of Learning Environments

ERIC Educational Resources Information Center

Allodi, Mara Westling

2010-01-01

This paper defines a broad model of the psychosocial climate in educational settings. The model was developed from a general theory of learning environments, on a theory of human values and on empirical studies of children's evaluations of their schools. The contents of the model are creativity, stimulation, achievement, self-efficacy, creativity,…

Evaluating the effects of climate change on summertime ozone using a relative reduction factor approach for policymakers

EPA Science Inventory

The impact of climate change on surface-level ozone is examined through a multiscale modeling effort that linked global and regional climate models to drive air quality model simulations. Results are quantified in terms of the relative response factor (RRFE), which estimates the ...

Modelling the effects of past and future climate on the risk of bluetongue emergence in Europe

PubMed Central

Guis, Helene; Caminade, Cyril; Calvete, Carlos; Morse, Andrew P.; Tran, Annelise; Baylis, Matthew

2012-01-01

Vector-borne diseases are among those most sensitive to climate because the ecology of vectors and the development rate of pathogens within them are highly dependent on environmental conditions. Bluetongue (BT), a recently emerged arboviral disease of ruminants in Europe, is often cited as an illustration of climate's impact on disease emergence, although no study has yet tested this association. Here, we develop a framework to quantitatively evaluate the effects of climate on BT's emergence in Europe by integrating high-resolution climate observations and model simulations within a mechanistic model of BT transmission risk. We demonstrate that a climate-driven model explains, in both space and time, many aspects of BT's recent emergence and spread, including the 2006 BT outbreak in northwest Europe which occurred in the year of highest projected risk since at least 1960. Furthermore, the model provides mechanistic insight into BT's emergence, suggesting that the drivers of emergence across Europe differ between the South and the North. Driven by simulated future climate from an ensemble of 11 regional climate models, the model projects increase in the future risk of BT emergence across most of Europe with uncertainty in rate but not in trend. The framework described here is adaptable and applicable to other diseases, where the link between climate and disease transmission risk can be quantified, permitting the evaluation of scale and uncertainty in climate change's impact on the future of such diseases. PMID:21697167

Watershed scale response to climate change--Trout Lake Basin, Wisconsin

USGS Publications Warehouse

Walker, John F.; Hunt, Randall J.; Hay, Lauren E.; Markstrom, Steven L.

2012-01-01

Fourteen basins for which the Precipitation Runoff Modeling System has been calibrated and evaluated were selected as study sites. Precipitation Runoff Modeling System is a deterministic, distributed parameter watershed model developed to evaluate the effects of various combinations of precipitation, temperature, and land use on streamflow and general basin hydrology. Output from five General Circulation Model simulations and four emission scenarios were used to develop an ensemble of climate-change scenarios for each basin. These ensembles were simulated with the corresponding Precipitation Runoff Modeling System model. This fact sheet summarizes the hydrologic effect and sensitivity of the Precipitation Runoff Modeling System simulations to climate change for the Trout River Basin at Trout Lake in northern Wisconsin.

Watershed scale response to climate change--Clear Creek Basin, Iowa

USGS Publications Warehouse

Christiansen, Daniel E.; Hay, Lauren E.; Markstrom, Steven L.

2012-01-01

Fourteen basins for which the Precipitation Runoff Modeling System has been calibrated and evaluated were selected as study sites. Precipitation Runoff Modeling System is a deterministic, distributed parameter watershed model developed to evaluate the effects of various combinations of precipitation, temperature, and land use on streamflow and general basin hydrology. Output from five General Circulation Model simulations and four emission scenarios were used to develop an ensemble of climate-change scenarios for each basin. These ensembles were simulated with the corresponding Precipitation Runoff Modeling System model. This fact sheet summarizes the hydrologic effect and sensitivity of the Precipitation Runoff Modeling System simulations to climate change for the Clear Creek Basin, near Coralville, Iowa.

Watershed scale response to climate change--Feather River Basin, California

USGS Publications Warehouse

Koczot, Kathryn M.; Markstrom, Steven L.; Hay, Lauren E.

2012-01-01

Fourteen basins for which the Precipitation Runoff Modeling System has been calibrated and evaluated were selected as study sites. Precipitation Runoff Modeling System is a deterministic, distributed parameter watershed model developed to evaluate the effects of various combinations of precipitation, temperature, and land use on streamflow and general basin hydrology. Output from five General Circulation Model simulations and four emission scenarios were used to develop an ensemble of climate-change scenarios for each basin. These ensembles were simulated with the corresponding Precipitation Runoff Modeling System model. This fact sheet summarizes the hydrologic effect and sensitivity of the Precipitation Runoff Modeling System simulations to climate change for the Feather River Basin, California.

Watershed scale response to climate change--South Fork Flathead River Basin, Montana

USGS Publications Warehouse

Chase, Katherine J.; Hay, Lauren E.; Markstrom, Steven L.

2012-01-01

Fourteen basins for which the Precipitation Runoff Modeling System has been calibrated and evaluated were selected as study sites. Precipitation Runoff Modeling System is a deterministic, distributed parameter watershed model developed to evaluate the effects of various combinations of precipitation, temperature, and land use on streamflow and general basin hydrology. Output from five General Circulation Model simulations and four emission scenarios were used to develop an ensemble of climate-change scenarios for each basin. These ensembles were simulated with the corresponding Precipitation Runoff Modeling System model. This fact sheet summarizes the hydrologic effect and sensitivity of the Precipitation Runoff Modeling System simulations to climate change for the South Fork Flathead River Basin, Montana.

Watershed scale response to climate change--Cathance Stream Basin, Maine

USGS Publications Warehouse

Dudley, Robert W.; Hay, Lauren E.; Markstrom, Steven L.; Hodgkins, Glenn A.

2012-01-01

Fourteen basins for which the Precipitation Runoff Modeling System has been calibrated and evaluated were selected as study sites. Precipitation Runoff Modeling System is a deterministic, distributed parameter watershed model developed to evaluate the effects of various combinations of precipitation, temperature, and land use on streamflow and general basin hydrology. Output from five General Circulation Model simulations and four emission scenarios were used to develop an ensemble of climate-change scenarios for each basin. These ensembles were simulated with the corresponding Precipitation Runoff Modeling System model. This fact sheet summarizes the hydrologic effect and sensitivity of the Precipitation Runoff Modeling System simulations to climate change for the Cathance Stream Basin, Maine.

Watershed scale response to climate change--Pomperaug River Watershed, Connecticut

USGS Publications Warehouse

Bjerklie, David M.; Hay, Lauren E.; Markstrom, Steven L.

2012-01-01

Fourteen basins for which the Precipitation Runoff Modeling System has been calibrated and evaluated were selected as study sites. Precipitation Runoff Modeling System is a deterministic, distributed parameter watershed model developed to evaluate the effects of various combinations of precipitation, temperature, and land use on streamflow and general basin hydrology. Output from five General Circulation Model simulations and four emission scenarios were used to develop an ensemble of climate-change scenarios for each basin. These ensembles were simulated with the corresponding Precipitation Runoff Modeling System model. This fact sheet summarizes the hydrologic effect and sensitivity of the Precipitation Runoff Modeling System simulations to climate change for the Pomperaug River Basin at Southbury, Connecticut.

Watershed scale response to climate change--Starkweather Coulee Basin, North Dakota

USGS Publications Warehouse

Vining, Kevin C.; Hay, Lauren E.; Markstrom, Steven L.

2012-01-01

Fourteen basins for which the Precipitation Runoff Modeling System has been calibrated and evaluated were selected as study sites. Precipitation Runoff Modeling System is a deterministic, distributed parameter watershed model developed to evaluate the effects of various combinations of precipitation, temperature, and land use on streamflow and general basin hydrology. Output from five General Circulation Model simulations and four emission scenarios were used to develop an ensemble of climate-change scenarios for each basin. These ensembles were simulated with the corresponding Precipitation Runoff Modeling System model. This fact sheet summarizes the hydrologic effect and sensitivity of the Precipitation Runoff Modeling System simulations to climate change for the Starkweather Coulee Basin near Webster, North Dakota.

Watershed scale response to climate change--Sagehen Creek Basin, California

USGS Publications Warehouse

Markstrom, Steven L.; Hay, Lauren E.; Regan, R. Steven

2012-01-01

Fourteen basins for which the Precipitation Runoff Modeling System has been calibrated and evaluated were selected as study sites. Precipitation Runoff Modeling System is a deterministic, distributed parameter watershed model developed to evaluate the effects of various combinations of precipitation, temperature, and land use on streamflow and general basin hydrology. Output from five General Circulation Model simulations and four emission scenarios were used to develop an ensemble of climate-change scenarios for each basin. These ensembles were simulated with the corresponding Precipitation Runoff Modeling System model. This fact sheet summarizes the hydrologic effect and sensitivity of the Precipitation Runoff Modeling System simulations to climate change for the Sagehen Creek Basin near Truckee, California.

Watershed scale response to climate change--Sprague River Basin, Oregon

USGS Publications Warehouse

Risley, John; Hay, Lauren E.; Markstrom, Steven L.

2012-01-01

Fourteen basins for which the Precipitation Runoff Modeling System has been calibrated and evaluated were selected as study sites. Precipitation Runoff Modeling System is a deterministic, distributed parameter watershed model developed to evaluate the effects of various combinations of precipitation, temperature, and land use on streamflow and general basin hydrology. Output from five General Circulation Model simulations and four emission scenarios were used to develop an ensemble of climate-change scenarios for each basin. These ensembles were simulated with the corresponding Precipitation Runoff Modeling System model. This fact sheet summarizes the hydrologic effect and sensitivity of the Precipitation Runoff Modeling System simulations to climate change for the Sprague River Basin near Chiloquin, Oregon.

Watershed scale response to climate change--Black Earth Creek Basin, Wisconsin

USGS Publications Warehouse

Hunt, Randall J.; Walker, John F.; Westenbroek, Steven M.; Hay, Lauren E.; Markstrom, Steven L.

2012-01-01

Fourteen basins for which the Precipitation Runoff Modeling System has been calibrated and evaluated were selected as study sites. Precipitation Runoff Modeling System is a deterministic, distributed parameter watershed model developed to evaluate the effects of various combinations of precipitation, temperature, and land use on streamflow and general basin hydrology. Output from five General Circulation Model simulations and four emission scenarios were used to develop an ensemble of climate-change scenarios for each basin. These ensembles were simulated with the corresponding Precipitation Runoff Modeling System model. This fact sheet summarizes the hydrologic effect and sensitivity of the Precipitation Runoff Modeling System simulations to climate change for the Black Earth Creek Basin, Wisconsin.

Watershed scale response to climate change--East River Basin, Colorado

USGS Publications Warehouse

Battaglin, William A.; Hay, Lauren E.; Markstrom, Steven L.

2012-01-01

Fourteen basins for which the Precipitation Runoff Modeling System has been calibrated and evaluated were selected as study sites. Precipitation Runoff Modeling System is a deterministic, distributed parameter watershed model developed to evaluate the effects of various combinations of precipitation, temperature, and land use on streamflow and general basin hydrology. Output from five General Circulation Model simulations and four emission scenarios were used to develop an ensemble of climate-change scenarios for each basin. These ensembles were simulated with the corresponding Precipitation Runoff Modeling System model. This fact sheet summarizes the hydrologic effect and sensitivity of the Precipitation Runoff Modeling System simulations to climate change for the East River Basin, Colorado.

Watershed scale response to climate change--Naches River Basin, Washington

USGS Publications Warehouse

Mastin, Mark C.; Hay, Lauren E.; Markstrom, Steven L.

2012-01-01

Fourteen basins for which the Precipitation Runoff Modeling System has been calibrated and evaluated were selected as study sites. Precipitation Runoff Modeling System is a deterministic, distributed parameter watershed model developed to evaluate the effects of various combinations of precipitation, temperature, and land use on streamflow and general basin hydrology. Output from five General Circulation Model simulations and four emission scenarios were used to develop an ensemble of climate-change scenarios for each basin. These ensembles were simulated with the corresponding Precipitation Runoff Modeling System model. This fact sheet summarizes the hydrologic effect and sensitivity of the Precipitation Runoff Modeling System simulations to climate change for the Naches River Basin below Tieton River in Washington.

Watershed scale response to climate change--Flint River Basin, Georgia

USGS Publications Warehouse

Hay, Lauren E.; Markstrom, Steven L.

2012-01-01

Fourteen basins for which the Precipitation Runoff Modeling System has been calibrated and evaluated were selected as study sites. Precipitation Runoff Modeling System is a deterministic, distributed parameter watershed model developed to evaluate the effects of various combinations of precipitation, temperature, and land use on streamflow and general basin hydrology. Output from five General Circulation Model simulations and four emission scenarios were used to develop an ensemble of climate-change scenarios for each basin. These ensembles were simulated with the corresponding Precipitation Runoff Modeling System model. This fact sheet summarizes the hydrologic effect and sensitivity of the Precipitation Runoff Modeling System simulations to climate change for the Flint River Basin at Montezuma, Georgia.

Simulation of climate characteristics and extremes of the Volta Basin using CCLM and RCA regional climate models

NASA Astrophysics Data System (ADS)

Darko, Deborah; Adjei, Kwaku A.; Appiah-Adjei, Emmanuel K.; Odai, Samuel N.; Obuobie, Emmanuel; Asmah, Ruby

2018-06-01

The extent to which statistical bias-adjusted outputs of two regional climate models alter the projected change signals for the mean (and extreme) rainfall and temperature over the Volta Basin is evaluated. The outputs from two regional climate models in the Coordinated Regional Climate Downscaling Experiment for Africa (CORDEX-Africa) are bias adjusted using the quantile mapping technique. Annual maxima rainfall and temperature with their 10- and 20-year return values for the present (1981-2010) and future (2051-2080) climates are estimated using extreme value analyses. Moderate extremes are evaluated using extreme indices (viz. percentile-based, duration-based, and intensity-based). Bias adjustment of the original (bias-unadjusted) models improves the reproduction of mean rainfall and temperature for the present climate. However, the bias-adjusted models poorly reproduce the 10- and 20-year return values for rainfall and maximum temperature whereas the extreme indices are reproduced satisfactorily for the present climate. Consequently, projected changes in rainfall and temperature extremes were weak. The bias adjustment results in the reduction of the change signals for the mean rainfall while the mean temperature signals are rather magnified. The projected changes for the original mean climate and extremes are not conserved after bias adjustment with the exception of duration-based extreme indices.

Quantitative Decision Support Requires Quantitative User Guidance

NASA Astrophysics Data System (ADS)

Smith, L. A.

2009-12-01

Is it conceivable that models run on 2007 computer hardware could provide robust and credible probabilistic information for decision support and user guidance at the ZIP code level for sub-daily meteorological events in 2060? In 2090? Retrospectively, how informative would output from today’s models have proven in 2003? or the 1930’s? Consultancies in the United Kingdom, including the Met Office, are offering services to “future-proof” their customers from climate change. How is a US or European based user or policy maker to determine the extent to which exciting new Bayesian methods are relevant here? or when a commercial supplier is vastly overselling the insights of today’s climate science? How are policy makers and academic economists to make the closely related decisions facing them? How can we communicate deep uncertainty in the future at small length-scales without undermining the firm foundation established by climate science regarding global trends? Three distinct aspects of the communication of the uses of climate model output targeting users and policy makers, as well as other specialist adaptation scientists, are discussed. First, a brief scientific evaluation of the length and time scales at which climate model output is likely to become uninformative is provided, including a note on the applicability the latest Bayesian methodology to current state-of-the-art general circulation models output. Second, a critical evaluation of the language often employed in communication of climate model output, a language which accurately states that models are “better”, have “improved” and now “include” and “simulate” relevant meteorological processed, without clearly identifying where the current information is thought to be uninformative and misleads, both for the current climate and as a function of the state of the (each) climate simulation. And thirdly, a general approach for evaluating the relevance of quantitative climate model output for a given problem is presented. Based on climate science, meteorology, and the details of the question in hand, this approach identifies necessary (never sufficient) conditions required for the rational use of climate model output in quantitative decision support tools. Inasmuch as climate forecasting is a problem of extrapolation, there will always be harsh limits on our ability to establish where a model is fit for purpose, this does not, however, limit us from identifying model noise as such, and thereby avoiding some cases of the misapplication and over interpretation of model output. It is suggested that failure to clearly communicate the limits of today’s climate model in providing quantitative decision relevant climate information to today’s users of climate information, would risk the credibility of tomorrow’s climate science and science based policy more generally.

Watershed scale response to climate change--Yampa River Basin, Colorado

USGS Publications Warehouse

Hay, Lauren E.; Battaglin, William A.; Markstrom, Steven L.

2012-01-01

General Circulation Model simulations of future climate through 2099 project a wide range of possible scenarios. To determine the sensitivity and potential effect of long-term climate change on the freshwater resources of the United States, the U.S. Geological Survey Global Change study, "An integrated watershed scale response to global change in selected basins across the United States" was started in 2008. The long-term goal of this national study is to provide the foundation for hydrologically based climate change studies across the nation. Fourteen basins for which the Precipitation Runoff Modeling System has been calibrated and evaluated were selected as study sites. Precipitation Runoff Modeling System is a deterministic, distributed parameter watershed model developed to evaluate the effects of various combinations of precipitation, temperature, and land use on streamflow and general basin hydrology. Output from five General Circulation Model simulations and four emission scenarios were used to develop an ensemble of climate-change scenarios for each basin. These ensembles were simulated with the corresponding Precipitation Runoff Modeling System model. This fact sheet summarizes the hydrologic effect and sensitivity of the Precipitation Runoff Modeling System simulations to climate change for the Yampa River Basin at Steamboat Springs, Colorado.

Why inputs matter: Selection of climatic variables for species distribution modelling in the Himalayan region

NASA Astrophysics Data System (ADS)

Bobrowski, Maria; Schickhoff, Udo

2017-04-01

Betula utilis is a major constituent of alpine treeline ecotones in the western and central Himalayan region. The objective of this study is to provide first time analysis of the potential distribution of Betula utilis in the subalpine and alpine belts of the Himalayan region using species distribution modelling. Using Generalized Linear Models (GLM) we aim at examining climatic factors controlling the species distribution under current climate conditions. Furthermore we evaluate the prediction ability of climate data derived from different statistical methods. GLMs were created using least correlated bioclimatic variables derived from two different climate models: 1) interpolated climate data (i.e. Worldclim, Hijmans et al., 2005) and 2) quasi-mechanistical statistical downscaling (i.e. Chelsa; Karger et al., 2016). Model accuracy was evaluated by the ability to predict the potential species distribution range. We found that models based on variables of Chelsa climate data had higher predictive power, whereas models using Worldclim climate data consistently overpredicted the potential suitable habitat for Betula utilis. Although climatic variables of Worldclim are widely used in modelling species distribution, our results suggest to treat them with caution when remote regions like the Himalayan mountains are in focus. Unmindful usage of climatic variables for species distribution models potentially cause misleading projections and may lead to wrong implications and recommendations for nature conservation. References: Hijmans, R.J., Cameron, S.E., Parra, J.L., Jones, P.G. & Jarvis, A. (2005) Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology, 25, 1965-1978. Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N., Linder, H.P. & Kessler, M. (2016) Climatologies at high resolution for the earth land surface areas. arXiv:1607.00217 [physics].

A prognostic pollen emissions model for climate models (PECM1.0)

NASA Astrophysics Data System (ADS)

Wozniak, Matthew C.; Steiner, Allison L.

2017-11-01

We develop a prognostic model called Pollen Emissions for Climate Models (PECM) for use within regional and global climate models to simulate pollen counts over the seasonal cycle based on geography, vegetation type, and meteorological parameters. Using modern surface pollen count data, empirical relationships between prior-year annual average temperature and pollen season start dates and end dates are developed for deciduous broadleaf trees (Acer, Alnus, Betula, Fraxinus, Morus, Platanus, Populus, Quercus, Ulmus), evergreen needleleaf trees (Cupressaceae, Pinaceae), grasses (Poaceae; C3, C4), and ragweed (Ambrosia). This regression model explains as much as 57 % of the variance in pollen phenological dates, and it is used to create a climate-flexible phenology that can be used to study the response of wind-driven pollen emissions to climate change. The emissions model is evaluated in the Regional Climate Model version 4 (RegCM4) over the continental United States by prescribing an emission potential from PECM and transporting pollen as aerosol tracers. We evaluate two different pollen emissions scenarios in the model using (1) a taxa-specific land cover database, phenology, and emission potential, and (2) a plant functional type (PFT) land cover, phenology, and emission potential. The simulated surface pollen concentrations for both simulations are evaluated against observed surface pollen counts in five climatic subregions. Given prescribed pollen emissions, the RegCM4 simulates observed concentrations within an order of magnitude, although the performance of the simulations in any subregion is strongly related to the land cover representation and the number of observation sites used to create the empirical phenological relationship. The taxa-based model provides a better representation of the phenology of tree-based pollen counts than the PFT-based model; however, we note that the PFT-based version provides a useful and climate-flexible emissions model for the general representation of the pollen phenology over the United States.

Overview of the Special Issue: A Multi-Model Framework to Achieve Consistent Evaluation of Climate Change Impacts in the United States

EPA Science Inventory

The Climate Change Impacts and Risk Analysis (CIRA) project establishes a new multi-model framework to systematically assess the impacts, economic damages, and risks from climate change in the United States. The primary goal of this framework to estimate how climate change impac...

Development and comparison of metrics for evaluating climate models and estimation of projection uncertainty

NASA Astrophysics Data System (ADS)

Ring, Christoph; Pollinger, Felix; Kaspar-Ott, Irena; Hertig, Elke; Jacobeit, Jucundus; Paeth, Heiko

2017-04-01

The COMEPRO project (Comparison of Metrics for Probabilistic Climate Change Projections of Mediterranean Precipitation), funded by the Deutsche Forschungsgemeinschaft (DFG), is dedicated to the development of new evaluation metrics for state-of-the-art climate models. Further, we analyze implications for probabilistic projections of climate change. This study focuses on the results of 4-field matrix metrics. Here, six different approaches are compared. We evaluate 24 models of the Coupled Model Intercomparison Project Phase 3 (CMIP3), 40 of CMIP5 and 18 of the Coordinated Regional Downscaling Experiment (CORDEX). In addition to the annual and seasonal precipitation the mean temperature is analysed. We consider both 50-year trend and climatological mean for the second half of the 20th century. For the probabilistic projections of climate change A1b, A2 (CMIP3) and RCP4.5, RCP8.5 (CMIP5,CORDEX) scenarios are used. The eight main study areas are located in the Mediterranean. However, we apply our metrics to globally distributed regions as well. The metrics show high simulation quality of temperature trend and both precipitation and temperature mean for most climate models and study areas. In addition, we find high potential for model weighting in order to reduce uncertainty. These results are in line with other accepted evaluation metrics and studies. The comparison of the different 4-field approaches reveals high correlations for most metrics. The results of the metric-weighted probabilistic density functions of climate change are heterogeneous. We find for different regions and seasons both increases and decreases of uncertainty. The analysis of global study areas is consistent with the regional study areas of the Medeiterrenean.

Improved climate model evaluation using a new, 750-year Antarctic-wide snow accumulation product

NASA Astrophysics Data System (ADS)

Medley, B.; Thomas, E. R.

2017-12-01

Snow that accumulates over the cold, dry grounded ice of Antarctica is an important component of its mass balance, mitigating the ice sheet's contribution to sea level. Secular trends in accumulation not only result trends in the mass balance of the Antarctic Ice Sheet, but also directly and indirectly impact surface height changes. Long-term and spatiotemporally complete records of snow accumulation are needed to understand part and present Antarctic-wide mass balance, to convert from altimetry derived volume change to mass change, and to evaluate the ability of climate models to reproduce the observed climate change. We need measurements in both time and space, yet they typically sample one dimension at the expense of the other. Here, we develop a spatially complete, annually resolved snow accumulation product for the Antarctic Ice Sheet over the past 750 years by combining a newly compiled database of ice core accumulation records with climate model output. We mainly focus on climate model evaluation. Because the product spans several centuries, we can evaluate model ability in representing the preindustrial as well as present day accumulation change. Significant long-term trends in snow accumulation are found over the Ross and Bellingshausen Sea sectors of West Antarctica, the Antarctic Peninsula, and several sectors in East Antarctica. These results suggest that change is more complex over the Antarctic Ice Sheet than a simple uniform change (i.e., more snowfall in a warming world), which highlights the importance of atmospheric circulation as a major driver of change. By evaluating several climate models' ability to reproduce the observed trends, we can deduce whether their projections are reasonable or potentially biased where the latter would result in a misrepresentation of the Antarctic contribution to sea level.

Shallow Horizontal GCHP Effectiveness in Arid Climate Soils

NASA Astrophysics Data System (ADS)

North, Timothy James

Ground coupled heat pumps (GCHPs) have been used successfully in many environments to improve the heating and cooling efficiency of both small and large scale buildings. In arid climate regions, such as the Phoenix, Arizona metropolitan area, where the air condi-tioning load is dominated by cooling in the summer, GCHPs are difficult to install and operate. This is because the nature of soils in arid climate regions, in that they are both dry and hot, renders them particularly ineffective at dissipating heat. The first part of this thesis addresses applying the SVHeat finite element modeling soft-ware to create a model of a GCHP system. Using real-world data from a prototype solar-water heating system coupled with a ground-source heat exchanger installed in Menlo Park, California, a relatively accurate model was created to represent a novel GCHP panel system installed in a shallow vertical trench. A sensitivity analysis was performed to evaluate the accuracy of the calibrated model. The second part of the thesis involved adapting the calibrated model to represent an ap-proximation of soil conditions in arid climate regions, using a range of thermal properties for dry soils. The effectiveness of the GCHP in the arid climate region model was then evaluated by comparing the thermal flux from the panel into the subsurface profile to that of the prototype GCHP. It was shown that soils in arid climate regions are particularly inefficient at heat dissipation, but that it is highly dependent on the thermal conductivity inputted into the model. This demonstrates the importance of proper site characterization in arid climate regions. Finally, several soil improvement methods were researched to evaluate their potential for use in improving the effectiveness of shallow horizontal GCHP systems in arid climate regions.

Evaluating the fidelity of CMIP5 models in producing large-scale meteorological patterns over the Northwestern United States

NASA Astrophysics Data System (ADS)

Lintner, B. R.; Loikith, P. C.; Pike, M.; Aragon, C.

2017-12-01

Climate change information is increasingly required at impact-relevant scales. However, most state-of-the-art climate models are not of sufficiently high spatial resolution to resolve features explicitly at such scales. This challenge is particularly acute in regions of complex topography, such as the Pacific Northwest of the United States. To address this scale mismatch problem, we consider large-scale meteorological patterns (LSMPs), which can be resolved by climate models and associated with the occurrence of local scale climate and climate extremes. In prior work, using self-organizing maps (SOMs), we computed LSMPs over the northwestern United States (NWUS) from daily reanalysis circulation fields and further related these to the occurrence of observed extreme temperatures and precipitation: SOMs were used to group LSMPs into 12 nodes or clusters spanning the continuum of synoptic variability over the regions. Here this observational foundation is utilized as an evaluation target for a suite of global climate models from the Fifth Phase of the Coupled Model Intercomparison Project (CMIP5). Evaluation is performed in two primary ways. First, daily model circulation fields are assigned to one of the 12 reanalysis nodes based on minimization of the mean square error. From this, a bulk model skill score is computed measuring the similarity between the model and reanalysis nodes. Next, SOMs are applied directly to the model output and compared to the nodes obtained from reanalysis. Results reveal that many of the models have LSMPs analogous to the reanalysis, suggesting that the models reasonably capture observed daily synoptic states.

Climate change impacts on tree ranges: model intercomparison facilitates understanding and quantification of uncertainty.

PubMed

Cheaib, Alissar; Badeau, Vincent; Boe, Julien; Chuine, Isabelle; Delire, Christine; Dufrêne, Eric; François, Christophe; Gritti, Emmanuel S; Legay, Myriam; Pagé, Christian; Thuiller, Wilfried; Viovy, Nicolas; Leadley, Paul

2012-06-01

Model-based projections of shifts in tree species range due to climate change are becoming an important decision support tool for forest management. However, poorly evaluated sources of uncertainty require more scrutiny before relying heavily on models for decision-making. We evaluated uncertainty arising from differences in model formulations of tree response to climate change based on a rigorous intercomparison of projections of tree distributions in France. We compared eight models ranging from niche-based to process-based models. On average, models project large range contractions of temperate tree species in lowlands due to climate change. There was substantial disagreement between models for temperate broadleaf deciduous tree species, but differences in the capacity of models to account for rising CO(2) impacts explained much of the disagreement. There was good quantitative agreement among models concerning the range contractions for Scots pine. For the dominant Mediterranean tree species, Holm oak, all models foresee substantial range expansion. © 2012 Blackwell Publishing Ltd/CNRS.

Uncertainty Quantification in Climate Modeling and Projection

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

Qian, Yun; Jackson, Charles; Giorgi, Filippo

The projection of future climate is one of the most complex problems undertaken by the scientific community. Although scientists have been striving to better understand the physical basis of the climate system and to improve climate models, the overall uncertainty in projections of future climate has not been significantly reduced (e.g., from the IPCC AR4 to AR5). With the rapid increase of complexity in Earth system models, reducing uncertainties in climate projections becomes extremely challenging. Since uncertainties always exist in climate models, interpreting the strengths and limitations of future climate projections is key to evaluating risks, and climate change informationmore » for use in Vulnerability, Impact, and Adaptation (VIA) studies should be provided with both well-characterized and well-quantified uncertainty. The workshop aimed at providing participants, many of them from developing countries, information on strategies to quantify the uncertainty in climate model projections and assess the reliability of climate change information for decision-making. The program included a mixture of lectures on fundamental concepts in Bayesian inference and sampling, applications, and hands-on computer laboratory exercises employing software packages for Bayesian inference, Markov Chain Monte Carlo methods, and global sensitivity analyses. The lectures covered a range of scientific issues underlying the evaluation of uncertainties in climate projections, such as the effects of uncertain initial and boundary conditions, uncertain physics, and limitations of observational records. Progress in quantitatively estimating uncertainties in hydrologic, land surface, and atmospheric models at both regional and global scales was also reviewed. The application of Uncertainty Quantification (UQ) concepts to coupled climate system models is still in its infancy. The Coupled Model Intercomparison Project (CMIP) multi-model ensemble currently represents the primary data for assessing reliability and uncertainties of climate change information. An alternative approach is to generate similar ensembles by perturbing parameters within a single-model framework. One of workshop’s objectives was to give participants a deeper understanding of these approaches within a Bayesian statistical framework. However, there remain significant challenges still to be resolved before UQ can be applied in a convincing way to climate models and their projections.« less

Evaluating the Effects of Climate Change on Summertime Ozone using a Relative Response Factor approach for Policy Makers

EPA Science Inventory

The impact of climate change on surface level ozone is examined through a multi-scale modeling effort that linked global and regional climate models to drive air quality model simulations. Results are quantified in terms of the Relative Response Factor (RRF_E), which es...

Climate change and watershed mercury export: a multiple projection and model analysis

USGS Publications Warehouse

Golden, Heather E.; Knightes, Christopher D.; Conrads, Paul; Feaster, Toby D.; Davis, Gary M.; Benedict, Stephen T.; Bradley, Paul M.

2013-01-01

Future shifts in climatic conditions may impact watershed mercury (Hg) dynamics and transport. An ensemble of watershed models was applied in the present study to simulate and evaluate the responses of hydrological and total Hg (THg) fluxes from the landscape to the watershed outlet and in-stream THg concentrations to contrasting climate change projections for a watershed in the southeastern coastal plain of the United States. Simulations were conducted under stationary atmospheric deposition and land cover conditions to explicitly evaluate the effect of projected precipitation and temperature on watershed Hg export (i.e., the flux of Hg at the watershed outlet). Based on downscaled inputs from 2 global circulation models that capture extremes of projected wet (Community Climate System Model, Ver 3 [CCSM3]) and dry (ECHAM4/HOPE-G [ECHO]) conditions for this region, watershed model simulation results suggest a decrease of approximately 19% in ensemble-averaged mean annual watershed THg fluxes using the ECHO climate-change model and an increase of approximately 5% in THg fluxes with the CCSM3 model. Ensemble-averaged mean annual ECHO in-stream THg concentrations increased 20%, while those of CCSM3 decreased by 9% between the baseline and projected simulation periods. Watershed model simulation results using both climate change models suggest that monthly watershed THg fluxes increase during the summer, when projected flow is higher than baseline conditions. The present study's multiple watershed model approach underscores the uncertainty associated with climate change response projections and their use in climate change management decisions. Thus, single-model predictions can be misleading, particularly in developmental stages of watershed Hg modeling.

The Relative Impact of Climate Change on the Extinction Risk of Tree Species in the Montane Tropical Andes.

PubMed

Tejedor Garavito, Natalia; Newton, Adrian C; Golicher, Duncan; Oldfield, Sara

2015-01-01

There are widespread concerns that anthropogenic climate change will become a major cause of global biodiversity loss. However, the potential impact of climate change on the extinction risk of species remains poorly understood, particularly in comparison to other current threats. The objective of this research was to examine the relative impact of climate change on extinction risk of upper montane tree species in the tropical Andes, an area of high biodiversity value that is particularly vulnerable to climate change impacts. The extinction risk of 129 tree species endemic to the region was evaluated according to the IUCN Red List criteria, both with and without the potential impacts of climate change. Evaluations were supported by development of species distribution models, using three methods (generalized additive models, recursive partitioning, and support vector machines), all of which produced similarly high AUC values when averaged across all species evaluated (0.82, 0.86, and 0.88, respectively). Inclusion of climate change increased the risk of extinction of 18-20% of the tree species evaluated, depending on the climate scenario. The relative impact of climate change was further illustrated by calculating the Red List Index, an indicator that shows changes in the overall extinction risk of sets of species over time. A 15% decline in the Red List Index was obtained when climate change was included in this evaluation. While these results suggest that climate change represents a significant threat to tree species in the tropical Andes, they contradict previous suggestions that climate change will become the most important cause of biodiversity loss in coming decades. Conservation strategies should therefore focus on addressing the multiple threatening processes currently affecting biodiversity, rather than focusing primarily on potential climate change impacts.

The Relative Impact of Climate Change on the Extinction Risk of Tree Species in the Montane Tropical Andes

PubMed Central

Tejedor Garavito, Natalia; Newton, Adrian C.; Golicher, Duncan; Oldfield, Sara

2015-01-01

There are widespread concerns that anthropogenic climate change will become a major cause of global biodiversity loss. However, the potential impact of climate change on the extinction risk of species remains poorly understood, particularly in comparison to other current threats. The objective of this research was to examine the relative impact of climate change on extinction risk of upper montane tree species in the tropical Andes, an area of high biodiversity value that is particularly vulnerable to climate change impacts. The extinction risk of 129 tree species endemic to the region was evaluated according to the IUCN Red List criteria, both with and without the potential impacts of climate change. Evaluations were supported by development of species distribution models, using three methods (generalized additive models, recursive partitioning, and support vector machines), all of which produced similarly high AUC values when averaged across all species evaluated (0.82, 0.86, and 0.88, respectively). Inclusion of climate change increased the risk of extinction of 18–20% of the tree species evaluated, depending on the climate scenario. The relative impact of climate change was further illustrated by calculating the Red List Index, an indicator that shows changes in the overall extinction risk of sets of species over time. A 15% decline in the Red List Index was obtained when climate change was included in this evaluation. While these results suggest that climate change represents a significant threat to tree species in the tropical Andes, they contradict previous suggestions that climate change will become the most important cause of biodiversity loss in coming decades. Conservation strategies should therefore focus on addressing the multiple threatening processes currently affecting biodiversity, rather than focusing primarily on potential climate change impacts. PMID:26177097

Assessing effects of variation in global climate data sets on spatial predictions from climate envelope models

USGS Publications Warehouse

Romañach, Stephanie; Watling, James I.; Fletcher, Robert J.; Speroterra, Carolina; Bucklin, David N.; Brandt, Laura A.; Pearlstine, Leonard G.; Escribano, Yesenia; Mazzotti, Frank J.

2014-01-01

Climate change poses new challenges for natural resource managers. Predictive modeling of species–environment relationships using climate envelope models can enhance our understanding of climate change effects on biodiversity, assist in assessment of invasion risk by exotic organisms, and inform life-history understanding of individual species. While increasing interest has focused on the role of uncertainty in future conditions on model predictions, models also may be sensitive to the initial conditions on which they are trained. Although climate envelope models are usually trained using data on contemporary climate, we lack systematic comparisons of model performance and predictions across alternative climate data sets available for model training. Here, we seek to fill that gap by comparing variability in predictions between two contemporary climate data sets to variability in spatial predictions among three alternative projections of future climate. Overall, correlations between monthly temperature and precipitation variables were very high for both contemporary and future data. Model performance varied across algorithms, but not between two alternative contemporary climate data sets. Spatial predictions varied more among alternative general-circulation models describing future climate conditions than between contemporary climate data sets. However, we did find that climate envelope models with low Cohen's kappa scores made more discrepant spatial predictions between climate data sets for the contemporary period than did models with high Cohen's kappa scores. We suggest conservation planners evaluate multiple performance metrics and be aware of the importance of differences in initial conditions for spatial predictions from climate envelope models.

An ARM data-oriented diagnostics package to evaluate the climate model simulation

NASA Astrophysics Data System (ADS)

Zhang, C.; Xie, S.

2016-12-01

A set of diagnostics that utilize long-term high frequency measurements from the DOE Atmospheric Radiation Measurement (ARM) program is developed for evaluating the regional simulation of clouds, radiation and precipitation in climate models. The diagnostics results are computed and visualized automatically in a python-based package that aims to serve as an easy entry point for evaluating climate simulations using the ARM data, as well as the CMIP5 multi-model simulations. Basic performance metrics are computed to measure the accuracy of mean state and variability of simulated regional climate. The evaluated physical quantities include vertical profiles of clouds, temperature, relative humidity, cloud liquid water path, total column water vapor, precipitation, sensible and latent heat fluxes, radiative fluxes, aerosol and cloud microphysical properties. Process-oriented diagnostics focusing on individual cloud and precipitation-related phenomena are developed for the evaluation and development of specific model physical parameterizations. Application of the ARM diagnostics package will be presented in the AGU session. This work is performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344, IM release number is: LLNL-ABS-698645.

Evaluation of skill at simulating heatwave and heat-humidity indices in Global and Regional Climate Models

NASA Astrophysics Data System (ADS)

Goldie, J. K.; Alexander, L. V.; Lewis, S. C.; Sherwood, S. C.

2017-12-01

A wide body of literature now establishes the harm of extreme heat on human health, and work is now emerging on the projection of future health impacts. However, heat-health relationships vary across different populations (Gasparrini et al. 2015), so accurate simulation of regional climate is an important component of joint health impact projection. Here, we evaluate the ability of nine Global Climate Models (GCMs) from CMIP5 and the NARCliM Regional Climate Model to reproduce a selection of 15 health-relevant heatwave and heat-humidity indices over the historical period (1990-2005) using the Perkins skill score (Perkins et al. 2007) in five Australian cities. We explore the reasons for poor model skill, comparing these modelled distributions to both weather station observations and gridded reanalysis data. Finally, we show changes in the modelled distributions from the highest-performing models under RCP4.5 and RCP8.5 greenhouse gas scenarios and discuss the implications of simulated heat stress for future climate change adaptation. ReferencesGasparrini, Antonio, Yuming Guo, Masahiro Hashizume, Eric Lavigne, Antonella Zanobetti, Joel Schwartz, Aurelio Tobias, et al. "Mortality Risk Attributable to High and Low Ambient Temperature: A Multicountry Observational Study." The Lancet 386, no. 9991 (July 31, 2015): 369-75. doi:10.1016/S0140-6736(14)62114-0. Perkins, S. E., A. J. Pitman, N. J. Holbrook, and J. McAneney. "Evaluation of the AR4 Climate Models' Simulated Daily Maximum Temperature, Minimum Temperature, and Precipitation over Australia Using Probability Density Functions." Journal of Climate 20, no. 17 (September 1, 2007): 4356-76. doi:10.1175/JCLI4253.1.

Evaluating the Sensitivity of Agricultural Model Performance to Different Climate Inputs: Supplemental Material

NASA Technical Reports Server (NTRS)

Glotter, Michael J.; Ruane, Alex C.; Moyer, Elisabeth J.; Elliott, Joshua W.

2015-01-01

Projections of future food production necessarily rely on models, which must themselves be validated through historical assessments comparing modeled and observed yields. Reliable historical validation requires both accurate agricultural models and accurate climate inputs. Problems with either may compromise the validation exercise. Previous studies have compared the effects of different climate inputs on agricultural projections but either incompletely or without a ground truth of observed yields that would allow distinguishing errors due to climate inputs from those intrinsic to the crop model. This study is a systematic evaluation of the reliability of a widely used crop model for simulating U.S. maize yields when driven by multiple observational data products. The parallelized Decision Support System for Agrotechnology Transfer (pDSSAT) is driven with climate inputs from multiple sources reanalysis, reanalysis that is bias corrected with observed climate, and a control dataset and compared with observed historical yields. The simulations show that model output is more accurate when driven by any observation-based precipitation product than when driven by non-bias-corrected reanalysis. The simulations also suggest, in contrast to previous studies, that biased precipitation distribution is significant for yields only in arid regions. Some issues persist for all choices of climate inputs: crop yields appear to be oversensitive to precipitation fluctuations but under sensitive to floods and heat waves. These results suggest that the most important issue for agricultural projections may be not climate inputs but structural limitations in the crop models themselves.

Evaluating the sensitivity of agricultural model performance to different climate inputs

PubMed Central

Glotter, Michael J.; Moyer, Elisabeth J.; Ruane, Alex C.; Elliott, Joshua W.

2017-01-01

Projections of future food production necessarily rely on models, which must themselves be validated through historical assessments comparing modeled to observed yields. Reliable historical validation requires both accurate agricultural models and accurate climate inputs. Problems with either may compromise the validation exercise. Previous studies have compared the effects of different climate inputs on agricultural projections, but either incompletely or without a ground truth of observed yields that would allow distinguishing errors due to climate inputs from those intrinsic to the crop model. This study is a systematic evaluation of the reliability of a widely-used crop model for simulating U.S. maize yields when driven by multiple observational data products. The parallelized Decision Support System for Agrotechnology Transfer (pDSSAT) is driven with climate inputs from multiple sources – reanalysis, reanalysis bias-corrected with observed climate, and a control dataset – and compared to observed historical yields. The simulations show that model output is more accurate when driven by any observation-based precipitation product than when driven by un-bias-corrected reanalysis. The simulations also suggest, in contrast to previous studies, that biased precipitation distribution is significant for yields only in arid regions. However, some issues persist for all choices of climate inputs: crop yields appear oversensitive to precipitation fluctuations but undersensitive to floods and heat waves. These results suggest that the most important issue for agricultural projections may be not climate inputs but structural limitations in the crop models themselves. PMID:29097985

Examining Interior Grid Nudging Techniques Using Two-Way Nesting in the WRF Model for Regional Climate Modeling

EPA Science Inventory

This study evaluates interior nudging techniques using the Weather Research and Forecasting (WRF) model for regional climate modeling over the conterminous United States (CONUS) using a two-way nested configuration. NCEP–Department of Energy Atmospheric Model Intercomparison Pro...

Impacts of uncertainties in European gridded precipitation observations on regional climate analysis.

PubMed

Prein, Andreas F; Gobiet, Andreas

2017-01-01

Gridded precipitation data sets are frequently used to evaluate climate models or to remove model output biases. Although precipitation data are error prone due to the high spatio-temporal variability of precipitation and due to considerable measurement errors, relatively few attempts have been made to account for observational uncertainty in model evaluation or in bias correction studies. In this study, we compare three types of European daily data sets featuring two Pan-European data sets and a set that combines eight very high-resolution station-based regional data sets. Furthermore, we investigate seven widely used, larger scale global data sets. Our results demonstrate that the differences between these data sets have the same magnitude as precipitation errors found in regional climate models. Therefore, including observational uncertainties is essential for climate studies, climate model evaluation, and statistical post-processing. Following our results, we suggest the following guidelines for regional precipitation assessments. (1) Include multiple observational data sets from different sources (e.g. station, satellite, reanalysis based) to estimate observational uncertainties. (2) Use data sets with high station densities to minimize the effect of precipitation undersampling (may induce about 60% error in data sparse regions). The information content of a gridded data set is mainly related to its underlying station density and not to its grid spacing. (3) Consider undercatch errors of up to 80% in high latitudes and mountainous regions. (4) Analyses of small-scale features and extremes are especially uncertain in gridded data sets. For higher confidence, use climate-mean and larger scale statistics. In conclusion, neglecting observational uncertainties potentially misguides climate model development and can severely affect the results of climate change impact assessments.

Expertly validated models and phylogenetically-controlled analysis suggests responses to climate change are related to species traits in the order lagomorpha.

PubMed

Leach, Katie; Kelly, Ruth; Cameron, Alison; Montgomery, W Ian; Reid, Neil

2015-01-01

Climate change during the past five decades has impacted significantly on natural ecosystems, and the rate of current climate change is of great concern among conservation biologists. Species Distribution Models (SDMs) have been used widely to project changes in species' bioclimatic envelopes under future climate scenarios. Here, we aimed to advance this technique by assessing future changes in the bioclimatic envelopes of an entire mammalian order, the Lagomorpha, using a novel framework for model validation based jointly on subjective expert evaluation and objective model evaluation statistics. SDMs were built using climatic, topographical, and habitat variables for all 87 lagomorph species under past and current climate scenarios. Expert evaluation and Kappa values were used to validate past and current models and only those deemed 'modellable' within our framework were projected under future climate scenarios (58 species). Phylogenetically-controlled regressions were used to test whether species traits correlated with predicted responses to climate change. Climate change is likely to impact more than two-thirds of lagomorph species, with leporids (rabbits, hares, and jackrabbits) likely to undertake poleward shifts with little overall change in range extent, whilst pikas are likely to show extreme shifts to higher altitudes associated with marked range declines, including the likely extinction of Kozlov's Pika (Ochotona koslowi). Smaller-bodied species were more likely to exhibit range contractions and elevational increases, but showing little poleward movement, and fecund species were more likely to shift latitudinally and elevationally. Our results suggest that species traits may be important indicators of future climate change and we believe multi-species approaches, as demonstrated here, are likely to lead to more effective mitigation measures and conservation management. We strongly advocate studies minimising data gaps in our knowledge of the Order, specifically collecting more specimens for biodiversity archives and targeting data deficient geographic regions.

A Bayesian Approach to Evaluating Consistency between Climate Model Output and Observations

NASA Astrophysics Data System (ADS)

Braverman, A. J.; Cressie, N.; Teixeira, J.

2010-12-01

Like other scientific and engineering problems that involve physical modeling of complex systems, climate models can be evaluated and diagnosed by comparing their output to observations of similar quantities. Though the global remote sensing data record is relatively short by climate research standards, these data offer opportunities to evaluate model predictions in new ways. For example, remote sensing data are spatially and temporally dense enough to provide distributional information that goes beyond simple moments to allow quantification of temporal and spatial dependence structures. In this talk, we propose a new method for exploiting these rich data sets using a Bayesian paradigm. For a collection of climate models, we calculate posterior probabilities its members best represent the physical system each seeks to reproduce. The posterior probability is based on the likelihood that a chosen summary statistic, computed from observations, would be obtained when the model's output is considered as a realization from a stochastic process. By exploring how posterior probabilities change with different statistics, we may paint a more quantitative and complete picture of the strengths and weaknesses of the models relative to the observations. We demonstrate our method using model output from the CMIP archive, and observations from NASA's Atmospheric Infrared Sounder.

Multi-Wheat-Model Ensemble Responses to Interannual Climate Variability

NASA Technical Reports Server (NTRS)

Ruane, Alex C.; Hudson, Nicholas I.; Asseng, Senthold; Camarrano, Davide; Ewert, Frank; Martre, Pierre; Boote, Kenneth J.; Thorburn, Peter J.; Aggarwal, Pramod K.; Angulo, Carlos

2016-01-01

We compare 27 wheat models' yield responses to interannual climate variability, analyzed at locations in Argentina, Australia, India, and The Netherlands as part of the Agricultural Model Intercomparison and Improvement Project (AgMIP) Wheat Pilot. Each model simulated 1981e2010 grain yield, and we evaluate results against the interannual variability of growing season temperature, precipitation, and solar radiation. The amount of information used for calibration has only a minor effect on most models' climate response, and even small multi-model ensembles prove beneficial. Wheat model clusters reveal common characteristics of yield response to climate; however models rarely share the same cluster at all four sites indicating substantial independence. Only a weak relationship (R2 0.24) was found between the models' sensitivities to interannual temperature variability and their response to long-termwarming, suggesting that additional processes differentiate climate change impacts from observed climate variability analogs and motivating continuing analysis and model development efforts.

Dry-bean production under climate change conditions in the north of Argentina: Risk assessment and economic implications

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

Feijoo, M.; Mestre, F.; Castagnaro, A.

This study evaluates the potential effect of climate change on Dry-bean production in Argentina, combining climate models, a crop productivity model and a yield response model estimation of climate variables on crop yields. The study was carried out in the North agricultural regions of Jujuy, Salta, Santiago del Estero and Tucuman which include the largest areas of Argentina where dry beans are grown as a high input crop. The paper combines the output from a crop model with different techniques of analysis. The scenarios used in this study were generated from the output of two General Circulation Models (GCMs): themore » Goddard Institute for Space Studies model (GISS) and the Canadian Climate Change Model (CCCM). The study also includes a preliminary evaluation of the potential changes in monetary returns taking into account the possible variability of yields and prices, using mean-Gini stochastic dominance (MGSD). The results suggest that large climate change may have a negative impact on the Argentine agriculture sector, due to the high relevance of this product in the export sector. The difference negative effect depends on the varieties of dry bean and also the General Circulation Model scenarios considered for double levels of atmospheric carbon dioxide.« less

The evaluation of the climate change effects on maize and fennel cultivation by means of an hydrological physically based model: the case study of an irrigated district of southern Italy

NASA Astrophysics Data System (ADS)

Bonfante, A.; Alfieri, M. S.; Basile, A.; De Lorenzi, F.; Fiorentino, N.; Menenti, M.

2012-04-01

The effect of climate change on irrigated agricultural systems will be different from area to area depending on some factors as: (i) water availability, (ii) crop water demand (iii) soil hydrological behavior and (iv) irrigation management strategy. The adaptation of irrigated crop systems to future climate change can be supported by physically based model which simulate the water and heat fluxes in the soil-vegetation-atmosphere system. The aim of this work is to evaluate the effects of climate change on the heat and water balance of a maize-fennel rotation. This was applied to a on-demand irrigation district of Southern Italy ("Destra Sele", Campania Region, 22.645 ha). Two climate scenarios were considered, current climate (1961-1990) and future climate (2021-2050), the latter constructed by applying statistical downscaling to GCMs scenarios. For each climate scenario the soil moisture regime of the selected study area was calculated by means of a simulation model of the soil-water-atmosphere system (SWAP). Synthetic indicators of the soil water regimes (e.g., crop water stress index - CWSI, available water content) have been calculated and impacts evaluated taking into account the yield response functions to water availability of different cultivars. Different irrigation delivering strategies were also simulated. The hydrological model SWAP was applied to the representative soils of the whole area (20 soil units) for which the soil hydraulic properties were derived by means of pedo-transfer function (HYPRES) tested and validated on the typical soils in the study area. Upper boundary conditions were derived from two climate scenarios, i.e. current and future. Unit gradient in soil water potential was set as lower boundary condition. Crop-specific input data and model parameters were derived from field experiments, in the same area, where the SWAP model was calibrated and validated. The results obtained have shown a significant increase of CWSI in the future climate scenario, and some spatial patterns strongly influenced by the soils characteristics. Adaptability of different maize cultivars has been evaluated. 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) Keywords: Plant Adaptative capacity, SWAP, Climate changes, Maize, Fennel

Forecasting conditional climate-change using a hybrid approach

USGS Publications Warehouse

Esfahani, Akbar Akbari; Friedel, Michael J.

2014-01-01

A novel approach is proposed to forecast the likelihood of climate-change across spatial landscape gradients. This hybrid approach involves reconstructing past precipitation and temperature using the self-organizing map technique; determining quantile trends in the climate-change variables by quantile regression modeling; and computing conditional forecasts of climate-change variables based on self-similarity in quantile trends using the fractionally differenced auto-regressive integrated moving average technique. The proposed modeling approach is applied to states (Arizona, California, Colorado, Nevada, New Mexico, and Utah) in the southwestern U.S., where conditional forecasts of climate-change variables are evaluated against recent (2012) observations, evaluated at a future time period (2030), and evaluated as future trends (2009–2059). These results have broad economic, political, and social implications because they quantify uncertainty in climate-change forecasts affecting various sectors of society. Another benefit of the proposed hybrid approach is that it can be extended to any spatiotemporal scale providing self-similarity exists.

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

Farm simulation: a tool for evaluating the mitigation of greenhouse gas emissions and the adaptation of dairy production to climate change

USDA-ARS?s Scientific Manuscript database

Process-level modeling at the farm scale provides a tool for evaluating both strategies for mitigating greenhouse gas emissions and strategies for adapting to climate change. The Integrated Farm System Model (IFSM) simulates representative crop, beef or dairy farms over many years of weather to pred...

Evaluation of CORDEX-Arctic daily precipitation and temperature-based climate indices over Canadian Arctic land areas

NASA Astrophysics Data System (ADS)

Diaconescu, Emilia Paula; Mailhot, Alain; Brown, Ross; Chaumont, Diane

2018-03-01

This study focuses on the evaluation of daily precipitation and temperature climate indices and extremes simulated by an ensemble of 12 Regional Climate Model (RCM) simulations from the ARCTIC-CORDEX experiment with surface observations in the Canadian Arctic from the Adjusted Historical Canadian Climate Dataset. Five global reanalyses products (ERA-Interim, JRA55, MERRA, CFSR and GMFD) are also included in the evaluation to assess their potential for RCM evaluation in data sparse regions. The study evaluated the means and annual anomaly distributions of indices over the 1980-2004 dataset overlap period. The results showed that RCM and reanalysis performance varied with the climate variables being evaluated. Most RCMs and reanalyses were able to simulate well climate indices related to mean air temperature and hot extremes over most of the Canadian Arctic, with the exception of the Yukon region where models displayed the largest biases related to topographic effects. Overall performance was generally poor for indices related to cold extremes. Likewise, only a few RCM simulations and reanalyses were able to provide realistic simulations of precipitation extreme indicators. The multi-reanalysis ensemble provided superior results to individual datasets for climate indicators related to mean air temperature and hot extremes, but not for other indicators. These results support the use of reanalyses as reference datasets for the evaluation of RCM mean air temperature and hot extremes over northern Canada, but not for cold extremes and precipitation indices.

Climate change and watershed mercury export: a multiple projection and model analysis.

PubMed

Golden, Heather E; Knightes, Christopher D; Conrads, Paul A; Feaster, Toby D; Davis, Gary M; Benedict, Stephen T; Bradley, Paul M

2013-09-01

Future shifts in climatic conditions may impact watershed mercury (Hg) dynamics and transport. An ensemble of watershed models was applied in the present study to simulate and evaluate the responses of hydrological and total Hg (THg) fluxes from the landscape to the watershed outlet and in-stream THg concentrations to contrasting climate change projections for a watershed in the southeastern coastal plain of the United States. Simulations were conducted under stationary atmospheric deposition and land cover conditions to explicitly evaluate the effect of projected precipitation and temperature on watershed Hg export (i.e., the flux of Hg at the watershed outlet). Based on downscaled inputs from 2 global circulation models that capture extremes of projected wet (Community Climate System Model, Ver 3 [CCSM3]) and dry (ECHAM4/HOPE-G [ECHO]) conditions for this region, watershed model simulation results suggest a decrease of approximately 19% in ensemble-averaged mean annual watershed THg fluxes using the ECHO climate-change model and an increase of approximately 5% in THg fluxes with the CCSM3 model. Ensemble-averaged mean annual ECHO in-stream THg concentrations increased 20%, while those of CCSM3 decreased by 9% between the baseline and projected simulation periods. Watershed model simulation results using both climate change models suggest that monthly watershed THg fluxes increase during the summer, when projected flow is higher than baseline conditions. The present study's multiple watershed model approach underscores the uncertainty associated with climate change response projections and their use in climate change management decisions. Thus, single-model predictions can be misleading, particularly in developmental stages of watershed Hg modeling. Copyright © 2013 SETAC.

Process-oriented Observational Metrics for CMIP6 Climate Model Assessments

NASA Astrophysics Data System (ADS)

Jiang, J. H.; Su, H.

2016-12-01

Observational metrics based on satellite observations have been developed and effectively applied during post-CMIP5 model evaluation and improvement projects. As new physics and parameterizations continue to be included in models for the upcoming CMIP6, it is important to continue objective comparisons between observations and model results. This talk will summarize the process-oriented observational metrics and methodologies for constraining climate models with A-Train satellite observations and support CMIP6 model assessments. We target parameters and processes related to atmospheric clouds and water vapor, which are critically important for Earth's radiative budget, climate feedbacks, and water and energy cycles, and thus reduce uncertainties in climate models.

Koeppen Bioclimatic Metrics for Evaluating CMIP5 Simulations of Historical Climate

NASA Astrophysics Data System (ADS)

Phillips, T. J.; Bonfils, C.

2012-12-01

The classic Koeppen bioclimatic classification scheme associates generic vegetation types (e.g. grassland, tundra, broadleaf or evergreen forests, etc.) with regional climate zones defined by the observed amplitude and phase of the annual cycles of continental temperature (T) and precipitation (P). Koeppen classification thus can provide concise, multivariate metrics for evaluating climate model performance in simulating the regional magnitudes and seasonalities of climate variables that are of critical importance for living organisms. In this study, 14 Koeppen vegetation types are derived from annual-cycle climatologies of T and P in some 3 dozen CMIP5 simulations of 1980-1999 climate, a period when observational data provides a reliable global validation standard. Metrics for evaluating the ability of the CMIP5 models to simulate the correct locations and areas of the vegetation types, as well as measures of overall model performance, also are developed. It is found that the CMIP5 models are most deficient in simulating 1) the climates of the drier zones (e.g. desert, savanna, grassland, steppe vegetation types) that are located in the Southwestern U.S. and Mexico, Eastern Europe, Southern Africa, and Central Australia, as well as 2) the climate of regions such as Central Asia and Western South America where topography plays a central role. (Detailed analysis of regional biases in the annual cycles of T and P of selected simulations exemplifying general model performance problems also are to be presented.) The more encouraging results include evidence for a general improvement in CMIP5 performance relative to that of older CMIP3 models. Within CMIP5 also, the more complex Earth Systems Models (ESMs) with prognostic biogeochemistry perform comparably to the corresponding global models that simulate only the "physical" climate. Acknowledgments This work was funded by the U.S. Department of Energy Office of Science and was performed at the Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.

AIRS Obs4MIPs V2 data set

NASA Astrophysics Data System (ADS)

Tian, B.

2017-12-01

The Coupled Model Intercomparison Project (CMIP) has become a central element of national and international assessments of climate change. The CMIP Phase 6 (CMIP6) model experiments will be the foundation for the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6), scheduled for publication around 2021. To increase the fidelity of the IPCC AR6, the CMIP6 model experiments need rigorous evaluation. The "Observations for Model Intercomparison Projects" (Obs4MIPs) collects, organizes and publishes various well-established satellite data sets for CMIP model evaluation. The Atmospheric Infrared Sounder (AIRS) and Advanced Microwave Sounding Unit (AMSU), the NASA's temperature and humidity sounding system on the Aqua satellite, has provided over a decade-long high-quality tropospheric temperature and moisture sounding data. Under the current support of the NASA Data for Operation and Assessment (NDOA) program, we are generating and publishing the AIRS Obs4MIPs V2 data set including the monthly mean tropospheric air temperature, specific humidity, and relative humidity profiles from September 2002 to September 2016. This will provide the latest AIRS data in Obs4MIPs and assist the climate modeling community to better use the AIRS data for CMIP (including CMIP3, CMIP5, and CMIP6) model evaluation. In this presentation, we will discuss the AIRS Obs4MIPs V2 data set and their possible use for CMIP6 climate model evaluation.

Impacts of Future Climate Change on California Perennial Crop Yields: Model Projections with Climate and Crop Uncertainties

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

Lobell, D; Field, C; Cahill, K

2006-01-10

Most research on the agricultural impacts of climate change has focused on the major annual crops, yet perennial cropping systems are less adaptable and thus potentially more susceptible to damage. Improved assessments of yield responses to future climate are needed to prioritize adaptation strategies in the many regions where perennial crops are economically and culturally important. These impact assessments, in turn, must rely on climate and crop models that contain often poorly defined uncertainties. We evaluated the impact of climate change on six major perennial crops in California: wine grapes, almonds, table grapes, oranges, walnuts, and avocados. Outputs from multiplemore » climate models were used to evaluate climate uncertainty, while multiple statistical crop models, derived by resampling historical databases, were used to address crop response uncertainties. We find that, despite these uncertainties, climate change in California is very likely to put downward pressure on yields of almonds, walnuts, avocados, and table grapes by 2050. Without CO{sub 2} fertilization or adaptation measures, projected losses range from 0 to >40% depending on the crop and the trajectory of climate change. Climate change uncertainty generally had a larger impact on projections than crop model uncertainty, although the latter was substantial for several crops. Opportunities for expansion into cooler regions are identified, but this adaptation would require substantial investments and may be limited by non-climatic constraints. Given the long time scales for growth and production of orchards and vineyards ({approx}30 years), climate change should be an important factor in selecting perennial varieties and deciding whether and where perennials should be planted.« less

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.

Rethinking the Default Construction of Multimodel Climate Ensembles

DOE PAGES

Rauser, Florian; Gleckler, Peter; Marotzke, Jochem

2015-07-21

Here, we discuss the current code of practice in the climate sciences to routinely create climate model ensembles as ensembles of opportunity from the newest phase of the Coupled Model Intercomparison Project (CMIP). We give a two-step argument to rethink this process. First, the differences between generations of ensembles corresponding to different CMIP phases in key climate quantities are not large enough to warrant an automatic separation into generational ensembles for CMIP3 and CMIP5. Second, we suggest that climate model ensembles cannot continue to be mere ensembles of opportunity but should always be based on a transparent scientific decision process.more » If ensembles can be constrained by observation, then they should be constructed as target ensembles that are specifically tailored to a physical question. If model ensembles cannot be constrained by observation, then they should be constructed as cross-generational ensembles, including all available model data to enhance structural model diversity and to better sample the underlying uncertainties. To facilitate this, CMIP should guide the necessarily ongoing process of updating experimental protocols for the evaluation and documentation of coupled models. Finally, with an emphasis on easy access to model data and facilitating the filtering of climate model data across all CMIP generations and experiments, our community could return to the underlying idea of using model data ensembles to improve uncertainty quantification, evaluation, and cross-institutional exchange.« less

The Agricultural Model Intercomparison and Improvement Project: Phase I Activities by a Global Community of Science. Chapter 1

NASA Technical Reports Server (NTRS)

Rosenzweig, Cynthia E.; Jones, James W.; Hatfield, Jerry L.; Antle, John M.; Ruane, Alexander C.; Mutter, Carolyn Z.

2015-01-01

The Agricultural Model Intercomparison and Improvement Project (AgMIP) was founded in 2010. Its mission is to improve substantially the characterization of world food security as affected by climate variability and change, and to enhance adaptation capacity in both developing and developed countries. The objectives of AgMIP are to: Incorporate state-of-the-art climate, crop/livestock, and agricultural economic model improvements into coordinated multi-model regional and global assessments of future climate impacts and adaptation and other key aspects of the food system. Utilize multiple models, scenarios, locations, crops/livestock, and participants to explore uncertainty and the impact of data and methodological choices. Collaborate with regional experts in agronomy, animal sciences, economics, and climate to build a strong basis for model applications, addressing key climate related questions and sustainable intensification farming systems. Improve scientific and adaptive capacity in modeling for major agricultural regions in the developing and developed world, with a focus on vulnerable regions. Improve agricultural data and enhance data-sharing based on their intercomparison and evaluation using best scientific practices. Develop modeling frameworks to identify and evaluate promising adaptation technologies and policies and to prioritize strategies.

Is the future already here? The impact of climate change on the distribution of the eastern coral snake (Micrurus fulvius).

PubMed

Archis, Jennifer N; Akcali, Christopher; Stuart, Bryan L; Kikuchi, David; Chunco, Amanda J

2018-01-01

Anthropogenic climate change is a significant global driver of species distribution change. Although many species have undergone range expansion at their poleward limits, data on several taxonomic groups are still lacking. A common method for studying range shifts is using species distribution models to evaluate current, and predict future, distributions. Notably, many sources of 'current' climate data used in species distribution modeling use the years 1950-2000 to calculate climatic averages. However, this does not account for recent (post 2000) climate change. This study examines the influence of climate change on the eastern coral snake ( Micrurus fulvius ). Specifically, we: (1) identified the current range and suitable environment of M. fulvius in the Southeastern United States, (2) investigated the potential impacts of climate change on the distribution of M. fulvius , and (3) evaluated the utility of future models in predicting recent (2001-2015) records. We used the species distribution modeling program Maxent and compared both current (1950-2000) and future (2050) climate conditions. Future climate models showed a shift in the distribution of suitable habitat across a significant portion of the range; however, results also suggest that much of the Southeastern United States will be outside the range of current conditions, suggesting that there may be no-analog environments in the future. Most strikingly, future models were more effective than the current models at predicting recent records, suggesting that range shifts may already be occurring. These results have implications for both M. fulvius and its Batesian mimics. More broadly, we recommend future Maxent studies consider using future climate data along with current data to better estimate the current distribution.

Multi-criteria evaluation of CMIP5 GCMs for climate change impact analysis

NASA Astrophysics Data System (ADS)

Ahmadalipour, Ali; Rana, Arun; Moradkhani, Hamid; Sharma, Ashish

2017-04-01

Climate change is expected to have severe impacts on global hydrological cycle along with food-water-energy nexus. Currently, there are many climate models used in predicting important climatic variables. Though there have been advances in the field, there are still many problems to be resolved related to reliability, uncertainty, and computing needs, among many others. In the present work, we have analyzed performance of 20 different global climate models (GCMs) from Climate Model Intercomparison Project Phase 5 (CMIP5) dataset over the Columbia River Basin (CRB) in the Pacific Northwest USA. We demonstrate a statistical multicriteria approach, using univariate and multivariate techniques, for selecting suitable GCMs to be used for climate change impact analysis in the region. Univariate methods includes mean, standard deviation, coefficient of variation, relative change (variability), Mann-Kendall test, and Kolmogorov-Smirnov test (KS-test); whereas multivariate methods used were principal component analysis (PCA), singular value decomposition (SVD), canonical correlation analysis (CCA), and cluster analysis. The analysis is performed on raw GCM data, i.e., before bias correction, for precipitation and temperature climatic variables for all the 20 models to capture the reliability and nature of the particular model at regional scale. The analysis is based on spatially averaged datasets of GCMs and observation for the period of 1970 to 2000. Ranking is provided to each of the GCMs based on the performance evaluated against gridded observational data on various temporal scales (daily, monthly, and seasonal). Results have provided insight into each of the methods and various statistical properties addressed by them employed in ranking GCMs. Further; evaluation was also performed for raw GCM simulations against different sets of gridded observational dataset in the area.

Expertly Validated Models and Phylogenetically-Controlled Analysis Suggests Responses to Climate Change Are Related to Species Traits in the Order Lagomorpha

PubMed Central

Leach, Katie; Kelly, Ruth; Cameron, Alison; Montgomery, W. Ian; Reid, Neil

2015-01-01

Climate change during the past five decades has impacted significantly on natural ecosystems, and the rate of current climate change is of great concern among conservation biologists. Species Distribution Models (SDMs) have been used widely to project changes in species’ bioclimatic envelopes under future climate scenarios. Here, we aimed to advance this technique by assessing future changes in the bioclimatic envelopes of an entire mammalian order, the Lagomorpha, using a novel framework for model validation based jointly on subjective expert evaluation and objective model evaluation statistics. SDMs were built using climatic, topographical, and habitat variables for all 87 lagomorph species under past and current climate scenarios. Expert evaluation and Kappa values were used to validate past and current models and only those deemed ‘modellable’ within our framework were projected under future climate scenarios (58 species). Phylogenetically-controlled regressions were used to test whether species traits correlated with predicted responses to climate change. Climate change is likely to impact more than two-thirds of lagomorph species, with leporids (rabbits, hares, and jackrabbits) likely to undertake poleward shifts with little overall change in range extent, whilst pikas are likely to show extreme shifts to higher altitudes associated with marked range declines, including the likely extinction of Kozlov’s Pika (Ochotona koslowi). Smaller-bodied species were more likely to exhibit range contractions and elevational increases, but showing little poleward movement, and fecund species were more likely to shift latitudinally and elevationally. Our results suggest that species traits may be important indicators of future climate change and we believe multi-species approaches, as demonstrated here, are likely to lead to more effective mitigation measures and conservation management. We strongly advocate studies minimising data gaps in our knowledge of the Order, specifically collecting more specimens for biodiversity archives and targeting data deficient geographic regions. PMID:25874407

Evaluation of a Mesoscale Convective System in Variable-Resolution CESM

NASA Astrophysics Data System (ADS)

Payne, A. E.; Jablonowski, C.

2017-12-01

Warm season precipitation over the Southern Great Plains (SGP) follows a well observed diurnal pattern of variability, peaking at night-time, due to the eastward propagation of mesoscale convection systems that develop over the eastern slopes of the Rockies in the late afternoon. While most climate models are unable to adequately capture the organization of convection and characteristic pattern of precipitation over this region, models with high enough resolution to explicitly resolve convection show improvement. However, high resolution simulations are computationally expensive and, in the case of regional climate models, are subject to boundary conditions. Newly developed variable resolution global climate models strike a balance between the benefits of high-resolution regional climate models and the large-scale dynamics of global climate models and low computational cost. Recently developed parameterizations that are insensitive to the model grid scale provide a way to improve model performance. Here, we present an evaluation of the newly available Cloud Layers Unified by Binormals (CLUBB) parameterization scheme in a suite of variable-resolution CESM simulations with resolutions ranging from 110 km to 7 km within a regionally refined region centered over the SGP Atmospheric Radiation Measurement (ARM) site. Simulations utilize the hindcast approach developed by the Department of Energy's Cloud-Associated Parameterizations Testbed (CAPT) for the assessment of climate models. We limit our evaluation to a single mesoscale convective system that passed over the region on May 24, 2008. The effects of grid-resolution on the timing and intensity of precipitation, as well as, on the transition from shallow to deep convection are assessed against ground-based observations from the SGP ARM site, satellite observations and ERA-Interim reanalysis.

Historical Climate Change Impacts on the Hydrological Processes of the Ponto-Caspian Basin

NASA Astrophysics Data System (ADS)

Koriche, Sifan A.; Singarayer, Joy S.; Coe, Michael T.; Nandini, Sri; Prange, Matthias; Cloke, Hannah; Lunt, Dan

2017-04-01

The Ponto-Caspian basin is one of the largest basins globally, composed of a closed basin (Caspian Sea) and open basins connecting to the global ocean (Black and Azov Sea). Over the historical time period (1850-present) Caspian Sea levels have varied between -25 and -29mbsl (Arpe et al., 2012), resulting in considerable changes to the area of the lake (currently 371,000 km2). Given projections of future climate change and the importance of the Caspian Sea for fisheries, agriculture, and industry, it is vital to understand how sea levels may vary in the future. Hydrological models can be used to assess the impacts of climate change on hydrological processes for future forecasts. However, it is critical to first evaluate such models using observational data for the present and recent past, and to understand the key hydrological processes driving past changes in sea level. In this study, the Terrestrial Hydrological Model (THMB) (Coe, 2000, 2002) is applied and evaluated to investigate the hydrological processes of the Ponto-Caspian basin for the historical period 1900 to 2000. The model has been forced using observational reanalysis datasets (ERA-Interim, ERA-20) and historical climate model data outputs (from CESM and HadCM3 models) to investigate the variability in the Caspian Sea level and the major river discharges. We examine the differences produced by driving the hydrological model with reanalysis data or climate models. We evaluate the model performance compared to observational discharge measurements and Caspian Sea level data. Secondly, we investigated the sensitivity of historical Caspian Sea level variations to different aspects of climate changes to examine the most important processes involved over this time period.

The evolution of climate. [climatic effects of polar wandering and continental drift

NASA Technical Reports Server (NTRS)

Donn, W. L.; Shaw, D.

1975-01-01

A quantitative evaluation is made of the climatic effects of polar wandering plus continental drift in order to determine wether this mechanism alone could explain the deterioration of climate that occurred from the warmth of Mesozoic time to the ice age conditions of the late Cenozoic. By way of procedure, to investigate the effect of the changing geography of the past on climate Adem's thermodynamic model was selected. The application of the model is discussed and preliminary results are given.

A model for evaluating effects of climate, water availability, and water management on wetland impoundments--a case study on Bowdoin, Long Lake, and Sand Lake National Wildlife Refuges

USGS Publications Warehouse

Tangen, Brian A.; Gleason, Robert A.; Stamm, John F.

2013-01-01

Many wetland impoundments managed by the U.S. Fish and Wildlife Service (USFWS) National Wildlife Refuge System throughout the northern Great Plains rely on rivers as a primary water source. A large number of these impoundments currently are being stressed from changes in water supplies and quality, and these problems are forecast to worsen because of projected changes to climate and land use. For example, many managed wetlands in arid regions have become degraded owing to the long-term accumulation of salts and increased salinity associated with evapotranspiration. A primary goal of the USFWS is to provide aquatic habitats for a diversity of waterbirds; thus, wetland managers would benefit from a tool that facilitates evaluation of wetland habitat quality in response to current and anticipated impacts of altered hydrology and salt balances caused by factors such as climate change, water availability, and management actions. A spreadsheet model that simulates the overall water and salinity balance (WSB model) of managed wetland impoundments is presented. The WSB model depicts various habitat metrics, such as water depth, salinity, and surface areas (inundated, dry), which can be used to evaluate alternative management actions under various water-availability and climate scenarios. The WSB model uses widely available spreadsheet software, is relatively simple to use, relies on widely available inputs, and is readily adaptable to specific locations. The WSB model was validated using data from three National Wildlife Refuges with direct and indirect connections to water resources associated with rivers, and common data limitations are highlighted. The WSB model also was used to conduct simulations based on hypothetical climate and management scenarios to demonstrate the utility of the model for evaluating alternative management strategies and climate futures. The WSB model worked well across a range of National Wildlife Refuges and could be a valuable tool for USFWS staff when evaluating system state and management alternatives and establishing long-term goals and objectives.

Linking climate change and karst hydrology to evaluate species vulnerability: The Edwards and Madison aquifers (Invited)

NASA Astrophysics Data System (ADS)

Mahler, B. J.; Long, A. J.; Stamm, J. F.; Poteet, M.; Symstad, A.

2013-12-01

Karst aquifers present an extreme case of flow along structurally variable pathways, making them highly dynamic systems and therefore likely to respond rapidly to climate change. In turn, many biological communities and ecosystems associated with karst are sensitive to hydrologic changes. We explored how three sites in the Edwards aquifer (Texas) and two sites in the Madison aquifer (South Dakota) might respond to projected climate change from 2011 to 2050. Ecosystems associated with these karst aquifers support federally listed endangered and threatened species and state-listed species of concern, including amphibians, birds, insects, and plants. The vulnerability of selected species associated with projected climate change was assessed. The Advanced Research Weather and Research Forecasting (WRF) model was used to simulate projected climate at a 36-km grid spacing for three weather stations near the study sites, using boundary and initial conditions from the global climate model Community Climate System Model (CCSM3) and an A2 emissions scenario. Daily temperature and precipitation projections from the WRF model were used as input for the hydrologic Rainfall-Response Aquifer and Watershed Flow (RRAWFLOW) model and the Climate Change Vulnerability Index (CCVI) model. RRAWFLOW is a lumped-parameter model that simulates hydrologic response at a single site, combining the responses of quick and slow flow that commonly characterize karst aquifers. CCVI uses historical and projected climate and hydrologic metrics to determine the vulnerability of selected species on the basis of species exposure to climate change, sensitivity to factors associated with climate change, and capacity to adapt to climate change. An upward trend in temperature was projected for 2011-2050 at all three weather stations; there was a trend (downward) in annual precipitation only for the weather station in Texas. A downward trend in mean annual spring flow or groundwater level was projected for all of the Edwards sites, but there was no significant trend for the Madison sites. Of 16 Edwards aquifer species evaluated (four amphibians, six arthropods, one fish, one mollusk, and four plants), 12 were scored as highly or moderately vulnerable under the projected climate change scenario. In contrast, all of the 8 Madison aquifer species evaluated (two mammals, one bird, one mollusk, and four plants) were scored as moderately vulnerable, stable, or intermediate between the two. The inclusion of hydrologic projections in the vulnerability assessment was essential for interpreting the effects of climate change on aquatic species of conservations concern, such as endemic salamanders. The linkage of climate, hydrologic, and vulnerability models provided a bridge to project the effects of global climate change on local karst aquifer and stream systems and selected species.

Measures of GCM Performance as Functions of Model Parameters Affecting Clouds and Radiation

NASA Astrophysics Data System (ADS)

Jackson, C.; Mu, Q.; Sen, M.; Stoffa, P.

2002-05-01

This abstract is one of three related presentations at this meeting dealing with several issues surrounding optimal parameter and uncertainty estimation of model predictions of climate. Uncertainty in model predictions of climate depends in part on the uncertainty produced by model approximations or parameterizations of unresolved physics. Evaluating these uncertainties is computationally expensive because one needs to evaluate how arbitrary choices for any given combination of model parameters affects model performance. Because the computational effort grows exponentially with the number of parameters being investigated, it is important to choose parameters carefully. Evaluating whether a parameter is worth investigating depends on two considerations: 1) does reasonable choices of parameter values produce a large range in model response relative to observational uncertainty? and 2) does the model response depend non-linearly on various combinations of model parameters? We have decided to narrow our attention to selecting parameters that affect clouds and radiation, as it is likely that these parameters will dominate uncertainties in model predictions of future climate. We present preliminary results of ~20 to 30 AMIPII style climate model integrations using NCAR's CCM3.10 that show model performance as functions of individual parameters controlling 1) critical relative humidity for cloud formation (RHMIN), and 2) boundary layer critical Richardson number (RICR). We also explore various definitions of model performance that include some or all observational data sources (surface air temperature and pressure, meridional and zonal winds, clouds, long and short-wave cloud forcings, etc...) and evaluate in a few select cases whether the model's response depends non-linearly on the parameter values we have selected.

Wildfire potential evaluation during a drought event with a regional climate model and NDVI

Treesearch

Y. Liu; J. Stanturf; S. Goodrick

2010-01-01

Regional climate modeling is a technique for simulating high-resolution physical processes in the atmosphere, soil and vegetation. It can be used to evaluate wildfire potential by either providing meteorological conditions for computation of fire indices or predicting soil moisture as a direct measure of fire potential. This study examines these roles using a regional...

Using Impact-Relevant Sensitivities to Efficiently Evaluate and Select Climate Change Scenarios

NASA Astrophysics Data System (ADS)

Vano, J. A.; Kim, J. B.; Rupp, D. E.; Mote, P.

2014-12-01

We outline an efficient approach to help researchers and natural resource managers more effectively use global climate model information in their long-term planning. The approach provides an estimate of the magnitude of change of a particular impact (e.g., summertime streamflow) from a large ensemble of climate change projections prior to detailed analysis. These estimates provide both qualitative information as an end unto itself (e.g., the distribution of future changes between emissions scenarios for the specific impact) and a judicious, defensible evaluation structure that can be used to qualitatively select a sub-set of climate models for further analysis. More specifically, the evaluation identifies global climate model scenarios that both (1) span the range of possible futures for the variable/s most important to the impact under investigation, and (2) come from global climate models that adequately simulate historical climate, providing plausible results for the future climate in the region of interest. To identify how an ecosystem process responds to projected future changes, we methodically sample, using a simple sensitivity analysis, how an impact variable (e.g., streamflow magnitude, vegetation carbon) responds locally to projected regional temperature and precipitation changes. We demonstrate our technique over the Pacific Northwest, focusing on two types of impacts each in three distinct geographic settings: (a) changes in streamflow magnitudes in critical seasons for water management in the Willamette, Yakima, and Upper Columbia River basins; and (b) changes in annual vegetation carbon in the Oregon and Washington Coast Ranges, Western Cascades, and Columbia Basin ecoregions.

Application of a Hybrid Forest Growth Model to Evaluate Climate Change Impacts on Productivity, Nutrient Cycling and Mortality in a Montane Forest Ecosystem.

PubMed

Seely, Brad; Welham, Clive; Scoullar, Kim

2015-01-01

Climate change introduces considerable uncertainty in forest management planning and outcomes, potentially undermining efforts at achieving sustainable practices. Here, we describe the development and application of the FORECAST Climate model. Constructed using a hybrid simulation approach, the model includes an explicit representation of the effect of temperature and moisture availability on tree growth and survival, litter decomposition, and nutrient cycling. The model also includes a representation of the impact of increasing atmospheric CO2 on water use efficiency, but no direct CO2 fertilization effect. FORECAST Climate was evaluated for its ability to reproduce the effects of historical climate on Douglas-fir and lodgepole pine growth in a montane forest in southern British Columbia, Canada, as measured using tree ring analysis. The model was subsequently used to project the long-term impacts of alternative future climate change scenarios on forest productivity in young and established stands. There was a close association between predicted sapwood production and measured tree ring chronologies, providing confidence that model is able to predict the relative impact of annual climate variability on tree productivity. Simulations of future climate change suggest a modest increase in productivity in young stands of both species related to an increase in growing season length. In contrast, results showed a negative impact on stemwood biomass production (particularly in the case of lodgepole pine) for established stands due to increased moisture stress mortality.

Application of a Hybrid Forest Growth Model to Evaluate Climate Change Impacts on Productivity, Nutrient Cycling and Mortality in a Montane Forest Ecosystem

PubMed Central

Seely, Brad; Welham, Clive; Scoullar, Kim

2015-01-01

Climate change introduces considerable uncertainty in forest management planning and outcomes, potentially undermining efforts at achieving sustainable practices. Here, we describe the development and application of the FORECAST Climate model. Constructed using a hybrid simulation approach, the model includes an explicit representation of the effect of temperature and moisture availability on tree growth and survival, litter decomposition, and nutrient cycling. The model also includes a representation of the impact of increasing atmospheric CO2 on water use efficiency, but no direct CO2 fertilization effect. FORECAST Climate was evaluated for its ability to reproduce the effects of historical climate on Douglas-fir and lodgepole pine growth in a montane forest in southern British Columbia, Canada, as measured using tree ring analysis. The model was subsequently used to project the long-term impacts of alternative future climate change scenarios on forest productivity in young and established stands. There was a close association between predicted sapwood production and measured tree ring chronologies, providing confidence that model is able to predict the relative impact of annual climate variability on tree productivity. Simulations of future climate change suggest a modest increase in productivity in young stands of both species related to an increase in growing season length. In contrast, results showed a negative impact on stemwood biomass production (particularly in the case of lodgepole pine) for established stands due to increased moisture stress mortality. PMID:26267446

Impacts of uncertainties in European gridded precipitation observations on regional climate analysis

PubMed Central

Gobiet, Andreas

2016-01-01

ABSTRACT Gridded precipitation data sets are frequently used to evaluate climate models or to remove model output biases. Although precipitation data are error prone due to the high spatio‐temporal variability of precipitation and due to considerable measurement errors, relatively few attempts have been made to account for observational uncertainty in model evaluation or in bias correction studies. In this study, we compare three types of European daily data sets featuring two Pan‐European data sets and a set that combines eight very high‐resolution station‐based regional data sets. Furthermore, we investigate seven widely used, larger scale global data sets. Our results demonstrate that the differences between these data sets have the same magnitude as precipitation errors found in regional climate models. Therefore, including observational uncertainties is essential for climate studies, climate model evaluation, and statistical post‐processing. Following our results, we suggest the following guidelines for regional precipitation assessments. (1) Include multiple observational data sets from different sources (e.g. station, satellite, reanalysis based) to estimate observational uncertainties. (2) Use data sets with high station densities to minimize the effect of precipitation undersampling (may induce about 60% error in data sparse regions). The information content of a gridded data set is mainly related to its underlying station density and not to its grid spacing. (3) Consider undercatch errors of up to 80% in high latitudes and mountainous regions. (4) Analyses of small‐scale features and extremes are especially uncertain in gridded data sets. For higher confidence, use climate‐mean and larger scale statistics. In conclusion, neglecting observational uncertainties potentially misguides climate model development and can severely affect the results of climate change impact assessments. PMID:28111497

Effect of the learning climate of residency programs on faculty's teaching performance as evaluated by residents.

PubMed

Lombarts, Kiki M J M H; Heineman, Maas Jan; Scherpbier, Albert J J A; Arah, Onyebuchi A

2014-01-01

To understand teaching performance of individual faculty, the climate in which residents' learning takes place, the learning climate, may be important. There is emerging evidence that specific climates do predict specific outcomes. Until now, the effect of learning climate on the performance of the individual faculty who actually do the teaching was unknown. THIS STUDY: (i) tested the hypothesis that a positive learning climate was associated with better teaching performance of individual faculty as evaluated by residents, and (ii) explored which dimensions of learning climate were associated with faculty's teaching performance. We conducted two cross-sectional questionnaire surveys amongst residents from 45 residency training programs and multiple specialties in 17 hospitals in the Netherlands. Residents evaluated the teaching performance of individual faculty using the robust System for Evaluating Teaching Qualities (SETQ) and evaluated the learning climate of residency programs using the Dutch Residency Educational Climate Test (D-RECT). The validated D-RECT questionnaire consisted of 11 subscales of learning climate. Main outcome measure was faculty's overall teaching (SETQ) score. We used multivariable adjusted linear mixed models to estimate the separate associations of overall learning climate and each of its subscales with faculty's teaching performance. In total 451 residents completed 3569 SETQ evaluations of 502 faculty. Residents also evaluated the learning climate of 45 residency programs in 17 hospitals in the Netherlands. Overall learning climate was positively associated with faculty's teaching performance (regression coefficient 0.54, 95% confidence interval: 0.37 to 0.71; P<0.001). Three out of 11 learning climate subscales were substantially associated with better teaching performance: 'coaching and assessment', 'work is adapted to residents' competence', and 'formal education'. Individual faculty's teaching performance evaluations are positively affected by better learning climate of residency programs.

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.

Characterizing and Addressing the Need for Statistical Adjustment of Global Climate Model Data

NASA Astrophysics Data System (ADS)

White, K. D.; Baker, B.; Mueller, C.; Villarini, G.; Foley, P.; Friedman, D.

2017-12-01

As part of its mission to research and measure the effects of the changing climate, the U. S. Army Corps of Engineers (USACE) regularly uses the World Climate Research Programme's Coupled Model Intercomparison Project Phase 5 (CMIP5) multi-model dataset. However, these data are generated at a global level and are not fine-tuned for specific watersheds. This often causes CMIP5 output to vary from locally observed patterns in the climate. Several downscaling methods have been developed to increase the resolution of the CMIP5 data and decrease systemic differences to support decision-makers as they evaluate results at the watershed scale. Evaluating preliminary comparisons of observed and projected flow frequency curves over the US revealed a simple framework for water resources decision makers to plan and design water resources management measures under changing conditions using standard tools. Using this framework as a basis, USACE has begun to explore to use of statistical adjustment to alter global climate model data to better match the locally observed patterns while preserving the general structure and behavior of the model data. When paired with careful measurement and hypothesis testing, statistical adjustment can be particularly effective at navigating the compromise between the locally observed patterns and the global climate model structures for decision makers.

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.

Assessment of CMIP5 historical simulations of rainfall over Southeast Asia

NASA Astrophysics Data System (ADS)

Raghavan, Srivatsan V.; Liu, Jiandong; Nguyen, Ngoc Son; Vu, Minh Tue; Liong, Shie-Yui

2018-05-01

We present preliminary analyses of the historical (1986-2005) climate simulations of a ten-member subset of the Coupled Model Inter-comparison Project Phase 5 (CMIP5) global climate models over Southeast Asia. The objective of this study was to evaluate the general circulation models' performance in simulating the mean state of climate over this less-studied climate vulnerable region, with a focus on precipitation. Results indicate that most of the models are unable to reproduce the observed state of climate over Southeast Asia. Though the multi-model ensemble mean is a better representation of the observations, the uncertainties in the individual models are far high. There is no particular model that performed well in simulating the historical climate of Southeast Asia. There seems to be no significant influence of the spatial resolutions of the models on the quality of simulation, despite the view that higher resolution models fare better. The study results emphasize on careful consideration of models for impact studies and the need to improve the next generation of models in their ability to simulate regional climates better.

SEEPLUS: A SIMPLE ONLINE CLIMATE MODEL

NASA Astrophysics Data System (ADS)

Tsutsui, Junichi

A web application for a simple climate model - SEEPLUS (a Simple climate model to Examine Emission Pathways Leading to Updated Scenarios) - has been developed. SEEPLUS consists of carbon-cycle and climate-change modules, through which it provides the information infrastructure required to perform climate-change experiments, even on a millennial-timescale. The main objective of this application is to share the latest scientific knowledge acquired from climate modeling studies among the different stakeholders involved in climate-change issues. Both the carbon-cycle and climate-change modules employ impulse response functions (IRFs) for their key processes, thereby enabling the model to integrate the outcome from an ensemble of complex climate models. The current IRF parameters and forcing manipulation are basically consistent with, or within an uncertainty range of, the understanding of certain key aspects such as the equivalent climate sensitivity and ocean CO2 uptake data documented in representative literature. The carbon-cycle module enables inverse calculation to determine the emission pathway required in order to attain a given concentration pathway, thereby providing a flexible way to compare the module with more advanced modeling studies. The module also enables analytical evaluation of its equilibrium states, thereby facilitating the long-term planning of global warming mitigation.

Evaluating Impacts of climate and land use changes on streamflow using SWAT and land use models based CESM1-CAM5 Climate scenarios

NASA Astrophysics Data System (ADS)

Lin, Tzu Ping; Lin, Yu Pin; Lien, Wan Yu

2015-04-01

Climate change projects have various levels of impacts on hydrological cycles around the world. The impact of climate change and uncertainty of climate projections from general circulation models (GCMs) from the Coupled Model Intercomparison Project (CMIP5) which has been just be released in Taiwan, 2014. Since the streamflow run into ocean directly due to the steep terrain and the rainfall difference between wet and dry seasons is apparent; as a result, the allocation water resource reasonable is very challenge in Taiwan, particularly under climate change. The purpose of this study was to evaluate the impacts of climate and land use changes on a small watershed in Taiwan. The AR5 General Circulation Models(GCM) output data was adopted in this study and was downscaled from the monthly to the daily weather data as the input data of hydrological model such as Soil and Water Assessment Tool (SWAT) model in this study. The spatially explicit land uses change model, the Conservation of Land Use and its Effects at Small regional extent (CLUE-s), was applied to simulate land use scenarios in 2020-2039. Combined climate and land use change scenarios were adopted as input data of the hydrological model, the SWAT model, to estimate the future streamflows. With the increasing precipitation, increasing urban area and decreasing agricultural and grass land, the annual streamflow in the most of twenty-three subbasins were also increased. Besides, due to the increasing rainfall in wet season and decreasing rainfall in dry season, the difference of streamflow between wet season and dry season are also increased. This result indicates a more stringent challenge on the water resource management in future. Therefore, impacts on water resource caused by climate change and land use change should be considered in water resource planning for the Datuan river watershed. Keywords: SWAT, GCM, CLUE-s, streamflow, climate change, land use change

Benefits of explicit urban parameterization in regional climate modeling to study climate and city interactions

NASA Astrophysics Data System (ADS)

Daniel, M.; Lemonsu, Aude; Déqué, M.; Somot, S.; Alias, A.; Masson, V.

2018-06-01

Most climate models do not explicitly model urban areas and at best describe them as rock covers. Nonetheless, the very high resolutions reached now by the regional climate models may justify and require a more realistic parameterization of surface exchanges between urban canopy and atmosphere. To quantify the potential impact of urbanization on the regional climate, and evaluate the benefits of a detailed urban canopy model compared with a simpler approach, a sensitivity study was carried out over France at a 12-km horizontal resolution with the ALADIN-Climate regional model for 1980-2009 time period. Different descriptions of land use and urban modeling were compared, corresponding to an explicit modeling of cities with the urban canopy model TEB, a conventional and simpler approach representing urban areas as rocks, and a vegetated experiment for which cities are replaced by natural covers. A general evaluation of ALADIN-Climate was first done, that showed an overestimation of the incoming solar radiation but satisfying results in terms of precipitation and near-surface temperatures. The sensitivity analysis then highlighted that urban areas had a significant impact on modeled near-surface temperature. A further analysis on a few large French cities indicated that over the 30 years of simulation they all induced a warming effect both at daytime and nighttime with values up to + 1.5 °C for the city of Paris. The urban model also led to a regional warming extending beyond the urban areas boundaries. Finally, the comparison to temperature observations available for Paris area highlighted that the detailed urban canopy model improved the modeling of the urban heat island compared with a simpler approach.

Predicting potential global distributions of two Miscanthus grasses: implications for horticulture, biofuel production, and biological invasions.

PubMed

Hager, Heather A; Sinasac, Sarah E; Gedalof, Ze'ev; Newman, Jonathan A

2014-01-01

In many regions, large proportions of the naturalized and invasive non-native floras were originally introduced deliberately by humans. Pest risk assessments are now used in many jurisdictions to regulate the importation of species and usually include an estimation of the potential distribution in the import area. Two species of Asian grass (Miscanthus sacchariflorus and M. sinensis) that were originally introduced to North America as ornamental plants have since escaped cultivation. These species and their hybrid offspring are now receiving attention for large-scale production as biofuel crops in North America and elsewhere. We evaluated their potential global climate suitability for cultivation and potential invasion using the niche model CLIMEX and evaluated the models' sensitivity to the parameter values. We then compared the sensitivity of projections of future climatically suitable area under two climate models and two emissions scenarios. The models indicate that the species have been introduced to most of the potential global climatically suitable areas in the northern but not the southern hemisphere. The more narrowly distributed species (M. sacchariflorus) is more sensitive to changes in model parameters, which could have implications for modelling species of conservation concern. Climate projections indicate likely contractions in potential range in the south, but expansions in the north, particularly in introduced areas where biomass production trials are under way. Climate sensitivity analysis shows that projections differ more between the selected climate change models than between the selected emissions scenarios. Local-scale assessments are required to overlay suitable habitat with climate projections to estimate areas of cultivation potential and invasion risk.

Hydrological Modeling in the Bull Run Watershed in Support of a Piloting Utility Modeling Applications (PUMA) Project

NASA Astrophysics Data System (ADS)

Nijssen, B.; Chiao, T. H.; Lettenmaier, D. P.; Vano, J. A.

2016-12-01

Hydrologic models with varying complexities and structures are commonly used to evaluate the impact of climate change on future hydrology. While the uncertainties in future climate projections are well documented, uncertainties in streamflow projections associated with hydrologic model structure and parameter estimation have received less attention. In this study, we implemented and calibrated three hydrologic models (the Distributed Hydrology Soil Vegetation Model (DHSVM), the Precipitation-Runoff Modeling System (PRMS), and the Variable Infiltration Capacity model (VIC)) for the Bull Run watershed in northern Oregon using consistent data sources and best practice calibration protocols. The project was part of a Piloting Utility Modeling Applications (PUMA) project with the Portland Water Bureau (PWB) under the umbrella of the Water Utility Climate Alliance (WUCA). Ultimately PWB would use the model evaluation to select a model to perform in-house climate change analysis for Bull Run Watershed. This presentation focuses on the experimental design of the comparison project, project findings and the collaboration between the team at the University of Washington and at PWB. After calibration, the three models showed similar capability to reproduce seasonal and inter-annual variations in streamflow, but differed in their ability to capture extreme events. Furthermore, the annual and seasonal hydrologic sensitivities to changes in climate forcings differed among models, potentially attributable to different model representations of snow and vegetation processes.

Overview of the Special Issue: A Multi-Model Framework to Achieve Consistent Evaluation of Climate Change Impacts in the United States

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

Waldhoff, Stephanie T.; Martinich, Jeremy; Sarofim, Marcus

2015-07-01

The Climate Change Impacts and Risk Analysis (CIRA) modeling exercise is a unique contribution to the scientific literature on climate change impacts, economic damages, and risk analysis that brings together multiple, national-scale models of impacts and damages in an integrated and consistent fashion to estimate climate change impacts, damages, and the benefits of greenhouse gas (GHG) mitigation actions in the United States. The CIRA project uses three consistent socioeconomic, emissions, and climate scenarios across all models to estimate the benefits of GHG mitigation policies: a Business As Usual (BAU) and two policy scenarios with radiative forcing (RF) stabilization targets ofmore » 4.5 W/m2 and 3.7 W/m2 in 2100. CIRA was also designed to specifically examine the sensitivity of results to uncertainties around climate sensitivity and differences in model structure. The goals of CIRA project are to 1) build a multi-model framework to produce estimates of multiple risks and impacts in the U.S., 2) determine to what degree risks and damages across sectors may be lowered from a BAU to policy scenarios, 3) evaluate key sources of uncertainty along the causal chain, and 4) provide information for multiple audiences and clearly communicate the risks and damages of climate change and the potential benefits of mitigation. This paper describes the motivations, goals, and design of the CIRA modeling exercise and introduces the subsequent papers in this special issue.« less

An Assessment of Actual and Potential Building Climate Zone Change and Variability From the Last 30 Years Through 2100 Using NASA's MERRA and CMIP5 Simulations

NASA Technical Reports Server (NTRS)

Stackhouse, Paul W., Jr.; Chandler, William S.; Hoell, James M.; Westberg, David; Zhang, Taiping

2015-01-01

Background: In the US, residential and commercial building infrastructure combined consumes about 40% of total energy usage and emits about 39% of total CO2 emission (DOE/EIA "Annual Energy Outlook 2013"). Building codes, as used by local and state enforcement entities are typically tied to the dominant climate within an enforcement jurisdiction classified according to various climate zones. These climate zones are based upon a 30-year average of local surface observations and are developed by DOE and ASHRAE. Establishing the current variability and potential changes to future building climate zones is very important for increasing the energy efficiency of buildings and reducing energy costs and emissions in the future. Objectives: This paper demonstrates the usefulness of using NASA's Modern Era Retrospective-analysis for Research and Applications (MERRA) atmospheric data assimilation to derive the DOE/ASHRAE building climate zone maps and then using MERRA to define the last 30 years of variability in climate zones for the Continental US. An atmospheric assimilation is a global atmospheric model optimized to satellite, atmospheric and surface in situ measurements. Using MERRA as a baseline, we then evaluate the latest Climate Model Inter-comparison Project (CMIP) climate model Version 5 runs to assess potential variability in future climate zones under various assumptions. Methods: We derive DOE/ASHRAE building climate zones using surface and temperature data products from MERRA. We assess these zones using the uncertainties derived by comparison to surface measurements. Using statistical tests, we evaluate variability of the climate zones in time and assess areas in the continental US for statistically significant trends by region. CMIP 5 produced a data base of over two dozen detailed climate model runs under various greenhouse gas forcing assumptions. We evaluate the variation in building climate zones for 3 different decades using an ensemble and quartile statistics to provide an assessment of potential building climate zone changes relative to the uncertainties demonstrated using MERRA. Findings and Conclusions: These results show that there is a statistically significant increase in the area covered by warmer climate zones and a tendency for a reduction of area in colder climate zones in some limited regions. The CMIP analysis shows that models vary from relatively little building climate zone change for the least sensitive and conservation assumptions to a warming of at most 3 zones for certain areas, particularly the north central US by the end of the 21st century.

Assessment of the Effect of Climate Change on Grain Yields in China

NASA Astrophysics Data System (ADS)

Chou, J.

2006-12-01

The paper elaborates the social background and research background; makes clear what the key scientific issues need to be resolved and where the difficulties are. In the research area of parasailing the grain yield change caused by climate change, massive works have been done both in the domestic and in the foreign. It is our upcoming work to evaluate how our countrywide climate change information provided by this pattern influence our economic and social development; and how to make related policies and countermeasures. the main idea in this paper is that the grain yield change is by no means the linear composition of social economy function effect and the climatic change function effect. This paper identifies the economic evaluation object, proposes one new concept - climate change output. The grain yields change affected by the social factors and the climatic change working together. Climate change influences the grain yields by the non ¨C linear function from both climate change and social factor changes, not only by climate change itself. Therefore, in my paper, the appraisal object is defined as: The social factors change based on actual social changing situations; under the two kinds of climate change situation, the invariable climate change situation and variable climate change situation; the difference of grain yield outputs is called " climate change output ", In order to solve this problem, we propose a method to analyze and imitate on the historical materials. Giving the condition that the climate is invariable, the social economic factor changes cause the grain yield change. However, this grain yield change is a tentative quantity index, not an actual quantity number. So we use the existing historical materials to exam the climate change output, based on the characteristic that social factor changes greater in year than in age, but the climate factor changes greater in age than in year. The paper proposes and establishes one economy - climate model (C-D-C model) to appraise the grain yield change caused by the climatic change. Also the preliminary test on this model has been done. In selection of the appraisal methods, we take the C-D production function model, which has been proved more mature in the economic research, as our fundamental model. Then, we introduce climate index (arid index) to the C-D model to develop one new model. This new model utilizes the climatic change factor in the economical model to appraise how the climatic change influence the grain yield change. The new way of appraise should have the better application prospect. The economy - climate model (The C-D-C model) has been applied on the eight Chinese regions that we divide; it has been proved satisfactory in its feasibility, rationality and the application prospect. So we can provide the theoretical fundamentals for policy-making under the more complex and uncertain climate change. Therefore, we open a new possible channel for the global climate change research moving toward the actual social, economic life.

Can increasing carbon dioxide cause climate change?

PubMed Central

Lindzen, Richard S.

1997-01-01

The realistic physical functioning of the greenhouse effect is reviewed, and the role of dynamic transport and water vapor is identified. Model errors and uncertainties are quantitatively compared with the forcing due to doubling CO2, and they are shown to be too large for reliable model evaluations of climate sensitivities. The possibility of directly measuring climate sensitivity is reviewed. A direct approach using satellite data to relate changes in globally averaged radiative flux changes at the top of the atmosphere to naturally occurring changes in global mean temperature is described. Indirect approaches to evaluating climate sensitivity involving the response to volcanic eruptions and Eocene climate change are also described. Finally, it is explained how, in principle, a climate that is insensitive to gross radiative forcing as produced by doubling CO2 might still be able to undergo major changes of the sort associated with ice ages and equable climates. PMID:11607742

2016 International Land Model Benchmarking (ILAMB) Workshop Report

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

Hoffman, Forrest M.; Koven, Charles D.; Keppel-Aleks, Gretchen

As Earth system models become increasingly complex, there is a growing need for comprehensive and multi-faceted evaluation of model projections. To advance understanding of biogeochemical processes and their interactions with hydrology and climate under conditions of increasing atmospheric carbon dioxide, new analysis methods are required that use observations to constrain model predictions, inform model development, and identify needed measurements and field experiments. Better representations of biogeochemistry–climate feedbacks and ecosystem processes in these models are essential for reducing uncertainties associated with projections of climate change during the remainder of the 21st century.

Clouds at Barbados are representative of clouds across the trade wind regions in observations and climate models.

PubMed

Medeiros, Brian; Nuijens, Louise

2016-05-31

Trade wind regions cover most of the tropical oceans, and the prevailing cloud type is shallow cumulus. These small clouds are parameterized by climate models, and changes in their radiative effects strongly and directly contribute to the spread in estimates of climate sensitivity. This study investigates the structure and variability of these clouds in observations and climate models. The study builds upon recent detailed model evaluations using observations from the island of Barbados. Using a dynamical regimes framework, satellite and reanalysis products are used to compare the Barbados region and the broader tropics. It is shown that clouds in the Barbados region are similar to those across the trade wind regions, implying that observational findings from the Barbados Cloud Observatory are relevant to clouds across the tropics. The same methods are applied to climate models to evaluate the simulated clouds. The models generally capture the cloud radiative effect, but underestimate cloud cover and show an array of cloud vertical structures. Some models show strong biases in the environment of the Barbados region in summer, weakening the connection between the regional biases and those across the tropics. Even bearing that limitation in mind, it is shown that covariations of cloud and environmental properties in the models are inconsistent with observations. The models tend to misrepresent sensitivity to moisture variations and inversion characteristics. These model errors are likely connected to cloud feedback in climate projections, and highlight the importance of the representation of shallow cumulus convection.

Clouds at Barbados are representative of clouds across the trade wind regions in observations and climate models

PubMed Central

Nuijens, Louise

2016-01-01

Trade wind regions cover most of the tropical oceans, and the prevailing cloud type is shallow cumulus. These small clouds are parameterized by climate models, and changes in their radiative effects strongly and directly contribute to the spread in estimates of climate sensitivity. This study investigates the structure and variability of these clouds in observations and climate models. The study builds upon recent detailed model evaluations using observations from the island of Barbados. Using a dynamical regimes framework, satellite and reanalysis products are used to compare the Barbados region and the broader tropics. It is shown that clouds in the Barbados region are similar to those across the trade wind regions, implying that observational findings from the Barbados Cloud Observatory are relevant to clouds across the tropics. The same methods are applied to climate models to evaluate the simulated clouds. The models generally capture the cloud radiative effect, but underestimate cloud cover and show an array of cloud vertical structures. Some models show strong biases in the environment of the Barbados region in summer, weakening the connection between the regional biases and those across the tropics. Even bearing that limitation in mind, it is shown that covariations of cloud and environmental properties in the models are inconsistent with observations. The models tend to misrepresent sensitivity to moisture variations and inversion characteristics. These model errors are likely connected to cloud feedback in climate projections, and highlight the importance of the representation of shallow cumulus convection. PMID:27185925

A multi-model assessment of the co-benefits of climate mitigation for global air quality

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

Rao, Shilpa; Klimont, Zbigniew; Leitao, Joana

The recent International Panel on Climate change (IPCC) report identifies significant co-benefits from climate policies on near-term ambient air pollution and related human health outcomes [1]. This is increasingly relevant for policy making as the health impacts of air pollution are a major global concern- the Global Burden of Disease (GBD) study identifies outdoor air pollution as the sixth major cause of death globally [2]. Integrated assessment models (IAMs) are an effective tool to evaluate future air pollution outcomes across a wide range of assumptions on socio-economic development and policy regimes. The Representative Concentration Pathways (RCPs) [3] were the firstmore » set of long-term global scenarios developed across multiple integrated assessment models that provided detailed estimates of a number of air pollutants until 2100. However these scenarios were primarily designed to cover a defined range of radiative forcing outcomes and thus did not specifically focus on the interactions of long-term climate goals on near-term air pollution impacts. More recently, [4] used the RCP4.5 scenario to evaluate the co-benefits of global GHG reductions on air quality and human health in 2030. [5-7] have further examined the interactions of more diverse pollution control regimes with climate policies. This paper extends the listed studies in a number of ways. Firstly it uses multiple IAMs to look into the co-benefits of a global climate policy for ambient air pollution under harmonized assumptions on near-term air pollution control. Multi-model frameworks have been extensively used in the analysis of climate change mitigation pathways, and the structural uncertainties regarding the underlying mechanisms (see for example [8-10]. This is to our knowledge the first time that a multi-model evaluation has been specifically designed and applied to analyze the co-benefits of climate change policy on ambient air quality, thus enabling a better understanding of at a detailed sector and region level. A second methodological advancement is a quantification of the co-benefits in terms of the associated atmospheric concentrations of fine particulate matter (PM2.5) and consequent mortality related outcomes across different models. This is made possible by the use of state-of the art simplified atmospheric model that allows for the first time a computationally feasible multi-model evaluation of such outcomes.« less

Integration of Linear Dynamic Emission and Climate Models with Air Traffic Simulations

NASA Technical Reports Server (NTRS)

Sridhar, Banavar; Ng, Hok K.; Chen, Neil Y.

2012-01-01

Future air traffic management systems are required to balance the conflicting objectives of maximizing safety and efficiency of traffic flows while minimizing the climate impact of aviation emissions and contrails. Integrating emission and climate models together with air traffic simulations improve the understanding of the complex interaction between the physical climate system, carbon and other greenhouse gas emissions and aviation activity. This paper integrates a national-level air traffic simulation and optimization capability with simple climate models and carbon cycle models, and climate metrics to assess the impact of aviation on climate. The capability can be used to make trade-offs between extra fuel cost and reduction in global surface temperature change. The parameters in the simulation can be used to evaluate the effect of various uncertainties in emission models and contrails and the impact of different decision horizons. Alternatively, the optimization results from the simulation can be used as inputs to other tools that monetize global climate impacts like the FAA s Aviation Environmental Portfolio Management Tool for Impacts.

Assessments of Future Maize Yield Potential Changes in the Korean Peninsula Using Multiple Crop Models

NASA Astrophysics Data System (ADS)

Kim, S. H.; Lim, C. H.; Kim, J.; Lee, W. K.; Kafatos, M.

2016-12-01

The Korean Peninsula has unique agricultural environment due to the differences of political and socio-economical system between Republic of Korea (SK, hereafter) and Democratic Peoples' Republic of Korea (NK, hereafter). NK has been suffering lack of food supplies caused by natural disasters, land degradation and political failure. The neighboring developed country SK has better agricultural system but very low food self-sufficiency rate. Maize is an important crop in both countries since it is staple food for NK and SK is No. 2 maize importing country in the world after Japan. Therefore, evaluating maize yield potential (Yp) in the two distinct regions is essential to assess food security under climate change and variability. In this study, we utilized multiple process-based crop models, having ability of regional scale assessment, to evaluate maize Yp and assess the model uncertainties -EPIC, GEPIC, DSSAT, and APSIM model that has capability of regional scale expansion (apsimRegions). First we evaluated each crop model for 3 years from 2012 to 2014 using reanalysis data (RDAPS; Regional Data Assimilation and Prediction System produced by Korea Meteorological Agency) and observed yield data. Each model performances were compared over the different regions in the Korean Peninsula having different local climate characteristics. To quantify of the major influence of at each climate variables, we also conducted sensitivity test using 20 years of climatology in historical period from 1981 to 2000. Lastly, the multi-crop model ensemble analysis was performed for future period from 2031 to 2050. The required weather variables projected for mid-century were employed from COordinated Regional climate Downscaling EXperiment (CORDEX) East Asia. The high-resolution climate data were obtained from multiple regional climate models (RCM) driven by multiple climate scenarios projected from multiple global climate models (GCMs) in conjunction with multiple greenhouse gas concentration pathways. The results indicate that the projected Yp in the Korean peninsula is significantly changed comparing to the historical period and proper adaptation strategies such as optimized planting dates can considerably alleviate Yp decrease.

Evaluating the impact of climate change on landslide occurrence, hazard, and risk: from global to regional scale.

NASA Astrophysics Data System (ADS)

Gariano, Stefano Luigi; Guzzetti, Fausto

2017-04-01

According to the fifth report of the Intergovernmental Panel on Climate Change, "warming of the climate system is unequivocal". The influence of climate changes on slope stability and landslides is also undisputable. Nevertheless, the quantitative evaluation of the impact of global warming, and the related changes in climate, on landslides remains a complex question to be solved. The evidence that climate and landslides act at only partially overlapping spatial and temporal scales complicates the evaluation. Different research fields, including e.g., climatology, physics, hydrology, geology, hydrogeology, geotechnics, soil science, environmental science, and social science, must be considered. Climatic, environmental, demographic, and economic changes are strictly correlated, with complex feedbacks, to landslide occurrence and variation. Thus, a holistic, multidisciplinary approach is necessary. We reviewed the literature on landslide-climate studies, and found a bias in their geographical distribution, with several studies centered in Europe and North America, and large parts of the world not investigated. We examined advantages and drawbacks of the approaches adopted to evaluate the effects of climate variations on landslides, including prospective modelling and retrospective methods that use landslide and climate records, and paleo-environmental information. We found that the results of landslide-climate studies depend more on the emission scenarios, the global circulation models, the regional climate models, and the methods to downscale the climate variables, than on the description of the variables controlling slope processes. Using ensembles of projections based on a range of emissions scenarios would reduce (or at least quantify) the uncertainties in the obtained results. We performed a preliminary global assessment of the future landslide impact, presenting a global distribution of the projected impact of climate change on landslide activity and abundance. Where global warming is expected to increase, the frequency and intensity of severe rainfall events, a primary trigger of shallow, rapid-moving landslides that cause many landslide fatalities, an increase in the number of people exposed to landslide risk is to be expected. Furthermore, we defined a group of objective and reproducible methods for the quantitative evaluation of the past and future (expected) variations in landslide occurrence and distribution, and in the impact and risk to the population, as a result of changes in climatic and environmental factors (particularly, land use changes), at regional scale. The methods were tested in a southern Italian region, but they can easily applied in other physiographic and climatic regions, where adequate information is available.

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.

Advance strategy for climate change adaptation and mitigation in cities

NASA Astrophysics Data System (ADS)

Varquez, A. C. G.; Kanda, M.; Darmanto, N. S.; Sueishi, T.; Kawano, N.

2017-12-01

An on-going 5-yr project financially supported by the Ministry of Environment, Japan, has been carried out to specifically address the issue of prescribing appropriate adaptation and mitigation measures to climate change in cities. Entitled "Case Study on Mitigation and Local Adaptation to Climate Change in an Asian Megacity, Jakarta", the project's relevant objectives is to develop a research framework that can consider both urbanization and climate change with the main advantage of being readily implementable for all cities around the world. The test location is the benchmark city, Jakarta, Indonesia, with the end focus of evaluating the benefits of various mitigation and adaptation strategies in Jakarta and other megacities. The framework was designed to improve representation of urban areas when conducting climate change investigations in cities; and to be able to quantify separately the impacts of urbanization and climate change to all cities globally. It is comprised of a sophisticated, top-down, multi-downscaling approach utilizing a regional model (numerical weather model) and a microscale model (energy balance model and CFD model), with global circulation models (GCM) as input. The models, except the GCM, were configured to reasonably consider land cover, urban morphology, and anthropogenic heating (AH). Equally as important, methodologies that can collect and estimate global distribution of urban parametric and AH datasets are continually being developed. Urban growth models, climate scenario matrices that match representative concentration pathways with shared socio-economic pathways, present distribution of socio-demographic indicators such as population and GDP, existing GIS datasets of urban parameters, are utilized. From these tools, future urbanization (urban morphological parameters and AH) can be introduced into the models. Sensitivity using various combinations of GCM and urbanization can be conducted. Furthermore, since the models utilize parameters that can be readily modified to suit certain countermeasures, adaptation and mitigation strategies can be evaluated using thermal comfort and other social indicators. With the approaches introduced through this project, a deeper understanding of urban-climate interactions in the changing global climate can be achieved.

The Operationalization of the Needs-Press Model: A Critique.

ERIC Educational Resources Information Center

May, Carolyn S.

The needs-press model, as operationalized by the Stern Activities Index (AI) and the Organizational Climate Index (OCI), was examined for its usefulness in evaluating and measuring organizational climate in a school setting. According to the model, needs represents personality and press represents the environment. This research was designed to…

OMIP contribution to CMIP6: experimental and diagnostic protocol for the physical component of the Ocean Model Intercomparison Project

NASA Astrophysics Data System (ADS)

Griffies, Stephen M.; Danabasoglu, Gokhan; Durack, Paul J.; Adcroft, Alistair J.; Balaji, V.; Böning, Claus W.; Chassignet, Eric P.; Curchitser, Enrique; Deshayes, Julie; Drange, Helge; Fox-Kemper, Baylor; Gleckler, Peter J.; Gregory, Jonathan M.; Haak, Helmuth; Hallberg, Robert W.; Heimbach, Patrick; Hewitt, Helene T.; Holland, David M.; Ilyina, Tatiana; Jungclaus, Johann H.; Komuro, Yoshiki; Krasting, John P.; Large, William G.; Marsland, Simon J.; Masina, Simona; McDougall, Trevor J.; Nurser, A. J. George; Orr, James C.; Pirani, Anna; Qiao, Fangli; Stouffer, Ronald J.; Taylor, Karl E.; Treguier, Anne Marie; Tsujino, Hiroyuki; Uotila, Petteri; Valdivieso, Maria; Wang, Qiang; Winton, Michael; Yeager, Stephen G.

2016-09-01

The Ocean Model Intercomparison Project (OMIP) is an endorsed project in the Coupled Model Intercomparison Project Phase 6 (CMIP6). OMIP addresses CMIP6 science questions, investigating the origins and consequences of systematic model biases. It does so by providing a framework for evaluating (including assessment of systematic biases), understanding, and improving ocean, sea-ice, tracer, and biogeochemical components of climate and earth system models contributing to CMIP6. Among the WCRP Grand Challenges in climate science (GCs), OMIP primarily contributes to the regional sea level change and near-term (climate/decadal) prediction GCs.OMIP provides (a) an experimental protocol for global ocean/sea-ice models run with a prescribed atmospheric forcing; and (b) a protocol for ocean diagnostics to be saved as part of CMIP6. We focus here on the physical component of OMIP, with a companion paper (Orr et al., 2016) detailing methods for the inert chemistry and interactive biogeochemistry. The physical portion of the OMIP experimental protocol follows the interannual Coordinated Ocean-ice Reference Experiments (CORE-II). Since 2009, CORE-I (Normal Year Forcing) and CORE-II (Interannual Forcing) have become the standard methods to evaluate global ocean/sea-ice simulations and to examine mechanisms for forced ocean climate variability. The OMIP diagnostic protocol is relevant for any ocean model component of CMIP6, including the DECK (Diagnostic, Evaluation and Characterization of Klima experiments), historical simulations, FAFMIP (Flux Anomaly Forced MIP), C4MIP (Coupled Carbon Cycle Climate MIP), DAMIP (Detection and Attribution MIP), DCPP (Decadal Climate Prediction Project), ScenarioMIP, HighResMIP (High Resolution MIP), as well as the ocean/sea-ice OMIP simulations.

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

The accuracy of climate models' simulated season lengths and the effectiveness of grid scale correction factors

DOE PAGES

Winterhalter, Wade E.

2011-09-01

Global climate change is expected to impact biological populations through a variety of mechanisms including increases in the length of their growing season. Climate models are useful tools for predicting how season length might change in the future. However, the accuracy of these models tends to be rather low at regional geographic scales. Here, I determined the ability of several atmosphere and ocean general circulating models (AOGCMs) to accurately simulate historical season lengths for a temperate ectotherm across the continental United States. I also evaluated the effectiveness of regional-scale correction factors to improve the accuracy of these models. I foundmore » that both the accuracy of simulated season lengths and the effectiveness of the correction factors to improve the model's accuracy varied geographically and across models. These results suggest that regional specific correction factors do not always adequately remove potential discrepancies between simulated and historically observed environmental parameters. As such, an explicit evaluation of the correction factors' effectiveness should be included in future studies of global climate change's impact on biological populations.« less

Collaborative Project: The problem of bias in defining uncertainty in computationally enabled strategies for data-driven climate model development. Final Technical Report.

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

Huerta, Gabriel

The objective of the project is to develop strategies for better representing scientific sensibilities within statistical measures of model skill that then can be used within a Bayesian statistical framework for data-driven climate model development and improved measures of model scientific uncertainty. One of the thorny issues in model evaluation is quantifying the effect of biases on climate projections. While any bias is not desirable, only those biases that affect feedbacks affect scatter in climate projections. The effort at the University of Texas is to analyze previously calculated ensembles of CAM3.1 with perturbed parameters to discover how biases affect projectionsmore » of global warming. The hypothesis is that compensating errors in the control model can be identified by their effect on a combination of processes and that developing metrics that are sensitive to dependencies among state variables would provide a way to select version of climate models that may reduce scatter in climate projections. Gabriel Huerta at the University of New Mexico is responsible for developing statistical methods for evaluating these field dependencies. The UT effort will incorporate these developments into MECS, which is a set of python scripts being developed at the University of Texas for managing the workflow associated with data-driven climate model development over HPC resources. This report reflects the main activities at the University of New Mexico where the PI (Huerta) and the Postdocs (Nosedal, Hattab and Karki) worked on the project.« less

A Reusable Framework for Regional Climate Model Evaluation

NASA Astrophysics Data System (ADS)

Hart, A. F.; Goodale, C. E.; Mattmann, C. A.; Lean, P.; Kim, J.; Zimdars, P.; Waliser, D. E.; Crichton, D. J.

2011-12-01

Climate observations are currently obtained through a diverse network of sensors and platforms that include space-based observatories, airborne and seaborne platforms, and distributed, networked, ground-based instruments. These global observational measurements are critical inputs to the efforts of the climate modeling community and can provide a corpus of data for use in analysis and validation of climate models. The Regional Climate Model Evaluation System (RCMES) is an effort currently being undertaken to address the challenges of integrating this vast array of observational climate data into a coherent resource suitable for performing model analysis at the regional level. Developed through a collaboration between the NASA Jet Propulsion Laboratory (JPL) and the UCLA Joint Institute for Regional Earth System Science and Engineering (JIFRESSE), the RCMES uses existing open source technologies (MySQL, Apache Hadoop, and Apache OODT), to construct a scalable, parametric, geospatial data store that incorporates decades of observational data from a variety of NASA Earth science missions, as well as other sources into a consistently annotated, highly available scientific resource. By eliminating arbitrary partitions in the data (individual file boundaries, differing file formats, etc), and instead treating each individual observational measurement as a unique, geospatially referenced data point, the RCMES is capable of transforming large, heterogeneous collections of disparate observational data into a unified resource suitable for comparison to climate model output. This facility is further enhanced by the availability of a model evaluation toolkit which consists of a set of Python libraries, a RESTful web service layer, and a browser-based graphical user interface that allows for orchestration of model-to-data comparisons by composing them visually through web forms. This combination of tools and interfaces dramatically simplifies the process of interacting with and utilizing large volumes of observational data for model evaluation research. We feel that the RCMES is particularly appealing in that it represents a principled, reusable architectural approach rather than a one-off technological implementation. In fact, early RCMES prototypes have already utilized a variety of implementation technologies in an effort to address different performance and scalability concerns. This has been greatly facilitated by the fact that, at the architectural level, the RCMES is fundamentally domain agnostic. Strictly separating the data model from the implementation has enabled us to create a reusable architecture that we believe can be modified and configured to suit the demands of researchers in other domains.

Short-term climate change impacts on Mara basin hydrology

NASA Astrophysics Data System (ADS)

Demaria, E. M.; Roy, T.; Valdés, J. B.; Lyon, B.; Valdés-Pineda, R.; Serrat-Capdevila, A.; Durcik, M.; Gupta, H.

2017-12-01

The predictability of climate diminishes significantly at shorter time scales (e.g. decadal). Both natural variability as well as sampling variability of climate can obscure or enhance climate change signals in these shorter scales. Therefore, in order to assess the impacts of climate change on basin hydrology, it is important to design climate projections with exhaustive climate scenarios. In this study, we first create seasonal climate scenarios by combining (1) synthetic precipitation projections generated from a Vector Auto-Regressive (VAR) model using the University of East Anglia Climate Research Unit (UEA-CRU) data with (2) seasonal trends calculated from 31 models in the Coupled Model Intercomparison Project Phase 5 (CMIP). The seasonal climate projections are then disaggregated to daily level using the Agricultural Modern-Era Retrospective Analysis for Research and Applications (AgMERRA) data. The daily climate data are then bias-corrected and used as forcings to the land-surface model, Variable Infiltration Capacity (VIC), to generate different hydrological projections for the Mara River basin in East Africa, which are then evaluated to study the hydrologic changes in the basin in the next three decades (2020-2050).

An economic evaluation of solar radiation management.

PubMed

Aaheim, Asbjørn; Romstad, Bård; Wei, Taoyuan; Kristjánsson, Jón Egill; Muri, Helene; Niemeier, Ulrike; Schmidt, Hauke

2015-11-01

Economic evaluations of solar radiation management (SRM) usually assume that the temperature will be stabilized, with no economic impacts of climate change, but with possible side-effects. We know from experiments with climate models, however, that unlike emission control the spatial and temporal distributions of temperature, precipitation and wind conditions will change. Hence, SRM may have economic consequences under a stabilization of global mean temperature even if side-effects other than those related to the climatic responses are disregarded. This paper addresses the economic impacts of implementing two SRM technologies; stratospheric sulfur injection and marine cloud brightening. By the use of a computable general equilibrium model, we estimate the economic impacts of climatic responses based on the results from two earth system models, MPI-ESM and NorESM. We find that under a moderately increasing greenhouse-gas concentration path, RCP4.5, the economic benefits of implementing climate engineering are small, and may become negative. Global GDP increases in three of the four experiments and all experiments include regions where the benefits from climate engineering are negative. Copyright © 2015 Elsevier B.V. All rights reserved.

A National Energy-Water System Assessment Framework (NEWS): Synopsis of Stage 1 Research Strategy and Results

NASA Astrophysics Data System (ADS)

Vorosmarty, C. J.; Miara, A.; Macknick, J.; Newmark, R. L.; Cohen, S.; Sun, Y.; Tidwell, V. C.; Corsi, F.; Melillo, J. M.; Fekete, B. M.; Proussevitch, A. A.; Glidden, S.; Suh, S.

2017-12-01

The focus of this talk is on climate adaptation and the reliability of power supply infrastructure when viewed through the lens of strategic water issues. Power supply is critically dependent upon water resources, particularly to cool thermoelectric plants, making the sector particularly sensitive to any shifts in the geography or seasonality of water supply. We report on results from an NSF-Funded Water Sustainability and Climate effort aimed at uncovering key energy and economic system vulnerabilities. We have developed the National Energy-Water System assessment framework (NEWS) to systematically evaluate: a) the performance of the nation's electricity sector under multiple climate scenarios; b) the feasibility of alternative pathways to improve climate adaptation; and, c) the impacts of energy technology and investment tradeoffs on the economic productivity, water availability and aquatic ecosystem condition. Our project combines core engineering and geophysical models (ReEDS [Regional Energy Deployment System], TP2M [Thermoelectric Power and Thermal Pollution], and WBM [Water Balance]) through unique digital "handshake" protocols that operate across different institutions and modeling platforms. Combined system outputs are fed into a regional-to-national scale economic input/output model to evaluate economic consequences of climate constraints, technology choices, and environmental regulation. The impact assessments in NEWS are carried out through a series of climate/energy policy scenario studies to 2050. We find that despite significant climate-water impacts on individual plants, the current US power supply infrastructure shows potential for adaptation to future climates by capitalizing on the size of regional power systems, grid configuration and improvements in thermal efficiencies. However, the magnitude and implications of climate-water impacts vary depending on the configuration of the future power sector. To evaluate future power supply performance, we model alternative electricity sector pathways in combination with varying climate-water conditions. Further, water-linked disruptions in electricity supply yield substantial impacts on regional economies yet system-level shocks can be attenuated through different technology mixes and infrastructure.

Tropical convection regimes in climate models: evaluation with satellite observations

NASA Astrophysics Data System (ADS)

Steiner, Andrea K.; Lackner, Bettina C.; Ringer, Mark A.

2018-04-01

High-quality observations are powerful tools for the evaluation of climate models towards improvement and reduction of uncertainty. Particularly at low latitudes, the most uncertain aspect lies in the representation of moist convection and interaction with dynamics, where rising motion is tied to deep convection and sinking motion to dry regimes. Since humidity is closely coupled with temperature feedbacks in the tropical troposphere, a proper representation of this region is essential. Here we demonstrate the evaluation of atmospheric climate models with satellite-based observations from Global Positioning System (GPS) radio occultation (RO), which feature high vertical resolution and accuracy in the troposphere to lower stratosphere. We focus on the representation of the vertical atmospheric structure in tropical convection regimes, defined by high updraft velocity over warm surfaces, and investigate atmospheric temperature and humidity profiles. Results reveal that some models do not fully capture convection regions, particularly over land, and only partly represent strong vertical wind classes. Models show large biases in tropical mean temperature of more than 4 K in the tropopause region and the lower stratosphere. Reasonable agreement with observations is given in mean specific humidity in the lower to mid-troposphere. In moist convection regions, models tend to underestimate moisture by 10 to 40 % over oceans, whereas in dry downdraft regions they overestimate moisture by 100 %. Our findings provide evidence that RO observations are a unique source of information, with a range of further atmospheric variables to be exploited, for the evaluation and advancement of next-generation climate models.

An AgMIP framework for improved agricultural representation in integrated assessment models

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

Ruane, Alex C.; Rosenzweig, Cynthia; Asseng, Senthold

Integrated assessment models (IAMs) hold great potential to assess how future agricultural systems will be shaped by socioeconomic development, technological innovation, and changing climate conditions. By coupling with climate and crop model emulators, IAMs have the potential to resolve important agricultural feedback loops and identify unintended consequences of socioeconomic development for agricultural systems. Here we propose a framework to develop robust representation of agricultural system responses within IAMs, linking downstream applications with model development and the coordinated evaluation of key climate responses from local to global scales. We survey the strengths and weaknesses of protocol-based assessments linked to the Agriculturalmore » Model Intercomparison and Improvement Project (AgMIP), each utilizing multiple sites and models to evaluate crop response to core climate changes including shifts in carbon dioxide concentration, temperature, and water availability, with some studies further exploring how climate responses are affected by nitrogen levels and adaptation in farm systems. Site-based studies with carefully calibrated models encompass the largest number of activities; however they are limited in their ability to capture the full range of global agricultural system diversity. Representative site networks provide more targeted response information than broadly-sampled networks, with limitations stemming from difficulties in covering the diversity of farming systems. Global gridded crop models provide comprehensive coverage, although with large challenges for calibration and quality control of inputs. Diversity in climate responses underscores that crop model emulators must distinguish between regions and farming system while recognizing model uncertainty. Finally, to bridge the gap between bottom-up and top-down approaches we recommend the deployment of a hybrid climate response system employing a representative network of sites to bias-correct comprehensive gridded simulations, opening the door to accelerated development and a broad range of applications.« less

An AgMIP framework for improved agricultural representation in integrated assessment models

NASA Astrophysics Data System (ADS)

Ruane, Alex C.; Rosenzweig, Cynthia; Asseng, Senthold; Boote, Kenneth J.; Elliott, Joshua; Ewert, Frank; Jones, James W.; Martre, Pierre; McDermid, Sonali P.; Müller, Christoph; Snyder, Abigail; Thorburn, Peter J.

2017-12-01

Integrated assessment models (IAMs) hold great potential to assess how future agricultural systems will be shaped by socioeconomic development, technological innovation, and changing climate conditions. By coupling with climate and crop model emulators, IAMs have the potential to resolve important agricultural feedback loops and identify unintended consequences of socioeconomic development for agricultural systems. Here we propose a framework to develop robust representation of agricultural system responses within IAMs, linking downstream applications with model development and the coordinated evaluation of key climate responses from local to global scales. We survey the strengths and weaknesses of protocol-based assessments linked to the Agricultural Model Intercomparison and Improvement Project (AgMIP), each utilizing multiple sites and models to evaluate crop response to core climate changes including shifts in carbon dioxide concentration, temperature, and water availability, with some studies further exploring how climate responses are affected by nitrogen levels and adaptation in farm systems. Site-based studies with carefully calibrated models encompass the largest number of activities; however they are limited in their ability to capture the full range of global agricultural system diversity. Representative site networks provide more targeted response information than broadly-sampled networks, with limitations stemming from difficulties in covering the diversity of farming systems. Global gridded crop models provide comprehensive coverage, although with large challenges for calibration and quality control of inputs. Diversity in climate responses underscores that crop model emulators must distinguish between regions and farming system while recognizing model uncertainty. Finally, to bridge the gap between bottom-up and top-down approaches we recommend the deployment of a hybrid climate response system employing a representative network of sites to bias-correct comprehensive gridded simulations, opening the door to accelerated development and a broad range of applications.

Evaluating the Relevance, Reliability, and Applicability of CMIP5 Climate Projections for Water Resources and Environmental Planning

NASA Astrophysics Data System (ADS)

Brekke, L. D.; Scott, J.; Ferguson, I. M.; Arnold, J.; Raff, D. A.; Webb, R. S.

2012-12-01

Water managers need to understand the applicability of climate projection information available for decision-support at the scale of their applications. Applicability depends on information reliability and relevance. This need to understand applicability stems from expectations that entities rationalize adaptation investments or decisions to delay investment. It is also occurring at a time when new global climate projections are being released through the World Climate Research Programme Coupled Model Intercomparison Project phase 5 (CMIP5), which introduces new information opportunities and interpretation challenges. This project involves an interagency collaboration to evaluate the applicability of CMIP5 projections for use in water and environmental resources planning. The overarching goal is to develop and demonstrate a framework that involves dual evaluations of relevance and reliability informing an ultimate discussion and judgment of applicability, which is expected to vary with decision-making context. The framework is being developed and demonstrated within the context of reservoir systems management in California's Sacramento and San Joaquin River basins. The relevance evaluation focuses on identifying the climate variables and statistical measures relevant to long-term management questions, which may depend on satisfying multiple objectives. Past studies' results are being considered in this evaluation, along with new results from system sensitivity analyses conducted through this effort. The reliability evaluation focuses on the CMIP5 climate models' ability to simulate past conditions relative to observed references. The evaluation is being conducted across the global domain using a large menu of climate variables and statistical measures, leveraging lessons learned from similar evaluations of CMIP3 climate models. The global focus addresses a broader project goal of producing a web resource that can serve reliability information to applicability discussions around the world, with evaluation results being served through a web-portal similar to that developed by NOAA/CIRES to serve CMIP3 information on future climate extremes (http://www.esrl.noaa.gov/psd/ipcc/extremes/). The framework concludes with an applicability discussion informed by relevance and reliability results. The goal is to observe the discussion process and identify features, choice points, and challenges that might be summarized and shared with other resource management groups facing applicability questions. This presentation will discuss the project framework and preliminary results. In addition to considering CMIP5 21st century projection information, the framework is being developed to support evaluation of CMIP5 decadal predictability experiment simulations and reconcile those simulations with 21st century projections. The presentation will also discuss implications of considering the applicability of bias-corrected and downscaled information within this framework.

Uncertainty Analysis of Coupled Socioeconomic-Cropping Models: Building Confidence in Climate Change Decision-Support Tools for Local Stakeholders

NASA Astrophysics Data System (ADS)

Malard, J. J.; Rojas, M.; Adamowski, J. F.; Gálvez, J.; Tuy, H. A.; Melgar-Quiñonez, H.

2015-12-01

While cropping models represent the biophysical aspects of agricultural systems, system dynamics modelling offers the possibility of representing the socioeconomic (including social and cultural) aspects of these systems. The two types of models can then be coupled in order to include the socioeconomic dimensions of climate change adaptation in the predictions of cropping models.We develop a dynamically coupled socioeconomic-biophysical model of agricultural production and its repercussions on food security in two case studies from Guatemala (a market-based, intensive agricultural system and a low-input, subsistence crop-based system). Through the specification of the climate inputs to the cropping model, the impacts of climate change on the entire system can be analysed, and the participatory nature of the system dynamics model-building process, in which stakeholders from NGOs to local governmental extension workers were included, helps ensure local trust in and use of the model.However, the analysis of climate variability's impacts on agroecosystems includes uncertainty, especially in the case of joint physical-socioeconomic modelling, and the explicit representation of this uncertainty in the participatory development of the models is important to ensure appropriate use of the models by the end users. In addition, standard model calibration, validation, and uncertainty interval estimation techniques used for physically-based models are impractical in the case of socioeconomic modelling. We present a methodology for the calibration and uncertainty analysis of coupled biophysical (cropping) and system dynamics (socioeconomic) agricultural models, using survey data and expert input to calibrate and evaluate the uncertainty of the system dynamics as well as of the overall coupled model. This approach offers an important tool for local decision makers to evaluate the potential impacts of climate change and their feedbacks through the associated socioeconomic system.

Farm Simulation: a tool for evaluating the mitigation of greenhouse gas emissions and the adaptation of dairy production to climate change

USDA-ARS?s Scientific Manuscript database

Farms both produce greenhouse gas emissions that drive human-induced climate change and are impacted by that climate change. Whole farm and global climate models provide useful tools for studying the benefits and costs of greenhouse gas mitigation and the adaptation of farms to changing climate. The...

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.

Uncertainties in Decadal Model Evaluation due to the Choice of Different Reanalysis Products

NASA Astrophysics Data System (ADS)

Illing, Sebastian; Kadow, Christopher; Kunst, Oliver; Cubasch, Ulrich

2014-05-01

In recent years decadal predictions have become very popular in the climate science community. A major task is the evaluation and validation of a decadal prediction system. Therefore hindcast experiments are performed and evaluated against observation based or reanalysis data-sets. That is, various metrics and skill scores like the anomaly correlation or the mean squared error skill score (MSSS) are calculated to estimate potential prediction skill of the model system. Our results will mostly feature the Baseline 1 hindcast experiments from the MiKlip decadal prediction system. MiKlip (www.fona-miklip.de) is a project for medium-term climate prediction funded by the Federal Ministry of Education and Research in Germany (BMBF) and has the aim to create a model system that can provide reliable decadal forecasts on climate and weather. There are various reanalysis and observation based products covering at least the last forty years which can be used for model evaluation, for instance the 20th Century Reanalysis from NOAA-CIRES, the Climate Forecast System Reanalysis from NCEP or the Interim Reanalysis from ECMWF. Each of them is based on different climate models and observations. We will show that the choice of the reanalysis product has a huge impact on the value of various skill metrics. In some cases this may actually lead to a change in the interpretation of the results, e.g. when one tries to compare two model versions and the anomaly correlation difference changes its sign for two different reanalysis products. We will also show first results of our studies investigating the influence and effect of this source of uncertainty for decadal model evaluation. Furthermore we point out regions which are most affected by this uncertainty and where one has to cautious interpreting skill scores. In addition we introduce some strategies to overcome or at least reduce this source of uncertainty.

Evaluation of global climate model on performances of precipitation simulation and prediction in the Huaihe River basin

NASA Astrophysics Data System (ADS)

Wu, Yenan; Zhong, Ping-an; Xu, Bin; Zhu, Feilin; Fu, Jisi

2017-06-01

Using climate models with high performance to predict the future climate changes can increase the reliability of results. In this paper, six kinds of global climate models that selected from the Coupled Model Intercomparison Project Phase 5 (CMIP5) under Representative Concentration Path (RCP) 4.5 scenarios were compared to the measured data during baseline period (1960-2000) and evaluate the simulation performance on precipitation. Since the results of single climate models are often biased and highly uncertain, we examine the back propagation (BP) neural network and arithmetic mean method in assembling the precipitation of multi models. The delta method was used to calibrate the result of single model and multimodel ensembles by arithmetic mean method (MME-AM) during the validation period (2001-2010) and the predicting period (2011-2100). We then use the single models and multimodel ensembles to predict the future precipitation process and spatial distribution. The result shows that BNU-ESM model has the highest simulation effect among all the single models. The multimodel assembled by BP neural network (MME-BP) has a good simulation performance on the annual average precipitation process and the deterministic coefficient during the validation period is 0.814. The simulation capability on spatial distribution of precipitation is: calibrated MME-AM > MME-BP > calibrated BNU-ESM. The future precipitation predicted by all models tends to increase as the time period increases. The order of average increase amplitude of each season is: winter > spring > summer > autumn. These findings can provide useful information for decision makers to make climate-related disaster mitigation plans.

Improving Climate Projections Using "Intelligent" Ensembles

NASA Technical Reports Server (NTRS)

Baker, Noel C.; Taylor, Patrick C.

2015-01-01

Recent changes in the climate system have led to growing concern, especially in communities which are highly vulnerable to resource shortages and weather extremes. There is an urgent need for better climate information to develop solutions and strategies for adapting to a changing climate. Climate models provide excellent tools for studying the current state of climate and making future projections. However, these models are subject to biases created by structural uncertainties. Performance metrics-or the systematic determination of model biases-succinctly quantify aspects of climate model behavior. Efforts to standardize climate model experiments and collect simulation data-such as the Coupled Model Intercomparison Project (CMIP)-provide the means to directly compare and assess model performance. Performance metrics have been used to show that some models reproduce present-day climate better than others. Simulation data from multiple models are often used to add value to projections by creating a consensus projection from the model ensemble, in which each model is given an equal weight. It has been shown that the ensemble mean generally outperforms any single model. It is possible to use unequal weights to produce ensemble means, in which models are weighted based on performance (called "intelligent" ensembles). Can performance metrics be used to improve climate projections? Previous work introduced a framework for comparing the utility of model performance metrics, showing that the best metrics are related to the variance of top-of-atmosphere outgoing longwave radiation. These metrics improve present-day climate simulations of Earth's energy budget using the "intelligent" ensemble method. The current project identifies several approaches for testing whether performance metrics can be applied to future simulations to create "intelligent" ensemble-mean climate projections. It is shown that certain performance metrics test key climate processes in the models, and that these metrics can be used to evaluate model quality in both current and future climate states. This information will be used to produce new consensus projections and provide communities with improved climate projections for urgent decision-making.

Predicting lodgepole pine site index from climatic parameters in Alberta.

Treesearch

Robert A. Monserud; Shongming Huang; Yuqing Yang

2006-01-01

We sought to evaluate the impact of climatic variables on site productivity of lodgepole pine (Pinus contorta var. latifolia Engelm.) for the province of Alberta. Climatic data were obtained from the Alberta Climate Model, which is based on 30-year normals from the provincial weather station network. Mapping methods were based...

On solar geoengineering and climate uncertainty

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

MacMartin, Douglas; Kravitz, Benjamin S.; Rasch, Philip J.

2015-09-03

Uncertainty in the climate system response has been raised as a concern regarding solar geoengineering. Here we show that model projections of regional climate change outcomes may have greater agreement under solar geoengineering than with CO2 alone. We explore the effects of geoengineering on one source of climate system uncertainty by evaluating the inter-model spread across 12 climate models participating in the Geoengineering Model Intercomparison project (GeoMIP). The model spread in regional temperature and precipitation changes is reduced with CO2 and a solar reduction, in comparison to the case with increased CO2 alone. That is, the intermodel spread in predictionsmore » of climate change and the model spread in the response to solar geoengineering are not additive but rather partially cancel. Furthermore, differences in efficacy explain most of the differences between models in their temperature response to an increase in CO2 that is offset by a solar reduction. These conclusions are important for clarifying geoengineering risks.« less

The potential impacts of climate change and variability on forests and forestry in the Mid-Atlantic Region

Treesearch

Mary McKenney-Easterling; David R. DeWalle; Louis R. Iverson; Anantha M. Prasad; Anthony R. Buda; Anthony R. Buda

2000-01-01

As part of the Mid-Atlantic Regional Assessment, an evaluation is being made of the impacts of climate variability and potential future climate change on forests and forestry in the Mid-Atlantic Region. This paper provides a brief overview of the current status of forests in the region, and then focuses on 2 components of this evaluation: (1) modeling of the potential...

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.

On the Hydrologic Adjustment of Climate-Model Projections: The Potential Pitfall of Potential Evapotranspiration

USGS Publications Warehouse

Milly, Paul C.D.; Dunne, Krista A.

2011-01-01

Hydrologic models often are applied to adjust projections of hydroclimatic change that come from climate models. Such adjustment includes climate-bias correction, spatial refinement ("downscaling"), and consideration of the roles of hydrologic processes that were neglected in the climate model. Described herein is a quantitative analysis of the effects of hydrologic adjustment on the projections of runoff change associated with projected twenty-first-century climate change. In a case study including three climate models and 10 river basins in the contiguous United States, the authors find that relative (i.e., fractional or percentage) runoff change computed with hydrologic adjustment more often than not was less positive (or, equivalently, more negative) than what was projected by the climate models. The dominant contributor to this decrease in runoff was a ubiquitous change in runoff (median -11%) caused by the hydrologic model’s apparent amplification of the climate-model-implied growth in potential evapotranspiration. Analysis suggests that the hydrologic model, on the basis of the empirical, temperature-based modified Jensen–Haise formula, calculates a change in potential evapotranspiration that is typically 3 times the change implied by the climate models, which explicitly track surface energy budgets. In comparison with the amplification of potential evapotranspiration, central tendencies of other contributions from hydrologic adjustment (spatial refinement, climate-bias adjustment, and process refinement) were relatively small. The authors’ findings highlight the need for caution when projecting changes in potential evapotranspiration for use in hydrologic models or drought indices to evaluate climate-change impacts on water.

Evaluating models of climate and forest vegetation

NASA Technical Reports Server (NTRS)

Clark, James S.

1992-01-01

Understanding how the biosphere may respond to increasing trace gas concentrations in the atmosphere requires models that contain vegetation responses to regional climate. Most of the processes ecologists study in forests, including trophic interactions, nutrient cycling, and disturbance regimes, and vital components of the world economy, such as forest products and agriculture, will be influenced in potentially unexpected ways by changing climate. These vegetation changes affect climate in the following ways: changing C, N, and S pools; trace gases; albedo; and water balance. The complexity of the indirect interactions among variables that depend on climate, together with the range of different space/time scales that best describe these processes, make the problems of modeling and prediction enormously difficult. These problems of predicting vegetation response to climate warming and potential ways of testing model predictions are the subjects of this chapter.

More than the sum of the parts: forest climate response from joint species distribution models

Treesearch

James S. Clark; Alan E. Gelfand; Christopher W. Woodall; Kai Zhu

2014-01-01

The perceived threat of climate change is often evaluated from species distribution models that are fitted to many species independently and then added together. This approach ignores the fact that species are jointly distributed and limit one another. Species respond to the same underlying climatic variables, and the abundance of any one species can be constrained by...

Crop Yield Simulations Using Multiple Regional Climate Models in the Southwestern United States

NASA Astrophysics Data System (ADS)

Stack, D.; Kafatos, M.; Kim, S.; Kim, J.; Walko, R. L.

2013-12-01

Agricultural productivity (described by crop yield) is strongly dependent on climate conditions determined by meteorological parameters (e.g., temperature, rainfall, and solar radiation). California is the largest producer of agricultural products in the United States, but crops in associated arid and semi-arid regions live near their physiological limits (e.g., in hot summer conditions with little precipitation). Thus, accurate climate data are essential in assessing the impact of climate variability on agricultural productivity in the Southwestern United States and other arid regions. To address this issue, we produced simulated climate datasets and used them as input for the crop production model. For climate data, we employed two different regional climate models (WRF and OLAM) using a fine-resolution (8km) grid. Performances of the two different models are evaluated in a fine-resolution regional climate hindcast experiment for 10 years from 2001 to 2010 by comparing them to the North American Regional Reanalysis (NARR) dataset. Based on this comparison, multi-model ensembles with variable weighting are used to alleviate model bias and improve the accuracy of crop model productivity over large geographic regions (county and state). Finally, by using a specific crop-yield simulation model (APSIM) in conjunction with meteorological forcings from the multi-regional climate model ensemble, we demonstrate the degree to which maize yields are sensitive to the regional climate in the Southwestern United States.

Multi-objective optimization of GENIE Earth system models.

PubMed

Price, Andrew R; Myerscough, Richard J; Voutchkov, Ivan I; Marsh, Robert; Cox, Simon J

2009-07-13

The tuning of parameters in climate models is essential to provide reliable long-term forecasts of Earth system behaviour. We apply a multi-objective optimization algorithm to the problem of parameter estimation in climate models. This optimization process involves the iterative evaluation of response surface models (RSMs), followed by the execution of multiple Earth system simulations. These computations require an infrastructure that provides high-performance computing for building and searching the RSMs and high-throughput computing for the concurrent evaluation of a large number of models. Grid computing technology is therefore essential to make this algorithm practical for members of the GENIE project.

Clouds and more: ARM climate modeling best estimate data: A new data product for climate studies

DOE PAGES

Xie, Shaocheng; McCoy, Renata B.; Klein, Stephen A.; ...

2010-01-01

The U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Program (www.arm.gov) was created in 1989 to address scientific uncertainties related to global climate change, with a focus on the crucial role of clouds and their influence on the transfer of radiation atmosphere. Here, a central activity is the acquisition of detailed observations of clouds and radiation, as well as related atmospheric variables for climate model evaluation and improvement.

The Value of GRACE Data in Improving, Assessing and Evaluating Land Surface and Climate Models

NASA Astrophysics Data System (ADS)

Yang, Z.

2011-12-01

I will review how the Gravity Recovery and Climate Experiment (GRACE) satellite measurements have improved land surface models that are developed for weather, climate, and hydrological studies. GRACE-derived terrestrial water storage (TWS) changes have been successfully used to assess and evaluate the improved representations of land-surface hydrological processes such as groundwater-soil moisture interaction, frozen soil and infiltration, and the topographic control on runoff production, as evident in the simulations from the latest Noah-MP, the Community Land Model, and the Community Climate System Model. GRACE data sets have made it possible to estimate key terrestrial water storage components (snow mass, surface water, groundwater or water table depth), biomass, and surface water fluxes (evapotranspiration, solid precipitation, melt of snow/ice). Many of the examples will draw from my Land, Environment and Atmosphere Dynamics group's work on land surface model developments, snow mass retrieval, and multi-sensor snow data assimilation using the ensemble Karman filter and the ensemble Karman smoother. Finally, I will briefly outline some future directions in using GRACE in land surface modeling.

Probabilistic modeling of the indoor climates of residential buildings using EnergyPlus

DOE PAGES

Buechler, Elizabeth D.; Pallin, Simon B.; Boudreaux, Philip R.; ...

2017-04-25

The indoor air temperature and relative humidity in residential buildings significantly affect material moisture durability, HVAC system performance, and occupant comfort. Therefore, indoor climate data is generally required to define boundary conditions in numerical models that evaluate envelope durability and equipment performance. However, indoor climate data obtained from field studies is influenced by weather, occupant behavior and internal loads, and is generally unrepresentative of the residential building stock. Likewise, whole-building simulation models typically neglect stochastic variables and yield deterministic results that are applicable to only a single home in a specific climate. The

Extra-Tropical Cyclones at Climate Scales: Comparing Models to Observations

NASA Astrophysics Data System (ADS)

Tselioudis, G.; Bauer, M.; Rossow, W.

2009-04-01

Climate is often defined as the accumulation of weather, and weather is not the concern of climate models. Justification for this latter sentiment has long been hidden behind coarse model resolutions and blunt validation tools based on climatological maps. The spatial-temporal resolutions of today's climate models and observations are converging onto meteorological scales, however, which means that with the correct tools we can test the largely unproven assumption that climate model weather is correct enough that its accumulation results in a robust climate simulation. Towards this effort we introduce a new tool for extracting detailed cyclone statistics from observations and climate model output. These include the usual cyclone characteristics (centers, tracks), but also adaptive cyclone-centric composites. We have created a novel dataset, the MAP Climatology of Mid-latitude Storminess (MCMS), which provides a detailed 6 hourly assessment of the areas under the influence of mid-latitude cyclones, using a search algorithm that delimits the boundaries of each system from the outer-most closed SLP contour. Using this we then extract composites of cloud, radiation, and precipitation properties from sources such as ISCCP and GPCP to create a large comparative dataset for climate model validation. A demonstration of the potential usefulness of these tools in process-based climate model evaluation studies will be shown.

Evaluation of the new EMAC-SWIFT chemistry climate model

NASA Astrophysics Data System (ADS)

Scheffler, Janice; Langematz, Ulrike; Wohltmann, Ingo; Rex, Markus

2016-04-01

It is well known that the representation of atmospheric ozone chemistry in weather and climate models is essential for a realistic simulation of the atmospheric state. Including atmospheric ozone chemistry into climate simulations is usually done by prescribing a climatological ozone field, by including a fast linear ozone scheme into the model or by using a climate model with complex interactive chemistry. While prescribed climatological ozone fields are often not aligned with the modelled dynamics, a linear ozone scheme may not be applicable for a wide range of climatological conditions. Although interactive chemistry provides a realistic representation of atmospheric chemistry such model simulations are computationally very expensive and hence not suitable for ensemble simulations or simulations with multiple climate change scenarios. A new approach to represent atmospheric chemistry in climate models which can cope with non-linearities in ozone chemistry and is applicable to a wide range of climatic states is the Semi-empirical Weighted Iterative Fit Technique (SWIFT) that is driven by reanalysis data and has been validated against observational satellite data and runs of a full Chemistry and Transport Model. SWIFT has recently been implemented into the ECHAM/MESSy (EMAC) chemistry climate model that uses a modular approach to climate modelling where individual model components can be switched on and off. Here, we show first results of EMAC-SWIFT simulations and validate these against EMAC simulations using the complex interactive chemistry scheme MECCA, and against observations.

International Land Model Benchmarking (ILAMB) Workshop Report, Technical Report DOE/SC-0186

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

Hoffman, Forrest M.; Koven, Charles D.; Kappel-Aleks, Gretchen

2016-11-01

As Earth system models become increasingly complex, there is a growing need for comprehensive and multi-faceted evaluation of model projections. To advance understanding of biogeochemical processes and their interactions with hydrology and climate under conditions of increasing atmospheric carbon dioxide, new analysis methods are required that use observations to constrain model predictions, inform model development, and identify needed measurements and field experiments. Better representations of biogeochemistry–climate feedbacks and ecosystem processes in these models are essential for reducing uncertainties associated with projections of climate change during the remainder of the 21st century.

Evaluation of mean climate in a chemistry-climate model simulation

NASA Astrophysics Data System (ADS)

Hong, S.; Park, H.; Wie, J.; Park, R.; Lee, S.; Moon, B. K.

2017-12-01

Incorporation of the interactive chemistry is essential for understanding chemistry-climate interactions and feedback processes in climate models. Here we assess a newly developed chemistry-climate model (GRIMs-Chem), which is based on the Global/Regional Integrated Model system (GRIMs) including the aerosol direct effect as well as stratospheric linearized ozone chemistry (LINOZ). We conducted GRIMs-Chem with observed sea surface temperature during the period of 1979-2010, and compared the simulation results with observations and also with CMIP models. To measure the relative performance of our model, we define the quantitative performance metric using the Taylor diagram. This metric allow us to assess overall features in simulating multiple variables. Overall, our model better reproduce the zonal mean spatial pattern of temperature, horizontal wind, vertical motion, and relative humidity relative to other models. However, the model did not produce good simulations at upper troposphere (200 hPa). It is currently unclear which model processes are responsible for this. AcknowledgementsThis research was supported by the Korea Ministry of Environment (MOE) as "Climate Change Correspondence Program."

Quantifying the impacts of climatic trend and fluctuation on crop yields in northern China.

PubMed

Qiao, Jianmin; Yu, Deyong; Liu, Yupeng

2017-10-01

Climate change plays a critical role in crop yield variations, which has attracted a great deal of concern worldwide. However, the mechanisms of how climatic trend and fluctuations affect crop yields are not well understood and need to be further investigated. Thus, using the GIS-based Environmental Policy Integrated Climate (EPIC) model, we simulated the yields of major crops (i.e., wheat, maize, and rice) and evaluated the impacts of climatic factors on crop yields in the Agro-Pastoral Transitional Zone (APTZ) of northern China between 1980 and 2010. The partial least squares regression model was used to assess the contribution rates of climatic factors (i.e., precipitation, photosynthetically active radiation (PAR), minimum temperature (T min ), maximum temperature (T max )) to the variation of crop yields. The Breaks for Additive Season and Trend (BFAST) model was adopted to decompose the climate factors into trend and fluctuation components, and the relative contributions of climate trend and fluctuation were then evaluated. The results indicated that the contributions of climatic factors to yield variations of wheat, maize, and rice were 31.7, 37.7, and 23.1%, respectively. That is, climate change had larger impacts on maize than wheat and rice. More cultivated areas were significantly and positively correlated with precipitation than with other climatic factors due to the limited precipitation in the APTZ. Also, climatic trend component had positive impacts on crop yields in the whole region, whereas the climate fluctuation was associated mainly with the areas where the crop yields decreased. This study helps improve our understanding of the mechanisms of climate change impacts on crop yields, and provides useful scientific information for designing regional-scale strategies of adaptation to climate change.

The Earth System Model

NASA Technical Reports Server (NTRS)

Schoeberl, Mark; Rood, Richard B.; Hildebrand, Peter; Raymond, Carol

2003-01-01

The Earth System Model is the natural evolution of current climate models and will be the ultimate embodiment of our geophysical understanding of the planet. These models are constructed from components - atmosphere, ocean, ice, land, chemistry, solid earth, etc. models and merged together through a coupling program which is responsible for the exchange of data from the components. Climate models and future earth system models will have standardized modules, and these standards are now being developed by the ESMF project funded by NASA. The Earth System Model will have a variety of uses beyond climate prediction. The model can be used to build climate data records making it the core of an assimilation system, and it can be used in OSSE experiments to evaluate. The computing and storage requirements for the ESM appear to be daunting. However, the Japanese ES theoretical computing capability is already within 20% of the minimum requirements needed for some 2010 climate model applications. Thus it seems very possible that a focused effort to build an Earth System Model will achieve succcss.

Analysis of shifts in the spatial distribution of vegetation due to climate change

NASA Astrophysics Data System (ADS)

del Jesus, Manuel; Díez-Sierra, Javier; Rinaldo, Andrea; Rodríguez-Iturbe, Ignacio

2017-04-01

Climate change will modify the statistical regime of most climatological variables, inducing changes on average values and in the natural variability of environmental variables. These environmental variables may be used to explain the spatial distribution of functional types of vegetation in arid and semiarid watersheds through the use of plant optimization theories. Therefore, plant optimization theories may be used to approximate the response of the spatial distribution of vegetation to a changing climate. Predicting changes in these spatial distributions is important to understand how climate change may affect vegetated ecosystems, but it is also important for hydrological engineering applications where climate change effects on water availability are assessed. In this work, Maximum Entropy Production (MEP) is used as the plant optimization theory that describes the spatial distribution of functional types of vegetation. Current climatological conditions are obtained from direct observations from meteorological stations. Climate change effects are evaluated for different temporal horizons and different climate change scenarios using numerical model outputs from the CMIP5. Rainfall estimates are downscaled by means of a stochastic point process used to model rainfall. The study is carried out for the Rio Salado watershed, located within the Sevilleta LTER site, in New Mexico (USA). Results show the expected changes in the spatial distribution of vegetation and allow to evaluate the expected variability of the changes. The updated spatial distributions allow to evaluate the vegetated ecosystem health and its updated resilience. These results can then be used to inform the hydrological modeling part of climate change assessments analyzing water availability in arid and semiarid watersheds.

Toward Evaluating the Predictability of Arctic-related Climate Variations: Initial Results from ArCS Project Theme 5

NASA Astrophysics Data System (ADS)

Hasumi, H.

2016-12-01

We present initial results from the theme 5 of the project ArCS, which is a national flagship project for Arctic research in Japan. The goal of theme 5 is to evaluate the predictability of Arctic-related climate variations, wherein we aim to: (1) establish the scientific basis of climate predictability; and (2) develop a method for predicting/projecting medium- and long-term climate variations. Variability in the Arctic environment remotely influences middle and low latitudes. Since some of the processes specific to the Arctic environment function as a long memory of the state of the climate, understanding of the process of remote connections would lead to higher-precision and longer-term prediction of global climate variations. Conventional climate models have large uncertainty in the Arctic region. By making Arctic processes in climate models more sophisticated, we aim to clarify the role of multi-sphere interaction in the Arctic environment. In this regard, our newly developed high resolution ice-ocean model has revealed the relationship between the oceanic heat transport into the Arctic Ocean and the synoptic scale atmospheric variability. We also aim to reveal the mechanism of remote connections by conducting climate simulations and analyzing various types of climate datasets. Our atmospheric model experiments under possible future situations of Arctic sea ice cover indicate that reduction of sea ice qualitatively alters the basic mechanism of remote connection. Also, our analyses of climate data have identified the cause of recent more frequent heat waves at Eurasian mid-to-high latitudes and clarified the dynamical process which forms the West Pacific pattern, a dominant mode of the atmospheric anomalous circulation in the West Pacific region which also exhibits a significant signal in the Arctic stratosphere.

Continuously on-­going regional climate hindcast simulations for impact applications

NASA Astrophysics Data System (ADS)

Anders, Ivonne; Piringer, Martin; Kaufmann, Hildegard; Knauder, Werner; Resch, Gernot; Andre, Konrad

2017-04-01

Observational data for e.g. temperature, precipitation, radiation, or wind are often used as meteorological forcing for different impact models, like e.g. crop models, urban models, economic models and energy system models. To assess a climate signal, the time period covered by the observation is often too short, they have gaps in between, and are inhomogeneous over time, due to changes in the measurements itself or in the near surrounding. Thus output from global and regional climate models can close the gap and provide homogeneous and physically consistent time series of meteorological parameters. CORDEX evaluation runs performed for the IPCC-AR5 provide a good base for the regional scale. However, with respect to climate services, continuously on-going hindcast simulations are required for regularly updated applications. The Climate Research group at the national Austrian weather service, ZAMG, is focusing on high mountain regions and, especially on the Alps. The hindcast-simulation performed with the regional climate model COSMO-CLM is forced by ERAinterim and optimized for the Alpine Region. The simulation available for the period of 1979-2015 in a spatial resolution of about 10km is prolonged ongoing and fullfils the customer's needs with respect of output variables, levels, intervals and statistical measures. One of the main tasks is to capture strong precipitation events which often occur during summer when low pressure systems develop over the Golf of Genoa, moving to the Northeast. This leads to floods and landslide events in Austria, Czech Republic and Germany. Such events are not sufficiently represented in the CORDEX-evaluation runs. ZAMG use high quality gridded precipitation and temperature data for the Alpine Region (1-6km) to evaluate the model performance. Data is provided e.g. to hydrological modellers (high water, low water), but also to assess icing capability of infrastructure or the calculation the separation distances between livestock farming and residential area.

Evaluation of DGVMs in tropical areas: linking patterns of vegetation cover, climate and fire to ecological processes

NASA Astrophysics Data System (ADS)

D'Onofrio, Donatella; von Hardenberg, Jost; Baudena, Mara

2017-04-01

Many current Dynamic Global Vegetation Models (DGVMs), including those incorporated into Earth System Models (ESMs), are able to realistically reproduce the distribution of the most worldwide biomes. However, they display high uncertainty in predicting the forest, savanna and grassland distributions and the transitions between them in tropical areas. These biomes are the most productive terrestrial ecosystems, and owing to their different biogeophysical and biogeochemical characteristics, future changes in their distributions could have also impacts on climate states. In particular, expected increasing temperature and CO2, modified precipitation regimes, as well as increasing land-use intensity could have large impacts on global biogeochemical cycles and precipitation, affecting the land-climate interactions. The difficulty of the DGVMs in simulating tropical vegetation, especially savanna structure and occurrence, has been associated with the way they represent the ecological processes and feedbacks between biotic and abiotic conditions. The inclusion of appropriate ecological mechanisms under present climatic conditions is essential for obtaining reliable future projections of vegetation and climate states. In this work we analyse observed relationships of tree and grass cover with climate and fire, and the current ecological understanding of the mechanisms driving the forest-savanna-grassland transition in Africa to evaluate the outcomes of a current state-of-the-art DGVM and to assess which ecological processes need to be included or improved within the model. Specifically, we analyse patterns of woody and herbaceous cover and fire return times from MODIS satellite observations, rainfall annual average and seasonality from TRMM satellite measurements and tree phenology information from the ESA global land cover map, comparing them with the outcomes of the LPJ-GUESS DGVM, also used by the EC-Earth global climate model. The comparison analysis with the LPJ-GUESS simulations suggests possible improvements in the model representations of tree-grass competition for water and in the vegetation-fire interaction. The proposed method could be useful for evaluating DGVMs in tropical areas, especially in the phase of model setting-up, before the coupling with Earth System Models. This could help in improving the simulations of ecological processes and consequently of land-climate interactions.

Newer classification and regression tree techniques: Bagging and Random Forests for ecological prediction

Treesearch

Anantha M. Prasad; Louis R. Iverson; Andy Liaw; Andy Liaw

2006-01-01

We evaluated four statistical models - Regression Tree Analysis (RTA), Bagging Trees (BT), Random Forests (RF), and Multivariate Adaptive Regression Splines (MARS) - for predictive vegetation mapping under current and future climate scenarios according to the Canadian Climate Centre global circulation model.

Can metric-based approaches really improve multi-model climate projections? A perfect model framework applied to summer temperature change in France.

NASA Astrophysics Data System (ADS)

Boé, Julien; Terray, Laurent

2014-05-01

Ensemble approaches for climate change projections have become ubiquitous. Because of large model-to-model variations and, generally, lack of rationale for the choice of a particular climate model against others, it is widely accepted that future climate change and its impacts should not be estimated based on a single climate model. Generally, as a default approach, the multi-model ensemble mean (MMEM) is considered to provide the best estimate of climate change signals. The MMEM approach is based on the implicit hypothesis that all the models provide equally credible projections of future climate change. This hypothesis is unlikely to be true and ideally one would want to give more weight to more realistic models. A major issue with this alternative approach lies in the assessment of the relative credibility of future climate projections from different climate models, as they can only be evaluated against present-day observations: which present-day metric(s) should be used to decide which models are "good" and which models are "bad" in the future climate? Once a supposedly informative metric has been found, other issues arise. What is the best statistical method to combine multiple models results taking into account their relative credibility measured by a given metric? How to be sure in the end that the metric-based estimate of future climate change is not in fact less realistic than the MMEM? It is impossible to provide strict answers to those questions in the climate change context. Yet, in this presentation, we propose a methodological approach based on a perfect model framework that could bring some useful elements of answer to the questions previously mentioned. The basic idea is to take a random climate model in the ensemble and treat it as if it were the truth (results of this model, in both past and future climate, are called "synthetic observations"). Then, all the other members from the multi-model ensemble are used to derive thanks to a metric-based approach a posterior estimate of climate change, based on the synthetic observation of the metric. Finally, it is possible to compare the posterior estimate to the synthetic observation of future climate change to evaluate the skill of the method. The main objective of this presentation is to describe and apply this perfect model framework to test different methodological issues associated with non-uniform model weighting and similar metric-based approaches. The methodology presented is general, but will be applied to the specific case of summer temperature change in France, for which previous works have suggested potentially useful metrics associated with soil-atmosphere and cloud-temperature interactions. The relative performances of different simple statistical approaches to combine multiple model results based on metrics will be tested. The impact of ensemble size, observational errors, internal variability, and model similarity will be characterized. The potential improvements associated with metric-based approaches compared to the MMEM is terms of errors and uncertainties will be quantified.

Integrated Assessment and the Relation Between Land-Use Change and Climate Change

DOE R&D Accomplishments Database

Dale, V. H.

1994-10-07

Integrated assessment is an approach that is useful in evaluating the consequences of global climate change. Understanding the consequences requires knowledge of the relationship between land-use change and climate change. Methodologies for assessing the contribution of land-use change to atmospheric CO{sub 2} concentrations are considered with reference to a particular case study area: south and southeast Asia. The use of models to evaluate the consequences of climate change on forests must also consider an assessment approach. Each of these points is discussed in the following four sections.

Beyond a Climate-Centric View of Plant Distribution: Edaphic Variables Add Value to Distribution Models

PubMed Central

Beauregard, Frieda; de Blois, Sylvie

2014-01-01

Both climatic and edaphic conditions determine plant distribution, however many species distribution models do not include edaphic variables especially over large geographical extent. Using an exceptional database of vegetation plots (n = 4839) covering an extent of ∼55000 km2, we tested whether the inclusion of fine scale edaphic variables would improve model predictions of plant distribution compared to models using only climate predictors. We also tested how well these edaphic variables could predict distribution on their own, to evaluate the assumption that at large extents, distribution is governed largely by climate. We also hypothesized that the relative contribution of edaphic and climatic data would vary among species depending on their growth forms and biogeographical attributes within the study area. We modelled 128 native plant species from diverse taxa using four statistical model types and three sets of abiotic predictors: climate, edaphic, and edaphic-climate. Model predictive accuracy and variable importance were compared among these models and for species' characteristics describing growth form, range boundaries within the study area, and prevalence. For many species both the climate-only and edaphic-only models performed well, however the edaphic-climate models generally performed best. The three sets of predictors differed in the spatial information provided about habitat suitability, with climate models able to distinguish range edges, but edaphic models able to better distinguish within-range variation. Model predictive accuracy was generally lower for species without a range boundary within the study area and for common species, but these effects were buffered by including both edaphic and climatic predictors. The relative importance of edaphic and climatic variables varied with growth forms, with trees being more related to climate whereas lower growth forms were more related to edaphic conditions. Our study identifies the potential for non-climate aspects of the environment to pose a constraint to range expansion under climate change. PMID:24658097

Beyond a climate-centric view of plant distribution: edaphic variables add value to distribution models.

PubMed

Beauregard, Frieda; de Blois, Sylvie

2014-01-01

Both climatic and edaphic conditions determine plant distribution, however many species distribution models do not include edaphic variables especially over large geographical extent. Using an exceptional database of vegetation plots (n = 4839) covering an extent of ∼55,000 km2, we tested whether the inclusion of fine scale edaphic variables would improve model predictions of plant distribution compared to models using only climate predictors. We also tested how well these edaphic variables could predict distribution on their own, to evaluate the assumption that at large extents, distribution is governed largely by climate. We also hypothesized that the relative contribution of edaphic and climatic data would vary among species depending on their growth forms and biogeographical attributes within the study area. We modelled 128 native plant species from diverse taxa using four statistical model types and three sets of abiotic predictors: climate, edaphic, and edaphic-climate. Model predictive accuracy and variable importance were compared among these models and for species' characteristics describing growth form, range boundaries within the study area, and prevalence. For many species both the climate-only and edaphic-only models performed well, however the edaphic-climate models generally performed best. The three sets of predictors differed in the spatial information provided about habitat suitability, with climate models able to distinguish range edges, but edaphic models able to better distinguish within-range variation. Model predictive accuracy was generally lower for species without a range boundary within the study area and for common species, but these effects were buffered by including both edaphic and climatic predictors. The relative importance of edaphic and climatic variables varied with growth forms, with trees being more related to climate whereas lower growth forms were more related to edaphic conditions. Our study identifies the potential for non-climate aspects of the environment to pose a constraint to range expansion under climate change.

Kuiper Prize Lecture - Present and past climates of the terrestrial planets

NASA Technical Reports Server (NTRS)

Pollack, James B.

1991-01-01

An evaluation is undertaken of the current understanding of factors shaping the current climates of Venus, Mars, and the earth, in conjunction with the ways in which these planetary climates may have been different in the past. Attention is given to modeling approaches of various levels of sophistication which both characterize current climates and elucidate prior climatic epochs; these are assessed in light of observational data in order to judge degrees of success thus far and formulate major remaining questions for future investigations. Venus is noted to offer excellent opportunities for modeling the greenhouse effect.

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.

Assessing the Value of Post-processed State-of-the-art Long-term Weather Forecast Ensembles within An Integrated Agronomic Modelling Framework

NASA Astrophysics Data System (ADS)

LI, Y.; Castelletti, A.; Giuliani, M.

2014-12-01

Over recent years, long-term climate forecast from global circulation models (GCMs) has been demonstrated to show increasing skills over the climatology, thanks to the advances in the modelling of coupled ocean-atmosphere dynamics. Improved information from long-term forecast is supposed to be a valuable support to farmers in optimizing farming operations (e.g. crop choice, cropping time) and for more effectively coping with the adverse impacts of climate variability. Yet, evaluating how valuable this information can be is not straightforward and farmers' response must be taken into consideration. Indeed, while long-range forecast are traditionally evaluated in terms of accuracy by comparison of hindcast and observed values, in the context of agricultural systems, potentially useful forecast information should alter the stakeholders' expectation, modify their decisions and ultimately have an impact on their annual benefit. Therefore, it is more desirable to assess the value of those long-term forecasts via decision-making models so as to extract direct indication of probable decision outcomes from farmers, i.e. from an end-to-end perspective. In this work, we evaluate the operational value of thirteen state-of-the-art long-range forecast ensembles against climatology forecast and subjective prediction (i.e. past year climate and historical average) within an integrated agronomic modeling framework embedding an implicit model of farmers' behavior. Collected ensemble datasets are bias-corrected and downscaled using a stochastic weather generator, in order to address the mismatch of the spatio-temporal scale between forecast data from GCMs and distributed crop simulation model. The agronomic model is first simulated using the forecast information (ex-ante), followed by a second run with actual climate (ex-post). Multi-year simulations are performed to account for climate variability and the value of the different climate forecast is evaluated against the perfect foresight scenario based on the expected crop productivity as well as the land-use decisions. Our results show that not all the products generate beneficial effects to farmers and that the forecast errors might be amplified by the farmers decisions.

High definition clouds and precipitation for climate prediction -results from a unified German research initiative on high resolution modeling and observations

NASA Astrophysics Data System (ADS)

Rauser, F.

2013-12-01

We present results from the German BMBF initiative 'High Definition Cloud and Precipitation for advancing Climate Prediction -HD(CP)2'. This initiative addresses most of the problems that are discussed in this session in one, unified approach: cloud physics, convection, boundary layer development, radiation and subgrid variability are approached in one organizational framework. HD(CP)2 merges both observation and high performance computing / model development communities to tackle a shared problem: how to improve the understanding of the most important subgrid-scale processes of cloud and precipitation physics, and how to utilize this knowledge for improved climate predictions. HD(CP)2 is a coordinated initiative to: (i) realize; (ii) evaluate; and (iii) statistically characterize and exploit for the purpose of both parameterization development and cloud / precipitation feedback analysis; ultra-high resolution (100 m in the horizontal, 10-50 m in the vertical) regional hind-casts over time periods (3-15 y) and spatial scales (1000-1500 km) that are climatically meaningful. HD(CP)2 thus consists of three elements (the model development and simulations, their observational evaluation and exploitation/synthesis to advance CP prediction) and its first three-year phase has started on October 1st 2012. As a central part of HD(CP)2, the HD(CP)2 Observational Prototype Experiment (HOPE) has been carried out in spring 2013. In this campaign, high resolution measurements with a multitude of instruments from all major centers in Germany have been carried out in a limited domain, to allow for unprecedented resolution and precision in the observation of microphysics parameters on a resolution that will allow for evaluation and improvement of ultra-high resolution models. At the same time, a local area version of the new climate model ICON of the Max Planck Institute and the German weather service has been developed that allows for LES-type simulations on high resolutions on limited domains. The advantage of modifying an existing, evolving climate model is to share insights from high resolution runs directly with the large-scale modelers and to allow for easy intercomparison and evaluation later on. Within this presentation, we will give a short overview on HD(CP)2 , show results from the observation campaign HOPE and the LES simulations of the same domain and conditions and will discuss how these will lead to an improved understanding and evaluation background for the efforts to improve fast physics in our climate model.

Watershed-scale response to climate change through the twenty-first century for selected basins across the United States

USGS Publications Warehouse

Hay, Lauren E.; Markstrom, Steven; Ward-Garrison, Christian D.

2011-01-01

The hydrologic response of different climate-change emission scenarios for the twenty-first century were evaluated in 14 basins from different hydroclimatic regions across the United States using the Precipitation-Runoff Modeling System (PRMS), a process-based, distributed-parameter watershed model. This study involves four major steps: 1) setup and calibration of the PRMS model in 14 basins across the United States by local U.S. Geological Survey personnel; 2) statistical downscaling of the World Climate Research Programme’s Coupled Model Intercomparison Project phase 3 climate-change emission scenarios to create PRMS input files that reflect these emission scenarios; 3) run PRMS for the climate-change emission scenarios for the 14 basins; and 4) evaluation of the PRMS output.This paper presents an overview of this project, details of the methodology, results from the 14 basin simulations, and interpretation of these results. A key finding is that the hydrological response of the different geographical regions of the United States to potential climate change may be very different, depending on the dominant physical processes of that particular region. Also considered is the tremendous amount of uncertainty present in the climate emission scenarios and how this uncertainty propagates through the hydrologic simulations. This paper concludes with a discussion of the lessons learned and potential for future work.

Modeling the impact of climate change in Germany with biosphere models for long-term safety assessment of nuclear waste repositories.

PubMed

Staudt, C; Semiochkina, N; Kaiser, J C; Pröhl, G

2013-01-01

Biosphere models are used to evaluate the exposure of populations to radionuclides from a deep geological repository. Since the time frame for assessments of long-time disposal safety is 1 million years, potential future climate changes need to be accounted for. Potential future climate conditions were defined for northern Germany according to model results from the BIOCLIM project. Nine present day reference climate regions were defined to cover those future climate conditions. A biosphere model was developed according to the BIOMASS methodology of the IAEA and model parameters were adjusted to the conditions at the reference climate regions. The model includes exposure pathways common to those reference climate regions in a stylized biosphere and relevant to the exposure of a hypothetical self-sustaining population at the site of potential radionuclide contamination from a deep geological repository. The end points of the model are Biosphere Dose Conversion factors (BDCF) for a range of radionuclides and scenarios normalized for a constant radionuclide concentration in near-surface groundwater. Model results suggest an increased exposure of in dry climate regions with a high impact of drinking water consumption rates and the amount of irrigation water used for agriculture. Copyright © 2012 Elsevier Ltd. All rights reserved.

Evaluating simplistic methods to understand current distributions and forecast distribution changes under climate change scenarios: An example with coypu (Myocastor coypus)

USGS Publications Warehouse

Jarnevich, Catherine S.; Young, Nicholas E; Sheffels, Trevor R.; Carter, Jacoby; Systma, Mark D.; Talbert, Colin

2017-01-01

Invasive species provide a unique opportunity to evaluate factors controlling biogeographic distributions; we can consider introduction success as an experiment testing suitability of environmental conditions. Predicting potential distributions of spreading species is not easy, and forecasting potential distributions with changing climate is even more difficult. Using the globally invasive coypu (Myocastor coypus [Molina, 1782]), we evaluate and compare the utility of a simplistic ecophysiological based model and a correlative model to predict current and future distribution. The ecophysiological model was based on winter temperature relationships with nutria survival. We developed correlative statistical models using the Software for Assisted Habitat Modeling and biologically relevant climate data with a global extent. We applied the ecophysiological based model to several global circulation model (GCM) predictions for mid-century. We used global coypu introduction data to evaluate these models and to explore a hypothesized physiological limitation, finding general agreement with known coypu distribution locally and globally and support for an upper thermal tolerance threshold. Global circulation model based model results showed variability in coypu predicted distribution among GCMs, but had general agreement of increasing suitable area in the USA. Our methods highlighted the dynamic nature of the edges of the coypu distribution due to climate non-equilibrium, and uncertainty associated with forecasting future distributions. Areas deemed suitable habitat, especially those on the edge of the current known range, could be used for early detection of the spread of coypu populations for management purposes. Combining approaches can be beneficial to predicting potential distributions of invasive species now and in the future and in exploring hypotheses of factors controlling distributions.

Convergence in France facing Big Data era and Exascale challenges for Climate Sciences

NASA Astrophysics Data System (ADS)

Denvil, Sébastien; Dufresne, Jean-Louis; Salas, David; Meurdesoif, Yann; Valcke, Sophie; Caubel, Arnaud; Foujols, Marie-Alice; Servonnat, Jérôme; Sénési, Stéphane; Derouillat, Julien; Voury, Pascal

2014-05-01

The presentation will introduce a french national project : CONVERGENCE that has been funded for four years. This project will tackle big data and computational challenges faced by climate modeling community in HPC context. Model simulations are central to the study of complex mechanisms and feedbacks in the climate system and to provide estimates of future and past climate changes. Recent trends in climate modelling are to add more physical components in the modelled system, increasing the resolution of each individual component and the more systematic use of large suites of simulations to address many scientific questions. Climate simulations may therefore differ in their initial state, parameter values, representation of physical processes, spatial resolution, model complexity, and degree of realism or degree of idealisation. In addition, there is a strong need for evaluating, improving and monitoring the performance of climate models using a large ensemble of diagnostics and better integration of model outputs and observational data. High performance computing is currently reaching the exascale and has the potential to produce this exponential increase of size and numbers of simulations. However, post-processing, analysis, and exploration of the generated data have stalled and there is a strong need for new tools to cope with the growing size and complexity of the underlying simulations and datasets. Exascale simulations require new scalable software tools to generate, manage and mine those simulations ,and data to extract the relevant information and to take the correct decision. The primary purpose of this project is to develop a platform capable of running large ensembles of simulations with a suite of models, to handle the complex and voluminous datasets generated, to facilitate the evaluation and validation of the models and the use of higher resolution models. We propose to gather interdisciplinary skills to design, using a component-based approach, a specific programming environment for scalable scientific simulations and analytics, integrating new and efficient ways of deploying and analysing the applications on High Performance Computing (HPC) system. CONVERGENCE, gathering HPC and informatics expertise that cuts across the individual partners and the broader HPC community, will allow the national climate community to leverage information technology (IT) innovations to address its specific needs. Our methodology consists in developing an ensemble of generic elements needed to run the French climate models with different grids and different resolution, ensuring efficient and reliable execution of these models, managing large volume and number of data and allowing analysis of the results and precise evaluation of the models. These elements include data structure definition and input-output (IO), code coupling and interpolation, as well as runtime and pre/post-processing environments. A common data and metadata structure will allow transferring consistent information between the various elements. All these generic elements will be open source and publicly available. The IPSL-CM and CNRM-CM climate models will make use of these elements that will constitute a national platform for climate modelling. This platform will be used, in its entirety, to optimise and tune the next version of the IPSL-CM model and to develop a global coupled climate model with a regional grid refinement. It will also be used, at least partially, to run ensembles of the CNRM-CM model at relatively high resolution and to run a very-high resolution prototype of this model. The climate models we developed are already involved in many international projects. For instance we participate to the CMIP (Coupled Model Intercomparison Project) project that is very demanding but has a high visibility: its results are widely used and are in particular synthesised in the IPCC (Intergovernmental Panel on Climate Change) assessment reports. The CONVERGENCE project will constitute an invaluable step for the French climate community to prepare and better contribute to the next phase of the CMIP project.

Joint Applications Pilot of the National Climate Predictions and Projections Platform and the North Central Climate Science Center: Delivering climate projections on regional scales to support adaptation planning

NASA Astrophysics Data System (ADS)

Ray, A. J.; Ojima, D. S.; Morisette, J. T.

2012-12-01

The DOI North Central Climate Science Center (NC CSC) and the NOAA/NCAR National Climate Predictions and Projections (NCPP) Platform and have initiated a joint pilot study to collaboratively explore the "best available climate information" to support key land management questions and how to provide this information. NCPP's mission is to support state of the art approaches to develop and deliver comprehensive regional climate information and facilitate its use in decision making and adaptation planning. This presentation will describe the evolving joint pilot as a tangible, real-world demonstration of linkages between climate science, ecosystem science and resource management. Our joint pilot is developing a deliberate, ongoing interaction to prototype how NCPP will work with CSCs to develop and deliver needed climate information products, including translational information to support climate data understanding and use. This pilot also will build capacity in the North Central CSC by working with NCPP to use climate information used as input to ecological modeling. We will discuss lessons to date on developing and delivering needed climate information products based on this strategic partnership. Four projects have been funded to collaborate to incorporate climate information as part of an ecological modeling project, which in turn will address key DOI stakeholder priorities in the region: Riparian Corridors: Projecting climate change effects on cottonwood and willow seed dispersal phenology, flood timing, and seedling recruitment in western riparian forests. Sage Grouse & Habitats: Integrating climate and biological data into land management decision models to assess species and habitat vulnerability Grasslands & Forests: Projecting future effects of land management, natural disturbance, and CO2 on woody encroachment in the Northern Great Plains The value of climate information: Supporting management decisions in the Plains and Prairie Potholes LCC. NCCSC's role in these projects is to provide the connections between climate data and running ecological models, and prototype these for future work. NCPP will develop capacities to provide enhanced climate information at relevant spatial and temporal scales, both for historical climate and projections of future climate, and will work to link expert guidance and understanding of modeling processes and evaluation of modeling with the use of numerical climate data. Translational information thus is a suite of information that aids in translation of numerical climate information into usable knowledge for applications, e.g. ecological response models, hydrologic risk studies. This information includes technical and scientific aspects including, but not limited to: 1) results of objective, quantitative evaluation of climate models & downscaling techniques, 2) guidance on appropriate uses and interpretation, i.e., understanding the advantages and limitations of various downscaling techniques for specific user applications, 3) characterizing and interpreting uncertainty, 4) Descriptions meaningful to applications, e.g. narratives. NCPP believes that translational information is best co-developed between climate scientists and applications scientists, such as the NC-CSC pilot.

The Seasonal cycle of the Tropical Lower Stratospheric Water Vapor in Chemistry-Climate Models in Comparison with Observations

NASA Astrophysics Data System (ADS)

Wang, X.; Dessler, A. E.

2017-12-01

The seasonal cycle is one of the key features of the tropical lower stratospheric water vapor, so it is important that the climate models reproduce it. In this analysis, we evaluate how well the Goddard Earth Observing System Chemistry Climate Model (GEOSCCM) and the Whole Atmosphere Community Climate Model (WACCM) reproduce the seasonal cycle of tropical lower stratospheric water vapor. We do this by comparing the models to observations from the Microwave Limb Sounder (MLS) and the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim (ERAi). We also evaluate if the chemistry-climate models (CCMs) reproduce the key transport and dehydration processes that regulate the seasonal cycle using a forward, domain filling, diabatic trajectory model. Finally, we explore the changes of the seasonal cycle during the 21st century in the two CCMs. Our results show general agreement in the seasonal cycles from the MLS, the ERAi, and the CCMs. Despite this agreement, there are some clear disagreements between the models and the observations on the details of transport and dehydration in the TTL. Finally, both the CCMs predict a moister seasonal cycle by the end of the 21st century. But they disagree on the changes of the seasonal amplitude, which is predicted to increase in the GEOSCCM and decrease in the WACCM.

CEOS SEO and GISS Meeting

NASA Technical Reports Server (NTRS)

Killough, Brian; Stover, Shelley

2008-01-01

The Committee on Earth Observation Satellites (CEOS) provides a brief to the Goddard Institute for Space Studies (GISS) regarding the CEOS Systems Engineering Office (SEO) and current work on climate requirements and analysis. A "system framework" is provided for the Global Earth Observation System of Systems (GEOSS). SEO climate-related tasks are outlined including the assessment of essential climate variable (ECV) parameters, use of the "systems framework" to determine relevant informational products and science models and the performance of assessments and gap analyses of measurements and missions for each ECV. Climate requirements, including instruments and missions, measurements, knowledge and models, and decision makers, are also outlined. These requirements would establish traceability from instruments to products and services allowing for benefit evaluation of instruments and measurements. Additionally, traceable climate requirements would provide a better understanding of global climate models.

Climate Influence on Emerging Risk Areas for Rift Valley Fever Epidemics in Tanzania.

PubMed

Mweya, Clement N; Mboera, Leonard E G; Kimera, Sharadhuli I

2017-07-01

Rift Valley Fever (RVF) is a climate-related arboviral infection of animals and humans. Climate is thought to represent a threat toward emerging risk areas for RVF epidemics globally. The objective of this study was to evaluate influence of climate on distribution of suitable breeding habitats for Culex pipiens complex, potential mosquito vector responsible for transmission and distribution of disease epidemics risk areas in Tanzania. We used ecological niche models to estimate potential distribution of disease risk areas based on vectors and disease co-occurrence data approach. Climatic variables for the current and future scenarios were used as model inputs. Changes in mosquito vectors' habitat suitability in relation to disease risk areas were estimated. We used partial receiver operating characteristic and the area under the curves approach to evaluate model predictive performance and significance. Habitat suitability for Cx. pipiens complex indicated broad-scale potential for change and shift in the distribution of the vectors and disease for both 2020 and 2050 climatic scenarios. Risk areas indicated more intensification in the areas surrounding Lake Victoria and northeastern part of the country through 2050 climate scenario. Models show higher probability of emerging risk areas spreading toward the western parts of Tanzania from northeastern areas and decrease in the southern part of the country. Results presented here identified sites for consideration to guide surveillance and control interventions to reduce risk of RVF disease epidemics in Tanzania. A collaborative approach is recommended to develop and adapt climate-related disease control and prevention strategies.

An Object-Based Approach to Evaluation of Climate Variability Projections and Predictions

NASA Astrophysics Data System (ADS)

Ammann, C. M.; Brown, B.; Kalb, C. P.; Bullock, R.

2017-12-01

Evaluations of the performance of earth system model predictions and projections are of critical importance to enhance usefulness of these products. Such evaluations need to address specific concerns depending on the system and decisions of interest; hence, evaluation tools must be tailored to inform about specific issues. Traditional approaches that summarize grid-based comparisons of analyses and models, or between current and future climate, often do not reveal important information about the models' performance (e.g., spatial or temporal displacements; the reason behind a poor score) and are unable to accommodate these specific information needs. For example, summary statistics such as the correlation coefficient or the mean-squared error provide minimal information to developers, users, and decision makers regarding what is "right" and "wrong" with a model. New spatial and temporal-spatial object-based tools from the field of weather forecast verification (where comparisons typically focus on much finer temporal and spatial scales) have been adapted to more completely answer some of the important earth system model evaluation questions. In particular, the Method for Object-based Diagnostic Evaluation (MODE) tool and its temporal (three-dimensional) extension (MODE-TD) have been adapted for these evaluations. More specifically, these tools can be used to address spatial and temporal displacements in projections of El Nino-related precipitation and/or temperature anomalies, ITCZ-associated precipitation areas, atmospheric rivers, seasonal sea-ice extent, and other features of interest. Examples of several applications of these tools in a climate context will be presented, using output of the CESM large ensemble. In general, these tools provide diagnostic information about model performance - accounting for spatial, temporal, and intensity differences - that cannot be achieved using traditional (scalar) model comparison approaches. Thus, they can provide more meaningful information that can be used in decision-making and planning. Future extensions and applications of these tools in a climate context will be considered.

Integrating a Detailed Agricultural Model in a Global Economic Framework: New methods for assessment of climate mitigation and adaptation opportunities

NASA Astrophysics Data System (ADS)

Thomson, A. M.; Izaurralde, R. C.; Calvin, K.; Zhang, X.; Wise, M.; West, T. O.

2010-12-01

Climate change and food security are global issues increasingly linked through human decision making that takes place across all scales from on-farm management actions to international climate negotiations. Understanding how agricultural systems can respond to climate change, through mitigation or adaptation, while still supplying sufficient food to feed a growing global population, thus requires a multi-sector tool in a global economic framework. Integrated assessment models are one such tool, however they are typically driven by historical aggregate statistics of production in combination with exogenous assumptions of future trends in agricultural productivity; they are not yet capable of exploring agricultural management practices as climate adaptation or mitigation strategies. Yet there are agricultural models capable of detailed biophysical modeling of farm management and climate impacts on crop yield, soil erosion and C and greenhouse gas emissions, although these are typically applied at point scales that are incompatible with coarse resolution integrated assessment modeling. To combine the relative strengths of these modeling systems, we are using the agricultural model EPIC (Environmental Policy Integrated Climate), applied in a geographic data framework for regional analyses, to provide input to the global economic model GCAM (Global Change Assessment Model). The initial phase of our approach focuses on a pilot region of the Midwest United States, a highly productive agricultural area. We apply EPIC, a point based biophysical process model, at 60 m spatial resolution within this domain and aggregate the results to GCAM agriculture and land use subregions for the United States. GCAM is then initialized with multiple management options for key food and bioenergy crops. Using EPIC to distinguish these management options based on grain yield, residue yield, soil C change and cost differences, GCAM then simulates the optimum distribution of the available management options to meet demands for food and energy over the next century. The coupled models provide a new platform for evaluating future changes in agricultural management based on food demand, bioenergy demand, and changes in crop yield and soil C under a changing climate. This framework can be applied to evaluate the economically and biophysically optimal distribution of management under future climates.

A Hybrid Evaluation System Framework (Shell & Web) with Standardized Access to Climate Model Data and Verification Tools for a Clear Climate Science Infrastructure on Big Data High Performance Computers

NASA Astrophysics Data System (ADS)

Kadow, C.; Illing, S.; Kunst, O.; Cubasch, U.

2014-12-01

The project 'Integrated Data and Evaluation System for Decadal Scale Prediction' (INTEGRATION) as part of the German decadal prediction project MiKlip develops a central evaluation system. The fully operational hybrid features a HPC shell access and an user friendly web-interface. It employs one common system with a variety of verification tools and validation data from different projects in- and outside of MiKlip. The evaluation system is located at the German Climate Computing Centre (DKRZ) and has direct access to the bulk of its ESGF node including millions of climate model data sets, e.g. from CMIP5 and CORDEX. The database is organized by the international CMOR standard using the meta information of the self-describing model, reanalysis and observational data sets. Apache Solr is used for indexing the different data projects into one common search environment. This implemented meta data system with its advanced but easy to handle search tool supports users, developers and their tools to retrieve the required information. A generic application programming interface (API) allows scientific developers to connect their analysis tools with the evaluation system independently of the programming language used. Users of the evaluation techniques benefit from the common interface of the evaluation system without any need to understand the different scripting languages. Facilitating the provision and usage of tools and climate data increases automatically the number of scientists working with the data sets and identify discrepancies. Additionally, the history and configuration sub-system stores every analysis performed with the evaluation system in a MySQL database. Configurations and results of the tools can be shared among scientists via shell or web-system. Therefore, plugged-in tools gain automatically from transparency and reproducibility. Furthermore, when configurations match while starting a evaluation tool, the system suggests to use results already produced by other users-saving CPU time, I/O and disk space. This study presents the different techniques and advantages of such a hybrid evaluation system making use of a Big Data HPC in climate science. website: www-miklip.dkrz.de visitor-login: guest password: miklip

A Hybrid Evaluation System Framework (Shell & Web) with Standardized Access to Climate Model Data and Verification Tools for a Clear Climate Science Infrastructure on Big Data High Performance Computers

NASA Astrophysics Data System (ADS)

Kadow, Christopher; Illing, Sebastian; Kunst, Oliver; Ulbrich, Uwe; Cubasch, Ulrich

2015-04-01

The project 'Integrated Data and Evaluation System for Decadal Scale Prediction' (INTEGRATION) as part of the German decadal prediction project MiKlip develops a central evaluation system. The fully operational hybrid features a HPC shell access and an user friendly web-interface. It employs one common system with a variety of verification tools and validation data from different projects in- and outside of MiKlip. The evaluation system is located at the German Climate Computing Centre (DKRZ) and has direct access to the bulk of its ESGF node including millions of climate model data sets, e.g. from CMIP5 and CORDEX. The database is organized by the international CMOR standard using the meta information of the self-describing model, reanalysis and observational data sets. Apache Solr is used for indexing the different data projects into one common search environment. This implemented meta data system with its advanced but easy to handle search tool supports users, developers and their tools to retrieve the required information. A generic application programming interface (API) allows scientific developers to connect their analysis tools with the evaluation system independently of the programming language used. Users of the evaluation techniques benefit from the common interface of the evaluation system without any need to understand the different scripting languages. Facilitating the provision and usage of tools and climate data increases automatically the number of scientists working with the data sets and identify discrepancies. Additionally, the history and configuration sub-system stores every analysis performed with the evaluation system in a MySQL database. Configurations and results of the tools can be shared among scientists via shell or web-system. Therefore, plugged-in tools gain automatically from transparency and reproducibility. Furthermore, when configurations match while starting a evaluation tool, the system suggests to use results already produced by other users-saving CPU time, I/O and disk space. This study presents the different techniques and advantages of such a hybrid evaluation system making use of a Big Data HPC in climate science. website: www-miklip.dkrz.de visitor-login: click on "Guest"

Simulating Aerosol Indirect Effects with Improved Aerosol-Cloud- Precipitation Representations in a Coupled Regional Climate Model

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

Zhang, Yang; Leung, L. Ruby; Fan, Jiwen

This is a collaborative project among North Carolina State University, Pacific Northwest National Laboratory, and Scripps Institution of Oceanography, University of California at San Diego to address the critical need for an accurate representation of aerosol indirect effect in climate and Earth system models. In this project, we propose to develop and improve parameterizations of aerosol-cloud-precipitation feedbacks in climate models and apply them to study the effect of aerosols and clouds on radiation and hydrologic cycle. Our overall objective is to develop, improve, and evaluate parameterizations to enable more accurate simulations of these feedbacks in high resolution regional and globalmore » climate models.« less

Performance of the Hydrological Portion of a Simple Water Quality Model in Different Climatic Regions

NASA Astrophysics Data System (ADS)

Moore, K.; Pierson, D.; Pettersson, K.; Naden, P.; Allott, N.; Jennings, E.; Tamm, T.; Järvet, A.; Nickus, U.; Thies, H.; Arvola, L.; Järvinen, M.; Schneiderman, E.; Zion, M.; Lounsbury, D.

2004-05-01

We are applying an existing watershed model in the EU CLIME (Climate and Lake Impacts in Europe) project to evaluate the effects of weather on seasonal and annual delivery of N, P, and DOC to lakes. Model calibration is based on long-term records of weather and water quality data collected from sites in different climatic regions spread across Europe and in New York State. The overall aim of the CLIME project is to develop methods and models to support lake and catchment management under current climate conditions and make predictions under future climate scenarios. Scientists from 10 partner countries are collaborating on developing a consistent approach to defining model parameters for the Generalized Watershed Loading Functions (GWLF) model, one of a larger suite of models used in the project. An example of the approach for the hydrological portion of the GWLF model will be presented, with consideration of the balance between model simplicity, ease of use, data requirements, and realistic predictions.

Predicting the Impacts of Climate Change on Central American Agriculture

NASA Astrophysics Data System (ADS)

Winter, J. M.; Ruane, A. C.; Rosenzweig, C.

2011-12-01

Agriculture is a vital component of Central America's economy. Poor crop yields and harvest reliability can produce food insecurity, malnutrition, and conflict. Regional climate models (RCMs) and agricultural models have the potential to greatly enhance the efficiency of Central American agriculture and water resources management under both current and future climates. A series of numerical experiments was conducted using Regional Climate Model Version 3 (RegCM3) and the Weather Research and Forecasting Model (WRF) to evaluate the ability of RCMs to reproduce the current climate of Central America and assess changes in temperature and precipitation under multiple future climate scenarios. Control simulations were thoroughly compared to a variety of observational datasets, including local weather station data, gridded meteorological data, and high-resolution satellite-based precipitation products. Future climate simulations were analyzed for both mean shifts in climate and changes in climate variability, including extreme events (droughts, heat waves, floods). To explore the impacts of changing climate on maize, bean, and rice yields in Central America, RCM output was used to force the Decision Support System for Agrotechnology Transfer Model (DSSAT). These results were synthesized to create climate change impacts predictions for Central American agriculture that explicitly account for evolving distributions of precipitation and temperature extremes.

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.

Evaluating impacts of climate change on future water scarcity in an intensively managed semi-arid region using a coupled model of biophysical processes and water rights

NASA Astrophysics Data System (ADS)

Han, B.; Flores, A. N.; Benner, S. G.

2017-12-01

In semiarid and arid regions where water supply is intensively managed, future water scarcity is a product of complex interactions between climate change and human activities. Evaluating future water scarcity under alternative scenarios of climate change, therefore, necessitates modeling approaches that explicitly represent the coupled biophysical and social processes responsible for the redistribution of water in these regions. At regional scales a particular challenge lies in adequately capturing not only the central tendencies of change in projections of climate change, but also the associated plausible range of variability in those projections. This study develops a framework that combines a stochastic weather generator, historical climate observations, and statistically downscaled General Circulation Model (GCM) projections. The method generates a large ensemble of daily climate realizations, avoiding deficiencies of using a few or mean values of individual GCM realizations. Three climate change scenario groups reflecting the historical, RCP4.5, and RCP8.5 future projections are developed. Importantly, the model explicitly captures the spatiotemporally varying irrigation activities as constrained by local water rights in a rapidly growing, semi-arid human-environment system in southwest Idaho. We use this modeling framework to project water use and scarcity patterns under the three future climate change scenarios. The model is built using the Envision alternative futures modeling framework. Climate projections for the region show future increases in both precipitation and temperature, especially under the RCP8.5 scenario. The increase of temperature has a direct influence on the increase of the irrigation water use and water scarcity, while the influence of increased precipitation on water use is less clear. The predicted changes are potentially useful in identifying areas in the watershed particularly sensitive to water scarcity, the relative importance of changes in precipitation versus temperature as a driver of scarcity, and potential shortcomings of the current water management framework in the region.

A model for evaluating stream temperature response to climate change scenarios in Wisconsin

USGS Publications Warehouse

Westenbroek, Stephen M.; Stewart, Jana S.; Buchwald, Cheryl A.; Mitro, Matthew G.; Lyons, John D.; Greb, Steven

2010-01-01

Global climate change is expected to alter temperature and flow regimes for streams in Wisconsin over the coming decades. Stream temperature will be influenced not only by the predicted increases in average air temperature, but also by changes in baseflow due to changes in precipitation patterns and amounts. In order to evaluate future stream temperature and flow regimes in Wisconsin, we have integrated two existing models in order to generate a water temperature time series at a regional scale for thousands of stream reaches where site-specific temperature observations do not exist. The approach uses the US Geological Survey (USGS) Soil-Water-Balance (SWB) model, along with a recalibrated version of an existing artificial neural network (ANN) stream temperature model. The ANN model simulates stream temperatures on the basis of landscape variables such as land use and soil type, and also includes climate variables such as air temperature and precipitation amounts. The existing ANN model includes a landscape variable called DARCY designed to reflect the potential for groundwater recharge in the contributing area for a stream segment. SWB tracks soil-moisture and potential recharge at a daily time step, providing a way to link changing climate patterns and precipitation amounts over time to baseflow volumes, and presumably to stream temperatures. The recalibrated ANN incorporates SWB-derived estimates of potential recharge to supplement the static estimates of groundwater flow potential derived from a topographically based model (DARCY). SWB and the recalibrated ANN will be supplied with climate drivers from a suite of general circulation models and emissions scenarios, enabling resource managers to evaluate possible changes in stream temperature regimes for Wisconsin.

Multi-model comparison highlights consistency in predicted effect of warming on a semi-arid shrub

USGS Publications Warehouse

Renwick, Katherine M.; Curtis, Caroline; Kleinhesselink, Andrew R.; Schlaepfer, Daniel R.; Bradley, Bethany A.; Aldridge, Cameron L.; Poulter, Benjamin; Adler, Peter B.

2018-01-01

A number of modeling approaches have been developed to predict the impacts of climate change on species distributions, performance, and abundance. The stronger the agreement from models that represent different processes and are based on distinct and independent sources of information, the greater the confidence we can have in their predictions. Evaluating the level of confidence is particularly important when predictions are used to guide conservation or restoration decisions. We used a multi-model approach to predict climate change impacts on big sagebrush (Artemisia tridentata), the dominant plant species on roughly 43 million hectares in the western United States and a key resource for many endemic wildlife species. To evaluate the climate sensitivity of A. tridentata, we developed four predictive models, two based on empirically derived spatial and temporal relationships, and two that applied mechanistic approaches to simulate sagebrush recruitment and growth. This approach enabled us to produce an aggregate index of climate change vulnerability and uncertainty based on the level of agreement between models. Despite large differences in model structure, predictions of sagebrush response to climate change were largely consistent. Performance, as measured by change in cover, growth, or recruitment, was predicted to decrease at the warmest sites, but increase throughout the cooler portions of sagebrush's range. A sensitivity analysis indicated that sagebrush performance responds more strongly to changes in temperature than precipitation. Most of the uncertainty in model predictions reflected variation among the ecological models, raising questions about the reliability of forecasts based on a single modeling approach. Our results highlight the value of a multi-model approach in forecasting climate change impacts and uncertainties and should help land managers to maximize the value of conservation investments.

Collaborative Proposal: Improving Decadal Prediction of Arctic Climate Variability and Change Using a Regional Arctic System Model (RASM)

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

Maslowski, Wieslaw

This project aims to develop, apply and evaluate a regional Arctic System model (RASM) for enhanced decadal predictions. Its overarching goal is to advance understanding of the past and present states of arctic climate and to facilitate improvements in seasonal to decadal predictions. In particular, it will focus on variability and long-term change of energy and freshwater flows through the arctic climate system. The project will also address modes of natural climate variability as well as extreme and rapid climate change in a region of the Earth that is: (i) a key indicator of the state of global climate throughmore » polar amplification and (ii) which is undergoing environmental transitions not seen in instrumental records. RASM will readily allow the addition of other earth system components, such as ecosystem or biochemistry models, thus allowing it to facilitate studies of climate impacts (e.g., droughts and fires) and of ecosystem adaptations to these impacts. As such, RASM is expected to become a foundation for more complete Arctic System models and part of a model hierarchy important for improving climate modeling and predictions.« less

A diagram for evaluating multiple aspects of model performance in simulating vector fields

NASA Astrophysics Data System (ADS)

Xu, Zhongfeng; Hou, Zhaolu; Han, Ying; Guo, Weidong

2016-12-01

Vector quantities, e.g., vector winds, play an extremely important role in climate systems. The energy and water exchanges between different regions are strongly dominated by wind, which in turn shapes the regional climate. Thus, how well climate models can simulate vector fields directly affects model performance in reproducing the nature of a regional climate. This paper devises a new diagram, termed the vector field evaluation (VFE) diagram, which is a generalized Taylor diagram and able to provide a concise evaluation of model performance in simulating vector fields. The diagram can measure how well two vector fields match each other in terms of three statistical variables, i.e., the vector similarity coefficient, root mean square length (RMSL), and root mean square vector difference (RMSVD). Similar to the Taylor diagram, the VFE diagram is especially useful for evaluating climate models. The pattern similarity of two vector fields is measured by a vector similarity coefficient (VSC) that is defined by the arithmetic mean of the inner product of normalized vector pairs. Examples are provided, showing that VSC can identify how close one vector field resembles another. Note that VSC can only describe the pattern similarity, and it does not reflect the systematic difference in the mean vector length between two vector fields. To measure the vector length, RMSL is included in the diagram. The third variable, RMSVD, is used to identify the magnitude of the overall difference between two vector fields. Examples show that the VFE diagram can clearly illustrate the extent to which the overall RMSVD is attributed to the systematic difference in RMSL and how much is due to the poor pattern similarity.

Evaluating CMIP5 Simulations of Historical Continental Climate with Koeppen Bioclimatic Metrics

NASA Astrophysics Data System (ADS)

Phillips, T. J.; Bonfils, C.

2013-12-01

The classic Koeppen bioclimatic classification scheme associates generic vegetation types (e.g. grassland, tundra, broadleaf or evergreen forests, etc.) with regional climate zones defined by their annual cycles of continental temperature (T) and precipitation (P), considered together. The locations or areas of Koeppen vegetation types derived from observational data thus can provide concise metrical standards for simultaneously evaluating climate simulations of T and P in naturally defined regions. The CMIP5 models' collective ability to correctly represent two variables that are critically important for living organisms at regional scales is therefore central to this evaluation. For this study, 14 Koeppen vegetation types are derived from annual-cycle climatologies of T and P in some 3 dozen CMIP5 simulations of the 1980-1999 period. Metrics for evaluating the ability of the CMIP5 models to simulate the correct locations and areas of each vegetation type, as well as measures of overall model performance, also are developed. It is found that the CMIP5 models are generally most deficient in simulating: 1) climates of drier Koeppen zones (e.g. desert, savanna, grassland, steppe vegetation types) located in the southwestern U.S. and Mexico, eastern Europe, southern Africa, and central Australia; 2) climates of regions such as central Asia and western South America where topography plays a key role. Details of regional T or P biases in selected simulations that exemplify general model performance problems also will be presented. Acknowledgments: This work was funded by the U.S. Department of Energy Office of Science and was performed at the Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344. Map of Koeppen vegetation types derived from observed T and P.

Predicting Nitrate Transport under Future Climate Scenarios beneath the Nebraska Management Systems Evaluation Area (MSEA) site

NASA Astrophysics Data System (ADS)

Li, Y.; Akbariyeh, S.; Gomez Peña, C. A.; Bartlet-Hunt, S.

2017-12-01

Understanding the impacts of future climate change on soil hydrological processes and solute transport is crucial to develop appropriate strategies to minimize adverse impacts of agricultural activities on groundwater quality. The goal of this work is to evaluate the direct effects of climate change on the fate and transport of nitrate beneath a center-pivot irrigated corn field in Nebraska Management Systems Evaluation Area (MSEA) site. Future groundwater recharge rate and actual evapotranspiration rate were predicted based on an inverse modeling approach using climate data generated by Weather Research and Forecasting (WRF) model under the RCP 8.5 scenario, which was downscaled from global CCSM4 model to a resolution of 24 by 24 km2. A groundwater flow model was first calibrated based on historical groundwater table measurement and was then applied to predict future groundwater table in the period 2057-2060. Finally, predicted future groundwater recharge rate, actual evapotranspiration rate, and groundwater level, together with future precipitation data from WRF, were used in a three-dimensional (3D) model, which was validated based on rich historic data set collected from 1993-1996, to predict nitrate concentration in soil and groundwater from the year 2057 to 2060. Future groundwater recharge was found to be decreasing in the study area compared to average groundwater recharge data from the literature. Correspondingly, groundwater elevation was predicted to decrease (1 to 2 ft) over the five years of simulation. Predicted higher transpiration data from climate model resulted in lower infiltration of nitrate concentration in subsurface within the root zone.

Predicting Nitrate Transport under Future Climate Scenarios beneath the Nebraska Management Systems Evaluation Area (MSEA) site

NASA Astrophysics Data System (ADS)

Li, Y.; Akbariyeh, S.; Gomez Peña, C. A.; Bartlet-Hunt, S.

2016-12-01

Understanding the impacts of future climate change on soil hydrological processes and solute transport is crucial to develop appropriate strategies to minimize adverse impacts of agricultural activities on groundwater quality. The goal of this work is to evaluate the direct effects of climate change on the fate and transport of nitrate beneath a center-pivot irrigated corn field in Nebraska Management Systems Evaluation Area (MSEA) site. Future groundwater recharge rate and actual evapotranspiration rate were predicted based on an inverse modeling approach using climate data generated by Weather Research and Forecasting (WRF) model under the RCP 8.5 scenario, which was downscaled from global CCSM4 model to a resolution of 24 by 24 km2. A groundwater flow model was first calibrated based on historical groundwater table measurement and was then applied to predict future groundwater table in the period 2057-2060. Finally, predicted future groundwater recharge rate, actual evapotranspiration rate, and groundwater level, together with future precipitation data from WRF, were used in a three-dimensional (3D) model, which was validated based on rich historic data set collected from 1993-1996, to predict nitrate concentration in soil and groundwater from the year 2057 to 2060. Future groundwater recharge was found to be decreasing in the study area compared to average groundwater recharge data from the literature. Correspondingly, groundwater elevation was predicted to decrease (1 to 2 ft) over the five years of simulation. Predicted higher transpiration data from climate model resulted in lower infiltration of nitrate concentration in subsurface within the root zone.

WRF-Cordex simulations for Europe: mean and extreme precipitation for present and future climates

NASA Astrophysics Data System (ADS)

Cardoso, Rita M.; Soares, Pedro M. M.; Miranda, Pedro M. A.

2013-04-01

The Weather Research and Forecast (WRF-ARW) model, version 3.3.1, was used to perform the European domain Cordex simulations, at 50km resolution. A first simulation, forced by ERA-Interim (1989-2009), was carried out to evaluate the models performance to represent the mean and extreme precipitation in present European climate. This evaluation is based in the comparison of WRF results against the ECAD regular gridded dataset of daily precipitation. Results are comparable to recent studies with other models for the European region, at this resolution. For the same domain a control and a future scenario (RCP8.5) simulation was performed to assess the climate change impact on the mean and extreme precipitation. These regional simulations were forced by EC-EARTH model results, and, encompass the periods from 1960-2006 and 2006-2100, respectively.

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.

Characterization and Quantification of Uncertainty in the NARCCAP Regional Climate Model Ensemble and Application to Impacts on Water Systems

NASA Astrophysics Data System (ADS)

Mearns, L. O.; Sain, S. R.; McGinnis, S. A.; Steinschneider, S.; Brown, C. M.

2015-12-01

In this talk we present the development of a joint Bayesian Probabilistic Model for the climate change results of the North American Regional Climate Change Assessment Program (NARCCAP) that uses a unique prior in the model formulation. We use the climate change results (joint distribution of seasonal temperature and precipitation changes (future vs. current)) from the global climate models (GCMs) that provided boundary conditions for the six different regional climate models used in the program as informative priors for the bivariate Bayesian Model. The two variables involved are seasonal temperature and precipitation over sub-regions (i.e., Bukovsky Regions) of the full NARCCAP domain. The basic approach to the joint Bayesian hierarchical model follows the approach of Tebaldi and Sansó (2009). We compare model results using informative (i.e., GCM information) as well as uninformative priors. We apply these results to the Water Evaluation and Planning System (WEAP) model for the Colorado Springs Utility in Colorado. We investigate the layout of the joint pdfs in the context of the water model sensitivities to ranges of temperature and precipitation results to determine the likelihoods of future climate conditions that cannot be accommodated by possible adaptation options. Comparisons may also be made with joint pdfs formed from the CMIP5 collection of global climate models and empirically downscaled to the region of interest.

Apache Open Climate Workbench: Building Open Source Climate Science Tools and Community at the Apache Software Foundation

NASA Astrophysics Data System (ADS)

Joyce, M.; Ramirez, P.; Boustani, M.; Mattmann, C. A.; Khudikyan, S.; McGibbney, L. J.; Whitehall, K. D.

2014-12-01

Apache Open Climate Workbench (OCW; https://climate.apache.org/) is a Top-Level Project at the Apache Software Foundation that aims to provide a suite of tools for performing climate science evaluations using model outputs from a multitude of different sources (ESGF, CORDEX, U.S. NCA, NARCCAP) with remote sensing data from NASA, NOAA, and other agencies. Apache OCW is the second NASA project to become a Top-Level Project at the Apache Software Foundation. It grew out of the Jet Propulsion Laboratory's (JPL) Regional Climate Model Evaluation System (RCMES) project, a collaboration between JPL and the University of California, Los Angeles' Joint Institute for Regional Earth System Science and Engineering (JIFRESSE). Apache OCW provides scientists and developers with tools for data manipulation, metrics for dataset comparisons, and a visualization suite. In addition to a powerful low-level API, Apache OCW also supports a web application for quick, browser-controlled evaluations, a command line application for local evaluations, and a virtual machine for isolated experimentation with minimal setup. This talk will look at the difficulties and successes of moving a closed community research project out into the wild world of open source. We'll explore the growing pains Apache OCW went through to become a Top-Level Project at the Apache Software Foundation as well as the benefits gained by opening up development to the broader climate and computer science communities.

First Evaluation of the CCAM Aerosol Simulation over Africa: Implications for Regional Climate Modeling

NASA Astrophysics Data System (ADS)

Horowitz, H.; Garland, R. M.; Thatcher, M. J.; Naidoo, M.; van der Merwe, J.; Landman, W.; Engelbrecht, F.

2015-12-01

An accurate representation of African aerosols in climate models is needed to understand the regional and global radiative forcing and climate impacts of aerosols, at present and under future climate change. However, aerosol simulations in regional climate models for Africa have not been well-tested. Africa contains the largest single source of biomass-burning smoke aerosols and dust globally. Although aerosols are short-lived relative to greenhouse gases, black carbon in particular is estimated to be second only to carbon dioxide in contributing to warming on a global scale. Moreover, Saharan dust is exported great distances over the Atlantic Ocean, affecting nutrient transport to regions like the Amazon rainforest, which can further impact climate. Biomass burning aerosols are also exported from Africa, westward from Angola over the Atlantic Ocean and off the southeastern coast of South Africa to the Indian Ocean. Here, we perform the first extensive quantitative evaluation of the Conformal-Cubic Atmospheric Model (CCAM) aerosol simulation against monitored data, focusing on aerosol optical depth (AOD) observations over Africa. We analyze historical regional simulations for 1999 - 2012 from CCAM consistent with the experimental design of CORDEX at 50 km global horizontal resolution, through the dynamical downscaling of ERA-Interim data reanalysis data, with the CMIP5 emissions inventory (RCP8.5 scenario). CCAM has a prognostic aerosol scheme for organic carbon, black carbon, sulfate, and dust, and non-prognostic sea salt. The CCAM AOD at 550nm was compared to AOD (observed at 440nm, adjusted to 550nm with the Ångström exponent) from long-term AERONET stations across Africa. Sites strongly impacted by dust and biomass burning and with long continuous records were prioritized. In general, the model captures the monthly trends of the AERONET data. This presentation provides a basis for understanding how well aerosol particles are represented over Africa in regional climate modeling and the potential impact on climate predictions, and is the first large scale climate model-measurement verification of aerosols over Africa that we are aware of. CCAM is widely used for regional climate modeling applications, and we also discuss further improvements to the aerosol parameterizations based on our results.

Use of the Köppen-Trewartha climate classification to evaluate climatic refugia in statistically derived ecoregions for the People’s Republic of China

Treesearch

B. Baker; Henry Diaz; William Hargrove; Forrest Hoffman

2010-01-01

Changes in climate as projected by state-of-the-art climate models are likely to result in novel combinations of climate and topo-edaphic factors that will have substantial impacts on the distribution and persistence of natural vegetation and animal species. We have used multivariate techniques to quantify some of these changes; the...

An assessment of aerosol optical properties from remote-sensing observations and regional chemistry-climate coupled models over Europe

NASA Astrophysics Data System (ADS)

Palacios-Peña, Laura; Baró, Rocío; Baklanov, Alexander; Balzarini, Alessandra; Brunner, Dominik; Forkel, Renate; Hirtl, Marcus; Honzak, Luka; María López-Romero, José; Montávez, Juan Pedro; Pérez, Juan Luis; Pirovano, Guido; San José, Roberto; Schröder, Wolfram; Werhahn, Johannes; Wolke, Ralf; Žabkar, Rahela; Jiménez-Guerrero, Pedro

2018-04-01

Atmospheric aerosols modify the radiative budget of the Earth due to their optical, microphysical and chemical properties, and are considered one of the most uncertain climate forcing agents. In order to characterise the uncertainties associated with satellite and modelling approaches to represent aerosol optical properties, mainly aerosol optical depth (AOD) and Ångström exponent (AE), their representation by different remote-sensing sensors and regional online coupled chemistry-climate models over Europe are evaluated. This work also characterises whether the inclusion of aerosol-radiation (ARI) or/and aerosol-cloud interactions (ACI) help improve the skills of modelling outputs.Two case studies were selected within the EuMetChem COST Action ES1004 framework when important aerosol episodes in 2010 all over Europe took place: a Russian wildfire episode and a Saharan desert dust outbreak that covered most of the Mediterranean Sea. The model data came from different regional air-quality-climate simulations performed by working group 2 of EuMetChem, which differed according to whether ARI or ACI was included or not. The remote-sensing data came from three different sensors: MODIS, OMI and SeaWIFS. The evaluation used classical statistical metrics to first compare satellite data versus the ground-based instrument network (AERONET) and then to evaluate model versus the observational data (both satellite and ground-based data).Regarding the uncertainty in the satellite representation of AOD, MODIS presented the best agreement with the AERONET observations compared to other satellite AOD observations. The differences found between remote-sensing sensors highlighted the uncertainty in the observations, which have to be taken into account when evaluating models. When modelling results were considered, a common trend for underestimating high AOD levels was observed. For the AE, models tended to underestimate its variability, except when considering a sectional approach in the aerosol representation. The modelling results showed better skills when ARI+ACI interactions were included; hence this improvement in the representation of AOD (above 30 % in the model error) and AE (between 20 and 75 %) is important to provide a better description of aerosol-radiation-cloud interactions in regional climate models.

ARM Data-Oriented Metrics and Diagnostics Package for Climate Model Evaluation Value-Added Product

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

Zhang, Chengzhu; Xie, Shaocheng

A Python-based metrics and diagnostics package is currently being developed by the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Infrastructure Team at Lawrence Livermore National Laboratory (LLNL) to facilitate the use of long-term, high-frequency measurements from the ARM Facility in evaluating the regional climate simulation of clouds, radiation, and precipitation. This metrics and diagnostics package computes climatological means of targeted climate model simulation and generates tables and plots for comparing the model simulation with ARM observational data. The Coupled Model Intercomparison Project (CMIP) model data sets are also included in the package to enable model intercomparison as demonstratedmore » in Zhang et al. (2017). The mean of the CMIP model can serve as a reference for individual models. Basic performance metrics are computed to measure the accuracy of mean state and variability of climate models. The evaluated physical quantities include cloud fraction, temperature, relative humidity, cloud liquid water path, total column water vapor, precipitation, sensible and latent heat fluxes, and radiative fluxes, with plan to extend to more fields, such as aerosol and microphysics properties. Process-oriented diagnostics focusing on individual cloud- and precipitation-related phenomena are also being developed for the evaluation and development of specific model physical parameterizations. The version 1.0 package is designed based on data collected at ARM’s Southern Great Plains (SGP) Research Facility, with the plan to extend to other ARM sites. The metrics and diagnostics package is currently built upon standard Python libraries and additional Python packages developed by DOE (such as CDMS and CDAT). The ARM metrics and diagnostic package is available publicly with the hope that it can serve as an easy entry point for climate modelers to compare their models with ARM data. In this report, we first present the input data, which constitutes the core content of the metrics and diagnostics package in section 2, and a user's guide documenting the workflow/structure of the version 1.0 codes, and including step-by-step instruction for running the package in section 3.« less

Manager personality, manager service quality orientation, and service climate: test of a model.

PubMed

Salvaggio, Amy Nicole; Schneider, Benjamin; Nishii, Lisa H; Mayer, David M; Ramesh, Anuradha; Lyon, Julie S

2007-11-01

This article conceptually and empirically explores the relationships among manager personality, manager service quality orientation, and climate for customer service. Data were collected from 1,486 employees and 145 managers in grocery store departments (N = 145) to test the authors' theoretical model. Largely consistent with hypotheses, results revealed that core self-evaluations were positively related to managers' service quality orientation, even after dimensions of the Big Five model of personality were controlled, and that service quality orientation fully mediated the relationship between personality and global service climate. Implications for personality and organizational climate research are discussed. (c) 2007 APA

Modeling potential hydrochemical responses to climate change and rising CO2 at the Hubbard Brook Experimental Forest using a dynamic biogeochemical model (PnET-BGC)

Treesearch

Afshin Pourmokhtarian; Charles T. Driscoll; John L. Campbell; Katharine Hayhoe

2012-01-01

Dynamic hydrochemical models are useful tools for understanding and predicting the interactive effects of climate change, atmospheric CO2, and atmospheric deposition on the hydrology and water quality of forested watersheds. We used the biogeochemical model, PnET-BGC, to evaluate the effects of potential future changes in temperature,...

Misleading prioritizations from modelling range shifts under climate change

Treesearch

Helen R. Sofaer; Catherine S. Jarnevich; Curtis H. Flather

2018-01-01

Conservation planning requires the prioritization of a subset of taxa and geographical locations to focus monitoring and management efforts. Integration of the threats and opportunities posed by climate change often relies on predictions from species distribution models, particularly for assessments of vulnerability or invasion risk for multiple taxa. We evaluated...

Uncertainty in BMP evaluation and optimization for watershed management

NASA Astrophysics Data System (ADS)

Chaubey, I.; Cibin, R.; Sudheer, K.; Her, Y.

2012-12-01

Use of computer simulation models have increased substantially to make watershed management decisions and to develop strategies for water quality improvements. These models are often used to evaluate potential benefits of various best management practices (BMPs) for reducing losses of pollutants from sources areas into receiving waterbodies. Similarly, use of simulation models in optimizing selection and placement of best management practices under single (maximization of crop production or minimization of pollutant transport) and multiple objective functions has increased recently. One of the limitations of the currently available assessment and optimization approaches is that the BMP strategies are considered deterministic. Uncertainties in input data (e.g. precipitation, streamflow, sediment, nutrient and pesticide losses measured, land use) and model parameters may result in considerable uncertainty in watershed response under various BMP options. We have developed and evaluated options to include uncertainty in BMP evaluation and optimization for watershed management. We have also applied these methods to evaluate uncertainty in ecosystem services from mixed land use watersheds. In this presentation, we will discuss methods to to quantify uncertainties in BMP assessment and optimization solutions due to uncertainties in model inputs and parameters. We have used a watershed model (Soil and Water Assessment Tool or SWAT) to simulate the hydrology and water quality in mixed land use watershed located in Midwest USA. The SWAT model was also used to represent various BMPs in the watershed needed to improve water quality. SWAT model parameters, land use change parameters, and climate change parameters were considered uncertain. It was observed that model parameters, land use and climate changes resulted in considerable uncertainties in BMP performance in reducing P, N, and sediment loads. In addition, climate change scenarios also affected uncertainties in SWAT simulated crop yields. Considerable uncertainties in the net cost and the water quality improvements resulted due to uncertainties in land use, climate change, and model parameter values.

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.

Comparing multiple model-derived aerosol optical properties to spatially collocated ground-based and satellite measurements

NASA Astrophysics Data System (ADS)

Ocko, Ilissa B.; Ginoux, Paul A.

2017-04-01

Anthropogenic aerosols are a key factor governing Earth's climate and play a central role in human-caused climate change. However, because of aerosols' complex physical, optical, and dynamical properties, aerosols are one of the most uncertain aspects of climate modeling. Fortunately, aerosol measurement networks over the past few decades have led to the establishment of long-term observations for numerous locations worldwide. Further, the availability of datasets from several different measurement techniques (such as ground-based and satellite instruments) can help scientists increasingly improve modeling efforts. This study explores the value of evaluating several model-simulated aerosol properties with data from spatially collocated instruments. We compare aerosol optical depth (AOD; total, scattering, and absorption), single-scattering albedo (SSA), Ångström exponent (α), and extinction vertical profiles in two prominent global climate models (Geophysical Fluid Dynamics Laboratory, GFDL, CM2.1 and CM3) to seasonal observations from collocated instruments (AErosol RObotic NETwork, AERONET, and Cloud-Aerosol Lidar with Orthogonal Polarization, CALIOP) at seven polluted and biomass burning regions worldwide. We find that a multi-parameter evaluation provides key insights on model biases, data from collocated instruments can reveal underlying aerosol-governing physics, column properties wash out important vertical distinctions, and improved models does not mean all aspects are improved. We conclude that it is important to make use of all available data (parameters and instruments) when evaluating aerosol properties derived by models.

On the hydrologic adjustment of climate-model projections: The potential pitfall of potential evapotranspiration

USGS Publications Warehouse

Milly, P.C.D.; Dunne, K.A.

2011-01-01

Hydrologic models often are applied to adjust projections of hydroclimatic change that come from climate models. Such adjustment includes climate-bias correction, spatial refinement ("downscaling"), and consideration of the roles of hydrologic processes that were neglected in the climate model. Described herein is a quantitative analysis of the effects of hydrologic adjustment on the projections of runoff change associated with projected twenty-first-century climate change. In a case study including three climate models and 10 river basins in the contiguous United States, the authors find that relative (i.e., fractional or percentage) runoff change computed with hydrologic adjustment more often than not was less positive (or, equivalently, more negative) than what was projected by the climate models. The dominant contributor to this decrease in runoff was a ubiquitous change in runoff (median 211%) caused by the hydrologic model's apparent amplification of the climate-model-implied growth in potential evapotranspiration. Analysis suggests that the hydrologic model, on the basis of the empirical, temperature-based modified Jensen-Haise formula, calculates a change in potential evapotranspiration that is typically 3 times the change implied by the climate models, which explicitly track surface energy budgets. In comparison with the amplification of potential evapotranspiration, central tendencies of other contributions from hydrologic adjustment (spatial refinement, climate-bias adjustment, and process refinement) were relatively small. The authors' findings highlight the need for caution when projecting changes in potential evapotranspiration for use in hydrologic models or drought indices to evaluate climatechange impacts on water. Copyright ?? 2011, Paper 15-001; 35,952 words, 3 Figures, 0 Animations, 1 Tables.

Performance evaluation of a non-hydrostatic regional climate model over the Mediterranean/Black Sea area and climate projections for the XXI century

NASA Astrophysics Data System (ADS)

Mercogliano, Paola; Bucchignani, Edoardo; Montesarchio, Myriam; Zollo, Alessandra Lucia

2013-04-01

In the framework of the Work Package 4 (Developing integrated tools for environmental assessment) of PERSEUS Project, high resolution climate simulations have been performed, with the aim of furthering knowledge in the field of climate variability at regional scale, its causes and impacts. CMCC is a no profit centre whose aims are the promotion, research coordination and scientific activities in the field of climate changes. In this work, we show results of numerical simulation performed over a very wide area (13W-46E; 29-56N) at spatial resolution of 14 km, which includes the Mediterranean and Black Seas, using the regional climate model COSMO-CLM. It is a non-hydrostatic model for the simulation of atmospheric processes, developed by the DWD-Germany for weather forecast services; successively, the model has been updated by the CLM-Community, in order to develop climatic applications. It is the only documented numerical model system in Europe designed for spatial resolutions down to 1 km with a range of applicability encompassing operational numerical weather prediction, regional climate modelling the dispersion of trace gases and aerosol and idealised studies and applicable in all regions of the world for a wide range of available climate simulations from global climate and NWP models. Different reasons justify the development of a regional model: the first is the increasing number of works in literature asserting that regional models have also the features to provide more detailed description of the climate extremes, that are often more important then their mean values for natural and human systems. The second one is that high resolution modelling shows adequate features to provide information for impact assessment studies. At CMCC, regional climate modelling is a part of an integrated simulation system and it has been used in different European and African projects to provide qualitative and quantitative evaluation of the hydrogeological and public health risks. A simulation covering the period 1971-2000 and driven by ERA40 reanalysis has been performed, in order to assess the capability of the model to reproduce the present climate, with "perfect boundary conditions". A comparison, in terms of 2-metre temperature and precipitation, with EOBS dataset will be shown and discussed, in order to analyze the capabilities in simulating the main features of the observed climate over a wide area, at high spatial resolution. Then, a comparison between the results of COSMO-CLM driven by the global model CMCC-MED (whose atmospheric component is ECHAM5) and by ERA40 will be provided for a characterization of the errors induced by the global model. Finally, climate projections on the examined area for the XXI century, considering the RCP4.5 emission scenario for the future, will be provided. In this work a special emphasis will be issued to the analysis of the capability to reproduce not only the average climate trend but also extremes of the present and future climate, in terms of temperature, precipitation and wind.

Climate change impact assessment on hydrology of a small watershed using semi-distributed model

NASA Astrophysics Data System (ADS)

Pandey, Brij Kishor; Gosain, A. K.; Paul, George; Khare, Deepak

2017-07-01

This study is an attempt to quantify the impact of climate change on the hydrology of Armur watershed in Godavari river basin, India. A GIS-based semi-distributed hydrological model, soil and water assessment tool (SWAT) has been employed to estimate the water balance components on the basis of unique combinations of slope, soil and land cover classes for the base line (1961-1990) and future climate scenarios (2071-2100). Sensitivity analysis of the model has been performed to identify the most critical parameters of the watershed. Average monthly calibration (1987-1994) and validation (1995-2000) have been performed using the observed discharge data. Coefficient of determination (R2), Nash-Sutcliffe efficiency (ENS) and root mean square error (RMSE) were used to evaluate the model performance. Calibrated SWAT setup has been used to evaluate the changes in water balance components of future projection over the study area. HadRM3, a regional climatic data, have been used as input of the hydrological model for climate change impact studies. In results, it was found that changes in average annual temperature (+3.25 °C), average annual rainfall (+28 %), evapotranspiration (28 %) and water yield (49 %) increased for GHG scenarios with respect to the base line scenario.

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.

Analysis of the Effect of Interior Nudging on Temperature and Precipitation Distributions of Multi-year Regional Climate Simulations

NASA Astrophysics Data System (ADS)

Nolte, C. G.; Otte, T. L.; Bowden, J. H.; Otte, M. J.

2010-12-01

There is disagreement in the regional climate modeling community as to the appropriateness of the use of internal nudging. Some investigators argue that the regional model should be minimally constrained and allowed to respond to regional-scale forcing, while others have noted that in the absence of interior nudging, significant large-scale discrepancies develop between the regional model solution and the driving coarse-scale fields. These discrepancies lead to reduced confidence in the ability of regional climate models to dynamically downscale global climate model simulations under climate change scenarios, and detract from the usability of the regional simulations for impact assessments. The advantages and limitations of interior nudging schemes for regional climate modeling are investigated in this study. Multi-year simulations using the WRF model driven by reanalysis data over the continental United States at 36km resolution are conducted using spectral nudging, grid point nudging, and for a base case without interior nudging. The means, distributions, and inter-annual variability of temperature and precipitation will be evaluated in comparison to regional analyses.

Representation of the Great Lakes in the Coupled Model Intercomparison Project Version 5

NASA Astrophysics Data System (ADS)

Briley, L.; Rood, R. B.

2017-12-01

The U.S. Great Lakes play a significant role in modifying regional temperatures and precipitation, and as the lakes change in response to a warming climate (i.e., warmer surface water temperatures, decreased ice cover, etc) lake-land-atmosphere dynamics are affected. Because the lakes modify regional weather and are a driver of regional climate change, understanding how they are represented in climate models is important to the reliability of model based information for the region. As part of the Great Lakes Integrated Sciences + Assessments (GLISA) Ensemble project, a major effort is underway to evaluate the Coupled Model Intercomparison Project version (CMIP) 5 global climate models for how well they physically represent the Great Lakes and lake-effects. The CMIP models were chosen because they are a primary source of information in many products developed for decision making (i.e., National Climate Assessment, downscaled future climate projections, etc.), yet there is very little description of how well they represent the lakes. This presentation will describe the results of our investigation of if and how the Great Lakes are represented in the CMIP5 models.

The DeepMIP Contribution to PMIP4: Experimental Design for Model Simulations of the EECO, PETM, and pre-PETM (version 1.0)

NASA Technical Reports Server (NTRS)

Lunt, Daniel J.; Huber, Matthew; Anagnostou, Eleni; Baatsen, Michiel L. J.; Caballero, Rodrigo; DeConto, Rob; Dijkstra, Henk A.; Donnadieu, Yannick; Evans, David; Feng, Ran;

Climate Literacy Through Learning-by-Doing: Engaging Communities in the Production of Accessible Research-Based Information

NASA Astrophysics Data System (ADS)

Bourqui, M.; Charriere, M. K. M.; Bolduc, C.

2016-12-01

This talk presents a case of a learning-by-doing approach used by the Climanosco organisation to produce research-based information written in a language accessible to a large public. In this model, engagement (the "doing") of members of the general public, alongside climate scientists, is fostered at various levels of this production of knowledge. In particular, this engagement plays a key role in our extended peer-review process as non-scientific referees are requested to review the accessibility of manuscripts for a large public. Members of the general public also participate to the scientific inquiry by inviting scientists to write on a particular topic or by co-authoring articles. Importantly, their participation, side-by-side with climate scientists, allows them to naturally raise their climate literacy (the "learning"). This model was tested in the context of a scientific challenge organised for the launch of Climanosco where climate scientists were invited to re-frame their research for the general public. This competition started in the fall 2015 and is due to end in September 2016. It led to 11 published articles and engaged the participation of 24 members of the general public. Six non-scientists participated to the jury alongside six climate scientists and evaluated the 11 articles. Their perceived increase in climate knowledge, as evaluated though a survey, will be presented in this talk. One important challenge now is to evaluate the potential of this model to support the teaching of climate sciences at schools. For that purpose, we are starting a dialog with various teachers in several countries. Progresses on this side will also be discussed in this talk.

Climate suitability for European ticks: assessing species distribution models against null models and projection under AR5 climate.

PubMed

Williams, Hefin Wyn; Cross, Dónall Eoin; Crump, Heather Louise; Drost, Cornelis Jan; Thomas, Christopher James

2015-08-28

There is increasing evidence that the geographic distribution of tick species is changing. Whilst correlative Species Distribution Models (SDMs) have been used to predict areas that are potentially suitable for ticks, models have often been assessed without due consideration for spatial patterns in the data that may inflate the influence of predictor variables on species distributions. This study used null models to rigorously evaluate the role of climate and the potential for climate change to affect future climate suitability for eight European tick species, including several important disease vectors. We undertook a comparative assessment of the performance of Maxent and Mahalanobis Distance SDMs based on observed data against those of null models based on null species distributions or null climate data. This enabled the identification of species whose distributions demonstrate a significant association with climate variables. Latest generation (AR5) climate projections were subsequently used to project future climate suitability under four Representative Concentration Pathways (RCPs). Seven out of eight tick species exhibited strong climatic signals within their observed distributions. Future projections intimate varying degrees of northward shift in climate suitability for these tick species, with the greatest shifts forecasted under the most extreme RCPs. Despite the high performance measure obtained for the observed model of Hyalomma lusitanicum, it did not perform significantly better than null models; this may result from the effects of non-climatic factors on its distribution. By comparing observed SDMs with null models, our results allow confidence that we have identified climate signals in tick distributions that are not simply a consequence of spatial patterns in the data. Observed climate-driven SDMs for seven out of eight species performed significantly better than null models, demonstrating the vulnerability of these tick species to the effects of climate change in the future.

Evaluation of Aerosol Mixing State Classes in the GISS Modele-matrix Climate Model Using Single-particle Mass Spectrometry Measurements

NASA Technical Reports Server (NTRS)

Bauer, Susanne E.; Ault, Andrew; Prather, Kimberly A.

2013-01-01

Aerosol particles in the atmosphere are composed of multiple chemical species. The aerosol mixing state, which describes how chemical species are mixed at the single-particle level, provides critical information on microphysical characteristics that determine the interaction of aerosols with the climate system. The evaluation of mixing state has become the next challenge. This study uses aerosol time-of-flight mass spectrometry (ATOFMS) data and compares the results to those of the Goddard Institute for Space Studies modelE-MATRIX (Multiconfiguration Aerosol TRacker of mIXing state) model, a global climate model that includes a detailed aerosol microphysical scheme. We use data from field campaigns that examine a variety of air mass regimens (urban, rural, and maritime). At all locations, polluted areas in California (Riverside, La Jolla, and Long Beach), a remote location in the Sierra Nevada Mountains (Sugar Pine) and observations from Jeju (South Korea), the majority of aerosol species are internally mixed. Coarse aerosol particles, those above 1 micron, are typically aged, such as coated dust or reacted sea-salt particles. Particles below 1 micron contain large fractions of organic material, internally-mixed with sulfate and black carbon, and few external mixtures. We conclude that observations taken over multiple weeks characterize typical air mass types at a given location well; however, due to the instrumentation, we could not evaluate mass budgets. These results represent the first detailed comparison of single-particle mixing states in a global climate model with real-time single-particle mass spectrometry data, an important step in improving the representation of mixing state in global climate models.

Challenges in the development of very high resolution Earth System Models for climate science

NASA Astrophysics Data System (ADS)

Rasch, Philip J.; Xie, Shaocheng; Ma, Po-Lun; Lin, Wuyin; Wan, Hui; Qian, Yun

2017-04-01

The authors represent the 20+ members of the ACME atmosphere development team. The US Department of Energy (DOE) has, like many other organizations around the world, identified the need for an Earth System Model capable of rapid completion of decade to century length simulations at very high (vertical and horizontal) resolution with good climate fidelity. Two years ago DOE initiated a multi-institution effort called ACME (Accelerated Climate Modeling for Energy) to meet this an extraordinary challenge, targeting a model eventually capable of running at 10-25km horizontal and 20-400m vertical resolution through the troposphere on exascale computational platforms at speeds sufficient to complete 5+ simulated years per day. I will outline the challenges our team has encountered in development of the atmosphere component of this model, and the strategies we have been using for tuning and debugging a model that we can barely afford to run on today's computational platforms. These strategies include: 1) evaluation at lower resolutions; 2) ensembles of short simulations to explore parameter space, and perform rough tuning and evaluation; 3) use of regionally refined versions of the model for probing high resolution model behavior at less expense; 4) use of "auto-tuning" methodologies for model tuning; and 5) brute force long climate simulations.

Ecoclimatic indicators to study climate suitability of areas for the cultivation of specific crops

NASA Astrophysics Data System (ADS)

Caubel, J.; Garcia de Cortazar Atauri, I.; Cufi, J.; Huard, F.; Launay, M.; Ripoche, D.; Graux, A.; deNoblet, N.

2013-12-01

Climatic conditions play a fundamental role in the suitability of geographical areas for cropping. In the context of climate change, we could expect changes in overall climatic conditions and so, on the suitability for cropping. Therefore, assessing the future climate suitability of areas for cropping is decisive for anticipating agriculture in a given area. Moreover, it is crucial to have access to the split up information concerning the effect of climate on the achievement of the main ecophysiological processes and cultural practices taking place during the crop cycle. In this way, stakeholders can envisage land use adaptations under climate change conditions, such as changes in cultural practices or development of new varieties for example. We proposed an aggregation tool of ecoclimatic indicators to design evaluation trees of climate suitability of areas for cropping, GETARI (Generic Evaluation Tool of Ecoclimatic Indicators). It calculates an overall climate suitability index at the annual scale, from a designed evaluation tree. This aggregation tool allows to characterize climate suitability according to crop ecophysiology, grain/fruit quality or crop management. GETARI proposes the major ecophysiological processes and cultural practices taking place during phenological periods, together with the climatic effects that are known to affect their achievement. The climatic effects on the ecophysiological processes (or cultural practices) during phenological periods are captured by the ecoclimatic indicators, which are agroclimatic indicators calculated over phenological periods. They give information about crop response to climate through ecophysiological or agronomic thresholds. Those indices of suitability are normalized and aggregated according to aggregation rules in order to compute an overall climate index. In order to illustrate how GETARI can be used, we designed evaluation trees in order to study the climate suitability for maize cropping regarding ecophysiology, for wheat cropping regarding its management and for grape cropping regarding its quality. The designed evaluation trees were developed in accordance with expert assessment and were applied in some past climatic conditions in France to verify their consistence. To conclude, the use of indicators does not replace models but represent additional tools for understanding and spatializing some results obtained by models. Their use can provide information about suitability of geographical areas for cropping in future climatic conditions and can enable to minimize the risk of crop failure. This work is carried out under the research program ORACLE (Opportunities and Risks of Agrosystems & forests in response to CLimate, socio-economic and policy changEs in France (and Europe).

Mid-Holocene and last glacial maximum climate simulations with the IPSL model: part II: model-data comparisons

NASA Astrophysics Data System (ADS)

Kageyama, Masa; Braconnot, Pascale; Bopp, Laurent; Mariotti, Véronique; Roy, Tilla; Woillez, Marie-Noëlle; Caubel, Arnaud; Foujols, Marie-Alice; Guilyardi, Eric; Khodri, Myriam; Lloyd, James; Lombard, Fabien; Marti, Olivier

2013-05-01

The climates of the mid-Holocene (MH, 6,000 years ago) and the Last Glacial Maximum (LGM, 21,000 years ago) have been extensively documented and as such, have become targets for the evaluation of climate models for climate contexts very different from the present. In Part 1 of the present work, we have studied the MH and LGM simulations performed with the last two versions of the IPSL model: IPSL_CM4, run for the PMIP2/CMIP3 (Coupled Model Intercomparion Project) projects and IPSL_CM5A, run for the most recent PMIP3/CMIP5 projets. We have shown that not only are these models different in their simulations of the PI climate, but also in their simulations of the climatic anomalies for the MH and LGM. In the Part 2 of this paper, we first examine whether palaeo-data can help discriminate between the model performances. This is indeed the case for the African monsoon for the MH or for North America south of the Laurentide ice sheet, the South Atlantic or the southern Indian ocean for the LGM. For the LGM, off-line vegetation modelling appears to offer good opportunities to distinguish climate model results because glacial vegetation proves to be very sensitive to even small differences in LGM climate. For other cases such as the LGM North Atlantic or the LGM equatorial Pacific, the large uncertainty on the SST reconstructions, prevents model discrimination. We have examined the use of other proxy-data for model evaluation, which has become possible with the inclusion of the biogeochemistry morel PISCES in the IPSL_CM5A model. We show a broad agreement of the LGM-PI export production changes with reconstructions. These changes are related to the mixed layer depth in most regions and to sea-ice variations in the high latitudes. We have also modelled foraminifer abundances with the FORAMCLIM model and shown that the changes in foraminifer abundance in the equatorial Pacific are mainly forced by changes in SSTs, hence confirming the SST-foraminifer abundance relationship. Yet, this is not the case in all regions in the North Atlantic, where food availability can have a strong impact of foraminifer abundances. Further work will be needed to exhaustively examine the role of factors other than climate in piloting changes in palaeo-indicators.

Climate Intervention as an Optimization Problem

NASA Astrophysics Data System (ADS)

Caldeira, Ken; Ban-Weiss, George A.

2010-05-01

Typically, climate models simulations of intentional intervention in the climate system have taken the approach of imposing a change (eg, in solar flux, aerosol concentrations, aerosol emissions) and then predicting how that imposed change might affect Earth's climate or chemistry. Computations proceed from cause to effect. However, humans often proceed from "What do I want?" to "How do I get it?" One approach to thinking about intentional intervention in the climate system ("geoengineering") is to ask "What kind of climate do we want?" and then ask "What pattern of radiative forcing would come closest to achieving that desired climate state?" This involves defining climate goals and a cost function that measures how closely those goals are attained. (An important next step is to ask "How would we go about producing these desired patterns of radiative forcing?" However, this question is beyond the scope of our present study.) We performed a variety of climate simulations in NCAR's CAM3.1 atmospheric general circulation model with a slab ocean model and thermodynamic sea ice model. We then evaluated, for a specific set of climate forcing basis functions (ie, aerosol concentration distributions), the extent to which the climate response to a linear combination of those basis functions was similar to a linear combination of the climate response to each basis function taken individually. We then developed several cost functions (eg, relative to the 1xCO2 climate, minimize rms difference in zonal and annual mean land temperature, minimize rms difference in zonal and annual mean runoff, minimize rms difference in a combination of these temperature and runoff indices) and then predicted optimal combinations of our basis functions that would minimize these cost functions. Lastly, we produced forward simulations of the predicted optimal radiative forcing patterns and compared these with our expected results. Obviously, our climate model is much simpler than reality and predictions from individual models do not provide a sound basis for action; nevertheless, our model results indicate that the general approach outlined here can lead to patterns of radiative forcing that make the zonal annual mean climate of a high CO2 world markedly more similar to that of a low CO2 world simultaneously for both temperature and hydrological indices, where degree of similarity is measured using our explicit cost functions. We restricted ourselves to zonally uniform aerosol concentrations distributions that can be defined in terms of a positive-definite quadratic equation on the sine of latitude. Under this constraint, applying an aerosol distribution in a 2xCO2 climate that minimized a combination of rms difference in zonal and annual mean land temperature and runoff relative to the 1xCO2 climate, the rms difference in zonal and annual mean temperatures was reduced by ~90% and the rms difference in zonal and annual mean runoff was reduced by ~80%. This indicates that there may be potential for stratospheric aerosols to diminish simultaneously both temperature and hydrological cycle changes caused by excess CO2 in the atmosphere. Clearly, our model does not include many factors (eg, socio-political consequences, chemical consequences, ocean circulation changes, aerosol transport and microphysics) so we do not argue strongly for our specific climate model results, however, we do argue strongly in favor of our methodological approach. The proposed approach is general, in the sense that cost functions can be developed that represent different valuations. While the choice of appropriate cost functions is inherently a value judgment, evaluating those functions for a specific climate simulation is a quantitative exercise. Thus, the use of explicit cost functions in evaluating model results for climate intervention scenarios is a clear way of separating value judgments from purely scientific and technical issues.

The global gridded crop model intercomparison: Data and modeling protocols for Phase 1 (v1.0)

DOE PAGES

Elliott, J.; Müller, C.; Deryng, D.; ...

2015-02-11

We present protocols and input data for Phase 1 of the Global Gridded Crop Model Intercomparison, a project of the Agricultural Model Intercomparison and Improvement Project (AgMIP). The project consist of global simulations of yields, phenologies, and many land-surface fluxes using 12–15 modeling groups for many crops, climate forcing data sets, and scenarios over the historical period from 1948 to 2012. The primary outcomes of the project include (1) a detailed comparison of the major differences and similarities among global models commonly used for large-scale climate impact assessment, (2) an evaluation of model and ensemble hindcasting skill, (3) quantification ofmore » key uncertainties from climate input data, model choice, and other sources, and (4) a multi-model analysis of the agricultural impacts of large-scale climate extremes from the historical record.« less

Climatic Forecasting of Net Infiltration at Yucca Mountain, Using Analogue Meteorological Data

NASA Astrophysics Data System (ADS)

Faybishenko, B.

2005-12-01

Net infiltration is a key hydrologic parameter that, throughout the unsaturated zone, controls the rate of deep percolation, the groundwater recharge, radionuclide transport, and seepage into underground tunnels. Because net infiltration is largely affected by climatic conditions, future changes in climatic conditions will potentially alter net infiltration. The objectives of this presentation are to: (1) Present a conceptual model and a semi-empirical approach for regional climatic forecasting of net infiltration, based on precipitation and temperature data from analogue meteorological stations; and (2) Demonstrate the results of forecasting net infiltration for future climates - interglacial, monsoon and glacial - over the Yucca Mountain region for a period of 500,000 years. Calculations of net infiltration were performed using a modified Budyko's water-balance model, and potential evapotranspiration was evaluated from the temperature-based Thornthwaite formula. (Both Budyko's and Thornthwaite's formulae have been used broadly in hydrological studies.) The results of these calculations were used for ranking net infiltration, along with aridity and precipitation-effectiveness (P-E) indices, for future climatic scenarios. Using this approach, we determined a general trend of increasing net infiltration from the present-day (interglacial) climate to the monsoon, intermediate (glacial transition) climate, a trend that continued into the glacial climate time frame. The ranking of aridity and P-E indices is practically the same as that for net infiltration. Validation of the computed net infiltration rates yielded a good match with other field and modeling study results related to groundwater recharge and net infiltration evaluation.

Using statistical model to simulate the impact of climate change on maize yield with climate and crop uncertainties

NASA Astrophysics Data System (ADS)

Zhang, Yi; Zhao, Yanxia; Wang, Chunyi; Chen, Sining

2017-11-01

Assessment of the impact of climate change on crop productions with considering uncertainties is essential for properly identifying and decision-making agricultural practices that are sustainable. In this study, we employed 24 climate projections consisting of the combinations of eight GCMs and three emission scenarios representing the climate projections uncertainty, and two crop statistical models with 100 sets of parameters in each model representing parameter uncertainty within the crop models. The goal of this study was to evaluate the impact of climate change on maize ( Zea mays L.) yield at three locations (Benxi, Changling, and Hailun) across Northeast China (NEC) in periods 2010-2039 and 2040-2069, taking 1976-2005 as the baseline period. The multi-models ensembles method is an effective way to deal with the uncertainties. The results of ensemble simulations showed that maize yield reductions were less than 5 % in both future periods relative to the baseline. To further understand the contributions of individual sources of uncertainty, such as climate projections and crop model parameters, in ensemble yield simulations, variance decomposition was performed. The results indicated that the uncertainty from climate projections was much larger than that contributed by crop model parameters. Increased ensemble yield variance revealed the increasing uncertainty in the yield simulation in the future periods.

Can air temperature be used to project influences of climate change on stream temperature?

Treesearch

Ivan Arismendi; Mohammad Safeeq; Jason B Dunham; Sherri L Johnson

2014-01-01

Worldwide, lack of data on stream temperature has motivated the use of regression-based statistical models to predict stream temperatures based on more widely available data on air temperatures. Such models have been widely applied to project responses of stream temperatures under climate change, but the performance of these models has not been fully evaluated. To...

Modeling transport of nutrients & sediment loads into Lake Tahoe under climate change

USGS Publications Warehouse

Riverson, John; Coats, Robert; Costa-Cabral, Mariza; Dettinger, Mike; Reuter, John; Sahoo, Goloka; Schladow, Geoffrey

2013-01-01

The outputs from two General Circulation Models (GCMs) with two emissions scenarios were downscaled and bias-corrected to develop regional climate change projections for the Tahoe Basin. For one model—the Geophysical Fluid Dynamics Laboratory or GFDL model—the daily model results were used to drive a distributed hydrologic model. The watershed model used an energy balance approach for computing evapotranspiration and snowpack dynamics so that the processes remain a function of the climate change projections. For this study, all other aspects of the model (i.e. land use distribution, routing configuration, and parameterization) were held constant to isolate impacts of climate change projections. The results indicate that (1) precipitation falling as rain rather than snow will increase, starting at the current mean snowline, and moving towards higher elevations over time; (2) annual accumulated snowpack will be reduced; (3) snowpack accumulation will start later; and (4) snowmelt will start earlier in the year. Certain changes were masked (or counter-balanced) when summarized as basin-wide averages; however, spatial evaluation added notable resolution. While rainfall runoff increased at higher elevations, a drop in total precipitation volume decreased runoff and fine sediment load from the lower elevation meadow areas and also decreased baseflow and nitrogen loads basin-wide. This finding also highlights the important role that the meadow areas could play as high-flow buffers under climatic change. Because the watershed model accounts for elevation change and variable meteorological patterns, it provided a robust platform for evaluating the impacts of projected climate change on hydrology and water quality.

Toward a consistent modeling framework to assess multi-sectoral climate impacts.

PubMed

Monier, Erwan; Paltsev, Sergey; Sokolov, Andrei; Chen, Y-H Henry; Gao, Xiang; Ejaz, Qudsia; Couzo, Evan; Schlosser, C Adam; Dutkiewicz, Stephanie; Fant, Charles; Scott, Jeffery; Kicklighter, David; Morris, Jennifer; Jacoby, Henry; Prinn, Ronald; Haigh, Martin

2018-02-13

Efforts to estimate the physical and economic impacts of future climate change face substantial challenges. To enrich the currently popular approaches to impact analysis-which involve evaluation of a damage function or multi-model comparisons based on a limited number of standardized scenarios-we propose integrating a geospatially resolved physical representation of impacts into a coupled human-Earth system modeling framework. Large internationally coordinated exercises cannot easily respond to new policy targets and the implementation of standard scenarios across models, institutions and research communities can yield inconsistent estimates. Here, we argue for a shift toward the use of a self-consistent integrated modeling framework to assess climate impacts, and discuss ways the integrated assessment modeling community can move in this direction. We then demonstrate the capabilities of such a modeling framework by conducting a multi-sectoral assessment of climate impacts under a range of consistent and integrated economic and climate scenarios that are responsive to new policies and business expectations.

Methodology to assess and map the potential development of forest ecosystems exposed to climate change and atmospheric nitrogen deposition: A pilot study in Germany.

PubMed

Schröder, Winfried; Nickel, Stefan; Jenssen, Martin; Riediger, Jan

2015-07-15

A methodology for mapping ecosystems and their potential development under climate change and atmospheric nitrogen deposition was developed using examples from Germany. The methodology integrated data on vegetation, soil, climate change and atmospheric nitrogen deposition. These data were used to classify ecosystem types regarding six ecological functions and interrelated structures. Respective data covering 1961-1990 were used for reference. The assessment of functional and structural integrity relies on comparing a current or future state with an ecosystem type-specific reference. While current functions and structures of ecosystems were quantified by measurements, potential future developments were projected by geochemical soil modelling and data from a regional climate change model. The ecosystem types referenced the potential natural vegetation and were mapped using data on current tree species coverage and land use. In this manner, current ecosystem types were derived, which were related to data on elevation, soil texture, and climate for the years 1961-1990. These relations were quantified by Classification and Regression Trees, which were used to map the spatial patterns of ecosystem type clusters for 1961-1990. The climate data for these years were subsequently replaced by the results of a regional climate model for 1991-2010, 2011-2040, and 2041-2070. For each of these periods, one map of ecosystem type clusters was produced and evaluated with regard to the development of areal coverage of ecosystem type clusters over time. This evaluation of the structural aspects of ecological integrity at the national level was added by projecting potential future values of indicators for ecological functions at the site level by using the Very Simple Dynamic soil modelling technique based on climate data and two scenarios of nitrogen deposition as input. The results were compared to the reference and enabled an evaluation of site-specific ecosystem changes over time which proved to be both, positive and negative. Copyright © 2015 Elsevier B.V. All rights reserved.

Impact of lakes and wetlands on present and future boreal climate

NASA Astrophysics Data System (ADS)

Poutou, E.; Krinner, G.; Genthon, C.

2002-12-01

Impact of lakes and wetlands on present and future boreal climate The role of lakes and wetlands in present-day high latitude climate is quantified using a general circulation model of the atmosphere. The atmospheric model includes a lake module which is presented and validated. Seasonal and spatial wetland distribution is calculated as a function of the hydrological budget of the wetlands themselves and of continental soil whose runoff feeds them. Wetland extent is simulated and discussed both in simulations forced by observed climate and in general circulation model simulations. In off-line simulations, forced by ECMWF reanalyses, the lake model simulates correctly observed lake ice durations, while the wetland extent is somewhat underestimated in the boreal regions. Coupled to the general circulation model, the lake model yields satisfying ice durations, although the climate model biases have impacts on the modeled lake ice conditions. Boreal wetland extents are overestimated in the general circulation model as simulated precipitation is too high. The impact of inundated surfaces on the simulated climate is strongest in summer when these surfaces are ice-free. Wetlands seem to play a more important role than lakes in cooling the boreal regions in summer and in humidifying the atmosphere. The role of lakes and wetlands in future climate change is evaluated by analyzing simulations of present and future climate with and without prescribed inland water bodies.

An Integrated Systems Approach to Designing Climate Change Adaptation Policy in Water Resources

NASA Astrophysics Data System (ADS)

Ryu, D.; Malano, H. M.; Davidson, B.; George, B.

2014-12-01

Climate change projections are characterised by large uncertainties with rainfall variability being the key challenge in designing adaptation policies. Climate change adaptation in water resources shows all the typical characteristics of 'wicked' problems typified by cognitive uncertainty as new scientific knowledge becomes available, problem instability, knowledge imperfection and strategic uncertainty due to institutional changes that inevitably occur over time. Planning that is characterised by uncertainties and instability requires an approach that can accommodate flexibility and adaptive capacity for decision-making. An ability to take corrective measures in the event that scenarios and responses envisaged initially derive into forms at some future stage. We present an integrated-multidisciplinary and comprehensive framework designed to interface and inform science and decision making in the formulation of water resource management strategies to deal with climate change in the Musi Catchment of Andhra Pradesh, India. At the core of this framework is a dialogue between stakeholders, decision makers and scientists to define a set of plausible responses to an ensemble of climate change scenarios derived from global climate modelling. The modelling framework used to evaluate the resulting combination of climate scenarios and adaptation responses includes the surface and groundwater assessment models (SWAT & MODFLOW) and the water allocation modelling (REALM) to determine the water security of each adaptation strategy. Three climate scenarios extracted from downscaled climate models were selected for evaluation together with four agreed responses—changing cropping patterns, increasing watershed development, changing the volume of groundwater extraction and improving irrigation efficiency. Water security in this context is represented by the combination of level of water availability and its associated security of supply for three economic activities (agriculture, urban, industrial) on a spatially distributed basis. The resulting combinations of climate scenarios and adaptation responses were subjected to a combined hydro-economic assessment based on the degree of water security together with its cost-effectiveness against the Business-as-usual scenario.

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.

Using critical evaluation to reappraise plausibility judgments: A critical cognitive component of conceptual change

NASA Astrophysics Data System (ADS)

Lombardi, D.

2011-12-01

Plausibility judgments-although well represented in conceptual change theories (see, for example, Chi, 2005; diSessa, 1993; Dole & Sinatra, 1998; Posner et al., 1982)-have received little empirical attention until our recent work investigating teachers' and students' understanding of and perceptions about human-induced climate change (Lombardi & Sinatra, 2010, 2011). In our first study with undergraduate students, we found that greater plausibility perceptions of human-induced climate accounted for significantly greater understanding of weather and climate distinctions after instruction, even after accounting for students' prior knowledge (Lombardi & Sinatra, 2010). In a follow-up study with inservice science and preservice elementary teachers, we showed that anger about the topic of climate change and teaching about climate change was significantly related to implausible perceptions about human-induced climate change (Lombardi & Sinatra, 2011). Results from our recent studies helped to inform our development of a model of the role of plausibility judgments in conceptual change situations. The model applies to situations involving cognitive dissonance, where background knowledge conflicts with an incoming message. In such situations, we define plausibility as a judgment on the relative potential truthfulness of incoming information compared to one's existing mental representations (Rescher, 1976). Students may not consciously think when making plausibility judgments, expending only minimal mental effort in what is referred to as an automatic cognitive process (Stanovich, 2009). However, well-designed instruction could facilitate students' reappraisal of plausibility judgments in more effortful and conscious cognitive processing. Critical evaluation specifically may be one effective method to promote plausibility reappraisal in a classroom setting (Lombardi & Sinatra, in progress). In science education, critical evaluation involves the analysis of how evidentiary data support a hypothesis and its alternatives. The presentation will focus on how instruction promoting critical evaluation can encourage individuals to reappraise their plausibility judgments and initiate knowledge reconstruction. In a recent pilot study, teachers experienced an instructional scaffold promoting critical evaluation of two competing climate change theories (i.e., human-induced and increasing solar irradiance) and significantly changed both their plausibility judgments and perceptions of correctness toward the scientifically-accepted model of human-induced climate change. A comparison group of teachers who did not experience the critical evaluation activity showed no significant change. The implications of these studies for future research and instruction will be discussed in the presentation, including effective ways to increase students' and teachers' ability to be critically evaluative and reappraise their plausibility judgments. With controversial science issues, such as climate change, such abilities may be necessary to facilitate conceptual change.

Development of a ground hydrology model suitable for global climate modeling using soil morphology and vegetation cover, and an evaluation of remotely sensed information

NASA Technical Reports Server (NTRS)

Zobler, L.; Lewis, R.

1988-01-01

The long-term purpose was to contribute to scientific understanding of the role of the planet's land surfaces in modulating the flows of energy and matter which influence the climate, and to quantify and monitor human-induced changes to the land environment that may affect global climate. Highlights of the effort include the following: production of geo-coded, digitized World Soil Data file for use with the Goddard Institute for Space Studies (GISS) climate model; contribution to the development of a numerical physically-based model of ground hydrology; and assessment of the utility of remote sensing for providing data on hydrologically significant land surface variables.

Climate and dengue transmission: evidence and implications.

PubMed

Morin, Cory W; Comrie, Andrew C; Ernst, Kacey

2013-01-01

Climate influences dengue ecology by affecting vector dynamics, agent development, and mosquito/human interactions. Although these relationships are known, the impact climate change will have on transmission is unclear. Climate-driven statistical and process-based models are being used to refine our knowledge of these relationships and predict the effects of projected climate change on dengue fever occurrence, but results have been inconsistent. We sought to identify major climatic influences on dengue virus ecology and to evaluate the ability of climate-based dengue models to describe associations between climate and dengue, simulate outbreaks, and project the impacts of climate change. We reviewed the evidence for direct and indirect relationships between climate and dengue generated from laboratory studies, field studies, and statistical analyses of associations between vectors, dengue fever incidence, and climate conditions. We assessed the potential contribution of climate-driven, process-based dengue models and provide suggestions to improve their performance. Relationships between climate variables and factors that influence dengue transmission are complex. A climate variable may increase dengue transmission potential through one aspect of the system while simultaneously decreasing transmission potential through another. This complexity may at least partly explain inconsistencies in statistical associations between dengue and climate. Process-based models can account for the complex dynamics but often omit important aspects of dengue ecology, notably virus development and host-species interactions. Synthesizing and applying current knowledge of climatic effects on all aspects of dengue virus ecology will help direct future research and enable better projections of climate change effects on dengue incidence.

Climate Model Diagnostic Analyzer Web Service System

NASA Astrophysics Data System (ADS)

Lee, S.; Pan, L.; Zhai, C.; Tang, B.; Jiang, J. H.

2013-12-01

The latest Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report stressed the need for the comprehensive and innovative evaluation of climate models with newly available global observations. The traditional approach to climate model evaluation, which compares a single parameter at a time, identifies symptomatic model biases and errors but fails to diagnose the model problems. The model diagnosis process requires physics-based multi-variable comparisons that typically involve large-volume and heterogeneous datasets, making them both computationally- and data-intensive. To address these challenges, we are developing a parallel, distributed web-service system that enables the physics-based multi-variable model performance evaluations and diagnoses through the comprehensive and synergistic use of multiple observational data, reanalysis data, and model outputs. We have developed a methodology to transform an existing science application code into a web service using a Python wrapper interface and Python web service frameworks (i.e., Flask, Gunicorn, and Tornado). The web-service system, called Climate Model Diagnostic Analyzer (CMDA), currently supports (1) all the datasets from Obs4MIPs and a few ocean datasets from NOAA and Argo, which can serve as observation-based reference data for model evaluation and (2) many of CMIP5 model outputs covering a broad range of atmosphere, ocean, and land variables from the CMIP5 specific historical runs and AMIP runs. Analysis capabilities currently supported by CMDA are (1) the calculation of annual and seasonal means of physical variables, (2) the calculation of time evolution of the means in any specified geographical region, (3) the calculation of correlation between two variables, and (4) the calculation of difference between two variables. A web user interface is chosen for CMDA because it not only lowers the learning curve and removes the adoption barrier of the tool but also enables instantaneous use, avoiding the hassle of local software installation and environment incompatibility. CMDA is planned to be used as an educational tool for the summer school organized by JPL's Center for Climate Science in 2014. The requirements of the educational tool are defined with the interaction with the school organizers, and CMDA is customized to meet the requirements accordingly. The tool needs to be production quality for 30+ simultaneous users. The summer school will thus serve as a valuable testbed for the tool development, preparing CMDA to serve the Earth-science modeling and model-analysis community at the end of the project. This work was funded by the NASA Earth Science Program called Computational Modeling Algorithms and Cyberinfrastructure (CMAC).

Assess Climate Change's Impact on Coastal Rivers using a Coupled Climate-Hydrology Model

NASA Astrophysics Data System (ADS)

Xue, Z. G.; Gochis, D.; Yu, W.; Zang, Z.; Sampson, K. M.; Keim, B. D.

2016-12-01

In this study we present a coupled climate-hydrological model reproducing the water cycle of three coastal river basins along the northern Gulf of Mexico for the past three decades (1985-2014). Model simulated climate condition, surface physics, and streamflow were well validated against in situ data and satellite-derived products, giving us the confidence that the newly developed WRF-Hydro model can be a robust tool for evaluating climate change's impact on hydrological regime. Trend analysis of model simulated monthly and annual time series indicates that local climate is getting hotter and dryer, specifically during the growing season. Wavelet analysis reveals that local evapotranspiration is strongly correlated with temperature, while soil moisture, water surplus, and streamflow are coupled with precipitation. In addition, local climate is closely correlated with large-scale climate dynamics such as AMO and ENSO. A possible change-point is detected around year 2004, after which, the monthly precipitation decreased by 14.2%, evapotranspiration increased by 2.9%, and water surplus decreased by 36.5%. The implication of the difference between the water surplus (runoff) calculated using the classic Thornthwaite method and river discharge estimated using streamflow records to the coastal environment is also discussed.

Converging Climate Sensitivities of European Forests Between Observed Radial Tree Growth and Vegetation Models

NASA Technical Reports Server (NTRS)

Zhang, Zhen; Babst, Flurin; Bellassen, Valentin; Frank, David; Launois, Thomas; Tan, Kun; Ciais, Philippe; Poulter, Benjamin

2017-01-01

The impacts of climate variability and trends on European forests are unevenly distributed across different bioclimatic zones and species. Extreme climate events are also becoming more frequent and it is unknown how they will affect feed backs of CO2 between forest ecosystems and the atmosphere. An improved understanding of species differences at the regional scale of the response of forest productivity to climate variation and extremes is thus important for forecasting forest dynamics. In this study, we evaluate the climate sensitivity of above ground net primary production (NPP) simulated by two dynamic global vegetation models (DGVM; ORCHIDEE and LPJ-wsl) against tree ring width (TRW) observations from about1000 sites distributed across Europe. In both the model simulations and the TRW observations, forests in northern Europe and the Alps respond positively to warmer spring and summer temperature, and their overall temperature sensitivity is larger than that of the soil-moisture-limited forests in central Europe and Mediterranean regions. Compared with TRW observations, simulated NPP from ORCHIDEE and LPJ-wsl appear to be overly sensitive to climatic factors. Our results indicate that the models lack biological processes that control time lags, such as carbohydrate storage and remobilization, that delay the effects of radial growth dynamics to climate. Our study highlights the need for re-evaluating the physiological controls on the climate sensitivity of NPP simulated by DGVMs. In particular, DGVMs could be further enhanced by a more detailed representation of carbon reserves and allocation that control year-to year variation in plant growth.

Using Remotely Sensed Data and Hydrologic Models to Evaluate the Effects of Climate Change on Shallow Aquatic Ecosystems in the Mobile Bay, AL Estuary

NASA Astrophysics Data System (ADS)

Estes, M. G.; Al-Hamdan, M. Z.; Thom, R.; Judd, C.; Ellis, J.; Woodruff, D.; Quattrochi, D.; Rose, K.; Swann, R.

2012-12-01

Coastal systems in the northern Gulf of Mexico, including the Mobile Bay, AL estuary, are subject to increasing pressure from a variety of activities including climate change. Climate changes have a direct effect on the discharge of rivers that drain into Mobile Bay and adjacent coastal water bodies. The outflows change water quality (temperature, salinity, and sediment concentrations) in the shallow aquatic areas and affect ecosystem functioning. Mobile Bay is a vital ecosystem that provides habitat for many species of fauna and flora. Historically, submerged aquatic vegetation (SAV) and seagrasses were found in this area of the northern Gulf of Mexico; however the extent of vegetation has significantly decreased over the last 60 years. The objectives of this research are to determine: how climate changes affect runoff and water quality in the estuary and how these changes will affect habitat suitability for SAV and seagrasses. Our approach is to use watershed and hydrodynamic modeling to evaluate the impact of climate change on shallow water aquatic ecosystems in Mobile Bay and adjacent coastal areas. Remotely sensed Landsat data were used for current land cover land use (LCLU) model input and the data provided by Intergovernmental Panel on Climate Change (IPCC) of the future changes in temperature, precipitation, and sea level rise were used to create the climate scenarios for the 2025 and 2050 model simulations. Project results are being shared with Gulf coast stakeholders through the Gulf of Mexico Data Atlas to benefit coastal policy and climate change adaptation strategies.

Using Remotely Sensed Data and Hydrologic Models to Evaluate the Effects of Climate Change on Shallow Aquatic Ecosystems in the Mobile Bay, AL Estuary

NASA Technical Reports Server (NTRS)

Estes, M. G.; Al-Hamdan, M. Z.; Thom, R.; Judd, C.; Woodruff, D.; Ellis, J. T.; Quattrochi, D.; Swann, R.

2012-01-01

Coastal systems in the northern Gulf of Mexico, including the Mobile Bay, AL estuary, are subject to increasing pressure from a variety of activities including climate change. Climate changes have a direct effect on the discharge of rivers that drain into Mobile Bay and adjacent coastal water bodies. The outflows change water quality (temperature, salinity, and sediment concentrations) in the shallow aquatic areas and affect ecosystem functioning. Mobile Bay is a vital ecosystem that provides habitat for many species of fauna and flora. Historically, submerged aquatic vegetation (SAV) and seagrasses were found in this area of the northern Gulf of Mexico; however the extent of vegetation has significantly decreased over the last 60 years. The objectives of this research are to determine: how climate changes affect runoff and water quality in the estuary and how these changes will affect habitat suitability for SAV and seagrasses. Our approach is to use watershed and hydrodynamic modeling to evaluate the impact of climate change on shallow water aquatic ecosystems in Mobile Bay and adjacent coastal areas. Remotely sensed Landsat data were used for current land cover land use (LCLU) model input and the data provided by Intergovernmental Panel on Climate Change (IPCC) of the future changes in temperature, precipitation, and sea level rise were used to create the climate scenarios for the 2025 and 2050 model simulations. Project results are being shared with Gulf coast stakeholders through the Gulf of Mexico Data Atlas to benefit coastal policy and climate change adaptation strategies.

Regional modeling of large wildfires under current and potential future climates in Colorado and Wyoming, USA

USGS Publications Warehouse

West, Amanda; Kumar, Sunil; Jarnevich, Catherine S.

2016-01-01

Regional analysis of large wildfire potential given climate change scenarios is crucial to understanding areas most at risk in the future, yet wildfire models are not often developed and tested at this spatial scale. We fit three historical climate suitability models for large wildfires (i.e. ≥ 400 ha) in Colorado andWyoming using topography and decadal climate averages corresponding to wildfire occurrence at the same temporal scale. The historical models classified points of known large wildfire occurrence with high accuracies. Using a novel approach in wildfire modeling, we applied the historical models to independent climate and wildfire datasets, and the resulting sensitivities were 0.75, 0.81, and 0.83 for Maxent, Generalized Linear, and Multivariate Adaptive Regression Splines, respectively. We projected the historic models into future climate space using data from 15 global circulation models and two representative concentration pathway scenarios. Maps from these geospatial analyses can be used to evaluate the changing spatial distribution of climate suitability of large wildfires in these states. April relative humidity was the most important covariate in all models, providing insight to the climate space of large wildfires in this region. These methods incorporate monthly and seasonal climate averages at a spatial resolution relevant to land management (i.e. 1 km2) and provide a tool that can be modified for other regions of North America, or adapted for other parts of the world.

AerChemMIP: Quantifying the effects of chemistry and aerosols in CMIP6

DOE PAGES

Collins, William J.; Lamarque, Jean -François; Schulz, Michael; ...

2017-02-09

The Aerosol Chemistry Model Intercomparison Project (AerChemMIP) is endorsed by the Coupled-Model Intercomparison Project 6 (CMIP6) and is designed to quantify the climate and air quality impacts of aerosols and chemically reactive gases. These are specifically near-term climate forcers (NTCFs: methane, tropospheric ozone and aerosols, and their precursors), nitrous oxide and ozone-depleting halocarbons. The aim of AerChemMIP is to answer four scientific questions. 1. How have anthropogenic emissions contributed to global radiative forcing and affected regional climate over the historical period? 2. How might future policies (on climate, air quality and land use) affect the abundances of NTCFs and theirmore » climate impacts? 3.How do uncertainties in historical NTCF emissions affect radiative forcing estimates? 4. How important are climate feedbacks to natural NTCF emissions, atmospheric composition, and radiative effects? These questions will be addressed through targeted simulations with CMIP6 climate models that include an interactive representation of tropospheric aerosols and atmospheric chemistry. These simulations build on the CMIP6 Diagnostic, Evaluation and Characterization of Klima (DECK) experiments, the CMIP6 historical simulations, and future projections performed elsewhere in CMIP6, allowing the contributions from aerosols and/or chemistry to be quantified. As a result, specific diagnostics are requested as part of the CMIP6 data request to highlight the chemical composition of the atmosphere, to evaluate the performance of the models, and to understand differences in behaviour between them.« less

AerChemMIP: Quantifying the effects of chemistry and aerosols in CMIP6

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

Collins, William J.; Lamarque, Jean -François; Schulz, Michael

The Aerosol Chemistry Model Intercomparison Project (AerChemMIP) is endorsed by the Coupled-Model Intercomparison Project 6 (CMIP6) and is designed to quantify the climate and air quality impacts of aerosols and chemically reactive gases. These are specifically near-term climate forcers (NTCFs: methane, tropospheric ozone and aerosols, and their precursors), nitrous oxide and ozone-depleting halocarbons. The aim of AerChemMIP is to answer four scientific questions. 1. How have anthropogenic emissions contributed to global radiative forcing and affected regional climate over the historical period? 2. How might future policies (on climate, air quality and land use) affect the abundances of NTCFs and theirmore » climate impacts? 3.How do uncertainties in historical NTCF emissions affect radiative forcing estimates? 4. How important are climate feedbacks to natural NTCF emissions, atmospheric composition, and radiative effects? These questions will be addressed through targeted simulations with CMIP6 climate models that include an interactive representation of tropospheric aerosols and atmospheric chemistry. These simulations build on the CMIP6 Diagnostic, Evaluation and Characterization of Klima (DECK) experiments, the CMIP6 historical simulations, and future projections performed elsewhere in CMIP6, allowing the contributions from aerosols and/or chemistry to be quantified. As a result, specific diagnostics are requested as part of the CMIP6 data request to highlight the chemical composition of the atmosphere, to evaluate the performance of the models, and to understand differences in behaviour between them.« less

Evaluation and comparison of different RCMs simulations of the Mediterranean climate: a view on the impact of model resolution and Mediterranean sea coupling.

NASA Astrophysics Data System (ADS)

Panthou, Gérémy; Vrac, Mathieu; Drobinski, Philippe; Bastin, Sophie; Somot, Samuel; Li, Laurent

2015-04-01

As regularly stated by numerous authors, the Mediterranean climate is considered as one major climate 'hot spot'. At least, three reasons may explain this statement. First, this region is known for being regularly affected by extreme hydro-meteorological events (heavy precipitation and flash-floods during the autumn season; droughts and heat waves during spring and summer). Second, the vulnerability of populations in regard of these extreme events is expected to increase during the XXIst century (at least due to the projected population growth in this region). At last, Global Circulation Models project that this regional climate will be highly sensitive to climate change. Moreover, global warming is expected to intensify the hydrological cycle and thus to increase the frequency of extreme hydro-meteorological events. In order to propose adaptation strategies, the robust estimation of the future evolution of the Mediterranean climate and the associated extreme hydro-meteorological events (in terms of intensity/frequency) is of great relevance. However, these projections are characterized by large uncertainties. Many components of the simulation chain can explain these large uncertainties : (i) uncertainties concerning the emission scenario; (ii) climate model simulations suffer of parametrization errors and uncertainties concerning the initial state of the climate; and (iii) the additional uncertainties given by the (dynamical or statistical) downscaling techniques and the impact model. Narrowing (as fine as possible) these uncertainties is a major challenge of the actual climate research. One way for that is to reduce the uncertainties associated with each component. In this study, we are interested in evaluating the potential improvement of : (i) coupled RCM simulations (with the Mediterranean Sea) in comparison with atmosphere only (stand-alone) RCM simulations and (ii) RCM simulations at a finer resolution in comparison with larger resolution. For that, three different RCMs (WRF, ALADIN, LMDZ4) were run, forced by ERA-Interim reanalyses, within the MED-CORDEX experiment. For each RCM, different versions (coupled/stand-alone, high/low resolution) were realized. A large set of scores was developed and applied in order to evaluate the performances of these different RCMs simulations. These scores were applied for three variables (daily precipitation amount, mean daily air temperature and the dry spell lengths). A particular attention was given to the RCM capability to reproduce the seasonal and spatial pattern of extreme statistics. Results show that the differences between coupled and stand-alone RCMs are localized very near the Mediterranean sea and that the model resolution has a slight impact on the scores obtained. Globally, the main differences between the RCM simulations come from the RCM used. Keywords: Mediterranean climate, extreme hydro-meteorological events, RCM simulations, evaluation of climate simulations

Estimation of landslides activities evolution due to land-use and climate change in a Pyrenean valley

NASA Astrophysics Data System (ADS)

Vandromme, Rosalie; Bernardie, Séverine; Houet, Thomas; Grémont, Marine; Grandjean, Gilles; Thiery, Yannick

2016-04-01

Global changes would have impacts worldwide, but their effects should be even more exacerbated in areas particularly vulnerable. Mountainous areas are among these vulnerable territories. Ecological systems are often at a fragile equilibrium, socio-economical activities are often climate-dependent and climate-driven natural hazards can be a major threat for human activities. In order to estimate the capacity of such mountainous valleys to face global changes (climate, but also climate- and human- induced land-use changes), it is necessary to be able to evaluate the evolution of the different threats. The present work shows a method to evaluate the influences of the evolution of both vegetation cover and climate on landslides activities over a whole valley until 2100, to propose adequate solutions for current and future forestry management. Firstly, the assessment of future land use is addressed through the construction of four prospective socio-economic scenarios up to 2050 and 2100, which are then spatially validated and modeled with LUCC models. Secondly, the climate change inputs of the project correspond to 2 scenarios of emission of greenhouse gases. The used simulations available on the portal DRIAS (http://www.drias-climat.fr) were performed with the GHG emissions scenarios (RCP: Representative concentration pathways, according to the standards defined by the GIEC) RCP 4.5 and RCP 8.5. The impact of land use and climate change is then addressed through the use of these scenarios into hazards computations. For that we use a large-scale slope stability assessment tool ALICE which combines a mechanical stability model (using finite slope analysis), a vegetation module which interfere with the first model, to take into account the effects of vegetation on the mechanical soil properties (cohesion and over-load), and an hydrogeological model. All these elements are interfaced within a GIS-based solution. In that way, future changes in temperature, precipitation and vegetation cover are analyzed, permitting to address the direct and indirect impacts of global change on mountain societies. The whole chain is applied to a 100-km² Pyrenean Valley, for the ANR Project SAMCO (Society Adaptation for coping with Mountain risks in a global change COntext), as a first step in the chain for risk assessment for different climate and economical development scenarios, to evaluate the resilience of mountainous areas.

Use of circulation types classifications to evaluate AR4 climate models over the Euro-Atlantic region

NASA Astrophysics Data System (ADS)

Pastor, M. A.; Casado, M. J.

2012-10-01

This paper presents an evaluation of the multi-model simulations for the 4th Assessment Report of the Intergovernmental Panel on Climate Change (IPCC) in terms of their ability to simulate the ERA40 circulation types over the Euro-Atlantic region in winter season. Two classification schemes, k-means and SANDRA, have been considered to test the sensitivity of the evaluation results to the classification procedure. The assessment allows establishing different rankings attending spatial and temporal features of the circulation types. Regarding temporal characteristics, in general, all AR4 models tend to underestimate the frequency of occurrence. The best model simulating spatial characteristics is the UKMO-HadGEM1 whereas CCSM3, UKMO-HadGEM1 and CGCM3.1(T63) are the best simulating the temporal features, for both classification schemes. This result agrees with the AR4 models ranking obtained when having analysed the ability of the same AR4 models to simulate Euro-Atlantic variability modes. This study has proved the utility of applying such a synoptic climatology approach as a diagnostic tool for models' assessment. The ability of the models to properly reproduce the position of ridges and troughs and the frequency of synoptic patterns, will therefore improve our confidence in the response of models to future climate changes.

Evaluation of GCMs in the context of regional predictive climate impact studies.

NASA Astrophysics Data System (ADS)

Kokorev, Vasily; Anisimov, Oleg

2016-04-01

Significant improvements in the structure, complexity, and general performance of earth system models (ESMs) have been made in the recent decade. Despite these efforts, the range of uncertainty in predicting regional climate impacts remains large. The problem is two-fold. Firstly, there is an intrinsic conflict between the local and regional scales of climate impacts and adaptation strategies, on one hand, and larger scales, at which ESMs demonstrate better performance, on the other. Secondly, there is a growing understanding that majority of the impacts involve thresholds, and are thus driven by extreme climate events, whereas accent in climate projections is conventionally made on gradual changes in means. In this study we assess the uncertainty in projecting extreme climatic events within a region-specific and process-oriented context by examining the skills and ranking of ESMs. We developed a synthetic regionalization of Northern Eurasia that accounts for the spatial features of modern climatic changes and major environmental and socio-economical impacts. Elements of such fragmentation could be considered as natural focus regions that bridge the gap between the spatial scales adopted in climate-impacts studies and patterns of climate change simulated by ESMs. In each focus region we selected several target meteorological variables that govern the key regional impacts, and examined the ability of the models to replicate their seasonal and annual means and trends by testing them against observations. We performed a similar evaluation with regard to extremes and statistics of the target variables. And lastly, we used the results of these analyses to select sets of models that demonstrate the best performance at selected focus regions with regard to selected sets of target meteorological parameters. Ultimately, we ranked the models according to their skills, identified top-end models that "better than average" reproduce the behavior of climatic parameters, and eliminated the outliers. Since the criteria of selecting the "best" models are somewhat loose, we constructed several regional ensembles consisting of different number of high-ranked models and compared results from these optimized ensembles with observations and with the ensemble of all models. We tested our approach in specific regional application of the terrestrial Russian Arctic, considering permafrost and Artic biomes as key regional climate-dependent systems, and temperature and precipitation characteristics governing their state as target meteorological parameters. Results of this case study are deposited on the web portal www.permafrost.su/gcms

IMPACTS OF CLIMATE CHANGE ON RICE YIELD: EVALUATION OF THE EFFICACITY OF DIFFERENT MODELING APPROACHES

EPA Science Inventory

Increasing concentrations of carbon dioxide (CO2) and other greenhouse gases are expected to modify the climate of the earth in the next 50-100 years. echanisms of plant response to these changes need to be incorporated in models that predict crop yield to obtain an understanding...

Resource selection by an ectothermic predator in a dynamic thermal landscape

Treesearch

Andrew D. George; Grant M. Connette; Frank R. Thompson; John Faaborg

2017-01-01

Predicting the effects of global climate change on species interactions has remained difficult because there is a spatiotemporal mismatch between regional climate models and microclimates experienced by organisms. We evaluated resource selection in a predominant ectothermic predator using a modeling approach that permitted us to assess the importance of habitat...

A Simple Water Balance Model Adapted for Arctic Hydrology Reveals Glacier and Streamflow Responses to Climate Change in the Copper River, Alaska

NASA Astrophysics Data System (ADS)

Valentin, M. M.; Hay, L.; Van Beusekom, A. E.; Viger, R. J.; Hogue, T. S.

2016-12-01

Forecasting the hydrologic response to climate change in Alaska's glaciated watersheds remains daunting for hydrologists due to sparse field data and few modeling tools, which frustrates efforts to manage and protect critical aquatic habitat. Approximately 20% of the 64,000 square kilometer Copper River watershed is glaciated, and its glacier-fed tributaries support renowned salmon fisheries that are economically, culturally, and nutritionally invaluable to the local communities. This study adapts a simple, yet powerful, conceptual hydrologic model to simulate changes in the timing and volume of streamflow in the Copper River, Alaska as glaciers change under plausible future climate scenarios. The USGS monthly water balance model (MWBM), a hydrologic tool used for two decades to evaluate a broad range of hydrologic questions in the contiguous U.S., was enhanced to include glacier melt simulations and remotely sensed data. In this presentation we summarize the technical details behind our MWBM adaptation and demonstrate its use in the Copper River Basin to evaluate glacier and streamflow responses to climate change.

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.

Evaluating Programs That Promote Climate and Energy Education-Meeting Teacher Needs for Online Resources

NASA Astrophysics Data System (ADS)

Lynds, S. E.; Buhr, S. M.

2011-12-01

The Climate Literacy and Energy Awareness Network (CLEAN) Pathway, is a National Science Digital Library (NSDL) Pathways project that was begun in 2010. The main goal of CLEAN is to generate a reviewed collection of educational resources that are aligned with the Essential Principles of Climate Science (EPCS). Another goal of the project is to support a community that will assist students, teachers, and citizens in climate literacy. A complementary program begun in 2010 is the ICEE (Inspiring Climate Education Excellence) program, which is developing online modules and courses designed around the climate literacy principles for use by teachers and other interested citizens. In these projects, we learn about teacher needs through a variety of evaluation mechanisms. The programs use evaluation to assist in the process of providing easy access to high quality climate and energy learning resources that meet classroom requirements. The internal evaluation of the CLEAN program is multidimensional. At the CLEAN resource review camps, teachers and scientists work together in small groups to assess the value of online resources for use in the classroom. The review camps are evaluated using observation and feedback surveys; the resulting evaluation reports provide information to managers to fine-tune future camps. In this way, a model for effective climate resource development meetings has been refined. Evaluation methods used in ICEE and CLEAN include teacher needs assessment surveys, teacher feedback at professional development opportunities, scientist feedback at resource review workshops, and regular analysis of online usage of resources, forums, and education modules. This paper will review the most successful strategies for evaluating the effectiveness of online climate and energy education resources and their use by educators and the general public.

Estimating Model Prediction Error: Should You Treat Predictions as Fixed or Random?

NASA Technical Reports Server (NTRS)

Wallach, Daniel; Thorburn, Peter; Asseng, Senthold; Challinor, Andrew J.; Ewert, Frank; Jones, James W.; Rotter, Reimund; Ruane, Alexander

2016-01-01

Crop models are important tools for impact assessment of climate change, as well as for exploring management options under current climate. It is essential to evaluate the uncertainty associated with predictions of these models. We compare two criteria of prediction error; MSEP fixed, which evaluates mean squared error of prediction for a model with fixed structure, parameters and inputs, and MSEP uncertain( X), which evaluates mean squared error averaged over the distributions of model structure, inputs and parameters. Comparison of model outputs with data can be used to estimate the former. The latter has a squared bias term, which can be estimated using hindcasts, and a model variance term, which can be estimated from a simulation experiment. The separate contributions to MSEP uncertain (X) can be estimated using a random effects ANOVA. It is argued that MSEP uncertain (X) is the more informative uncertainty criterion, because it is specific to each prediction situation.

The climate4impact portal: bridging CMIP5 data to impact users

NASA Astrophysics Data System (ADS)

Som de Cerff, Wim; Plieger, Maarten; Page, Christian; Hutjes, Ronald; de Jong, Fokke; Barring, Lars; Sjökvist, Elin

2013-04-01

Together with seven other partners (CERFACS, CNRS-IPSL, SMHI, INHGA, CMCC, WUR, MF-CNRM), KNMI is involved in the FP7 project IS-ENES (http://is.enes.org), which supports the European climate modeling infrastructure, in the work package 'Bridging Climate Research Data and the Needs of the Impact Community'. The aim of this work package is to enhance the use of climate model data and to enhance the interaction with climate effect/impact communities. The portal is based on 17 impact use cases from 5 different European countries, and is evaluated by a user panel consisting of use case owners. As the climate impact community is very broad, the focus is mainly on the scientific impact community. This work has resulted in a prototype portal, the ENES portal interface for climate impact communities, that can be visited at www.climate4impact.eu. The portal is connected to all Earth System Grid Federation (ESGF) nodes containing global climate model data (GCM data) from the fifth phase of the Coupled Model Intercomparison Project (CMIP5) and later from the Coordinated Regional Climate Downscaling Experiment (CORDEX). This global network of all major climate model data centers offers services for data description, discovery and download. The climate4impact portal connects to these services and offers a user interface for searching, visualizing and downloading global climate model data and more. A challenging task was to describe the available model data and how it can be used. The portal tries to inform users about possible caveats when using model data. All impact use cases are described in the documentation section, using highlighted keywords pointing to detailed information in the glossary. The current portal is a Prototype. It is built to explore state-of-art technologies to provide improved access to climate model data. The prototype will be evaluated and is the basis for development of an operational service. The portal and services provided will be sustained and supported during the development of these operational services (2013-2016) in the second phase of the FP7 IS-ENES project, ISENES2. In this presentation the architecture and following items will be detailed: • Security: Login using OpenID for access to the ESGF data nodes. The ESGF works in conjunction with several external websites and systems. The portal provides access to several distributed archives, most importantly the ESGF nodes. Single Sign-on (SSO) is used to let these websites and systems work together. • Discovery: Intelligent search based on e.g. variable name, model, institute. A catalog browser allows for browsing through CMIP5 and other climate model data catalogues (e.g. ESSENCE, EOBS, UNIDATA). • Download: Directly from ESGF nodes and other THREDDS catalogs • Visualization: Visualize any data directly on a map (ADAGUC Map services). • Transformation: Transform your data into other formats, perform basic calculations and extractions

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.

EVALUATING SHORT-TERM CLIMATE VARIABILITY IN THE LATE HOLOCENE OF THE NORTHERN GREAT PLAINS

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

Joseph H. Hartman

1999-09-01

This literature study investigated methods and areas to deduce climate change and climate patterns, looking for short-term cycle phenomena and the means to interpret them. Many groups are actively engaged in intensive climate-related research. Ongoing research might be (overly) simplified into three categories: (1) historic data on weather that can be used for trend analysis and modeling; (2) detailed geological, biological (subfossil), and analytical (geochemical, radiocarbon, etc.) studies covering the last 10,000 years (about since last glaciation); and (3) geological, paleontological, and analytical (geochemical, radiometric, etc.) studies over millions of years. Of importance is our ultimate ability to join thesemore » various lines of inquiry into an effective means of interpretation. At this point, the process of integration is fraught with methodological troubles and misconceptions about what each group can contribute. This project has met its goals to the extent that it provided an opportunity to study resource materials and consider options for future effort toward the goal of understanding the natural climate variation that has shaped our current civilization. A further outcome of this project is a proposed methodology based on ''climate sections'' that provides spatial and temporal correlation within a region. The method would integrate cultural and climate data to establish the climate history of a region with increasing accuracy with progressive study and scientific advancement (e. g., better integration of regional and global models). The goal of this project is to better understand natural climatic variations in the recent past (last 5000 years). The information generated by this work is intended to provide better context within which to examine global climate change. The ongoing project will help to establish a basis upon which to interpret late Holocene short-term climate variability as evidenced in various studies in the northern Great Plains, northern hemisphere, and elsewhere. Finally these data can be integrated into a history of climate change and predictive climate models. This is not a small undertaking. The goals of researchers and the methods used vary considerably. The primary task of this project was literature research to (1) evaluate existing methodologies used in geologic climate change studies and evidence for short-term cycles produced by these methodologies and (2) evaluate late Holocene climate patterns and their interpretations.« less

Impact of climate change on hydrological conditions in a tropical West African catchment using an ensemble of climate simulations

NASA Astrophysics Data System (ADS)

Yira, Yacouba; Diekkrüger, Bernd; Steup, Gero; Yaovi Bossa, Aymar

2017-04-01

This study evaluates climate change impacts on water resources using an ensemble of six regional climate models (RCMs)-global climate models (GCMs) in the Dano catchment (Burkina Faso). The applied climate datasets were performed in the framework of the COordinated Regional climate Downscaling Experiment (CORDEX-Africa) project.

After evaluation of the historical runs of the climate models' ensemble, a statistical bias correction (empirical quantile mapping) was applied to daily precipitation. Temperature and bias corrected precipitation data from the ensemble of RCMs-GCMs was then used as input for the Water flow and balance Simulation Model (WaSiM) to simulate water balance components.

The mean hydrological and climate variables for two periods (1971-2000 and 2021-2050) were compared to assess the potential impact of climate change on water resources up to the middle of the 21st century under two greenhouse gas concentration scenarios, the Representative Concentration Pathways (RCPs) 4.5 and 8.5. The results indicate (i) a clear signal of temperature increase of about 0.1 to 2.6 °C for all members of the RCM-GCM ensemble; (ii) high uncertainty about how the catchment precipitation will evolve over the period 2021-2050; (iii) the applied bias correction method only affected the magnitude of the climate change signal; (iv) individual climate models results lead to opposite discharge change signals; and (v) the results for the RCM-GCM ensemble are too uncertain to give any clear direction for future hydrological development. Therefore, potential increase and decrease in future discharge have to be considered in climate change adaptation strategies in the catchment. The results further underline on the one hand the need for a larger ensemble of projections to properly estimate the impacts of climate change on water resources in the catchment and on the other hand the high uncertainty associated with climate projections for the West African region. A water-energy budget analysis provides further insight into the behavior of the catchment.

Managing burned landscapes: Evaluating future management strategies for resilient forests under a warming climate

Treesearch

K. L. Shive; P. Z. Fule; C. H. Sieg; B. A. Strom; M. E. Hunter

2014-01-01

Climate change effects on forested ecosystems worldwide include increases in drought-related mortality, changes to disturbance regimes and shifts in species distributions. Such climate-induced changes will alter the outcomes of current management strategies, complicating the selection of appropriate strategies to promote forest resilience. We modelled forest growth in...

Evaluating climate model performance in the tropics with retrievals of water isotopic composition from Aura TES

NASA Astrophysics Data System (ADS)

Field, Robert; Kim, Daehyun; Kelley, Max; LeGrande, Allegra; Worden, John; Schmidt, Gavin

2014-05-01

Observational and theoretical arguments suggest that satellite retrievals of the stable isotope composition of water vapor could be useful for climate model evaluation. The isotopic composition of water vapor is controlled by the same processes that control water vapor amount, but the observed distribution of isotopic composition is distinct from amount itself . This is due to the fractionation that occurs between the abundant H216O isotopes (isotopologues) and the rare and heavy H218O and HDO isotopes during evaporation and condensation. The fractionation physics are much simpler than the underlying moist physics; discrepancies between observed and modeled isotopic fields are more likely due to problems in the latter. Isotopic measurements therefore have the potential for identifying problems that might not be apparent from more conventional measurements. Isotopic tracers have existed in climate models since the 1980s but it is only since the mid 2000s that there have been enough data for meaningful model evaluation in this sense, in the troposphere at least. We have evaluated the NASA GISS ModelE2 general circulation model over the tropics against water isotope (HDO/H2O) retrievals from the Aura Tropospheric Emission Spectrometer (TES), alongside more conventional measurements. A small ensemble of experiments was performed with physics perturbations to the cumulus and planetary boundary layer schemes, done in the context of the normal model development process. We examined the degree to which model-data agreement could be used to constrain a select group of internal processes in the model, namely condensate evaporation, entrainment strength, and moist convective air mass flux. All are difficult to parameterize, but exert strong influence over model performance. We found that the water isotope composition was significantly more sensitive to physics changes than precipitation, temperature or relative humidity through the depth of the tropical troposphere. Among the processes considered, this was most closely, and fairly exclusively, related to mid-tropospheric entrainment strength. This demonstrates that water isotope retrievals have considerable potential alongside more conventional measurements for climate model evaluation and development.

Evaluating Global Land-use Change Scenario: Carbon Emission in RCP Scenarios and its Effects on Climate Response

NASA Astrophysics Data System (ADS)

Kato, E.; Kawamiya, M.

2011-12-01

In CMIP5 experiments, new emissions scenarios for GCMs and Earth System Models (ESMs) have been constructed as Representative Concentration Pathways (RCPs) by a community effort of Integrated Assessment Modeling (IAM) groups. In RCP scenarios, regional land-use scenarios have been depicted based on the socio-economic assumption of IAMs, and also downscaled spatially explicit land-use maps from the regional scenarios are prepared. In the land-use harmonization project, integrated gridded land-use transition data for the past and future time period has been developed from the reconstruction based on HYDE 3 agricultural data and FAO wood harvest data, and the future land-use scenarios from IAMs. These gridded land-use dataset are used as a forcing of some ESMs participating to the CMIP5 experiments, to assess the biogeochemical and biogeophysical effects of land-use and land cover change in the climate change simulation. In this study, global net CO2 emissions from land-use change for RCP scenarios are evaluated with an offline terrestrial biogeochemical model, VISIT (Vegetation Integrative SImulation Tool). Also the emissions are evaluated with coupled ESM, MIROC-ESM following the LUCID-CMIP5 protocol to see the effect of land-use and land cover change on climate response. Using the model output, consistency of the land-use change CO2 emission scenarios provided by RCPs are evaluated in terms of effect of CO2 fertilization, climate change, and land-use transition itself including the effect of biomass crops production with CCS. We find that a land-use scenario with decreased agricultural land-use intensity such as RCP 6.0 shows possibility of further absorption of CO2 through the climate-carbon feedback, and cooling effect through both biogeochemical and biogeophysical effects.

Analysis of Solar Chimneys in Different Climate Zones - Case of Social Housing in Ecuador

NASA Astrophysics Data System (ADS)

Godoy-Vaca, Luis; Almaguer, Manuel; Martínez-Gómez, Javier; Lobato, Andrea; Palme, Massimo

2017-10-01

The aim of this research is to simulate the performance of a solar chimney located in different macro-zones in Ecuador. The proposed solar chimney model was simulated using a python script in order to predict the temperature distribution and the mass flow over time. The results obtained were firstly compared with experimental data for dry-warm climate. Then, the model was evaluated and tested in real weather conditions: dry-warm, moist-warm and rainy-cold. In addition, the assumed chimney dimensions were chosen according to the literature for the studied conditions. In spite of evaluating the best nightly ventilation, different chimney wall materials were tested: solid brick, common brick and reinforced concrete. The results showed that concrete in a dry-warm climate, a metallic layer on the gap with solid brick in a moist-warm climate and reinforced concrete in a rainy cold climate used for the absorbent wall improve the thermal inertia of the social housing.

Sustainable Water Resources for Communities under Climate Change: Can State-of-the-Art Forecasting Inform Decision-Making in Data Sparse Regions?

NASA Astrophysics Data System (ADS)

Mayer, A.; Vivoni, E.; Halvorsen, K.; Robles-Morua, A.; Dana, K.; Che, D.; Mirchi, A.; Kossak, D.; Casteneda, M.

2013-05-01

In this project, we are studying decision-making for water resources management in anticipation of climate change in the Sonora River Basin, Mexico as a case study for the broader arid and semiarid southwestern North America. The goal of the proposed project is to determine whether water resources systems modeling, developed within a participatory framework, can contribute to the building of management strategies in a context of water scarcity, conflicting water uses and highly variable and changing climate conditions. The participatory modeling approach will be conducted through a series of three workshops, designed to encourage substantive participation from a broad range of actors, including representatives from federal and local government agencies, water use sectors, non-governmental organizations, and academics. Participants will guide the design of supply- and demand-side management strategies and selection of climate change and infrastructure management scenarios using state-of-the-art engineering tools. These tools include a water resources systems framework, a spatially-explicit hydrologic model, the use of forecasted climate scenarios under 21st century climate change, and observations obtained from field and satellite sensors. Through the theory of planned behavior, the participatory modeling process will be evaluated to understand if, and to what extent, the engineering tools are useful in the uncertain and politically-complex setting. Pre- and post-workshop surveys will be used in this evaluation. For this contribution, we present the results of the first collaborative modeling workshop that will be held in March 2013, where we will develop the initial modeling framework in collaboration with workshop participants.

Development and Performance of Alternative Electricity Sector Pathways Subject to Multiple Climate and Water Projections

NASA Astrophysics Data System (ADS)

Newmark, R. L.; Vorosmarty, C. J.; Miara, A.; Cohen, S.; Macknick, J.; Sun, Y.; Corsi, F.; Fekete, B. M.; Tidwell, V. C.

2017-12-01

Climate change impacts on air temperatures and water availability have the potential to alter future electricity sector investment decisions as well as the reliability and performance of the power sector. Different electricity sector configurations are more or less vulnerable to climate-induced changes. For example, once-through cooled thermal facilities are the most cost-effective and efficient technologies under cooler and wetter conditions, but can be substantially affected by and vulnerable to warmer and drier conditions. Non-thermal renewable technologies, such as PV and wind, are essentially "drought-proof" but have other integration and reliability challenges. Prior efforts have explored the impacts of climate change on electric sector development for a limited set of climate and electricity scenarios. Here, we provide a comprehensive suite of scenarios that evaluate how different electricity sector pathways could be affected by a range of climate and water resource conditions. We use four representative concentration pathway (RCP) scenarios under five global circulation models (GCM) as climate drivers to a Water Balance Model (WBM), to provide twenty separate future climate-water conditions. These climate-water conditions influence electricity sector development from present day to 2050 as determined using the Regional Energy Deployment Systems (ReEDS) model. Four unique electricity sector pathways will be considered, including business-as-usual, carbon cap, high renewable energy technology costs, and coal reliance scenarios. The combination of climate-water and electricity sector pathway scenarios leads to 80 potential future cases resulting in different national and regional electricity infrastructure configurations. The vulnerability of these configurations in relation to climate change (including in-stream thermal pollution impacts and environmental regulations) is evaluated using the Thermoelectric Power and Thermal Pollution (TP2M) model, providing quantitative estimates of the power sector's ability to meet loads, given changes in air temperature, water temperature, and water availability.

Process evaluation of sea salt aerosol concentrations at remote marine locations

NASA Astrophysics Data System (ADS)

Struthers, H.; Ekman, A. M.; Nilsson, E. D.

2011-12-01

Sea salt, an important natural aerosol, is generated by bubbles bursting at the surface of the ocean. Sea salt aerosol contributes significantly to the global aerosol burden and radiative budget and are a significant source of cloud condensation nuclei in remote marine areas (Monahan et al., 1986). Consequently, changes in marine aerosol abundance is expected to impact on climate forcing. Estimates of the atmospheric burden of sea salt aerosol mass derived from chemical transport and global climate models vary greatly both in the global total and the spatial distribution (Texor et al. 2006). This large uncertainty in the sea salt aerosol distribution in turn contributes to the large uncertainty in the current estimates of anthropogenic aerosol climate forcing (IPCC, 2007). To correctly attribute anthropogenic climate change and to veraciously project future climate, natural aerosols including sea salt must be understood and accurately modelled. In addition, the physical processes that determine the sea salt aerosol concentration are susceptible to modification due to climate change (Carslaw et al., 2010) which means there is the potential for feedbacks within the climate/aerosol system. Given the large uncertainties in sea salt aerosol modelling, there is an urgent need to evaluate the process description of sea salt aerosols in global models. An extremely valuable source of data for model evaluation is the long term measurements of PM10 sea salt aerosol mass available from a number of remote marine observation sites around the globe (including the GAW network). Sea salt aerosol concentrations at remote marine locations depend strongly on the surface exchange (emission and deposition) as well as entrainment or detrainment to the free troposphere. This suggests that the key parameters to consider in any analysis include the sea surface water temperature, wind speed, precipitation rate and the atmospheric stability. In this study, the sea salt aerosol observations are analysed to quantify the key sensitivities of the processes connecting the physical drivers of sea salt aerosol to the mass tendency. The analysis employs a semi-empirical model based on the time-tendency of the aerosol mass. This approach of focusing on the time-tendency of the sea salt aerosol concentration provides a framework for the process evaluation of sea salt aerosol concentrations in global models. The same analysis methodology can be applied to output from global models. A process of comparing the sensitivity parameters derived from observations and models will reveal model inadequacies and thus guide model improvements. Carslaw, K. S., Boucher, O., Spracklen, D. V., Mann G. W., Rae, J. G. L, Woodward, S., Kulmala, M. (2010). Atmos. Chem. Phys., 10, 1701-1737 IPCC (2007). Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, Solomon, S., D. Monahan, E. C., Spiel, D. E., Davidson, K. L. (1986) Oceanic Whitecaps ed. Monahan E. C. & MacNiochaill, D. Reidel, Norwell, Mass. Texor, C., et al. (2006) Atmos. Chem. Phys., 6, 1777-1813.

Koppen bioclimatic evaluation of CMIP historical climate simulations

DOE PAGES

Phillips, Thomas J.; Bonfils, Celine J. W.

2015-06-05

Köppen bioclimatic classification relates generic vegetation types to characteristics of the interactive annual-cycles of continental temperature (T) and precipitation (P). In addition to predicting possible bioclimatic consequences of past or prospective climate change, a Köppen scheme can be used to pinpoint biases in model simulations of historical T and P. In this study a Köppen evaluation of Coupled Model Intercomparison Project (CMIP) simulations of historical climate is conducted for the period 1980–1999. Evaluation of an example CMIP5 model illustrates how errors in simulating Köppen vegetation types (relative to those derived from observational reference data) can be deconstructed and related tomore » model-specific temperature and precipitation biases. Measures of CMIP model skill in simulating the reference Köppen vegetation types are also developed, allowing the bioclimatic performance of a CMIP5 simulation of T and P to be compared quantitatively with its CMIP3 antecedent. Although certain bioclimatic discrepancies persist across model generations, the CMIP5 models collectively display an improved rendering of historical T and P relative to their CMIP3 counterparts. Additionally, the Köppen-based performance metrics are found to be quite insensitive to alternative choices of observational reference data or to differences in model horizontal resolution.« less

Plant community dynamics and restoring Louisiana's wetland ecosystems

NASA Astrophysics Data System (ADS)

Duke-Sylvester, S. M.; Visser, J.

2017-12-01

We have developed a computational model of plant community dynamics. Our model is designed to evaluate the effects of management actions on the structure and health of Louisiana's coastal wetland plant communities. A number of projects have been initiated or proposed to preserve and restore this ecosystem while still allowing the area to support Louisiana's economy. These projects involve both modification of the flow of freshwater as well as restoring natural wetlands. Evaluating the long term effects of these projects is complex and involves numerous moving pieces operating over an extensive and diverse landscape. The situation is further complicated by in sea level rise and climate change associated with global warming. The vegetation model is part of a larger set of linked models that include hydrology and soil morphology. Using hydrological conditions projected by the linked hydrology models, we are able to evaluate the effects of anthropogenic and climatic changes on Louisiana's wetland plant communities. Unique features of our model include replacing the division of wetlands into coarse groups defined by salinity conditions with species level responses to environmental conditions and extending the spatial scale of modeling to encompass the entirety of Louisiana's Gulf coast. Model results showing the potential impact of alternative management and climate change scenarios are presented.

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.

Climate Models

NASA Technical Reports Server (NTRS)

Druyan, Leonard M.

2012-01-01

Climate models is a very broad topic, so a single volume can only offer a small sampling of relevant research activities. This volume of 14 chapters includes descriptions of a variety of modeling studies for a variety of geographic regions by an international roster of authors. The climate research community generally uses the rubric climate models to refer to organized sets of computer instructions that produce simulations of climate evolution. The code is based on physical relationships that describe the shared variability of meteorological parameters such as temperature, humidity, precipitation rate, circulation, radiation fluxes, etc. Three-dimensional climate models are integrated over time in order to compute the temporal and spatial variations of these parameters. Model domains can be global or regional and the horizontal and vertical resolutions of the computational grid vary from model to model. Considering the entire climate system requires accounting for interactions between solar insolation, atmospheric, oceanic and continental processes, the latter including land hydrology and vegetation. Model simulations may concentrate on one or more of these components, but the most sophisticated models will estimate the mutual interactions of all of these environments. Advances in computer technology have prompted investments in more complex model configurations that consider more phenomena interactions than were possible with yesterday s computers. However, not every attempt to add to the computational layers is rewarded by better model performance. Extensive research is required to test and document any advantages gained by greater sophistication in model formulation. One purpose for publishing climate model research results is to present purported advances for evaluation by the scientific community.

The Cloud Feedback Model Intercomparison Project (CFMIP) Diagnostic Codes Catalogue – metrics, diagnostics and methodologies to evaluate, understand and improve the representation of clouds and cloud feedbacks in climate models

DOE PAGES

Tsushima, Yoko; Brient, Florent; Klein, Stephen A.; ...

2017-11-27

The CFMIP Diagnostic Codes Catalogue assembles cloud metrics, diagnostics and methodologies, together with programs to diagnose them from general circulation model (GCM) outputs written by various members of the CFMIP community. This aims to facilitate use of the diagnostics by the wider community studying climate and climate change. Here, this paper describes the diagnostics and metrics which are currently in the catalogue, together with examples of their application to model evaluation studies and a summary of some of the insights these diagnostics have provided into the main shortcomings in current GCMs. Analysis of outputs from CFMIP and CMIP6 experiments willmore » also be facilitated by the sharing of diagnostic codes via this catalogue. Any code which implements diagnostics relevant to analysing clouds – including cloud–circulation interactions and the contribution of clouds to estimates of climate sensitivity in models – and which is documented in peer-reviewed studies, can be included in the catalogue. We very much welcome additional contributions to further support community analysis of CMIP6 outputs.« less

The Cloud Feedback Model Intercomparison Project (CFMIP) Diagnostic Codes Catalogue – metrics, diagnostics and methodologies to evaluate, understand and improve the representation of clouds and cloud feedbacks in climate models

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

Tsushima, Yoko; Brient, Florent; Klein, Stephen A.

The CFMIP Diagnostic Codes Catalogue assembles cloud metrics, diagnostics and methodologies, together with programs to diagnose them from general circulation model (GCM) outputs written by various members of the CFMIP community. This aims to facilitate use of the diagnostics by the wider community studying climate and climate change. Here, this paper describes the diagnostics and metrics which are currently in the catalogue, together with examples of their application to model evaluation studies and a summary of some of the insights these diagnostics have provided into the main shortcomings in current GCMs. Analysis of outputs from CFMIP and CMIP6 experiments willmore » also be facilitated by the sharing of diagnostic codes via this catalogue. Any code which implements diagnostics relevant to analysing clouds – including cloud–circulation interactions and the contribution of clouds to estimates of climate sensitivity in models – and which is documented in peer-reviewed studies, can be included in the catalogue. We very much welcome additional contributions to further support community analysis of CMIP6 outputs.« less

Integrated watershed-scale response to climate change for selected basins across the United States

USGS Publications Warehouse

Markstrom, Steven L.; Hay, Lauren E.; Ward-Garrison, D. Christian; Risley, John C.; Battaglin, William A.; Bjerklie, David M.; Chase, Katherine J.; Christiansen, Daniel E.; Dudley, Robert W.; Hunt, Randall J.; Koczot, Kathryn M.; Mastin, Mark C.; Regan, R. Steven; Viger, Roland J.; Vining, Kevin C.; Walker, John F.

2012-01-01

A study by the U.S. Geological Survey (USGS) evaluated the hydrologic response to different projected carbon emission scenarios of the 21st century using a hydrologic simulation model. This study involved five major steps: (1) setup, calibrate and evaluated the Precipitation Runoff Modeling System (PRMS) model in 14 basins across the United States by local USGS personnel; (2) acquire selected simulated carbon emission scenarios from the World Climate Research Programme's Coupled Model Intercomparison Project; (3) statistical downscaling of these scenarios to create PRMS input files which reflect the future climatic conditions of these scenarios; (4) generate PRMS projections for the carbon emission scenarios for the 14 basins; and (5) analyze the modeled hydrologic response. This report presents an overview of this study, details of the methodology, results from the 14 basin simulations, and interpretation of these results. A key finding is that the hydrological response of the different geographical regions of the United States to potential climate change may be different, depending on the dominant physical processes of that particular region. Also considered is the tremendous amount of uncertainty present in the carbon emission scenarios and how this uncertainty propagates through the hydrologic simulations.

Climate data induced uncertainty in model-based estimations of terrestrial primary productivity

NASA Astrophysics Data System (ADS)

Wu, Zhendong; Ahlström, Anders; Smith, Benjamin; Ardö, Jonas; Eklundh, Lars; Fensholt, Rasmus; Lehsten, Veiko

2017-06-01

Model-based estimations of historical fluxes and pools of the terrestrial biosphere differ substantially. These differences arise not only from differences between models but also from differences in the environmental and climatic data used as input to the models. Here we investigate the role of uncertainties in historical climate data by performing simulations of terrestrial gross primary productivity (GPP) using a process-based dynamic vegetation model (LPJ-GUESS) forced by six different climate datasets. We find that the climate induced uncertainty, defined as the range among historical simulations in GPP when forcing the model with the different climate datasets, can be as high as 11 Pg C yr-1 globally (9% of mean GPP). We also assessed a hypothetical maximum climate data induced uncertainty by combining climate variables from different datasets, which resulted in significantly larger uncertainties of 41 Pg C yr-1 globally or 32% of mean GPP. The uncertainty is partitioned into components associated to the three main climatic drivers, temperature, precipitation, and shortwave radiation. Additionally, we illustrate how the uncertainty due to a given climate driver depends both on the magnitude of the forcing data uncertainty (climate data range) and the apparent sensitivity of the modeled GPP to the driver (apparent model sensitivity). We find that LPJ-GUESS overestimates GPP compared to empirically based GPP data product in all land cover classes except for tropical forests. Tropical forests emerge as a disproportionate source of uncertainty in GPP estimation both in the simulations and empirical data products. The tropical forest uncertainty is most strongly associated with shortwave radiation and precipitation forcing, of which climate data range contributes higher to overall uncertainty than apparent model sensitivity to forcing. Globally, precipitation dominates the climate induced uncertainty over nearly half of the vegetated land area, which is mainly due to climate data range and less so due to the apparent model sensitivity. Overall, climate data ranges are found to contribute more to the climate induced uncertainty than apparent model sensitivity to forcing. Our study highlights the need to better constrain tropical climate, and demonstrates that uncertainty caused by climatic forcing data must be considered when comparing and evaluating carbon cycle model results and empirical datasets.

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.

A Statistical Modeling Framework for Projecting Future Ambient Ozone and its Health Impact due to Climate Change

PubMed Central

Chang, Howard H.; Hao, Hua; Sarnat, Stefanie Ebelt

2014-01-01

The adverse health effects of ambient ozone are well established. Given the high sensitivity of ambient ozone concentrations to meteorological conditions, the impacts of future climate change on ozone concentrations and its associated health effects are of concern. We describe a statistical modeling framework for projecting future ozone levels and its health impacts under a changing climate. This is motivated by the continual effort to evaluate projection uncertainties to inform public health risk assessment. The proposed approach was applied to the 20-county Atlanta metropolitan area using regional climate model (RCM) simulations from the North American Regional Climate Change Assessment Program. Future ozone levels and ozone-related excesses in asthma emergency department (ED) visits were examined for the period 2041–2070. The computationally efficient approach allowed us to consider 8 sets of climate model outputs based on different combinations of 4 RCMs and 4 general circulation models. Compared to the historical period of 1999–2004, we found consistent projections across climate models of an average 11.5% higher ozone levels (range: 4.8%, 16.2%), and an average 8.3% (range: −7% to 24%) higher number of ozone exceedance days. Assuming no change in the at-risk population, this corresponds to excess ozone-related ED visits ranging from 267 to 466 visits per year. Health impact projection uncertainty was driven predominantly by uncertainty in the health effect association and climate model variability. Calibrating climate simulations with historical observations reduced differences in projections across climate models. PMID:24764746

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.

A Biophysical Model for Hawaiian Coral Reefs: Coupling Local Ecology, Larval Transport and Climate Change

NASA Astrophysics Data System (ADS)

Kapur, M. R.

2016-02-01

Simulative models of reef ecosystems have been used to evaluate ecological responses to a myriad of disturbance events, including fishing pressure, coral bleaching, invasion by alien species, and nutrient loading. The Coral Reef Scenario Evaluation Tool (CORSET), has been developed and instantiated for both the Meso-American Reef (MAR) and South China Sea (SCS) regions. This model is novel in that it accounts for the many scales at which reef ecosystem processes take place; is comprised of a "bottom-up" structure wherein complex behaviors are not pre-programmed, but emergent and highly portable to new systems. Local-scale dynamics are coupled across regions through larval connectivity matrices, derived sophisticated particle transport simulations that include key elements of larval behavior. By this approach, we are able to directly evaluate some of the potential consequences of larval connectivity patterns across a range of spatial scales and under multiple climate scenarios. This work develops and applies the CORSET (Coral Reef Scenario Evaluation Tool) to the Main Hawaiian Islands under a suite of climate and ecological scenarios. We introduce an adaptation constant into reef-building coral dynamics to simulate observed resiliencies to bleaching events. This presentation will share results from the model's instantiation under two Resource Concentration Pathway climate scenarios, with emphasis upon larval connectivity dynamics, emergent coral tolerance to increasing thermal anomalies, and patterns of spatial fishing closures. Results suggest that under a business-as-usual scenario, thermal tolerance and herbivore removal will have synergistic effects on reef resilience.

Ancillary health effects of climate mitigation scenarios as drivers of policy uptake: a review of air quality, transportation and diet co-benefits modeling studies

NASA Astrophysics Data System (ADS)

Chang, Kelly M.; Hess, Jeremy J.; Balbus, John M.; Buonocore, Jonathan J.; Cleveland, David A.; Grabow, Maggie L.; Neff, Roni; Saari, Rebecca K.; Tessum, Christopher W.; Wilkinson, Paul; Woodward, Alistair; Ebi, Kristie L.

2017-11-01

Background: Significant mitigation efforts beyond the Nationally Determined Commitments (NDCs) coming out of the 2015 Paris Climate Agreement are required to avoid warming of 2 °C above pre-industrial temperatures. Health co-benefits represent selected near term, positive consequences of climate policies that can offset mitigation costs in the short term before the beneficial impacts of those policies on the magnitude of climate change are evident. The diversity of approaches to modeling mitigation options and their health effects inhibits meta-analyses and syntheses of results useful in policy-making. Methods/Design: We evaluated the range of methods and choices in modeling health co-benefits of climate mitigation to identify opportunities for increased consistency and collaboration that could better inform policy-making. We reviewed studies quantifying the health co-benefits of climate change mitigation related to air quality, transportation, and diet published since the 2009 Lancet Commission ‘Managing the health effects of climate change’ through January 2017. We documented approaches, methods, scenarios, health-related exposures, and health outcomes. Results/Synthesis: Forty-two studies met the inclusion criteria. Air quality, transportation, and diet scenarios ranged from specific policy proposals to hypothetical scenarios, and from global recommendations to stakeholder-informed local guidance. Geographic and temporal scope as well as validity of scenarios determined policy relevance. More recent studies tended to use more sophisticated methods to address complexity in the relevant policy system. Discussion: Most studies indicated significant, nearer term, local ancillary health benefits providing impetus for policy uptake and net cost savings. However, studies were more suited to describing the interaction of climate policy and health and the magnitude of potential outcomes than to providing specific accurate estimates of health co-benefits. Modeling the health co-benefits of climate policy provides policy-relevant information when the scenarios are reasonable, relevant, and thorough, and the model adequately addresses complexity. Greater consistency in selected modeling choices across the health co-benefits of climate mitigation research would facilitate evaluation of mitigation options particularly as they apply to the NDCs and promote policy uptake.

Significance of model credibility in estimating climate projection distributions for regional hydroclimatological risk assessments

USGS Publications Warehouse

Brekke, L.D.; Dettinger, M.D.; Maurer, E.P.; Anderson, M.

2008-01-01

Ensembles of historical climate simulations and climate projections from the World Climate Research Programme's (WCRP's) Coupled Model Intercomparison Project phase 3 (CMIP3) multi-model dataset were investigated to determine how model credibility affects apparent relative scenario likelihoods in regional risk assessments. Methods were developed and applied in a Northern California case study. An ensemble of 59 twentieth century climate simulations from 17 WCRP CMIP3 models was analyzed to evaluate relative model credibility associated with a 75-member projection ensemble from the same 17 models. Credibility was assessed based on how models realistically reproduced selected statistics of historical climate relevant to California climatology. Metrics of this credibility were used to derive relative model weights leading to weight-threshold culling of models contributing to the projection ensemble. Density functions were then estimated for two projected quantities (temperature and precipitation), with and without considering credibility-based ensemble reductions. An analysis for Northern California showed that, while some models seem more capable at recreating limited aspects twentieth century climate, the overall tendency is for comparable model performance when several credibility measures are combined. Use of these metrics to decide which models to include in density function development led to local adjustments to function shapes, but led to limited affect on breadth and central tendency, which were found to be more influenced by 'completeness' of the original ensemble in terms of models and emissions pathways. ?? 2007 Springer Science+Business Media B.V.

Quantifying climate feedbacks in polar regions.

PubMed

Goosse, Hugues; Kay, Jennifer E; Armour, Kyle C; Bodas-Salcedo, Alejandro; Chepfer, Helene; Docquier, David; Jonko, Alexandra; Kushner, Paul J; Lecomte, Olivier; Massonnet, François; Park, Hyo-Seok; Pithan, Felix; Svensson, Gunilla; Vancoppenolle, Martin

2018-05-15

The concept of feedback is key in assessing whether a perturbation to a system is amplified or damped by mechanisms internal to the system. In polar regions, climate dynamics are controlled by both radiative and non-radiative interactions between the atmosphere, ocean, sea ice, ice sheets and land surfaces. Precisely quantifying polar feedbacks is required for a process-oriented evaluation of climate models, a clear understanding of the processes responsible for polar climate changes, and a reduction in uncertainty associated with model projections. This quantification can be performed using a simple and consistent approach that is valid for a wide range of feedbacks, offering the opportunity for more systematic feedback analyses and a better understanding of polar climate changes.

Data-based information gain on the response behaviour of hydrological models at catchment scale

NASA Astrophysics Data System (ADS)

Willems, Patrick

2013-04-01

A data-based approach is presented to analyse the response behaviour of hydrological models at the catchment scale. The approach starts with a number of sequential time series processing steps, applied to available rainfall, ETo and river flow observation series. These include separation of the high frequency (e.g., hourly, daily) river flow series into subflows, split of the series in nearly independent quick and slow flow hydrograph periods, and the extraction of nearly independent peak and low flows. Quick-, inter- and slow-subflow recession behaviour, sub-responses to rainfall and soil water storage are derived from the time series data. This data-based information on the catchment response behaviour can be applied on the basis of: - Model-structure identification and case-specific construction of lumped conceptual models for gauged catchments; or diagnostic evaluation of existing model structures; - Intercomparison of runoff responses for gauged catchments in a river basin, in order to identify similarity or significant differences between stations or between time periods, and relate these differences to spatial differences or temporal changes in catchment characteristics; - (based on the evaluation of the temporal changes in previous point:) Detection of temporal changes/trends and identification of its causes: climate trends, or land use changes; - Identification of asymptotic properties of the rainfall-runoff behaviour towards extreme peak or low flow conditions (for a given catchment) or towards extreme catchment conditions (for regionalization, ungauged basin prediction purposes); hence evaluating the performance of the model in making extrapolations beyond the range of available stations' data; - (based on the evaluation in previous point:) Evaluation of the usefulness of the model for making extrapolations to more extreme climate conditions projected by for instance climate models. Examples are provided for river basins in Belgium, Ethiopia, Kenya, Ecuador, Bolivia and China. References: Van Steenbergen, N., Willems, P. (2012), 'Method for testing the accuracy of rainfall-runoff models in predicting peak flow changes due to rainfall changes, in a climate changing context', Journal of Hydrology, 414-415, 425-434, doi:10.1016/j.jhydrol.2011.11.017 Mora, D., Willems, P. (2012), 'Decadal oscillations in rainfall and air temperature in the Paute River Basin - Southern Andes of Ecuador', Theoretical and Applied Climatology, 108(1), 267-282, doi:0.1007/s00704-011-0527-4 Taye, M.T., Willems, P. (2011). 'Influence of climate variability on representative QDF predictions of the upper Blue Nile Basin', Journal of Hydrology, 411, 355-365, doi:10.1016/j.jhydrol.2011.10.019 Taye, M.T., Willems, P. (2012). 'Temporal variability of hydro-climatic extremes in the Blue Nile basin', Water Resources Research, 48, W03513, 13p. Vansteenkiste, Th., Tavakoli, M., Ntegeka, V., Willems, P., De Smedt, F., Batelaan, O. (in press), 'Climate change impact on river flows and catchment hydrology: a comparison of two spatially distributed models', Hydrological Processes; doi: 10.1002/hyp.9480 [in press

Demonstrating the climate4impact portal: bridging the CMIP5 data infrastructure to impact users

NASA Astrophysics Data System (ADS)

Plieger, Maarten; Som de Cerff, Wim; Page, Christian; Hutjes, Ronald; de Jong, Fokke; Bärring, Lars; Sjökvist, Elin

2013-04-01

Together with seven other partners (CERFACS, CNRS-IPSL, SMHI, INHGA, CMCC, WUR, MF-CNRM), KNMI is involved in the FP7 project IS-ENES (http://is.enes.org), which supports the European climate modeling infrastructure, in the work package 'Bridging Climate Research Data and the Needs of the Impact Community'. The aim of this work package is to enhance the use of climate model data and to enhance the interaction with climate effect/impact communities. The portal is based on 17 impact use cases from 5 different European countries, and is evaluated by a user panel consisting of use case owners. As the climate impact community is very broad, the focus is mainly on the scientific impact community. This work has resulted in a prototype portal, the ENES portal interface for climate impact communities, that can be visited at www.climate4impact.eu. The portal is connected to all Earth System Grid Federation (ESGF) nodes containing global climate model data (GCM data) from the fifth phase of the Coupled Model Intercomparison Project (CMIP5) and later from the Coordinated Regional Climate Downscaling Experiment (CORDEX). This global network of all major climate model data centers offers services for data description, discovery and download. The climate4impact portal connects to these services and offers a user interface for searching, visualizing and downloading global climate model data and more. During the project, the content management system Drupal was used to enable partners to contribute on the documentation section. The following topics will be demonstrated: - Security: Login using OpenID for access to the ESG data nodes. The ESG works in conjunction with several external websites and systems. The climate4impact portal uses X509 based short lived credentials, generated on behalf of the user with a MyProxy service. Single Sign-on (SSO) is used to make these websites and systems work together. - Discovery: Facetted search based on e.g. variable name, model and institute using the ESG search services. A catalog browser allows for browsing through CMIP5 and other climate model data catalogues (e.g. ESSENCE, EOBS, UNIDATA). - Download: Directly from ESG nodes and other THREDDS catalogs - Visualization: Visualize any data directly using ADAGUC dynamic Web Map Services. - Transformation: Transform your data into other formats, perform basic calculations and extractions using OCG Web Processing Services The current portal is a Prototype. It is built to explore state-of-art technologies to provide improved access to climate model data. The prototype will be evaluated and is the basis for development of an operational service. The portal and services provided will be sustained and supported during the development of these operational services (2013-2016) in the second phase of the FP7 IS-ENES project, ISENES2.

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

Buechler, Elizabeth D.; Pallin, Simon B.; Boudreaux, Philip R.

The indoor air temperature and relative humidity in residential buildings significantly affect material moisture durability, HVAC system performance, and occupant comfort. Therefore, indoor climate data is generally required to define boundary conditions in numerical models that evaluate envelope durability and equipment performance. However, indoor climate data obtained from field studies is influenced by weather, occupant behavior and internal loads, and is generally unrepresentative of the residential building stock. Likewise, whole-building simulation models typically neglect stochastic variables and yield deterministic results that are applicable to only a single home in a specific climate. The

Collaborative Project. Understanding the effects of tides and eddies on the ocean dynamics, sea ice cover and decadal/centennial climate prediction using the Regional Arctic Climate Model (RACM)

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

Hutchings, Jennifer; Joseph, Renu

2013-09-14

The goal of this project is to develop an eddy resolving ocean model (POP) with tides coupled to a sea ice model (CICE) within the Regional Arctic System Model (RASM) to investigate the importance of ocean tides and mesoscale eddies in arctic climate simulations and quantify biases associated with these processes and how their relative contribution may improve decadal to centennial arctic climate predictions. Ocean, sea ice and coupled arctic climate response to these small scale processes will be evaluated with regard to their influence on mass, momentum and property exchange between oceans, shelf-basin, ice-ocean, and ocean-atmosphere. The project willmore » facilitate the future routine inclusion of polar tides and eddies in Earth System Models when computing power allows. As such, the proposed research addresses the science in support of the BER’s Climate and Environmental Sciences Division Long Term Measure as it will improve the ocean and sea ice model components as well as the fully coupled RASM and Community Earth System Model (CESM) and it will make them more accurate and computationally efficient.« less

How does the sensitivity of climate affect stratospheric solar radiation management?

NASA Astrophysics Data System (ADS)

Ricke, K.; Rowlands, D. J.; Ingram, W.; Keith, D.; Morgan, M. G.

2011-12-01

If implementation of proposals to engineer the climate through solar radiation management (SRM) ever occurs, it is likely to be contingent upon climate sensitivity. Despite this, no modeling studies have examined how the effectiveness of SRM forcings differs between the typical Atmosphere-Ocean General Circulation Models (AOGCMs) with climate sensitivities close to the Coupled Model Intercomparison Project (CMIP) mean and ones with high climate sensitivities. Here, we use a perturbed physics ensemble modeling experiment to examine variations in the response of climate to SRM under different climate sensitivities. When SRM is used as a substitute for mitigation its ability to maintain the current climate state gets worse with increased climate sensitivity and with increased concentrations of greenhouse gases. However, our results also demonstrate that the potential of SRM to slow climate change, even at the regional level, grows with climate sensitivity. On average, SRM reduces regional rates of temperature change by more than 90 percent and rates of precipitation change by more than 50 percent in these higher sensitivity model configurations. To investigate how SRM might behave in models with high climate sensitivity that are also consistent with recent observed climate change we perform a "perturbed physics" ensemble (PPE) modelling experiment with the climateprediction.net (cpdn) version of the HadCM3L AOGCM. Like other perturbed physics climate modelling experiments, we simulate past and future climate scenarios using a wide range of model parameter combinations that both reproduce past climate within a specified level of accuracy and simulate future climates with a wide range of climate sensitivities. We chose 43 members ("model versions") from a subset of the 1,550 from the British Broadcasting Corporation (BBC) climateprediction.net project that have data that allow restarts. We use our results to explore how much assessments of SRM that use best-estimate models, and so near-median climate sensitivity, may be ignoring important contingencies associated with implementing SRM in reality. A primary motivation for studying SRM via the injection of aerosols in the stratosphere is to evaluate its potential effectiveness as "insurance" in the case of higher-than-expected climate response to global warming. We find that this is precisely when SRM appears to be least effective in returning regional climates to their baseline states and reducing regional rates of precipitation change. On the other hand, given the very high regional temperature anomalies associated with rising greenhouse gas concentrations in high sensitivity models, it is also where SRM is most effective in reducing rates of change relative to a no SRM alternative.

Catchments as non-linear filters: evaluating data-driven approaches for spatio-temporal predictions in ungauged basins

NASA Astrophysics Data System (ADS)

Bellugi, D. G.; Tennant, C.; Larsen, L.

2016-12-01

Catchment and climate heterogeneity complicate prediction of runoff across time and space, and resulting parameter uncertainty can lead to large accumulated errors in hydrologic models, particularly in ungauged basins. Recently, data-driven modeling approaches have been shown to avoid the accumulated uncertainty associated with many physically-based models, providing an appealing alternative for hydrologic prediction. However, the effectiveness of different methods in hydrologically and geomorphically distinct catchments, and the robustness of these methods to changing climate and changing hydrologic processes remain to be tested. Here, we evaluate the use of machine learning techniques to predict daily runoff across time and space using only essential climatic forcing (e.g. precipitation, temperature, and potential evapotranspiration) time series as model input. Model training and testing was done using a high quality dataset of daily runoff and climate forcing data for 25+ years for 600+ minimally-disturbed catchments (drainage area range 5-25,000 km2, median size 336 km2) that cover a wide range of climatic and physical characteristics. Preliminary results using Support Vector Regression (SVR) suggest that in some catchments this nonlinear-based regression technique can accurately predict daily runoff, while the same approach fails in other catchments, indicating that the representation of climate inputs and/or catchment filter characteristics in the model structure need further refinement to increase performance. We bolster this analysis by using Sparse Identification of Nonlinear Dynamics (a sparse symbolic regression technique) to uncover the governing equations that describe runoff processes in catchments where SVR performed well and for ones where it performed poorly, thereby enabling inference about governing processes. This provides a robust means of examining how catchment complexity influences runoff prediction skill, and represents a contribution towards the integration of data-driven inference and physically-based models.

A method for physically based model analysis of conjunctive use in response to potential climate changes

USGS Publications Warehouse

Hanson, R.T.; Flint, L.E.; Flint, A.L.; Dettinger, M.D.; Faunt, C.C.; Cayan, D.; Schmid, W.

2012-01-01

Potential climate change effects on aspects of conjunctive management of water resources can be evaluated by linking climate models with fully integrated groundwater-surface water models. The objective of this study is to develop a modeling system that links global climate models with regional hydrologic models, using the California Central Valley as a case study. The new method is a supply and demand modeling framework that can be used to simulate and analyze potential climate change and conjunctive use. Supply-constrained and demand-driven linkages in the water system in the Central Valley are represented with the linked climate models, precipitation-runoff models, agricultural and native vegetation water use, and hydrologic flow models to demonstrate the feasibility of this method. Simulated precipitation and temperature were used from the GFDL-A2 climate change scenario through the 21st century to drive a regional water balance mountain hydrologic watershed model (MHWM) for the surrounding watersheds in combination with a regional integrated hydrologic model of the Central Valley (CVHM). Application of this method demonstrates the potential transition from predominantly surface water to groundwater supply for agriculture with secondary effects that may limit this transition of conjunctive use. The particular scenario considered includes intermittent climatic droughts in the first half of the 21st century followed by severe persistent droughts in the second half of the 21st century. These climatic droughts do not yield a valley-wide operational drought but do cause reduced surface water deliveries and increased groundwater abstractions that may cause additional land subsidence, reduced water for riparian habitat, or changes in flows at the Sacramento-San Joaquin River Delta. The method developed here can be used to explore conjunctive use adaptation options and hydrologic risk assessments in regional hydrologic systems throughout the world.

Modeling climate change effects on runoff and soil erosion in southeastern Arizona rangelands and implications for mitigation with rangeland conservation practices

USDA-ARS?s Scientific Manuscript database

Climate change is expected to impact runoff and soil erosion on rangelands in the southwestern United States. This study was done to evaluate the potential impacts of precipitation changes on soil erosion and surface runoff in southeastern Arizona using seven GCM models with three emission scenarios...

Modelling the impact of climate change and atmospheric N deposition on French forests biodiversity.

PubMed

Rizzetto, Simon; Belyazid, Salim; Gégout, Jean-Claude; Nicolas, Manuel; Alard, Didier; Corcket, Emmanuel; Gaudio, Noémie; Sverdrup, Harald; Probst, Anne

2016-06-01

A dynamic coupled biogeochemical-ecological model was used to simulate the effects of nitrogen deposition and climate change on plant communities at three forest sites in France. The three sites had different forest covers (sessile oak, Norway spruce and silver fir), three nitrogen loads ranging from relatively low to high, different climatic regions and different soil types. Both the availability of vegetation time series and the environmental niches of the understory species allowed to evaluate the model for predicting the composition of the three plant communities. The calibration of the environmental niches was successful, with a model performance consistently reasonably high throughout the three sites. The model simulations of two climatic and two deposition scenarios showed that climate change may entirely compromise the eventual recovery from eutrophication of the simulated plant communities in response to the reductions in nitrogen deposition. The interplay between climate and deposition was strongly governed by site characteristics and histories in the long term, while forest management remained the main driver of change in the short term. Copyright © 2016 Elsevier Ltd. All rights reserved.

Review of the Global Models Used Within Phase 1 of the Chemistry-Climate Model Initiative (CCMI)

NASA Technical Reports Server (NTRS)

Morgenstern, Olaf; Hegglin, Michaela I.; Rozanov, Eugene; O’Connor, Fiona M.; Abraham, N. Luke; Akiyoshi, Hideharu; Archibald, Alexander T.; Bekki, Slimane; Butchart, Neal; Chipperfield, Martyn P.;

Present-day Antarctic climatology of the NCAR Community Climate Model Version 1

NASA Technical Reports Server (NTRS)

Tzeng, Ren-Yow; Bromwich, David H.; Parish, Thomas R.

1993-01-01

The ability of the NCAR Community Climate Model Version 1 (CCM1) with R 15 resolution to simulate the present-day climate of Antarctica was evaluated using the five-year seasonal cycle output produced by the CCM1 and comparing the model results with observed horizontal syntheses and point data. The results showed that the CCM1 with R 15 resolution can simulate to some extent the dynamics of Antarctic climate on the synoptic scale as well as some mesoscale features. The model can also simulate the phase and the amplitude of the annual and semiannual variation of the temperature, sea level pressure, and zonally averaged zonal (E-W) wind. The main shortcomings of the CCM1 model are associated with the model's anomalously large precipitation amounts at high latitudes, due to the tendency of the scheme to suppress negative moisture values.

[Research on quality regionalization of cultivated Pseudostellaria heterophylla based on climate factors].

PubMed

Kang, Chuan-Zhi; Zhou, Tao; Jiang, Wei-Ke; Guo, Lan-Ping; Zhang, Xiao-Bo; Xiao, Cheng-Hong; Zhao, Dan

2016-07-01

Maxent model was applied in the study to filtering the climate factors layer by layer. Polysaccharides and pseudostellarin B the two internal quality evaluation index were combined to analyse the interlinkages between climate factors and chemical constituents in order to search for the critical climate factors of Pseudostellaria heterophylla. Then based on the key climate factors to explicit the quality spatial distribution of P. heterophylla. The results showed that polysaccharides and climatic factors had no significant correlation, suggesting that the indicator was not climate-driven metabolites. Pseudostellarin B could construct regression model with the precipitation. And quality regionalization results showed that pseudostellarin B content presented firstly increased and then decreased trend from southeast to northwest, which was the consistent change with precipitation. It clearly proposed that precipitation was the key climate factor, which affected the accumulation of cyclopeptide compound for Pseudostellariae Radix. Copyright© by the Chinese Pharmaceutical Association.

Evaluation of global horizontal irradiance to plane-of-array irradiance models at locations across the United States

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

Lave, Matthew; Hayes, William; Pohl, Andrew

2015-02-02

We report an evaluation of the accuracy of combinations of models that estimate plane-of-array (POA) irradiance from measured global horizontal irradiance (GHI). This estimation involves two steps: 1) decomposition of GHI into direct and diffuse horizontal components and 2) transposition of direct and diffuse horizontal irradiance (DHI) to POA irradiance. Measured GHI and coincident measured POA irradiance from a variety of climates within the United States were used to evaluate combinations of decomposition and transposition models. A few locations also had DHI measurements, allowing for decoupled analysis of either the decomposition or the transposition models alone. Results suggest that decompositionmore » models had mean bias differences (modeled versus measured) that vary with climate. Transposition model mean bias differences depended more on the model than the location. Lastly, when only GHI measurements were available and combinations of decomposition and transposition models were considered, the smallest mean bias differences were typically found for combinations which included the Hay/Davies transposition model.« less

Evaluation of Offline Models Used to Simulate Components of the Permafrost Carbon Feedback: Experience from the Permafrost Carbon Network Model Integration Group

NASA Astrophysics Data System (ADS)

McGuire, A. D.

2016-12-01

The Model Integration Group of the Permafrost Carbon Network (see http://www.permafrostcarbon.org/) has conducted studies to evaluate the sensitivity of offline terrestrial permafrost and carbon models to both historical and projected climate change. These studies indicate that there is a wide range of (1) initial states permafrost extend and carbon stocks simulated by these models and (2) responses of permafrost extent and carbon stocks to both historical and projected climate change. In this study, we synthesize what has been learned about the variability in initial states among models and the driving factors that contribute to variability in the sensitivity of responses. We conclude the talk with a discussion of efforts needed by (1) the modeling community to standardize structural representation of permafrost and carbon dynamics among models that are used to evaluate the permafrost carbon feedback and (2) the modeling and observational communities to jointly develop data sets and methodologies to more effectively benchmark models.

Global Air Quality and Climate Impacts of Mitigating Short-lived Climate Pollution in China

NASA Astrophysics Data System (ADS)

Harper, K.; Unger, N.; Heyes, C.; Kiesewetter, G.; Klimont, Z.; Schoepp, W.; Wagner, F.

2014-12-01

China is a major emitter of harmful air pollutants, including the short-lived climate pollutants (SLCPs) and their precursors. Implementation of pollution control technologies provides a mechanism for simultaneously protecting human and ecosystem health and achieving near-term climate co-benefits; however, predicting the outcomes of technical and policy interventions is challenging because the SLCPs participate in both climate warming and cooling and share many common emission sources. Here, we present the results of a combined regional integrated assessment and global climate modeling study aimed at quantifying the near-term climate and air quality co-benefits of selective control of Chinese air pollution emissions. Results from IIASA's Greenhouse Gas - Air Pollution Interactions and Synergies (GAINS) integrated assessment model indicate that methane emission reductions make up > 75% of possible CO2-equivalent emission reductions of the SLCPs and their precursors in China in 2030. A multi-pollutant emission reduction scenario incorporating the 2030 Chinese pollution control measures with the highest potential for future climate impact is applied to the NASA ModelE2 - Yale Interactive Terrestrial Biosphere (NASA ModelE2-YIBs) global carbon - chemistry - climate model to assess the regional and long-range impacts of Chinese SLCP mitigation measures. Using model simulations that incorporate dynamic methane emissions and photosynthesis-dependent isoprene emissions, we quantify the impacts of Chinese reductions of the short-lived air pollutants on radiative forcing and on surface ozone and particulate air pollution. Present-day modeled methane mole fractions are evaluated against SCIAMACHY methane columns and NOAA ESRL/GMD surface flask measurements.

Cross - Scale Intercomparison of Climate Change Impacts Simulated by Regional and Global Hydrological Models in Eleven Large River Basins

NASA Technical Reports Server (NTRS)

Hattermann, F. F.; Krysanova, V.; Gosling, S. N.; Dankers, R.; Daggupati, P.; Donnelly, C.; Florke, M.; Huang, S.; Motovilov, Y.; Buda, S.;

Climate Change Impacts on Crop Production in Nigeria

NASA Astrophysics Data System (ADS)

Mereu, V.; Gallo, A.; Carboni, G.; Spano, D.

2011-12-01

The agricultural sector in Nigeria is particularly important for the country's food security, natural resources, and growth agenda. The cultivable areas comprise more than 70% of the total area; however, the cultivated area is about the 35% of the total area. The most important components in the food basket of the nation are cereals and tubers, which include rice, maize, corn, millet, sorghum, yam, and cassava. These crops represent about 80% of the total agricultural product in Nigeria (from NPAFS). The major crops grown in the country can be divided into food crops (produced for consumption) and export products. Despite the importance of the export crops, the primary policy of agriculture is to make Nigeria self-sufficient in its food and fiber requirements. The projected impacts of future climate change on agriculture and water resources are expected to be adverse and extensive in these area. This implies the need for actions and measures to adapt to climate change impacts, and especially as they affect agriculture, the primary sector for Nigerian economy. In the framework of the Project Climate Risk Analysis in Nigeria (founded by World Bank Contract n.7157826), a study was made to assess the potential impact of climate change on the main crops that characterize Nigerian agriculture. The DSSAT-CSM (Decision Support System for Agrotechnology Transfer - Cropping System Model) software, version 4.5 was used for the analysis. Crop simulation models included in DSSAT are tools that simulate physiological processes of crop growth, development and production by combining genetic crop characteristics and environmental (soil and weather) conditions. For each selected crop, the models were calibrated to evaluate climate change impacts on crop production. The climate data used for the analysis are derived by the Regional Circulation Model COSMO-CLM, from 1971 to 2065, at 8 km of spatial resolution. The RCM model output was "perturbed" with 10 Global Climate Models to have a wide variety of possible climate projections for the impact analysis. Multiple combinations of soil and climate conditions and crop management and varieties were considered for each Agro-Ecological Zone (AEZ) of Nigeria. A sensitivity analysis was made to evaluate the model response to changes in precipitation and temperature. The climate impact assessment was made by comparing the yield obtained with the climate data for the present period and the yield obtainable under future climate conditions. The results were analyzed at state, AEZ and country levels. The analysis shows a general reduction in crop yields in particular in the dryer regions of northern Nigeria.

Carbon Cycle Model Linkage Project (CCMLP): Evaluating Biogeochemical Process Models with Atmospheric Measurements and Field Experiments

NASA Astrophysics Data System (ADS)

Heimann, M.; Prentice, I. C.; Foley, J.; Hickler, T.; Kicklighter, D. W.; McGuire, A. D.; Melillo, J. M.; Ramankutty, N.; Sitch, S.

2001-12-01

Models of biophysical and biogeochemical proceses are being used -either offline or in coupled climate-carbon cycle (C4) models-to assess climate- and CO2-induced feedbacks on atmospheric CO2. Observations of atmospheric CO2 concentration, and supplementary tracers including O2 concentrations and isotopes, offer unique opportunities to evaluate the large-scale behaviour of models. Global patterns, temporal trends, and interannual variability of the atmospheric CO2 concentration and its seasonal cycle provide crucial benchmarks for simulations of regionally-integrated net ecosystem exchange; flux measurements by eddy correlation allow a far more demanding model test at the ecosystem scale than conventional indicators, such as measurements of annual net primary production; and large-scale manipulations, such as the Duke Forest Free Air Carbon Enrichment (FACE) experiment, give a standard to evaluate modelled phenomena such as ecosystem-level CO2 fertilization. Model runs including historical changes of CO2, climate and land use allow comparison with regional-scale monthly CO2 balances as inferred from atmospheric measurements. Such comparisons are providing grounds for some confidence in current models, while pointing to processes that may still be inadequately treated. Current plans focus on (1) continued benchmarking of land process models against flux measurements across ecosystems and experimental findings on the ecosystem-level effects of enhanced CO2, reactive N inputs and temperature; (2) improved representation of land use, forest management and crop metabolism in models; and (3) a strategy for the evaluation of C4 models in a historical observational context.

Adaptive management of irrigation and crops' biodiversity: a case study on tomato

NASA Astrophysics Data System (ADS)

De Lorenzi, Francesca; Alfieri, Silvia Maria; Basile, Angelo; Bonfante, Antonello; Monaco, Eugenia; Riccardi, Maria; Menenti, Massimo

2013-04-01

We have assessed the impacts of climate change and evaluated options to adapt irrigation management in the face of predicted changes of agricultural water demand. We have evaluated irrigation scheduling and its effectiveness (versus crop transpiration), and cultivars' adaptability. The spatial and temporal variations of effectiveness and adaptability were studied in an irrigated district of Southern Italy. Two climate scenarios were considered: reference (1961-90) and future (2021-2050) climate, the former from climatic statistics, and the latter from statistical downscaling of general circulation models (AOGCM). Climatic data consist of daily time series of maximum and minimum temperature, and daily rainfall on a grid with a spatial resolution of 35 km. The work was carried out in the Destra Sele irrigation scheme (18.000 ha. Twenty-five soil units were identified and their hydrological properties were determined (measured or estimated from texture through pedo-transfer functions). A tomato crop, in a rotation typical of the area, was considered. A mechanistic model of water flow in the soil-plant-atmosphere system (SWAP) was used to study crop water requirements and water consumption. The model was calibrated and validated in the same area for many different crops. Tomato crop input data and model parameters were estimated on the basis of scientific literature and assumed to be generically representative of the species. Simulations were performed for reference and future climate, and for different irrigation scheduling options. In all soil units, six levels of irrigation volumes were applied: full irrigation (100%), deficit irrigation (80%, 60%, 40%, 20%), no irrigation. From simulation runs, indicators of soil water availability were calculated, moreover the marginal increases of transpiration per unit of irrigation volume, i.e. the effectiveness of irrigation (ΔT/I), were computed, in both climate scenarios. Indicators and marginal increases were used to evaluate the tomato crop adaptability to future climate. To this purpose, for several tomato cultivars, threshold values of their yield responses to soil water availability were determined (data from scientific literature). Cultivars' threshold values were evaluated, in all soil units, against the indicators' values, for irrigation levels with different ΔT/I. Less water intensive cultivars and irrigation volumes that optimize transpiration (and yield) could thus be identified in both climate scenarios, and irrigation management scenarios were determined taking into account soils' hydrological properties, crop biodiversity, and efficient use of water resource. 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) Keywords: climate change, adaptation, simulation models, deficit irrigation, water resource efficiency, SWAP

Biodiversity of Terrestrial Vegetation during Past Warm Periods

NASA Astrophysics Data System (ADS)

Davies-Barnard, T.; Valdes, P. J.; Ridgwell, A.

2016-12-01

Previous modelling studies of vegetation have generally used a small number of plant functional types to understand how the terrestrial biosphere responds to climate changes. Whilst being useful for understanding first order climate feedbacks, this climate-envelope approach makes a lot of assumptions about past vegetation being very similar to modern. A trait-based method has the advantage for paleo modelling in that there are substantially less assumptions made. In a novel use of the trait-based dynamic vegetation model JeDi, forced with output from climate model HadCM3, we explore past biodiversity and vegetation carbon changes. We use JeDi to model an optimal 2000 combinations of fifteen different traits to enable assessment of the overall level of biodiversity as well as individual growth strategies. We assess the vegetation shifts and biodiversity changes in past greenhouse periods to better understand the impact on the terrestrial biosphere. This work provides original insights into the response of vegetation and terrestrial carbon to climate and hydrological changes in high carbon dioxide climates over time, including during the Late Permian and Cretaceous. We evaluate how the location of biodiversity hotspots and species richness in past greenhouse climates is different to the present day.

Devon Ice cap's future: results from climate and ice dynamics modelling via surface mass balance modelling

NASA Astrophysics Data System (ADS)

Rodehacke, C. B.; Mottram, R.; Boberg, F.

2017-12-01

The Devon Ice Cap is an example of a relatively well monitored small ice cap in the Canadian Arctic. Close to Greenland, it shows a similar surface mass balance signal to glaciers in western Greenland. Here we various boundary conditions, ranging from ERA-Interim reanalysis data via global climate model high resolution (5km) output from the regional climate model HIRHAM5, to determine the surface mass balance of the Devon ice cap. These SMB estimates are used to drive the PISM glacier model in order to model the present day and future prospects of this small Arctic ice cap. Observational data from the Devon Ice Cap in Arctic Canada is used to evaluate the surface mass balance (SMB) data output from the HIRHAM5 model for simulations forced with the ERA-Interim climate reanalysis data and the historical emissions scenario run by the EC-Earth global climate model. The RCP8.5 scenario simulated by EC-Earth is also downscaled by HIRHAM5 and this output is used to force the PISM model to simulate the likely future evolution of the Devon Ice Cap under a warming climate. We find that the Devon Ice Cap is likely to continue its present day retreat, though in the future increased precipitation partly offsets the enhanced melt rates caused by climate change.

Application of the NASA A-Train to Evaluate Clouds Simulated by the Weather Research and Forecast Model

NASA Technical Reports Server (NTRS)

Molthan, Andrew L.; Jedlovec, Gary J.; Lapenta, William M.

2008-01-01

The CloudSat Mission, part of the NASA A-Train, is providing the first global survey of cloud profiles and cloud physical properties, observing seasonal and geographical variations that are pertinent to evaluating the way clouds are parameterized in weather and climate forecast models. CloudSat measures the vertical structure of clouds and precipitation from space through the Cloud Profiling Radar (CPR), a 94 GHz nadir-looking radar measuring the power backscattered by clouds as a function of distance from the radar. One of the goals of the CloudSat mission is to evaluate the representation of clouds in forecast models, thereby contributing to improved predictions of weather, climate and the cloud-climate feedback problem. This paper highlights potential limitations in cloud microphysical schemes currently employed in the Weather Research and Forecast (WRF) modeling system. The horizontal and vertical structure of explicitly simulated cloud fields produced by the WRF model at 4-km resolution are being evaluated using CloudSat observations in concert with products derived from MODIS and AIRS. A radiative transfer model is used to produce simulated profiles of radar reflectivity given WRF input profiles of hydrometeor mixing ratios and ambient atmospheric conditions. The preliminary results presented in the paper will compare simulated and observed reflectivity fields corresponding to horizontal and vertical cloud structures associated with midlatitude cyclone events.

Results from the BRACE 1.5 study: Climate change impacts of 1.5 C and 2 C warming

NASA Astrophysics Data System (ADS)

O'Neill, B. C.; Anderson, B.; Monaghan, A. J.; Ren, X.; Sanderson, B.; Tebaldi, C.

2017-12-01

In 2015, 195 countries negotiated the Paris Agreement on climate change, which set long-term goals of limiting global mean warming to well below 2 C and possibly 1.5 C. This event stimulated substantial scientific interest in climate outcomes and impacts on society associated with those levels of warming. Recently, the first set of global climate model simulations explicitly designed to meet those targets were undertaken with the Community Earth System Model (CESM) for use by the research community (Sanderson et al, accepted). The BRACE 1.5 project models societal impacts from these climate outcomes, combined with assumptions about future socioeconomic conditions according to the Shared Socioeconomic Pathways. These analyses build on a recently completed study of the Benefits of Reduced Anthropogenic Climate changE (BRACE), published as a set of 20 papers in Climatic Change, which examined the difference in impacts between two higher scenarios resulting in about 2.5 C and 3.7 C warming by late this century. BRACE 1.5 consists of a set of six papers to be submitted to a special collection in Environmental Research Letters that takes a similar approach but focuses on impacts at 1.5 and 2 C warming. We ask whether impacts differ substantially between the two climate scenarios, accounting for uncertainty in climate outcomes through the use of initial condition ensembles of CESM simulations, and in societal conditions by using alternative SSP-based development pathways. Impact assessment focuses on the health and agricultural sectors; modeling approaches include the use of a global mutli-region CGE model for economic analysis, both a process-based and an empirical crop model, a model of spatial population change, a model of climatic suitability for the aedes aegypti mosquito, and an epidemiological model of heat-related mortality. A methodological analysis also evaluates the use of climate model emulation techniques for providing climate information sufficient to support impact assessment in low warming scenarios.

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.

A description and evaluation of an air quality model nested within global and regional composition-climate models using MetUM

NASA Astrophysics Data System (ADS)

Neal, Lucy S.; Dalvi, Mohit; Folberth, Gerd; McInnes, Rachel N.; Agnew, Paul; O'Connor, Fiona M.; Savage, Nicholas H.; Tilbee, Marie

2017-11-01

There is a clear need for the development of modelling frameworks for both climate change and air quality to help inform policies for addressing these issues simultaneously. This paper presents an initial attempt to develop a single modelling framework, by introducing a greater degree of consistency in the meteorological modelling framework by using a two-step, one-way nested configuration of models, from a global composition-climate model (GCCM) (140 km resolution) to a regional composition-climate model covering Europe (RCCM) (50 km resolution) and finally to a high (12 km) resolution model over the UK (AQUM). The latter model is used to produce routine air quality forecasts for the UK. All three models are based on the Met Office's Unified Model (MetUM). In order to better understand the impact of resolution on the downscaling of projections of future climate and air quality, we have used this nest of models to simulate a 5-year period using present-day emissions and under present-day climate conditions. We also consider the impact of running the higher-resolution model with higher spatial resolution emissions, rather than simply regridding emissions from the RCCM. We present an evaluation of the models compared to in situ air quality observations over the UK, plus a comparison against an independent 1 km resolution gridded dataset, derived from a combination of modelling and observations, effectively producing an analysis of annual mean surface pollutant concentrations. We show that using a high-resolution model over the UK has some benefits in improving air quality modelling, but that the use of higher spatial resolution emissions is important to capture local variations in concentrations, particularly for primary pollutants such as nitrogen dioxide and sulfur dioxide. For secondary pollutants such as ozone and the secondary component of PM10, the benefits of a higher-resolution nested model are more limited and reasons for this are discussed. This study highlights the point that the resolution of models is not the only factor in determining model performance - consistency between nested models is also important.

Creating "Intelligent" Ensemble Averages Using a Process-Based Framework

NASA Astrophysics Data System (ADS)

Baker, Noel; Taylor, Patrick

2014-05-01

The CMIP5 archive contains future climate projections from over 50 models provided by dozens of modeling centers from around the world. Individual model projections, however, are subject to biases created by structural model uncertainties. As a result, ensemble averaging of multiple models is used to add value to individual model projections and construct a consensus projection. Previous reports for the IPCC establish climate change projections based on an equal-weighted average of all model projections. However, individual models reproduce certain climate processes better than other models. Should models be weighted based on performance? Unequal ensemble averages have previously been constructed using a variety of mean state metrics. What metrics are most relevant for constraining future climate projections? This project develops a framework for systematically testing metrics in models to identify optimal metrics for unequal weighting multi-model ensembles. The intention is to produce improved ("intelligent") unequal-weight ensemble averages. A unique aspect of this project is the construction and testing of climate process-based model evaluation metrics. A climate process-based metric is defined as a metric based on the relationship between two physically related climate variables—e.g., outgoing longwave radiation and surface temperature. Several climate process metrics are constructed using high-quality Earth radiation budget data from NASA's Clouds and Earth's Radiant Energy System (CERES) instrument in combination with surface temperature data sets. It is found that regional values of tested quantities can vary significantly when comparing the equal-weighted ensemble average and an ensemble weighted using the process-based metric. Additionally, this study investigates the dependence of the metric weighting scheme on the climate state using a combination of model simulations including a non-forced preindustrial control experiment, historical simulations, and several radiative forcing Representative Concentration Pathway (RCP) scenarios. Ultimately, the goal of the framework is to advise better methods for ensemble averaging models and create better climate predictions.

Predicting future US water yield and ecosystem productivity by linking an ecohydrological model to WRF dynamically downscaled climate projections

NASA Astrophysics Data System (ADS)

Sun, S.; Sun, G.; Cohen, E.; McNulty, S. G.; Caldwell, P.; Duan, K.; Zhang, Y.

2015-12-01

Quantifying the potential impacts of climate change on water yield and ecosystem productivity (i.e., carbon balances) is essential to developing sound watershed restoration plans, and climate change adaptation and mitigation strategies. This study links an ecohydrological model (Water Supply and Stress Index, WaSSI) with WRF (Weather Research and Forecasting Model) dynamically downscaled climate projections of the HadCM3 model under the IPCC SRES A2 emission scenario. We evaluated the future (2031-2060) changes in evapotranspiration (ET), water yield (Q) and gross primary productivity (GPP) from the baseline period of 1979-2007 across the 82 773 watersheds (12 digit Hydrologic Unit Code level) in the conterminous US (CONUS), and evaluated the future annual and monthly changes of hydrology and ecosystem productivity for the 18 Water Resource Regions (WRRs) or 2-digit HUCs. Across the CONUS, the future multi-year means show increases in annual precipitation (P) of 45 mm yr-1 (6 %), 1.8 °C increase in temperature (T), 37 mm yr-1 (7 %) increase in ET, 9 mm yr-1 (3 %) increase in Q, and 106 g C m-2 yr-1 (9 %) increase in GPP. Response to climate change was highly variable across the 82, 773 watersheds, but in general, the majority would see consistent increases in all variables evaluated. Over half of the 82 773 watersheds, mostly found in the northeast and the southern part of the southwest would have an increase in annual Q (>100 mm yr-1 or 20 %). This study provides an integrated method and example for comprehensive assessment of the potential impacts of climate change on watershed water balances and ecosystem productivity at high spatial and temporal resolutions. Results will be useful for policy-makers and land managers in formulating appropriate watershed-specific strategies for sustaining water and carbon sources in the face of climate change.

Misleading prioritizations from modelling range shifts under climate change

USGS Publications Warehouse

Sofaer, Helen R.; Jarnevich, Catherine S.; Flather, Curtis H.

2018-01-01

AimConservation planning requires the prioritization of a subset of taxa and geographical locations to focus monitoring and management efforts. Integration of the threats and opportunities posed by climate change often relies on predictions from species distribution models, particularly for assessments of vulnerability or invasion risk for multiple taxa. We evaluated whether species distribution models could reliably rank changes in species range size under climate and land use change.LocationConterminous U.S.A.Time period1977–2014.Major taxa studiedPasserine birds.MethodsWe estimated ensembles of species distribution models based on historical North American Breeding Bird Survey occurrences for 190 songbirds, and generated predictions to recent years given c. 35 years of observed land use and climate change. We evaluated model predictions using standard metrics of discrimination performance and a more detailed assessment of the ability of models to rank species vulnerability to climate change based on predicted range loss, range gain, and overall change in range size.ResultsSpecies distribution models yielded unreliable and misleading assessments of relative vulnerability to climate and land use change. Models could not accurately predict range expansion or contraction, and therefore failed to anticipate patterns of range change among species. These failures occurred despite excellent overall discrimination ability and transferability to the validation time period, which reflected strong performance at the majority of locations that were either always or never occupied by each species.Main conclusionsModels failed for the questions and at the locations of greatest interest to conservation and management. This highlights potential pitfalls of multi-taxa impact assessments under global change; in our case, models provided misleading rankings of the most impacted species, and spatial information about range changes was not credible. As modelling methods and frameworks continue to be refined, performance assessments and validation efforts should focus on the measures of risk and vulnerability useful for decision-making.

Interactive effects of water diversion and climate change for juvenile chinook salmon in the lemhi river basin (USA.).

PubMed

Walters, Annika W; Bartz, Krista K; McClure, Michelle M

2013-12-01

The combined effects of water diversion and climate change are a major conservation challenge for freshwater ecosystems. In the Lemhi Basin, Idaho (U.S.A.), water diversion causes changes in streamflow, and climate change will further affect streamflow and temperature. Shifts in streamflow and temperature regimes can affect juvenile salmon growth, movement, and survival. We examined the potential effects of water diversion and climate change on juvenile Chinook salmon (Oncorhynchus tshawytscha), a species listed as threatened under the U.S. Endangered Species Act (ESA). To examine the effects for juvenile survival, we created a model relating 19 years of juvenile survival data to streamflow and temperature and found spring streamflow and summer temperature were good predictors of juvenile survival. We used these models to project juvenile survival for 15 diversion and climate-change scenarios. Projected survival was 42-58% lower when streamflows were diverted than when streamflows were undiverted. For diverted streamflows, 2040 climate-change scenarios (ECHO-G and CGCM3.1 T47) resulted in an additional 11-39% decrease in survival. We also created models relating habitat carrying capacity to streamflow and made projections for diversion and climate-change scenarios. Habitat carrying capacity estimated for diverted streamflows was 17-58% lower than for undiverted streamflows. Climate-change scenarios resulted in additional decreases in carrying capacity for the dry (ECHO-G) climate model. Our results indicate climate change will likely pose an additional stressor that should be considered when evaluating the effects of anthropogenic actions on salmon population status. Thus, this type of analysis will be especially important for evaluating effects of specific actions on a particular species. Efectos Interactivos de la Desviación del Agua y el Cambio Climático en Individuos Juveniles de Salmón Chinook en la Cuenca del Río Lemhi (E.U.A.). Conservation Biology © 2013 Society for Conservation Biology No claim to original US government works.

Developing quantitative criteria to evaluate AOGCMs for application to regional climate assessments

NASA Astrophysics Data System (ADS)

Hayhoe, K.; Wake, C.; Bradbury, J.; Degaetano, A.; Hertel, A.

2006-12-01

Climate projections are the foundation for regional assessments of potential climate impacts. However, the soundness of regional assessments depends on the ability of global climate models to reproduce key processes responsible for regional climate trends. Here, we develop a systematic method to compare observed climate with historical atmosphere-ocean general circulation model (AOGCM) simulations, to assess the degree to which AOGCMs are able to reproduce regional circulation patterns. Applying this methodology to the U.S. Northeast (NE), we find that nearly all AOGCMs simulate a reasonable winter NAO pattern and seasonal positions of the Jet Stream and the East Coast Trough. However, not all models capture observed correlations between these circulation patterns and seasonal climate anomalies in the NE. Using only those AOGCMs that meet the criteria in each of these areas, we then develop projections of future climate change in the NE. The primary changes projected to occur over the next century - slightly greater temperature increases in summer than winter, and increases in winter precipitation - are consistent with projected trends in regional climate processes and are relatively independent of model or scale. These suggest confidence in the direction and potential range of the most notable regional climate trends, with the absolute magnitude of change depending on both the sensitivity of the climate system to human forcing as well as on human emissions over coming decades.

Modelling climate impact on floods under future emission scenarios using an ensemble of climate model projections

NASA Astrophysics Data System (ADS)

Wetterhall, F.; Cloke, H. L.; He, Y.; Freer, J.; Pappenberger, F.

2012-04-01

Evidence provided by modelled assessments of climate change impact on flooding is fundamental to water resource and flood risk decision making. Impact models usually rely on climate projections from Global and Regional Climate Models, and there is no doubt that these provide a useful assessment of future climate change. However, cascading ensembles of climate projections into impact models is not straightforward because of problems of coarse resolution in Global and Regional Climate Models (GCM/RCM) and the deficiencies in modelling high-intensity precipitation events. Thus decisions must be made on how to appropriately pre-process the meteorological variables from GCM/RCMs, such as selection of downscaling methods and application of Model Output Statistics (MOS). In this paper a grand ensemble of projections from several GCM/RCM are used to drive a hydrological model and analyse the resulting future flood projections for the Upper Severn, UK. The impact and implications of applying MOS techniques to precipitation as well as hydrological model parameter uncertainty is taken into account. The resultant grand ensemble of future river discharge projections from the RCM/GCM-hydrological model chain is evaluated against a response surface technique combined with a perturbed physics experiment creating a probabilisic ensemble climate model outputs. The ensemble distribution of results show that future risk of flooding in the Upper Severn increases compared to present conditions, however, the study highlights that the uncertainties are large and that strong assumptions were made in using Model Output Statistics to produce the estimates of future discharge. The importance of analysing on a seasonal basis rather than just annual is highlighted. The inability of the RCMs (and GCMs) to produce realistic precipitation patterns, even in present conditions, is a major caveat of local climate impact studies on flooding, and this should be a focus for future development.

Positive water vapour feedback in climate models confirmed by satellite data

NASA Technical Reports Server (NTRS)

Rind, D.; Lerner, J.; Chiou, E.-W.; Chu, W.; Larsen, J.; Mccormick, M. P.; Mcmaster, L.

1991-01-01

It has recently been suggested that GCMs used to evaluate climate change overestimate the greenhouse effect due to increased concentrations of trace gases in the atmosphere. Here, new satellite-generated water vapor data are used to compare summer and winter moisture values in regions of the middle and upper troposphere that have previously been difficult to observe with confidence. It is found that, as the hemispheres warm, increased convection leads to increased water vapor above 500 mbar in approximate quantitative agreement with results from current climate models. The same conclusion is reached by comparing the tropical western and eastern Pacific regions. Thus, water vapor feedback is not overestimated in models and should amplify the climate response to increased trace-gas concentrations.

The DACCIWA model evaluation project: representation of the meteorology of southern West Africa in state-of-the-art weather, seasonal and climate prediction models

NASA Astrophysics Data System (ADS)

Kniffka, Anke; Benedetti, Angela; Knippertz, Peter; Stanelle, Tanja; Brooks, Malcolm; Deetz, Konrad; Maranan, Marlon; Rosenberg, Philip; Pante, Gregor; Allan, Richard; Hill, Peter; Adler, Bianca; Fink, Andreas; Kalthoff, Norbert; Chiu, Christine; Vogel, Bernhard; Field, Paul; Marsham, John

2017-04-01

DACCIWA (Dynamics-Aerosol-Chemistry-Cloud Interactions in West Africa) is an EU-funded project that aims to determine the influence of anthropogenic and natural emissions on the atmospheric composition, air quality, weather and climate over southern West Africa. DACCIWA organised a major international field campaign in June-July 2016 and involves a wide range of modelling activities. Here we report about the coordinated model evaluation performed in the framework of DACCIWA focusing on meteorological fields. This activity consists of two elements: (a) the quality of numerical weather prediction during the field campaign, (b) the ability of seasonal and climate models to represent the mean state and its variability. For the first element, the extensive observations from the main field campaign in West Africa in June-July 2016 (ground supersites, radiosondes, aircraft measurements) will be combined with conventional data (synoptic stations, satellites data from various sensors) to evaluate models against. The forecasts include operational products from centres such as the ECMWF, UK MetOffice and the German Weather Service and runs specifically conducted for the planning and the post-analysis of the field campaign using higher resolutions (e.g., WRF, COSMO). The forecast and the observations are analysed in a concerted way to assess the ability of the models to represent the southern West African weather systems and secondly to provide a comprehensive synoptic overview of the state of the atmosphere. In a second step the process will be extended to long-term modelling periods. This includes both seasonal and climate models, respectively. In this case, the observational dataset contains long-term satellite observations and station data, some of which were digitised from written records in the framework of DACCIWA. Parameter choice and spatial averaging will build directly on the weather forecasting evaluation to allow an assessment of the impact of short-term errors on long-term simulations.

Evaluating cloud processes in large-scale models: Of idealized case studies, parameterization testbeds and single-column modelling on climate time-scales

NASA Astrophysics Data System (ADS)

Neggers, Roel

2016-04-01

Boundary-layer schemes have always formed an integral part of General Circulation Models (GCMs) used for numerical weather and climate prediction. The spatial and temporal scales associated with boundary-layer processes and clouds are typically much smaller than those at which GCMs are discretized, which makes their representation through parameterization a necessity. The need for generally applicable boundary-layer parameterizations has motivated many scientific studies, which in effect has created its own active research field in the atmospheric sciences. Of particular interest has been the evaluation of boundary-layer schemes at "process-level". This means that parameterized physics are studied in isolated mode from the larger-scale circulation, using prescribed forcings and excluding any upscale interaction. Although feedbacks are thus prevented, the benefit is an enhanced model transparency, which might aid an investigator in identifying model errors and understanding model behavior. The popularity and success of the process-level approach is demonstrated by the many past and ongoing model inter-comparison studies that have been organized by initiatives such as GCSS/GASS. A red line in the results of these studies is that although most schemes somehow manage to capture first-order aspects of boundary layer cloud fields, there certainly remains room for improvement in many areas. Only too often are boundary layer parameterizations still found to be at the heart of problems in large-scale models, negatively affecting forecast skills of NWP models or causing uncertainty in numerical predictions of future climate. How to break this parameterization "deadlock" remains an open problem. This presentation attempts to give an overview of the various existing methods for the process-level evaluation of boundary-layer physics in large-scale models. This includes i) idealized case studies, ii) longer-term evaluation at permanent meteorological sites (the testbed approach), and iii) process-level evaluation at climate time-scales. The advantages and disadvantages of each approach will be identified and discussed, and some thoughts about possible future developments will be given.

Alternative future analysis for assessing the potential impact of climate change on urban landscape dynamics.

PubMed

He, Chunyang; Zhao, Yuanyuan; Huang, Qingxu; Zhang, Qiaofeng; Zhang, Da

2015-11-01

Assessing the impact of climate change on urban landscape dynamics (ULD) is the foundation for adapting to climate change and maintaining urban landscape sustainability. This paper demonstrates an alternative future analysis by coupling a system dynamics (SD) and a cellular automata (CA) model. The potential impact of different climate change scenarios on ULD from 2009 to 2030 was simulated and evaluated in the Beijing-Tianjin-Tangshan megalopolis cluster area (BTT-MCA). The results suggested that the integrated model, which combines the advantages of the SD and CA model, has the strengths of spatial quantification and flexibility. Meanwhile, the results showed that the influence of climate change would become more severe over time. In 2030, the potential urban area affected by climate change will be 343.60-1260.66 km(2) (5.55 -20.37 % of the total urban area, projected by the no-climate-change-effect scenario). Therefore, the effects of climate change should not be neglected when designing and managing urban landscape. Copyright © 2015 Elsevier B.V. All rights reserved.

Educational and Scientific Applications of Climate Model Diagnostic Analyzer

NASA Astrophysics Data System (ADS)

Lee, S.; Pan, L.; Zhai, C.; Tang, B.; Kubar, T. L.; Zhang, J.; Bao, Q.

2016-12-01

Climate Model Diagnostic Analyzer (CMDA) is a web-based information system designed for the climate modeling and model analysis community to analyze climate data from models and observations. CMDA provides tools to diagnostically analyze climate data for model validation and improvement, and to systematically manage analysis provenance for sharing results with other investigators. CMDA utilizes cloud computing resources, multi-threading computing, machine-learning algorithms, web service technologies, and provenance-supporting technologies to address technical challenges that the Earth science modeling and model analysis community faces in evaluating and diagnosing climate models. As CMDA infrastructure and technology have matured, we have developed the educational and scientific applications of CMDA. Educationally, CMDA supported the summer school of the JPL Center for Climate Sciences for three years since 2014. In the summer school, the students work on group research projects where CMDA provide datasets and analysis tools. Each student is assigned to a virtual machine with CMDA installed in Amazon Web Services. A provenance management system for CMDA is developed to keep track of students' usages of CMDA, and to recommend datasets and analysis tools for their research topic. The provenance system also allows students to revisit their analysis results and share them with their group. Scientifically, we have developed several science use cases of CMDA covering various topics, datasets, and analysis types. Each use case developed is described and listed in terms of a scientific goal, datasets used, the analysis tools used, scientific results discovered from the use case, an analysis result such as output plots and data files, and a link to the exact analysis service call with all the input arguments filled. For example, one science use case is the evaluation of NCAR CAM5 model with MODIS total cloud fraction. The analysis service used is Difference Plot Service of Two Variables, and the datasets used are NCAR CAM total cloud fraction and MODIS total cloud fraction. The scientific highlight of the use case is that the CAM5 model overall does a fairly decent job at simulating total cloud cover, though simulates too few clouds especially near and offshore of the eastern ocean basins where low clouds are dominant.

C4MIP - The Coupled Climate-Carbon Cycle Model Intercomparison Project: experimental protocol for CMIP6

NASA Astrophysics Data System (ADS)

Jones, Chris D.; Arora, Vivek; Friedlingstein, Pierre; Bopp, Laurent; Brovkin, Victor; Dunne, John; Graven, Heather; Hoffman, Forrest; Ilyina, Tatiana; John, Jasmin G.; Jung, Martin; Kawamiya, Michio; Koven, Charlie; Pongratz, Julia; Raddatz, Thomas; Randerson, James T.; Zaehle, Sönke

2016-08-01

Coordinated experimental design and implementation has become a cornerstone of global climate modelling. Model Intercomparison Projects (MIPs) enable systematic and robust analysis of results across many models, by reducing the influence of ad hoc differences in model set-up or experimental boundary conditions. As it enters its 6th phase, the Coupled Model Intercomparison Project (CMIP6) has grown significantly in scope with the design and documentation of individual simulations delegated to individual climate science communities. The Coupled Climate-Carbon Cycle Model Intercomparison Project (C4MIP) takes responsibility for design, documentation, and analysis of carbon cycle feedbacks and interactions in climate simulations. These feedbacks are potentially large and play a leading-order contribution in determining the atmospheric composition in response to human emissions of CO2 and in the setting of emissions targets to stabilize climate or avoid dangerous climate change. For over a decade, C4MIP has coordinated coupled climate-carbon cycle simulations, and in this paper we describe the C4MIP simulations that will be formally part of CMIP6. While the climate-carbon cycle community has created this experimental design, the simulations also fit within the wider CMIP activity, conform to some common standards including documentation and diagnostic requests, and are designed to complement the CMIP core experiments known as the Diagnostic, Evaluation and Characterization of Klima (DECK). C4MIP has three key strands of scientific motivation and the requested simulations are designed to satisfy their needs: (1) pre-industrial and historical simulations (formally part of the common set of CMIP6 experiments) to enable model evaluation, (2) idealized coupled and partially coupled simulations with 1 % per year increases in CO2 to enable diagnosis of feedback strength and its components, (3) future scenario simulations to project how the Earth system will respond to anthropogenic activity over the 21st century and beyond. This paper documents in detail these simulations, explains their rationale and planned analysis, and describes how to set up and run the simulations. Particular attention is paid to boundary conditions, input data, and requested output diagnostics. It is important that modelling groups participating in C4MIP adhere as closely as possible to this experimental design.

A Multi-Model Framework to Achieve Consistent Evaluation of Climate Change Impacts in the United States

NASA Astrophysics Data System (ADS)

Sarofim, M. C.; Martinich, J.; Waldhoff, S.; DeAngelo, B. J.; McFarland, J.; Jantarasami, L.; Shouse, K.; Crimmins, A.; Li, J.

2014-12-01

The Climate Change Impacts and Risk Analysis (CIRA) project establishes a new multi-model framework to systematically assess the physical impacts, economic damages, and risks from climate change. The primary goal of this framework is to estimate the degree to which climate change impacts and damages in the United States are avoided or reduced in the 21st century under multiple greenhouse gas (GHG) emissions mitigation scenarios. The first phase of the CIRA project is a modeling exercise that included two integrated assessment models and 15 sectoral models encompassing five broad impacts sectors: water resources, electric power, infrastructure, human health, and ecosystems. Three consistent socioeconomic and climate scenarios are used to analyze the benefits of global GHG mitigation targets: a reference scenario and two policy scenarios with total radiative forcing targets in 2100 of 4.5 W/m2 and 3.7 W/m2. In this exercise, the implications of key uncertainties are explored, including climate sensitivity, climate model, natural variability, and model structures and parameters. This presentation describes the motivations and goals of the CIRA project; the design and academic contribution of the first CIRA modeling exercise; and briefly summarizes several papers published in a special issue of Climatic Change. The results across impact sectors show that GHG mitigation provides benefits to the United States that increase over time, the effects of climate change can be strongly influenced by near-term policy choices, adaptation can reduce net damages, and impacts exhibit spatial and temporal patterns that may inform mitigation and adaptation policy discussions.

Assessment of extreme flood events in a changing climate for a long-term planning of socio-economic infrastructure in the Russian Arctic

NASA Astrophysics Data System (ADS)

Shevnina, Elena; Kourzeneva, Ekaterina; Kovalenko, Viktor; Vihma, Timo

2017-05-01

Climate warming has been more acute in the Arctic than at lower latitudes and this tendency is expected to continue. This generates major challenges for economic activity in the region. Among other issues is the long-term planning and development of socio-economic infrastructure (dams, bridges, roads, etc.), which require climate-based forecasts of the frequency and magnitude of detrimental flood events. To estimate the cost of the infrastructure and operational risk, a probabilistic form of long-term forecasting is preferable. In this study, a probabilistic model to simulate the parameters of the probability density function (PDF) for multi-year runoff based on a projected climatology is applied to evaluate changes in extreme floods for the territory of the Russian Arctic. The model is validated by cross-comparison of the modelled and empirical PDFs using observations from 23 sites located in northern Russia. The mean values and coefficients of variation (CVs) of the spring flood depth of runoff are evaluated under four climate scenarios, using simulations of six climate models for the period 2010-2039. Regions with substantial expected changes in the means and CVs of spring flood depth of runoff are outlined. For the sites located within such regions, it is suggested to account for the future climate change in calculating the maximal discharges of rare occurrence. An example of engineering calculations for maximal discharges with 1 % exceedance probability is provided for the Nadym River at Nadym.

Modelling hydrological responses of Nerbioi River Basin to Climate Change

NASA Astrophysics Data System (ADS)

Mendizabal, Maddalen; Moncho, Roberto; Chust, Guillem; Torp, Peter

2010-05-01

Future climate change will affect aquatic systems on various pathways. Regarding the hydrological cycle, which is a very important pathway, changes in hydrometeorological variables (air temperature, precipitation, evapotranspiration) in first order impact discharges. The fourth report assessment of the Intergovernmental Panel for Climate Change indicates there is evidence that the recent warming of the climate system would result in more frequent extreme precipitation events, increased winter flood likelihoods, increased and widespread melting of snow and ice, longer and more widespread droughts, and rising sea level. Available research and climate model outputs indicate a range of hydrological impacts with likely to very likely probabilities (67 to 99%). For example, it is likely that up to 20% of the world population will live in areas where river flood potential could increase by the 2080s. In Spain, within the Atlantic basin, the hydrological variability will increase in the future due to the intensification of the positive phase of the North Atlantic Oscillation (NAO) index. This might cause flood frequency decreases, but its magnitude does not decrease. The generation of flood, its duration and magnitude are closely linked to changes in winter precipitation. The climatic conditions and relief of the Iberian Peninsula favour the generation of floods. In Spain, floods had historically strong socio-economic impacts, with more than 1525 victims in the past five decades. This upward trend of hydrological variability is expected to remain in the coming decades (medium uncertainty) when the intensification of the positive phase of the NAO index (MMA, 2006) is considered. In order to adapt or minimize climate change impacts in water resources, it is necessary to use climate projections as well as hydrological modelling tools. The main objective of this paper is to evaluate and assess the hydrological response to climate changes in flow conditions in Nerbioi river basin (Basque Country, North of Spain). So that adaptation strategies can be defined. In order to fulfil this objective four subobjectives are defined: (1)selection of the future climate projections for the case study area from a wide spectrum of possibilities; (2) model the hydrological processes of the basin with a physically distributed complex hydrological model; (3) validation of the hydrological model with observation data; and (4) runoff simulation introducing regional climate model data selected. The analysis of climate models suggests that extreme precipitation in the Basque Country increased by about 10% during the twenty-first century. This increase of extreme precipitations raised discharge and water level in Nerbioi river basin. That is why in the 21st century it is expected that the flood-prone area will expand for precipitation with a return period of 50 years. In this context, it is necessary to define and evaluate different adaptation options which are already in practice or conceivable according to the current scientific knowledge. As well as evaluate the adaptation measures in terms of their ability to lower the vulnerability of water resources to climate change. For example, land use change could be a useful tool to adapt our basin systems. The land use plays an important role on the water balance of a river by varying the proportion of precipitation that runs off and the fraction that is lost by evapotranspiration. Therefore, both climate change and adaptation strategies will have an impact on the hydrodynamic conditions of rivers; particularly the changes in flow conditions will have a severe ecological, economical and social impact. As future work, adaptation measures will introduce in the future runoff simulation in order to evaluate the effectiveness and as a decision-making tool to operational organisations.

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

Evaluating water quality ecosystem services of wetlands under historic and future climate

NASA Astrophysics Data System (ADS)

Records, R.; Arabi, M.; Fassnacht, S. R.; Duffy, W.; Ahmadi, M.; Hegewisch, K.

2013-12-01

Potential hydrologic effects of climate change have been assessed extensively; however, possible impacts of changing climate on in-stream water quality at the watershed scale have received little study. We assessed potential impacts of climate change on water quantity and quality in the mountainous Sprague River watershed, Oregon, USA, where high total phosphorus (TP) and sediment loads are associated with lake eutrophication and mortality of endangered fish species. Additionally, we analyzed water quality impacts of wetland and riparian zone loss and gain under present-day climate and future climate scenarios. We utilized the hydrologic model Soil and Water Assessment Tool (SWAT) forced with six distinct climate scenarios derived from Coupled Model Intercomparison Project 5 (CMIP5) General Circulation Models to assess magnitude and direction of trends in streamflow, sediment and TP fluxes in the mid-21st century (2030-2059). Model results showed little significant trend in average annual streamflow under most climate scenarios, but trends in annual and monthly streamflow, sediment, and TP fluxes were more pronounced and were generally increasing. Results also suggest that future loss of present-day wetlands and riparian zones under land use or climatic change could result in substantial increases in sediment and TP loads at the Sprague River outlet.

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.

The climate4impact portal: bridging the CMIP5 data infrastructure to impact users

NASA Astrophysics Data System (ADS)

Plieger, Maarten; Som de Cerff, Wim; Page, Christian; Hutjes, Ronald; de Jong, Fokke; Bärring, Lars; Sjökvist, Elin

2013-04-01

Together with seven other partners (CERFACS, CNRS-IPSL, SMHI, INHGA, CMCC, WUR, MF-CNRM), KNMI is involved in the FP7 project IS-ENES (http://is.enes.org), which supports the European climate modeling infrastructure, in the work package 'Bridging Climate Research Data and the Needs of the Impact Community'. The aim of this work package is to enhance the use of climate model data and to enhance the interaction with climate effect/impact communities. The portal is based on 17 impact use cases from 5 different European countries, and is evaluated by a user panel consisting of use case owners. As the climate impact community is very broad, the focus is mainly on the scientific impact community. This work has resulted in a prototype portal, the ENES portal interface for climate impact communities, that can be visited at www.climate4impact.eu. The portal is connected to all Earth System Grid Federation (ESGF) nodes containing global climate model data (GCM data) from the fifth phase of the Coupled Model Intercomparison Project (CMIP5) and later from the Coordinated Regional Climate Downscaling Experiment (CORDEX). This global network of all major climate model data centers offers services for data description, discovery and download. The climate4impact portal connects to these services and offers a user interface for searching, visualizing and downloading global climate model data and more. A challenging task was to describe the available model data and how it can be used. The portal tries to inform users about possible caveats when using GCM data. All impact use cases are described in the documentation section, using highlighted keywords pointing to detailed information in the glossary. During the project, the content management system Drupal was used to enable partners to contribute on the documentation section. In this presentation the architecture and following items will be detailed: - Security: Login using OpenID for access to the ESG data nodes. The ESG works in conjunction with several external websites and systems. The climate4impact portal uses X509 based short lived credentials, generated on behalf of the user with a MyProxy service. Single Sign-on (SSO) is used to make these websites and systems work together. - Discovery: Facetted search based on e.g. variable name, model and institute using the ESG search services. A catalog browser allows for browsing through CMIP5 and other climate model data catalogues (e.g. ESSENCE, EOBS, UNIDATA). - Download: Directly from ESG nodes and other THREDDS catalogs - Visualization: Visualize any data directly using ADAGUC dynamic Web Map Services. - Transformation: Transform your data into other formats, perform basic calculations and extractions using OCG Web Processing Services The current portal is a Prototype. It is built to explore state-of-art technologies to provide improved access to climate model data. The prototype will be evaluated and is the basis for development of an operational service. The portal and services provided will be sustained and supported during the development of these operational services (2013-2016) in the second phase of the FP7 IS-ENES project, ISENES2.

Modelling the influence of climate change on the chemical concentrations in the Baltic Sea region with the POPCYCLING-Baltic model.

PubMed

Kong, Deguo; MacLeod, Matthew; Cousins, Ian T

2014-09-01

The effect of projected future changes in temperature, wind speed, precipitation and particulate organic carbon on concentrations of persistent organic chemicals in the Baltic Sea regional environment is evaluated using the POPCYCLING-Baltic multimedia chemical fate model. Steady-state concentrations of hypothetical perfectly persistent chemicals with property combinations that encompass the entire plausible range for non-ionizing organic substances are modelled under two alternative climate change scenarios (IPCC A2 and B2) and compared to a baseline climate scenario. The contributions of individual climate parameters are deduced in model experiments in which only one of the four parameters is changed from the baseline scenario. Of the four selected climate parameters, temperature is the most influential, and wind speed is least. Chemical concentrations in the Baltic region are projected to change by factors of up to 3.0 compared to the baseline climate scenario. For chemicals with property combinations similar to legacy persistent organic pollutants listed by the Stockholm Convention, modelled concentration ratios between two climate change scenarios and the baseline scenario range from factors of 0.5 to 2.0. This study is a first step toward quantitatively assessing climate change-induced changes in the environmental concentrations of persistent organic chemicals in the Baltic Sea region. Copyright © 2014 Elsevier Ltd. All rights reserved.

Climate data induced uncertainty in model based estimations of terrestrial primary productivity

NASA Astrophysics Data System (ADS)

Wu, Z.; Ahlström, A.; Smith, B.; Ardö, J.; Eklundh, L.; Fensholt, R.; Lehsten, V.

2016-12-01

Models used to project global vegetation and carbon cycle differ in their estimates of historical fluxes and pools. These differences arise not only from differences between models but also from differences in the environmental and climatic data that forces the models. Here we investigate the role of uncertainties in historical climate data, encapsulated by a set of six historical climate datasets. We focus on terrestrial gross primary productivity (GPP) and analyze the results from a dynamic process-based vegetation model (LPJ-GUESS) forced by six different climate datasets and two empirical datasets of GPP (derived from flux towers and remote sensing). We find that the climate induced uncertainty, defined as the difference among historical simulations in GPP when forcing the model with the different climate datasets, can be as high as 33 Pg C yr-1 globally (19% of mean GPP). The uncertainty is partitioned into the three main climatic drivers, temperature, precipitation, and shortwave radiation. Additionally, we illustrate how the uncertainty due to a given climate driver depends both on the magnitude of the forcing data uncertainty (the data range) and the sensitivity of the modeled GPP to the driver (the ecosystem sensitivity). The analysis is performed globally and stratified into five land cover classes. We find that the dynamic vegetation model overestimates GPP, compared to empirically based GPP data over most areas, except for the tropical region. Both the simulations and empirical estimates agree that the tropical region is a disproportionate source of uncertainty in GPP estimation. This is mainly caused by uncertainties in shortwave radiation forcing, of which climate data range contributes slightly higher uncertainty than ecosystem sensitivity to shortwave radiation. We also find that precipitation dominated the climate induced uncertainty over nearly half of terrestrial vegetated surfaces, which is mainly due to large ecosystem sensitivity to precipitation. Overall, climate data ranges are found to contribute more to the climate induced uncertainty than ecosystem sensitivity. Our study highlights the need to better constrain tropical climate and demonstrate that uncertainty caused by climatic forcing data must be considered when comparing and evaluating model results and empirical datasets.

Assessing environmental attributes and effects of climate change on Sphagnum peatland distributions in North America using single- and multi-species models.

PubMed

Oke, Tobi A; Hager, Heather A

2017-01-01

The fate of Northern peatlands under climate change is important because of their contribution to global carbon (C) storage. Peatlands are maintained via greater plant productivity (especially of Sphagnum species) than decomposition, and the processes involved are strongly mediated by climate. Although some studies predict that warming will relax constraints on decomposition, leading to decreased C sequestration, others predict increases in productivity and thus increases in C sequestration. We explored the lack of congruence between these predictions using single-species and integrated species distribution models as proxies for understanding the environmental correlates of North American Sphagnum peatland occurrence and how projected changes to the environment might influence these peatlands under climate change. Using Maximum entropy and BIOMOD modelling platforms, we generated single and integrated species distribution models for four common Sphagnum species in North America under current climate and a 2050 climate scenario projected by three general circulation models. We evaluated the environmental correlates of the models and explored the disparities in niche breadth, niche overlap, and climate suitability among current and future models. The models consistently show that Sphagnum peatland distribution is influenced by the balance between soil moisture deficit and temperature of the driest quarter-year. The models identify the east and west coasts of North America as the core climate space for Sphagnum peatland distribution. The models show that, at least in the immediate future, the area of suitable climate for Sphagnum peatland could expand. This result suggests that projected warming would be balanced effectively by the anticipated increase in precipitation, which would increase Sphagnum productivity.

Assessing environmental attributes and effects of climate change on Sphagnum peatland distributions in North America using single- and multi-species models

PubMed Central

Oke, Tobi A.; Hager, Heather A.

2017-01-01

The fate of Northern peatlands under climate change is important because of their contribution to global carbon (C) storage. Peatlands are maintained via greater plant productivity (especially of Sphagnum species) than decomposition, and the processes involved are strongly mediated by climate. Although some studies predict that warming will relax constraints on decomposition, leading to decreased C sequestration, others predict increases in productivity and thus increases in C sequestration. We explored the lack of congruence between these predictions using single-species and integrated species distribution models as proxies for understanding the environmental correlates of North American Sphagnum peatland occurrence and how projected changes to the environment might influence these peatlands under climate change. Using Maximum entropy and BIOMOD modelling platforms, we generated single and integrated species distribution models for four common Sphagnum species in North America under current climate and a 2050 climate scenario projected by three general circulation models. We evaluated the environmental correlates of the models and explored the disparities in niche breadth, niche overlap, and climate suitability among current and future models. The models consistently show that Sphagnum peatland distribution is influenced by the balance between soil moisture deficit and temperature of the driest quarter-year. The models identify the east and west coasts of North America as the core climate space for Sphagnum peatland distribution. The models show that, at least in the immediate future, the area of suitable climate for Sphagnum peatland could expand. This result suggests that projected warming would be balanced effectively by the anticipated increase in precipitation, which would increase Sphagnum productivity. PMID:28426754

Impact of climate change on global malaria distribution.

PubMed

Caminade, Cyril; Kovats, Sari; Rocklov, Joacim; Tompkins, Adrian M; Morse, Andrew P; Colón-González, Felipe J; Stenlund, Hans; Martens, Pim; Lloyd, Simon J

2014-03-04

Malaria is an important disease that has a global distribution and significant health burden. The spatial limits of its distribution and seasonal activity are sensitive to climate factors, as well as the local capacity to control the disease. Malaria is also one of the few health outcomes that has been modeled by more than one research group and can therefore facilitate the first model intercomparison for health impacts under a future with climate change. We used bias-corrected temperature and rainfall simulations from the Coupled Model Intercomparison Project Phase 5 climate models to compare the metrics of five statistical and dynamical malaria impact models for three future time periods (2030s, 2050s, and 2080s). We evaluated three malaria outcome metrics at global and regional levels: climate suitability, additional population at risk and additional person-months at risk across the model outputs. The malaria projections were based on five different global climate models, each run under four emission scenarios (Representative Concentration Pathways, RCPs) and a single population projection. We also investigated the modeling uncertainty associated with future projections of populations at risk for malaria owing to climate change. Our findings show an overall global net increase in climate suitability and a net increase in the population at risk, but with large uncertainties. The model outputs indicate a net increase in the annual person-months at risk when comparing from RCP2.6 to RCP8.5 from the 2050s to the 2080s. The malaria outcome metrics were highly sensitive to the choice of malaria impact model, especially over the epidemic fringes of the malaria distribution.

Impact of climate change on global malaria distribution

PubMed Central

Caminade, Cyril; Kovats, Sari; Rocklov, Joacim; Tompkins, Adrian M.; Morse, Andrew P.; Colón-González, Felipe J.; Stenlund, Hans; Martens, Pim; Lloyd, Simon J.

2014-01-01

Malaria is an important disease that has a global distribution and significant health burden. The spatial limits of its distribution and seasonal activity are sensitive to climate factors, as well as the local capacity to control the disease. Malaria is also one of the few health outcomes that has been modeled by more than one research group and can therefore facilitate the first model intercomparison for health impacts under a future with climate change. We used bias-corrected temperature and rainfall simulations from the Coupled Model Intercomparison Project Phase 5 climate models to compare the metrics of five statistical and dynamical malaria impact models for three future time periods (2030s, 2050s, and 2080s). We evaluated three malaria outcome metrics at global and regional levels: climate suitability, additional population at risk and additional person-months at risk across the model outputs. The malaria projections were based on five different global climate models, each run under four emission scenarios (Representative Concentration Pathways, RCPs) and a single population projection. We also investigated the modeling uncertainty associated with future projections of populations at risk for malaria owing to climate change. Our findings show an overall global net increase in climate suitability and a net increase in the population at risk, but with large uncertainties. The model outputs indicate a net increase in the annual person-months at risk when comparing from RCP2.6 to RCP8.5 from the 2050s to the 2080s. The malaria outcome metrics were highly sensitive to the choice of malaria impact model, especially over the epidemic fringes of the malaria distribution. PMID:24596427

Climate change impact on the annual water balance in the northwest Florida coastal

NASA Astrophysics Data System (ADS)

Alizad, K.; Wang, D.; Alimohammadi, N.; Hagen, S. C.

2012-12-01

As the largest tributary to the Apalachicola River, the Chipola River originates in southern Alabama, flows through Florida Panhandle and ended to Gulf of Mexico. The Chipola watershed is located in an intermediate climate environment with aridity index around one. Watershed provides habitat for a number of threatened and endangered animal and plant species. However, climate change affects hydrologic cycle of Chipola River watershed at various temporal and spatial scales. Studying the effects of climate variations is of great importance for water and environmental management purposes in this catchment. This research is mainly focuses on assessing climate change impact on the partitioning pattern of rainfall from mean annual to inter-annual and to seasonal scales. At the mean annual scale, rainfall is partitioned into runoff and evaporation assuming negligible water storage changes. Mean annual runoff is controlled by both mean annual precipitation and potential evaporation. Changes in long term mean runoff caused by variations of long term mean precipitation and potential evaporation will be evaluated based on Budyko hypothesis. At the annual scale, rainfall is partitioned into runoff, evaporation, and storage change. Inter-annual variability of runoff and evaporation are mainly affected by the changes of mean annual climate variables as well as their inter-annual variability. In order to model and evaluate each component of water balance at the annual scale, parsimonious but reliable models, are developed. Budyko hypothesis on the existing balance between available water and energy supply is reconsidered and redefined for the sub-annual time scale and reconstructed accordingly in order to accurately model seasonal hydrologic balance of the catchment. Models are built in the seasonal time frame with a focus on the role of storage change in water cycle. Then for Chipola catchment, models are parameterized based on a sufficient time span of historical data and the their coefficients are quantified. For necessary future predictions, data obtained from climate regional models starting 2040 to 2069 will be utilized. To accommodate the inherent uncertainty of climate projections, an ensemble of regional climate models will be used to assess changes of rainfall and potential evaporation. Then, the climate change impact on seasonal and annual runoff, evaporation, and water storage changes will be projected.

Evaluation of methodology for detecting/predicting migration of forest species

Treesearch

Dale S. Solomon; William B. Leak

1996-01-01

Available methods for analyzing migration of forest species are evaluated, including simulation models, remeasured plots, resurveys, pollen/vegetation analysis, and age/distance trends. Simulation models have provided some of the most drastic estimates of species changes due to predicted changes in global climate. However, these models require additional testing...

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".

Conceptual Model of Climate Change Impacts at LANL

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

Dewart, Jean Marie

Goal 9 of the LANL FY15 Site Sustainability Plan (LANL 2014a) addresses Climate Change Adaptation. As part of Goal 9, the plan reviews many of the individual programs the Laboratory has initiated over the past 20 years to address climate change impacts to LANL (e.g. Wildland Fire Management Plan, Forest Management Plan, etc.). However, at that time, LANL did not yet have a comprehensive approach to climate change adaptation. To fill this gap, the FY15 Work Plan for the LANL Long Term Strategy for Environmental Stewardship and Sustainability (LANL 2015) included a goal of (1) establishing a comprehensive conceptual modelmore » of climate change impacts at LANL and (2) establishing specific climate change indices to measure climate change and impacts at Los Alamos. Establishing a conceptual model of climate change impacts will demonstrate that the Laboratory is addressing climate change impacts in a comprehensive manner. This paper fulfills the requirement of goal 1. The establishment of specific indices of climate change at Los Alamos (goal 2), will improve our ability to determine climate change vulnerabilities and assess risk. Future work will include prioritizing risks, evaluating options/technologies/costs, and where appropriate, taking actions. To develop a comprehensive conceptual model of climate change impacts, we selected the framework provided in the National Oceanic and Atmospheric Administration (NOAA) Climate Resilience Toolkit (http://toolkit.climate.gov/).« less

Advancing Climate Change and Impacts Science Through Climate Informatics

NASA Astrophysics Data System (ADS)

Lenhardt, W.; Pouchard, L. C.; King, A. W.; Branstetter, M. L.; Kao, S.; Wang, D.

2010-12-01

This poster will outline the work to date on developing a climate informatics capability at Oak Ridge National Laboratory (ORNL). The central proposition of this effort is that the application of informatics and information science to the domain of climate change science is an essential means to bridge the realm of high performance computing (HPC) and domain science. The goal is to facilitate knowledge capture and the creation of new scientific insights. For example, a climate informatics capability will help with the understanding and use of model results in domain sciences that were not originally in the scope. From there, HPC can also benefit from feedback as the new approaches may lead to better parameterization in the models. In this poster we will summarize the challenges associated with climate change science that can benefit from the systematic application of informatics and we will highlight our work to date in creating the climate informatics capability to address these types of challenges. We have identified three areas that are particularly challenging in the context of climate change science: 1) integrating model and observational data across different spatial and temporal scales, 2) model linkages, i.e. climate models linked to other models such as hydrologic models, and 3) model diagnostics. Each of these has a methodological component and an informatics component. Our project under way at ORNL seeks to develop new approaches and tools in the context of linking climate change and water issues. We are basing our work on the following four use cases: 1) Evaluation/test of CCSM4 biases in hydrology (precipitation, soil water, runoff, river discharge) over the Rio Grande Basin. User: climate modeler. 2) Investigation of projected changes in hydrology of Rio Grande Basin using the VIC (Variable Infiltration Capacity Macroscale) Hydrologic Model. User: watershed hydrologist/modeler. 3) Impact of climate change on agricultural productivity of the Rio Grande Basin. User: climate impact scientist, agricultural economist. 4) Renegotiation of the 1944 “Treaty for the Utilization of Waters of the Colorado and Tijuana Rivers and of the Rio Grande”. User: A US State Department analyst or their counterpart in Mexico.

Graphical approach to assess the soil fertility evaluation model validity for rice (case study: southern area of Merapi Mountain, Indonesia)

NASA Astrophysics Data System (ADS)

Julianto, E. A.; Suntoro, W. A.; Dewi, W. S.; Partoyo

2018-03-01

Climate change has been reported to exacerbate land resources degradation including soil fertility decline. The appropriate validity use on soil fertility evaluation could reduce the risk of climate change effect on plant cultivation. This study aims to assess the validity of a Soil Fertility Evaluation Model using a graphical approach. The models evaluated were the Indonesian Soil Research Center (PPT) version model, the FAO Unesco version model, and the Kyuma version model. Each model was then correlated with rice production (dry grain weight/GKP). The goodness of fit of each model can be tested to evaluate the quality and validity of a model, as well as the regression coefficient (R2). This research used the Eviews 9 programme by a graphical approach. The results obtained three curves, namely actual, fitted, and residual curves. If the actual and fitted curves are widely apart or irregular, this means that the quality of the model is not good, or there are many other factors that are still not included in the model (large residual) and conversely. Indeed, if the actual and fitted curves show exactly the same shape, it means that all factors have already been included in the model. Modification of the standard soil fertility evaluation models can improve the quality and validity of a model.

Clime: analyzing and producing climate data in GIS environment

NASA Astrophysics Data System (ADS)

Cattaneo, Luigi; Rillo, Valeria; Mercogliano, Paola

2014-05-01

In the last years, Impacts on Soil and Coasts Division (ISC) of CMCC (Euro-Mediterranean Center on Climate Change) had several collaboration experiences with impact communities, including IS-ENES (FP7-INF) and SafeLand (FP7-ENV) projects, which involved a study of landslide risk in Europe, and is currently active in GEMINA (FIRB) and ORIENTGATE (SEE Transnational Cooperation Programme) research projects. As a result, it has brought research activities about different impact of climate changes as flood and landslide hazards, based on climate simulation obtained from the high resolution regional climate models COSMO CLM, developed at CMCC as member of the consortium CLM Assembly. ISC-Capua also collaborates with local institutions interested in atmospherical climate change and also of their impacts on the soil, such as river basin authorities in the Campania region, ARPA Emilia Romagna and ARPA Calabria. Impact models (e.g. hydraulic or stability models) are usually developed in a GIS environment, since they need an accurate territory description, so Clime has been designed to bridge the usually existing gap between climate data - both observed and simulated - gathered from different sources, and impact communities. The main goal of Clime, special purpose Geographic Information System (GIS) software integrated in ESRI ArcGIS Desktop 10, is to easily evaluate multiple climate features and study climate changes over specific geographical domains with their related effects on environment, including impacts on soil. Developed as an add-in tool, this software has been conceived for research activities of ISC Division in order to provide a substantial contribution during post-processing and validation phase. Therefore, it is possible to analyze and compare multiple datasets (observations, climate simulations, etc.) through processes involving statistical functions, percentiles, trends test and evaluation of extreme events with a flexible system of temporal and spatial filtering, and to represent results as maps, temporal and statistic plots (time series, seasonal cycles, PDFs, scatter plots, Taylor diagrams) or Excel tables; in addition, it features bias correction techniques for climate model results. Summarizing, Clime is able to provide users a simple and fast way to retrieve analysis over simulated climate data and observations within any geographical site of interest (provinces, regions, countries, etc.).

Modeling potential climate change impacts on the trees of the northeastern United States

Treesearch

Louis Iverson; Anantha Prasad; Stephen Matthews

2008-01-01

We evaluated 134 tree species from the eastern United States for potential response to several scenarios of climate change, and summarized those responses for nine northeastern United States. We modeled and mapped each species individually and show current and potential future distributions for two emission scenarios (A1fi [higher emission] and B1 [lower emission]) and...

Validation of a model with climatic and flow scenario analysis: case of Lake Burrumbeet in southeastern Australia.

PubMed

Yihdego, Yohannes; Webb, John

2016-05-01

Forecast evaluation is an important topic that addresses the development of reliable hydrological probabilistic forecasts, mainly through the use of climate uncertainties. Often, validation has no place in hydrology for most of the times, despite the parameters of a model are uncertain. Similarly, the structure of the model can be incorrectly chosen. A calibrated and verified dynamic hydrologic water balance spreadsheet model has been used to assess the effect of climate variability on Lake Burrumbeet, southeastern Australia. The lake level has been verified to lake level, lake volume, lake surface area, surface outflow and lake salinity. The current study aims to increase lake level confidence model prediction through historical validation for the year 2008-2013, under different climatic scenario. Based on the observed climatic condition (2008-2013), it fairly matches with a hybridization of scenarios, being the period interval (2008-2013), corresponds to both dry and wet climatic condition. Besides to the hydrologic stresses uncertainty, uncertainty in the calibrated model is among the major drawbacks involved in making scenario simulations. In line with this, the uncertainty in the calibrated model was tested using sensitivity analysis and showed that errors in the model can largely be attributed to erroneous estimates of evaporation and rainfall, and surface inflow to a lesser. The study demonstrates that several climatic scenarios should be analysed, with a combination of extreme climate, stream flow and climate change instead of one assumed climatic sequence, to improve climate variability prediction in the future. Performing such scenario analysis is a valid exercise to comprehend the uncertainty with the model structure and hydrology, in a meaningful way, without missing those, even considered as less probable, ultimately turned to be crucial for decision making and will definitely increase the confidence of model prediction for management of the water resources.

Dynamic coupling of regional atmosphere to biosphere in the new generation regional climate system model REMO-iMOVE

NASA Astrophysics Data System (ADS)

Wilhelm, C.; Rechid, D.; Jacob, D.

2013-05-01

The main objective of this study is the coupling of the regional climate model REMO to a 3rd generation land surface scheme and the evaluation of the new model version of REMO, called REMO with interactive MOsaic-based VEgetation: REMO-iMOVE. Attention is paid to the documentation of the technical aspects of the new model constituents and the coupling mechanism. We compare simulation results of REMO-iMOVE and of the reference version REMO2009, to investigate the sensitivity of the regional model to the new land surface scheme. An 11 yr climate model run (1995-2005), forced with ECMWF ERA-Interim lateral boundary conditions, over Europe in 0.44° resolution of both model versions was carried out, to represent present day European climate. The result of these experiments are compared to multiple temperature, precipitation, heat flux and leaf area index observation data, to determine the differences in the model versions. The new model version has further the ability to model net primary productivity for the given plant functional types. This new feature is thoroughly evaluated by literature values of net primary productivity of different plant species in European climatic regions. The new model version REMO-iMOVE is able to model the European climate in the same quality as the parent model version REMO2009 does. The differences in the results of the two model versions stem from the differences in the dynamics of vegetation cover and density and can be distinct in some regions, due to the influences of these parameters to the surface heat and moisture fluxes. The modeled inter-annual variability in the phenology as well as the net primary productivity lays in the range of observations and literature values for most European regions. This study also reveals the need for a more sophisticated soil moisture representation in the newly developed model version REMO-iMOVE to be able to treat the differences in plant functional types. This gets especially important if the model will be used in dynamic vegetation studies.

Application of empirical and dynamical closure methods to simple climate models

NASA Astrophysics Data System (ADS)

Padilla, Lauren Elizabeth

This dissertation applies empirically- and physically-based methods for closure of uncertain parameters and processes to three model systems that lie on the simple end of climate model complexity. Each model isolates one of three sources of closure uncertainty: uncertain observational data, large dimension, and wide ranging length scales. They serve as efficient test systems toward extension of the methods to more realistic climate models. The empirical approach uses the Unscented Kalman Filter (UKF) to estimate the transient climate sensitivity (TCS) parameter in a globally-averaged energy balance model. Uncertainty in climate forcing and historical temperature make TCS difficult to determine. A range of probabilistic estimates of TCS computed for various assumptions about past forcing and natural variability corroborate ranges reported in the IPCC AR4 found by different means. Also computed are estimates of how quickly uncertainty in TCS may be expected to diminish in the future as additional observations become available. For higher system dimensions the UKF approach may become prohibitively expensive. A modified UKF algorithm is developed in which the error covariance is represented by a reduced-rank approximation, substantially reducing the number of model evaluations required to provide probability densities for unknown parameters. The method estimates the state and parameters of an abstract atmospheric model, known as Lorenz 96, with accuracy close to that of a full-order UKF for 30-60% rank reduction. The physical approach to closure uses the Multiscale Modeling Framework (MMF) to demonstrate closure of small-scale, nonlinear processes that would not be resolved directly in climate models. A one-dimensional, abstract test model with a broad spatial spectrum is developed. The test model couples the Kuramoto-Sivashinsky equation to a transport equation that includes cloud formation and precipitation-like processes. In the test model, three main sources of MMF error are evaluated independently. Loss of nonlinear multi-scale interactions and periodic boundary conditions in closure models were dominant sources of error. Using a reduced order modeling approach to maximize energy content allowed reduction of the closure model dimension up to 75% without loss in accuracy. MMF and a comparable alternative model peformed equally well compared to direct numerical simulation.

Precipitation forecast verification over Brazilian watersheds on present and future climate

NASA Astrophysics Data System (ADS)

Xavier, L.; Bruyere, C. L.; Rotunno, O.

2016-12-01

Evaluating the quality of precipitation forecast is an essential step for hydrological studies, among other applications, which is particularly relevant when taking into account climate change and the consequent likely modification of precipitation patterns. In this study we analyzed daily precipitation forecasts given by the global model CESM and the regional model WRF on present and future climate. For present runs, CESM data have been considered from 1980 to 2005, and WRF data from 1990 to 2000. CESM future runs were available for 3 RCP scenarios (4.5, 6.0 and 8.5), over 2005-2100 period; for WRF, future runs spanned 4 different 11-year periods (2020-2030, 2030-2040, 2050-2060 and 2080-2090). WRF simulations had been driven by bias-corrected forcings, and had been done on present climate for a 24 members ensemble created by varying the adopted parameterization schemes. On WRF future climate simulations, data from 3 members out of the original ensemble were available. Precipitation data have been spatially averaged over some large Brazilian watersheds (Amazon and subbasins, Tocantins, Sao Francisco, 4 of Parana`s subbasins) and have been evaluated for present climate against a gauge gridded dataset and ERA Interim data both spanning the 1980-2013 period. The evaluation was focused on the analysis of precipitation forecasts probabilities distribution. Taking into account daily and monthly mean precipitation aggregated on 3-month periods (DJF,MAM,JJA,SON), we adopted some skill measures, amongst them, the Perkins Skill Score (PSS). From the results we verified that on present climate WRF ensemble mean led to clearly better results when compared with CESM data for Amazon, Tocantins and Sao Francisco, but model was not as skillful to the other basins, which could be also been observed for future climate. PSS results from future runs showed that few changes would be observed over the different periods for the considered basins.

Quantifying climate feedbacks in polar regions

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

Goosse, Hugues; Kay, Jennifer E.; Armour, Kyle C.

The concept of feedback is key in assessing whether a perturbation to a system is amplified or damped by mechanisms internal to the system. In polar regions, climate dynamics are controlled by both radiative and non-radiative interactions between the atmosphere, ocean, sea ice, ice sheets and land surfaces. Precisely quantifying polar feedbacks is required for a process-oriented evaluation of climate models, a clear understanding of the processes responsible for polar climate changes, and a reduction in uncertainty associated with model projections. This quantification can be performed using a simple and consistent approach that is valid for a wide range ofmore » feedbacks, thus offering the opportunity for more systematic feedback analyses and a better understanding of polar climate changes.« less

Quantifying climate feedbacks in polar regions

DOE PAGES

Goosse, Hugues; Kay, Jennifer E.; Armour, Kyle C.; ...

2018-05-15

The concept of feedback is key in assessing whether a perturbation to a system is amplified or damped by mechanisms internal to the system. In polar regions, climate dynamics are controlled by both radiative and non-radiative interactions between the atmosphere, ocean, sea ice, ice sheets and land surfaces. Precisely quantifying polar feedbacks is required for a process-oriented evaluation of climate models, a clear understanding of the processes responsible for polar climate changes, and a reduction in uncertainty associated with model projections. This quantification can be performed using a simple and consistent approach that is valid for a wide range ofmore » feedbacks, thus offering the opportunity for more systematic feedback analyses and a better understanding of polar climate changes.« less

Drought impacts on ecosystem functions of the U.S. National Forests and Grasslands: Part I evaluation of a water and carbon balance model

Treesearch

Shanlei Sun; Ge Sun; Peter Caldwell; Steven G. McNulty; Erika Cohen; Jingfeng Xiao; Yang Zhang

2015-01-01

Understanding and quantitatively evaluating the regional impacts of climate change and variability (e.g., droughts) on forest ecosystem functions (i.e., water yield, evapotranspiration, and productivity) and services (e.g., fresh water supply and carbon sequestration) is of great importance for developing climate change adaptation strategies for National Forests and...

Influence of three common calibration metrics on the diagnosis of climate change impacts on water resources

NASA Astrophysics Data System (ADS)

Seiller, G.; Roy, R.; Anctil, F.

2017-04-01

Uncertainties associated to the evaluation of the impacts of climate change on water resources are broad, from multiple sources, and lead to diagnoses sometimes difficult to interpret. Quantification of these uncertainties is a key element to yield confidence in the analyses and to provide water managers with valuable information. This work specifically evaluates the influence of hydrological modeling calibration metrics on future water resources projections, on thirty-seven watersheds in the Province of Québec, Canada. Twelve lumped hydrologic models, representing a wide range of operational options, are calibrated with three common objective functions derived from the Nash-Sutcliffe efficiency. The hydrologic models are forced with climate simulations corresponding to two RCP, twenty-nine GCM from CMIP5 (Coupled Model Intercomparison Project phase 5) and two post-treatment techniques, leading to future projections in the 2041-2070 period. Results show that the diagnosis of the impacts of climate change on water resources are quite affected by the hydrologic models selection and calibration metrics. Indeed, for the four selected hydrological indicators, dedicated to water management, parameters from the three objective functions can provide different interpretations in terms of absolute and relative changes, as well as projected changes direction and climatic ensemble consensus. The GR4J model and a multimodel approach offer the best modeling options, based on calibration performance and robustness. Overall, these results illustrate the need to provide water managers with detailed information on relative changes analysis, but also absolute change values, especially for hydrological indicators acting as security policy thresholds.

Evaluating wind extremes in CMIP5 climate models

NASA Astrophysics Data System (ADS)

Kumar, Devashish; Mishra, Vimal; Ganguly, Auroop R.

2015-07-01

Wind extremes have consequences for renewable energy sectors, critical infrastructures, coastal ecosystems, and insurance industry. Considerable debates remain regarding the impacts of climate change on wind extremes. While climate models have occasionally shown increases in regional wind extremes, a decline in the magnitude of mean and extreme near-surface wind speeds has been recently reported over most regions of the Northern Hemisphere using observed data. Previous studies of wind extremes under climate change have focused on selected regions and employed outputs from the regional climate models (RCMs). However, RCMs ultimately rely on the outputs of global circulation models (GCMs), and the value-addition from the former over the latter has been questioned. Regional model runs rarely employ the full suite of GCM ensembles, and hence may not be able to encapsulate the most likely projections or their variability. Here we evaluate the performance of the latest generation of GCMs, the Coupled Model Intercomparison Project phase 5 (CMIP5), in simulating extreme winds. We find that the multimodel ensemble (MME) mean captures the spatial variability of annual maximum wind speeds over most regions except over the mountainous terrains. However, the historical temporal trends in annual maximum wind speeds for the reanalysis data, ERA-Interim, are not well represented in the GCMs. The historical trends in extreme winds from GCMs are statistically not significant over most regions. The MME model simulates the spatial patterns of extreme winds for 25-100 year return periods. The projected extreme winds from GCMs exhibit statistically less significant trends compared to the historical reference period.

What and How Are We Evaluating? Meta-Evaluation Study of the NASA Innovations in Climate Education (NICE) Portfolio

NASA Astrophysics Data System (ADS)

Martin, A. M.; Barnes, M. H.; Chambers, L. H.; Pippin, M. R.

2011-12-01

As part of NASA's Minority University Research and Education Program (MUREP), the NASA Innovations in Climate Education (NICE) project at Langley Research Center has funded 71 climate education initiatives since 2008. The funded initiatives span across the nation and contribute to the development of a climate-literate public and the preparation of a climate-related STEM workforce through research experiences, professional development opportunities, development of data access and modeling tools, and educational opportunities in both K-12 and higher education. Each of the funded projects proposes and carries out its own evaluation plan, in collaboration with external or internal evaluation experts. Using this portfolio as an exemplar case, NICE has undertaken a systematic meta-evaluation of these plans, focused primarily on evaluation questions, approaches, and methods. This meta-evaluation study seeks to understand the range of evaluations represented in the NICE portfolio, including descriptive information (what evaluations, questions, designs, approaches, and methods are applied?) and questions of value (do these evaluations meet the needs of projects and their staff, and of NASA/NICE?). In the current climate, as federal funders of climate change and STEM education projects seek to better understand and incorporate evaluation into their decisions, evaluators and project leaders are also seeking to build robust understanding of program effectiveness. Meta-evaluations like this provide some baseline understanding of the current status quo and the kinds of evaluations carried out within such funding portfolios. These explorations are needed to understand the common ground between evaluative best practices, limited resources, and agencies' desires, capacity, and requirements. When NASA asks for evaluation of funded projects, what happens? Which questions are asked and answered, using which tools? To what extent do the evaluations meet the needs of projects and program officers? How do they contribute to best practices in climate science education? These questions are important to ask about STEM and climate literacy work more generally; the NICE portfolio provides a broad test case for thinking strategically, critically, and progressively about evaluation in our community. Our findings can inform the STEM education, communication, and public outreach communities, and prompt us to consider a broad range of informative evaluation options. During this presentation, we will consider the breadth, depth and utility of evaluations conducted through a NASA climate education funding opportunity. We will examine the relationship between what we want to know about education programs, what we want to achieve with our interventions, and what we ask in our evaluations.

What and How Are We Evaluating? Meta-Evaluation Study of the NASA Innovations in Climate Education (NICE) Portfolio

NASA Astrophysics Data System (ADS)

Martin, A. M.; Barnes, M. H.; Chambers, L. H.; Pippin, M. R.

2013-12-01

As part of NASA's Minority University Research and Education Program (MUREP), the NASA Innovations in Climate Education (NICE) project at Langley Research Center has funded 71 climate education initiatives since 2008. The funded initiatives span across the nation and contribute to the development of a climate-literate public and the preparation of a climate-related STEM workforce through research experiences, professional development opportunities, development of data access and modeling tools, and educational opportunities in both K-12 and higher education. Each of the funded projects proposes and carries out its own evaluation plan, in collaboration with external or internal evaluation experts. Using this portfolio as an exemplar case, NICE has undertaken a systematic meta-evaluation of these plans, focused primarily on evaluation questions, approaches, and methods. This meta-evaluation study seeks to understand the range of evaluations represented in the NICE portfolio, including descriptive information (what evaluations, questions, designs, approaches, and methods are applied?) and questions of value (do these evaluations meet the needs of projects and their staff, and of NASA/NICE?). In the current climate, as federal funders of climate change and STEM education projects seek to better understand and incorporate evaluation into their decisions, evaluators and project leaders are also seeking to build robust understanding of program effectiveness. Meta-evaluations like this provide some baseline understanding of the current status quo and the kinds of evaluations carried out within such funding portfolios. These explorations are needed to understand the common ground between evaluative best practices, limited resources, and agencies' desires, capacity, and requirements. When NASA asks for evaluation of funded projects, what happens? Which questions are asked and answered, using which tools? To what extent do the evaluations meet the needs of projects and program officers? How do they contribute to best practices in climate science education? These questions are important to ask about STEM and climate literacy work more generally; the NICE portfolio provides a broad test case for thinking strategically, critically, and progressively about evaluation in our community. Our findings can inform the STEM education, communication, and public outreach communities, and prompt us to consider a broad range of informative evaluation options. During this presentation, we will consider the breadth, depth and utility of evaluations conducted through a NASA climate education funding opportunity. We will examine the relationship between what we want to know about education programs, what we want to achieve with our interventions, and what we ask in our evaluations.

The ARM Cloud Radar Simulator for Global Climate Models: A New Tool for Bridging Field Data and Climate Models

DOE PAGES

Zhang, Yuying; Xie, Shaocheng; Klein, Stephen A.; ...

2017-08-11

Clouds play an important role in Earth’s radiation budget and hydrological cycle. However, current global climate models (GCMs) have difficulties in accurately simulating clouds and precipitation. To improve the representation of clouds in climate models, it is crucial to identify where simulated clouds differ from real world observations of them. This can be difficult, since significant differences exist between how a climate model represents clouds and what instruments observe, both in terms of spatial scale and the properties of the hydrometeors which are either modeled or observed. To address these issues and minimize impacts of instrument limitations, the concept ofmore » instrument “simulators”, which convert model variables into pseudo-instrument observations, has evolved with the goal to facilitate and to improve the comparison of modeled clouds with observations. Many simulators have been (and continue to be) developed for a variety of instruments and purposes. Finally, a community satellite simulator package, the Cloud Feedback Model Intercomparison Project (CFMIP) Observation Simulator Package (COSP; Bodas-Salcedo et al. 2011), contains several independent satellite simulators and is being widely used in the global climate modeling community to exploit satellite observations for model cloud evaluation (e.g., Kay et al. 2012; Klein et al. 2013; Suzuki et al. 2013; Zhang et al. 2010).« less

The ARM Cloud Radar Simulator for Global Climate Models: A New Tool for Bridging Field Data and Climate Models

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

Zhang, Yuying; Xie, Shaocheng; Klein, Stephen A.

Clouds play an important role in Earth’s radiation budget and hydrological cycle. However, current global climate models (GCMs) have difficulties in accurately simulating clouds and precipitation. To improve the representation of clouds in climate models, it is crucial to identify where simulated clouds differ from real world observations of them. This can be difficult, since significant differences exist between how a climate model represents clouds and what instruments observe, both in terms of spatial scale and the properties of the hydrometeors which are either modeled or observed. To address these issues and minimize impacts of instrument limitations, the concept ofmore » instrument “simulators”, which convert model variables into pseudo-instrument observations, has evolved with the goal to facilitate and to improve the comparison of modeled clouds with observations. Many simulators have been (and continue to be) developed for a variety of instruments and purposes. Finally, a community satellite simulator package, the Cloud Feedback Model Intercomparison Project (CFMIP) Observation Simulator Package (COSP; Bodas-Salcedo et al. 2011), contains several independent satellite simulators and is being widely used in the global climate modeling community to exploit satellite observations for model cloud evaluation (e.g., Kay et al. 2012; Klein et al. 2013; Suzuki et al. 2013; Zhang et al. 2010).« less

Effects of Climate Change on Diffuse Pollution in Lake Mogan Watershed

NASA Astrophysics Data System (ADS)

Alp, E.; Özcan, Z.

2017-12-01

Climate change is putting increasing pressure on water bodies. It can affect the behavior of pollutants in the environment and their interaction with the hydrological cycle. For instance, changing precipitation patterns may result in higher volumes of runoff containing numerous contaminants to water bodies and eventually loss of life-supporting function of them. The purpose of this study is to evaluate the impacts of climate change on diffuse pollution in Lake Mogan watershed located in a climate change vulnerable region and where agricultural diffuse pollution is one of the significant concerns. Lake Mogan watershed has an area of 970 km2 and it is dominated by dry agricultural practices and characterized by intermittent creeks. The lake was declared as a special environmental protection region in 1990. In this study, the impacts of climate change on diffuse pollution in the Lake Mogan watershed was evaluated using with a water quality model, SWAT (Soil and Water Assessment Tool). SWAT is a conceptual, continuous time model that operates on a daily time step. The model has been used in many studies to estimate the impacts of climate change, to calculate pollutant loads and to evaluate the best management practices all over the world. The required inputs for SWAT model can be categorized under the following basic categories: topography, land use/land cover, soil properties, land management practices occurring in the watershed, and meteorological inputs. According to Turkish Ministry of Forestry and Water Affairs (2016), it is estimated that the annual average temperature values will increase up to 3.3°C during the 85 year projection period as compared to reference period in the RCP4.5 scenario in the study area. This increase is predicted as up to 5.7°C based on the RCP8.5 scenario. The calibrated SWAT model for the Lake Mogan Watershed is used for the climate change scenarios for a period of 2010 and 2100. It is aimed that the outcomes of this study will help decision makers to develop beneficial management strategies so that the sustainable management of the specially protected water body is provided.

Assessments of Maize Yield Potential in the Korean Peninsula Using Multiple Crop Models

NASA Astrophysics Data System (ADS)

Kim, S. H.; Myoung, B.; Lim, C. H.; Lee, S. G.; Lee, W. K.; Kafatos, M.

2015-12-01

The Korean Peninsular has unique agricultural environments due to the differences in the political and socio-economical systems between the Republic of Korea (SK, hereafter) and the Democratic Peoples' Republic of Korea (NK, hereafter). NK has been suffering from the lack of food supplies caused by natural disasters, land degradation and failed political system. The neighboring developed country SK has a better agricultural system but very low food self-sufficiency rate (around 1% of maize). Maize is an important crop in both countries since it is staple food for NK and SK is No. 2 maize importing country in the world after Japan. Therefore evaluating maize yield potential (Yp) in the two distinct regions is essential to assess food security under climate change and variability. In this study, we have utilized multiple process-based crop models capable of regional-scale assessments to evaluate maize Yp over the Korean Peninsula - the GIS version of EPIC model (GEPIC) and APSIM model that can be expanded to regional scales (APSIM regions). First we evaluated model performance and skill for 20 years from 1991 to 2010 using reanalysis data (Local Data Assimilation and Prediction System (LDAPS); 1.5km resolution) and observed data. Each model's performances were compared over different regions within the Korean Peninsula of different regional climate characteristics. To quantify the major influence of individual climate variables, we also conducted a sensitivity test using 20 years of climatology. Lastly, a multi-model ensemble analysis was performed to reduce crop model uncertainties. The results will provide valuable information for estimating the climate change or variability impacts on Yp over the Korean Peninsula.

WRF model forecasts and their use for hydroclimate monitoring over southern South America

NASA Astrophysics Data System (ADS)

Muller, Omar; Lovino, Miguel; Berbery, E. Hugo

2017-04-01

Weather forecasting and monitoring systems based on regional models are becoming increasingly relevant for decision support in agriculture and water management. This work evaluates the predictive and monitoring capabilities of a system based on WRF model simulations at 15 km grid spacing over a domain that encompasses La Plata Basin (LPB) in southern South America, where agriculture and water resources are essential. The model's skill up to a lead-time of 7 days is evaluated with daily precipitation and 2m temperature in-situ observations. Results show high prediction performance with 7 days lead-time throughout the domain and particularly over LPB, where about 70% of rain and no-rain days are correctly predicted. The scores tend to be better over humid climates than over arid-to-semiarid climates. Compared to the arid-semiarid climate, the humid climate has a higher probability of detection and less false alarms. The ranges of the skill scores are similar to those found over the United States, suggesting that proper choice of parameterizations lead to no loss of performance of the model. Daily mean, minimum and maximum forecast temperatures are highly correlated with observations up to 7 day lead time. The best performance is for daily mean temperature, followed by minimum temperature and a slightly weaker performance for maximum temperature over arid regions. The usefulness of WRF products for hydroclimate monitoring was tested for an unprecedented drought in southern Brazil and for a slightly above normal precipitation season in northeastern Argentina. In both cases the model products reproduce the observed precipitation conditions with consistent impacts on soil moisture, evapotranspiration and runoff. This evaluation validates the model's usefulness to fore-cast weather up to one week and to monitor climate conditions in real time. The scores suggest that the forecast lead-time can be extended into week two, while bias correction methods can reduce part of the systematic errors.

ISMIP6: Ice Sheet Model Intercomparison Project for CMIP6

NASA Technical Reports Server (NTRS)

Nowicki, S.

2015-01-01

ISMIP6 (Ice Sheet Model Intercomparison Project for CMIP6) targets the Cryosphere in a Changing Climate and the Future Sea Level Grand Challenges of the WCRP (World Climate Research Program). Primary goal is to provide future sea level contribution from the Greenland and Antarctic ice sheets, along with associated uncertainty. Secondary goal is to investigate feedback due to dynamic ice sheet models. Experiment design uses and augment the existing CMIP6 (Coupled Model Intercomparison Project Phase 6) DECK (Diagnosis, Evaluation, and Characterization of Klima) experiments. Additonal MIP (Model Intercomparison Project)- specific experiments will be designed for ISM (Ice Sheet Model). Effort builds on the Ice2sea, SeaRISE (Sea-level Response to Ice Sheet Evolution) and COMBINE (Comprehensive Modelling of the Earth System for Better Climate Prediction and Projection) efforts.

The Strengths and Limitations of Satellite Data for Evaluating Tropospheric Processes in Chemistry-Climate Models

NASA Technical Reports Server (NTRS)

Duncan, Bryan

2012-01-01

There is now a wealth of satellite data products available with which to evaluate a model fs simulation of tropospheric composition and other model processes. All of these data products have their strengths and limitations that need to be considered for this purpose. For example, uncertainties are introduced into a data product when 1) converting a slant column to a vertical column and 2) estimating the amount of a total column of a trace gas (e.g., ozone, nitrogen dioxide) that resides in the troposphere. Oftentimes, these uncertainties are not well quantified and the satellite data products are not well evaluated against in situ observations. However, these limitations do not preclude us from using these data products to evaluate our model processes if we understand these strengths and limitations when developing diagnostics. I will show several examples of how satellite data products are being used to evaluate particular model processes with a focus on the strengths and limitations of these data products. In addition, I will introduce the goals of a newly formed team to address issues on the topic of "satellite data for improved model evaluation and process studies" that is established in support of the IGAC/SPARC Global Chemistry ]Climate Modeling and Evaluation Workshop.

Agricultural policy environmental eXtender model simulation of climate change impacts on runoff from a small no-till watershed

USDA-ARS?s Scientific Manuscript database

Long-term hydrologic data sets are required to quantify the impacts of management, and climate on runoff at the field scale where management practices are applied. This study was conducted to evaluate the impacts of long-term management and climate on runoff from a small watershed managed with no-ti...

Vulnerability of water supply from the Oregon Cascades to changing climate: linking science to users and policy

Treesearch

Kathleen A. Farley; Christina Tague; Gordon E. Grant

2011-01-01

Despite improvements in understanding biophysical response to climate change, a better understanding of how such changes will affect societies is still needed. We evaluated effects of climate change on the coupled human-environmental system of the McKenzie River watershed in the Oregon Cascades in order to assess its vulnerability. Published empirical and modeling...

2016 International Land Model Benchmarking (ILAMB) Workshop Report

NASA Technical Reports Server (NTRS)

Hoffman, Forrest M.; Koven, Charles D.; Keppel-Aleks, Gretchen; Lawrence, David M.; Riley, William J.; Randerson, James T.; Ahlstrom, Anders; Abramowitz, Gabriel; Baldocchi, Dennis D.; Best, Martin J.;

Ensembles modeling approach to study Climate Change impacts on Wheat

NASA Astrophysics Data System (ADS)

Ahmed, Mukhtar; Claudio, Stöckle O.; Nelson, Roger; Higgins, Stewart

2017-04-01

Simulations of crop yield under climate variability are subject to uncertainties, and quantification of such uncertainties is essential for effective use of projected results in adaptation and mitigation strategies. In this study we evaluated the uncertainties related to crop-climate models using five crop growth simulation models (CropSyst, APSIM, DSSAT, STICS and EPIC) and 14 general circulation models (GCMs) for 2 representative concentration pathways (RCP) of atmospheric CO2 (4.5 and 8.5 W m-2) in the Pacific Northwest (PNW), USA. The aim was to assess how different process-based crop models could be used accurately for estimation of winter wheat growth, development and yield. Firstly, all models were calibrated for high rainfall, medium rainfall, low rainfall and irrigated sites in the PNW using 1979-2010 as the baseline period. Response variables were related to farm management and soil properties, and included crop phenology, leaf area index (LAI), biomass and grain yield of winter wheat. All five models were run from 2000 to 2100 using the 14 GCMs and 2 RCPs to evaluate the effect of future climate (rainfall, temperature and CO2) on winter wheat phenology, LAI, biomass, grain yield and harvest index. Simulated time to flowering and maturity was reduced in all models except EPIC with some level of uncertainty. All models generally predicted an increase in biomass and grain yield under elevated CO2 but this effect was more prominent under rainfed conditions than irrigation. However, there was uncertainty in the simulation of crop phenology, biomass and grain yield under 14 GCMs during three prediction periods (2030, 2050 and 2070). We concluded that to improve accuracy and consistency in simulating wheat growth dynamics and yield under a changing climate, a multimodel ensemble approach should be used.

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

Donner, Leo J.; O'Brien, Travis A.; Rieger, Daniel

Both climate forcing and climate sensitivity persist as stubborn uncertainties limiting the extent to which climate models can provide actionable scientific scenarios for climate change. A key, explicit control on cloud-aerosol interactions, the largest uncertainty in climate forcing, is the vertical velocity of cloud-scale updrafts. Model-based studies of climate sensitivity indicate that convective entrainment, which is closely related to updraft speeds, is an important control on climate sensitivity. Updraft vertical velocities also drive many physical processes essential to numerical weather prediction. Vertical velocities and their role in atmospheric physical processes have been given very limited attention in models for climatemore » and numerical weather prediction. The relevant physical scales range down to tens of meters and are thus frequently sub-grid and require parameterization. Many state-of-science convection parameterizations provide mass fluxes without specifying vertical velocities, and parameterizations which do provide vertical velocities have been subject to limited evaluation against what have until recently been scant observations. Atmospheric observations imply that the distribution of vertical velocities depends on the areas over which the vertical velocities are averaged. Distributions of vertical velocities in climate models may capture this behavior, but it has not been accounted for when parameterizing cloud and precipitation processes in current models. New observations of convective vertical velocities offer a potentially promising path toward developing process-level cloud models and parameterizations for climate and numerical weather prediction. Taking account of scale-dependence of resolved vertical velocities offers a path to matching cloud-scale physical processes and their driving dynamics more realistically, with a prospect of reduced uncertainty in both climate forcing and sensitivity.« less

Effects of Projected Future Climate Change on Groundwater Recharge and Storage for Two Coastal Aquifers in Guanacaste Province, Costa Rica

NASA Astrophysics Data System (ADS)

Kolb, C.

2017-12-01

Climate change is expected to pose a significant threat to water resources in the future. Guanacaste Province, located in northwestern Costa Rica, has a unique climate that is influenced by the Pacific Ocean and Caribbean Sea, as well as the Central Cordillera mountain range. Although the region experiences a marked rainy season between May and November, the hot, dry summers often stress water resources. Climate change projections suggest increased temperatures and reduced precipitation for the region, which will further stress water supplies. This study focuses on the effects of climate change on groundwater resources for two coastal aquifers, Potrero and Brasilito. The UZF model package coupled with the finite difference groundwater flow model MODFLOW were used to evaluate the effect of climate change on groundwater recharge and storage. A potential evapotranspiration model was used to estimate groundwater infiltration rates used in the MODFLOW model. Climate change projections for temperature, precipitation, and sea level rise were used to develop climate scenarios, which were compared to historical data. Preliminary results indicate that climate change could reduce future recharge, especially during the dry season. Additionally, the coastal aquifers are at increased risk of reduced storage and increased salinization due to the reductions in groundwater recharge and sea level rise. Climate change could also affect groundwater quality in the region, disrupting the ecosystem and impairing a primary source of drinking water.

Effects of climate change, invasive species, and disease on the distribution of native European crayfishes.

PubMed

Capinha, César; Larson, Eric R; Tricarico, Elena; Olden, Julian D; Gherardi, Francesca

2013-08-01

Climate change will require species to adapt to new conditions or follow preferred climates to higher latitudes or elevations, but many dispersal-limited freshwater species may be unable to move due to barriers imposed by watershed boundaries. In addition, invasive nonnative species may expand into new regions under future climate conditions and contribute to the decline of native species. We evaluated future distributions for the threatened European crayfish fauna in response to climate change, watershed boundaries, and the spread of invasive crayfishes, which transmit the crayfish plague, a lethal disease for native European crayfishes. We used climate projections from general circulation models and statistical models based on Mahalanobis distance to predict climate-suitable regions for native and invasive crayfishes in the middle and at the end of the 21st century. We identified these suitable regions as accessible or inaccessible on the basis of major watershed boundaries and present occurrences and evaluated potential future overlap with 3 invasive North American crayfishes. Climate-suitable areas decreased for native crayfishes by 19% to 72%, and the majority of future suitable areas for most of these species were inaccessible relative to native and current distributions. Overlap with invasive crayfish plague-transmitting species was predicted to increase. Some native crayfish species (e.g., noble crayfish [Astacus astacus]) had no future refugia that were unsuitable for the modeled nonnative species. Our results emphasize the importance of preventing additional introductions and spread of invasive crayfishes in Europe to minimize interactions between the multiple stressors of climate change and invasive species, while suggesting candidate regions for the debatable management option of assisted colonization. © 2013 Society for Conservation Biology.

Effects of different representations of transport in the new EMAC-SWIFT chemistry climate model

NASA Astrophysics Data System (ADS)

Scheffler, Janice; Langematz, Ulrike; Wohltmann, Ingo; Kreyling, Daniel; Rex, Markus

2017-04-01

It is well known that the representation of atmospheric ozone chemistry in weather and climate models is essential for a realistic simulation of the atmospheric state. Interactively coupled chemistry climate models (CCMs) provide a means to realistically simulate the interaction between atmospheric chemistry and dynamics. The calculation of chemistry in CCMs, however, is computationally expensive which renders the use of complex chemistry models not suitable for ensemble simulations or simulations with multiple climate change scenarios. In these simulations ozone is therefore usually prescribed as a climatological field or included by incorporating a fast linear ozone scheme into the model. While prescribed climatological ozone fields are often not aligned with the modelled dynamics, a linear ozone scheme may not be applicable for a wide range of climatological conditions. An alternative approach to represent atmospheric chemistry in climate models which can cope with non-linearities in ozone chemistry and is applicable to a wide range of climatic states is the Semi-empirical Weighted Iterative Fit Technique (SWIFT) that is driven by reanalysis data and has been validated against observational satellite data and runs of a full Chemistry and Transport Model. SWIFT has been implemented into the ECHAM/MESSy (EMAC) chemistry climate model that uses a modular approach to climate modelling where individual model components can be switched on and off. When using SWIFT in EMAC, there are several possibilities to represent the effect of transport inside the polar vortex: the semi-Lagrangian transport scheme of EMAC and a transport parameterisation that can be useful when using SWIFT in models not having transport of their own. Here, we present results of equivalent simulations with different handling of transport, compare with EMAC simulations with full interactive chemistry and evaluate the results with observations.

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.

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

A Practical Philosophy of Complex Climate Modelling

NASA Technical Reports Server (NTRS)

Schmidt, Gavin A.; Sherwood, Steven

2014-01-01

We give an overview of the practice of developing and using complex climate models, as seen from experiences in a major climate modelling center and through participation in the Coupled Model Intercomparison Project (CMIP).We discuss the construction and calibration of models; their evaluation, especially through use of out-of-sample tests; and their exploitation in multi-model ensembles to identify biases and make predictions. We stress that adequacy or utility of climate models is best assessed via their skill against more naive predictions. The framework we use for making inferences about reality using simulations is naturally Bayesian (in an informal sense), and has many points of contact with more familiar examples of scientific epistemology. While the use of complex simulations in science is a development that changes much in how science is done in practice, we argue that the concepts being applied fit very much into traditional practices of the scientific method, albeit those more often associated with laboratory work.

Uncertainty quantification and validation of combined hydrological and macroeconomic analyses.

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

Hernandez, Jacquelynne; Parks, Mancel Jordan; Jennings, Barbara Joan

2010-09-01

Changes in climate can lead to instabilities in physical and economic systems, particularly in regions with marginal resources. Global climate models indicate increasing global mean temperatures over the decades to come and uncertainty in the local to national impacts means perceived risks will drive planning decisions. Agent-based models provide one of the few ways to evaluate the potential changes in behavior in coupled social-physical systems and to quantify and compare risks. The current generation of climate impact analyses provides estimates of the economic cost of climate change for a limited set of climate scenarios that account for a small subsetmore » of the dynamics and uncertainties. To better understand the risk to national security, the next generation of risk assessment models must represent global stresses, population vulnerability to those stresses, and the uncertainty in population responses and outcomes that could have a significant impact on U.S. national security.« less

Regional modelling of nitrate leaching from Swiss organic and conventional cropping systems under climate change

NASA Astrophysics Data System (ADS)

Calitri, Francesca; Necpalova, Magdalena; Lee, Juhwan; Zaccone, Claudio; Spiess, Ernst; Herrera, Juan; Six, Johan

2016-04-01

Organic cropping systems have been promoted as a sustainable alternative to minimize the environmental impacts of conventional practices. Relatively little is known about the potential to reduce NO3-N leaching through the large-scale adoption of organic practices. Moreover, the potential to mitigate NO3-N leaching and thus the N pollution under future climate change through organic farming remain unknown and highly uncertain. Here, we compared regional NO3-N leaching from organic and conventional cropping systems in Switzerland using a terrestrial biogeochemical process-based model DayCent. The objectives of this study are 1) to calibrate and evaluate the model for NO3-N leaching measured under various management practices from three experiments at two sites in Switzerland; 2) to estimate regional NO3-N leaching patterns and their spatial uncertainty in conventional and organic cropping systems (with and without cover crops) for future climate change scenario A1B; 3) to explore the sensitivity of NO3-N leaching to changes in soil and climate variables; and 4) to assess the nitrogen use efficiency for conventional and organic cropping systems with and without cover crops under climate change. The data for model calibration/evaluation were derived from field experiments conducted in Liebefeld (canton Bern) and Eschikon (canton Zürich). These experiments evaluated effects of various cover crops and N fertilizer inputs on NO3-N leaching. The preliminary results suggest that the model was able to explain 50 to 83% of the inter-annual variability in the measured soil drainage (RMSE from 12.32 to 16.89 cm y-1). The annual NO3-N leaching was also simulated satisfactory (RMSE = 3.94 to 6.38 g N m-2 y-1), although the model had difficulty to reproduce the inter-annual variability in the NO3-N leaching losses correctly (R2 = 0.11 to 0.35). Future climate datasets (2010-2099) from the 10 regional climate models (RCM) were used in the simulations. Regional NO3-N leaching predictions for conventional cropping system with a three years rotation (silage maize, potatoes and winter wheat) in Zurich and Bern cantons varied from 6.30 to 16.89 g N m-2 y-1 over a 30-years period. Further simulations and analyses will follow to provide insights into understanding of driving variables and patterns of N losses by leaching in response to changes from conventional to organic cropping systems, and climate change.

Assessing the impact of extreme air temperature on fruit trees by modeling weather dependent phenology with variety-specific thermal requirements

NASA Astrophysics Data System (ADS)

Alfieri, Silvia Maria; De Lorenzi, Francesca; Missere, Daniele; Buscaroli, Claudio; Menenti, Massimo

2013-04-01

Extremely high and extremely low temperature may have a terminal impact on the productivity of fruit tree if occurring at critical phases of development. Notorious examples are frost during flowering or extremely high temperature during fruit setting. The dates of occurrence of such critical phenological stages depend on the weather history from the start of the yearly development cycle in late autumn, thus the impact of climate extremes can only be evaluated correctly if the phenological development is modeled taking into account the weather history of the specific year being evaluated. Climate change impact may lead to a shift in timing of phenological stages and change in the duration of vegetative and reproductive phases. A changing climate can also exhibit a greater climatic variability producing quite large changes in the frequency of extreme climatic events. We propose a two-stage approach to evaluate the impact of predicted future climate on the productivity of fruit trees. The phenological development is modeled using phase - specific thermal times and variety specific thermal requirements for several cultivars of pear, apricot and peach. These requirements were estimated using phenological observations over several years in Emilia Romagna region and scientific literature. We calculated the dates of start and end of rest completion, bud swell, flowering, fruit setting and ripening stages , from late autumn through late summer. Then phase-specific minimum and maximum cardinal temperature were evaluated for present and future climate to estimate how frequently they occur during any critically sensitive phenological phase. This analysis has been done for past climate (1961 - 1990) and fifty realizations of a year representative of future climate (2021 - 2050). A delay in rest completion of about 10-20 days has been predicted for future climate for most of the cultivars. On the other hand the predicted rise in air temperature causes an earlier development of crops thus a reduction in the length of the different phenological stages. Despite the earlier timing of phenological phases may expose the crops to frost hazard, the mean increase of air temperature avoids relevant impacts on crops. The frequency of air temperatures higher than the cardinal temperatures is expected to increase by 5% compared with the reference 1961 - 1990 climate. 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)

A simple integrated assessment approach to global change simulation and evaluation

NASA Astrophysics Data System (ADS)

Ogutu, Keroboto; D'Andrea, Fabio; Ghil, Michael

2016-04-01

We formulate and study the Coupled Climate-Economy-Biosphere (CoCEB) model, which constitutes the basis of our idealized integrated assessment approach to simulating and evaluating global change. CoCEB is composed of a physical climate module, based on Earth's energy balance, and an economy module that uses endogenous economic growth with physical and human capital accumulation. A biosphere model is likewise under study and will be coupled to the existing two modules. We concentrate on the interactions between the two subsystems: the effect of climate on the economy, via damage functions, and the effect of the economy on climate, via a control of the greenhouse gas emissions. Simple functional forms of the relation between the two subsystems permit simple interpretations of the coupled effects. The CoCEB model is used to make hypotheses on the long-term effect of investment in emission abatement, and on the comparative efficacy of different approaches to abatement, in particular by investing in low carbon technology, in deforestation reduction or in carbon capture and storage (CCS). The CoCEB model is very flexible and transparent, and it allows one to easily formulate and compare different functional representations of climate change mitigation policies. Using different mitigation measures and their cost estimates, as found in the literature, one is able to compare these measures in a coherent way.

Risk assessment of climate systems for national security.

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

Backus, George A.; Boslough, Mark Bruce Elrick; Brown, Theresa Jean

2012-10-01

Climate change, through drought, flooding, storms, heat waves, and melting Arctic ice, affects the production and flow of resource within and among geographical regions. The interactions among governments, populations, and sectors of the economy require integrated assessment based on risk, through uncertainty quantification (UQ). This project evaluated the capabilities with Sandia National Laboratories to perform such integrated analyses, as they relate to (inter)national security. The combining of the UQ results from climate models with hydrological and economic/infrastructure impact modeling appears to offer the best capability for national security risk assessments.

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.

Reliability of regional climate simulations

NASA Astrophysics Data System (ADS)

Ahrens, W.; Block, A.; Böhm, U.; Hauffe, D.; Keuler, K.; Kücken, M.; Nocke, Th.

2003-04-01

Quantification of uncertainty becomes more and more a key issue for assessing the trustability of future climate scenarios. In addition to the mean conditions, climate impact modelers focus in particular on extremes. Before generating such scenarios using e.g. dynamic regional climate models, a careful validation of present-day simulations should be performed to determine the range of errors for the quantities of interest under recent conditions as a raw estimate of their uncertainty in the future. Often, multiple aspects shall be covered together, and the required simulation accuracy depends on the user's demand. In our approach, a massive parallel regional climate model shall be used on the one hand to generate "long-term" high-resolution climate scenarios for several decades, and on the other hand to provide very high-resolution ensemble simulations of future dry spells or heavy rainfall events. To diagnosis the model's performance for present-day simulations, we have recently developed and tested a first version of a validation and visualization chain for this model. It is, however, applicable in a much more general sense and could be used as a common test bed for any regional climate model aiming at this type of simulations. Depending on the user's interest, integrated quality measures can be derived for near-surface parameters using multivariate techniques and multidimensional distance measures in a first step. At this point, advanced visualization techniques have been developed and included to allow for visual data mining and to qualitatively identify dominating aspects and regularities. Univariate techniques that are especially designed to assess climatic aspects in terms of statistical properties can then be used to quantitatively diagnose the error contributions of the individual used parameters. Finally, a comprehensive in-depth diagnosis tool allows to investigate, why the model produces the obtained near-surface results to answer the question if the model performs well from the modeler's point of view. Examples will be presented for results obtained using this approach for assessing the risk of potential total agricultural yield loss under drought conditions in Northeast Brazil and for evaluating simulation results for a 10-year period for Europe. To support multi-run simulations and result evaluation, the model will be embedded into an already existing simulation environment that provides further postprocessing tools for sensitivity studies, behavioral analysis and Monte-Carlo simulations, but also for ensemble scenario analysis in one of the next steps.

Implementation and evaluation of a monthly water balance model over the US on an 800 m grid

USGS Publications Warehouse

Hostetler, Steven W.; Alder, Jay R.

2016-01-01

We simulate the 1950–2010 water balance for the conterminous U.S. (CONUS) with a monthly water balance model (MWBM) using the 800 m Parameter-elevation Regression on Independent Slopes Model (PRISM) data set as model input. We employed observed snow and streamflow data sets to guide modification of the snow and potential evapotranspiration components in the default model and to evaluate model performance. Based on various metrics and sensitivity tests, the modified model yields reasonably good simulations of seasonal snowpack in the West (range of bias of ±50 mm at 68% of 713 SNOTEL sites), the gradients and magnitudes of actual evapotranspiration, and runoff (median correlation of 0.83 and median Nash-Sutcliff efficiency of 0.6 between simulated and observed annual time series at 1427 USGS gage sites). The model generally performs well along the Pacific Coast, the high elevations of the Basin and Range and over the Midwest and East, but not as well over the dry areas of the Southwest and upper Plains regions due, in part, to the apportioning of direct versus delayed runoff. Sensitivity testing and application of the MWBM to simulate the future water balance at four National Parks when driven by 30 climate models from the Climate Model Intercomparison Program Phase 5 (CMIP5) demonstrate that the model is useful for evaluating first-order, climate driven hydrologic change on monthly and annual time scales.

Significance of hydrological model choice and land use changes when doing climate change impact assessment

NASA Astrophysics Data System (ADS)

Bjørnholt Karlsson, Ida; Obel Sonnenborg, Torben; Refsgaard, Jens Christian; Høgh Jensen, Karsten

2014-05-01

Uncertainty in impact studies arises both from Global Climate Models (GCM), emission projections, statistical downscaling, Regional Climate Models (RCM), hydrological models and calibration techniques (Refsgaard et al. 2013). Some of these uncertainties have been evaluated several times in the literature; however few studies have investigated the effect of hydrological model choice on the assessment results (Boorman & Sefton 1997; Jiang et al. 2007; Bastola et al. 2011). These studies have found that model choice results in large differences, up to 70%, in the predicted discharge changes depending on the climate input. The objective of the study is to investigate the impact of climate change on hydrology of the Odense catchment, Denmark both in response to (a) different climate projections (GCM-RCM combinations); (b) different hydrological models and (c) different land use scenarios. This includes: 1. Separation of the climate model signal; the hydrological model signal and the land use signal 2. How do the different hydrological components react under different climate and land use conditions for the different models 3. What land use scenario seems to provide the best adaptation for the challenges of the different future climate change scenarios from a hydrological perspective? Four climate models from the ENSEMBLES project (Hewitt & Griggs 2004): ECHAM5 - HIRHAM5, ECHAM5 - RCA3, ARPEGE - RM5.1 and HadCM3 - HadRM3 are used, assessing the climate change impact in three periods: 1991-2010 (present), 2041-2060 (near future) and 2081-2100 (far future). The four climate models are used in combination with three hydrological models with different conceptual layout: NAM, SWAT and MIKE SHE. Bastola, S., C. Murphy and J. Sweeney (2011). "The role of hydrological modelling uncertainties in climate change impact assessments of Irish river catchments." Advances in Water Resources 34: 562-576. Boorman, D. B. and C. E. M. Sefton (1997). "Recognising the uncertainty in the quantification of the effects of climate change on hydrological response." Climate Change 35: 415-434. Hewitt, C. D. and D. J. Griggs (2004). "Ensembles-based predictions of climate changes and their impacts." Eos, Transactions American Geophysical Union 85: 1-566. Jiang, T., Y. D. Chen, C. Xu, X. Chen, X. Chen and V. P. Singh (2007). "Comparison of hydrological impacts of climate change simulated by six hydrological models in the Dongjiang Basin, South China." Journal of hydrology 336: 316-333. Refsgaard, J. C., K. Arnbjerg-Nielsen, M. Drews, K. Halsnæs, E. Jeppesen, H. Madsen, A. Markandya, J. E. Olesen, J. R. Porter and J. H. Christensen (2013). "The role of uncertainty in climate change adaptation strategies - A Danish water management example." Mitigation and Adaptation Strategies for Global Change 18: 337-359.

A plant’s perspective of extremes: Terrestrial plant responses to changing climatic variability

PubMed Central

Reyer, C.; Leuzinger, S.; Rammig, A.; Wolf, A.; Bartholomeus, R. P.; Bonfante, A.; de Lorenzi, F.; Dury, M.; Gloning, P.; Abou Jaoudé, R.; Klein, T.; Kuster, T. M.; Martins, M.; Niedrist, G.; Riccardi, M.; Wohlfahrt, G.; de Angelis, P.; de Dato, G.; François, L.; Menzel, A.; Pereira, M.

2013-01-01

We review observational, experimental and model results on how plants respond to extreme climatic conditions induced by changing climatic variability. Distinguishing between impacts of changing mean climatic conditions and changing climatic variability on terrestrial ecosystems is generally underrated in current studies. The goals of our review are thus (1) to identify plant processes that are vulnerable to changes in the variability of climatic variables rather than to changes in their mean, and (2) to depict/evaluate available study designs to quantify responses of plants to changing climatic variability. We find that phenology is largely affected by changing mean climate but also that impacts of climatic variability are much less studied but potentially damaging. We note that plant water relations seem to be very vulnerable to extremes driven by changes in temperature and precipitation and that heatwaves and flooding have stronger impacts on physiological processes than changing mean climate. Moreover, interacting phenological and physiological processes are likely to further complicate plant responses to changing climatic variability. Phenological and physiological processes and their interactions culminate in even more sophisticated responses to changing mean climate and climatic variability at the species and community level. Generally, observational studies are well suited to study plant responses to changing mean climate, but less suitable to gain a mechanistic understanding of plant responses to climatic variability. Experiments seem best suited to simulate extreme events. In models, temporal resolution and model structure are crucial to capture plant responses to changing climatic variability. We highlight that a combination of experimental, observational and /or modeling studies have the potential to overcome important caveats of the respective individual approaches. PMID:23504722

High-resolution regional climate model evaluation using variable-resolution CESM over California

NASA Astrophysics Data System (ADS)

Huang, X.; Rhoades, A.; Ullrich, P. A.; Zarzycki, C. M.

2015-12-01

Understanding the effect of climate change at regional scales remains a topic of intensive research. Though computational constraints remain a problem, high horizontal resolution is needed to represent topographic forcing, which is a significant driver of local climate variability. Although regional climate models (RCMs) have traditionally been used at these scales, variable-resolution global climate models (VRGCMs) have recently arisen as an alternative for studying regional weather and climate allowing two-way interaction between these domains without the need for nudging. In this study, the recently developed variable-resolution option within the Community Earth System Model (CESM) is assessed for long-term regional climate modeling over California. Our variable-resolution simulations will focus on relatively high resolutions for climate assessment, namely 28km and 14km regional resolution, which are much more typical for dynamically downscaled studies. For comparison with the more widely used RCM method, the Weather Research and Forecasting (WRF) model will be used for simulations at 27km and 9km. All simulations use the AMIP (Atmospheric Model Intercomparison Project) protocols. The time period is from 1979-01-01 to 2005-12-31 (UTC), and year 1979 was discarded as spin up time. The mean climatology across California's diverse climate zones, including temperature and precipitation, is analyzed and contrasted with the Weather Research and Forcasting (WRF) model (as a traditional RCM), regional reanalysis, gridded observational datasets and uniform high-resolution CESM at 0.25 degree with the finite volume (FV) dynamical core. The results show that variable-resolution CESM is competitive in representing regional climatology on both annual and seasonal time scales. This assessment adds value to the use of VRGCMs for projecting climate change over the coming century and improve our understanding of both past and future regional climate related to fine-scale processes. This assessment is also relevant for addressing the scale limitation of current RCMs or VRGCMs when next-generation model resolution increases to ~10km and beyond.

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.

Determining spatially discretized surface flow and baseflow in the context of climate change and water quality management

NASA Astrophysics Data System (ADS)

Raimonet, M.; Oudin, L.; Rabouille, C.; Garnier, J.; Silvestre, M.; Vautard, R.; Thieu, V.

2016-12-01

Water quality management of fresh and marine aquatic systems requires modelling tools along the land-ocean continuum in order to evaluate the effect of climate change on nutrient transfer and on potential ecosystem dysfonctioning (e.g. eutrophication, anoxia). In addition to direct effects of climate change on water temperature, it is essential to consider indirect effects of precipitation and temperature changes on hydrology since nutrient transfers are particularly sensitive to the partition of streamflow between surface flow and baseflow. Yet, the determination of surface flow and baseflow, their spatial repartition on drainage basins, and their relative potential evolution under climate change remains challenging. In this study, we developed a generic approach to determine 10-day surface flow and baseflow using a regionalized hydrological model applied at a high spatial resolution (unitary catchments of area circa 10km²). Streamflow data at gauged basins were used to calibrate hydrological model parameters that were then applied on neighbor ungauged basins to estimate streamflow at the scale of the French territory. The proposed methodology allowed representing spatialized surface flow and baseflow that are consistent with climatic and geomorphological settings. The methodology was then used to determine the effect of climate change on the spatial repartition of surface flow and baseflow on the Seine drainage bassin. Results showed large discrepancies of both the amount and the spatial repartition of changes of surface flow and baseflow according to the several GCM and RCM used to derive projected climatic forcing. Consequently, it is expected that the impact of climate change on nutrient transfer might also be quite heterogeneous for the Seine River. This methodology could be applied in any drainage basin where at least several gauged hydrometric stations are available. The estimated surface flow and baseflow can then be used in hydro-ecological models in order to evaluate direct and indirect impacts of climate change on nutrient transfers and potential ecosystem dysfunctioning along the land-ocean continuum.

Modelling the climate and surface mass balance of polar ice sheets using RACMO2 - Part 1: Greenland (1958-2016)

NASA Astrophysics Data System (ADS)

Noël, Brice; van de Berg, Willem Jan; Melchior van Wessem, J.; van Meijgaard, Erik; van As, Dirk; Lenaerts, Jan T. M.; Lhermitte, Stef; Kuipers Munneke, Peter; Smeets, C. J. P. Paul; van Ulft, Lambertus H.; van de Wal, Roderik S. W.; van den Broeke, Michiel R.

2018-03-01

We evaluate modelled Greenland ice sheet (GrIS) near-surface climate, surface energy balance (SEB) and surface mass balance (SMB) from the updated regional climate model RACMO2 (1958-2016). The new model version, referred to as RACMO2.3p2, incorporates updated glacier outlines, topography and ice albedo fields. Parameters in the cloud scheme governing the conversion of cloud condensate into precipitation have been tuned to correct inland snowfall underestimation: snow properties are modified to reduce drifting snow and melt production in the ice sheet percolation zone. The ice albedo prescribed in the updated model is lower at the ice sheet margins, increasing ice melt locally. RACMO2.3p2 shows good agreement compared to in situ meteorological data and point SEB/SMB measurements, and better resolves the spatial patterns and temporal variability of SMB compared with the previous model version, notably in the north-east, south-east and along the K-transect in south-western Greenland. This new model version provides updated, high-resolution gridded fields of the GrIS present-day climate and SMB, and will be used for projections of the GrIS climate and SMB in response to a future climate scenario in a forthcoming study.

How Unusual were Hurricane Harvey's Rains?

NASA Astrophysics Data System (ADS)

Emanuel, K.

2017-12-01

We apply an advanced technique for hurricane risk assessment to evaluate the probability of hurricane rainfall of Harvey's magnitude. The technique embeds a detailed computational hurricane model in the large-scale conditions represented by climate reanalyses and by climate models. We simulate 3700 hurricane events affecting the state of Texas, from each of three climate reanalyses spanning the period 1980-2016, and 2000 events from each of six climate models for each of two periods: the period 1981-2000 from historical simulations, and the period 2081-2100 from future simulations under Representative Concentration Pathway (RCP) 8.5. On the basis of these simulations, we estimate that hurricane rain of Harvey's magnitude in the state of Texas would have had an annual probability of 0.01 in the late twentieth century, and will have an annual probability of 0.18 by the end of this century, with remarkably small scatter among the six climate models downscaled. If the event frequency is changing linearly over time, this would yield an annual probability of 0.06 in 2017.

Evaluation of surface water budget and assessment the global water cycle for the IPCC AR4 A1B scenario simulations

NASA Astrophysics Data System (ADS)

Baek, H.; Park, E.; Kwon, W.

2009-12-01

Water balance calculations are becoming increasingly important for earth-system studies, because humans require water for their survival. Especially, the relationship between climate change and freshwater resources is of primary concern to human society and also has implications for all living species. The goal of this study is to assess the closure and annual variations of the water cycles based on the multi-model ensemble approach. In this study, the projection results of the previous works focusing on global and six sub-regions are updated using sixteen atmosphere-ocean general circulation model (AOGCM) simulations based on the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) A1B scenario. Before projecting future climate, model performances are evaluated on the simulation of the present-day climate. From the result, we construct and use mainly multi-model ensembles (MMEs), which is referred to as MME9, defined from nine selected AOGCMs of higher performance. Analyzed variables include annual and seasonal precipitation, evaporation, and runoff. The overall projection results from MME9 show that most regions will experience warmer and wetter climate at the end of 21st century. The evaporation shows a very similar trend to precipitation, but not in the runoff projection. The internal and inter-model variabilities are larger in the runoff than both precipitation and evaporation. Moreover, the runoff is notably reduced in Europe at the end of 21st century.

Thermoregulation of two sympatric species of horned lizards in the Chihuahuan Desert and their local extinction risk.

PubMed

Lara-Reséndiz, Rafael A; Gadsden, Héctor; Rosen, Philip C; Sinervo, Barry; Méndez-De la Cruz, Fausto R

2015-02-01

Thermoregulatory studies of ectothermic organisms are an important tool for ecological physiology, evolutionary ecology and behavior, and recently have become central for evaluating and predicting global climate change impacts. Here, we present a novel combination of field, laboratory, and modeling approaches to examine body temperature regulation, habitat thermal quality, and hours of thermal restriction on the activity of two sympatric, aridlands horned lizards (Phrynosoma cornutum and Phrynosoma modestum) at three contrasting Chihuahuan Desert sites in Mexico. Using these physiological data, we estimate local extinction risk under predicted climate change within their current geographical distribution. We followed the Hertz et al. (1993, Am. Nat., 142, 796-818) protocol for evaluating thermoregulation and the Sinervo et al. (2010, Science, 328, 894-899) eco-physiological model of extinction under climatic warming. Thermoregulatory indices suggest that both species thermoregulate effectively despite living in habitats of low thermal quality, although high environmental temperatures restrict the activity period of both species. Based on our measurements, if air temperature rises as predicted by climate models, the extinction model projects that P. cornutum will become locally extinct at 6% of sites by 2050 and 18% by 2080 and P. modestum will become extinct at 32% of sites by 2050 and 60% by 2080. The method we apply, using widely available or readily acquired thermal data, along with the modeling, appeared to identify several unique ecological traits that seemingly exacerbate climate sensitivity of P. modestum. Copyright © 2014 Elsevier Ltd. All rights reserved.

An assessment of global climate model-simulated climate for the western cordillera of Canada (1961-90)

NASA Astrophysics Data System (ADS)

Bonsal, Barrie R.; Prowse, Terry D.; Pietroniro, Alain

2003-12-01

Climate change is projected to significantly affect future hydrologic processes over many regions of the world. This is of particular importance for alpine systems that provide critical water supplies to lower-elevation regions. The western cordillera of Canada is a prime example where changes to temperature and precipitation could have profound hydro-climatic impacts not only for the cordillera itself, but also for downstream river systems and the drought-prone Canadian Prairies. At present, impact researchers primarily rely on global climate models (GCMs) for future climate projections. The main objective of this study is to assess several GCMs in their ability to simulate the magnitude and spatial variability of current (1961-90) temperature and precipitation over the western cordillera of Canada. In addition, several gridded data sets of observed climate for the study region are evaluated.Results reveal a close correspondence among the four gridded data sets of observed climate, particularly for temperature. There is, however, considerable variability regarding the various GCM simulations of this observed climate. The British, Canadian, German, Australian, and US GFDL models are superior at simulating the magnitude and spatial variability of mean temperature. The Japanese GCM is of intermediate ability, and the US NCAR model is least representative of temperature in this region. Nearly all the models substantially overestimate the magnitude of total precipitation, both annually and on a seasonal basis. An exception involves the British (Hadley) model, which best represents the observed magnitude and spatial variability of precipitation. This study improves our understanding regarding the accuracy of GCM climate simulations over the western cordillera of Canada. The findings may assist in producing more reliable future scenarios of hydro-climatic conditions over various regions of the country. Copyright

Are Atmospheric Updrafts a Key to Unlocking Climate Forcing and Sensitivity?

DOE PAGES

Donner, Leo J.; O'Brien, Travis A.; Rieger, Daniel; ...

2016-06-08

Both climate forcing and climate sensitivity persist as stubborn uncertainties limiting the extent to which climate models can provide actionable scientific scenarios for climate change. A key, explicit control on cloud-aerosol interactions, the largest uncertainty in climate forcing, is the vertical velocity of cloud-scale updrafts. Model-based studies of climate sensitivity indicate that convective entrainment, which is closely related to updraft speeds, is an important control on climate sensitivity. Updraft vertical velocities also drive many physical processes essential to numerical weather prediction. Vertical velocities and their role in atmospheric physical processes have been given very limited attention in models for climatemore » and numerical weather prediction. The relevant physical scales range down to tens of meters and are thus frequently sub-grid and require parameterization. Many state-of-science convection parameterizations provide mass fluxes without specifying vertical velocities, and parameterizations which do provide vertical velocities have been subject to limited evaluation against what have until recently been scant observations. Atmospheric observations imply that the distribution of vertical velocities depends on the areas over which the vertical velocities are averaged. Distributions of vertical velocities in climate models may capture this behavior, but it has not been accounted for when parameterizing cloud and precipitation processes in current models. New observations of convective vertical velocities offer a potentially promising path toward developing process-level cloud models and parameterizations for climate and numerical weather prediction. Taking account of scale-dependence of resolved vertical velocities offers a path to matching cloud-scale physical processes and their driving dynamics more realistically, with a prospect of reduced uncertainty in both climate forcing and sensitivity.« less

Animating climate model data

NASA Astrophysics Data System (ADS)

DaPonte, John S.; Sadowski, Thomas; Thomas, Paul

2006-05-01

This paper describes a collaborative project conducted by the Computer Science Department at Southern Connecticut State University and NASA's Goddard Institute for Space Science (GISS). Animations of output from a climate simulation math model used at GISS to predict rainfall and circulation have been produced for West Africa from June to September 2002. These early results have assisted scientists at GISS in evaluating the accuracy of the RM3 climate model when compared to similar results obtained from satellite imagery. The results presented below will be refined to better meet the needs of GISS scientists and will be expanded to cover other geographic regions for a variety of time frames.

Implications of climate change mitigation for sustainable development

NASA Astrophysics Data System (ADS)

Jakob, Michael; Steckel, Jan Christoph

2016-10-01

Evaluating the trade-offs between the risks related to climate change, climate change mitigation as well as co-benefits requires an integrated scenarios approach to sustainable development. We outline a conceptual multi-objective framework to assess climate policies that takes into account climate impacts, mitigation costs, water and food availability, technological risks of nuclear energy and carbon capture and sequestration as well as co-benefits of reducing local air pollution and increasing energy security. This framework is then employed as an example to different climate change mitigation scenarios generated with integrated assessment models. Even though some scenarios encompass considerable challenges for sustainability, no scenario performs better or worse than others in all dimensions, pointing to trade-offs between different dimensions of sustainable development. For this reason, we argue that these trade-offs need to be evaluated in a process of public deliberation that includes all relevant social actors.

Assessing modelled spatial distributions of ice water path using satellite data

NASA Astrophysics Data System (ADS)

Eliasson, S.; Buehler, S. A.; Milz, M.; Eriksson, P.; John, V. O.

2010-05-01

The climate models used in the IPCC AR4 show large differences in monthly mean cloud ice. The most valuable source of information that can be used to potentially constrain the models is global satellite data. For this, the data sets must be long enough to capture the inter-annual variability of Ice Water Path (IWP). PATMOS-x was used together with ISCCP for the annual cycle evaluation in Fig. 7 while ECHAM-5 was used for the correlation with other models in Table 3. A clear distinction between ice categories in satellite retrievals, as desired from a model point of view, is currently impossible. However, long-term satellite data sets may still be used to indicate the climatology of IWP spatial distribution. We evaluated satellite data sets from CloudSat, PATMOS-x, ISCCP, MODIS and MSPPS in terms of monthly mean IWP, to determine which data sets can be used to evaluate the climate models. IWP data from CloudSat cloud profiling radar provides the most advanced data set on clouds. As CloudSat data are too short to evaluate the model data directly, it was mainly used here to evaluate IWP from the other satellite data sets. ISCCP and MSPPS were shown to have comparatively low IWP values. ISCCP shows particularly low values in the tropics, while MSPPS has particularly low values outside the tropics. MODIS and PATMOS-x were in closest agreement with CloudSat in terms of magnitude and spatial distribution, with MODIS being the best of the two. As PATMOS-x extends over more than 25 years and is in fairly close agreement with CloudSat, it was chosen as the reference data set for the model evaluation. In general there are large discrepancies between the individual climate models, and all of the models show problems in reproducing the observed spatial distribution of cloud-ice. Comparisons consistently showed that ECHAM-5 is the GCM from IPCC AR4 closest to satellite observations.

Introduction. Progress in Earth science and climate studies.

PubMed

Thompson, J Michael T

2008-12-28

In this introductory paper, I review the 'visions of the future' articles prepared by top young scientists for the second of the two Christmas 2008 Triennial Issues of Phil. Trans. R. Soc.A, devoted respectively to astronomy and Earth science. Topics covered in the Earth science issue include: trace gases in the atmosphere; dynamics of the Antarctic circumpolar current; a study of the boundary between the Earth's rocky mantle and its iron core; and two studies of volcanoes and their plumes. A final section devoted to ecology and climate covers: the mathematical modelling of plant-soil interactions; the effects of the boreal forests on the Earth's climate; the role of the past palaeoclimate in testing and calibrating today's numerical climate models; and the evaluation of these models including the quantification of their uncertainties.

Defining metrics of the Quasi-Biennial Oscillation in global climate models

NASA Astrophysics Data System (ADS)

Schenzinger, Verena; Osprey, Scott; Gray, Lesley; Butchart, Neal

2017-06-01

As the dominant mode of variability in the tropical stratosphere, the Quasi-Biennial Oscillation (QBO) has been subject to extensive research. Though there is a well-developed theory of this phenomenon being forced by wave-mean flow interaction, simulating the QBO adequately in global climate models still remains difficult. This paper presents a set of metrics to characterize the morphology of the QBO using a number of different reanalysis datasets and the FU Berlin radiosonde observation dataset. The same metrics are then calculated from Coupled Model Intercomparison Project 5 and Chemistry-Climate Model Validation Activity 2 simulations which included a representation of QBO-like behaviour to evaluate which aspects of the QBO are well captured by the models and which ones remain a challenge for future model development.

Douglas-fir plantations in Europe: a retrospective test of assisted migration to address climate change.

PubMed

Isaac-Renton, Miriam G; Roberts, David R; Hamann, Andreas; Spiecker, Heinrich

2014-08-01

We evaluate genetic test plantations of North American Douglas-fir provenances in Europe to quantify how tree populations respond when subjected to climate regime shifts, and we examined whether bioclimate envelope models developed for North America to guide assisted migration under climate change can retrospectively predict the success of these provenance transfers to Europe. The meta-analysis is based on long-term growth data of 2800 provenances transferred to 120 European test sites. The model was generally well suited to predict the best performing provenances along north-south gradients in Western Europe, but failed to predict superior performance of coastal North American populations under continental climate conditions in Eastern Europe. However, model projections appear appropriate when considering additional information regarding adaptation of Douglas-fir provenances to withstand frost and drought, even though the model partially fails in a validation against growth traits alone. We conclude by applying the partially validated model to climate change scenarios for Europe, demonstrating that climate trends observed over the last three decades warrant changes to current use of Douglas-fir provenances in plantation forestry throughout Western and Central Europe. © 2014 John Wiley & Sons Ltd.

Insights on the energy-water nexus through modeling of the integrated water cycle

NASA Astrophysics Data System (ADS)

Leung, L. R.; Li, H. Y.; Zhang, X.; Wan, W.; Voisin, N.; Leng, G.

2016-12-01

For sustainable energy planning, understanding the impacts of climate change, land use change, and water management is essential as they all exert notable controls on streamflow and stream temperature that influence energy production. An integrated water model representing river processes, irrigation water use and water management has been developed and coupled to a land surface model to investigate the energy-water nexus. Simulations driven by two climate change projections with the RCP 4.5 and RCP 8.5 emissions scenarios, with and without water management, are analyzed to evaluate the individual and combined effects of climate change and water management on streamflow and stream temperature. The simulations revealed important impacts of climate change and water management on both floods and droughts. The simulations also revealed the dynamics of competition between changes in water demand and water availability in the climate mitigation (RCP 4.5) and business as usual (RCP 8.5) scenarios that influence streamflow and stream temperature, with important consequences to energy production. The integrated water model is being implemented to the Accelerated Climate Modeling for Energy (ACME) to enable investigation of the energy-water nexus in the fully coupled Earth system.

Climate variability in Andalusia (southern Spain) during the period 1701-1850 AD from documentary sources: evaluation and comparison with climate model simulations

NASA Astrophysics Data System (ADS)

Rodrigo, F. S.; Gómez-Navarro, J. J.; Montávez Gómez, J. P.

2011-07-01

In this work, a reconstruction of climatic conditions in Andalusia (southern Iberia Peninsula) during the period 1701-1850, as well as an evaluation of its associated uncertainties, is presented. This period is interesting because it is characterized by a minimum in the solar irradiance (Dalton Minimum, around 1800), as well as intense volcanic activity (for instance, the eruption of the Tambora in 1815), when the increasing atmospheric CO2 concentrations were of minor importance. The reconstruction is based on the analysis of a wide variety of documentary data. The reconstruction methodology is based on accounting the number of extreme events in past, and inferring mean value and standard deviation using the assumption of normal distribution for the seasonal means of climate variables. This reconstruction methodology is tested within the pseudoreality of a high-resolution paleoclimate simulation performed with the regional climate model MM5 coupled to the global model ECHO-G. Results show that the reconstructions are influenced by the reference period chosen and the threshold values used to define extreme values. This creates uncertainties which are assesed within the context of the climate simulation. An ensemble of reconstructions was obtained using two different reference periods and two pairs of percentiles as threshold values. Results correspond to winter temperature, and winter, spring, and autumn rainfall, and they are compared with simulations of the climate model for the considered period. The comparison of the distribution functions corresponding to 1790-1820 and 1960-1990 periods indicates that during the Dalton Minimum the frequency of dry and warm (wet and cold) winters was lesser (higher) than during the reference period. In spring and autumn it was detected an increase (decrease) in the frequency of wet (dry) seasons. Future research challenges are outlined.

Investigating Impacts of Climate Change on Irrigation Water Demands and Its Resulting Consequences on Groundwater Using CMIP5 Models.

PubMed

Goodarzi, Mustafa; Abedi-Koupai, Jahangir; Heidarpour, Manouchehr

2018-04-15

In this study, the impacts of climate change on crop water requirements and irrigation water requirements on the regional cropping pattern were evaluated using two climate change scenarios and combinations of 20 GCM models. Different models including CROPWAT, MODFLOW, and statistical models were used to evaluate the climate change impacts. The results showed that in the future period (2017 to 2046) the temperature in all months of the year will increase at all stations. The average annual precipitation decline in Isfahan, Tiran, Flavarjan, and Lenj stations for RCP 4.5 and RCP 8.5 scenarios are 18.6 and 27.6%, 15.2 and 18%, 22.5 and 31.5%, and 10.5 and 12.1%, respectively. The average increase in the evapotranspiration for RCP 4.5 and RCP 8.5 scenarios are about 2.5 and 4.1%, respectively. The irrigation water demands increases considerably and for some crops, on average 18%. Among the existing crops in the cropping pattern, barley, cumin, onion, wheat, and forage crops are more sensitive and their water demand will increase significantly. Results indicate that climate change could have a significant impact on water resources consumption. By considering irrigation efficiency in the region, climate change impacts will result in about 35 to 50 million m 3 /year, over-extraction from the aquifer. This additional exploitation causes an extra drop of 0.4 to 0.8 m in groundwater table per year in the aquifer. Therefore, with regard to the critical condition of the aquifer, management and preventive measures to deal with climate change in the future is absolutely necessary. © 2018, National Ground Water Association.

Deducing Climatic Elasticity to Assess Projected Climate Change Impacts on Streamflow Change across China

NASA Astrophysics Data System (ADS)

Liu, Jianyu; Zhang, Qiang; Zhang, Yongqiang; Chen, Xi; Li, Jianfeng; Aryal, Santosh K.

2017-10-01

Climatic elasticity has been widely applied to assess streamflow responses to climate changes. To fully assess impacts of climate under global warming on streamflow and reduce the error and uncertainty from various control variables, we develop a four-parameter (precipitation, catchment characteristics n, and maximum and minimum temperatures) climatic elasticity method named PnT, based on the widely used Budyko framework and simplified Makkink equation. We use this method to carry out the first comprehensive evaluation of the streamflow response to potential climate change for 372 widely spread catchments in China. The PnT climatic elasticity was first evaluated for a period 1980-2000, and then used to evaluate streamflow change response to climate change based on 12 global climate models under Representative Concentration Pathway 2.6 (RCP2.6) and RCP 8.5 emission scenarios. The results show that (1) the PnT climatic elasticity method is reliable; (2) projected increasing streamflow takes place in more than 60% of the selected catchments, with mean increments of 9% and 15.4% under RCP2.6 and RCP8.5 respectively; and (3) uncertainties in the projected streamflow are considerable in several regions, such as the Pearl River and Yellow River, with more than 40% of the selected catchments showing inconsistent change directions. Our results can help Chinese policy makers to manage and plan water resources more effectively, and the PnT climatic elasticity should be applied to other parts of the world.

Evaluation of a new satellite-based precipitation dataset for climate studies in the Xiang River basin, Southern China

NASA Astrophysics Data System (ADS)

Zhu, Q.; Xu, Y. P.; Hsu, K. L.

2017-12-01

A new satellite-based precipitation dataset, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) with long-term time series dating back to 1983 can be one valuable dataset for climate studies. This study investigates the feasibility of using PERSIANN-CDR as a reference dataset for climate studies. Sixteen CMIP5 models are evaluated over the Xiang River basin, southern China, by comparing their performance on precipitation projection and streamflow simulation, particularly on extreme precipitation and streamflow events. The results show PERSIANN-CDR is a valuable dataset for climate studies, even on extreme precipitation events. The precipitation estimates and their extreme events from CMIP5 models are improved significantly compared with rain gauge observations after bias-correction by the PERSIANN-CDR precipitation estimates. Given streamflows simulated with raw and bias-corrected precipitation estimates from 16 CMIP5 models, 10 out of 16 are improved after bias-correction. The impact of bias-correction on extreme events for streamflow simulations are unstable, with eight out of 16 models can be clearly claimed they are improved after the bias-correction. Concerning the performance of raw CMIP5 models on precipitation, IPSL-CM5A-MR excels the other CMIP5 models, while MRI-CGCM3 outperforms on extreme events with its better performance on six extreme precipitation metrics. Case studies also show that raw CCSM4, CESM1-CAM5, and MRI-CGCM3 outperform other models on streamflow simulation, while MIROC5-ESM-CHEM, MIROC5-ESM and IPSL-CM5A-MR behaves better than the other models after bias-correction.

Regional Climate Models as a Tool for Assessing Changes in the Laurentian Great Lakes Net Basin Supply

NASA Astrophysics Data System (ADS)

Music, B.; Mailhot, E.; Nadeau, D.; Irambona, C.; Frigon, A.

2017-12-01

Over the last decades, there has been growing concern about the effects of climate change on the Great Lakes water supply. Most of the modelling studies focusing on the Laurentian Great Lakes do not allow two-way exchanges of water and energy between the atmosphere and the underlying surface, and therefore do not account for important feedback mechanisms. Moreover, energy budget constraint at the land surface is not usually taken into account. To address this issue, several recent climate change studies used high resolution Regional Climate Models (RCMs) for evaluating changes in the hydrological regime of the Great Lakes. As RCMs operate on the concept of water and energy conservation, an internal consistency of the simulated energy and water budget components is assured. In this study we explore several recently generated Regional Climate Model (RCM) simulations to investigate the Great Lakes' Net Basin Supply (NBS) in a changing climate. These include simulations of the Canadian Regional Climate Model (CRCM5) supplemented by simulations from several others RCMs participating to the North American CORDEX project (CORDEX-NA). The analysis focuses on the NBS extreme values under nonstationary conditions. The results are expected to provide useful information to the industries in the Great Lakes that all need to include accurate climate change information in their long-term strategy plans to better anticipate impacts of low and/or high water levels.

Drivers and uncertainties of forecasted range shifts for warm-water fishes under climate and land cover change

USGS Publications Warehouse

Bouska, Kristen; Whitledge, Gregory W.; Lant, Christopher; Schoof, Justin

2018-01-01

Land cover is an important determinant of aquatic habitat and is projected to shift with climate changes, yet climate-driven land cover changes are rarely factored into climate assessments. To quantify impacts and uncertainty of coupled climate and land cover change on warm-water fish species’ distributions, we used an ensemble model approach to project distributions of 14 species. For each species, current range projections were compared to 27 scenario-based projections and aggregated to visualize uncertainty. Multiple regression and model selection techniques were used to identify drivers of range change. Novel, or no-analogue, climates were assessed to evaluate transferability of models. Changes in total probability of occurrence ranged widely across species, from a 63% increase to a 65% decrease. Distributional gains and losses were largely driven by temperature and flow variables and underscore the importance of habitat heterogeneity and connectivity to facilitate adaptation to changing conditions. Finally, novel climate conditions were driven by mean annual maximum temperature, which stresses the importance of understanding the role of temperature on fish physiology and the role of temperature-mitigating management practices.

ARM - Midlatitude Continental Convective Clouds

DOE Data Explorer

Jensen, Mike; Bartholomew, Mary Jane; Genio, Anthony Del; Giangrande, Scott; Kollias, Pavlos

2012-01-19

Convective processes play a critical role in the Earth's energy balance through the redistribution of heat and moisture in the atmosphere and their link to the hydrological cycle. Accurate representation of convective processes in numerical models is vital towards improving current and future simulations of Earths climate system. Despite improvements in computing power, current operational weather and global climate models are unable to resolve the natural temporal and spatial scales important to convective processes and therefore must turn to parameterization schemes to represent these processes. In turn, parameterization schemes in cloud-resolving models need to be evaluated for their generality and application to a variety of atmospheric conditions. Data from field campaigns with appropriate forcing descriptors have been traditionally used by modelers for evaluating and improving parameterization schemes.

ARM - Midlatitude Continental Convective Clouds (comstock-hvps)

DOE Data Explorer

Jensen, Mike; Comstock, Jennifer; Genio, Anthony Del; Giangrande, Scott; Kollias, Pavlos

2012-01-06

Convective processes play a critical role in the Earth's energy balance through the redistribution of heat and moisture in the atmosphere and their link to the hydrological cycle. Accurate representation of convective processes in numerical models is vital towards improving current and future simulations of Earths climate system. Despite improvements in computing power, current operational weather and global climate models are unable to resolve the natural temporal and spatial scales important to convective processes and therefore must turn to parameterization schemes to represent these processes. In turn, parameterization schemes in cloud-resolving models need to be evaluated for their generality and application to a variety of atmospheric conditions. Data from field campaigns with appropriate forcing descriptors have been traditionally used by modelers for evaluating and improving parameterization schemes.

Evaluation of large-scale meteorological patterns associated with temperature extremes in the NARCCAP regional climate model simulations

NASA Astrophysics Data System (ADS)

Loikith, Paul C.; Waliser, Duane E.; Lee, Huikyo; Neelin, J. David; Lintner, Benjamin R.; McGinnis, Seth; Mearns, Linda O.; Kim, Jinwon

2015-12-01

Large-scale meteorological patterns (LSMPs) associated with temperature extremes are evaluated in a suite of regional climate model (RCM) simulations contributing to the North American Regional Climate Change Assessment Program. LSMPs are characterized through composites of surface air temperature, sea level pressure, and 500 hPa geopotential height anomalies concurrent with extreme temperature days. Six of the seventeen RCM simulations are driven by boundary conditions from reanalysis while the other eleven are driven by one of four global climate models (GCMs). Four illustrative case studies are analyzed in detail. Model fidelity in LSMP spatial representation is high for cold winter extremes near Chicago. Winter warm extremes are captured by most RCMs in northern California, with some notable exceptions. Model fidelity is lower for cool summer days near Houston and extreme summer heat events in the Ohio Valley. Physical interpretation of these patterns and identification of well-simulated cases, such as for Chicago, boosts confidence in the ability of these models to simulate days in the tails of the temperature distribution. Results appear consistent with the expectation that the ability of an RCM to reproduce a realistically shaped frequency distribution for temperature, especially at the tails, is related to its fidelity in simulating LMSPs. Each ensemble member is ranked for its ability to reproduce LSMPs associated with observed warm and cold extremes, identifying systematically high performing RCMs and the GCMs that provide superior boundary forcing. The methodology developed here provides a framework for identifying regions where further process-based evaluation would improve the understanding of simulation error and help guide future model improvement and downscaling efforts.

A continuous latitudinal energy balance model to explore non-uniform climate engineering strategies

NASA Astrophysics Data System (ADS)

Bonetti, F.; McInnes, C. R.

2016-12-01

Current concentrations of atmospheric CO2 exceed measured historical levels in modern times, largely attributed to anthropogenic forcing since the industrial revolution. The required decline in emissions rates has never been achieved leading to recent interest in climate engineering for future risk-mitigation strategies. Climate engineering aims to offset human-driven climate change. It involves techniques developed both to reduce the concentration of CO2 in the atmosphere (Carbon Dioxide Removal (CDR) methods) and to counteract the radiative forcing that it generates (Solar Radiation Management (SRM) methods). In order to investigate effects of SRM technologies for climate engineering, an analytical model describing the main dynamics of the Earth's climate has been developed. The model is a time-dependent Energy Balance Model (EBM) with latitudinal resolution and allows for the evaluation of non-uniform climate engineering strategies. A significant disadvantage of climate engineering techniques involving the management of solar radiation is regional disparities in cooling. This model offers an analytical approach to design multi-objective strategies that counteract climate change on a regional basis: for example, to cool the Artic and restrict undesired impacts at mid-latitudes, or to control the equator-to-pole temperature gradient. Using the Green's function approach the resulting partial differential equation allows for the computation of the surface temperature as a function of time and latitude when a 1% per year increase in the CO2 concentration is considered. After the validation of the model through comparisons with high fidelity numerical models, it will be used to explore strategies for the injection of the aerosol precursors in the stratosphere. In particular, the model involves detailed description of the optical properties of the particles, the wash-out dynamics and the estimation of the radiative cooling they can generate.

Personal Vehicles Evaluated against Climate Change Mitigation Targets.

PubMed

Miotti, Marco; Supran, Geoffrey J; Kim, Ella J; Trancik, Jessika E

2016-10-18

Meeting global climate change mitigation goals will likely require that transportation-related greenhouse gas emissions begin to decline within the next two decades and then continue to fall. A variety of vehicle technologies and fuels are commercially available to consumers today that can reduce the emissions of the transportation sector. Yet what are the best options, and do any suffice to meet climate policy targets? Here, we examine the costs and carbon intensities of 125 light-duty vehicle models on the U.S. market today and evaluate these models against U.S. emission-reduction targets for 2030, 2040, and 2050 that are compatible with the goal of limiting mean global temperature rise to 2 °C above preindustrial levels. Our results show that consumers are not required to pay more for a low-carbon-emitting vehicle. Across the diverse set of vehicle models and powertrain technologies examined, a clean vehicle is usually a low-cost vehicle. Although the average carbon intensity of vehicles sold in 2014 exceeds the climate target for 2030 by more than 50%, we find that most hybrid and battery electric vehicles available today meet this target. By 2050, only electric vehicles supplied with almost completely carbon-free electric power are expected to meet climate-policy targets.

Uncertainty of climate change impact on groundwater reserves - Application to a chalk aquifer

NASA Astrophysics Data System (ADS)

Goderniaux, Pascal; Brouyère, Serge; Wildemeersch, Samuel; Therrien, René; Dassargues, Alain

2015-09-01

Recent studies have evaluated the impact of climate change on groundwater resources for different geographical and climatic contexts. However, most studies have either not estimated the uncertainty around projected impacts or have limited the analysis to the uncertainty related to climate models. In this study, the uncertainties around impact projections from several sources (climate models, natural variability of the weather, hydrological model calibration) are calculated and compared for the Geer catchment (465 km2) in Belgium. We use a surface-subsurface integrated model implemented using the finite element code HydroGeoSphere, coupled with climate change scenarios (2010-2085) and the UCODE_2005 inverse model, to assess the uncertainty related to the calibration of the hydrological model. This integrated model provides a more realistic representation of the water exchanges between surface and subsurface domains and constrains more the calibration with the use of both surface and subsurface observed data. Sensitivity and uncertainty analyses were performed on predictions. The linear uncertainty analysis is approximate for this nonlinear system, but it provides some measure of uncertainty for computationally demanding models. Results show that, for the Geer catchment, the most important uncertainty is related to calibration of the hydrological model. The total uncertainty associated with the prediction of groundwater levels remains large. By the end of the century, however, the uncertainty becomes smaller than the predicted decline in groundwater levels.

Reproduction of 20th century inter- to multi-decadel surface temperature variablilty in radiatively forced coupled climate models

USDA-ARS?s Scientific Manuscript database

Coupled Model Intercomparison Project 3 simulations of surface temperature were evaluated over the period 1902-1999 to assess their ability to reproduce historical temperature variability at 211 global locations. Model performance was evaluated using the running Mann Whitney-Z method, a technique th...

Climate Model Evaluation using New Datasets from the Clouds and the Earth's Radiant Energy System (CERES)

NASA Technical Reports Server (NTRS)

Loeb, Norman G.; Wielicki, Bruce A.; Doelling, David R.

2008-01-01

There are some in the science community who believe that the response of the climate system to anthropogenic radiative forcing is unpredictable and we should therefore call off the quest . The key limitation in climate predictability is associated with cloud feedback. Narrowing the uncertainty in cloud feedback (and therefore climate sensitivity) requires optimal use of the best available observations to evaluate and improve climate model processes and constrain climate model simulations over longer time scales. The Clouds and the Earth s Radiant Energy System (CERES) is a satellite-based program that provides global cloud, aerosol and radiative flux observations for improving our understanding of cloud-aerosol-radiation feedbacks in the Earth s climate system. CERES is the successor to the Earth Radiation Budget Experiment (ERBE), which has widely been used to evaluate climate models both at short time scales (e.g., process studies) and at decadal time scales. A CERES instrument flew on the TRMM satellite and captured the dramatic 1998 El Nino, and four other CERES instruments are currently flying aboard the Terra and Aqua platforms. Plans are underway to fly the remaining copy of CERES on the upcoming NPP spacecraft (mid-2010 launch date). Every aspect of CERES represents a significant improvement over ERBE. While both CERES and ERBE measure broadband radiation, CERES calibration is a factor of 2 better than ERBE. In order to improve the characterization of clouds and aerosols within a CERES footprint, we use coincident higher-resolution imager observations (VIRS, MODIS or VIIRS) to provide a consistent cloud-aerosol-radiation dataset at climate accuracy. Improved radiative fluxes are obtained by using new CERES-derived Angular Distribution Models (ADMs) for converting measured radiances to fluxes. CERES radiative fluxes are a factor of 2 more accurate than ERBE overall, but the improvement by cloud type and at high latitudes can be as high as a factor of 5. Diurnal cycles are explicitly resolved by merging geostationary satellite observations with CERES and MODIS. Atmospheric state data are provided from a frozen version of the Global Modeling and Assimilation Office- Data Assimilation System at the NASA Goddard Space Flight Center. In addition to improving the accuracy of top-of-atmosphere (TOA) radiative fluxes, CERES also produces radiative fluxes at the surface and at several levels in the atmosphere using radiative transfer modeling, constrained at the TOA by CERES (ERBE was limited to the TOA). In all, CERES uses 11 instruments on 7 spacecraft all integrated to obtain climate accuracy in TOA to surface fluxes. This presentation will provide an overview of several new CERES datasets of interest to the climate community (including a new adjusted TOA flux dataset constrained by estimates of heat storage in the Earth system), show direct comparisons between CERES ad ERBE, and provide a detailed error analysis of CERES fluxes at various time and space scales. We discuss how observations can be used to reduce uncertainties in cloud feedback and climate sensitivity and strongly argue why we should NOT "call off the quest".

Modeled ecohydrological responses to climate change at seven small watersheds in the northeastern United States

Treesearch

Afshin Pourmokhtarian; Charles T. Driscoll; John L. Campbell; Katharine Hayhoe; Anne M. K. Stoner; Mary Beth Adams; Douglas Burns; Ivan Fernandez; Myron J. Mitchell; James B. Shanley

2016-01-01

A cross-site analysis was conducted on seven diverse, forested watersheds in the northeastern United States to evaluate hydrological responses (evapotranspiration, soil moisture, seasonal and annual streamflow, and water stress) to projections of future climate. We used output from four atmosphere–ocean general circulation models (AOGCMs; CCSM4, HadGEM2-CC, MIROC5, and...

A set of scientific issues being considered by the Environmental Protection Agency regarding: pesticide exposure modeling and climate change. SAP Minutes No. 2011-01. USEPA FIFRA Scientific Advisory Panel

USDA-ARS?s Scientific Manuscript database

The USEPA Office of Pesticide Programs (OPP) reviewed most of its human and ecological exposure assessment models for conventional pesticides to evaluate which inputs and parameters may be affected by changing climate conditions. To illustrate the approach used for considering potential effects of c...

The impact of global warming on the range distribution of different climatic groups of Aspidoscelis costata costata.

PubMed

Güizado-Rodríguez, Martha Anahí; Ballesteros-Barrera, Claudia; Casas-Andreu, Gustavo; Barradas-Miranda, Victor Luis; Téllez-Valdés, Oswaldo; Salgado-Ugarte, Isaías Hazarmabeth

2012-12-01

The ectothermic nature of reptiles makes them especially sensitive to global warming. Although climate change and its implications are a frequent topic of detailed studies, most of these studies are carried out without making a distinction between populations. Here we present the first study of an Aspidoscelis species that evaluates the effects of global warming on its distribution using ecological niche modeling. The aims of our study were (1) to understand whether predicted warmer climatic conditions affect the geographic potential distribution of different climatic groups of Aspidoscelis costata costata and (2) to identify potential altitudinal changes of these groups under global warming. We used the maximum entropy species distribution model (MaxEnt) to project the potential distributions expected for the years 2020, 2050, and 2080 under a single simulated climatic scenario. Our analysis suggests that some climatic groups of Aspidoscelis costata costata will exhibit reductions and in others expansions in their distribution, with potential upward shifts toward higher elevation in response to climate warming. Different climatic groups were revealed in our analysis that subsequently showed heterogeneous responses to climatic change illustrating the complex nature of species geographic responses to environmental change and the importance of modeling climatic or geographic groups and/or populations instead of the entire species' range treated as a homogeneous entity.

The use of perturbed physics ensembles and emulation in palaeoclimate reconstruction (Invited)

NASA Astrophysics Data System (ADS)

Edwards, T. L.; Rougier, J.; Collins, M.

2010-12-01

Climate is a coherent process, with correlations and dependencies across space, time, and climate variables. However, reconstructions of palaeoclimate traditionally consider individual pieces of information independently, rather than making use of this covariance structure. Such reconstructions are at risk of being unphysical or at least implausible. Climate simulators such as General Circulation Models (GCMs), on the other hand, contain climate system theory in the form of dynamical equations describing physical processes, but are imperfect and computationally expensive. These two datasets - pointwise palaeoclimate reconstructions and climate simulator evaluations - contain complementary information, and a statistical synthesis can produce a palaeoclimate reconstruction that combines them while not ignoring their limitations. We use an ensemble of simulators with perturbed parameterisations, to capture the uncertainty about the simulator variant, and our method also accounts for structural uncertainty. The resulting reconstruction contains a full expression of climate uncertainty, not just pointwise but also jointly over locations. Such joint information is crucial in determining spatially extensive features such as isotherms, or the location of the tree-line. A second outcome of the statistical analysis is a refined distribution for the simulator parameters. In this way, information from palaeoclimate observations can be used directly in quantifying uncertainty in future climate projections. The main challenge is the expense of running a large scale climate simulator: each evaluation of an atmosphere-ocean GCM takes several months of computing time. The solution is to interpret the ensemble of evaluations within an 'emulator', which is a statistical model of the simulator. This technique has been used fruitfully in the statistical field of Computer Models for two decades, and has recently been applied in estimating uncertainty in future climate predictions in the UKCP09 (http://ukclimateprojections.defra.gov.uk). But only in the last couple of years has it developed to the point where it can be applied to large-scale spatial fields. We construct an emulator for the mid-Holocene (6000 calendar years BP) temperature anomaly over North America, at the resolution of our simulator (2.5° latitude by 3.75° longitude). This allows us to explore the behaviour of simulator variants that we could not afford to evaluate directly. We introduce the technique of 'co-emulation' of two versions of the climate simulator: the coupled atmosphere-ocean model HadCM3, and an equivalent with a simplified ocean, HadSM3. Running two different versions of a simulator is a powerful tool for increasing the information yield from a fixed budget of computer time, but the results must be combined statistically to account for the reduced fidelity of the quicker version. Emulators provide the appropriate framework.

Understanding Climate Uncertainty with an Ocean Focus

NASA Astrophysics Data System (ADS)

Tokmakian, R. T.

2009-12-01

Uncertainty in climate simulations arises from various aspects of the end-to-end process of modeling the Earth’s climate. First, there is uncertainty from the structure of the climate model components (e.g. ocean/ice/atmosphere). Even the most complex models are deficient, not only in the complexity of the processes they represent, but in which processes are included in a particular model. Next, uncertainties arise from the inherent error in the initial and boundary conditions of a simulation. Initial conditions are the state of the weather or climate at the beginning of the simulation and other such things, and typically come from observations. Finally, there is the uncertainty associated with the values of parameters in the model. These parameters may represent physical constants or effects, such as ocean mixing, or non-physical aspects of modeling and computation. The uncertainty in these input parameters propagates through the non-linear model to give uncertainty in the outputs. The models in 2020 will no doubt be better than today’s models, but they will still be imperfect, and development of uncertainty analysis technology is a critical aspect of understanding model realism and prediction capability. Smith [2002] and Cox and Stephenson [2007] discuss the need for methods to quantify the uncertainties within complicated systems so that limitations or weaknesses of the climate model can be understood. In making climate predictions, we need to have available both the most reliable model or simulation and a methods to quantify the reliability of a simulation. If quantitative uncertainty questions of the internal model dynamics are to be answered with complex simulations such as AOGCMs, then the only known path forward is based on model ensembles that characterize behavior with alternative parameter settings [e.g. Rougier, 2007]. The relevance and feasibility of using "Statistical Analysis of Computer Code Output" (SACCO) methods for examining uncertainty in ocean circulation due to parameter specification will be described and early results using the ocean/ice components of the CCSM climate model in a designed experiment framework will be shown. Cox, P. and D. Stephenson, Climate Change: A Changing Climate for Prediction, 2007, Science 317 (5835), 207, DOI: 10.1126/science.1145956. Rougier, J. C., 2007: Probabilistic Inference for Future Climate Using an Ensemble of Climate Model Evaluations, Climatic Change, 81, 247-264. Smith L., 2002, What might we learn from climate forecasts? Proc. Nat’l Academy of Sciences, Vol. 99, suppl. 1, 2487-2492 doi:10.1073/pnas.012580599.

Evaluation of Data-Driven Models for Predicting Solar Photovoltaics Power Output

DOE PAGES

Moslehi, Salim; Reddy, T. Agami; Katipamula, Srinivas

2017-09-10

This research was undertaken to evaluate different inverse models for predicting power output of solar photovoltaic (PV) systems under different practical scenarios. In particular, we have investigated whether PV power output prediction accuracy can be improved if module/cell temperature was measured in addition to climatic variables, and also the extent to which prediction accuracy degrades if solar irradiation is not measured on the plane of array but only on a horizontal surface. We have also investigated the significance of different independent or regressor variables, such as wind velocity and incident angle modifier in predicting PV power output and cell temperature.more » The inverse regression model forms have been evaluated both in terms of their goodness-of-fit, and their accuracy and robustness in terms of their predictive performance. Given the accuracy of the measurements, expected CV-RMSE of hourly power output prediction over the year varies between 3.2% and 8.6% when only climatic data are used. Depending on what type of measured climatic and PV performance data is available, different scenarios have been identified and the corresponding appropriate modeling pathways have been proposed. The corresponding models are to be implemented on a controller platform for optimum operational planning of microgrids and integrated energy systems.« less

Impacts of fine particulate matter on premature mortality under future climate change

NASA Astrophysics Data System (ADS)

Park, S.; Allen, R.; Lim, C. H.

2016-12-01

Climate change modulates concentration of fine particulate matter (PM2.5) via modifying atmospheric circulation and the hydrological cycle. Furthermore, surface PM2.5 is significantly associated with respiratory diseases and premature mortality. In this study, we assess the response of PM2.5 concentration to climate change in the future (end of 21st century) and its effects on year of life lost (YLL) and premature mortality. We use outputs from five models participating in the Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP) to evaluate climate change effects on PM2.5: for present climate with current aerosol emissions and greenhouse gas concentrations, and for future climate, also with present-day aerosol emissions, but with end-of-the century greenhouse gas concentrations, sea surface temperatures and sea-ice. The results show that climate change is associated with an increase in PM2.5 concentration. Combined with global future population data from the United Nation (UN), we also find an increase in premature mortality and YLL.

Diagnosis and Quantification of Climatic Sensitivity of Carbon Fluxes in Ensemble Global Ecosystem Models

NASA Astrophysics Data System (ADS)

Wang, W.; Hashimoto, H.; Milesi, C.; Nemani, R. R.; Myneni, R.

2011-12-01

Terrestrial ecosystem models are primary scientific tools to extrapolate our understanding of ecosystem functioning from point observations to global scales as well as from the past climatic conditions into the future. However, no model is nearly perfect and there are often considerable structural uncertainties existing between different models. Ensemble model experiments thus become a mainstream approach in evaluating the current status of global carbon cycle and predicting its future changes. A key task in such applications is to quantify the sensitivity of the simulated carbon fluxes to climate variations and changes. Here we develop a systematic framework to address this question solely by analyzing the inputs and the outputs from the models. The principle of our approach is to assume the long-term (~30 years) average of the inputs/outputs as a quasi-equlibrium of the climate-vegetation system while treat the anomalies of carbon fluxes as responses to climatic disturbances. In this way, the corresponding relationships can be largely linearized and analyzed using conventional time-series techniques. This method is used to characterize three major aspects of the vegetation models that are mostly important to global carbon cycle, namely the primary production, the biomass dynamics, and the ecosystem respiration. We apply this analytical framework to quantify the climatic sensitivity of an ensemble of models including CASA, Biome-BGC, LPJ as well as several other DGVMs from previous studies, all driven by the CRU-NCEP climate dataset. The detailed analysis results are reported in this study.

Growth response of conifers in Adirondack plantations to changing environment: Model approaches based on stem-analysis

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

Pan, Y.

1993-01-01

Based on model approaches, three conifer species, red pine, Norway spruce and Scots pine grown in plantations at Pack Demonstration Forest, in the southeastern Adirondack mountains of New York, were chosen to study growth response to different environmental changes, including silvicultural treatments and changes in climate and chemical environment. Detailed stem analysis data provided a basis for constructing tree growth models. These models were organized into three groups: morphological, dynamic and predictive. The morphological model was designed to evaluate relationship between tree attributes and interactive influences of intrinsic and extrinsic factors on the annual increments. Three types of morphological patternsmore » have been characterized: space-time patterns of whole-stem rings, intrinsic wood deposition pattern along the tree-stem, and bolewood allocation ratio patterns along the tree-stem. The dynamic model reflects the growth process as a system which responds to extrinsic signal inputs, including fertilization pulses, spacing effects and climatic disturbance, as well as intrinsic feedback. Growth signals indicative of climatic effects were used to construct growth-climate models using both multivariate analysis and Kalman filter methods. The predictive model utilized GCMs and growth-climate relationships to forecast tree growth responses in relation to future scenarios of CO[sub 2]-induced climate change. Prediction results indicate that different conifer species have individualistic growth response to future climatic change and suggest possible changes in future growth and distribution of naturally occurring conifers in this region.« less

Increasing the relevance of GCM simulations for Climate Services

NASA Astrophysics Data System (ADS)

Smith, L. A.; Suckling, E.

2012-12-01

The design and interpretation of model simulations for climate services differ significantly from experimental design for the advancement of the fundamental research on predictability that underpins it. Climate services consider the sources of best information available today; this calls for a frank evaluation of model skill in the face of statistical benchmarks defined by empirical models. The fact that Physical simulation models are thought to provide the only reliable method for extrapolating into conditions not previously observed has no bearing on whether or not today's simulation models outperform empirical models. Evidence on the length scales on which today's simulation models fail to outperform empirical benchmarks is presented; it is illustrated that this occurs even on global scales in decadal prediction. At all timescales considered thus far (as of July 2012), predictions based on simulation models are improved by blending with the output of statistical models. Blending is shown to be more interesting in the climate context than it is in the weather context, where blending with a history-based climatology is straightforward. As GCMs improve and as the Earth's climate moves further from that of the last century, the skill from simulation models and their relevance to climate services is expected to increase. Examples from both seasonal and decadal forecasting will be used to discuss a third approach that may increase the role of current GCMs more quickly. Specifically, aspects of the experimental design in previous hind cast experiments are shown to hinder the use of GCM simulations for climate services. Alternative designs are proposed. The value in revisiting Thompson's classic approach to improving weather forecasting in the fifties in the context of climate services is discussed.

Climate Controls AM Fungal Distributions from Global to Local Scales

NASA Astrophysics Data System (ADS)

Kivlin, S. N.; Hawkes, C.; Muscarella, R.; Treseder, K. K.; Kazenel, M.; Lynn, J.; Rudgers, J.

2016-12-01

Arbuscular mycorrhizal (AM) fungi have key functions in terrestrial biogeochemical processes; thus, determining the relative importance of climate, edaphic factors, and plant community composition on their geographic distributions can improve predictions of their sensitivity to global change. Local adaptation by AM fungi to plant hosts, soil nutrients, and climate suggests that all of these factors may control fungal geographic distributions, but their relative importance is unknown. We created species distribution models for 142 AM fungal taxa at the global scale with data from GenBank. We compared climate variables (BioClim and soil moisture), edaphic variables (phosphorus, carbon, pH, and clay content), and plant variables using model selection on models with (1) all variables, (2) climatic variables only (including soil moisture) and (3) resource-related variables only (all other soil parameters and NPP) using the MaxEnt algorithm evaluated with ENMEval. We also evaluated whether drivers of AM fungal distributions were phylogenetically conserved. To test whether global correlates of AM fungal distributions were reflected at local scales, we then surveyed AM fungi in nine plant hosts along three elevation gradients in the Upper Gunnison Basin, Colorado, USA. At the global scale, the distributions of 55% of AM fungal taxa were affected by both climate and soil resources, whereas 16% were only affected by climate and 29% were only affected by soil resources. Even for AM fungi that were affected by both climate and resources, the effects of climatic variables nearly always outweighed those of resources. Soil moisture and isothermality were the main climatic and NPP and soil carbon the main resource related factors influencing AM fungal distributions. Distributions of closely related AM fungal taxa were similarly affected by climate, but not by resources. Local scale surveys of AM fungi across elevations confirmed that climate was a key driver of AM fungal composition and root colonization, with weaker influences of plant identity and soil nutrients. These two studies across scales suggest prevailing effects of climate on AM fungal distributions. Thus, incorporating climate when forecasting future ranges of AM fungi will enhance predictions of AM fungal abundance and associated ecosystem functions.

Results of a community-based survey of construction safety climate for Hispanic workers.

PubMed

Marin, Luz S; Cifuentes, Manuel; Roelofs, Cora

2015-01-01

Hispanic construction workers experience high rates of occupational injury, likely influenced by individual, organizational, and social factors. To characterize the safety climate of Hispanic construction workers using worker, contractor, and supervisor perceptions of the workplace. We developed a 40-item interviewer-assisted survey with six safety climate dimensions and administered it in Spanish and English to construction workers, contractors, and supervisors. A safety climate model, comparing responses and assessing contributing factors was created based on survey responses. While contractors and construction supervisors' (n = 128) scores were higher, all respondents shared a negative perception of safety climate. Construction workers had statistically significantly lower safety climate scores compared to supervisors and contractors (30·6 vs 46·5%, P<0·05). Safety climate scores were not associated with English language ability or years lived in the United States. We found that Hispanic construction workers in this study experienced a poor safety climate. The Hispanic construction safety climate model we propose can serve as a framework to guide organizational safety interventions and evaluate safety climate improvements.

Results of a community-based survey of construction safety climate for Hispanic workers

PubMed Central

Marin, Luz S; Cifuentes, Manuel; Roelofs, Cora

2015-01-01

Background: Hispanic construction workers experience high rates of occupational injury, likely influenced by individual, organizational, and social factors. Objectives: To characterize the safety climate of Hispanic construction workers using worker, contractor, and supervisor perceptions of the workplace. Methods: We developed a 40-item interviewer-assisted survey with six safety climate dimensions and administered it in Spanish and English to construction workers, contractors, and supervisors. A safety climate model, comparing responses and assessing contributing factors was created based on survey responses. Results: While contractors and construction supervisors’ (n = 128) scores were higher, all respondents shared a negative perception of safety climate. Construction workers had statistically significantly lower safety climate scores compared to supervisors and contractors (30.6 vs 46.5%, P<0.05). Safety climate scores were not associated with English language ability or years lived in the United States. Conclusions: We found that Hispanic construction workers in this study experienced a poor safety climate. The Hispanic construction safety climate model we propose can serve as a framework to guide organizational safety interventions and evaluate safety climate improvements. PMID:26145454

Promoting Scientific Thinking and Conceptual Change about Alternative Explanations of Climate Change and Other Controversial Socio-scientific Topics

NASA Astrophysics Data System (ADS)

Lombardi, D.; Sinatra, G. M.

2013-12-01

Critical evaluation and plausibility reappraisal of scientific explanations have been underemphasized in many science classrooms (NRC, 2012). Deep science learning demands that students increase their ability to critically evaluate the quality of scientific knowledge, weigh alternative explanations, and explicitly reappraise their plausibility judgments. Therefore, this lack of instruction about critical evaluation and plausibility reappraisal has, in part, contributed to diminished understanding about complex and controversial topics, such as global climate change. The Model-Evidence Link (MEL) diagram (originally developed by researchers at Rutgers University under an NSF-supported project; Chinn & Buckland, 2012) is an instructional scaffold that promotes students to critically evaluate alternative explanations. We recently developed a climate change MEL and found that the students who used the MEL experienced a significant shift in their plausibility judgments toward the scientifically accepted model of human-induced climate change. Using the MEL for instruction also resulted in conceptual change about the causes of global warming that reflected greater understanding of fundamental scientific principles. Furthermore, students sustained this conceptual change six months after MEL instruction (Lombardi, Sinatra, & Nussbaum, 2013). This presentation will discuss recent educational research that supports use of the MEL to promote critical evaluation, plausibility reappraisal, and conceptual change, and also, how the MEL may be particularly effective for learning about global climate change and other socio-scientific topics. Such instruction to develop these fundamental thinking skills (e.g., critical evaluation and plausibility reappraisal) is demanded by both the Next Generation Science Standards (Achieve, 2013) and the Common Core State Standards for English Language Arts and Mathematics (CCSS Initiative-ELA, 2010; CCSS Initiative-Math, 2010), as well as a society that is equipped to deal with challenges in a way that is beneficial to our national and global community.

Implication of Agricultural Land Use Change on Regional Climate Projection

NASA Astrophysics Data System (ADS)

Wang, G.; Ahmed, K. F.; You, L.

2015-12-01

Agricultural land use plays an important role in land-atmosphere interaction. Agricultural activity is one of the most important processes driving human-induced land use land cover change (LULCC) in a region. In addition to future socioeconomic changes, climate-induced changes in crop yield represent another important factor shaping agricultural land use. In feedback, the resulting LULCC influences the direction and magnitude of global, regional and local climate change by altering Earth's radiative equilibrium. Therefore, assessment of climate change impact on future agricultural land use and its feedback is of great importance in climate change study. In this study, to evaluate the feedback of projected land use changes to the regional climate in West Africa, we employed an asynchronous coupling between a regional climate model (RegCM) and a prototype land use projection model (LandPro). The LandPro model, which was developed to project the future change in agricultural land use and the resulting shift in natural vegetation in West Africa, is a spatially explicit model that can account for both climate and socioeconomic changes in projecting future land use changes. In the asynchronously coupled modeling framework, LandPro was run for every five years during the period of 2005-2050 accounting for climate-induced change in crop yield and socioeconomic changes to project the land use pattern by the mid-21st century. Climate data at 0.5˚ was derived from RegCM to drive the crop model DSSAT for each of the five-year periods to simulate crop yields, which was then provided as input data to LandPro. Subsequently, the land use land cover map required to run RegCM was updated every five years using the outputs from the LandPro simulations. Results from the coupled model simulations improve the understanding of climate change impact on future land use and the resulting feedback to regional climate.

Climate conditions and drought assessment with the Palmer Drought Severity Index in Iran: evaluation of CORDEX South Asia climate projections (2070-2099)

NASA Astrophysics Data System (ADS)

Senatore, Alfonso; Hejabi, Somayeh; Mendicino, Giuseppe; Bazrafshan, Javad; Irannejad, Parviz

2018-03-01

Climate change projections were evaluated over both the whole Iran and six zones having different precipitation regimes considering the CORDEX South Asia dataset, for assessing space-time distribution of drought occurrences in the future period 2070-2099 under RCP4.5 scenario. Initially, the performances of eight available CORDEX South Asia Regional Climate Models (RCMs) were assessed for the baseline period 1970-2005 through the GPCC v.7 precipitation dataset and the CFSR temperature dataset, which were previously selected as the most reliable within a set of five global datasets compared to 41 available synoptic stations. Though the CCLM RCM driven by the MPI-ESM-LR General Circulation Model is in general the most suitable for temperature and, together with the REMO 2009 RCM also driven by MPI-ESM-LR, for precipitation, their performances do not overwhelm other models for every season and zone in which Iranian territory was divided according to a principal component analysis approach. Hence, a weighting approach was tested and adopted to take into account useful information from every RCM in each of the six zones. The models resulting more reliable compared to current climate show a strong precipitation decrease. Weighted average predicts an overall yearly precipitation decrease of about 20%. Temperature projections provide a mean annual increase of 2.4 °C. Future drought scenarios were depicted by means of the self-calibrating version of the Palmer drought severity index (SC-PDSI) model. Weighted average predicts a sharp drying that can be configured as a real shift in mean climate conditions, drastically affecting water resources of the country.

Modeling perceptions of climatic risk in crop production.

PubMed

Reinmuth, Evelyn; Parker, Phillip; Aurbacher, Joachim; Högy, Petra; Dabbert, Stephan

2017-01-01

In agricultural production, land-use decisions are components of economic planning that result in the strategic allocation of fields. Climate variability represents an uncertainty factor in crop production. Considering yield impact, climatic influence is perceived during and evaluated at the end of crop production cycles. In practice, this information is then incorporated into planning for the upcoming season. This process contributes to attitudes toward climate-induced risk in crop production. In the literature, however, the subjective valuation of risk is modeled as a risk attitude toward variations in (monetary) outcomes. Consequently, climatic influence may be obscured by political and market influences so that risk perceptions during the production process are neglected. We present a utility concept that allows the inclusion of annual risk scores based on mid-season risk perceptions that are incorporated into field-planning decisions. This approach is exemplified and implemented for winter wheat production in the Kraichgau, a region in Southwest Germany, using the integrated bio-economic simulation model FarmActor and empirical data from the region. Survey results indicate that a profitability threshold for this crop, the level of "still-good yield" (sgy), is 69 dt ha-1 (regional mean Kraichgau sample) for a given season. This threshold governs the monitoring process and risk estimators. We tested the modeled estimators against simulation results using ten projected future weather time series for winter wheat production. The mid-season estimators generally proved to be effective. This approach can be used to improve the modeling of planning decisions by providing a more comprehensive evaluation of field-crop response to climatic changes from an economic risk point of view. The methodology further provides economic insight in an agrometeorological context where prices for crops or inputs are lacking, but farmer attitudes toward risk should still be included in the analysis.

Present and Future Projections of Habitat Suitability of the Asian Tiger Mosquito, a Vector of Viral Pathogens, from Global Climate Simulations.

NASA Astrophysics Data System (ADS)

Proestos, Y.; Christophides, G.; Erguler, K.; Tanarhte, M.; Waldock, J.; Lelieveld, J.

2014-12-01

Climate change can influence the transmission of vector borne diseases (VBDs) through altering the habitat suitability of insect vectors. Here we present global climate model simulations and evaluate the associated uncertainties in view of the main meteorological factors that may affect the distribution of the Asian Tiger mosquito (Aedes albopictus), which can transmit pathogens that cause Chikungunya, Dengue fever, yellow fever and various encephalitides. Using a general circulation model (GCM) at 50 km horizontal resolution to simulate mosquito survival variables including temperature, precipitation and relative humidity, we present both global and regional projections of the habitat suitability up to the middle of the 21st century. The model resolution of 50 km allows evaluation against previous projections for Europe and provides a basis for comparative analyses with other regions. Model uncertainties and performance are addressed in light of the recent CMIP5 ensemble climate model simulations for the RCP8.5 concentration pathway and using meteorological re-analysis data (ERA-Interim/ECMWF) for the recent past. Uncertainty ranges associated with the thresholds of meteorological variables that may affect the distribution of Ae. albopictus are diagnosed using fuzzy-logic methodology, notably to assess the influence of selected meteorological criteria and combinations of criteria that influence mosquito habitat suitability. From the climate projections for 2050, and adopting a habitat suitability index larger than 70%, we estimate that about 2.4 billion individuals in a land area of nearly 20 million square kilometres will potentially be exposed to Ae. albopictus. The synthesis of fuzzy-logic based on mosquito biology and climate change analysis provides new insights into the regional and global spreading of VBDs to support disease control and policy making.

Modeling perceptions of climatic risk in crop production

PubMed Central

Parker, Phillip; Aurbacher, Joachim; Högy, Petra; Dabbert, Stephan

2017-01-01

In agricultural production, land-use decisions are components of economic planning that result in the strategic allocation of fields. Climate variability represents an uncertainty factor in crop production. Considering yield impact, climatic influence is perceived during and evaluated at the end of crop production cycles. In practice, this information is then incorporated into planning for the upcoming season. This process contributes to attitudes toward climate-induced risk in crop production. In the literature, however, the subjective valuation of risk is modeled as a risk attitude toward variations in (monetary) outcomes. Consequently, climatic influence may be obscured by political and market influences so that risk perceptions during the production process are neglected. We present a utility concept that allows the inclusion of annual risk scores based on mid-season risk perceptions that are incorporated into field-planning decisions. This approach is exemplified and implemented for winter wheat production in the Kraichgau, a region in Southwest Germany, using the integrated bio-economic simulation model FarmActor and empirical data from the region. Survey results indicate that a profitability threshold for this crop, the level of “still-good yield” (sgy), is 69 dt ha-1 (regional mean Kraichgau sample) for a given season. This threshold governs the monitoring process and risk estimators. We tested the modeled estimators against simulation results using ten projected future weather time series for winter wheat production. The mid-season estimators generally proved to be effective. This approach can be used to improve the modeling of planning decisions by providing a more comprehensive evaluation of field-crop response to climatic changes from an economic risk point of view. The methodology further provides economic insight in an agrometeorological context where prices for crops or inputs are lacking, but farmer attitudes toward risk should still be included in the analysis. PMID:28763471

Impacts of Climate Policy on Regional Air Quality, Health, and Air Quality Regulatory Procedures

NASA Astrophysics Data System (ADS)

Thompson, T. M.; Selin, N. E.

2011-12-01

Both the changing climate, and the policy implemented to address climate change can impact regional air quality. We evaluate the impacts of potential selected climate policies on modeled regional air quality with respect to national pollution standards, human health and the sensitivity of health uncertainty ranges. To assess changes in air quality due to climate policy, we couple output from a regional computable general equilibrium economic model (the US Regional Energy Policy [USREP] model), with a regional air quality model (the Comprehensive Air Quality Model with Extensions [CAMx]). USREP uses economic variables to determine how potential future U.S. climate policy would change emissions of regional pollutants (CO, VOC, NOx, SO2, NH3, black carbon, and organic carbon) from ten emissions-heavy sectors of the economy (electricity, coal, gas, crude oil, refined oil, energy intensive industry, other industry, service, agriculture, and transportation [light duty and heavy duty]). Changes in emissions are then modeled using CAMx to determine the impact on air quality in several cities in the Northeast US. We first calculate the impact of climate policy by using regulatory procedures used to show attainment with National Ambient Air Quality Standards (NAAQS) for ozone and particulate matter. Building on previous work, we compare those results with the calculated results and uncertainties associated with human health impacts due to climate policy. This work addresses a potential disconnect between NAAQS regulatory procedures and the cost/benefit analysis required for and by the Clean Air Act.

Methods and Model Dependency of Extreme Event Attribution: The 2015 European Drought

NASA Astrophysics Data System (ADS)

Hauser, Mathias; Gudmundsson, Lukas; Orth, René; Jézéquel, Aglaé; Haustein, Karsten; Vautard, Robert; van Oldenborgh, Geert J.; Wilcox, Laura; Seneviratne, Sonia I.

2017-10-01

Science on the role of anthropogenic influence on extreme weather events, such as heatwaves or droughts, has evolved rapidly in the past years. The approach of "event attribution" compares the occurrence-probability of an event in the present, factual climate with its probability in a hypothetical, counterfactual climate without human-induced climate change. Several methods can be used for event attribution, based on climate model simulations and observations, and usually researchers only assess a subset of methods and data sources. Here, we explore the role of methodological choices for the attribution of the 2015 meteorological summer drought in Europe. We present contradicting conclusions on the relevance of human influence as a function of the chosen data source and event attribution methodology. Assessments using the maximum number of models and counterfactual climates with pre-industrial greenhouse gas concentrations point to an enhanced drought risk in Europe. However, other evaluations show contradictory evidence. These results highlight the need for a multi-model and multi-method framework in event attribution research, especially for events with a low signal-to-noise ratio and high model dependency such as regional droughts.

Statistical framework for evaluation of climate model simulations by use of climate proxy data from the last millennium - Part 1: Theory

NASA Astrophysics Data System (ADS)

Sundberg, R.; Moberg, A.; Hind, A.

2012-08-01

A statistical framework for comparing the output of ensemble simulations from global climate models with networks of climate proxy and instrumental records has been developed, focusing on near-surface temperatures for the last millennium. This framework includes the formulation of a joint statistical model for proxy data, instrumental data and simulation data, which is used to optimize a quadratic distance measure for ranking climate model simulations. An essential underlying assumption is that the simulations and the proxy/instrumental series have a shared component of variability that is due to temporal changes in external forcing, such as volcanic aerosol load, solar irradiance or greenhouse gas concentrations. Two statistical tests have been formulated. Firstly, a preliminary test establishes whether a significant temporal correlation exists between instrumental/proxy and simulation data. Secondly, the distance measure is expressed in the form of a test statistic of whether a forced simulation is closer to the instrumental/proxy series than unforced simulations. The proposed framework allows any number of proxy locations to be used jointly, with different seasons, record lengths and statistical precision. The goal is to objectively rank several competing climate model simulations (e.g. with alternative model parameterizations or alternative forcing histories) by means of their goodness of fit to the unobservable true past climate variations, as estimated from noisy proxy data and instrumental observations.

Estimating climate change effects on net primary production of rangelands in the United States

Treesearch

Matthew C. Reeves; Adam L. Moreno; Karen E. Bagne; Steven W. Running

2014-01-01

The potential effects of climate change on net primary productivity (NPP) of U.S. rangelands were evaluated using estimated climate regimes from the A1B, A2 and B2 global change scenarios imposed on the biogeochemical cycling model, Biome-BGC from 2001 to 2100. Temperature, precipitation, vapor pressure deficit, day length, solar radiation, CO2 enrichment and nitrogen...

Decision- rather than scenario-centred downscaling: Towards smarter use of climate model outputs

NASA Astrophysics Data System (ADS)

Wilby, Robert L.

2013-04-01

Climate model output has been used for hydrological impact assessments for at least 25 years. Scenario-led methods raise awareness about risks posed by climate variability and change to the security of supplies, performance of water infrastructure, and health of freshwater ecosystems. However, it is less clear how these analyses translate into actionable information for adaptation. One reason is that scenario-led methods typically yield very large uncertainty bounds in projected impacts at regional and river catchment scales. Consequently, there is growing interest in vulnerability-based frameworks and strategies for employing climate model output in decision-making contexts. This talk begins by summarising contrasting perspectives on climate models and principles for testing their utility for water sector applications. Using selected examples it is then shown how water resource systems may be adapted with varying levels of reliance on climate model information. These approaches include the conventional scenario-led risk assessment, scenario-neutral strategies, safety margins and sensitivity testing, and adaptive management of water systems. The strengths and weaknesses of each approach are outlined and linked to selected water management activities. These cases show that much progress can be made in managing water systems without dependence on climate models. Low-regret measures such as improved forecasting, better inter-agency co-operation, and contingency planning, yield benefits regardless of the climate outlook. Nonetheless, climate model scenarios are useful for evaluating adaptation portfolios, identifying system thresholds and fixing weak links, exploring the timing of investments, improving operating rules, or developing smarter licensing regimes. The most problematic application remains the climate change safety margin because of the very low confidence in extreme precipitation and river flows generated by climate models. In such cases, it is necessary to understand the trade-offs that exist between the additional costs of a scheme and the level of risk that is accommodated.

Evaluation of local adaptation strategies to climate change of maize crop in Andalusia for the first half of 21st century

NASA Astrophysics Data System (ADS)

Gabaldón, Clara; Lorite, Ignacio J.; Inés Mínguez, M.; Dosio, Alessandro; Sánchez-Sánchez, Enrique; Ruiz-Ramos, Margarita

2013-04-01

The objective of this work is to generate and analyse adaptation strategies to cope with impacts of climate change on cereal cropping systems in Andalusia (Southern Spain) in a semi-arid environment, with focus on extreme events. In Andalusia, located in the South of the Iberian Peninsula, cereals crops may be affected by the increase in average temperatures, the precipitation variability and the possible extreme events. Those impacts may cause a decrease in both water availability and the pollination rate resulting on a decrease in yield and the farmer's profitability. Designing local and regional adaptation strategies to reduce these negative impacts is necessary. This study is focused on irrigated maize on five Andalusia locations. The Andalusia Network of Agricultural Trials (RAEA in Spanish) provided the experimental crop and soil data, and the observed climate data were obtained from the Agroclimatic Information Network of Andalusia and the Spanish National Meteorological Agency (AEMET in Spanish). The data for future climate scenarios (2013-2050) were generated by Dosio and Paruolo (2011) and Dosio et al. (2012), who corrected the bias of ENSEMBLES data for maximum and minimum temperatures and precipitation. ENSEMBLES data were the results of numerical simulations obtained from a group of regional climate models at high resolution (25 km) from the European Project ENSEMBLES (http://www.ensembles-eu.org/). Crop models considered were CERES-maize (Jones and Kiniry, 1986) under DSSAT platform, and CropSyst (Stockle et al., 2003). Those crop models were applied only on locations were calibration and validation were done. The effects of the adaptations strategies, such as changes in sowing dates or choice of cultivar, were evaluated regarding water consumption; changes in phenological dates were also analysed to compare with occurrence of extreme events of maximum temperature. These events represent a threat on summer crops due to the reduction on the duration of grain filling period with the consequent reduction in yield (Ruiz-Ramos et al., 2011) and with the supraoptimal temperatures in pollination. Finally, results of simulated impacts and adaptations were compared to previous studies done without bias correction of climatic projections, at low resolution and with previous versions of crop models (Mínguez et al., 2007). This study will contribute to MACSUR knowledge Hub within the Joint Programming Initiative on Agriculture, Food Security and Climate Change (FACCE - JPI) of EU and is financed by MULCLIVAR project (CGL2012-38923-C02-02) and IFAPA project AGR6126 from Junta de Andalucía, Spain. References Dosio A. and Paruolo P., 2011. Bias correction of the ENSEMBLES high-resolution climate change projections for use by impact models: Evaluation on the present climate. Journal of Geophysical Research, VOL. 116, D16106, doi:10.1029/2011JD015934 Dosio A., Paruolo P. and Rojas R., 2012. Bias correction of the ENSEMBLES high resolution climate change projections for use by impact models: Analysis of the climate change signal. Journal of Geophysical Research, Volume 117, D17, doi: 0.1029/2012JD017968 Jones, C.A., and J.R. Kiniry. 1986. CERES-Maize: A simulation model of maize growth and development. Texas A&M Univ. Press, College Station. Mínguez, M.I., M. Ruiz-ramos, C.H. Díaz-Ambrona, and M. Quemada. 2007. First-order impacts on winter and summer crops assessed with various high-resolution climate models in the Iberian Peninsula. Climatic Change 81: 343-355. Ruiz-Ramos, M., E. Sanchez, C. Galllardo, and M.I. Minguez. 2011. Impacts of projected maximum temperature extremes for C21 by an ensemble of regional climate models on cereal cropping systems in the Iberian Peninsula. Natural Hazards and Earth System Science 11: 3275-3291. Stockle, C.O., M. Donatelli, and R. Nelson. 2003. CropSyst , a cropping systems simulation model. European Journal of Agronomy18: 289-307.

Evaluation of the Enhanced Integrated Climatic Model for modulus-based construction specification for Oklahoma pavements.

DOT National Transportation Integrated Search

2013-07-01

The study provides estimation of site specific variation in environmental factors that can be : used in predicting seasonal and long-term variations in moduli of unbound materials. Using : these site specific estimates, the EICM climatic input files ...

Leachate generation from landfill in a semi-arid climate: A qualitative and quantitative study from Sousse, Tunisia.

PubMed

Frikha, Youssef; Fellner, Johann; Zairi, Moncef

2017-09-01

Despite initiatives for enhanced recycling and waste utilization, landfill still represents the dominant disposal path for municipal solid waste (MSW). The environmental impacts of landfills depend on several factors, including waste composition, technical barriers, landfill operation and climatic conditions. A profound evaluation of all factors and their impact is necessary in order to evaluate the environmental hazards emanating from landfills. The present paper investigates a sanitary landfill located in a semi-arid climate (Tunisia) and highlights major differences in quantitative and qualitative leachate characteristics compared to landfills situated in moderate climates. Besides the qualitative analysis of leachate samples, a quantitative analysis including the simulation of leachate generation (using the HELP model) has been conducted. The results of the analysis indicate a high load of salts (Cl, Na, inorganic nitrogen) in the leachate compared to other landfills. Furthermore the simulations with HELP model highlight that a major part of the leachate generated originates form the water content of waste.

Multidirectional abundance shifts among North American birds and the relative influence of multifaceted climate factors.

PubMed

Huang, Qiongyu; Sauer, John R; Dubayah, Ralph O

2017-09-01

Shifts in species distributions are major fingerprint of climate change. Examining changes in species abundance structures at a continental scale enables robust evaluation of climate change influences, but few studies have conducted these evaluations due to limited data and methodological constraints. In this study, we estimate temporal changes in abundance from North American Breeding Bird Survey data at the scale of physiographic strata to examine the relative influence of different components of climatic factors and evaluate the hypothesis that shifting species distributions are multidirectional in resident bird species in North America. We quantify the direction and velocity of the abundance shifts of 57 permanent resident birds over 44 years using a centroid analysis. For species with significant abundance shifts in the centroid analysis, we conduct a more intensive correlative analysis to identify climate components most strongly associated with composite change of abundance within strata. Our analysis focus on two contrasts: the relative importance of climate extremes vs. averages, and of temperature vs. precipitation in strength of association with abundance change. Our study shows that 36 species had significant abundance shifts over the study period. The average velocity of the centroid is 5.89 km·yr -1 . The shifted distance on average covers 259 km, 9% of range extent. Our results strongly suggest that the climate change fingerprint in studied avian distributions is multidirectional. Among 6 directions with significant abundance shifts, the northwestward shift was observed in the largest number of species (n = 13). The temperature/average climate model consistently has greater predictive ability than the precipitation/extreme climate model in explaining strata-level abundance change. Our study shows heterogeneous avian responses to recent environmental changes. It highlights needs for more species-specific approaches to examine contributing factors to recent distributional changes and for comprehensive conservation planning for climate change adaptation. Published 2017. This article is a U.S. Government work and is in the public domain in the USA.

Quantitative Assessment of Antarctic Climate Variability and Change

NASA Astrophysics Data System (ADS)

Ordonez, A.; Schneider, D. P.

2013-12-01

The Antarctic climate is both extreme and highly variable, but there are indications it may be changing. As the climate in Antarctica can affect global sea level and ocean circulation, it is important to understand and monitor its behavior. Observational and model data have been used to study climate change in Antarctica and the Southern Ocean, though observational data is sparse and models have difficulty reproducing many observed climate features. For example, a leading hypothesis that ozone depletion has been responsible for sea ice trends is struggling with the inability of ozone-forced models to reproduce the observed sea ice increase. The extent to which this data-model disagreement represents inadequate observations versus model biases is unknown. This research assessed a variety of climate change indicators to present an overview of Antarctic climate that will allow scientists to easily access this data and compare indicators with other observational data and model output. Indicators were obtained from observational and reanalysis data for variables such as temperature, sea ice area, and zonal wind stress. Multiple datasets were used for key variables. Monthly and annual anomaly data from Antarctica and the Southern Ocean as well as tropical indices were plotted as time series on common axes for comparison. Trends and correlations were also computed. Zonal wind, surface temperature, and austral springtime sea ice had strong relationships and were further discussed in terms of how they may relate to climate variability and change in the Antarctic. This analysis will enable hypothesized mechanisms of Antarctic climate change to be critically evaluated.

Assessment of the impact of climate shifts on malaria transmission in the Sahel.

PubMed

Bomblies, Arne; Eltahir, Elfatih A B

2009-09-01

Climate affects malaria transmission through a complex network of causative pathways. We seek to evaluate the impact of hypothetical climate change scenarios on malaria transmission in the Sahel by using a novel mechanistic, high spatial- and temporal-resolution coupled hydrology and agent-based entomology model. The hydrology model component resolves individual precipitation events and individual breeding pools. The impact of future potential climate shifts on the representative Sahel village of Banizoumbou, Niger, is estimated by forcing the model of Banizoumbou environment with meteorological data from two locations along the north-south climatological gradient observed in the Sahel--both for warmer, drier scenarios from the north and cooler, wetter scenarios from the south. These shifts in climate represent hypothetical but historically realistic climate change scenarios. For Banizoumbou climatic conditions (latitude 13.54 N), a shift toward cooler, wetter conditions may dramatically increase mosquito abundance; however, our modeling results indicate that the increased malaria transmissibility is not simply proportional to the precipitation increase. The cooler, wetter conditions increase the length of the sporogonic cycle, dampening a large vectorial capacity increase otherwise brought about by increased mosquito survival and greater overall abundance. Furthermore, simulations varying rainfall event frequency demonstrate the importance of precipitation patterns, rather than simply average or time-integrated precipitation, as a controlling factor of these dynamics. Modeling results suggest that in addition to changes in temperature and total precipitation, changes in rainfall patterns are very important to predict changes in disease susceptibility resulting from climate shifts. The combined effect of these climate-shift-induced perturbations can be represented with the aid of a detailed mechanistic model.

Characterization of the Sahelian-Sudan rainfall based on observations and regional climate models

NASA Astrophysics Data System (ADS)

Salih, Abubakr A. M.; Elagib, Nadir Ahmed; Tjernström, Michael; Zhang, Qiong

2018-04-01

The African Sahel region is known to be highly vulnerable to climate variability and change. We analyze rainfall in the Sahelian Sudan in terms of distribution of rain-days and amounts, and examine whether regional climate models can capture these rainfall features. Three regional models namely, Regional Model (REMO), Rossby Center Atmospheric Model (RCA) and Regional Climate Model (RegCM4), are evaluated against gridded observations (Climate Research Unit, Tropical Rainfall Measuring Mission, and ERA-interim reanalysis) and rain-gauge data from six arid and semi-arid weather stations across Sahelian Sudan over the period 1989 to 2008. Most of the observed rain-days are characterized by weak (0.1-1.0 mm/day) to moderate (> 1.0-10.0 mm/day) rainfall, with average frequencies of 18.5% and 48.0% of the total annual rain-days, respectively. Although very strong rainfall events (> 30.0 mm/day) occur rarely, they account for a large fraction of the total annual rainfall (28-42% across the stations). The performance of the models varies both spatially and temporally. RegCM4 most closely reproduces the observed annual rainfall cycle, especially for the more arid locations, but all of the three models fail to capture the strong rainfall events and hence underestimate its contribution to the total annual number of rain-days and rainfall amount. However, excessive moderate rainfall compensates this underestimation in the models in an annual average sense. The present study uncovers some of the models' limitations in skillfully reproducing the observed climate over dry regions, will aid model users in recognizing the uncertainties in the model output and will help climate and hydrological modeling communities in improving models.

Evaluation of a distributed energy balance model for a high-altitude glacier on the Tibetan Plateau using a time lapse camera system

NASA Astrophysics Data System (ADS)

Huintjes, Eva; Sauter, Tobias; Krenscher, Tobias; Maussion, Fabien; Kropacek, Jan; Yang, Wei; Zhang, Guoshuai; Kang, Shichang; Buchroithner, Manfred; Scherer, Dieter; Schneider, Christoph

2013-04-01

In the remote and high-altitude mountain areas of the Tibetan Plateau, climate observations as well as glacier-wide mass and energy balance determinations are scarce. Therefore, the application of models to determine reliable information on mass balance and runoff is important. Simultaneously, these circumstances make it difficult to evaluate the models. Since 2009, we operate an automatic weather station (AWS) in the ablation zone of Zhadang Glacier (5.665 m a.s.l.). The glacier is easily accessible. It is situated in the southern-central part of the Tibetan Plateau (30.5°N) in the Nam Co drainage basin and ranges between 5.400 and 5.900 m a.s.l. Based on these measurements over 2009-2012, we run and evaluate a physically based, distributed energy and mass balance model. The applied model couples an energy balance to a multilayer snow model and therefore accounts for subsurface processes like refreezing, subsurface melt and densification of the snowpack. First, the model is evaluated at point scale against measurements from the AWS. The results show that modelled accumulation and ablation patterns reproduce the observed changes in surface height very well. To evaluate the distributed model, we use daily images of a time lapse camera system installed nearby the glacier over 2010-2012. Therefore the non calibrated slope images had to be orthorectified using ground control points measured during field campaigns. The temporally and spatially highly resolved time series allows a detailed evaluation of the distributed energy balance model by analyzing the spatial and temporal heterogeneity of the snow line during the ablation season. First results show that the model captures the observed spatial heterogeneity of melt on the glacier surface. Subsequently to the evaluation the model will be applied on several glaciers and small ice caps in remote areas on the Tibetan Plateau to determine the linkages between climate fluctuations and glacier variability. The work is part of research projects funded by the DFG Priority Programme 1372: "Tibetan Plateau: Formation-Climate-Ecosystems" (TiP) and the BMBF research program "Central Asia and Tibet: Monsoon dynamics and geo-ecosystems" (CAME).

Climate Change Impacts on Hydrology and Water Management of the San Juan Basin

NASA Astrophysics Data System (ADS)

Rich, P. M.; Weintraub, L. H.; Chen, L.; Herr, J.

2005-12-01

Recent climatic events, including regional drought and increased storm severity, have accentuated concerns that climatic extremes may be increasing in frequency and intensity due to global climate change. As part of the ZeroNet Water-Energy Initiative, the San Juan Decision Support System includes a basin-scale modeling tool to evaluate effects of climate change on water budgets under different climate and management scenarios. The existing Watershed Analysis Risk Management Framework (WARMF) was enhanced with iterative modeling capabilities to enable construction of climate scenarios based on historical and projected data. We applied WARMF to 42,000 km2 (16,000 mi2) of the San Juan Basin (CO, NM) to assess impacts of extended drought and increased temperature on surface water balance. Simulations showed that drought and increased temperature impact water availability for all sectors (agriculture, energy, municipal, industry), and lead to increased frequency of critical shortages. Implementation of potential management alternatives such as "shortage sharing" or degraded water usage during critical years helps improve available water supply. In the face of growing concern over climate change, limited water resources, and competing demands, integrative modeling tools can enable better understanding of complex interconnected systems, and enable better decisions.

Evaluating Modeled Impact Metrics for Human Health, Agriculture Growth, and Near-Term Climate

NASA Astrophysics Data System (ADS)

Seltzer, K. M.; Shindell, D. T.; Faluvegi, G.; Murray, L. T.

2017-12-01

Simulated metrics that assess impacts on human health, agriculture growth, and near-term climate were evaluated using ground-based and satellite observations. The NASA GISS ModelE2 and GEOS-Chem models were used to simulate the near-present chemistry of the atmosphere. A suite of simulations that varied by model, meteorology, horizontal resolution, emissions inventory, and emissions year were performed, enabling an analysis of metric sensitivities to various model components. All simulations utilized consistent anthropogenic global emissions inventories (ECLIPSE V5a or CEDS), and an evaluation of simulated results were carried out for 2004-2006 and 2009-2011 over the United States and 2014-2015 over China. Results for O3- and PM2.5-based metrics featured minor differences due to the model resolutions considered here (2.0° × 2.5° and 0.5° × 0.666°) and model, meteorology, and emissions inventory each played larger roles in variances. Surface metrics related to O3 were consistently high biased, though to varying degrees, demonstrating the need to evaluate particular modeling frameworks before O3 impacts are quantified. Surface metrics related to PM2.5 were diverse, indicating that a multimodel mean with robust results are valuable tools in predicting PM2.5-related impacts. Oftentimes, the configuration that captured the change of a metric best over time differed from the configuration that captured the magnitude of the same metric best, demonstrating the challenge in skillfully simulating impacts. These results highlight the strengths and weaknesses of these models in simulating impact metrics related to air quality and near-term climate. With such information, the reliability of historical and future simulations can be better understood.

Analysis of consistency of global net land-use change carbon emission scenario using offline vegetation model and earth system model

NASA Astrophysics Data System (ADS)

Kato, E.; Kawamiya, M.

2010-12-01

For CMIP5 experiments, emissions scenarios data sets for climate models are prepared as Representative Concentration Pathways (RCPs) by the Integrated Assessment Models (IAMs). IAMs also have depicted regional land-use scenarios based on the socioeconomic assumption of the future scenarios of RCPs. In the land-use harmonization project, gridded land-use transition data has been constructed from the regional IAMs future land-use scenarios which smoothly connects historical reconstructions of land-use based on HYDE 3 data and FAO wood harvest data. In this study, using the gridded transition land-use scenario data, global net CO2 emission from land-use change for each RCPs scenarios is evaluated with a offline version of terrestrial biogeochemical model, VISIT (Vegetation Integrative SImulation Tool), utilizing a protocol to estimate carbon emission from deforested biomass considering delayed decomposition of product pools, and regrowth absorption from the secondary lands with abandoned agricultural lands. From the model output, effect of CO2 fertilization and land-use scenario itself on the emission is assessed to see the consistency of the scenarios. In addition, to see the effect of climate change and the climate-carbon feedback on terrestrial ecosystems, net land-use change CO2 emission is also evaluated with an earth system model, MIROC-ESM incorporating a DGVM with land-use change component. In the simulations with earth system model, RCP 6.0 scenario has been evaluated by model runs with and without land-use change forcing.

Projecting water yield and ecosystem productivity across the United States by linking an ecohydrological model to WRF dynamically downscaled climate data

NASA Astrophysics Data System (ADS)

Sun, Shanlei; Sun, Ge; Cohen, Erika; McNulty, Steven G.; Caldwell, Peter V.; Duan, Kai; Zhang, Yang

2016-03-01

Quantifying the potential impacts of climate change on water yield and ecosystem productivity is essential to developing sound watershed restoration plans, and ecosystem adaptation and mitigation strategies. This study links an ecohydrological model (Water Supply and Stress Index, WaSSI) with WRF (Weather Research and Forecasting Model) using dynamically downscaled climate data of the HadCM3 model under the IPCC SRES A2 emission scenario. We evaluated the future (2031-2060) changes in evapotranspiration (ET), water yield (Q) and gross primary productivity (GPP) from the baseline period of 1979-2007 across the 82 773 watersheds (12-digit Hydrologic Unit Code level) in the coterminous US (CONUS). Across the CONUS, the future multi-year means show increases in annual precipitation (P) of 45 mm yr-1 (6 %), 1.8° C increase in temperature (T), 37 mm yr-1 (7 %) increase in ET, 9 mm yr-1 (3 %) increase in Q, and 106 gC m-2 yr-1 (9 %) increase in GPP. We found a large spatial variability in response to climate change across the CONUS 12-digit HUC watersheds, but in general, the majority would see consistent increases all variables evaluated. Over half of the watersheds, mostly found in the northeast and the southern part of the southwest, would see an increase in annual Q (> 100 mm yr-1 or 20 %). In addition, we also evaluated the future annual and monthly changes of hydrology and ecosystem productivity for the 18 Water Resource Regions (WRRs) or two-digit HUCs. The study provides an integrated method and example for comprehensive assessment of the potential impacts of climate change on watershed water balances and ecosystem productivity at high spatial and temporal resolutions. Results may be useful for policy-makers and land managers to formulate appropriate watershed-specific strategies for sustaining water and carbon sources in the face of climate change.

Impacts of Changing Climatic Drivers and Land use features on Future Stormwater Runoff in the Northwest Florida Basin: A Large-Scale Hydrologic Modeling Assessment

NASA Astrophysics Data System (ADS)

Khan, M.; Abdul-Aziz, O. I.

2017-12-01

Potential changes in climatic drivers and land cover features can significantly influence the stormwater budget in the Northwest Florida Basin. We investigated the hydro-climatic and land use sensitivities of stormwater runoff by developing a large-scale process-based rainfall-runoff model for the large basin by using the EPA Storm Water Management Model (SWMM 5.1). Climatic and hydrologic variables, as well as land use/cover features were incorporated into the model to account for the key processes of coastal hydrology and its dynamic interactions with groundwater and sea levels. We calibrated and validated the model by historical daily streamflow observations during 2009-2012 at four major rivers in the basin. Downscaled climatic drivers (precipitation, temperature, solar radiation) projected by twenty GCMs-RCMs under CMIP5, along with the projected future land use/cover features were also incorporated into the model. The basin storm runoff was then simulated for the historical (2000s = 1976-2005) and two future periods (2050s = 2030-2059, and 2080s = 2070-2099). Comparative evaluation of the historical and future scenarios leads to important guidelines for stormwater management in Northwest Florida and similar regions under a changing climate and environment.

Observationally-based Metrics of Ocean Carbon and Biogeochemical Variables are Essential for Evaluating Earth System Model Projections

NASA Astrophysics Data System (ADS)

Russell, J. L.; Sarmiento, J. L.

2017-12-01

The Southern Ocean is central to the climate's response to increasing levels of atmospheric greenhouse gases as it ventilates a large fraction of the global ocean volume. Global coupled climate models and earth system models, however, vary widely in their simulations of the Southern Ocean and its role in, and response to, the ongoing anthropogenic forcing. Due to its complex water-mass structure and dynamics, Southern Ocean carbon and heat uptake depend on a combination of winds, eddies, mixing, buoyancy fluxes and topography. Understanding how the ocean carries heat and carbon into its interior and how the observed wind changes are affecting this uptake is essential to accurately projecting transient climate sensitivity. Observationally-based metrics are critical for discerning processes and mechanisms, and for validating and comparing climate models. As the community shifts toward Earth system models with explicit carbon simulations, more direct observations of important biogeochemical parameters, like those obtained from the biogeochemically-sensored floats that are part of the Southern Ocean Carbon and Climate Observations and Modeling project, are essential. One goal of future observing systems should be to create observationally-based benchmarks that will lead to reducing uncertainties in climate projections, and especially uncertainties related to oceanic heat and carbon uptake.

Assessment of bias correction under transient climate change

NASA Astrophysics Data System (ADS)

Van Schaeybroeck, Bert; Vannitsem, Stéphane

2015-04-01

Calibration of climate simulations is necessary since large systematic discrepancies are generally found between the model climate and the observed climate. Recent studies have cast doubt upon the common assumption of the bias being stationary when the climate changes. This led to the development of new methods, mostly based on linear sensitivity of the biases as a function of time or forcing (Kharin et al. 2012). However, recent studies uncovered more fundamental problems using both low-order systems (Vannitsem 2011) and climate models, showing that the biases may display complicated non-linear variations under climate change. This last analysis focused on biases derived from the equilibrium climate sensitivity, thereby ignoring the effect of the transient climate sensitivity. Based on the linear response theory, a general method of bias correction is therefore proposed that can be applied on any climate forcing scenario. The validity of the method is addressed using twin experiments with a climate model of intermediate complexity LOVECLIM (Goosse et al., 2010). We evaluate to what extent the bias change is sensitive to the structure (frequency) of the applied forcing (here greenhouse gases) and whether the linear response theory is valid for global and/or local variables. To answer these question we perform large-ensemble simulations using different 300-year scenarios of forced carbon-dioxide concentrations. Reality and simulations are assumed to differ by a model error emulated as a parametric error in the wind drag or in the radiative scheme. References [1] H. Goosse et al., 2010: Description of the Earth system model of intermediate complexity LOVECLIM version 1.2, Geosci. Model Dev., 3, 603-633. [2] S. Vannitsem, 2011: Bias correction and post-processing under climate change, Nonlin. Processes Geophys., 18, 911-924. [3] V.V. Kharin, G. J. Boer, W. J. Merryfield, J. F. Scinocca, and W.-S. Lee, 2012: Statistical adjustment of decadal predictions in a changing climate, Geophys. Res. Lett., 39, L19705.

US forest response to projected climate-related stress: a tolerance perspective.

PubMed

Liénard, Jean; Harrison, John; Strigul, Nikolay

2016-08-01

Although it is widely recognized that climate change will require a major spatial reorganization of forests, our ability to predict exactly how and where forest characteristics and distributions will change has been rather limited. Current efforts to predict future distribution of forested ecosystems as a function of climate include species distribution models (for fine-scale predictions) and potential vegetation climate envelope models (for coarse-grained, large-scale predictions). Here, we develop and apply an intermediate approach wherein we use stand-level tolerances of environmental stressors to understand forest distributions and vulnerabilities to anticipated climate change. In contrast to other existing models, this approach can be applied at a continental scale while maintaining a direct link to ecologically relevant, climate-related stressors. We first demonstrate that shade, drought, and waterlogging tolerances of forest stands are strongly correlated with climate and edaphic conditions in the conterminous United States. This discovery allows the development of a tolerance distribution model (TDM), a novel quantitative tool to assess landscape level impacts of climate change. We then focus on evaluating the implications of the drought TDM. Using an ensemble of 17 climate change models to drive this TDM, we estimate that 18% of US ecosystems are vulnerable to drought-related stress over the coming century. Vulnerable areas include mostly the Midwest United States and Northeast United States, as well as high-elevation areas of the Rocky Mountains. We also infer stress incurred by shifting climate should create an opening for the establishment of forest types not currently seen in the conterminous United States. © 2016 John Wiley & Sons Ltd.

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.

Modeling daily soil temperature over diverse climate conditions in Iran—a comparison of multiple linear regression and support vector regression techniques

NASA Astrophysics Data System (ADS)

Delbari, Masoomeh; Sharifazari, Salman; Mohammadi, Ehsan

2018-02-01

The knowledge of soil temperature at different depths is important for agricultural industry and for understanding climate change. The aim of this study is to evaluate the performance of a support vector regression (SVR)-based model in estimating daily soil temperature at 10, 30 and 100 cm depth at different climate conditions over Iran. The obtained results were compared to those obtained from a more classical multiple linear regression (MLR) model. The correlation sensitivity for the input combinations and periodicity effect were also investigated. Climatic data used as inputs to the models were minimum and maximum air temperature, solar radiation, relative humidity, dew point, and the atmospheric pressure (reduced to see level), collected from five synoptic stations Kerman, Ahvaz, Tabriz, Saghez, and Rasht located respectively in the hyper-arid, arid, semi-arid, Mediterranean, and hyper-humid climate conditions. According to the results, the performance of both MLR and SVR models was quite well at surface layer, i.e., 10-cm depth. However, SVR performed better than MLR in estimating soil temperature at deeper layers especially 100 cm depth. Moreover, both models performed better in humid climate condition than arid and hyper-arid areas. Further, adding a periodicity component into the modeling process considerably improved the models' performance especially in the case of SVR.

Creating "Intelligent" Climate Model Ensemble Averages Using a Process-Based Framework

NASA Astrophysics Data System (ADS)

Baker, N. C.; Taylor, P. C.

2014-12-01

The CMIP5 archive contains future climate projections from over 50 models provided by dozens of modeling centers from around the world. Individual model projections, however, are subject to biases created by structural model uncertainties. As a result, ensemble averaging of multiple models is often used to add value to model projections: consensus projections have been shown to consistently outperform individual models. Previous reports for the IPCC establish climate change projections based on an equal-weighted average of all model projections. However, certain models reproduce climate processes better than other models. Should models be weighted based on performance? Unequal ensemble averages have previously been constructed using a variety of mean state metrics. What metrics are most relevant for constraining future climate projections? This project develops a framework for systematically testing metrics in models to identify optimal metrics for unequal weighting multi-model ensembles. A unique aspect of this project is the construction and testing of climate process-based model evaluation metrics. A climate process-based metric is defined as a metric based on the relationship between two physically related climate variables—e.g., outgoing longwave radiation and surface temperature. Metrics are constructed using high-quality Earth radiation budget data from NASA's Clouds and Earth's Radiant Energy System (CERES) instrument and surface temperature data sets. It is found that regional values of tested quantities can vary significantly when comparing weighted and unweighted model ensembles. For example, one tested metric weights the ensemble by how well models reproduce the time-series probability distribution of the cloud forcing component of reflected shortwave radiation. The weighted ensemble for this metric indicates lower simulated precipitation (up to .7 mm/day) in tropical regions than the unweighted ensemble: since CMIP5 models have been shown to overproduce precipitation, this result could indicate that the metric is effective in identifying models which simulate more realistic precipitation. Ultimately, the goal of the framework is to identify performance metrics for advising better methods for ensemble averaging models and create better climate predictions.

Integrated Assessment Model Evaluation

NASA Astrophysics Data System (ADS)

Smith, S. J.; Clarke, L.; Edmonds, J. A.; Weyant, J. P.

2012-12-01

Integrated assessment models of climate change (IAMs) are widely used to provide insights into the dynamics of the coupled human and socio-economic system, including emission mitigation analysis and the generation of future emission scenarios. Similar to the climate modeling community, the integrated assessment community has a two decade history of model inter-comparison, which has served as one of the primary venues for model evaluation and confirmation. While analysis of historical trends in the socio-economic system has long played a key role in diagnostics of future scenarios from IAMs, formal hindcast experiments are just now being contemplated as evaluation exercises. Some initial thoughts on setting up such IAM evaluation experiments are discussed. Socio-economic systems do not follow strict physical laws, which means that evaluation needs to take place in a context, unlike that of physical system models, in which there are few fixed, unchanging relationships. Of course strict validation of even earth system models is not possible (Oreskes etal 2004), a fact borne out by the inability of models to constrain the climate sensitivity. Energy-system models have also been grappling with some of the same questions over the last quarter century. For example, one of "the many questions in the energy field that are waiting for answers in the next 20 years" identified by Hans Landsberg in 1985 was "Will the price of oil resume its upward movement?" Of course we are still asking this question today. While, arguably, even fewer constraints apply to socio-economic systems, numerous historical trends and patterns have been identified, although often only in broad terms, that are used to guide the development of model components, parameter ranges, and scenario assumptions. IAM evaluation exercises are expected to provide useful information for interpreting model results and improving model behavior. A key step is the recognition of model boundaries, that is, what is inside and outside the IAM. All IAM projections to date are conditional on assumed inputs such as population dynamics and economic growth. A key part of evaluation exercises will be the substantial effort needed to develop the necessary historical datasets. Given the fundamentally uncertain characteristics of the socio-economic system, alternative formulations of the evaluation question may turn out to be useful. For example, is is likely useful to ask: how much needs to be specified on order to be able to reproduce historical trends to within a given accuracy? There is also a close, and fundamental, link between evaluation and diagnostic exercises that aim to evaluate the characteristics of future scenarios (rates of growth, technology diffusion, etc.) against historical behavior. These exercises are currently being conducted by individual groups due, in part, due to the large diversity if IAM designs and goals. While all climate models are, to first order, modeling the same system, boundary conditions, and physical laws, this is not true for IAMs. The structure, and even feasibility, of a hindcast-style evaluation exercise can be very different depending on the structure of each specific integrated assessment model.

Carbon-Temperature-Water Change Analysis for Peanut Production Under Climate Change: A Prototype for the AgMIP Coordinated Climate-Crop Modeling Project (C3MP)

NASA Technical Reports Server (NTRS)

Ruane, Alex C.; McDermid, Sonali; Rosenzweig, Cynthia; Baigorria, Guillermo A.; Jones, James W.; Romero, Consuelo C.; Cecil, L. DeWayne

2014-01-01

Climate change is projected to push the limits of cropping systems and has the potential to disrupt the agricultural sector from local to global scales. This article introduces the Coordinated Climate-Crop Modeling Project (C3MP), an initiative of the Agricultural Model Intercomparison and Improvement Project (AgMIP) to engage a global network of crop modelers to explore the impacts of climate change via an investigation of crop responses to changes in carbon dioxide concentration ([CO2]), temperature, and water. As a demonstration of the C3MP protocols and enabled analyses, we apply the Decision Support System for Agrotechnology Transfer (DSSAT) CROPGRO-Peanut crop model for Henry County, Alabama, to evaluate responses to the range of plausible [CO2], temperature changes, and precipitation changes projected by climate models out to the end of the 21st century. These sensitivity tests are used to derive crop model emulators that estimate changes in mean yield and the coefficient of variation for seasonal yields across a broad range of climate conditions, reproducing mean yields from sensitivity test simulations with deviations of ca. 2% for rain-fed conditions. We apply these statistical emulators to investigate how peanuts respond to projections from various global climate models, time periods, and emissions scenarios, finding a robust projection of modest (<10%) median yield losses in the middle of the 21st century accelerating to more severe (>20%) losses and larger uncertainty at the end of the century under the more severe representative concentration pathway (RCP8.5). This projection is not substantially altered by the selection of the AgMERRA global gridded climate dataset rather than the local historical observations, differences between the Third and Fifth Coupled Model Intercomparison Project (CMIP3 and CMIP5), or the use of the delta method of climate impacts analysis rather than the C3MP impacts response surface and emulator approach.

Using a dynamic vegetation model for future projections of crop yields: application to Belgium in the framework of the VOTES and MASC projects

NASA Astrophysics Data System (ADS)

Jacquemin, Ingrid; Henrot, Alexandra-Jane; Fontaine, Corentin M.; Dendoncker, Nicolas; Beckers, Veronique; Debusscher, Bos; Tychon, Bernard; Hambuckers, Alain; François, Louis

2016-04-01

Dynamic vegetation models (DVM) were initially designed to describe the dynamics of natural ecosystems as a function of climate and soil, to study the role of the vegetation in the carbon cycle. These models are now directly coupled with climate models in order to evaluate feedbacks between vegetation and climate. But DVM characteristics allow numerous other applications, leading to amelioration of some of their modules (e.g., evaluating sensitivity of the hydrological module to land surface changes) and developments (e.g., coupling with other models like agent-based models), to be used in ecosystem management and land use planning studies. It is in this dynamic context about DVMs that we have adapted the CARAIB (CARbon Assimilation In the Biosphere) model. One of the main improvements is the implementation of a crop module, allowing the assessment of climate change impacts on crop yields. We try to validate this module at different scales: - from the plot level, with the use of eddy-covariance data from agricultural sites in the FLUXNET network, such as Lonzée (Belgium) or other Western European sites (Grignon, Dijkgraaf,…), - to the country level, for which we compare the crop yield calculated by CARAIB to the crop yield statistics for Belgium and for different agricultural regions of the country. Another challenge for the CARAIB DVM was to deal with the landscape dynamics, which is not directly possible due to the lack of consideration of anthropogenic factors in the system. In the framework of the VOTES and the MASC projects, CARAIB is coupled with an agent-based model (ABM), representing the societal component of the system. This coupled module allows the use of climate and socio-economic scenarios, particularly interesting for studies which aim at ensuring a sustainable approach. This module has particularly been exploited in the VOTES project, where the objective was to provide a social, biophysical and economic assessment of the ecosystem services in four municipalities under urban pressure in the center of Belgium. The biophysical valuation was carried out with the coupled module, allowing a quantitative evaluation of key ecosystem services as a function of three climatic and socio-economic scenarios.

Potential for added value in precipitation simulated by high-resolution nested Regional Climate Models and observations

NASA Astrophysics Data System (ADS)

di Luca, Alejandro; de Elía, Ramón; Laprise, René

2012-03-01

Regional Climate Models (RCMs) constitute the most often used method to perform affordable high-resolution regional climate simulations. The key issue in the evaluation of nested regional models is to determine whether RCM simulations improve the representation of climatic statistics compared to the driving data, that is, whether RCMs add value. In this study we examine a necessary condition that some climate statistics derived from the precipitation field must satisfy in order that the RCM technique can generate some added value: we focus on whether the climate statistics of interest contain some fine spatial-scale variability that would be absent on a coarser grid. The presence and magnitude of fine-scale precipitation variance required to adequately describe a given climate statistics will then be used to quantify the potential added value (PAV) of RCMs. Our results show that the PAV of RCMs is much higher for short temporal scales (e.g., 3-hourly data) than for long temporal scales (16-day average data) due to the filtering resulting from the time-averaging process. PAV is higher in warm season compared to cold season due to the higher proportion of precipitation falling from small-scale weather systems in the warm season. In regions of complex topography, the orographic forcing induces an extra component of PAV, no matter the season or the temporal scale considered. The PAV is also estimated using high-resolution datasets based on observations allowing the evaluation of the sensitivity of changing resolution in the real climate system. The results show that RCMs tend to reproduce relatively well the PAV compared to observations although showing an overestimation of the PAV in warm season and mountainous regions.

Impacts of climate change, land-use change and phosphorus reduction on phytoplankton in the River Thames (UK).

PubMed

Bussi, Gianbattista; Whitehead, Paul G; Bowes, Michael J; Read, Daniel S; Prudhomme, Christel; Dadson, Simon J

2016-12-01

Potential increases of phytoplankton concentrations in river systems due to global warming and changing climate could pose a serious threat to the anthropogenic use of surface waters. Nevertheless, the extent of the effect of climatic alterations on phytoplankton concentrations in river systems has not yet been analysed in detail. In this study, we assess the impact of a change in precipitation and temperature on river phytoplankton concentration by means of a physically-based model. A scenario-neutral methodology has been employed to evaluate the effects of climate alterations on flow, phosphorus concentration and phytoplankton concentration of the River Thames (southern England). In particular, five groups of phytoplankton are considered, representing a range of size classes and pigment phenotypes, under three different land-use/land-management scenarios to assess their impact on phytoplankton population levels. The model results are evaluated within the framework of future climate projections, using the UK Climate Projections 09 (UKCP09) for the 2030s. The results of the model demonstrate that an increase in average phytoplankton concentration due to climate change is highly likely to occur, with the magnitude varying depending on the location along the River Thames. Cyanobacteria show significant increases under future climate change and land use change. An expansion of intensive agriculture accentuates the growth in phytoplankton, especially in the upper reaches of the River Thames. However, an optimal phosphorus removal mitigation strategy, which combines reduction of fertiliser application and phosphorus removal from wastewater, can help to reduce this increase in phytoplankton concentration, and in some cases, compensate for the effect of rising temperature. Copyright © 2016 Elsevier B.V. All rights reserved.

Multilevel models in the explanation of the relationship between safety climate and safe behavior.

PubMed

Cheyne, Alistair; Tomás, José M; Oliver, Amparo

2013-01-01

This study examines the relationships between components of organizational safety climate, including employee attitudes to organizational safety issues; perceptions of the physical working environment, and evaluations of worker engagement with safety issues; and relates these to self-reported levels of safety behavior. It attempts to explore the relationships between these variables in 1189 workers across 78 work groups in a large transportation organization. Evaluations of safety climate, the working environment and worker engagement, as well as safe behaviors, were collected using a self report questionnaire. The multilevel analysis showed that both levels of evaluation (the work group and the individual), and some cross-level interactions, were significant in explaining safe behaviors. Analyses revealed that a number of variables, at both levels, were associated with worker engagement and safe behaviors. The results suggest that, while individual evaluations of safety issues are important, there is also a role for the fostering of collective safety climates in encouraging safe behaviors and therefore reducing accidents.

Evaluating atmospheric blocking in the global climate model EC-Earth

NASA Astrophysics Data System (ADS)

Hartung, Kerstin; Hense, Andreas; Kjellström, Erik

2013-04-01

Atmospheric blocking is a phenomenon of the midlatitudal troposphere, which plays an important role in climate variability. Therefore a correct representation of blocking in climate models is necessary, especially for evaluating the results of climate projections. In my master's thesis a validation of blocking in the coupled climate model EC-Earth is performed. Blocking events are detected based on the Tibaldi-Molteni Index. At first, a comparison with the reanalysis dataset ERA-Interim is conducted. The blocking frequency depending on longitude shows a small general underestimation of blocking in the model - a well known problem. Scaife et al. (2011) proposed the correction of model bias as a way to solve this problem. However, applying the correction to the higher resolution EC-Earth model does not yield any improvement. Composite maps show a link between blocking events and surface variables. One example is the formation of a positive surface temperature anomaly north and a negative anomaly south of the blocking anticyclone. In winter the surface temperature in EC-Earth can be reproduced quite well, but in summer a cold bias over the inner-European ocean is present. Using generalized linear models (GLMs) I want to study the connection between regional blocking and global atmospheric variables further. GLMs have the advantage of being applicable to non-Gaussian variables. Therefore the blocking index at each longitude, which is Bernoulli distributed, can be analysed statistically with GLMs. I applied a logistic regression between the blocking index and the geopotential height at 500 hPa to study the teleconnection of blocking events at midlatitudes with global geopotential height. GLMs also offer the possibility of quantifying the connections shown in composite maps. The implementation of the logistic regression can even be expanded to a search for trends in blocking frequency, for example in the scenario simulations.

Addressing Climate Change in Long-Term Water Planning Using Robust Decisionmaking

NASA Astrophysics Data System (ADS)

Groves, D. G.; Lempert, R.

2008-12-01

Addressing climate change in long-term natural resource planning is difficult because future management conditions are deeply uncertain and the range of possible adaptation options are so extensive. These conditions pose challenges to standard optimization decision-support techniques. This talk will describe a methodology called Robust Decisionmaking (RDM) that can complement more traditional analytic approaches by utilizing screening-level water management models to evaluate large numbers of strategies against a wide range of plausible future scenarios. The presentation will describe a recent application of the methodology to evaluate climate adaptation strategies for the Inland Empire Utilities Agency in Southern California. This project found that RDM can provide a useful way for addressing climate change uncertainty and identify robust adaptation strategies.

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.

A Centroid Model of Species Distribution to Analyize Multi-directional Climate Change Finger Print in Avian Distribution in North America

NASA Astrophysics Data System (ADS)

Huang, Q.; Sauer, J.; Dubayah, R.

2015-12-01

Species distribution shift (or referred to as "fingerprint of climate change") as a primary mechanism to adapt climate change has been of great interest to ecologists and conservation practitioners. Recent meta-analyses have concluded that a wide range of animal and plant species are already shifting their distribution. However majority of the literature has focused on analyzing recent poleward and elevationally upward shift of species distribution. However if measured only in poleward shifts, the fingerprint of climate change will be underestimated significantly. In this study, we demonstrate a centroid model for range-wide analysis of distribution shifts using the North American Breeding Bird Survey. The centroid model is based on a hierarchical Bayesian framework which models population change within physiographic strata while accounting for several factors affecting species detectability. We used the centroid approach to examine large number of species permanent resident species in North America and evaluated the dreiction and magnitude of their shifting distribution. To examine the inferential ability of mean temperature and precipitation, we test a hypothesis based on climate velocity theory that species would be more likely to shift their distribution or would shift with greater magnitude in in regions with high climate change velocity. For species with significant shifts of distribution, we establish a precipitation model and a temperature model to explain their change of abundance at the strata level. Two models which are composed of mean and extreme climate indices respectively are also established to test the influences of changes in gradual and extreme climate trends.

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.

Projected climate change impacts and short term predictions on staple crops in Sub-Saharan Africa

NASA Astrophysics Data System (ADS)

Mereu, V.; Spano, D.; Gallo, A.; Carboni, G.

2013-12-01

Agriculture in Sub-Saharan Africa (SSA) drives the economy of many African countries and it is mainly rain-fed agriculture used for subsistence. Increasing temperatures, changed precipitation patterns and more frequent droughts may lead to a substantial decrease of crop yields. The projected impacts of future climate change on agriculture are expected to be significant and extensive in the SSA due to the shortening of the growing seasons and the increasing of water-stress risk. Differences in Agro-Ecological Zones and geographical characteristics of SSA influence the diverse impacts of climate change, which can greatly differ across the continent and within countries. The vulnerability of African Countries to climate change is aggravated by the low adaptive capacity of the continent, due to the increasing of its population, the widespread poverty, and other social factors. In this contest, the assessment of climate change impact on agricultural sector has a particular interest to stakeholder and policy makers, in order to identify specific agricultural sectors and Agro-Ecological Zones that could be more vulnerable to changes in climatic conditions and to develop the most appropriate policies to cope with these threats. For these reasons, the evaluation of climate change impacts for key crops in SSA was made exploring climate uncertainty and focusing on short period monitoring, which is particularly useful for food security and risk management analysis. The DSSAT-CSM (Decision Support System for Agrotechnology Transfer - Cropping System Model) software, version 4.5 was used for the analysis. Crop simulation models included in DSSAT-CSM are tools that allow to simulate physiological process of crop growth, development and production, by combining genetic crop characteristics and environmental (soil and weather) conditions. For each selected crop, the models were used, after a parameterization phase, to evaluate climate change impacts on crop phenology and production. Multiple combinations of soils and climate conditions, crop management and varieties were considered for the different Agro-Ecological Zones. The climate impact was assessed using future climate prediction, statistically and/or dynamically downscaled, for specific areas. Direct and indirect effects of different CO2 concentrations projected for the future periods were separately explored to estimate their effects on crops. Several adaptation strategies (e.g., introduction of full irrigation, shift of the ordinary sowing/planting date, changes in the ordinary fertilization management) were also evaluated with the aim to reduce the negative impact of climate change on crop production. The results of the study, analyzed at local, AEZ and country level, will be discussed.

Use of Climatic Information In Regional Water Resources Assessment

NASA Astrophysics Data System (ADS)

Claps, P.

Relations between climatic parameters and hydrological variables at the basin scale are investigated, with the aim of evaluating in a parsimonious way physical parameters useful both for a climatic classification of an area and for supporting statistical models of water resources assessment. With reference to the first point, literature methods for distributed evaluation of parameters such as temperature, global and net solar radiation, precipitation, have been considered at the annual scale with the aim of considering the viewpoint of the robust evaluation of parameters based on few basic physical variables of simple determination. Elevation, latitude and average annual number of sunny days have demonstrated to be the essential parameters with respect to the evaluation of climatic indices related to the soil water deficit and to the radiative balance. The latter term was evaluated at the monthly scale and validated (in the `global' term) with measured data. in questo caso riferite al bilancio idrico a scala annuale. Budyko, Thornthwaite and Emberger climatic indices were evaluated on the 10,000 km2 territory of the Basilicata region (southern Italy) based on a 1.1. km grid. They were compared in terms of spatial variability and sensitivity to the variation of the basic variables in humid and semi-arid areas. The use of the climatic index data with respect to statistical parameters of the runoff series in some gauging stations of the region demonstrated the possibility to support regionalisation of the annual runoff using climatic information, with clear distinction of the variability of the coefficient of variation in terms of the humidity-aridity of the basin.

Investigating the Capacity of Hydrological Models to Project Impacts of Climate Change in the Context of Water Allocation

NASA Astrophysics Data System (ADS)

Velez, Carlos; Maroy, Edith; Rocabado, Ivan; Pereira, Fernando

2017-04-01

To analyse the impacts of climate changes, hydrological models are used to project the hydrology responds under future conditions that normally differ from those for which they were calibrated. The challenge is to assess the validity of the projected effects when there is not data to validate it. A framework for testing the ability of models to project climate change was proposed by Refsgaard et al., (2014). The authors recommend the use of the differential-split sample test (DSST) in order to build confidence in the model projections. The method follow three steps: 1. A small number of sub-periods are selected according to one climate characteristics, 2. The calibration - validation test is applied on these periods, 3. The validation performances are compered to evaluate whether they vary significantly when climatic characteristics differ between calibration and validation. DSST rely on the existing records of climate and hydrological variables; and performances are estimated based on indicators of error between observed and simulated variables. Other authors suggest that, since climate models are not able to reproduce single events but rather statistical properties describing the climate, this should be reflected when testing hydrological models. Thus, performance criteria such as RMSE should be replaced by for instance flow duration curves or other distribution functions. Using this type of performance criteria, Van Steenbergen and Willems, (2012) proposed a method to test the validity of hydrological models in a climate changing context. The method is based on the evaluation of peak flow increases due to different levels of rainfall increases. In contrast to DSST, this method use the projected climate variability and it is especially useful to compare different modelling tools. In the framework of a water allocation project for the region of Flanders (Belgium) we calibrated three hydrological models: NAM, PDM and VHM; for 67 gauged sub-catchments with approx. 40 years of records. This paper investigates the capacity of the three hydrological models to project the impacts of climate change scenarios. It is proposed a general testing framework which combine the use of the existing information through an adapted form of DSST with the approach proposed by Van Steenbergen and Willems, (2012) adapted to assess statistical properties of flows useful in the context of water allocation. To assess the model we use robustness criteria based on a Log Nash-Sutcliffe, BIAS on cummulative volumes and relative changes based on Q50/Q90 estimated from the duration curve. The three conceptual rainfall-runoff models yielded different results per sub-catchments. A relation was found between robustness criteria and changes in mean rainfall and changes in mean potential evapotranspiration. Biases are greatly affected by changes in precipitation, especially when the climate scenarios involve changes in precipitation volume beyond the range used for calibration. Using the combine approach we were able to classify the modelling tools per sub-catchments and create an ensemble of best models to project the impacts of climate variability for the catchments of 10 main rivers in Flanders. Thus, managers could understand better the usability of the modelling tools and the credibility of its outputs for water allocation applications. References Refsgaard, J.C., Madsen, H., Andréassian, V., Arnbjerg-Nielsen, K., Davidson, T.A., Drews, M., Hamilton, D.P., Jeppesen, E., Kjellström, E., Olesen, J.E., Sonnenborg, T.O., Trolle, D., Willems, P., Christensen, J.H., 2014. A framework for testing the ability of models to project climate change and its impacts. Clim. Change. Van Steenbergen, N., Willems, P., 2012. Method for testing the accuracy of rainfall - runoff models in predicting peak flow changes due to rainfall changes , in a climate changing context. J. Hydrol. 415, 425-434.

Climate, Anchovy, and Sardine.

PubMed

Checkley, David M; Asch, Rebecca G; Rykaczewski, Ryan R

2017-01-03

Anchovy and sardine populated productive ocean regions over hundreds of thousands of years under a naturally varying climate, and are now subject to climate change of equal or greater magnitude occurring over decades to centuries. We hypothesize that anchovy and sardine populations are limited in size by the supply of nitrogen from outside their habitats originating from upwelling, mixing, and rivers. Projections of the responses of anchovy and sardine to climate change rely on a range of model types and consideration of the effects of climate on lower trophic levels, the effects of fishing on higher trophic levels, and the traits of these two types of fish. Distribution, phenology, nutrient supply, plankton composition and production, habitat compression, fishing, and acclimation and adaptation may be affected by ocean warming, acidification, deoxygenation, and altered hydrology. Observations of populations and evaluation of model skill are essential to resolve the effects of climate change on these fish.

Climate, Anchovy, and Sardine

NASA Astrophysics Data System (ADS)

Checkley, David M.; Asch, Rebecca G.; Rykaczewski, Ryan R.

2017-01-01

Anchovy and sardine populated productive ocean regions over hundreds of thousands of years under a naturally varying climate, and are now subject to climate change of equal or greater magnitude occurring over decades to centuries. We hypothesize that anchovy and sardine populations are limited in size by the supply of nitrogen from outside their habitats originating from upwelling, mixing, and rivers. Projections of the responses of anchovy and sardine to climate change rely on a range of model types and consideration of the effects of climate on lower trophic levels, the effects of fishing on higher trophic levels, and the traits of these two types of fish. Distribution, phenology, nutrient supply, plankton composition and production, habitat compression, fishing, and acclimation and adaptation may be affected by ocean warming, acidification, deoxygenation, and altered hydrology. Observations of populations and evaluation of model skill are essential to resolve the effects of climate change on these fish.

The value of superpower-submitted INDCs in cooperative and non-cooperative action scenarios: economic impact, dynamic risk, and temperature rise

NASA Astrophysics Data System (ADS)

Augustin, C. M.

2015-12-01

As the 2015 Paris climate talks near, policy discussions are focused on "intended nationally determined contributions" (INDCs) submitted in advance of the discussions. As the major global emitters - specifically the United States and China - have already submitted their INDCs, we have a point of comparison for evaluating the relative potential impacts of the proposed targets. By applying integrated assessment models to robust, publicly available data sets,we aim to evaluate the interplay between climate change and economic development, comment on emissions reduction scenarios in cooperative and non-cooperative situations, and assess the dynamic risks of multiple regional emissions scenarios. We use both the RICE model and the C-ROADS model to examine alternative regional outcomes for emissions, climate change, and damages,under different reduction scenarios, including a scenario where geo-engineering plays a prominent role. These simulators allow us to vary emissions, population, and economic levels in China and the United States specifically to comment on the international climate risk impact of actors working jointly - or not - toward a global climate goal. In a complementary piece of analysis we seek to understand the value judgments, trade-offs, and regional policies that would lead to favorable climate finance flows. To reach an international sample of industry decision-makers, we propose a novel application of a standard discrete-choice survey methodology. A conjoint analysis requires a participant to chose between combinations of attributes and identify trade-offs while allowing the researcher to determine the relative importance of each individual attribute by mathematically assessing the impact each attribute could have on total item utility. As climate policy negotiations will consist of allocation of scarce resources and rejection of certain attributes, a conjoint analysis is an ideal tool for evaluating policy outcomes. This research program seeks to provide a commentary useful to policy makers on the most desirable outcomes of the negotiations and other international cooperation.

Evaluation of a new CNRM-CM6 model version for seasonal climate predictions

NASA Astrophysics Data System (ADS)

Volpi, Danila; Ardilouze, Constantin; Batté, Lauriane; Dorel, Laurant; Guérémy, Jean-François; Déqué, Michel

2017-04-01

This work presents the quality assessment of a new version of the Météo-France coupled climate prediction system, which has been developed in the EU COPERNICUS Climate Change Services framework to carry out seasonal forecast. The system is based on the CNRM-CM6 model, with Arpege-Surfex 6.2.2 as atmosphere/land component and Nemo 3.2 as ocean component, which has directly embedded the sea-ice component Gelato 6.0. In order to have a robust diagnostic, the experiment is composed by 60 ensemble members generated with stochastic dynamic perturbations. The experiment has been performed over a 37-year re-forecast period from 1979 to 2015, with two start dates per year, respectively in May 1st and November 1st. The evaluation of the predictive skill of the model is shown under two perspectives: on the one hand, the ability of the model to faithfully respond to positive or negative ENSO, NAO and QBO events, independently of the predictability of these events. Such assessment is carried out through a composite analysis, and shows that the model succeeds in reproducing the main patterns for 2-meter temperature, precipitation and geopotential height at 500 hPa during the winter season. On the other hand, the model predictive skill of the same events (positive and negative ENSO, NAO and QBO) is evaluated.