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To understand potential climate change impacts on ecosystems, water resources, and numerous other natural and managed resources, climate change data and projections must be downscaled from coarse global climate models to much finer resolutions and more applicable formats. This project conducted comparative analyses to better understand the accuracy and properties of these downscaled climate simulations and climate-change projections. Interpretation, guidance and evaluation, including measures of uncertainties, strengths and weaknesses of the different methodologies for each simulation, can enable potential users with the necessary information to select and apply the models.
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Climate change information simulated by global climate models is downscaled using statistical methods to translate spatially course regional projections to finer resolutions needed by researchers and managers to assess local climate impacts. Several statistical downscaling methods have been developed over the past fifteen years, resulting in multiple datasets derived by different methods. We apply a simple monthly water-balance model (MWBM) to demonstrate how the differences among these datasets result in disparate projections of snow loss and future changes in runoff. We apply the MWBM to six statistically downscaled datasets for 14 general circulation models (GCMs) from the Climate Model Intercomparison Program...


    map background search result map search result map Analysis of Downscaled Climate Simulations and Projections and Their Use in Decision Making for the Southwest Data Release for The dependence of hydroclimate projections in snow-dominated regions of the western U.S. on the choice of statistically downscaled climate data Analysis of Downscaled Climate Simulations and Projections and Their Use in Decision Making for the Southwest Data Release for The dependence of hydroclimate projections in snow-dominated regions of the western U.S. on the choice of statistically downscaled climate data