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The U.S. Great Plains is known for frequent hazardous convective weather and climate extremes. Across this region, climate change is expected to cause more severe droughts, more intense heavy rainfall events, and subsequently more flooding episodes. These potential changes in climate will adversely affect habitats, ecosystems, and landscapes as well as the fish and wildlife they support. Better understanding and simulation of regional precipitation can help natural resource managers mitigate and adapt to these adverse impacts. In this project, we aim to achieve a better precipitation downscaling in the Great Plains with the Weather Research and Forecast (WRF) model and use the high quality dynamic downscaling results...
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Six global climate models (GCMs) from the Coupled Model Intercomparison Project Phase Five (CMIP5) were dynamically downscaled to 25-km grid spacing according to the representative concentration pathway 8.5 (RCP8.5) scenario using the International Centre for Theoretical Physics (ICTP) Regional Climate Model Version Four (RegCM4), interactively coupled to a 1D lake model to represent the Great Lakes. These GCMs include the Centre National de Recherches Meteorologiques Coupled Global Climate Model Version Five (CNRM-CM5), the Model for Interdisciplinary Research on Climate Version Five (MIROC5), the Institut Pierre Simon Laplace Coupled Model Version Five-Medium Resolution (IPSL-CM5-MR), the Meteorological Research...
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Precipitation-related indicators for seasonal precipiation, extreme precipiation, and drought have been generated for 8701 weather stations covering the entire United States, and for a 198x337 grid (on a resolution of 1/16 th degree) covering the South Central region from the future downscaled projections using the Asynchronous Regional Regression Model. The data covers the period from 1950 to 2100. The high-resolution future projections are statistically downscaled from simulations by 12 global climate models from the Coupled Model Intercomparison Project phase 5 (ACCESS1-0, ACCESS1-3, CCSM4, CMCC-CM, CNRM-CM5, CSIRO-Mk3.6.0, MPI-ESM-LR, HadGEM2-CC, INMCM4, IPSL-CM5A-LR, MIROC5 and MRI-CGCM under the lower Representative...
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Global climate models (GCMs) are numerically complex, computationally intensive, physics-based research tools used to simulate our planet’s inter-connected climate system. In addition to improving the scientific understanding of how the large-scale climate system works, GCM simulations of past and future climate conditions can be useful in applied research contexts. When seeking to apply information from global-scale climate projections to address local- and regional-scale climate questions, GCM-generated datasets often undergo statistical post-processing generally known as statistical downscaling (hereafter, SD). There are many different SD techniques, with all using information from observations to address GCM...
    map background search result map search result map Dynamical Downscaling for the Midwest and Great Lakes Basin Very High-Resolution Dynamic Downscaling of Regional Climate for Use in Long-term Hydrologic Planning along the Red River Valley System High-Resolution Precipitation Projections for the South Central U.S. South Central Climate Projections Evaluation Project (C-PrEP) High-Resolution Precipitation Projections for the South Central U.S. South Central Climate Projections Evaluation Project (C-PrEP) Dynamical Downscaling for the Midwest and Great Lakes Basin Very High-Resolution Dynamic Downscaling of Regional Climate for Use in Long-term Hydrologic Planning along the Red River Valley System