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Very High-Resolution Dynamic Downscaling of Regional Climate for Use in Long-term Hydrologic Planning along the Red River Valley System 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... Categories: Data; Types: NetCDF OPeNDAP Service; Tags: Red River, climatologyMeteorologyAtmosphere, dataset, downscaling, service South Central Climate Projections Evaluation Project (C-PrEP) 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... Categories: Data; Types: NetCDF OPeNDAP Service; Tags: climate, climate change, climate projections, climatologyMeteorologyAtmosphere, dataset, All tags... downscaling, service, Fewer tags Dynamical Downscaling for the Midwest and Great Lakes Basin 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... Categories: Data; Types: NetCDF OPeNDAP Service; Tags: Great Lakes, climatologyMeteorologyAtmosphere, dataset, downscaling, service Very fine resolution dynamically downscaled climate data for Hawaii Clouds often come in contact with vegetation (often named fogs) within a certain elevation range on Hawai‘i’s mountains. Propelled by strong winds, cloud droplets are driven onto the stems and leaves of plants where they are deposited. Some of the water that accumulates on the plants in this way drips to the ground, adding additional water over and above the water supplied by rainfall. Prior observations show that the amount of cloud water intercepted by vegetation is substantial, but also quite variable from place to place. It is, therefore, important to create a map for the complex spatial patterns of cloud water interception (CWI) in Hawai‘i. In this project, we propose to create the CWI map at 0.8-km resolution... Categories: Data; Types: NetCDF OPeNDAP Service; Tags: Hawaii, Maui, climate, climatologyMeteorologyAtmosphere, dataset, All tags... downscaling, service, Fewer tags Very High-Resolution Dynamic Downscaling of Regional Climate for Use in Long-term Hydrologic Planning along the Red River Valley System 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... Categories: Data; Types: NetCDF OPeNDAP Service; Tags: Red River, climatologyMeteorologyAtmosphere, dataset, downscaling, service Dynamical Downscaling for the Midwest and Great Lakes Basin 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... Categories: Data; Types: NetCDF OPeNDAP Service; Tags: Great Lakes, climatologyMeteorologyAtmosphere, dataset, downscaling, service South Central Climate Projections Evaluation Project (C-PrEP) 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... Categories: Data; Types: NetCDF OPeNDAP Service; Tags: climate, climate change, climate projections, climatologyMeteorologyAtmosphere, dataset, All tags... downscaling, service, Fewer tags Very fine resolution dynamically downscaled climate data for Hawaii Clouds often come in contact with vegetation (often named fogs) within a certain elevation range on Hawai‘i’s mountains. Propelled by strong winds, cloud droplets are driven onto the stems and leaves of plants where they are deposited. Some of the water that accumulates on the plants in this way drips to the ground, adding additional water over and above the water supplied by rainfall. Prior observations show that the amount of cloud water intercepted by vegetation is substantial, but also quite variable from place to place. It is, therefore, important to create a map for the complex spatial patterns of cloud water interception (CWI) in Hawai‘i. In this project, we propose to create the CWI map at 0.8-km resolution... Categories: Data; Types: NetCDF OPeNDAP Service; Tags: Hawaii, Maui, climate, climatologyMeteorologyAtmosphere, dataset, All tags... downscaling, service, Fewer tags