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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...
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To meet the climate change planning and adaptation needs of Alaska managers and decision makers, I developed a set of statewide summaries of available climate change projections that can be further subset using GIS techniques for requests by management unit, watershed, or other location. This facilitates the development of tailored climate futures for decision makers’ regional or subregional management context. This file describes the source data and summaries for purposes of technical /scientific documentation. The methods and presentation for these datasets were adapted from products in previous USGS-approved IP products for the AKCASC Building Resilience Today project (e.g, Community of Kotlik et al. 2019)....
Abstract (from http://link.springer.com/article/10.1007/s10584-016-1598-0): Empirical statistical downscaling (ESD) methods seek to refine global climate model (GCM) outputs via processes that glean information from a combination of observations and GCM simulations. They aim to create value-added climate projections by reducing biases and adding finer spatial detail. Analysis techniques, such as cross-validation, allow assessments of how well ESD methods meet these goals during observational periods. However, the extent to which an ESD method’s skill might differ when applied to future climate projections cannot be assessed readily in the same manner. Here we present a “perfect model” experimental design that quantifies...
The MC2 model projects an overall increase in carbon capture in conterminous United States during the 21st century while also simulating a rise in fire causing much carbon loss. Carbon sequestration in soils is critical to prevent carbon losses from future disturbances, and we show that natural ecosystems store more carbon belowground than managed systems do. Natural and human-caused disturbances affect soil processes that shape ecosystem recovery and competitive interactions between native, exotics, and climate refugees. Tomorrow's carbon budgets will depend on how land use, natural disturbances, and climate variability will interact and affect the balance between carbon capture and release.
This fact sheet provides highlights from a comprehensive U.S. Geological Survey report that evaluates six widely used downscaled climate projections covering the southeastern United States and recommends best practices for use of downscaled datasets for ecological modeling and decision-making.
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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...
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The California Basin Characterization Model (CA-BCM 2014) dataset provides historical and projected climate and hydrologic surfaces for the region that encompasses the state of California and all the streams that flow into it (California hydrologic region ). The CA-BCM 2014 applies a monthly regional water-balance model to simulate hydrologic responses to climate at the spatial resolution of a 270-m grid. The model has been calibrated using a total of 159 relatively unimpaired watersheds for the California region. The historical data is based on 800m PRISM data spatially downscaled to 270 m using the gradient-inverse distance squared approach (GIDS), and the projected climate surfaces include five CMIP-3 (GFDL,...
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The California Basin Characterization Model (CA-BCM 2014) dataset provides historical and projected climate and hydrologic surfaces for the region that encompasses the state of California and all the streams that flow into it (California hydrologic region ). The CA-BCM 2014 applies a monthly regional water-balance model to simulate hydrologic responses to climate at the spatial resolution of a 270-m grid. The model has been calibrated using a total of 159 relatively unimpaired watersheds for the California region. The historical data is based on 800m PRISM data spatially downscaled to 270 m using the gradient-inverse distance squared approach (GIDS), and the projected climate surfaces include five CMIP-3 (GFDL,...
This final report is for the Pacific Islands-funded project "21st Century High-Resolution Climate Projections for Guam and American Samoa". We provide the projected fine-resolution future climate changes over Guam and American Samoa by the late 21st century (2080-2099) with both a high emissions scenario (RCP8.5) and a medium emission scenario (RCP4.5). We show that the surface air temperature (SAT) over Guam is likely to increase by 1.5 – 2.0 °C for RCP4.5 and by 3.0 – 3.5 °C for RCP8.5, while the projected SAT increases over American Samoa are slightly smaller. The projected annual mean future rainfall changes for Guam are not statistically significant in any location in either the RCP4.5 or RCP8.5 scenarios....
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The Regional Climate Downloader (RCD) is a web application that can be used to visualize and download climate data. The RCD uses the Web Mapping Service (WMS) that is built into the Thredds Data Server , (see Disclaimer and Terms of Use ) that allows users to map and download monthly average data for over 60 surface variables generated by the Dynamical Downscaling project. The time coverage of these simulations ranges from 1968 to 2100, depending on the region of simulation and the driving GCM. Decadal, monthly and daily data are available for the Dynamical Downscaling project. Other datasets such as CIG are also available from within RCD. Data are available in NetCDF format.


    map background search result map search result map USGS Regional Climate Downloader California Basin Characterization Model Downscaled Climate and Hydrology Data Integration Workshop in Support of the Coastal Temperate Rainforest of Southeast Alaska and British Columbia Geospatial and climatic data layers for coastal and temperate rainforest biome Final NPLCC Project Report - NPLCC Cross-Border Final Report & Appendices 2014 Data Integration Workshop in Support of the Coastal Temperate Rainforest of Southeast Alaska and British Columbia Final Report South Central Climate Projections Evaluation Project (C-PrEP) South Central Climate Projections Evaluation Project (C-PrEP) California Basin Characterization Model Downscaled Climate and Hydrology Geospatial and climatic data layers for coastal and temperate rainforest biome Final NPLCC Project Report - NPLCC Cross-Border Final Report & Appendices 2014 Data Integration Workshop in Support of the Coastal Temperate Rainforest of Southeast Alaska and British Columbia Final Report Data Integration Workshop in Support of the Coastal Temperate Rainforest of Southeast Alaska and British Columbia California Basin Characterization Model Downscaled Climate and Hydrology California Basin Characterization Model Downscaled Climate and Hydrology South Central Climate Projections Evaluation Project (C-PrEP) South Central Climate Projections Evaluation Project (C-PrEP) USGS Regional Climate Downloader