This archive contains daily statistically downscaled climate projections and simulated land surface water and energy fluxes for the western United States and southern British Columbia at 1/16th (0.0625) degree resolution. Climate and hydrologic variables (21 total) are as follows: precipitation, temperature (avg./max./min.), outgoing longwave radiation, incoming shortwave radiation, relative humidity, vapor pressure deficit, evapotranspiration, runoff, baseflow, soil moisture (3-layers), snow water equivalent, snow depth, and potential evapotranspiration (5 vegetation references). The downscaling used is the Modified Delta approach (see Littell et al. 2011), based on 10 models from Phase 3 of the Coupled Model Intercomparison Project (CMIP3), a critical source of data to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR4). Documentation home: http://cses.washington.edu/cig/data/wus.shtml Note that time-stamps on these data are not in the future. See the statistical downscaling chapter from this report for more information. http://warm.atmos.washington.edu/2860/r7climate/study_report/CBCCSP_chap4_gcm_final.pdf Reference: This research was sponsored by a grant from the Department of the Interior, USGS NW Climate Science Center, a multi-institution DOI-funded project located at the University of Washington, Oregon State University, and the University of Idaho. We also acknowledge the modeling groups, the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and the WCRP's Working Group on Coupled Modelling (WGCM) for their roles in making available the WCRP CMIP3 multi-model dataset. Support of this dataset is provided by the Office of Science, U.S. Department of Energy.

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 Phase 5 (CMIP5) for the RCP 8.5 emission scenario (1950 - 2099). The statistically downscaled datasets are as follows: BCCA: Bias Corrected Constructed Analogs (Reclamation, 2013) BCSD-C: Bias Corrected Spatial Disaggregation (Reclamation, 2013) BCSD-F: Bias Corrected Spatial Disaggregation (Thrasher et al., 2013) LOCA: Localized Constructed Analogs (Pierce et al., 2014) MACA-L: Multivariate Adaptive Constructed Analogs (Abatzoglou & Brown, 2012, bias corrected by Livneh et al., 2013) MACA-M: Multivariate Adaptive Constructed Analogs (Abatzoglou & Brown, 2012, bias corrected by METDATA, Abatzoglou, 2013) Users interested in the downscaled temperature and precipitation files are referred to the dataset home pages: BCCA, BCSD-C: http://gdo-dcp.ucllnl.org/downscaled_cmip_projections/dcpInterface.html BCSD-F: https://cds.nccs.nasa.gov/nex/ LOCA: http://loca.ucsd.edu/ MACA-L, MACA-M: http://maca.northwestknowledge.net The GCMs are the following: bcc-csm1-1, CanESM2, CNRM-CM5, CSIRO-Mk3-6-0, GFDL-ESM2G, GFDL-ESM2M, inmcm4, IPSL-CM5A-LR, IPSL-CM5A-MR, MIROC-ESM, MIROC-ESM-CHEM, MIROC5, MRI-CGCM3, NorESM1-M

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 Phase 5 (CMIP5) for the RCP 8.5 emission scenario (1950 - 2099). The statistically downscaled datasets are as follows: BCCA: Bias Corrected Constructed Analogs (Reclamation, 2013) BCSD-C: Bias Corrected Spatial Disaggregation (Reclamation, 2013) BCSD-F: Bias Corrected Spatial Disaggregation (Thrasher et al., 2013) LOCA: Localized Constructed Analogs (Pierce et al., 2014) MACA-L: Multivariate Adaptive Constructed Analogs (Abatzoglou & Brown, 2012, bias corrected by Livneh et al., 2013) MACA-M: Multivariate Adaptive Constructed Analogs (Abatzoglou & Brown, 2012, bias corrected by METDATA, Abatzoglou, 2013) Users interested in the downscaled temperature and precipitation files are referred to the dataset home pages: BCCA, BCSD-C: http://gdo-dcp.ucllnl.org/downscaled_cmip_projections/dcpInterface.html BCSD-F: https://cds.nccs.nasa.gov/nex/ LOCA: http://loca.ucsd.edu/ MACA-L, MACA-M: http://maca.northwestknowledge.net The GCMs are the following: bcc-csm1-1, CanESM2, CNRM-CM5, CSIRO-Mk3-6-0, GFDL-ESM2G, GFDL-ESM2M, inmcm4, IPSL-CM5A-LR, IPSL-CM5A-MR, MIROC-ESM, MIROC-ESM-CHEM, MIROC5, MRI-CGCM3, NorESM1-M

This collection contains three statistically downscaled time series (datasets) for the Red River Basin (South Central U.S.), and one dataset used as historical observations. In particular, three different Global Climate Models (MPI-ESM-LR, CCSM4 and MIROC5) were downscaled using three different quantile mapping methods (CDFt, EDQM and BCQM). We do not recommend the use of the BCQM method, as the CDFt method is considered an improvement of it. The datasets created using the BCQM method are published as a demonstration of the risks of using flawed methods. The variables of interest are: daily maximum and minimum temperature, and daily precipitation. The spatial resolution of the datasets in the collection is 1/10th of a degree (~ 11 km). The statistically downscaled datasets include local climate projections of three different Representative Concentration Pathways (RCPs - 2.6, 4.5 and 8.5) for the 21st century (2006 – 2099), and for the historical (1961-2005) period. The project was funded by the USGS – South Central Climate Science Center.

This collection contains three statistically downscaled time series (datasets) for the Red River Basin (South Central U.S.), and one dataset used as historical observations. In particular, three different Global Climate Models (MPI-ESM-LR, CCSM4 and MIROC5) were downscaled using three different quantile mapping methods (CDFt, EDQM and BCQM). We do not recommend the use of the BCQM method, as the CDFt method is considered an improvement of it. The datasets created using the BCQM method are published as a demonstration of the risks of using flawed methods. The variables of interest are: daily maximum and minimum temperature, and daily precipitation. The spatial resolution of the datasets in the collection is 1/10th of a degree (~ 11 km). The statistically downscaled datasets include local climate projections of three different Representative Concentration Pathways (RCPs - 2.6, 4.5 and 8.5) for the 21st century (2006 – 2099), and for the historical (1961-2005) period. The project was funded by the CIDA – South Central Climate Science Center.

Projected change from historical (1950-2005) in several hydrometerological variables under three Global Circulation Models for two time periods (2050s and 2080s) under RCP 8.5

Projected change from historical (1950-2005) in several hydrometerological variables under three Global Circulation Models for two time periods (2050s and 2080s) under RCP 8.5

This data release contains monthly 270-meter resolution Basin Characterization Model (BCMv8) climate and hydrologic variables for Localized Constructed Analog (LOCA; Pierce et al., 2014)-downscaled HadGEM2-ES Global Climate Model (GCM) for Representative Concentration Pathway (RCP) 4.5 (medium-low emissions) and 8.5 (high emissions) for hydrologic California. The LOCA climate scenarios span water years 1950 to 2099 with greenhouse-gas forcings beginning in 2006. The LOCA downscaling method has been shown to produce better estimates of extreme events and reduces the common downscaling problem of too many low-precipitation days (Pierce et al., 2014). Ten GCMs were selected from the full ensemble of models from the fifth Coupled Model Intercomparison Project from the World Climate Research Programme (CMIP5) based on GCM historical performance to address specific needs for California water-resource planning (California Department of Water Resources Climate Change Technical Advisory Group, 2015). The 10 GCMs with RCP 4.5 and 8.5 each were statistically downscaled using the LOCA method (Pierce et al., 2014) from 2-degree (approximately 222-kilometer; km) quadrangles to 6-km resolution. Next, the scenarios were spatially downscaled from 6 km to 270 meters (Flint and Flint, 2012) and run through the BCMv8 using the same model parameters and input files as the historical BCM model (BCMv8; Flint et al., 2021). Downscaled gridded climate variables include precipitation (ppt), minimum temperature (tmn), maximum temperature (tmx), and potential evapotranspiration (pet). Gridded hydrologic variables include: actual evapotranspiration (aet), climatic water deficit (cwd), snowpack (pck), recharge (rch), runoff (run), and soil storage (str). The units for temperature variables are degrees Celsius, and all other variables are in millimeters per month. Monthly variables from water years 1951 to 2099 are summarized into water year files (for example, water year 1951 includes October 1950 - September 1951) and 30-year average summaries from 1951 to 2099. Raster grids are in the NAD83 California Teale Albers, (meters) projection in an open format ascii text file (*.asc). This data release includes a child item for each RCP (4.5 & 8.5) for the HadGEM2-ES GCM. Each RCP child item contains 4 child items: 1. 30-year summaries (Water year files averaged for selected 30-year periods, zipped by variable) 2. Monthly BCM hydrology variables (monthly BCM hydrology variables zipped by decade) 3. Monthly climate variables (monthly climate variables zipped by decade) 4. Water year summaries (monthly files summed (aet, cwd, pck, rch, run, str, pet, and ppt) or averaged (tmn and tmx) by water year, zipped by variable) References cited: California Department of Water Resources Climate Change Technical Advisory Group, 2015, Perspectives and guidance for climate change analysis: Sacramento, Calif., California Department of Water Resources Technical Information Record, 142 p. Flint, L.E., Flint, A.L., and Stern, M.A., 2021, The Basin Characterization Model - A monthly regional water balance software package (BCMv8) data release and model archive for hydrologic California (ver. 3.0, June 2023): U.S. Geological Survey data release, https://doi.org/10.5066/P9PT36UI. Flint, L.E., and Flint, A.L., 2012, Downscaling future climate scenarios to fine scales for hydrologic and ecological modeling and analysis: Ecological Processes, v. 1, no. 2, 15 p., https://doi.org/10.1186/2192-1709-1-2. Pierce, D.W., Cayan, D.R. and Thrasher, B.L., 2014. Statistical downscaling using localized constructed analogs (LOCA). Journal of hydrometeorology, 15(6), pp.2558-2585.