Statistically Downscaled and Bias Corrected Climate Model Outputs for Climate Changes Impact Assessment in the U.S. Northeast
This dataset includes 1/8 degree (~12.5km) resolution daily precipitation, maximum temperature, and minimum temperature data for the period 2046-2065. It is derived through statistical downscaling and bias correction (SDBC) of daily output from six GCM and five RCMs. The domain covers the New England region, ranging from 67.0625 W to 75.0625 W in longitudes and from 38.8125 N to 48.8125 N in latitudes. The names of the files are self-explanatory. All data are in binary format. A sample GrADS control file is provided (for GrADS users), and a sample Fortran code reading the binary data is also provided.
The GCMs include CCSM, GFDL, PCM, CGCM, MPI and MIROC, and their CMIP3 (IPCC AR4) simulations are used. These GCMs are so chosen to cover the full range of model sensitivity to atmospheric CO2 concentration changes as found in IPCC AR4. The RCM simulations are taken from the NARCCAP database that provides 50-km resolution climate simulation using different RCMs driven by outputs from a number of GCMs. The RCM-GCM combinations include RCM-GFDL, RCM-CGCM, CRCM-CGCM, CRCM-CCSM and WRFG-CCSM. These combinations are chosen based on data availability when the SDBC work first started.
The development of this dataset was jointly supported by funding from USDA (Grant no. 2008-003237), NSF (AGS-1049017), and the University of Connecticut Center for Environmental Sciences and Engineering (CESE). The SDBC approach and some analysis based on this dataset are described in Ahmed et al. (2012). Users of this dataset should acknowledge the following publication:
Ahmed KF, Wang GL, Silander J, Wilson MA, Allen JM, Horton R, Anyah R, 2013: Statistical downscaling and bias correction of climate model outputs for climate change impact assessment in the U.S. Northeast. Global and Planetary Change, 100, 320-332
Please direct questions or comments to gwang@engr.uconn.edu. If you are using this dataset, we encourage you to let us know so that we can keep you posted in case of potential bug corrections, data updates, and new additions.