Downscaled and bias corrected dataset for multiple GCMs and variables

Abstract

Understanding the impact of climate variability and change is of great importance for developing adaptation and mitigation strategies. Coarse resolution data sets such as simulations of general circulation models (GCMs) are important for reconstructing historical climate and predicting the future. However, scale discrepancy and biases limit the coarse resolution data sets from being directly used for impact assessments and decision making. One solution for bridging this gap is to downscale and bias correct coarse resolution data to the local scale.

Purpose

We bias corrected and downscaled 9 climate variables for 5 popular GCMs from the latest CMIP6 with a widly used method, namely quantile delta mapping (QDM). The dataset has spatial resolution of 0.25 degree and daily temporal scale. For each GCM, two emission levels are included(ssp126 as low emission level and ssp585 as high emission level). The names of the 5 GCMs are EC-Earth3, MPI-ESM1-2-HR, MRI-ESM2-0, IPSL-CM6A-LR,and GFDL-ESM4. The 9 variables include daily total precipitation (pr), daily maximum near-surface air temperature(tasmax), daily minimum near-surface air temperature (tasmin),eastward near-surface wind (uas),northward near-surface wind (vas),near-surface relative humidity (hurs), surface downwelling shortwave radiation (rsds), surface downwelling longwave radiation (rlds) and sea level pressure (psl). The dataset will be used as forcings for the watershed modeling and physical/biogeochemical modeling around the bay area to explore adaptation and mitigation strategies.

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