Bias-corrected CMIP6 global dataset for dynamical downscaling of the historical and future climate (1979-2100).

ABSTRACT: Dynamical downscaling is an important approach to obtaining fine-scale weather and climate information. However, dynamical downscaling simulations are often degraded by biases in the large-scale forcing itself. We constructed a bias-corrected global dataset based on 18 models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) and the European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5) dataset. The bias-corrected data have an ERA5-based mean climate and interannual variance, but with a non-linear trend from the ensemble mean of the 18 CMIP6 models. The dataset spans the historical time period 1979-2014 and future scenarios (SSP245 and SSP585) for 2015-2100 with a horizontal grid spacing of (1.25° × 1.25°) at six-hourly intervals. Our evaluation suggests that t

SUBMITTER: Xu Z 

PROVIDER: S-EPMC8569144 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature