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 the bias-corrected data are of better quality than the individual CMIP6 models in terms of the climatological mean, interannual variance and extreme events. This dataset will be useful for dynamical downscaling projections of the Earth’s future climate, atmospheric environment, hydrology, agriculture, wind power, etc.
Highlights
Background & SummaryProjections of the Earth’s future climate at a finer scale are of great importance in climate-related studies, such as studies of climate extremes, water resources, agriculture, air quality and wind power
Previous studies have suggested that the MPI-M Earth system models show a generally good performance in the simulation of the sea surface temperature (SST) and atmospheric circulation among the Coupled Model Intercomparison Project Phase 5 (CMIP5) and Coupled Model Intercomparison Project Phase 6 (CMIP6) models[32,33,34]
The multivariable integrated skill score (MISS) is defined based on vector field statistics and can summarize the overall performance of the model in simulating multiple fields[41,42,43]
Summary
Projections of the Earth’s future climate at a finer scale are of great importance in climate-related studies, such as studies of climate extremes, water resources, agriculture, air quality and wind power. Traditional dynamical downscaling of the future climate involves integrating a regional climate model (RCM) with the initial and lateral boundary conditions from a general circulation model (GCM)[6,11]. This traditional dynamical downscaling approach has been widely reported[12,13]. The bias corrections were applied to historical simulations over the time period 1979–2014 and two future scenarios of SSP245 and SSP585 over the time period 2015–2100 This bias-corrected dataset provides high-quality large-scale forcing for dynamical downscaling simulations and will improve the reliability of future projections of the regional climate and environment
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