Numerical simulation of surface solar radiation over Southern Africa. Part 1: Evaluation of regional and global climate models

摘要

This study evaluates the performance of climate models in reproducing surface solar radiation (SSR) over Southern Africa (SA) by validating five Regional Climate Models (RCM, including CCLM4, HIRHAM5, RACMO22T, RCA4 and REMO2009) that participated in the Coordinated Regional Downscaling Experiment program over Africa (CORDEX-Africa) along with their ten driving General Circulation Models (GCMs) from the Coupled Model Intercomparison Project Phase 5 over SA. The model simulated SSR was thereby compared to reference data from ground-based measurements, satellite-derived products and reanalyses over the period 1990-2005. Results show that (1) the references obtained from satellite retrievals and reanalyses overall overestimate SSR by up to 10W/m(2) on average when compared to ground-based measurements from the Global Energy Balance Archive, which are located mainly over the eastern part of the southern African continent. (2) Compared to one of the satellite products (Surface Solar Radiation Data SetHeliosat Edition 2; SARAH-2): GCMs overestimate SSR over SA in terms of their multi-model mean by about 1W/m(2) (compensation of opposite biases over sub-regions) and 7.5W/m(2) in austral summer and winter respectively; RCMs driven by GCMs show in their multimodel mean underestimations of SSR in both seasons with Mean Bias Errors (MBEs) of about -30W/m(2) in austral summer and about -14W/m(2) in winter compared to SARAH-2. This multi-model mean low bias is dominated by the simulations of the CCLM4, with negative biases up to -76W/m(2) in summer and -32W/m(2) in winter. (3) The discrepancies in the simulated SSR over SA are larger in the RCMs than in the GCMs. (4) In terms of trend during the brightening period 1990-2005, both GCMs and RCMs (driven by European Centre for Medium-Range Weather Forecasts Reanalysis ERA-Interim, short as ERAINT and GCMs) simulate an SSR trend of less than 1W/m(2) per decade. However, variations of SSR trend exist among different references data. (5) For individual RCM models, their SSR bias fields seem rather insensitive with respect to the different lateral forcings provided by ERAINT and various GCMs, in line with previous findings over Europe. (6) Biases in SSR are overall qualitatively consistent with those in total cloud cover. The information obtained in present study is of crucial importance for understanding future climate projections of SSR and for relevant impact studies.