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

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Abstract

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 10 W/m2 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 Set—Heliosat Edition 2; SARAH-2): GCMs overestimate SSR over SA in terms of their multi-model mean by about 1 W/m2 (compensation of opposite biases over sub-regions) and 7.5 W/m2 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 − 30 W/m2 in austral summer and about − 14 W/m2 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 − 76 W/m2 in summer and − 32 W/m2 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 1 W/m2 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.

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Authors and Affiliations

  1. Laboratoire d’Energétique, d’Electronique et Procédés, Université de La Réunion, La Réunion, France

    Chao Tang, Béatrice Morel & Miloud Bessafi

  2. Institute for Atmospheric and Climate Science, ETH Zurich, Universitätsstr. 16, 8092, Zurich, Switzerland

    Martin Wild

  3. Centre de Recherches de Climatologie, UMR6282 Biogéosciences, CNRS/Université de Bourgogne Franche-Comté, 6 boulevard Gabriel, 21000, Dijon, France

    Benjamin Pohl

  4. Climate System Analysis Group, Department of Environmental and Geographical Sciences, University of Cape Town, Cape Town, South Africa

    Babatunde Abiodun

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  1. Chao Tang
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  2. Béatrice Morel
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Correspondence to Chao Tang.

Appendix

Appendix

See Figs. 12 and 13 and Table 6.

Fig. 12
figure 12

Latitudinal variation of the monthly SSR Mean Absolute Bias (MAB) from SARAH-2 (red), ERAINT (light green), SRB (green) and CFSR (blue) at the GEBA stations (Fig. 1) during 1990–2005. Each MAB is an averaged value from all the stations located in a 5° latitude interval. The number of stations is indicated by the gray bar in each interval

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Fig. 13
figure 13

Multi-model seasonal mean SSR (CLT) differences with respect to SARAH-2 (CLARA-A2) in DJF and JJA seasons over the period 1990–2005. In order to avoid overrepresentation of those RCMs having many simulations (4 in CCLM4 and 10 in RCA4, 3 in HIRHAM4 and 2 in RACM22T and REMO2009), each model bias is firstly determined as average over the simulations conducted by this model, and then only the 5 remaining bias fields were averaged to obtain the multimodel mean. However, the resulting multimodel mean SSR field shows only negligible differences compared an unweighted average over all 20 RCMs simulations, so the “overrepresentation” is not crucial in this study

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Table 6 Aerosols adapted in the regional climate models
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Tang, C., Morel, B., Wild, M. et al. Numerical simulation of surface solar radiation over Southern Africa. Part 1: Evaluation of regional and global climate models. Clim Dyn 52, 457–477 (2019). https://doi.org/10.1007/s00382-018-4143-1

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