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Numerical simulation of surface solar radiation over Southern Africa. Part 2: projections of regional and global climate models

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Abstract

In the second part of this study, possible impacts of climate change on Surface Solar Radiation (SSR) in Southern Africa (SA) are evaluated. We use outputs from 20 regional climate simulations from five Regional Climate Models (RCM) that participate in the Coordinated Regional Downscaling Experiment program over the African domain (CORDEX-Africa) along with their 10 driving Global Climate Models (GCM) from the Coupled Model Intercomparison Project Phase 5 (CMIP5). Multi-model mean projections of SSR trends are consistent between the GCMs and their nested RCMs. However, this consistency is not found for each GCM/RCM setup. Over the centre of SA, GCMs and RCMs project a statistically significant increase in SSR by 2099 of about + 1 W/m2 per decade in RCP4.5 (+ 1.5 W/m2 per decade in RCP8.5) during the DJF season in their multi-model means. Over Eastern Equatorial Africa (EA-E) a statistically significant decrease in SSR of about − 1.5 W/m2 per decade in RCP4.5 (− 2 W/m2 per decade in RCP8.5) is found in the ensemble means in DJF, whereas in JJA SSR is predicted to increase by about + 0.5 W/m2 per decade under RCP4.5 (+ 1 W/m2 per decade in RCP8.5). SSR projections are fairly similar between RCP8.5 and RCP4.5 before 2050 and then the differences between those two scenarios increase up to about 1 W/m2 per decade with larger changes in RCP8.5 than in RCP4.5 scenario. These SSR evolutions are generally consistent with projected changes in Cloud Cover Fraction over SA and may also related to the changes in atmosphere water vapor content. SSR change signals emerge earlier out of internal variability estimated from reanalyses (European Centre for Medium-Range Weather Forecasts Reanalysis ERA-Interim, ERAIN) in DJF in RCMs than in GCMs, which suggests a higher sensitivity of RCMs to the forcing RCP scenarios than their driving GCMs in simulating SSR changes. Uncertainty in SSR change projections over SA is dominated by the internal climate variability before 2050, and after that model and scenario uncertainties become as important as internal variability until the end of the 21st century.

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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 & Chris Lennard

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  1. Chao Tang
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  2. Béatrice Morel
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  3. Martin Wild
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  4. Benjamin Pohl
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  5. Babatunde Abiodun
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  6. Chris Lennard
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  7. Miloud Bessafi
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Correspondence to Chao Tang.

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Appendix

Appendix

See Figs. 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23 and 24.

Fig. 12
figure 12

Projected annual CLT changes (2070–2099) from GCMs (1st column) and five embedded RCMs (2nd and 6th columns) in RCP8.5, expressed in absolute % with respect with the annual mean value of the reference period 1970–1999

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Fig. 13
figure 13

Smoothed seasonal series of SSR anomalies of ten driving GCMs and 20 embedded RCMs along the 21st century under RCP8.5 for JJA for different sub-regions from GCMs (green) and the RCMs (red). Anomalies are computed with respect to the mean values in the reference period 1970–1999 and expressed in W/m2. Solid lines depict the multi-model mean values, with shadows showing the ensemble spread. Vertical bars depict 0 ± the multi-model means (GCMs or RCMs) of the standard deviation of the seasonal SSR anomalies in the period of 1970–2099 as representative of the internal variability, the “back-ground noise” of the climate change signal

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Fig. 14
figure 14

The same as Fig. 13 but for CLT in absolute %

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Fig. 15
figure 15

Standard deviation of detrended seasonal (JJA) series of SSR (left) from climate models (arithmetic mean of 20 RCMs or 10 GCMs) during period of 1970–2099, satellite data SARAH-2 during 1983–2015 and reanalysis ERA-Interim (ERAIN) during 1979–2015. Ratio between RCMs/GCMs and SARAH-2/ERAIN are on the right. The white area in the bottom right of SARAH-2 is out of its coverage

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Fig. 16
figure 16

Standard deviation of detrended seasonal (DJF) series of SSR during the reference period of 1970–2099 from every single RCM and GCM. The unit is W/m2

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Fig. 17
figure 17

Standard deviation of detrended seasonal (JJA) series of SSR during the reference period of 1970–2099 from every single RCM and GCM. The unit is W/m2

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Fig. 18
figure 18

Time of Emergence (ToE) of SSR projections estimated using the mean internal variability from ERAIN, for JJA season

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Fig. 19
figure 19

Time of Emergence (ToE) of SSR projections estimated using the mean internal variability from RCM/GCM models for DJF season

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Fig. 20
figure 20

Time of Emergence (ToE) of SSR projections estimated using the mean internal variability from RCM/GCM models for JJA season

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Fig. 21
figure 21

Time of Emergence (ToE) of SSR projections estimated using the internal variability from every single RCM/GCM model for DJF season in RCP8.5

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Fig. 22
figure 22

Time of Emergence (ToE) of SSR projections estimated using the internal variability from every single RCM/GCM model for JJA season in RCP8.5

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Fig. 23
figure 23

Projections of seasonal (JJA) mean SSR anomalies with respect to the reference period calculated as the mean value of the period from 1970 to 1999, with bold solid line indicating the multi-model mean (in the top row); different components of SSR variance estimated from different sources (Internal variability, scenario uncertainty and RCM/GCM uncertainty, mid row) and their fractional uncertainty (in 90% confidence level) as the ratio to the SSR change signal (bottom row) using outputs of simulations in Group I (7 RCM experiments under RCP2.6, RCP4.5 and RCP8.5, on the left) and Group II (all the 20 RCM experiments, but under only RCP4.5 and RCP8.5, on the right)

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Fig. 24
figure 24

Multi-model mean atmosphere water vapor content trend (2070–2099 vs 1970–1999) in relative percentage with respect with the mean value in reference period, i.e. 1970–1999 in the unit of % per decade. Only significant trends at the 95% level are represented. Note that for the GCM trend (on the left), not all the 10 GCMs in Table 1 are included, with exception of PSL-CM5A-MR and NorESM1-M, because of data availability. For RCM trend (on the right), only outputs from 16 experiments conducted by 3 RCMs, namely CCLM, RCA4, and RACMO22T (see Table 2) are considered for the same reason. GCM outputs are remapped to the same typical GCM horizontal resolution of 1.12° × 1.12° (the resolution of EC-EARTH) for facilitating the calculations

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Tang, C., Morel, B., Wild, M. et al. Numerical simulation of surface solar radiation over Southern Africa. Part 2: projections of regional and global climate models. Clim Dyn 53, 2197–2227 (2019). https://doi.org/10.1007/s00382-019-04817-x

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