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Both natural and managed systems in the Limpopo River basin in Southern Africa are highly dependent on rainfall. Information on total seasonal rainfall amounts is of great value for resource management and decision making for adaptation and building resilience to climate variability. Recent progress in the development of digital tools allowing the integrated representation of complex natural systems, the so-called digital twins, represent an opportunity for making. This note reports the assessment of the seasonal forecast of rainfall from eight state-of-the-art operational general circulation models belonging to the Copernicus Climate Change Service (C3S) over the Limpopo River basin, which is part of the CGIAR Digital Innovation Initiative. A series of performance indicators to evaluate both individual models and the multi-model ensemble (MME) during the main rainfall season from October to March for the period 1993-2016 (hindcast), 1-month lead time, were used. The ERA5 reanalysis was used as an observational reference. The results show variable results in terms of the biases in representing total rainfall, but a more homogeneous in Spearman correlation and the root mean square error are observed. These indicators account for differences in the performance of the models, being models such as ECMWF and UKMO more skilful. Nevertheless, the multi-model ensemble generated using both an unweighted and weighted approach shows a slightly better forecast skill than individual models, suggesting that their use should provide a more reliable forecast.
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United States of America
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CGIAR
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CGIAR Initiatives
Initiative
Digital Innovation
Impact Area
Poverty reduction, livelihoods & jobs
Action Area
Systems Transformation
Donor or Funder
CGIAR Trust Fund