Abstract
In the era of Anthropocene climate that the world is currently experiencing, accurate climate models that exhibit minimal uncertainties for precise estimation of the sporadic extreme climate anomalies is urgently needed. To address this gap, the present study quantified the added value in the recently released NEX-GDDP-CMIP6 precipitation models as compared to their native CMIP6 models over 9 climatic zones in Africa in order to identify the best performing models with minimal biases. Accordingly, 22 NEX-GDDP-CMIP6 precipitation models and similar number for native CMIP6 precipitation models were evaluated with respect to two observational products (CHIRPS and CPC). With robust statistical techniques employed, the results showed that at annual and seasonal scales, the NEX-GDDP-CMIP6 GCMs and their multi-model ensemble (MME) reproduced a coherent spatial pattern of precipitation to the observed better than the native CMIP6 GCMs. The NEX-GDDP-CMIP6 GCMs and their MME also exhibited a stronger spatial pattern with higher correlation coefficients, lower mean bias and root mean square error recorded, than in the CMIP6 GCMs. The differences and improvements exhibited by the NEX-GDDP-CMIP6 GCMs, highlight the significance of the improved bias correction method and finer spatial resolution of 0.25*0.25 which characterize the newly published NEX-GDDP-CMIP6 GCMs. The Taylor Skill Score and the Interannual Variability Scores were used to rank the NEX-GDDP-CMIP6 GCMs after evaluation and the results confirmed they were better than the native CMIP6 GCMs in simulating daily precipitation over diverse climate zones of Africa. It is recommended that new future projections of precipitation under whatever scenario (SSPs) or region should adopt this better improved dataset.
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Data Availability Statement
Data and materials will be made available upon request.
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Dioha, E.C., Chung, ES., Ayugi, B.O. et al. Quantifying the Added Value in the NEX-GDDP-CMIP6 Models as Compared to Native CMIP6 in Simulating Africa’s Diverse Precipitation Climatology. Earth Syst Environ (2024). https://doi.org/10.1007/s41748-024-00397-x
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DOI: https://doi.org/10.1007/s41748-024-00397-x