Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Evaluation of the Global Climate Models in CMIP6 over Uganda

Version 1 : Received: 30 December 2020 / Approved: 31 December 2020 / Online: 31 December 2020 (09:29:01 CET)

How to cite: Ngoma, H.; Wen, W.; Ayugi, B.; Babaousmail, H.; Karim, R.; Ongoma, V. Evaluation of the Global Climate Models in CMIP6 over Uganda . Preprints 2020, 2020120782 (doi: 10.20944/preprints202012.0782.v1). Ngoma, H.; Wen, W.; Ayugi, B.; Babaousmail, H.; Karim, R.; Ongoma, V. Evaluation of the Global Climate Models in CMIP6 over Uganda . Preprints 2020, 2020120782 (doi: 10.20944/preprints202012.0782.v1).

Abstract

This study employed 15 CMIP6 GCMs and evaluated their ability to simulate rainfall over Uganda during 1981-2019. The models and the ensemble mean were assessed based on the ability to reproduce the annual climatologyseasonal rainfall distribution, trend, and statistical metrics, including mean bias error, root mean square error, and pattern correlation coefficient. The Taylor diagram and Taylor skill score (TSS) were used in ranking the models. The models performance varies greatly from one season to the other. The models reproduced the observed bimodal rainfall pattern of March to May (MAM) and September to November (SON) rains occurring over the region. Some models slightly overestimated, while some slightly underestimated, the MAM rainfall. However, there was a high rainfall overestimation during SON by most models. The models showed a positive spatial correlation with observed dataset, whereas a low correlation was shown interannually. Some models could not capture the rainfall patterns around local-scale features, for example, around the Lake Victoria basin and mountainous areas. The best performing models identified in the study include GFDL-ESM4, BCC-CMC-MR, IPSL-CM6A-LR, CanESM5, GDFL-CM4-gr1, and GFDL-CM4-gr2. The models CNRM-CM6-1 and CNRM-ESM2 underestimated rainfall throughout the annual cycle and mean climatology. However, these two models better reproduced the spatial trends of rainfall during both MAM and SON. The model spread in CMIP6 over the study area calls for further investigation on the attributions and possible implementation of robust approaches of Machine learning to minimize the biases.

Subject Areas

rainfall; CMIP6; CHIRPS; Uganda; East Africa

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