MBOGBA NYOBE Jacques Beaudelaire: Evaluation of CMIP6 Model Performance in Simulating Rare Precipitation and Temperature Extremes over Central Africa
Beaudelaire J. N. Mbogba 1 ; Roméo Chamani 1,2 ; Benoît C. D. Menzoh 1
1 Laboratory of Environmental Modeling and Atmospheric Physic, Faculty of Science, University of Yaounde 1 P.O.Box: 812, Yaounde/Cameroon
2 Department of Physics, Faculty of Science, University of Yaoundé I, P.O.Box: 812, Yaounde/Cameroon
Faced with the threat posed by extreme temperatures and precipitations, climate modeling is one of the major challenges in sub–Saharan Africa. Although this modeling is effective in some parts of the world, doubts remain about its effectiveness in Central Africa due to the poor spatial and temporal coverage of ground-based climate observation tools in this region of sub-Saharan Africa.This study is part of this approach in that we examine the performance of the multi–model ensemble of phase 6 of the Coupled Model Inter-comparison Project (CMIP6) in simulating rare extremes of temperature and precipitation in Central Africa. To perform this, we use the 30–year return levels (RL) of annual maximum daily temperatures (TXx), annual daily minimum temperatures (TNn), and annual maximum 1–day precipitation (Rx1day) from the median of the CMIP6 multi-model ensemble were compared with those from the 5th generation of global climate reanalysis data (ERA5) over the period 1985–2014. The multi–model median generally reproduces well the spatial patterns of Txx, TNn, and Rx1day in Central Africa, with the particularity of better capturing the spatial configuration of hot extremes (TXx). However, overall, it underestimates these rare extremes by a relatively modest amount. The sub-regions located in the Sudano–Sahelian zone, north of the equator, and in the western equatorial zone appear to be those where the models perform best for TXx and Rx1day. Although these results would be a proven contribution to improving CMIP6 simulations in Central Africa, they remind us of the caution that should be exercised when constructing projections of extreme events such as heat waves and floods in this region based on these simulations.
Keywords: Temperature, Precipitation, Return Level, Climate Models, Biais