Machine-learning unravels spatial shifting in homogeneous rainfall subregions in Central Africa under global warming - Alain T Tamoffo; Fernand L Mouassom; Torsten Weber (Environmental Research Communications)
Modifications in precipitation regimes significantly affect various socio-economic sectors, including agriculture and water resource management. Although the rainfall regimes characterizing Central Africa (CA) have just recently been described, it is equally urgent to investigate potential changes in their spatial extent under different global warming pathways, which motivates the present study. For this purpose, we utilized results from the dynamical downscaling performed by regional climate models (RCMs) under the CORDEX-CORE (Coordinated Regional Climate Downscaling Experiment–Coordinated Output for Regional Evaluations) initiative. The warming pathways are based on low (RCP2.6) and high (RCP8.5) emission scenarios. The K-means clustering technique is employed to classify areas with homogeneous rainfall regimes. Our findings indicate that the ability of experiments to mimic the spatial patterns of these subregions is model-dependent. REMO and CCLM5 RCMs outperform RegCM4, achieving the highest Adjusted Rand (AR) index values compared to the observational datasets CHIRPS2 and TAMSAT3.1. Projections based on individual experiments and the multimodel ensemble-mean suggest that the warming level will influence clusters’ spatial extent. Broadly, the ensemble mean shows that an expansion of Equatorial CA is projected (4.8% and 9.7%, respectively), while a contraction of Southern CA is anticipated (4.2% and 4.5%, respectively), consistently under both scenarios. In contrast, the signal of change in Northern CA differs between the two warming pathways. Under the highly mitigated RCP2.6 scenario, an expansion of the cluster is projected (1%), whereas the low-mitigation RCP8.5 scenario projects a shrinking of its spatial extent (0.8%).