Abstract

A set of numerical experiments was conducted in order to investigate the impacts of global warming on West African monsoon rainfall for selected five years. The experiments varied different cumulus, microphysics and planetary boundary layer parameterization schemes. Rainfall characteristics over ... Show moreA set of numerical experiments was conducted in order to investigate the impacts of global warming on West African monsoon rainfall for selected five years. The experiments varied different cumulus, microphysics and planetary boundary layer parameterization schemes. Rainfall characteristics over three climatic zones, Guinea Coast, Savannah and Sahel, was analyzed. The potential change associated with global warming is assessed by the pseudo global warming (PGW) downscaling method. Multiple PGW runs were conducted using climate perturbation from the 40-member ensemble of the Community Earth System Model version 1 (CESM1) coupled with Community Atmospheric Model version 5.2 (CAM5.2) component large ensemble project. The model output was compared with TRMM and GPCP rainfall and atmospheric parameters from ECMWF reanalysis datasets. Results show that the rainfall amount in the 2070s estimated from the PGW runs substantially increases, especially in the eastern Sahel due to enhanced moisture convergence, compared to the current climate. The percentage change in simulated total rainfall amount can increase or decrease by 50% in the PGW runs and the theoretical rainfall computed based on Clausius-Clapeyron relation. Also, found is an increase (decrease) in heavy (both light and moderate) rainfall amount. These results, however, depend on the GCM used as the boundary conditions of the RCM. This suggests that the 4 degrees C change in average surface temperature derived from the 40-member ensemble model strongly influenced the increased rainfall simulated by the PGW experiments. Thus, highlighting the advantage of using the PGW technique to estimate the likely difference between present and future climate with reduced large-scale model biases and computational resources. Show less