The credibility of regional climate simulations over West Africa stands and falls with the ability to reproduce the West African monsoon (WAM) whose precipitation plays a pivotal role for people’s livelihood. In this study, we simulate the WAM for the wet year 1999 with a 27-member multi-physics ensemble of the Weather Research and Forecasting (WRF) model. We investigate the inter-member differences in a process-based manner in order to extract generalizable information on the behavior of the tested cumulus (CU), microphysics (MP), and planetary boundary layer (PBL) schemes. Precipitation, temperature and atmospheric dynamics are analyzed in comparison to the Tropical Rainfall Measuring Mission (TRMM) rainfall estimates, the Global Precipitation Climatology Centre (GPCC) gridded gauge-analysis, the Global Historical Climatology Network (GHCN) gridded temperature product and the forcing data (ERA-Interim) to explore interdependencies of processes leading to a certain WAM regime. We find that MP and PBL schemes contribute most to the ensemble spread (147 mm month⁻¹) for monsoon precipitation over the study region. Furthermore, PBL schemes have a strong influence on the movement of the WAM rainband because of their impact on the cloud fraction, that ranges from 8 to 20 % at 600 hPa during August. More low- and mid-level clouds result in less incoming radiation and a weaker monsoon. Ultimately, we identify the differing intensities of the moist Hadley-type meridional circulation that connects the monsoon winds to the Tropical Easterly Jet as the main source for inter-member differences. The ensemble spread of Sahel precipitation and associated dynamics for August 1999 is comparable to the observed inter-annual spread (1979–2010) between dry and wet years, emphasizing the strong potential impact of regional processes and the need for a careful selection of model parameterizations.

Significance: Soil moisture plays a key role in the climate system by affecting rainfall and drought over land. Through its impact on temperature, humidity, and wind in the lower atmosphere, it can influence where thunderstorms initiate. However, in many regions of the world, traveling storm clusters known as mesoscale convective systems (MCSs) are the dominant source of rainfall, and very little is known about their response to surface conditions once triggered. We use satellite observations to demonstrate that dry soils at scales≥200 km frequently create atmospheric conditions that intensify mature MCSs in the Sahel, long after their initiation. This surface-driven predictability of hazardous weather has potentially important applications, particularly in Africa, where the population is increasingly exposed to flood risk.

Convection‐permitting models perform better at representing the diurnal cycle and the intermittency of convective rainfall over land than parameterized‐convection models. However, most of the previous model assessments have been from an Eulerian point of view, while key impacts of the rainfall depend on a storm‐relative perspective of the system lifecycle. Here a storm‐tracking algorithm is used to generate storm‐centered Lagrangian lifecycle statistics of precipitation over West Africa from regional climate model simulations and observations. Two versions of the Met Office Unified Model with and without convection parameterization at 4‐, 12‐, and 25‐km resolution were analyzed. In both of the parameterized‐convection simulations, storm lifetimes are too short compared to observations, and storms have no preferred propagation direction; the diurnal cycle of initiations and dissipations and the spatial distribution of storms are also inaccurate. The storms in the convection‐permitting simulations have more realistic diurnal cycles and lifetimes but are not as large as the largest observed storms. The convection‐permitting model storms propagate in the correct direction, although not as fast as observed storms, and they have a much improved spatial distribution. The rainfall rate of convection‐permitting storms is likely too intense compared to observations. The improved representation of the statistics of organized convective lifecycles shows that convection‐permitting models provide better simulation of a number of aspects of high‐impact weather, which are critical to climate impacts in this important geographic region, providing the high rainfall rates can be taken into account.

Focusing on West Africa, a region riddled with in situ data scarcity, we evaluate the summer monsoon monthly rainfall characteristics of five global reanalysis datasets: ERA5, ERA-Interim, JRA-55, MERRA2, and NCEP-R2. Their performance in reproducing the West African monsoon (WAM) climatology, interannual variability, and long-term trends for the main monsoon months are compared to gauge-only and satellite products. We further examine their ability to reproduce teleconnections between sea surface temperatures and monsoon rainfall. All reanalyses are able to represent the average rainfall patterns and seasonal cycle; however, regional biases can be marked. ERA5, ERA-Interim, and NCEP-R2 underestimate rainfall over areas of peak rainfall, with ERA5 showing the strongest underestimation, particularly over the Guinea Highlands. The meridional northward extent of the monsoon rainband is well captured by JRA-55 and MERRA2 but is too narrow in ERA-Interim, for which rainfall stays close to the Guinea Coast. Differences in rainband displacement become particularly evident when comparing strong El Niño Southern Oscillation (ENSO) years, where all reanalyses except ERA-Interim reproduce wetter Sahelian conditions for La Niña, while overestimating dry conditions at the coast except for NCEP-R2. Precipitation trends are not coherent across reanalyses and magnitudes are generally overestimated compared to observations, with only JRA-55 and NCEP-R2 displaying the expected positive trend in the Sahel. ERA5 generally outperforms ERA-Interim, highlighting clear improvements over its predecessor. Ultimately, we find the strengths of reanalyses to strongly vary across the region.