Capability of CAM5.1 in simulating maximum air temperature patterns over West Africa during boreal spring

This study classifies maximum air temperature patterns over West Africa into sixgroups and evaluates the capability of a global climate model (Community Atmospheric Model version 5.1; CAM) to simulate them. We analyzed 45-year (1961–2005) multi-ensemble (50 members) simulations from CAM and compared the results with those of the Climate Research Unit (CRU) and the twentieth Century Reanalysis data sets.Using Self Organizing Map algorithmto classify the spatial patterns of maximum air temperature during boreal spring, the study revealsthe temperature patterns that CAM can simulate welland those the modelstruggles to reproduce. The results show that the best agreements between the composites of observation and CAM occur in the first temperature patterngroup (which features positivetemperaturesanomalies over theSahel) and Node 2 (which featuresnear-normal temperature) pattern of the third group. CAM succeeded in reproducing some of the associated regional atmospheric dynamics and thermodynamic features in winds(horizontal and vertical), temperature fields, the cloud fractions, and the mean sea-level pressure. Although CAM struggles to capture the relationship between air temperaturepatterns and tele-connection indices during the boreal spring season over West Africa, it agrees with observations that temperature patterns over the sub-region cannot be associated with a single climate index. Anensemble member (SIM48) captures the inter-annual variation of the observed temperaure patterns with high sycronization (ɳ > 44%), much better than that ofensembles mean (ɳ < 30%). SIM48 also captures adequately four of the spatial patterns in comparison to three captured by the ensembles mean. This indicates that, for better seasonal forecasts and more reliable future climate projections, the practice whereby an ensemble mean is based on uniformly averaging the members rather than the performance of individual ensemble members needs to be reviewed. The results of the studymay be used to improve the perfomance of CAM over West Africa, therebystrengthening the on-going efforts to include CAM as part of multi-model forecasting system over West Africa.