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Although climate models are important for making projections of future climate, little attention has been devoted to model simulation of the complex climate of Central Africa (CA). This study investigates rainfall biases through processes in three versions of the Met Office Unified Model over CA with both coupled and atmosphere‐only formulations for each version. The study shows that the models depict a wet (dry) bias over the eastern (coastal western) CA in the September–November season with the wet (dry) bias stronger in coupled (atmosphere‐only) models. Here, we explore potential regional to large‐scale causes of these biases. Results reveal that the overestimation of the simulated sinking branch of the Atlantic‐Congo zonal overturning cell is associated with a strong near‐surface temperature and pressure gradient between CA and the eastern Atlantic Ocean. This leads to strong low‐level westerlies (LLWs) which dry the coastal western CA and strengthen the intensity of the Congo basin cell. Over eastern CA, the wet bias is partially due to the misrepresentation in the strength of both African easterly jet components that shift the mid‐tropospheric moisture flux convergence southward favouring more convection south of the equator. Furthermore, the overestimation in the simulated width and intensity of the Congo basin cell is associated with a strong low‐level moisture convergence over eastern CA which contributes to more precipitation. Remote contributions to the wet bias come from both the Atlantic and the Indian Oceans. The simulated Atlantic‐Congo zonal overturning circulation dries the coast through its overestimated lower branch (LLWs) which moves further into the continent and advects more moisture to the eastern CA. In the meantime, during positive Indian Ocean Dipole years, the advected moisture from the Indian Ocean to the CA region is overestimated in models, much more in coupled models and contributes to the eastern CA wet bias. [ABSTRACT FROM AUTHOR]

Using both observational and reanalysis datasets, this study examines the influence of extreme Indian Ocean Dipole (IOD) events on Central Africa (CA) rainfall from September to December over the period 1980–2016. It is shown that during extreme positive IOD events (hereafter pIOD), CA experiences enhanced moisture supply from the Indian Ocean that results in increased rainfall while the opposite process is recorded during the negative phase (hereafter nIOD), with the magnitude of pIOD events anomalies stronger than those of nIOD events. Furthermore, the inflow is strongest in the lower troposphere over the ocean while it is strongest in the mid‐troposphere over CA because much of the lower‐level inflow is blocked by the topography to the east of CA. The intensification of mid‐tropospheric moisture transport over CA is associated with the African Easterly Jet which is present during these months. From 1980 to 2016 the IOD and the El Niño‐Southern Oscillation (ENSO) showed a strong positive relationship so that extreme pIOD (nIOD) are mostly coincident with El Niño (La Niña). To assess the possible role of non‐ENSO influences on the IOD influence over CA, the linear relationship between the Niño‐3.4 and dipole mode index (DMI) was removed from the DMI. It appears that the influence of the pIOD on CA rainfall is significant with the non‐El Niño effect and this is characterized by an increase in moisture advection toward CA that contributes to an enhancement of rainfall intensity. However, the IOD seems to contribute strongly to the rainfall variability in the CA regions south of 5°N, especially over the eastern Democratic Republic of Congo, and over the area from Gabon to the Republic of Congo where about 20–32% of the total rainfall variance is associated with the IOD independently of ENSO from November to December. [ABSTRACT FROM AUTHOR]

In this study, an analysis of present day climate simulation (1998–2008) is presented for the Central African (CA) region with the COnsortium for Small‐scale MOdelling in CLimate Mode (CCLM) regional climate model, forced by the ERA‐Interim (ERAINT) reanalysis data. The ability of the CCLM to simulate the observed precipitation with particular focus on the mean spatial pattern, low‐level circulation, seasonal cycles, and daily characteristics is evaluated. Likewise, the added value of the regional model CCLM compared to the driving ERAINT reanalysis is also investigated. It is shown that ERAINT and CCLM exhibit quite different sign of bias, which is an indication of the importance of internal variability and fine scale processes representation for the simulation of surface climate. Despite the CCLM is constantly dry over southern CA, the model succeeds to reproduce reasonably the mean spatial patterns of precipitation and low‐level circulation features, along with the associated seasonal cycles over the whole CA and majority of the five selected analysis sub‐regions. Results also show that daily precipitation indices are well represented, although the better performance greatly depends on the considered seasons. Nevertheless, CCLM substantially outperforms the ERAINT daily precipitation characteristics, thus highlighting the added value of the downscaling exercise over the region. The analysis of daily precipitation indices also reveals that the dry character of the model could probably be connected to the underestimation of the simulated less intense events, which in turn result to an overestimation of the simulated dry spell duration. [ABSTRACT FROM AUTHOR]

This paper investigates the performance of 10 Regional Climate Models (RCMs) hindcasts from the Coordinated Regional Climate Downscaling Experiments (CORDEX) over Central Africa, covering the period 1998–2008 and performed over a common model grid spacing 0.44° (∼50 km). Multiple observational data sets are used to evaluate model performances over four targeted subregions. Throughout the work, a measure of observational uncertainty is made and we discuss whether or not the models are found within or outside the range of observational uncertainty. Results indicate that RCMs generally capture rainfall and temperature basic features, though important biases exist and vary for models and seasons. Dry (wet) biases are common features over the Congo basin (northern and southern part of the domain). In terms of precipitation and temperature in both seasonal and annual scale, most RCMs along with their ensemble mean generally fall in the range of observational uncertainty. Furthermore, most RCMs show a good spread of grid points where the added value of RCMs is found although the added value in temperature is not as great as with precipitation. UC‐WRF is among models adding less value on ERAINT and this could explain why whatever the time scale of variability, UC‐WRF outputs are generally out from the observational uncertainty. The multimodel ensemble mean is generally found within observational range when most models are there as well. This highlights the fact that the ensemble mean, built from the equal treatment of RCMs, does not generally outperform individual RCMs realization as it is reported in several previous studies. Key Points: Most RCMs along with their ensemble mean generally fall in the range of observational uncertaintyThe ensemble mean of models is generally found within the observational range when most models are there as wellThe ensemble mean of models does not generally outperform individual RCMs realization as it is reported in several previous studies [ABSTRACT FROM AUTHOR]