Search

We evaluate and compare the simulation of the main features (low-level westerlies (LLWs) and the Congo basin (CB) cell) of low-level circulation in Central Equatorial Africa (CEA) with eight climate models from Phase 6 of the Coupled Model Intercomparison Project (CMIP6) and the corresponding eight previous models from CMIP5. Results reveal that, although the main characteristics of the two features are reasonably well depicted by the models, they bear some biases. The strength of LLWs is generally overestimated in CMIP5 models. The overestimation is attributed to both divergent and rotational components of the total wind with the rotational component contributing the most in the overestimation. In CMIP6 models, thanks to a better performance in the simulation of both divergent and rotational circulation, LLWs are slightly less strong compared to the CMIP5 models. The improvement in the simulated divergent component is associated with a better representation of the near-surface pressure and/or temperature difference between the Central Africa landmass and the coastal Atlantic Ocean. Regarding the rotational circulation, and especially for HadGEM3-GC31-LL and BCC-CSM2-MR, a simulated higher 850 hPa pressure is associated with less pronounced negative vorticity and a better representation of the rotational circulation. Most CMIP5 models also overestimate the CB cell intensity and width in association with the simulated strength of LLWs. However, in CMIP6 models, the strength of key cell characteristics (intensity and width) are reduced compared to CMIP5 models. This depicts an improvement in the representation of the cell in CMIP6 models and this is associated with the improvement in the simulated LLWs. [ABSTRACT FROM AUTHOR]

Climate model evaluation presents a crucial pathway into the investigation of the simulation of future climate. It presents the only lens through which the future states of the climate of the planet can be explored. In this study, reference datasets including reanalysis products have been used to demonstrate the climatological annual migration of the West African Monsoon System (WAMS) in three components, namely, the West African Heat Low (WAHL), the West African Heat Band (WAHB) and the West African Rain Band (WARB). We have used 8 Global Climate Models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) to assess how the models represent the climatological annual migration of these three features over the same domain for the same period. The results show close linkages between the WAHL, WAHB and the WARB as signature components of the WAMS in their climatological annual dynamics. All three features migrate from the south (with the minimum intensities) in January to the north (with the maximum intensities) in August over the region. The GCMs represent the climatological annual dynamics of all three features to some extent. However, in most of the models analysed the WAHL is too weak, and the WAHB and WARB are located too far south. Insights from this study can be beneficial for investigations of the future state of the WAMS. Also, development of models for the region should target the improved simulation of these features, specifically the intensity of the WAHL, and the locations of the WAHB and WARB. [ABSTRACT FROM AUTHOR]

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]

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]

In this paper, daily characteristics of the Central Africa rainfall are assessed using the regional model REMO in the framework of contributions to the CORDEX-Africa project. The model is used to dynamically downscale two global climate models (MPI-ESM-LR and EC-EARTH) for the present (1981–2005) and future (2041–2065, 2071–2095) climate under the Representative Concentration Pathway (RCP) 2.6, 4.5, and 8.5 emission scenarios. A substantial spatio-temporal variability of the daily precipitation characteristics is obtained, as well as varying inferences for individual indices. For the present days, both REMO's runs capture reasonably well the mean seasonal rainfall, the frequency of wet days, the threshold of extreme rainfall, and the cumulative frequency of daily rainfall. The model better simulates the frequency of rainy days than their intensity. It is found that origins of model biases differ as a function of regions. Over the continent, boundary conditions tend to influence the spatial distribution of rainfall whereas over oceanic and coastal regions, REMO's physics seems to dominate over the boundary forcing. The projected frequency of wet days shows a decrease along the twenty-first century over most part of the continent. Throughout the century, all scenarios of REMO decrease the rate of rainfall with increasing intensity, and which will be noticeable in the Sahelian region at late twenty-first century. Furthermore, the extreme event thresholds decrease over Sahelian regions and increase along the coastal regions. [ABSTRACT FROM AUTHOR]

Understanding the processes responsible for precipitation and its future change is important to develop plausible and sustainable climate change adaptation strategies, especially in regions with few available observed data like Congo Basin (CB). This paper investigates the atmospheric circulation processes associated with climate model biases in CB rainfall, and explores drivers of projected rainfall changes. Here we use an ensemble of simulations from the Swedish Regional Climate Model (RCM) RCA4, driven by eight General Circulation Models (GCMs) from the Coupled Model Intercomparison Project Phase 5 (CMIP5), for the 1.5 ∘ C and 2 ∘ C global warming levels (GWLs), and under the representative concentration pathways (RCPs) 4.5 and 8.5. RCA4 captures reasonably well the observed patterns of CB rainfall seasonality, but shows dry biases independent of seasons and large scale driving atmospheric conditions. While simulations mimic observed peaks in transition seasons (March–May and September–November), the rain-belt is misplaced southward (northward) in December–February (June–August), reducing the latitudinal extent of rainfall. Moreover, ERA-Interim reanalysis driven RCM simulation and RCM–GCM combinations show similar results, indicating the dominance of systematic biases. Modelled dry biases are associated with dry upper-tropospheric layers, resulting from a western outflow stronger than the eastern inflow and related to the northern component of African Easterly Jet. From the analysis of the climate change signal, we found that regional scale responses to anthropogenic forcings vary across GWLs and seasons. Changes of rainfall and moisture divergence are correlated, with values higher in March–May than in September–November, and larger for global warming of 2.0 ∘ C than at 1.5 ∘ C . There is an increase of zonal moisture divergence fluxes in upper atmospheric layers ( > 700 hPa ) under RCP8.5 compared to RCP4.5. Moreover, it is found that additional warming of 0.5 ∘ C will change the hydrological cycle and water availability in the CB, with potential to cause challenges to water resource management, agriculture, hydro-power generation, sanitation and ecosystems. [ABSTRACT FROM AUTHOR]

In this paper, the regional climate model REMO is used to investigate the added value of downscaling low resolutions global climate models (GCMs) and the climate change projections over Central Africa. REMO was forced by two GCMs (EC-Earth and MPI-ESM), for the period from 1950 to 2100 under the Representative Concentration Pathway 8.5 scenario. The performance of the REMO simulations for current climate is compared first with REMO simulation driven by ERA-Interim reanalysis, then by the corresponding GCMs in order to determine whether REMO outputs are able to effectively lead to added value at local scale. We found that REMO is generally able to better represent some aspects of the rainfall inter-annual variability, the daily rainfall intensity distribution as well as the intra-seasonal variability of the Central African monsoon, though few biases are still evident. It is also found that the boundary conditions strongly influences the spatial distribution of seasonal 2-m temperature and rainfall. From the analysis of the climate change signal from the present period 1976-2005 to the future 2066-2095, we found that all models project a warming at the end of the twenty-first century although the details of the climate change differ between REMO and the driving GCMs, specifically in REMO where we observe a general decrease in rainfall. This rainfall decrease is associated with delayed onset and anticipated recession of the Central African monsoon and a shortening of the rainy season. Small-scales variability of the climate change signal for 2-m temperature are usually smaller than that of the large-scales climate change part. For rainfall however, small-scales induce change of about 70% compared to the present climate statistics. [ABSTRACT FROM AUTHOR]