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Climate extremes pose threats to lives and properties. It is therefore critical to assess the dynamics of climate extremes in the future. Knowledge of the appropriate models to be used for projection helps reduces uncertainties in model projections. The study assesses the evolution of 8 extreme temperature indices over the Volta Basin for the 1985–2014 period. Consequently, the ability of 40 Coupled Model Intercomparison Project Phase 6 (CMIP6) models in reproducing these extremes is assessed. The biases and the M-scores of the annual spatial distribution of indices are determined. It is observed that the temperature extremes are frequent and profound in the Sahel, indicating that the Sahel is susceptible to extreme temperature changes. The warm indices showed positive trends, while the cold indices mainly showed negative trends. Most of the models had difficulty reproducing the climatologies of the indices. However, the CMCC-ESM2, MPI-ESM-1-2-HAM, MIROC6, and AWI-CM-1-1-MR reproduced the TNN, TNX, TXN, and TXX well while the MIROC6, MRI-ESM2-0, INM-CM4-8, and MIROC6 showed robust performance in reproducing the TN10P, TN90P, TX10P, and TX90P respectively. Poor performance of models may be due to poor simulation of sub-grid features such as vegetation cover, and clouds as a result of coarse grid sizes. Warming of the basin poses a significant impact on agriculture, drought occurrence, and seasonal to annual scale precipitation distribution. Southern sections of the basin may be vulnerable to temperature-related extremes as a result of deforestation and urbanization. [ABSTRACT FROM AUTHOR]

The ability of any climate model to reproduce present-day climate fields is very important for the confidence in the projections of the future state of the same fields. It is against this background that the performance of the Global Coupled configuration 2 (GC2) of the Unified Model (UM), a Global Climate Model (GCM), was evaluated over the West African region (a climate-sensitive region) for two periods on 3 climatological time scales for precipitation (using the Global Precipitation Climatology Center data) and 2-m temperature where the Climate Research Unit Time Series dataset was used. The GC2's performance is also compared with 4 Coupled Model Intercomparison Project phase 6 (CMIP6) GCMs. Results indicate that spatial biases are of similar patterns across the domain for the two study periods for both variables, demonstrating the consistency of the model in reproducing the two climate variables over West Africa (WA). The simulation of temperature by the UM is better than precipitation. Biases are larger in the UM and the CMIP6 GCMs than the levels of observational uncertainties for precipitation, but for temperature, biases in all the models are within the levels of uncertainties found in the observations. Also, the holistic performance of the UM in the two fields is relatively better than the 4 CMIP6 GCMs. The two variables are also simulated better in the southern parts of the region than the northern parts of the domain by the UM. The analysis of the UM suggests a strong linkage between the two variables and therefore, a detailed investigation in how the model reproduces precipitation and temperature-based processes over WA is recommended in order to enhance the model's simulations of these climate variables. Also, the model is recommended for future investigations of precipitation and temperature climatologies over WA. [ABSTRACT FROM AUTHOR]

A selected number of global climate models (GCMs) from the fifth Coupled Model Intercomparison Project (CMIP5) were evaluated over the Volta Basin for precipitation. Biases in models were computed by taking the differences between the averages over the period (1950–2004) of the models and the observation, normalized by the average of the observed for the annual and seasonal timescales. The Community Earth System Model, version 1-Biogeochemistry (CESM1-BGC), the Community Climate System Model Version 4 (CCSM4), the Max Planck Institute Earth System Model, Medium Range (MPI-ESM-MR), the Norwegian Earth System Model (NorESM1-M), and the multimodel ensemble mean were able to simulate the observed climatological mean of the annual total precipitation well (average biases of 1.9% to 7.5%) and hence were selected for the seasonal and monthly timescales. Overall, all the models (CESM1-BGC, CCSM4, MPI-ESM-MR, and NorESM1-M) scored relatively low for correlation (<0.5) but simulated the observed temporal variability differently ranging from 1.0 to 3.0 for the seasonal total. For the annual cycle of the monthly total, the CESM1-BGC, the MPI-ESM-MR, and the NorESM1-M were able to simulate the peak of the observed rainy season well in the Soudano-Sahel, the Sahel, and the entire basin, respectively, while all the models had difficulty in simulating the bimodal pattern of the Guinea Coast. The ensemble mean shows high performance compared to the individual models in various timescales. [ABSTRACT FROM AUTHOR]

The article offers information on the University Corporation for Atmospheric Research (UCAR) Africa Initiative (AI), an effort to help constrain the development of climate and weather in Africa. UCAR and AI worked together to improve the atmospheric sciences infrastructure in the continent. AI is making effort to encourage universities and government agencies to take on other efforts, such as atmospheric science capacity building, in different ways, in Africa. UCAR is expanding those activities it collaborated with AI. A weather radar network project is currently in progress as part of the effort to improve the atmospheric sciences infrastructure in the continent.