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The West African Monsoon (WAM) system plays a crucial role in the West African climate system because it transports moisture from the Atlantic Ocean into the subcontinent in summer. This study evaluates the capability of the Model for Prediction Across Scales-Atmosphere (MPAS-A) to simulate the characteristic reproduce the WAM system and the associated rainfall-producing features. The MPAS model was used to perform a 30-year global climate simulation (1981–2010) at a regular grid (uniform resolution of 60 km). The simulation was initialized with the Climate Forecast System Reanalysis (CSFR) dataset. The results showed that MPAS simulate well the rainfall pattern over West Africa and reproduces the different phases of the monsoon dynamics system (i.e., the northward progression, the peak period, and the southward retreat). The model also reasonably replicates the pattern of the zonal components of wind and the vertical velocity. However, MPAS underestimates the orographic rainfall over the Guinea Coast, Jos Plateau, and Mount Cameroon. It also underestimates the vertical velocity and zonal wind magnitudes over the region. In addition, the model features a weaker temperature gradient than in the reanalysis. Understanding and correcting the sources of these model biases will enhance the suitability of MPAS for weather and seasonal forecasts over West Africa. Research highlights: In this study, we investigated the ability of the Model for Prediction Across Scales (MPAS) to simulate the West African monsoon rainfall, the associated atmospheric features controlling the variability of the seasonal and annual cycle, and the thermal wind conditions. First, the model is initialized using the CSFR reanalysis. Then, we run the model simulation for 30 years, from 1981 to 2010, using a regular grid (uniform resolution of 60 km). We showed that the model realistically simulated the spatial pattern of rainfall over West Africa. However, it has some problems when it comes to capturing the monsoon features. [ABSTRACT FROM AUTHOR]

A large ensemble of 51 simulations with the Model for Prediction Across Scales (MPAS) has been applied to assess its ability to reproduce extreme temperatures and heat waves in the area of West Africa. With its global approach the model avoids transition errors influencing the performance of limited area climate models. The MPAS simulations were driven with sea surface temperature (SST) and sea ice extent as the only boundary condition. The results reveal moderate cold biases in the range from −0.6° to −0.9°C for the daily mean temperature and −1.2° to −2.0°C for the area mean of the daily maximum temperature. The bias in the number of tropical nights ranges from +3 to −10 days. An underestimation by up to 50% is also present regarding the number of summer days. The heat wave duration index is underestimated regionally by 10%–60%. MPAS simulations are generally closer to the reanalysis results than they are to the observational reference. The results from long term runs and from short term runs with selected SST years are similar. Shortcomings in the reproduction of the temperature and precipitation indices found in the present investigation indicate that the global MPAS approach does provide a fidelity similar to that of the regional climate models. Plain Language Summary: A large number of simulations with the global weather and climate model Model for Prediction Across Scales (MPAS) has been applied to investigate extreme temperatures and related heat waves. The considered area is West Africa. In the simulations sea surface temperature and sea ice extent were the only boundary condition. The results reveal moderate underestimation in the range from −0.6° to −0.9°C for the daily mean temperature. The bias of the area mean of the daily maximum temperature was −1.2° to −2.0°C. An underestimation by up to 50% is also present in the number of summer days. The heat wave duration index is underestimated regionally by 10%–60%. Obtained results in the reproduction of the observed temperatures and precipitation show that the global MPAS model provides results similar to that of regional climate models. Key Points: Multiple Model for Prediction Across Scales (MPAS) runs with SST and sea ice extent as the only boundary condition are used to investigate extremes of temperature and heat wavesMPAS reveals moderate cold biases for all investigated temperature indicesLong term runs as well as short term runs with selected SST years yield similar results [ABSTRACT FROM AUTHOR]

Renewable energy development is growing fast and is expected to expand in the next decades in West Africa as a contribution to addressing the power demand and climate change mitigation. However, the future impacts of climate change on solar PV and the wind energy potential in the region are still unclear. This study investigates the expected future impacts of climate change on solar PV and wind energy potential over West Africa using an ensemble of three regional climate models (RCMs). Each RCM is driven by three global climate models (GCMs) from the new coordinated high-resolution output for regional evaluations (CORDEX-CORE) under the RCP8.5 scenario. Two projection periods were used: the near future (2021–2050) and the far future (2071–2100). For the model evaluation, reanalysis data from ERA5 and satellite-based climate data (SARAH-2) were used. The models and their ensemble mean (hereafter Mean) show acceptable performance for the simulations of the solar PV potential, the wind power density, and related variables with some biases. The Mean predicts a general decrease in the solar PV potential over the region of about −2% in the near future and −4% in the far future. The wind power density (WPD) is expected to increase by about 20% in the near future and 40% in the far future. The changes for solar PV potential seem to be consistent, although the intensity differs according to the RCM used. For the WPD, there are some discrepancies among the RCMs in terms of intensity and direction. This study can guide governments and policymakers in decision making for future solar and wind energy projects in the region. [ABSTRACT FROM AUTHOR]

Classification of atmospheric circulation patterns (CP) is a common tool for downscaling rainfall, but it is rarely used for West Africa. In this study, a two‐step classification procedure is proposed for this region, which is applied from 1989 to 2010 for the Sudan‐Sahel zone (Central Burkina Faso) with a focus on heavy rainfall. The approach is based on a classification of large‐scale atmospheric CPs (e.g., Saharan Heat Low) of the West African Monsoon using a fuzzy rule‐based method to describe the seasonal rainfall variability. The wettest CPs are further classified using meso‐scale monsoon patterns to better describe the daily rainfall variability during the monsoon period. A comprehensive predictor screening for the seasonal classification indicates that the best performing predictor variables (e.g., surface pressure, meridional moisture fluxes) are closely related to the main processes of the West African Monsoon. In the second classification step, the stream function at 700 hPa for identifying troughs and ridges of tropical waves shows the highest performance, providing an added value to the overall performance of the classification. Thus, the new approach can better distinguish between dry and wet CPs during the rainy season. Moreover, CPs are identified that are of high relevance for daily heavy rainfall in the study area. The two wettest CPs caused roughly half of the extremes on about 6.5% of days. Both wettest patterns are characterized by an intensified Saharan Heat Low and a cyclonic rotation near the study area, indicating a tropical wave trough. Since the classification can be used to condition other statistical approaches used in climate sciences and other disciplines, the presented classification approach opens many different applications for the West African Monsoon region. [ABSTRACT FROM AUTHOR]

Climate model simulations typically exhibit a bias, which can be corrected using statistical approaches. In this study, a geostatistical approach for bias correction of daily precipitation at ungauged locations is presented. The method utilizes a double quantile mapping with dry day correction for future periods. The transfer function of the bias correction for the ungauged locations is established using distribution functions estimated by ordinary kriging with anisotropic variograms. The methodology was applied to the daily precipitation simulations of the entire CORDEX‐Africa ensemble for a study region located in the West African Sudanian Savanna. This ensemble consists of 23 regional climate models (RCM) that were run for three different future scenarios (RCP 2.6, RCP 4.5, and RCP 8.5). The evaluation of the approach for a historical 50‐year period (1950–2005) showed that the method can reduce the inherent strong precipitation bias of RCM simulations, thereby reproducing the main climatological features of the observed data. Moreover, the bias correction technique preserves the climate change signal of the uncorrected RCM simulations. However, the ensemble spread is increased due to an overestimation of the rainfall probability of uncorrected RCM simulations. The application of the bias correction method to the future period (2006–2100) revealed that annual precipitation increases for most models in the near (2020–2049) and far future (2070–2099) with a mean increase of up to 165mm⋅a−1 (18%). An analysis of the monthly and daily time series showed a slightly delayed onset and intensification of the rainy season. [ABSTRACT FROM AUTHOR]

Reliable long‐term observations from precipitation stations are often required for climatological studies but are strongly limited in many regions of the world. To improve this limitation for West Africa, we compiled daily and monthly observations from more than 20 national, continental and global databases, to establish a historical precipitation archive with a focus on four countries (Burkina Faso, Ghana, Benin and Togo). The new archive contains long‐term daily and monthly precipitation measurements from 1819 to 2013 for more than 1,000 sites. It is, therefore, the most comprehensive historical dataset with daily and monthly precipitation observations for this region. To produce a quality‐controlled and harmonized precipitation dataset for the focal region, various statistical algorithms have been implemented. These algorithms rely on straightforward geostatistical approaches (e.g., spatial correlograms) and corresponding statistical tests for identification and elimination of unreliable time series, in addition to various standard approaches used by global data centers. Although the quality control revealed various data errors and uncertainties for measurements and meta‐information (e.g., unit conversion errors, temporal offsets, frequent and long data gaps), a spatial interpolation using the quality‐controlled and harmonized dataset produced relatively reliable precipitation patterns for different target variables (e.g., monthly precipitation amount and daily precipitation probability). A major remaining challenge is providing free access to this database for research and other noncommercial purposes, due to national data protection regulations. However, several further tasks have been initiated and implemented (e.g., free provision of gridded precipitation datasets and point statistics) to improve the access and availability of station‐based precipitation observations and related data products for this climatologically challenging region. [ABSTRACT FROM AUTHOR]

The interaction between the land surface and the atmosphere is a crucial driver of atmospheric processes. Soil moisture and precipitation are key components in this feedback. Both variables are intertwined in a cycle, that is, the soil moisture – precipitation feedback for which involved processes and interactions are still discussed. In this study the soil moisture – precipitation feedback is compared for the sempiternal humid Ammer catchment in Southern Germany and for the semiarid to subhumid Sissili catchment in West Africa during the warm season, using precipitation datasets from the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), from the German Weather Service (REGNIE) and simulation datasets from the Weather Research and Forecasting (WRF) model and the hydrologically enhanced WRF‐Hydro model. WRF and WRF‐Hydro differ by their representation of terrestrial water flow. With this setup we want to investigate the strength, sign and variables involved in the soil moisture – precipitation feedback for these two regions. The normalized model spread between the two simulation results shows linkages between precipitation variability and diagnostic variables surface fluxes, moisture flux convergence above the surface and convective available potential energy in both study regions. The soil moisture – precipitation feedback is evaluated with a classification of soil moisture spatial heterogeneity based on the strength of the soil moisture gradients. This allows us to assess the impact of soil moisture anomalies on surface fluxes, moisture flux convergence, convective available potential energy and precipitation. In both regions the amount of precipitation generally increases with soil moisture spatial heterogeneity. For the Ammer region the soil moisture – precipitation feedback has a weak negative sign with more rain near drier patches while it has a positive signal for the Sissili region with more rain over wetter patches. At least for the observed moderate soil moisture values and the spatial scale of the Ammer region, the spatial variability of soil moisture is more important for surface‐atmosphere interactions than the actual soil moisture content. Overall, we found that soil moisture heterogeneity can greatly affect the soil moisture – precipitation feedback. [ABSTRACT FROM AUTHOR]

Background The terrestrial land surface in West Africa is made up several types of savanna ecosystems differing in land use changes which modulate gas exchanges between their vegetation and the overlying atmosphere. This study compares diurnal and seasonal estimates of CO2 fluxes from three contrasting ecosystems, a grassland, a mixture of fallow and cropland, and nature reserve in the Sudanian Savanna and relate them to water availability and land use characteristics. Results Over the study period, and for the three study sites, low soil moisture availability, high vapour pressure deficit and low ecosystem respiration were prevalent during the dry season (November to March), but the contrary occurred during the rainy season (May to October). Carbon uptake predominantly took place in the rainy season, while net carbon efflux occurred in the dry season as well as the dry to wet and wet to dry transition periods (AM and ND) respectively. Carbon uptake decreased in the order of the nature reserve, a mixture of fallow and cropland, and grassland. Only the nature reserve ecosystem at the Nazinga Park served as a net sink of CO2, mostly by virtue of a several times larger carbon uptake and ecosystem water use efficiency during the rainy season than at the other sites. These differences were influenced by albedo, LAI, EWUE, PPFD and climatology during the period of study. Conclusion These results suggest that land use characteristics affect plant physiological processes that lead to flux exchanges over the Sudanian Savanna ecosystems. It affects the diurnal, seasonal and annual changes in NEE and its composite signals, GPP and RE. GPP and NEE were generally related as NEE scaled with photosynthesis with higher CO2 assimilation leading to higher GPP. However, CO2 effluxes over the study period suggest that besides biomass regrowth, other processes, most likely from the soil might have also contributed to the enhancement of ecosystem respiration. [ABSTRACT FROM AUTHOR]

Ghana suffers from frequent power outages, which can be compensated by off-grid energy solutions. Photovoltaic-hybrid systems become more and more important for rural electrification due to their potential to offer a clean and cost-effective energy supply. However, uncertainties related to the prediction of electrical loads and solar irradiance result in inefficient system control and can lead to an unstable electricity supply, which is vital for the high reliability required for applications within the health sector. Model predictive control (MPC) algorithms present a viable option to tackle those uncertainties compared to rule-based methods, but strongly rely on the quality of the forecasts. This study tests and evaluates (a) a seasonal autoregressive integrated moving average (SARIMA) algorithm, (b) an incremental linear regression (ILR) algorithm, (c) a long short-term memory (LSTM) model, and (d) a customized statistical approach for electrical load forecasting on real load data of a Ghanaian health facility, considering initially limited knowledge of load and pattern changes through the implementation of incremental learning. The correlation of the electrical load with exogenous variables was determined to map out possible enhancements within the algorithms. Results show that all algorithms show high accuracies with a median normalized root mean square error (nRMSE) <0.1 and differing robustness towards load-shifting events, gradients, and noise. While the SARIMA algorithm and the linear regression model show extreme error outliers of nRMSE >1, methods via the LSTM model and the customized statistical approaches perform better with a median nRMSE of 0.061 and stable error distribution with a maximum nRMSE of <0.255. The conclusion of this study is a favoring towards the LSTM model and the statistical approach, with regard to MPC applications within photovoltaic-hybrid system solutions in the Ghanaian health sector. [ABSTRACT FROM AUTHOR]

Seasonal forecasts for monsoonal rainfall characteristics like the onset of the rainy seasons (ORS) are crucial for national weather services in semi-arid regions to better support decision-making in rain-fed agriculture. In this study an approach for seasonal forecasting of the ORS is proposed using precipitation information from a global seasonal ensemble prediction system. It consists of a quantile–quantile-transformation for eliminating systematic differences between ensemble forecasts and observations, a fuzzy-rule based method for estimating the ORS date and graphical methods for an improved visualization of probabilistic ORS forecasts. The performance of the approach is tested for several climate zones (the Sahel, Sudan and Guinean zone) in West Africa for a period of eleven years (2000 to 2010), using hindcasts from the Seasonal Forecasting System 4 of ECMWF. We indicated that seasonal ORS forecasts can be skillful for individual years and specific regions (e.g., the Guinean coasts), but also associated with large uncertainties. A spatial verification of the ORS fields emphasizes the importance of selecting appropriate performance measures (e.g., the anomaly correlation coefficient) to avoid an overestimation of the forecast skill. The graphical methods consist of several common formats used in seasonal forecasting and a new index-based method for a quicker interpretation of probabilistic ORS forecast. The new index can also be applied to other seasonal forecast variables, providing an important alternative to the common forecast formats used in seasonal forecasting. Moreover, the forecasting approach proposed in this study is not computationally intensive and is therefore operational applicable for forecasting centers in tropical and subtropical regions where computing power and bandwidth are often limited. [ABSTRACT FROM AUTHOR]

• Assessment of the performance of WRF-Solar model in 3 AWS in Ghana. • WRF-Solar model performs better in clear skies, uncertainties arise under cloudy conditions. • Poor performance of WRF-Solar under high aerosol levels. • Notable Biases in CFC Predictions from the WRF-Solar model. • WRF-Solar outperforms in predicting 72-hour GHI at Kologo under clear skies. Accurate global horizontal irradiance (GHI) forecasting is critical for integrating solar energy into the power grid and operating solar power plants. The Weather Research and Forecasting model with its solar radiation extension (WRF-Solar) has been used to forecast solar irradiance in different regions around the world. However, the application of the WRF-Solar model to the prediction of GHI in West Africa, particularly Ghana, has not yet been investigated. The aim of this study is to evaluate the performance of the WRF-Solar model for predicting GHI in Ghana, focusing on three automatic weather stations (Akwatia, Kumasi and Kologo) for the year 2021. We used two one-way nested domains (D1 = 15 km and D2 = 3 km) to investigate the ability of the fully coupled WRF-Solar model to forecast GHI up to 72-hour ahead under different atmospheric conditions. The initial and lateral boundary conditions were taken from the ECMWF high-resolution operational forecasts. Our findings reveal that the WRF-Solar model performs better under clear skies than cloudy skies. Under clear skies, Kologo performed best in predicting 72-hour GHI, with a first day nRMSE of 9.62 %. However, forecasting GHI under cloudy skies at all three sites had significant uncertainties. Additionally, WRF-Solar model is able to reproduce the observed GHI diurnal cycle under high AOD conditions in most of the selected days. This study enhances the understanding of the WRF-Solar model's capabilities and limitations for GHI forecasting in West Africa, particularly in Ghana. The findings provide valuable information for stakeholders involved in solar energy generation and grid integration towards optimized management in the region. [ABSTRACT FROM AUTHOR]

Estimates of global horizontal irradiance (GHI) from reanalysis and satellite-based data are the most important information for the design and monitoring of PV systems in Africa, but their quality is unknown due to the lack of in situ measurements. In this study, we evaluate the performance of hourly GHI from state-of-the-art reanalysis and satellite-based products (ERA5, MERRA-2, CAMS, and SARAH-2) with 37 quality-controlled in situ measurements from novel meteorological networks established in Burkina Faso and Ghana under different weather conditions for the year 2020. The effects of clouds and aerosols are also considered in the analysis by using common performance measures for the main quality attributes and a new overall performance value for the joint assessment. The results show that satellite data performs better than reanalysis data under different atmospheric conditions. Nevertheless, both data sources exhibit significant bias of more than 150 W/m2 in terms of RMSE under cloudy skies compared to clear skies. The new measure of overall performance clearly shows that the hourly GHI derived from CAMS and SARAH-2 could serve as viable alternative data for assessing solar energy in the different climatic zones of West Africa. [ABSTRACT FROM AUTHOR]

• Bias correction (BC) methods can better represent the spatial patterns of the climate and derived agricultural impacts. • Uncertainties from BC can be twice as high compared to the uncertainties from the GCM-RCM combinations. • Climate change impacts studies (CCIS) without BC or relying on any single BC approach should be interpreted with great caution. • In CCIS, a BC ensemble is suggested for generating climate projections following a super-ensemble probabilistic assessment. Many open questions and unresolved issues surround the topic of bias correction (BC) in climate change impact studies (CCIS). One question relates to the contribution of downscaling of climate change scenarios on the uncertainties in results obtained using impact models for agriculture. In particular, for large area or regional agricultural impact assessments, the question of bias correction is of high relevance. Relatively few studies exist looking at the quantification of the impacts of BC methods in general circulation model (GCM) and regional climate model (RCM) data on results of such impact studies. Here, we quantify the impact of different BC methods on temperature (T) and precipitation (P) from different CORDEX GCM-RCM combinations, and how the debiased T&P signal may propagate through agricultural impact models. Specifically, we estimate the impact of BC on (i) an empirical P- and fuzzy rule-based algorithm to estimate the crop planting date, and (ii) a mechanistic T&P-based approach to quantify the crop suitability index (CSI) for the main staple crops in West Africa (i.e. groundnut, maize, pearl millet, sorghum). Both approaches serve as a proxy for more complex process-based ecophysiological crop models. Depending on the choice of the BC method, the uncertainties in the assessment of the CSI can be more than twice as high compared to the uncertainties from the GCM-RCM model selection. Comparing the estimated CSI values with observed harvest area, it is found that BC generally improves the performance for models with low hit rates (< 30 –35%), but decreases the performance for models with relatively high hit rates (> 35 %). Such consequences can also be expected for process-based crop models, which are developed to operate on field-scale but are driven by coarser scale RCMs. It is concluded that such agriculturally oriented climate impact models as investigated here should be interpreted with great caution if applied without BC or relying on a single BC approach only. Rather, we suggest to include different BC approaches in the generation of climate projections for CCIS and quantify their uncertainties following a super-ensemble probabilistic assessment. [ABSTRACT FROM AUTHOR]