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Abstract: Scientifically sound decisions in sustainable water management are usually based on hydrological modeling which can only be accomplished by meteorological driving information. Especially in regions with weak infrastructure this task is hampered by limited hydro-meteorological information in sufficient spatial and temporal resolution. We investigated three approaches to provide required meteorological fields driving the distributed hydrological model: the results of the mesoscale meteorological model MM5 which are available near real time, the TRMM product 3B42 available with approximately one month delay, and station data available with a delay of one year or more. The study site is the White Volta catchment in the semi-arid environment of West Africa. The results for 2004 show that the meteorological model is able to provide meteorological input data for near real time water balance estimations. In this study the TRMM product does not improve the simulation results. Besides missing important meteorological data, also gridded information on land surface properties (albedo, LAI, etc.) is usually difficult to obtain, albeit it is an essential input for distributed hydrological models. This information is commonly taken from “static” tables depending on the land use. Satellite remote sensing provides worldwide spatially detailed information on land surface properties which is particularly suitable for large regions in remote settings. Therefore the MODIS products for albedo and LAI were processed to annual time series including the identification and replacement of low quality observations by interpolation. The impact using MODIS data on the spatial distribution of water balance variables occurs mainly on local scale. The hydrological simulations using MODIS LAI and albedo values result in higher annual evapotranspiration and lower total discharge sums for 2004. Altogether it is concluded that hydrological decision support systems in regions with weak infrastructure can benefit significantly from the integration of atmospheric modeling and satellite-derived land surface data. [Copyright &y& Elsevier]

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]

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]

This research examines the suitability of four land surface schemes (LSSs) in Weather Research and Forecasting (WRF) model over different West Africa's (WA) climatological zones, namely Guinea, Savanna, and Sahel. The four LSSs include Noah, Noah (Multiparameterization) MP, Noah MP with the groundwater option (Noah GW), and Community Land Model Version 4 (CLM4). A 3-month simulation was carried out with each LSS during July–September 2012. Temperature and dew point temperature (Dpt) were evaluated using the ERA5 dataset, while precipitation was evaluated using the TRMM product. Based on the three variables, CLM4 is most suitable for the Guinea zone; Noah, Noah MP, and CLM4 perform equally well for Savanna; and Noah MP is most suitable for the Sahel zone. In general, and over all zones, Noah MP is most suitable for precipitation simulation, while CLM4 is the best for dew point temperature as Noah MP and Noah GW are equally suitable for 2 m temperature. Noah GW overestimates surface moisture, altering surface fluxes, increasing evaporative fraction, and increasing convective activities (especially in semi-arid areas), which led to a significant bias in temperature and precipitation. Also, over the Savanna and Sahel zone, a strong African Easterly Jet (AEJ) with a weak Tropical Easterly Jet (TEJ) in Noah led to lower rainfall. In contrast, weak AEJ with strong TEJ in Noah MP and Noah GW caused higher rainfall. Further studies would be to evaluate the sensitivity of each LSS under different initial conditions, and their land-atmosphere interactions strength over the zones. [ABSTRACT FROM AUTHOR]

We present high-resolution rainfall maps from commercial microwave link (CML) data in the city of Ouagadougou, Burkina Faso. Rainfall was quantified based on data from 100 CMLs along unique paths and interpolated to achieve rainfall maps with a 5-min temporal and 0.55-km spatial resolution for the monsoon season of 2020. Established processing methods were combined with newly developed filtering methods, minimizing the loss of data availability. The rainfall maps were analyzed qualitatively both at a 5-min and aggregated daily scales. We observed high spatiotemporal variability on the 5-min scale that cannot be captured with any existing measurement infrastructure in West Africa. For the quantitative evaluation, only one rain gauge with a daily resolution was available. Comparing the gauge data with the corresponding CML rainfall map pixel showed a high agreement, with a Pearson correlation coefficient > 0.95 and an underestimation of the CML rainfall maps of ∼10%. Because the CMLs closest to the gauge have the largest influence on the map pixel at the gauge location, we thinned out the CML network around the rain gauge synthetically in several steps and repeated the interpolation. The performance of these rainfall maps dropped only when a radius of 5 km was reached and approximately one-half of all CMLs were removed. We further compared ERA5 and GPM IMERG data with the rain gauge and found that they had much lower correlation than data from the CML rainfall maps. This clearly highlights the large benefit that CML data can provide in the data-scarce but densely populated African cities. Significance Statement: In this study, we investigate the possibility of deriving accurate high-resolution rainfall maps from commercial microwave link (CML) data in West Africa. The main challenges are the lack of reference data in this area and the adoption of existing processing tools without reference data. We show CML rainfall maps for Ouagadougou, Burkina Faso, with a resolution of 5 min and 0.55 km, which is unprecedented in this region. The comparison with the only available rain gauge, which provides data only at a daily resolution, yields a Pearson correlation of >0.95. An analysis of synthetically thinned-out networks shows that this accuracy is valid for the whole domain. Comparing reanalysis and satellite data with the rain gauge and CML data showed a poor performance of these gridded reference datasets. Also, a high coincidence of temporal dynamics between CML rainfall maps and satellite products was observed. Overall, these findings support the potential of CMLs for future hydrometeorological applications in West Africa. [ABSTRACT FROM AUTHOR]

The number of solar power plants has increased in West Africa in recent years. Reliable reanalysis data and short-term forecasting of solar irradiance from numerical weather prediction models could provide an economic advantage for the planning and operation of solar power plants, especially in data-poor regions such as West Africa. This study presents a detailed assessment of different shortwave (SW) radiation schemes from the Weather Research and Forecasting (WRF) Model option Solar (WRF-Solar), with appropriate configurations for different atmospheric conditions in Ghana and the southern part of Burkina Faso. We applied two 1-way nested domains (D1 = 15 km and D2 = 3 km) to investigate four different SW schemes, namely, the Community Atmosphere Model, Dudhia, RRTMG, Goddard, and RRTMG without aerosol and with aerosol inputs (RRTMG_AERO). The simulation results were validated using hourly measurements from different automatic weather stations established in the study region in recent years. The results show that the RRTMG_AERO_D01 generally outperforms the other SW radiation schemes to simulate global horizontal irradiance under all-sky condition [RMSE = 235 W m−2 (19%); MAE = 172 W m−2 (14%)] and also under cloudy skies. Moreover, RRTMG_AERO_D01 shows the best performance on a seasonal scale. Both the RRTMG_AERO and Dudhia experiments indicate a good performance under clear skies. However, the sensitivity study of different SW radiation schemes in the WRF-Solar model suggests that RRTMG_AERO gives better results. Therefore, it is recommended that it be used for solar irradiance forecasts over Ghana and the southern part of Burkina Faso. [ABSTRACT FROM AUTHOR]

Using 2001 and 2016 land use (LU) data and three developed deforestation LU scenarios, namely Deforest I, II and III, the possible impacts of the ongoing land use land cover change (LULCC) over West Africa (WA) and the uncertainty regarding the impacts on regional climate were assessed. Twelve Weather Research and Forecasting (WRF) model simulations were configured with the default and integrated MODIS satellite dataset for two sets of 3 months (December–February DJF 2011–2012 and July–September JAS 2012) over WA. Between 2001 and 2016, changes that occurred in precipitation, temperature and dew point temperature due to LULCC are mostly not significant at the 95% level with a decrease of 0.8, 0.0 and 0.1%, respectively. Deforest I, II and III caused significant changes with an increase of 44.4, 44.7, 41.5%; 0.8, 0.9, 1.1%; and 0.3, 0.1, −0.2%, respectively, for precipitation, temperature and dew point temperature. Precipitation was lower in the simulation with 2016 LU than it was with 2001 LU, which may have also been influenced by the reforestation that occurred between the periods. However, Deforest III resulted in a widespread nonsignificant rise in precipitation, as the modified areas showed a significant decrease during JAS period. Areas where forests were converted into barren lands revealed severe increase in the surface albedo which is a strong factor that its alteration is capable of influencing precipitation and other variables over WA. LULCC also altered the atmospheric column by modifying the strength of the African Easterly Jet (AEJ), Tropical Easterly Jet (TEJ), wind speed, and monsoonal, vertical, meridional and zonal winds. Heat related fields, such as dew point and air temperature, and moisture fields such as relative humidity were also modified by LULCC. Therefore, as WA reacts strongly to LULCC, sustainable land management practices should be encouraged to control its adverse effects. [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]

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]

• 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]

The Volta Basin in West Africa is a region sensitive to water shortage. Future climate conditions therefore may put additional stress on the competition for the scarce water resources between industry, agriculture, and households. For an investigation of the sensitivity of the hydrological regime to global climate change in the data-sparse and poorly gauged region of the Volta Basin, joint regional climate-hydrology simulations were performed. MM5 was used as a regional climate model to downscale two time slices of a global ECHAM4 simulation to a resolution of 9 km. These regional climate simulations were used to drive a physically based, distributed hydrological model at 1 km resolution. The performance of the model components and the joint model system was evaluated for different historical periods. Results show that discharge in the Volta Basin reacts highly sensitively to precipitation differences. The pronounced rainfall decrease at the beginning of the rainy season is not transferred to discharge changes. During the rainy season most of the surplus rainfall evaporates due to a strong increase in evaporation as a consequence of higher near-surface air temperatures. The average change signal in precipitation, as well as surface and subsurface hydrology variables, lies in most variables within the range of inter-annual variability, but regionally stronger signals are also observed. [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]

A 26-year simulation (1980-2005) was performed with the Weather Research and Forecast (WRF) model over the Volta Basin in West Africa. This was to investigate the ability of a climate version of WRF to reproduce present day temperature and precipitation over the Volta Basin. The ERA-Interim reanalysis and one realization of the ECHAM6 global circulation model (GCM) data were dynamically downscaled using two nested domains within the WRF model. The outer domain had a horizontal resolution of 50 km and covered the whole of West Africa while the inner domain had a horizontal resolution of 10 km. It was observed that biases in the respective forcing data were carried over to the RCM, but also the RCM itself contributed to the mean bias of the model. Also, the biases in the 50-km domain were transferred unchanged, especially in the case of temperature, to the 10-km domain, but, for precipitation, the higher-resolution simulations increased the mean bias in some cases. While in general, WRF underestimated temperature in both the outer (mean biases of −1.6 and −2.3 K for ERA-Interim and ECHAM6, respectively) and the inner (mean biases of −0.9 K for the reanalysis and −1.8 K for the GCM) domains, WRF slightly underestimated precipitation in the coarser domain but overestimated precipitation in the finer domain over the Volta Basin. The performance of the GCM, in general, is good, particularly for temperature with mean bias of −0.7 K over the outer domain. However, for precipitation, the added value of the RCM cannot be overlooked, especially over the whole West African region on the annual time scale (mean biases of −3% for WRF and −8% for ECHAM6). Over the whole Volta Basin and the Soudano-Sahel for the month of April and spring (MAM) rainfall, respectively, mean bias close to 0% was simulated. Biases in the interannual variability in both temperature and precipitation over the basin were smaller in the WRF than the ECHAM6. High spatial pattern correlations between 0.7 and 0.8 were achieved for the autumn precipitation and low spatial correlation in the range of 0.0 and 0.2 for the winter season precipitation over the whole basin and all the three belts over the basin. [ABSTRACT FROM AUTHOR]

Climate change and constant population growth pose severe challenges to 21st century rural Africa. Within the framework of the West African Science Service Center on Climate Change and Adapted Land Use (WASCAL), an ensemble of high-resolution regional climate change scenarios for the greater West African region is provided to support the development of effective adaptation and mitigation measures. This contribution presents the overall concept of the WASCAL regional climate simulations, as well as detailed information on the experimental design, and provides information on the format and dissemination of the available data. All data are made available to the public at the CERA long-term archive of the German Climate Computing Center (DKRZ) with a subset available at the PANGAEA Data Publisher for Earth & Environmental Science portal (https://doi.pangaea.de/10.1594/PANGAEA.880512). A brief assessment of the data are presented to provide guidance for future users. Regional climate projections are generated at high (12 km) and intermediate (60 km) resolution using the Weather Research and Forecasting Model (WRF). The simulations cover the validation period 1980-2010 and the two future periods 2020-2050 and 2070-2100. A brief comparison to observations and two climate change scenarios from the Coordinated Regional Downscaling Experiment (CORDEX) initiative is presented to provide guidance on the data set to future users and to assess their climate change signal. Under the RCP4.5 (Representative Concentration Pathway 4.5) scenario, the results suggest an increase in temperature by 1.5 °C at the coast of Guinea and by up to 3 °C in the northern Sahel by the end of the 21st century, in line with existing climate projections for the region. They also project an increase in precipitation by up to 300mm per year along the coast of Guinea, by up to 150mm per year in the Soudano region adjacent in the north and almost no change in precipitation in the Sahel. This stands in contrast to existing regional climate projections, which predict increasingly drier conditions. The high spatial and temporal resolution of the data, the extensive list of output variables, the large computational domain and the long time periods covered make this data set a unique resource for follow-up analyses and impact modelling studies over the greaterWest African region. The comprehensive documentation and standardisation of the data facilitate and encourage their use within and outside of the WASCAL community. [ABSTRACT FROM AUTHOR]

Climate change and constant population growth pose severe challenges to 21st century rural Africa. Within the framework of the West African Science Service Center on Climate Change and Adapted Land Use (WASCAL), an ensemble of high-resolution regional climate change scenarios for the greater West African region are provided to support the development of effective adaptation and mitigation measures. This contribution presents the overall concept of the WASCAL regional climate simulations as well as detailed information on the experiment design, and provides information on the format and dissemination of the available data. All data is made available to the public at the CERA long-term archive of the German Climate Computing Center (DKRZ) with a subset available at the PANGAEA Data Publisher for Earth & Environmental Science portal (https://doi.pangaea.de/10.1594/PANGAEA.880512). Regional climate projections are generated at high (12 km) and intermediate (60 km) resolution using the Weather Research & Forecasting Model (WRF). The simulations cover the validation period 1980-2010 and the two future periods 2020-2050 and 2070-2100. A brief comparison to observations and two climate change scenarios from the CORDEX initiative is presented to provide guidance on the data set to future users and to assess their climate change signal. Under the RCP4.5 scenario, the results suggest an increase in temperature by 1.5 °C at the Coast of Guinea and by up to 3 °C in the northern Sahel by the end of the 21st century, in line with existing climate projections for the region. They also project an increase in precipitation by up to 300 mm per year along the Coast of Guinea, by up to 150 mm per year in the Soudano region adjacent in the North, and almost no change in precipitation in the Sahel. This stands in contrast to existing regional climate projections, which predict increasingly drier conditions. The high spatial and temporal resolution of the data, the extensive list of output variables, the large computational domain and the long time periods covered make this data set a unique resource for follow-up analyses and impact modelling studies over the greater West African region. The comprehensive documentation and standardisation of the data facilitate and encourage its use within and outside of the WASCAL community. [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]

Abstract: Savannahs are highly dynamic ecosystems but many of their properties and the related balances of energy, carbon, nitrogen, and water are still poorly understood. A particular scientific issue is the quantification of trace gases emitted from the soil of savannah ecosystems and their interaction with regional and global climate and air chemistry. Therefore it is important to develop and evaluate land-surface models that on the one hand represent vegetation and soil dynamics and on the other hand provide energy and water fluxes in a temporal resolution suitable for the application in climate/air chemistry models. In this paper, we present a consistent coupling between a common land-surface model (OSU) and a widely used biogeochemical model (DNDC) that is a first step for a full coupling of climate/air chemistry and biogeochemical processes. For consistency reasons, both models are linked to a general physiologically based plant model to provide the physical boundary conditions as well as the carbon and nitrogen in- and output variables. Evaluation is carried out with measurements of soil temperature, latent heat flux, soil water content, and soil emission data from two vegetation periods collected at a natural grassland site in Bontioli Nature Reserve, Burkina Faso (Africa). The results demonstrate that simulations of biogeochemical processes based on soil environmental conditions, calculated either with the land-surface model or with the unchanged biogeochemical model, do not differ significantly from each other. The OSU model simulates more realistic day-to-day variation of soil temperature as DNDC but the sensitivity of the biogeochemical simulation to this variation is small. In contrast, the sensitivity to differences in soil water content is high, but simulation results of both models are very similar on the daily scale and hardly depend on spatial soil resolution. [Copyright &y& Elsevier]

• 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]

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]

It is expected that global climate change will increase the probability of extreme hydrometeorological events like droughts and floods. In order to adapt to this changing climate and to maintain basic water-, power-, and food supply, especially water-scarse regions require reliable knowledge about the available freshwater resources and potential extreme events during the next season. However, the headwaters of semi-arid catchments are often characterized by complex terrain and strong topographic gradients. To predict seasonal extreme events in such regions, hydrological models require high-resolution forecasts of precipitation and temperature. A 35 km horizontal resolution of the latest global seasonal forecast products is not suited for regional hydrological applications in such catchments. Therefore, it is mandatory to improve the spatial resolution of the hydrometeorological information to better represent the regional conditions in river basins with complex terrain. In particular, if such information is used as driving data in a one-way coupled model chain with subsequent hydrological models, a high-resolution and high-quality meteorological input is a key factor.In this study, we demonstrate the concept of achieving this improved and regionalized meteorological input. For eight river basins in semi-arid regions like Iran, Brazil, Sudan, and West-Africa, we perform a dynamical downscaling of the global atmospheric fields with the use of the Weather Research and Forecasting (WRF) model. The best possible regionalization of the global fields is identified by testing up to 32 parameterization combinations of the WRF model for different representations of subgrid-scale convection, cloud microphysics, radiation physics and boundary layer physics for each individual region. For these configuration runs we use ERA-Interim re-analyses which also serve as the initialization for the latest seasonal predictions of the European Centre for Medium-Range Weather Forecasts (ECMWF SEAS5) re-forecasts. Five consecutive years for each of the 32 parameterization combinations are simulated and their performance is analyzed with the use of local station data for identifying an optimized model setup for each region. Preliminary results already indicate that this identified configuration does not only depend on the performance of the modeled meteorological information, but also on the required computation time that strongly differs between the individual configurations. With this optimized WRF setup for each region, we finally downscale the global SEAS5 forecasts and thereby provide the regionalized input for the subsequent hydrological model systems. Our study is the first step towards an one-way coupled model chain that finally allows to provide high-resolution hydrometeorological and hydrological forecasts for the next season. In the end, this derived information has the potential to significantly improve the regional water management, especially in semi-arid regions and in the context of extreme events like e.g. droughts. [ABSTRACT FROM AUTHOR]

West Africa's Sudanian savannas are one of Africa's high-potential "bread baskets", where surplus food (e.g. millet, sorghum, cowpea and livestock) is being produced. These ecosystems currently undergo pronounced land use changes and will likely face shorter rainy seasons with more extreme rainfall and droughts in the future. This could have serious impacts on the vegetation and its carbon dioxide (CO2) exchange with potentially increasing CO2 emissions accelerating climate warming. Understanding how the CO2 fluxes in this area respond to land use changes and environmental fluctuations, in particular rain events, is essential to design sustainable land use strategies. However, this understanding is hampered by a limited data availability. In this study, we monitored net ecosystem exchange (NEE) of CO2, rainfall and other weather and vegetation parameters during four years (2013-2016) at three savanna sites in southern Burkina Faso and northern Ghana. To this end, a monitoring network of three eddy-covariance stations and nine automatic weather stations was established. The three savannas were characterized by different vegetation due to different land use: i) woody and nearly pristine, ii) a mixture of cropland and grassland and iii) grassland under intensive grazing. Other site characteristics such as climate, soil, or topography chosen were as similar as possible. The impact of rain events on CO2 exchange for these three contrasting ecosystems was analyzed for single rain events (short-term) and on a yearly time scale (long-term). We found that the pristine savanna site was a prominent sink of CO2 (-864 to 605 g CO2 m-2 y-1) while the two degraded sites were net CO2 sources (118 to 605 g CO2 m-2 y-1) with a complex relation with annual rainfall amounts. The NEE responses to single rain events revealed that daytime rain systematically decreased the sink strengths at all sites, which might be associated with decreased light availability. At the degraded sites, additional factors increasing CO2 losses were rain duration and dry spell length. The observed patterns of immediate CO2 flux responses to rainfall at differently used savannas indicate strong internal feedbacks between vegetation and land use changes and raise the question whether the CO2 sink potentials might be overestimated with possible implications for global CO2 budgets. Our findings also suggest that carbon sequestration needs to be taken into account when designing sustainable land-use strategies. [ABSTRACT FROM AUTHOR]

Large-scale experimental sites for studying Critical Zone processes are relatively rare for savannas. However, they are essential for a better understanding of environmental changes and their impacts on ecosystem functioning and services. This study gives an overview of a novel field observatory which was established for mesoscale watersheds in the Sudan Savanna near the border of Burkina Faso and Ghana as part of the WASCAL (West African Science Service Centre on Climate Change and Adapted Land Use) programme. The study area is characterized by severe land cover changes due to a strongly increasing demand of agricultural land. The observatory is designed for long-term measurements of selected eco-hydrological processes in fine temporal resolution focusing on land-atmosphere exchange processes. It consists of a network of automatic weather stations, agro-meteorological stations, eddy covariance towers and various hydrological measurement devices, which are complemented by long-term historical measurements from national meteorological and hydrological networks. A unique component of the observatory is a micrometeorological experiment using the eddy covariance towers which is conducted since the initiation of the observatory in 2013. The experiment was implemented at three contrasting sites with different vegetation cover and contrasting land use practices (pristine savanna, mixture of cropland, degraded grassland) to investigate the impact of land use changes on water, energy, and CO2 fluxes. Moreover, the datasets of the new observatory are the basis of various field, data analysis and modelling studies conducted by other disciplines (i.e. agriculture, biology, ecology, remote sensing) in this region and are made available via a new database, the WASCAL Data Discovery Portal. The new large-scale field experiment also forms an excellent platform for future field campaigns and long-term experimental sites and can be used as foundation for an observatory that addresses key processes and components of the Critical Zone. In this presentation we give a detailed overview about the new field observatory with its different components and its advantages and drawbacks in comparison to other large-scale experimental sites for savanna ecosystems (e.g. AMMA catch observatory). In addition, recent outcomes from the long-term micro-meteorological field experiment are shown. Finally, the current challenges and future perspectives are addressed for operating this type of observatory in this region. [ABSTRACT FROM AUTHOR]