Malaria is a major public health problem in West Africa. Previous studies have shown that climate variability significantly affects malaria transmission. The lack of continuous observed weather station data and the absence of surveillance data for malaria over long periods have led to the use of reanalysis data to drive malaria models. In this study, we use the Liverpool Malaria Model (LMM) to simulate spatiotemporal variability of malaria in West Africa using daily rainfall and temperature from the following: Twentieth Century Reanalysis (20th CR), National Center for Environmental Prediction (NCEP), European Centre for Medium-Range Weather Forecasts (ECMWF) Atmospheric Reanalysis of the Twentieth Century (ERA20C), and interim ECMWF Re-Analysis (ERA-Interim). Malaria case data from the national surveillance program in Senegal are used for model validation between 2001 and 2016. The warm temperatures found over the Sahelian fringe of West Africa can lead to high malaria transmission during wet years. The rainfall season peaks in July to September over West Africa and Senegal, and the malaria season lasts from September to November, about 1–2 months after the rainfall peak. The long-term trends exhibit interannual and decadal variabilities. The LMM shows acceptable performance in simulating the spatial distribution of malaria incidence. However, some discrepancies are found. These results are useful for decision-makers who plan public health and control measures in affected West African countries. The study would have substantial implications for directing malaria surveillance activities and health policy. In addition, this malaria modeling framework could lead to the development of an early warning system for malaria in West Africa.

The analysis of the spatial and temporal variability of climate parameters is crucial to study the impact of climate-sensitive vector-borne diseases such as malaria. The use of malaria models is an alternative way of producing potential malaria historical data for Senegal due to the lack of reliable observations for malaria outbreaks over a long time period. Consequently, here we use the Liverpool Malaria Model (LMM), driven by different climatic datasets, in order to study and validate simulated malaria parameters over Senegal. The findings confirm that the risk of malaria transmission is mainly linked to climate variables such as rainfall and temperature as well as specific landscape characteristics. For the whole of Senegal, a lag of two months is generally observed between the peak of rainfall in August and the maximum number of reported malaria cases in October. The malaria transmission season usually takes place from September to November, corresponding to the second peak of temperature occurring in October. Observed malaria data from the Programme National de Lutte contre le Paludisme (PNLP, National Malaria control Programme in Senegal) and outputs from the meteorological data used in this study were compared. The malaria model outputs present some consistencies with observed malaria dynamics over Senegal, and further allow the exploration of simulations performed with reanalysis data sets over a longer time period. The simulated malaria risk significantly decreased during the 1970s and 1980s over Senegal. This result is consistent with the observed decrease of malaria vectors and malaria cases reported by field entomologists and clinicians in the literature. The main differences between model outputs and observations regard amplitude, but can be related not only to reanalysis deficiencies but also to other environmental and socio-economic factors that are not included in this mechanistic malaria model framework. The present study can be considered as a validation of the reliability of reanalysis to be used as inputs for the calculation of malaria parameters in the Sahel using dynamical malaria models.

The impact of desert aerosols on climate, atmospheric processes, and the environment is still debated in the scientific community. The extent of their influence remains to be determined and particularly requires a better understanding of the variability of their distribution. In this work, we studied the variability of these aerosols in western Africa using different types of satellite observations. SeaWiFS (Sea-Viewing Wide Field-of-View Sensor) and OMI (Ozone Monitoring Instrument) data have been used to characterize the spatial distribution of mineral aerosols from their optical and physical properties over the period 2005–2010. In particular, we focused on the variability of the transition between continental western African and the eastern Atlantic Ocean. Data provided by the lidar scrolling CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) onboard the satellite CALIPSO (Cloud Aerosol Lidar and Infrared Pathfinder Satellite Observations) for the period 2007–2013 were then used to assess the seasonal variability of the vertical distribution of desert aerosols. We first obtained a good representation of aerosol optical depth (AOD) and single-scattering albedo (SSA) from the satellites SeaWiFS and OMI, respectively, in comparison with AERONET estimates, both above the continent and the ocean. Dust occurrence frequency is higher in spring and boreal summer. In spring, the highest occurrences are located between the surface and 3 km above sea level, while in summer the highest occurrences are between 2 and 5 km altitude. The vertical distribution given by CALIOP also highlights an abrupt change at the coast from spring to fall with a layer of desert aerosols confined in an atmospheric layer uplifted from the surface of the ocean. This uplift of the aerosol layer above the ocean contrasts with the winter season during which mineral aerosols are confined in the atmospheric boundary layer. Radiosondes at Dakar Weather Station (17.5° W, 14.74° N) provide basic thermodynamic variables which partially give a causal relationship between the layering of the atmospheric circulation over western Africa and their aerosol contents throughout the year. A SSA increase is observed in winter and spring at the transition between the continent and the ocean. The analysis of mean NCEP (National Centers for Environmental Prediction) winds at 925 hPa between 2000 and 2012 suggest a significant contribution of coastal sand sources from Mauritania in winter which would increase SSA over the ocean.

Land Use Land-Cover Change (LULCC), such as deforestation, affects the climate system and land-atmosphere interactions. Using simulations carried out within the LUCID (Land Use and Climate, IDentification of robust Impacts) project framework, we first quantify the role of historical land-cover change induced by human activities on surface climate in West Africa. Focusing on two contrasted African regions, we find that climate responses of land-use changes are small but they are still statistically significant. In Western Sahel, a statistically significant near-surface atmospheric cooling and a decrease in water recycling are simulated in summer in response to LULCC. Over the Guinean zone, models simulate a significant decrease in precipitation and water recycling in autumn in response to LULCC. This signal is comparable in magnitude with the effect induced by the increase in greenhouse gases. Simulated climate changes due to historical LULCC could however be underestimated because: (i) the prescribed LULCC can be underestimated in those regions; (ii) the climate models underestimate the coupling strength between West African surface climate and leaf area index (LAI) and (iii) the lack of interactive LAI in some models. Finally, our study reveals indirect atmospheric processes triggered by LULCC. Over the Western Sahel, models reveal that a significant decrease in solar reflection tend to cool down the surface and thus counteract the atmospheric feedback. Conversely, over the Guinea zone, models reveal that the indirect atmospheric processes and turbulent heat fluxes dominate the climatic responses over the direct effects of LULCC.

The West African seaboard is one of the upwelling sectors that has received the least attention, and in situ observations relevant to its dynamics are particularly scarce. The current system in this sector is not well known and understood, e.g., in terms of seasonal variability, across-shore structure, and forcing processes. This knowledge gap is addressed in two studies that analyze the mean seasonal cycle of an eddy-permitting numerical simulation of the tropical Atlantic. Part 1 is concerned with the circulation over the West African continental slope at the southernmost reach of the Canary Current system, between ∼ 8 and 20° N. The focus is on the depth range most directly implicated in the wind-driven circulation (offshore and coastal upwellings and Sverdrup transport) located above the potential density σ_t = 26.7 kg m⁻³ in the model (approx. above 250 m of depth). In this sector and for this depth range, the flow is predominantly poleward as a direct consequence of positive wind stress curl forcing, but the degree to which the magnitude of the upper ocean poleward transport reflects Sverdrup theory varies with latitude. The model poleward flow also exhibits a marked semiannual cycle with transport maxima in spring and fall. Dynamical rationalizations of these characteristics are offered in terms of wind forcing of coastal trapped waves and Rossby wave dynamics. Remote forcing by seasonal fluctuations of coastal winds in the Gulf of Guinea plays an instrumental role in the fall intensification of the poleward flow. The spring intensification appears to be related to wind fluctuations taking place at shorter distances north of the Gulf of Guinea entrance and also locally. Rossby wave activity accompanying the semiannual fluctuations of the poleward flow in the coastal waveguide varies greatly with latitude, which in turn exerts a major influence on the vertical structure of the poleward flow. Although the realism of the model West African boundary currents is difficult to determine precisely, the present in-depth investigation provides a renewed framework for future observational programs in the region.

This study focuses on the ability of the global Land Data Assimilation System, LDAS-Monde, to improve the representation of land surface variables (LSVs) over Burkina-Faso through the joint assimilation of satellite derived surface soil moisture (SSM) and leaf area index (LAI) from January 2001 to June 2018. The LDAS-Monde offline system is forced by the latest European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis ERA5 as well as ERA-Interim former reanalysis, leading to reanalyses of LSVs at 0.25° × 0.25° and 0.50° × 0.50° spatial resolution, respectively. Within LDAS-Monde, SSM and LAI observations from the Copernicus Global Land Service (CGLS) are assimilated with a simplified extended Kalman filter (SEKF) using the CO2-responsive version of the ISBA (Interactions between Soil, Biosphere, and Atmosphere) land surface model (LSM). First, it is shown that ERA5 better represents precipitation and incoming solar radiation than ERA-Interim former reanalysis from ECMWF based on in situ data. Results of four experiments are then compared: Open-loop simulation (i.e., no assimilation) and analysis (i.e., joint assimilation of SSM and LAI) forced by either ERA5 or ERA-Interim. After jointly assimilating SSM and LAI, it is noticed that the assimilation is able to impact soil moisture in the first top soil layers (the first 20 cm), and also in deeper soil layers (from 20 cm to 60 cm and below), as reflected by the structure of the SEKF Jacobians. The added value of using ERA5 reanalysis over ERA-Interim when used in LDAS-Monde is highlighted. The assimilation is able to improve the simulation of both SSM and LAI: The analyses add skill to both configurations, indicating the healthy behavior of LDAS-Monde. For LAI in particular, the southern region of the domain (dominated by a Sudan-Guinean climate) highlights a strong impact of the assimilation compared to the other two sub-regions of Burkina-Faso (dominated by Sahelian and Sudan-Sahelian climates). In the southern part of the domain, differences between the model and the observations are the largest, prior to any assimilation. These differences are linked to the model failing to represent the behavior of some specific vegetation species, which are known to put on leaves before the first rains of the season. The LDAS-Monde analysis is very efficient at compensating for this model weakness. Evapotranspiration estimates from the Global Land Evaporation Amsterdam Model (GLEAM) project as well as upscaled carbon uptake from the FLUXCOM project and sun-induced fluorescence from the Global Ozone Monitoring Experiment-2 (GOME-2) are used in the evaluation process, again demonstrating improvements in the representation of evapotranspiration and gross primary production after assimilation.