Benjamin Sultan

Extreme weather events have increasingly disrupted Haiti’s socio-economic development. This study assesses long-term trends in mean and extreme temperature, precipitation, and near-surface humidity indices across Haiti for 1981–2020, using ERA5-Land temperature and humidity fields together with Multi-Source Weighted-Ensemble Precipitation (MSWEP), both at 0.1° resolution. Trends were quantified with the Theil–Sen estimator and tested with Mann–Kendall (MK) after Trend-Free Pre-Whitening (TFPW). Warm days (TX90p, percentage of days when daily maximum temperature exceeds the 90th percentile of the reference period) and warm nights (TN90p, percentage of days when daily minimum temperature exceeds the 90th percentile) increase nationwide by about 12 and 20 additional days per decade, while mean maximum and minimum temperatures warm by 0.17 °C and 0.22 °C per decade, respectively. Nocturnal warming is accompanied by a significant rise in near-surface water vapor pressure (+0.15 hPa per decade), whose detrended interannual variability correlates strongly with TN90p (r = 0.59), identifying the water-vapor greenhouse effect as a key driver of the faster warming of nights than days. Precipitation shows no significant national trend, except for very wet days (R90p, percentage of days with precipitation above the 90th percentile of wet days, +2.18% per decade) and a decline in rainy days (RR1, number of days with precipitation ≥ 1 mm, –4 days per decade). These results provide the first country-wide baseline of temperature and precipitation extremes for climate-risk assessment in Haiti.

Previous studies have shown that rainfed maize yield can be reduced by increases in heavy rainfall events and the associated loss of nitrogen (N) in low-input cropping systems of Sub-Saharan Africa (SSA). However, the attribution of these yield-reducing factors to human-induced climate change has not yet been performed. Here we assess the impact of climate change on maize productivity at five representative locations in SSA (Mali, Benin, Ghana, Ethiopia, and Rwanda) during 2010–2019 using an ensemble of eight process-based crop models.According to factual (all forcings) and counterfactual (no anthropogenic forcing) climate simulations, climate change altered both temperature and precipitation patterns in SSA compared to 1850-fixed greenhouse gas levels. Local warming reduced potential maize yield by an average of 2% despite elevated CO₂, mostly due to shortened growing periods. In Rwanda, Ghana, and Ethiopia, reduced drought stress reversed this decrease and increased water-limited yield. In Benin and Mali, changes were less pronounced, and both potential and water-limited yields decreased.Crop models depict how climate change locally affects combined water-and-N stresses and resulting limited yields. We show that N stress increased in all locations except Mali due to enhanced N leaching associated with increased rainfall. In Ethiopia and Rwanda, increased N stress outweighed reduced drought stress, decreasing water-and-N-limited yield. Water-and-N-limited yield also decreased in Benin and Mali but increased in Ghana, depending on the balance between increased N stress and reduced drought stress. Overall, water-and-N-limited yield decreased in four of the five locations due to human-induced climate change.Finally, N-use efficiency decreased in Rwanda, Mali, and Benin but increased in Ethiopia and Ghana, indicating contrasting opportunities for intensification through additional N inputs. Although crop model uncertainty remains large, these trends were identified by at least 60% of the models, providing a robust basis for implementing agronomic adaptation practices.

The ability of species and populations to adapt to their environment faces increasing challenges as climate change accelerates. Recent methods based on genomic offset (GO) statistics aim to quantify the risk of non‐adaptation of populations to future climates. While several studies have evaluated the ability of different offset statistics to predict population (mal)adaptation, the impact of the chosen climate data—which could vary in relevance and quality—remains unexplored. In this study, we tested how the choice of environmental variables impacts the predictive performance of GO statistics. Our assessment leverages a common garden experiment to evaluate how well GO values correspond to population variation in fitness. We analyzed 157 pearl millet ( Pennisetum glaucum (L.) R.Br. syn Cenchrus americanus (L.) Morrone) landraces cultivated in West Africa, for which fitness proxies were measured during field trials conducted over 2 years. We calculated geometric and gradient forest GO statistics using three different historical climate datasets: bioclimatic variables from WorldClim (v.2.1), from CHELSA (v.2.1), and a dataset specifically designed for this study derived from the EWEMBI (v.1) climate database. The predictive power of these statistics varied threefold when using the mean weight of seeds as the fitness trait, from the lowest correlation with GO at R ² = 0.20, to the highest at R ² = 0.60. Climate datasets have a stronger effect on the predictive performance of the offset statistics than the chosen statistical method itself. We showed that considering climate variables that are closely linked to the crop life cycle globally improves the relationship between GO values and estimated fitness. Our results emphasize the importance of using climate metrics that are relevant to the study system, along with experimental validation to enhance the reliability of GO predictions. This will ultimately strengthen future breeding and conservation strategies aimed at mitigating the impact of climate change.

Malnutrition, particularly its impact on child morbidity and mortality, is one of the top five health effects of climate change. However, quantifying the portion of malnutrition attributed to climate remains challenging due to various confounding factors. This study examines the relationship between climate and acute malnutrition in Niger, a country highly vulnerable to climate change and disasters. Since climate’s effect on malnutrition is indirect, mediated by crop production, we combine rainfall data from TAMSAT satellite estimates with the SARRA-O crop model, which simulates the impact of rainfall variability on crop yields. Our analysis reveals a significant correlation between malnutrition and both rainfall and crop production from the previous year, but not within the same year. The strongest correlation (R = −0.72) was found with the previous year’s crop production. No significant links were found with temperature or intra-seasonal rainfall indices, like the start or duration of the rainy season. Although national correlations between global malnutrition, rainfall, and crop yields were stronger, they were weaker or absent at the regional level and, for Severe Acute Malnutrition crises, are less likely driven by climate variability. However, the one-year lag in the correlation allows for the prediction of future food crises, providing an opportunity to implement early intervention measures.

We calculate the hourly wet bulb globe temperature (WBGT) values for the last 50 years over Western Africa to assess the emergence of new heatwave hotspots and the interplay between moist and dry heatwaves. In the formulation used, WBGT is derived using the grided data from ERA5 and ERA5-HEAT: 2-m air temperature, relative humidity, 10-m wind speed and mean radiant temperature (a measure of incidence of radiation on a body), and is thus representative of outdoor conditions.We find that the heat stress estimated through WBGT does not peak over the same geographical regions as the air temperature, suggesting an important role of humidity in intensifying heatwaves over certain regions. While the highest temperatures are reached in the northern Sahel and Saharan regions, the highest heat stress values are found further to the south, in the region bordering Senegal, Mauritania and Mali and in southwest Niger. These are the same regions where the WBGT threshold of 33 °C (conditions dangerous even at resting metabolic rates (MR) < 115 W) have recently been crossed for up to 40 hours per year.The duration of exposure to WBGT > 30 °C (conditions dangerous at light physical activity, MR < 180 W) has been increasing over almost the entire West Africa, at rates from 30 to 100 hours/decade. Over the Senegal - Mauritania - Mali border and southern Niger, exposure to WBGT > 33 °C has been increasing by 1-4 hours/decade.Dangerous WBGT thresholds can be crossed at a wide range of temperatures and are often not associated with the highest temperature percentiles. For example, in Niamey, a WBGT of 30 °C has been crossed in the temperature range from ~ 29 to 44 °C, in Thies (Dakar) and Ouagadougou from ~ 28 to 43 °C, and in Abidjan from ~ 28 - 36 °C. In September 2019 and July 2020, in Niamey we find the first occurrences of air temperatures below 36 °C being associated with very dangerous heat stress values (WBGT > 33 ° C).We conclude that for much of continental West Africa, and particularly for the Senegal - Mauritania - Mali border region and southern Niger, extreme heat alerts should at a minimum include indicators accounting for temperature and humidity, in order to capture the dangerous moist heatwave conditions occurring at temperatures well below the highest temperature percentiles. More complex indicators that additionally account for wind and radiation are very desirable for estimates of outdoor safety.

In sub-Saharan Africa, temperatures are generally conducive to malaria transmission, and rainfall provides mosquitoes with optimal breeding conditions. The objective of this study is to assess the impact of future climate change on malaria transmission in West Africa using community-based vector-borne disease models, TRIeste (VECTRI). This VECTRI model, based on bias-corrected data from the Phase 6 Coupled Model Intercomparison Project (CMIP6), was used to simulate malaria parameters, such as the entomological inoculation rate (EIR). Due to the lack of data on confirmed malaria cases throughout West Africa, we first validated the forced VECTRI model with CMIP6 data in Senegal. This comparative study between observed malaria data from the National Malaria Control Program in Senegal (Programme National de Lutte contre le Paludisme, PNLP, PNLP) and malaria simulation data with the VECTRI (EIR) model has shown the ability of the biological model to simulate malaria transmission in Senegal. We then used the VECTRI model to reproduce the historical characteristics of malaria in West Africa and quantify the projected changes with two Shared Socio-economic Pathways (SSPs). The method adopted consists of first studying the climate in West Africa for a historical period (1950–2014), then evaluating the performance of VECTRI to simulate malaria over the same period (1950–2014), and finally studying the impact of projected climate change on malaria in a future period (2015–2100) according to the ssp245 ssp585 scenario. The results showed that low-latitude (southern) regions with abundant rainfall are the areas most affected by malaria transmission. Two transmission peaks are observed in June and October, with a period of high transmission extending from May to November. In contrast to regions with high latitudes in the north, semi-arid zones experience a relatively brief transmission period that occurs between August, September, and October, with the peak observed in September. Regarding projections based on the ssp585 scenario, the results indicate that, in general, malaria prevalence will gradually decrease in West Africa in the distant future. But the period of high transmission will tend to expand in the future. In addition, the shift of malaria prevalence from already affected areas to more suitable areas due to climate change is observed. Similar results were also observed with the ssp245 scenario regarding the projection of malaria prevalence. In contrast, the ssp245 scenario predicts an increase in malaria prevalence in the distant future, while the ssp585 scenario predicts a decrease. These findings are valuable for decision makers in developing public health initiatives in West Africa to mitigate the impact of this disease in the region in the context of climate change.

Food insecurity is among one of the greatest risks posed by climate change in Africa, where 90 to 95% of African food production is rainfed and a large proportion of the population already faces chronic hunger and malnutrition. Although, several studies have found robust evidence of future crop yield losses under climate change scenarios, there is wide variation among crops and regions as well as large modeling uncertainties. A large part of this uncertainty stems from climate projections, as climate models may differ in simulating future changes in precipitation and temperature, which could lead to different future crop production scenarios. This work examines the impacts of climate change on crop yields of maize, millet and sorghum in West Africa using climate change projections from the Coupled Model Intercomparison Project 5th Phase (CMIP5) and from the new generation of climate models from the Coupled Model Intercomparison Project 6th Phase (CMIP6). We use the SIMPLACE crop modeling framework to simulate historical and future crop yields, and bootstrap techniques to evaluate projected changes in crop productivity between the CMIP5 and CMIP6 ensembles. Using the new generation of climate models CMIP6, we find that the negative crop yield projections shown by CMIP5 simulations are largely reduced, with even large increases in crop yields when the effect of atmospheric CO₂ concentration is considered in the crop model. These differences in crop yield impacts between the CMIP5 and CMIP6 simulations are mainly due to different climate projections of temperature and precipitation in West Africa; CMIP6 projections being significantly wetter and cooler by mid-century and to a lesser extent by the end of the century. Such results highlight the large uncertainties that remain in assessing the impacts of climate change in the region and the consequent difficulty for end-users to anticipate adaptation strategies.

On the climate-health issue, studies have already attempted to understand the influence of climate change on the transmission of malaria. Extreme weather events such as floods, droughts, or heat waves can alter the course and distribution of malaria. This study aims to understand the impact of future climate change on malaria transmission using, for the first time in Senegal, the ICTP’s community-based vector-borne disease model, TRIeste (VECTRI). This biological model is a dynamic mathematical model for the study of malaria transmission that considers the impact of climate and population variability. A new approach for VECTRI input parameters was also used. A bias correction technique, the cumulative distribution function transform (CDF-t) method, was applied to climate simulations to remove systematic biases in the Coupled Model Intercomparison Project Phase 5 (CMIP5) global climate models (GCMs) that could alter impact predictions. Beforehand, we use reference data for validation such as CPC global unified gauge-based analysis of daily precipitation (CPC for Climate Prediction Center), ERA5-land reanalysis, Climate Hazards InfraRed Precipitation with Station data (CHIRPS), and African Rainfall Climatology 2.0 (ARC2). The results were analyzed for two CMIP5 scenarios for the different time periods: assessment: 1983–2005; near future: 2006–2028; medium term: 2030–2052; and far future: 2077–2099). The validation results show that the models reproduce the annual cycle well. Except for the IPSL-CM5B model, which gives a peak in August, all the other models (ACCESS1–3, CanESM2, CSIRO, CMCC-CM, CMCC-CMS, CNRM-CM5, GFDL-CM3, GFDL-ESM2G, GFDL-ESM2M, inmcm4, and IPSL-CM5B) agree with the validation data on a maximum peak in September with a period of strong transmission in August–October. With spatial variation, the CMIP5 model simulations show more of a difference in the number of malaria cases between the south and the north. Malaria transmission is much higher in the south than in the north. However, the results predicted by the models on the occurrence of malaria by 2100 show differences between the RCP8.5 scenario, considered a high emission scenario, and the RCP4.5 scenario, considered an intermediate mitigation scenario. The CanESM2, CMCC-CM, CMCC-CMS, inmcm4, and IPSL-CM5B models predict decreases with the RCP4.5 scenario. However, ACCESS1–3, CSIRO, NRCM-CM5, GFDL-CM3, GFDL-ESM2G, and GFDL-ESM2M predict increases in malaria under all scenarios (RCP4.5 and RCP8.5). The projected decrease in malaria in the future with these models is much more visible in the RCP8.5 scenario. The results of this study are of paramount importance in the climate-health field. These results will assist in decision-making and will allow for the establishment of preventive surveillance systems for local climate-sensitive diseases, including malaria, in the targeted regions of Senegal.

The Sahelian Great Green Wall (SGGW) is an influential project to combat desertification and promote sustainable land management on a large scale, involving 11 countries in the Sahel region of Africa. The UNCCD’s 2020 progress report showed a mixed picture concerning the meeting of the initial targets. At the One Planet Summit in 2021, announcements were made to consolidate the implementation of the SGGW, most notably with the creation of the Great Green Wall Accelerator. In this context, our paper sets out to review the scientific work conducted with regard to the SGGW. We have thus carried out a bibliometric analysis of the literature on SGGW. Although the initiative involves 11 countries and covers a large spectrum of scientific disciplines, our results show the predominance of ecological studies in the SGGW literature and a concentration of studies in certain geographies of interest, such as northern Senegal. Moreover, based on a secondary analysis of publications on land restoration and sustainable ecosystem management in Sahelian countries, we show that the literature relevant to SGGW topics is richer and fills in the information gaps we have identified at thematic and geographical levels. By showing that SGGW studies are overly focused on certain topics and geographical areas, our paper argues for a better interdisciplinary mobilization of researchers working on GGW-related topics. The scientific and operational success of the project depends on stronger networking between the different research teams and themes, both in Africa and internationally.