We examine the role of zonal Ekman transport along the coast of Senegal on 30 August, 2015 when the tropical disturbance associated with Tropical Cyclone Fred was located to the west of Senegal causing considerable coastal damage to coastal areas south of Dakar, Senegal. Ten-meter winds from three Weather Research and Forecast model simulations were used to estimate zonal Ekman transport, with the largest values found during the 30 August. The simulations are in agreement with limited coastal observations showing increasing southerly wind speeds during 30 August but are overestimated relative to the 3 coastal stations. The strong meridional winds translate into increased zonal Ekman transport to the coast of Senegal on 30 August. The use of a coupled ocean model will improve the estimates of Ekman transport along the Guinea-Senegalese coast. The observed damage suggests that artificial and natural barriers (mangroves) should be strengthened to protect coastal communities in Senegal.

This study analyses uncertainties associated with the main features of the annual cycle of West African rainfall (amplitude, timing, duration) in 15 CMIP5 simulations over the Sahelian and Guinean regions with satellite daily precipitation estimates. The annual cycle of indices based on daily rainfall such as the frequency and the intensity of wet days, the consecutive dry (CDD) and wet days (CWD), the 95th percentile of daily rainfall (R95) have been assessed. Over both regions, satellite datasets provide more consistent results on the annual cycle of monthly precipitation than on higher-frequency rainfall indices, especially over the Guinean region. CMIP5 simulations display much higher uncertainties in both the mean precipitation climatology and higher-frequency indices. Over both regions, most of them overestimate the frequency of wet days. Over the Guinean region, the difficulty of models to represent the bimodality of the annual cycle of precipitation involves systematic biases the frequency of wet days. Likewise, we found strong uncertainties in the simulation of the CWD and the CDD over both areas. Finally, models generally provide too early (late) onset dates over the Sahel (the Guinean region) and overestimate rainfall during the early and late monsoon phases. These errors are strongly coupled errors in the latitudinal position of the ITCZ and do not compensate at the annual scale nor when considering West Africa as a whole. wet days. We found strong uncertainties in the simulation of the CWD and he CDD over both areas. Conversely for R95p and R95PTOT, the ncertainties in CMIP5 models appear somewhat weaker, but the magnitude f R95 is largely underestimated in most models.

Malaria is a constant reminder of the climate change impacts on health. Many studies have investigated the influence of climatic parameters on the of malaria transmission. Climate conditions can modulate malaria transmission through increased temperature, which reduces the duration of the parasite's reproductive cycle inside the mosquito. The intensity and frequency of the rainfall modulate the development of the mosquito population. In this study, the Liverpool Malaria Model (LMM) is used to simulate the spatio-temporal variation of the malaria incidence in Senegal. The simulations are based on the WATCH Forcing Data applied to ERA-Interim data (WFDEI) used as a point of reference, and biased-corrected CMIP6 models, separating historical and projections for 3 Shared Socio-economic Pathways scenarios (SSP126, SSP245 and SSP585). Our results highlight a strong increase in temperatures, especially towards eastern Senegal under the SSP245 but mainly the SSP585 scenarios. The ability of the LMM model to simulate the seasonality of malaria incidence is assessed. The model reveals a period of high malaria transmission between September and November with a maximum reached in October. Results indicate a decrease in malaria incidence in certain regions of the country for the far future and for the extreme scenario. This study is importance for the planning, prioritization, and implementation of control activities in Senegal.

This study investigates the spatio-temporal variability of the onset, offset, and Length of the rainy season in Senegal over the 1981-2018 period. The onset, offset dates, and length of the rainy season are crucial parameters for agricultural planning in West Africa, which exhibits high interannual and spatial variability in rainfall. The objective is to detect and spatially classify these phenological parameters across Senegal using different approaches. Daily precipitation data from the Climate Hazards group InfraRed Precipitation with Stations (CHIRPS) dataset over the study period and ERA5 reanalysis were used. The method of was applied to detect the onset, offset dates, and length of the rainy season. The Kohonen algorithm was then used to spatially classify these parameters on topological maps. A hierarchical ascendant classification was also performed to reduce the number of classes obtained. Finally, a composite analysis of moisture transport before the onset and end of the 1984 and 2007 seasons was conducted with ERA5 data. The results show a meridional gradient of the start of the season, progressively later from southeast to northwest, while the end follows a north-south gradient. The duration of the rainy season ranges from 45 days in northern Senegal to 150 days in the south. The maximum number of dry days also decreases from north to south. The Kohonen self-organizing maps allow spatial prediction of the onset, end date, and duration of the rainy season across Senegal. The dynamics analysis reveals that early onset years are associated with stronger and earlier northward moisture transport over West Africa, underscoring the role of this parameter in triggering the rainy season. These promising results can provide an early warning to policymakers and farmers on the start and end dates of the season in each area of Senegal.