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Bacterial meningitis is an ongoing threat for the population of the African Meningitis Belt, a region characterized by the highest incidence rates worldwide. The determinants of the disease dynamics are still poorly understood; nevertheless, it is often advocated that climate and mineral dust have a large impact. Over the last decade, several studies have investigated this relationship at a large scale. In this analysis, we scaled down to the district-level weekly scale (which is used for in-year response to emerging epidemics), and used wavelet and phase analysis methods to define and compare the time-varying periodicities of meningitis, climate and dust in Niger. We mostly focused on detecting time-lags between the signals that were consistent across districts. Results highlighted the special case of dust in comparison to wind, humidity or temperature: a strong similarity between districts is noticed in the evolution of the time-lags between the seasonal component of dust and meningitis. This result, together with the assumption of dust damaging the pharyngeal mucosa and easing bacterial invasion, reinforces our confidence in dust forcing on meningitis seasonality. Dust data should now be integrated in epidemiological and forecasting models to make them more realistic and usable in a public health perspective.

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An intercomparison of seven gridded rainfall products incorporating satellite data (ARC, CHIRPS, CMORPH, PERSIANN, TAPEER, TARCAT, TMPA) is carried out over Central Africa, by evaluating them against three observed datasets: (a) the WaTFor database, consisting of 293 (monthly records) and 154 (daily records) rain‐gauge stations collected from global datasets, national meteorological services and monitoring projects, (b) the WorldClim v2 gridded database, and (c) a set of stations expanded from the FAOCLIM network, these two latter sets describing climate normals. All products fairly well reproduce the mean rainfall regimes and the spatial patterns of mean annual rainfall, although with some discrepancies in the east–west gradient. A systematic positive bias is found in the CMORPH product. Despite its lower spatial resolution, TAPEER shows reasonable skills. When considering daily rainfall amounts, TMPA shows best skills, followed by CMORPH, but over the central part of the Democratic Republic of the Congo, TARCAT is amongst the best products. Skills ranking is however different at the interannual time‐scale, with CHIRPS and TMPA performing best, though PERSIANN has comparable skills when only fully independent stations are used as reference. A preliminary study of Southern Hemisphere dry season variability, from the example of Kinshasa, shows that it is a difficult variable to capture with satellite‐based rainfall products. Users should still be careful when using any product in the most data‐sparse regions, especially for trend assessment.

The interannual and intraseasonal variability of West African vegetation over the period 1982-2002 is studied using the normalized difference vegetation index (NDVI) from the Advanced Very High Resolution Radiometer (AVHRR).The novel independent component analysis (ICA) technique is applied to extract the main modes of the interannual variability of the vegetation, among which two modes are worth describing. The first component (IC1) describes NDVI variability over the Sahel from August to October. A strong photosynthetic activity over the Sahel is related to above-normal convection and rainfall within the intertropical convergence zone (ITCZ) in summertime and is partly associated with colder (warmer) SST in the eastern tropical Pacific (the Mediterranean). The second component (IC2) depicts a dipole pattern between the Sahelian and Guinean regions during the northern summer followed by a southward-propagating signal from October to December. It is associated with a north-south dipole in convection and rainfall induced by variations in the latitudinal location of the ITCZ as a response to the occurrence of the tropical Atlantic dipole.The analysis of the intraseasonal variability of the Sahelian vegetation relies on the analysis of the seasonal marches and their main phenological stages. Green-up usually starts in early July and shows a very low year-to-year variability, while senescence ends by mid-November and is prone to larger interannual variability. Six types of vegetative seasonal marches are discriminated according to variations in the timing of phenological stages as well as in the greening intensity. These types appear to be strongly dependent on rainfall distribution and amount, particularly those recorded in late August. Finally, year-to-year memory effects are highlighted: NDVI recorded during the green-up phase in year j appears to be strongly related to the maximum NDVI value recorded at year j Ϫ 1.

A more complete picture of the timing and patterns of the ENSO signal during the seasonal cycle for the whole of Africa over the three last decades is provided using the normalized difference vegetation index (NDVI). Indeed, NDVI has a higher spatial resolution and is more frequently updated than in situ climate databases, and highlights the impact of ENSO on vegetation dynamics as a combined result of ENSO on rainfall, solar radiation, and temperature.The month-by-month NDVI-Niño-3.4 correlation patterns evolve as follows. From July to September, negative correlations are observed over the Sahel, the Gulf of Guinea coast, and regions from the northern Democratic Republic of Congo to Ethiopia. However, they are not uniform in space and are moderate (;0.3). Conversely, positive correlations are recorded over the winter rainfall region of South Africa. In October-November, negative correlations over Ethiopia, Sudan, and Uganda strengthen while positive correlations emerge in the Horn of Africa and in the southeast coast of South Africa. By December with the settlement of the ITCZ south of the equator, positive correlations over the Horn of Africa spread southward and westward while negative correlations appear over Mozambique, Zimbabwe, and South Africa. This pattern strengthens and a dipole at 188S is well established in February-March with reduced (enhanced) greenness during ENSO years south (north) of 188S. At the same time, at ;28N negative correlations spread northward. Last, from April to June negative correlations south of 188S spread to the north (to 108S) and to the east (to the south of Tanzania).

[1] Over 15 years of Normalized Difference Vegetation Index (NDVI) data from the Advanced Very High Resolution Radiometers (AVHRR) are used to study the response of vegetation activity to rainfall in three semi-arid regions of Africa. The relationships between annual NDVI and annual precipitation (PPT) time series are examined using statistical approaches (simple and partial correlations, linear multiple regressions). It appears that annual NDVI highly depends on PPT of the concurrent year and the previous year. An analysis of particularly dry and wet years enables to better diagnose two distinct responses of vegetation activity to rainfall. The ''recovery'' effect represents the difficulty of vegetation to recover from previous two-year drought conditions. The ''memory'' effect represents the capacity of semi-arid ecosystems to benefit from a water surplus at a one-year time-lag.