Pierre Honore Kamsu-Tamo

Cette étude s'inscrit dans le cadre de la documentation de la variabilité intrasaisonnière atmosphérique et l'analyse de la prévisibilité sur les régions Afrique Centrale et Golfe de Guinée. Elle porte sur les saisons de l'année pour lesquelles la ZCIT est au dessus de l'équateur. Des travaux menés distinctement sur les mois de Mars à Juin et de Septembre à Novembre, il ressort que les activités convective et pluvieuse au cours de ces saisons sont régies par trois modes principaux de variabilité assez proches. Au cours de ces deux saisons, les systèmes individuels générateurs de pluie se déplacent d'est en ouest, et leur activité est régulée par des enveloppes convectives se déplaçant vers l'est. Des analyses spécifiques ont mis en lumière la forte empreinte de signaux équatoriaux de type onde de Kelvin se propageant vers l'est et dont les phases régulent l'organisation des systèmes convectifs. L'impact relatif d'ondes équatoriales se propageant vers l'ouest (Rossby en particulier) et celui d'advections de masses d'air méditerranéennes n'est pas à négliger, d'autant plus qu'elles sont susceptibles d'interagir avec les ondes de Kelvin, et donc de moduler les phases de l'activité convective. Les forçages externes ainsi identités constituent des sources potentielles de prévisibilité pour les modes intrasaisonniers mis en évidence. Utilisant les données de la base multi-modèle TIGGE, l'analyse de la prévisibilité de chacun des modes principaux de variabilité est réalisée. Se focalisant sur les phases spécifiques de ces modes, les scores obtenus augurent une prévisibilité au delà de 10 jours surtout pour des prévisions initialisées lorsque les principales sources sont actives.

In this study, hindcasts from eight Copernicus Climate Change Service (C3S) and three North American Multi‐Model Ensemble (NMME) operational seasonal forecast systems, based on dynamical climate models, are employed to investigate the influence of the South Atlantic Ocean Dipole (SAOD) on the predictive skill of Central Africa (CA) rainfall. The focus is primarily on the June–July–August season for 1993–2016. The findings reveal that, when regionally averaged, all models exhibit positive skill in predicting CA rainfall, except for the Geophysical Fluid Dynamics Laboratory (GFDL‐SPEAR) model. Notably, there are significant spatial variations in skill across different regions. Model performance is particularly low (high) in the Central African Republic and Congo Basin (Gabon and Chad) and tends to deteriorate with increasing lead‐time. Models that demonstrate a strong connection between SAOD and CA rainfall tend to exhibit better predictive skills in forecasting rainfall, in contrast to models with weaker connections. This leads to a significant in‐phase relationship between the predictive skills of rainfall and the strength of the SAOD–rainfall connection among the models. Furthermore, the atmospheric circulation responding to SST forcing associated with the El Niño–Southern Oscillation exerts a significant influence on the robust atmospheric circulation associated with the climatological mean of SST over the SAO. This suggests that mean state bias in the SAO/equatorial Pacific region plays a role in modulating the strength of the simulated SAOD–CA rainfall connection and, consequently, the prediction skill of CA rainfall. In general, both NMME and C3S models appear to be valuable tools capable of providing essential seasonal information several months in advance. These insights can aid decision‐makers in the region in making informed decisions regarding adaptation and mitigation measures.

Central African citizens are highly vulnerable to extreme hydroclimatic events due to excess precipitation or to dry spells. This study makes use of CHIRPS precipitation data gridded at 0.05° × 0.05° resolution and extended from 1981 to 2019 to analyze spatial variabilities and trends of six extreme precipitation indices defined by the Expert Team on Climate Change Detection and Indices (ETCCDI) over Cameroon. They are the number of wet days (RR1), the simple daily intensity index (SDII), the annual total precipitation from days greater than the 95th percentile (R95ptot), the maximum number of consecutive wet days (CWD), the maximum number of consecutive dry days (CDD), the number of very heavy rainfall (RR20). The standard precipitation index (SPI) time series were also examined in the five agro-climatic regions of the domain. The pattern of annual precipitation was first checked over the entire domain. We obtain a well-known pattern showing a decreased precipitation northward with the highest values around the Atlantic Ocean coast. The analysis shows that all indices represent patterns approximately similar to that of annual rainfall except CDD where the spatial south-north gradient is reversed. RR20 shows the lowest spatial variability. Trend study of RR1 indicates negative values south of the domain and predominated positive values in the northern part, where CDD, on the contrary, shows a decreased trend. The highest trends are observed in the northernmost area for CWD and around the coast for SDII and R95ptot. SPI time series indicate an alternative dry and wet period and the years between 1990 and 2000 witnessed more annual wet conditions. Such a study is very important in this domain where variabilities of climatic components are very high due to climate change impact and diversified relief. The results can serve as a reference for agricultural activity, hydropower management, civil engineering, planning of economic activities and can contribute to the understanding of the climate system in Cameroon.