Dr. Abdou Karim Gueye is the son of a farmer. As local drought was a major problem for his family in Senegal during his childhood, one day he decided that he would find a way to help them face this critical situation. Thanks to his strong motivation, he started a brilliant career as a researcher at the University of Dakar. Today, Dr. Gueye is conducting his research in France, where he focuses on precipitation in the Sahel, which has been identified by the United Nations as one of the world’s nine “hot spots” of global environmental change. Based on rainfed crops, agriculture in the Sahel entirely depends on water resources, which are extremely scarce. The ability to forecast climatic fluctuations within days or months of the event is the first step to reducing poverty, since this can make a real difference for the strategies used by rural populations in Africa to adapt to climate change. Dr. Gueye is a specialist in processing environmental data using advanced statistical tools, especially climate and weather tools. His research may contribute to decision making by highlighting the most relevant meteorological variables so that they can be included in the operational systems of national weather services. These predictions could contribute to the development of “index insurance,*” a new type of policy created for people living in vulnerable regions, whose livelihoods are closely linked to their environment. Instead of taking into account losses suffered by the policy holder and caused by weather hazards, this type of policy is linked to the fluctuation of a weather variable. Insurance payouts are therefore based on the performance of a weather index, such as rainfall, regardless of the actual loss suffered by the farmer. However, if uncertainty about weather forecast is too high, premiums can become unaffordable. Dr. Gueye aims to reduce this uncertainty through a better understanding of climate change over the coming decades. By improving weather predictions, Dr. Gueye’s research may contribute to mitigating climate risks in the sub-Saharan Africa region. It may also significantly improve the economic conditions of farmers by helping them access the insurance coverage and bank loans that are essential for their livelihood.
The long term control integrations of the IPSL model will be considered to evaluate its own decadal variability over Sub-Saharan Africa and the Atlantic. Then the skill of the decadal forecasts will be evaluated by comparing the different hindcasts (starting in 1960, 1965, ..., 2000) to the observations. Multi-dimensional statistical analysis tools (EOF, SVD, regression,...) will be used to address these issues and to compare with Mohino et al. results. The spin-up of the model at the beginning of the simulations will have to be examined in details and removed before analyzing the AMO-Sahel links and predictability