Seasonal forecasts of the rainy season onset over Africa: Preliminary results from the FOCUS-Africa project

Precipitation seasonality is the main factor controlling vegetation phenology in many tropical and subtropical regions. Anticipating the rain onset is of paramount importance for field preparation and seeding. This is of particular importance in various African countries that rely on agriculture as a main source of food, subsistence and income. In such countries, skilful and accurate onset forecasts could also inform early warning and early actions, such as aids logistics planning, for food security. Here, we assess the skill of the seasonal forecast data provided by the Copernicus Climate Change Service in predicting the rain onset over Africa. The skill, i.e. the accuracy of the seasonal forecasts simulation ensemble compared to the climatology, is computed in a probabilistic fashion by accounting for the frequencies of normal, early and late onsets predicted by the forecast system. We compute the skill using the hindcasts (forecast simulations conducted for the past) starting at the beginning of each month in the period 1993-2016. We detect the onset timing of the rainy season using a nonparametric method that accounts for double seasonality and is suitable for the specific time-window of the seasonal forecast simulations. We find positive skills in some key African agricultural regions some months in advance. Overall, the multi-model ensemble outperforms any individual model ensemble. We provide targeted recommendations to develop a useful climate service for the agricultural sector in Africa.

Automated summary

Precipitation seasonality plays a crucial role in determining vegetation phenology in tropical and subtropical regions. Accurate prediction of the rain onset is of great importance for agricultural activities in African countries. The study evaluates the skill of seasonal forecast data provided by the Copernicus Climate Change Service in predicting the onset of rainy season in Africa. It finds positive skills in certain agricultural regions, with the multi-model ensemble performing better than individual models.