Assessing seasonal and intra-seasonal skill at Kaffrine station in Senegal

Abstract

Predicting rainfall during the July-August-September season in Senegal is crucial for farmers relying on rainfed agriculture. Previous studies have shown that high skill can be achieved across the Sahel. This study examined skill at the station level in Kaffrine, Senegal, which is also the site of a Climate Change and Food Security pilot project site. Using the ECHAM4.5 model forced with Climate Forecasting System sea surface temperatures as part of a two- tiered forecast system, the study examines the ability to predict different variables and how they effect millet crop yields. Results show good seasonal rainfall skill can be achieved at the station level in Kaffrine. They also show that overall millet yield is most highly correlated with the onset of the rainy season.