Role of Ocean Initialization in Skillful Prediction of Sahel Rainfall on the Decadal Time Scale

Abstract

Abstract Sahel summer rainfall has undergone persistent drought from the 1970s to 1980s, causing severe human life and economic losses. Many studies pointed out that the decadal variations of Sahel rainfall are mainly modulated by low-frequency sea surface temperature (SST) variations in different ocean basins. However, how this modulation contributes to the decadal prediction skill of Sahel rainfall remains unknown. This study provided an affirmative response using the decadal hindcasts initialized by a dimensional-reduced projection four-dimensional variational (DRP-4DVar) data assimilation method to incorporate only ocean analysis data into the gridpoint version 2 of the Flexible Global Ocean–Atmosphere–Land System Model (FGOALS-g2). The hindcasts reveal the benefits of the DRP-4DVar approach for improving the Sahel rainfall decadal prediction skill measured by the anomaly correlation coefficient (ACC), root-mean-square error, ACC difference, and mean square skill score. The decadal variations of SSTs in the Atlantic, Mediterranean Sea, Indian Ocean, and Pacific as well as correct representations of the associated Sahel rainfall–SST relationships are well predicted, thus leading to skillful predictions of Sahel rainfall. In particular, the initialization of SSTs in the Atlantic and Mediterranean Sea plays a more important role in skillful Sahel rainfall predictions than in the other basins. The prediction skill of Sahel rainfall by the FGOALS-g2 prediction system is significantly higher than those by most phase 5 and 6 of the Coupled Model Intercomparison Project (CMIP5&6) prediction systems initialized only with ocean analysis data. This result is likely attributed to a more accurate relationship between Sahel rainfall and SST by the FGOALS-g2 prediction system than by the CMIP5&6 prediction systems. Significance Statement Previous studies have shown limited success in predicting Sahel rainfall. By using an advanced coupled data assimilation method constrained only by ocean observational data, we achieve a high decadal prediction skill for Sahel rainfall. The successful prediction is attributed to accurately predicted decadal variations of sea surface temperatures in the Atlantic, Mediterranean Sea, Indian Ocean, and Pacific as well as their relationships with Sahel rainfall. Our results can provide references for future decadal predictions of Sahel rainfall and motivate the need to evaluate the contributions of the initialization of the ocean versus the other climate components (e.g., atmosphere or land) to Sahel rainfall predictions.