Predictive skill of North American Multi-Model Ensemble seasonal forecasts for the climate rainfall over Central Africa.

This study evaluates the predictive performance of the North American Multi-model Ensemble (NMME) over Central Africa (CA) using the historical rainfall data. The African Rainfall Climatology Version 2 (ARC2) is used as a substitute for reference observational data to examine the capability of 11 NMME and their NMME ensemble mean (MME) in simulating rainfall. Using the Kling- Gupta efficiency (KGE), Taylor skill score (TSS), and Heidke skill score, the predictive evaluation of the models is performed from lead 0 to lead 5 of each season. The results show that the NMME models satisfactorily reproduce the bimodal and unimodal structure of rainfall in CA at the lead 0 level of different seasons: December–February (DJF), March–May (MAM), June–August (JJA), and September–November (SON). The pattern correlation coefficient (PCC) shows values of NMME and MME greater than ~0.69 and TSS > 0.60 for all four seasons. The MME presents a maximum in DJF 0.~96 between 0 and 1 month lead time. With the same time scale, just over 85% of the NMME have a KGE between 0 and 0.42. It follows that as the forecast lead time increases, the PCC and TSS of each model become small, with PCC 0:~12 in JJA and DJF, TSS<0.21 in JJA at lead 5. The NMME models exhibit an important rainfall bias and the calculated scores show the quality of the forecast decreases with increasing lead time; this may justify a constraint on the models to keep the good quality of the long-term seasonal forecast in CA. [ABSTRACT FROM AUTHOR]