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

Abstract

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.

Performance‐based evaluation of NMME and C3S models in forecasting the June–August Central African rainfall under the influence of the South Atlantic Ocean Dipole

“…In regions where rainfall skill is higher, it is worth noting that the NMME models tend to exhibit lower skill compared to the C3S models. This observation regarding the NMME models is consistent with the findings of Feudjio Tchinda et al (2022), which indicate reasonably good skill in JJA rainfall predictions over CA up to a 1‐month lead‐time.…”

Editorial: Atmospheric processes and applications in urban, coastal and mountainous terrain

“…In this regard, it is important to evaluate the reliability of models at different spatial and temporal scales, which can be used as decision‐support tools for adequate planning of water usage. In this Special Issue, Feudjio Tchinda et al (2022) focus on seasonal forecasts, assessing the predictive skill of the North American Multi‐model Ensemble (NMME) in Central Africa, while Onwukwe et al (2022) evaluate the performance of year‐long simulations with the WRF model to reproduce precipitation in a coastal valley in British Columbia (Canada). Furthermore, long‐term planning and strategic decisions can be supported by the climatological assessment of rainfall trends from the analysis of sufficiently long time series.…”