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

• Published in: International journal of climatology 2024-06, Vol.44 (7), p.2462-2483
• Main Authors: Nana, Hermann N., Tamoffo, Alain T., Kaissassou, Samuel, Djiotang Tchotchou, Lucie A., Tanessong, Roméo S., Kamsu‐Tamo, Pierre H., Kenfack, Kevin, Vondou, Derbetini A.
• Format: Article
• Language: English
• Subjects: Atmospheric circulation; Atmospheric circulation models; C3S; Central Africa; Climate change; Climate models; Climatological means; Dipoles; El Nino; El Nino phenomena; El Nino-Southern Oscillation event; Equatorial regions; Fluid dynamics; Forecasting; Geophysical fluids; Hydrodynamics; Mitigation; NMME; Oceans; Performance evaluation; Precipitation; Rainfall; Rainfall forecasting; rainfall predictability; Rainfall simulators; SAOD; Sea surface; Seasonal forecasting; Skills; Southern Oscillation; Spatial variations
• ISSN: 0899-8418; 1097-0088
• DOI: 10.1002/joc.8463

In this study, hindcasts from eight Copernicus Climate Change Service (C3S) and three North American Multi‐Model Ensemble (NMME) operational seasonal forecast systems, based on dynamical climate models, are employed to investigate the influence of the South Atlantic Ocean Dipole (SAOD) on the predictive skill of Central Africa (CA) rainfall. The focus is primarily on the June–July–August season for 1993–2016. The findings reveal that, when regionally averaged, all models exhibit positive skill in predicting CA rainfall, except for the Geophysical Fluid Dynamics Laboratory (GFDL‐SPEAR) model. Notably, there are significant spatial variations in skill across different regions. Model performance is particularly low (high) in the Central African Republic and Congo Basin (Gabon and Chad) and tends to deteriorate with increasing lead‐time. Models that demonstrate a strong connection between SAOD and CA rainfall tend to exhibit better predictive skills in forecasting rainfall, in contrast to models with weaker connections. This leads to a significant in‐phase relationship between the predictive skills of rainfall and the strength of the SAOD–rainfall connection among the models. Furthermore, the atmospheric circulation responding to SST forcing associated with the El Niño–Southern Oscillation exerts a significant influence on the robust atmospheric circulation associated with the climatological mean of SST over the SAO. This suggests that mean state bias in the SAO/equatorial Pacific region plays a role in modulating the strength of the simulated SAOD–CA rainfall connection and, consequently, the prediction skill of CA rainfall. In general, both NMME and C3S models appear to be valuable tools capable of providing essential seasonal information several months in advance. These insights can aid decision‐makers in the region in making informed decisions regarding adaptation and mitigation measures. Model performance used in this study is particularly low (high) in the Central African Republic and Congo Basin (Gabon and Chad) and tends to deteriorate with increasing lead‐time. In general, both forecast models appear to be valuable tools capable of providing essential seasonal information several months in advance. These insights can aid decision‐makers in the region in making informed decisions regarding adaptation and mitigation measures.