Atlantic Niño/Niña Prediction Skills in NMME Models

Abstract

The Atlantic Niño/Niña, one of the dominant interannual variability in the equatorial Atlantic, exerts prominent influence on the Earth’s climate, but its prediction skill shown previously was unsatisfactory and limited to two to three months. By diagnosing the recently released North American Multimodel Ensemble (NMME) models, we find that the Atlantic Niño/Niña prediction skills are improved, with the multi-model ensemble (MME) reaching five months. The prediction skills are season-dependent. Specifically, they show a marked dip in boreal spring, suggesting that the Atlantic Niño/Niña prediction suffers a “spring predictability barrier” like ENSO. The prediction skill is higher for Atlantic Niña than for Atlantic Niño, and better in the developing phase than in the decaying phase. The amplitude bias of the Atlantic Niño/Niña is primarily attributed to the amplitude bias in the annual cycle of the equatorial sea surface temperature (SST). The anomaly correlation coefficient scores of the Atlantic Niño/Niña, to a large extent, depend on the prediction skill of the Niño3.4 index in the preceding boreal winter, implying that the precedent ENSO may greatly affect the development of Atlantic Niño/Niña in the following boreal summer.

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

“…The analysis reveals that the prediction skills of the SAOD index decline at a slightly faster rate compared to those of the ENSO–SAOD connection as the lead‐time increases, both in the case of individual models and the MME. When employing a correlation coefficient skill cutoff value of 0.6 (indicated by the vertical dashed black line), consistent with the previous studies by Liu et al (2023) and Wang et al (2021), it is noteworthy that more than half of the models demonstrate the ability to skilfully predict the SAOD index up to 4 months in advance. The MME, based on the SAOD indices, exhibits higher correlation coefficient skills than nearly all individual models along the x ‐axis.…”

“…They showed that models with a stronger relationship between the September–November IOD and the following DJF Atlantic Niño appear to have a higher skill in predicting the DJF Atlantic Niño as opposed to those featuring a weaker connection. Wang et al (2021) and Nnamchi et al (2021) showed that, due to the relatively short‐lived ocean–atmosphere coupling processes in the equatorial Atlantic, the seasonal predictability of the Atlantic Niño is usually limited. They also showed that many of the NMME seasonal forecast models can provide a skilful prediction of the Atlantic Niño as long as 3 months lead‐time.…”

Impact of Indian Ocean Dipole on Atlantic Niño predictive skill

“…Due to the relatively short-lived ocean-atmosphere coupling processes in the equatorial Atlantic (Nnamchi et al 2015(Nnamchi et al , 2021, the seasonal predictability of the Atlantic Niño is usually limited (Stockdale et al 2006. Only half of the seasonal forecast models from the North American Multi-Model Ensemble (NMME) project can provide a skillful prediction of the Atlantic Niño as long as 3 months in advance during 1980-2010 (Wang et al 2021).…”

An attempt using equatorial waves to predict tropical sea surface temperature anomalies associated with the Atlantic zonal mode

“…Previous studies have shown that the skilful prediction for Atlantic Niño/Niña events by either statistical model or dynamic model is restricted in a short lead time (Li et al, 2020;Counillon et al, 2021;Wang et al, 2021). Li et al (2020) established two statistical models based on linear inverse modelling (LIM) and analogue forecast with a coupled model data, and found them barely incapable of improving the prediction skill of SST in the Atlantic 3 region (ATL3, 3°S-3°N, 20°W-0°) to surpass the statistical forecast using SST persistence.…”

“…Li et al (2020) established two statistical models based on linear inverse modelling (LIM) and analogue forecast with a coupled model data, and found them barely incapable of improving the prediction skill of SST in the Atlantic 3 region (ATL3, 3°S-3°N, 20°W-0°) to surpass the statistical forecast using SST persistence. Wang et al (2021) illustrated that the recently released dynamical models from North American Multimodel Ensemble (NMME) models are generally more skilful than statistical models, but still within only four month lead skilful predictions are provide by the most models. The prediction challenge could owe to the multiple causes of the event onset Richter et al (2013).…”