Quantifying the Relative Contributions of the Global Oceans to ENSO Predictability With Deep Learning

Abstract

Abstract We propose a unified statistical method based on deep learning and heatmap analysis to quantify the relative contributions of the global oceans to El Niño–Southern Oscillation (ENSO) predictability. By incorporating subsurface signals in the Indian Ocean and Atlantic, the forecast lead can be skillfully extended by about one season. This skill enhancement mainly originates from the tropical Indian Ocean, presumably related to signals of the Indian Ocean Dipole passing to the tropical Pacific through the Indonesian Throughflow. The sea surface temperature anomaly (SSTA) in the Indian Ocean accounts for nearly 50% of surface contributions to both El Niño and La Niña predictions at a 15‐month lead. The north tropical Atlantic SSTA has a moderate impact on La Niña at a 9‐month lead. The Pacific Meridional Mode plays a significant role in both ENSO phases at a 12‐month lead. Thus, our study suggests that trans‐basin effects for ENSO are more vigorous than previously thought.

Keywords

• ENSO prediction
• trans‐basin effects for ENSO
• deep learning