Explainable AI in lengthening ENSO prediction from western north pacific precursor

Abstract

In this short communication, we report initial success in utilizing existing Explainable Artificial Intelligence (XAI) methodology to investigate an emerging precursor of the El Niño-Southern Oscillation (ENSO), manifest as sea surface temperature anomalies (SSTA) in the Western North Pacific (WNP), and its impact on enhancing ENSO prediction accuracy. Our analysis reveals that integrating WNP SSTA with established XAI techniques significantly increases the predictability of ENSO states. We found marked improvement in prediction accuracy, from a 60 % baseline to over 85 % for forecasting moderate warm, cold, and neutral ENSO states one year ahead. For higher magnitude events, precision surpasses 90 %. This work, intended as a follow-up to recent studies, underscores the potential of augmenting emerging XAI with additional SST signals to advance long-term climate forecasting capabilities.