A low-dimensional recursive deep learning model for El Niño-Southern Oscillation simulation

In this study, we develop a low-dimensional recursive model using deep learning (DL) to understand the dynamics of the El Niño-Southern Oscillation (ENSO). Unlike most existing research that relies on Coupled General Circulation Models (CGCMs), we explore a DL technique as an alternative approach to simulate ENSO characteristics. To replicate the observed stochastically excited oscillations, we incorporate stochastic noise into the recursive process of the DL model. Our long-term simulations demonstrate that the DL model effectively reproduces ENSO characteristics comparable to those captured by CGCMs. Additionally, we conduct experiments to analyze the interactions between ENSO and the Indian and Atlantic Oceans, evaluating their impacts on ENSO dynamics. Beyond capturing ENSO characteristics, the DL model exhibits skillful ENSO prediction capabilities. Using eXplainable AI (XAI) methods, we identify the contributions of each variable to ENSO predictability. Our findings suggest that this DL model serves as a valuable tool for understanding climate dynamics at a relatively low computational cost, providing an alternative to complex physically-based models.