A low-dimensional recursive deep learning model for El Niño-Southern Oscillation simulation
Jiho Ko, Na-Yeon Shin, Jonghun Kam, Yoo-Geun Ham & Jong-Seong Kug
Abstract:
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.
pdf : 110. Ko et al. (2025)