Superiority of a Convolutional Neural Network Model over Dynamical Models in Predicting Central Pacific ENSO

The application of deep learning is fast developing in climate prediction, in which El Niño–Southern Oscillation (ENSO), as the most dominant disaster-causing climate event, is a key target. Previous studies have shown that deep learning methods possess a certain level of superiority in predicting ENSO indices. The present study develops a deep learning model for predicting the spatial pattern of sea surface temperature anomalies (SSTAs) in the equatorial Pacific by training a convolutional neural network (CNN) model with historical simulations from CMIP6 models. Compared with dynamical models, the CNN model has higher skill in predicting the SSTAs in the equatorial western-central Pacific, but not in the eastern Pacific. The CNN model can successfully capture the small-scale precursors in the initial SSTAs for the development of central Pacific ENSO to distinguish the spatial mode up to a lead time of seven months. A fusion model combining the predictions of the CNN model and the dynamical models achieves higher skill than each of them for both central and eastern Pacific ENSO.