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

Abstract

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.

摘 要

深度学习在气候预测中的应用正在快速发展, 其中厄尔尼诺-南方涛动(El Niño–Southern Oscillation, 以下简称ENSO)作为引发灾害性气候事件的主要模态, 是气候预测的一个关键研究对象. 过去的研究表明, 深度学习方法在预测ENSO指数方面具有一定的优势. 本研究通过使用CMIP6模式的历史模拟数据, 训练卷积神经网络(CNN)模型, 开发了一个用于预测赤道太平洋海表温度异常(SSTAs)空间形态的深度学习模型. 与动力模式相比, CNN模型对赤道中西太平洋地区的SSTAs具有更高的预测技巧, 但在东太平洋却不如动力模式. CNN模型可以成功捕捉到中太平洋(CP)ENSO发展初期SSTAs中的小尺度前兆, 以此能在超前七个月有效区分ENSO的空间形态. 将CNN模型和动力模式的预测结果相结合得到集合模型(fusion model), 其对CP和东太平洋(EP)ENSO事件的预测技巧均优于CNN模型和动力模式单独预测.

Electronic Supplementary Material to

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