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

作　　者：Tingyu WANG Ping HUANG 

机构地区：[1]Center for Monsoon System Research,Institute of Atmospheric Physics,Chinese Academy of Sciences,Beijing 100029,China [2]Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters(CIC-FEMD),Nanjing University of Information Science&Technology,Nanjing 210044,China [3]State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics,Institute of Atmospheric Physics,Chinese Academy of Sciences,Beijing 100029,China [4]University of Chinese Academy of Sciences,Beijing 100049,China

出　　处：《Advances in Atmospheric Sciences》2024年第1期141-154,共14页大气科学进展（英文版）

基　　金：supported by the National Key R&D Program of China(Grant No.2019YFA0606703);the National Natural Science Foundation of China(Grant No.41975116);the Youth Innovation Promotion Association of the Chinese Academy of Sciences(Grant No.Y202025)。

摘　　要：The application of deep learning is fast developing in climate prediction,in which El Ni?o–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.

关 键 词：ENSO diversity deep learning ENSO prediction dynamical forecast system 

分 类 号：P73[天文地球—海洋科学]