Retrospective ENSO predictions using an intermediate ocean-atmosphere coupled model by integrating deep-learning sea surface wind stress

Shuangying DU; Rong-Hua ZHANG; Chuan GAO

doi:10.1007/s00343-025-5166-1

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Retrospective ENSO predictions using an intermediate ocean-atmosphere coupled model by integrating deep-learning sea surface wind stress

Physics | Updated：2026-05-07

- Retrospective ENSO predictions using an intermediate ocean-atmosphere coupled model by integrating deep-learning sea surface wind stress
- Journal of Oceanology and Limnology Vol. 44, Issue 2, Pages: 477-491(2026)
- Affiliations：
  
  1.Key Laboratory of Ocean Observation and Forecasting and Key Laboratory of Ocean Circulation and Waves, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266000, China
  2.State Key Laboratory of Climate System Prediction and Risk Management/School of Marine Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China
  3.University of Chinese Academy of Sciences, Beijing 100049, China
  4.Laoshan Laboratory, Qingdao 266237, China
- Author bio：
  
  rzhang@nuist.edu.cn
  gaochuan@qdio.ac.cn
- Funds：
- DOI：10.1007/s00343-025-5166-1
  CLC：
- Received：22 May 2024，
  
  Accepted：16 June 2025，
  
  Online First：25 June 2025，
  
  Published：01 March 2026
- Accepted：
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DU Shuangying,ZHANG Rong-Hua,GAO Chuan.Retrospective ENSO predictions using an intermediate ocean-atmosphere coupled model by integrating deep-learning sea surface wind stress[J].Journal of Oceanology and Limnology,2026,44(02):477-491.
DOI：

DU Shuangying,ZHANG Rong-Hua,GAO Chuan.Retrospective ENSO predictions using an intermediate ocean-atmosphere coupled model by integrating deep-learning sea surface wind stress[J].Journal of Oceanology and Limnology,2026,44(02):477-491. DOI： 10.1007/s00343-025-5166-1.

摘要

Abstract

Various physics-based dynamical and data-based statistical models have been developed for uses in predicting sea surface temperature (SST) evolution in relation to the El Niño-Southern Oscillation (ENSO) over the tropical Pacific. At present

clear limitations remain in their ENSO predictions

with predicted SST anomalies (SSTAs) being widely spread across diverse models and considerable inter-model uncertainty. Fortunately

deep learning (DL)-based modeling has recently made promising advances in ENSO prediction tasks; numerous neural networks (NNs) have been constructed for ENSO predictions. However

most NNs themselves are purely data-driven and lack constraints of the necessary physical processes in the coupled system; there are few studies in which DL models are directly integrated with physics-based dynamical models. Previously

such a new type of intermediate coupled models (ICMs) was developed by directly integrating U-Net-derived sea surface wind stress models with an intermediate ocean dynamical model (denoted as ICM-UNet)

with demonstrated success in simulating ENSO evolutions in freely coupled runs. It is thus natural to take a step further for prediction applications. In this study

this new ICM-UNet is applied for retrospective ENSO predictions

the first time that such a fusion of DL atmospheric model and dynamical oceanic model with different architectures can be achieved to make ENSO predictions. The overall evaluations indicate that the ICM-UNet yields valid retrospective predictions during the period 1995–2023

confirming that the ICM-UNet is a credible ocean-atmosphere coupled model for ENSO predictions. In case studies during 2020–2023

the ICM-UNet predictions reveal that SSTAs over the equatorial Pacific evolved into a second-year cooling in late 2021 and a warming tendency in 2023

forming a three-year La Niña and an El Niño event thereafter

which is consistent with the reality. The ICM-UNet successful fusion

taking advantage of both the physical constraints due to dynamical oceanic models and nonlinear representations of wind responses due to DL capacity

further underscores the high adaptability of integrating data-driven NNs into the ocean-atmosphere coupled modeling for ENSO-related studies.

关键词

Keywords

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