A deep learning-based hybrid model for improved SST prediction in the tropical Pacific Ocean

Yuanzhe MA; Bowen XIE; Zhongkun FENG; Guimin SUN; Cong ZHANG; Shuguo YANG

doi:10.1007/s00343-025-4333-8

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A deep learning-based hybrid model for improved SST prediction in the tropical Pacific Ocean

Physics | Updated：2025-12-16

- A deep learning-based hybrid model for improved SST prediction in the tropical Pacific Ocean
- A deep learning-based hybrid model for improved SST prediction in the tropical Pacific Ocean
- 海洋湖沼学报(英文) 2025年43卷第6期页码：1709-1725
- Affiliations：
  
  1.School of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao 266061, China
  2.CAS Key Laboratory of Ocean Circulation and Waves, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China
  3.University of Chinese Academy of Sciences, Beijing 100049, China
  4.Marine Sciences Research Institute of Shandong Province, Qingdao 266104, China
- Author bio：
  
  xiebw@mails.qust.edu.cn
- Funds：
  
  the National Natural Science Foundation of China(42476024;42176010);the National Key Research and Development Program of China(2022YFF0801400)
- DOI：10.1007/s00343-025-4333-8
  中图分类号：
- 收稿：2024-12-09，
  
  纸质出版：2025-11-01
- Accepted：
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Yuanzhe MA, Bowen XIE, Zhongkun FENG, 等. A deep learning-based hybrid model for improved SST prediction in the tropical Pacific Ocean[J]. 海洋湖沼学报(英文), 2025,43(6):1709-1725.

MA Yuanzhe,XIE Bowen,FENG Zhongkun,et al.A deep learning-based hybrid model for improved SST prediction in the tropical Pacific Ocean[J].Journal of Oceanology and Limnology,2025,43(06):1709-1725.
Yuanzhe MA, Bowen XIE, Zhongkun FENG, 等. A deep learning-based hybrid model for improved SST prediction in the tropical Pacific Ocean[J]. 海洋湖沼学报(英文), 2025,43(6):1709-1725. DOI： 10.1007/s00343-025-4333-8.

MA Yuanzhe,XIE Bowen,FENG Zhongkun,et al.A deep learning-based hybrid model for improved SST prediction in the tropical Pacific Ocean[J].Journal of Oceanology and Limnology,2025,43(06):1709-1725. DOI： 10.1007/s00343-025-4333-8.

摘要

Abstract

Sea surface temperature (SST) is an important ocean variable affecting climate change. It plays an important role in the interactions between the ocean and the atmosphere

and it also has an effect on the transport of heat

freshwater

and carbon. Therefore

accurate SST prediction is necessary for understanding climate change and protecting ocean ecosystems. In this study

we proposed a hybrid model to predict SST in the tropical Pacific Ocean based on two single deep-learning models. Results indicate that the proposed hybrid model shows superior prediction accuracy at all lead times compared to the single model. Specifically

during El Niño periods

the root mean square error

mean absolute error

and Pearson correlation coefficient of the hybrid model forecasts were approximately 0.54 °C

0.40 °C

and 0.98

respectively

while during La Niña periods

these metrics were 0.55 °C

0.39 °C

and 0.98

respectively. Notably

the hybrid model was able to capture the spatial distribution of SSTs during the El Niño-Southern Oscillation (ENSO) events more accurately relative to a single model. Moreover

the prediction results of the hybrid model in different ocean regions exhibited lower prediction errors and higher correlations. The ablation experiments showed that sea surface wind (SSW) had different effects on SST at different times. By combining SST and SSW data

the model can make more-accurate predictions under different climatic conditions. The proposed hybrid model is able to predict SSTs quickly and accurately with better robustness during ENSO.

关键词

Keywords

references

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