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Advancing ocean subsurface thermal structure estimation in the Pacific Ocean: A multi-model ensemble machine learning approach

文献类型：期刊论文

作者Qi, Jifeng2,3; Zhang, Linlin2,3; Yin, Baoshu2,3,4; Li, Delei2,3; Xie, Bowen1; Sun, Guimin2,3
刊名DYNAMICS OF ATMOSPHERES AND OCEANS
出版日期2023-12-01
卷号104页码:16
ISSN号0377-0265
关键词Ensemble machine learning model Satellite observations Ocean subsurface thermal structure Pacific ocean
DOI10.1016/j.dynatmoce.2023.101403
通讯作者Qi, Jifeng(jfqi@qdio.ac.cn)
英文摘要Estimation of the ocean subsurface thermal structure (OSTS) is important for understanding thermodynamic processes and climate variability. In the present study, a novel multi-model ensemble machine learning (Ensemble-ML) model is developed to retrieve subsurface thermal structure in the Pacific Ocean by integrating sea surface data with Argo observations. The Ensemble-ML model integrates four individual machine learning models to enhance estimation accuracy and reliability. Our results exhibit good agreement between the satellite sea surface temperature (SST) and sea surface salinity (SSS) data and Argo observations, providing validation for the utilization of these datasets in the Ensemble-ML model. The Ensemble-ML model exhibits better performance compared to individual machine learning models, with an average root mean square error (RMSE) of 0.3273 degrees C and an average coefficient of determination (R2) of 0.9905. Notably, incorporating geographical information as input variables enhance model performance, emphasizing the importance of considering spatial context in OSTS estimation. The Ensemble-ML model accurately captures the spatial distribution of OSTS across depths and seasons in the Pacific Ocean, effectively reproducing critical temperature features while maintaining strong agreement with Argo observations. Nevertheless, its performance shows relative weakness within the thermocline layer and the equatorial Pacific region (spanning from 10 degrees S to 10 degrees N latitude), which are characterized by complex circulation systems. Despite these challenges, the Ensemble-ML model effectively reproduces the spatial distribution of OSTS of the Pacific Ocean. This indicates the potential of machine learning models, particularly ensemble models, for enhancing OSTS estimation in the Pacific Ocean and other regions, offering valuable insights for future research and applications in physical oceanography.
WOS关键词SEA-SURFACE SALINITY ; IN-SITU ; DATA ASSIMILATION ; TEMPERATURE ; CIRCULATION ; IMPACT ; MODEL
资助项目National Key Research and Development Program of China[2022YFF0801400] ; National Key Research and Development Program of China[LSKJ202202403] ; National Natural Science Foundation of China[42176010]
WOS研究方向Geochemistry & Geophysics ; Meteorology & Atmospheric Sciences ; Oceanography
语种英语
出版者ELSEVIER
WOS记录号WOS:001150093200001
源URL[http://ir.qdio.ac.cn/handle/337002/184296]  
专题海洋研究所_海洋环流与波动重点实验室
海洋研究所_海洋生态与环境科学重点实验室
通讯作者Qi, Jifeng
作者单位1.Qingdao Univ Sci & Technol, Sch Math & Phys, Qingdao, Peoples R China
2.Chinese Acad Sci, Inst Oceanol, CAS Key Lab Ocean Circulat & Waves, Qingdao, Peoples R China
3.Univ Chinese Acad Sci, Beijing, Peoples R China
4.Chinese Acad Sci, CAS Engn Lab Marine Ranching, Inst Oceanol, Qingdao, Peoples R China
推荐引用方式
GB/T 7714		 Qi, Jifeng,Zhang, Linlin,Yin, Baoshu,et al. Advancing ocean subsurface thermal structure estimation in the Pacific Ocean: A multi-model ensemble machine learning approach[J]. DYNAMICS OF ATMOSPHERES AND OCEANS,2023,104:16.
APA Qi, Jifeng,Zhang, Linlin,Yin, Baoshu,Li, Delei,Xie, Bowen,&Sun, Guimin.(2023).Advancing ocean subsurface thermal structure estimation in the Pacific Ocean: A multi-model ensemble machine learning approach.DYNAMICS OF ATMOSPHERES AND OCEANS,104,16.
MLA Qi, Jifeng,et al."Advancing ocean subsurface thermal structure estimation in the Pacific Ocean: A multi-model ensemble machine learning approach".DYNAMICS OF ATMOSPHERES AND OCEANS 104(2023):16.

入库方式： OAI收割

来源：海洋研究所

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