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

doi:10.1016/j.dynatmoce.2023.101403

IOCAS-IR > 海洋环流与波动重点实验室

	Advancing ocean subsurface thermal structure estimation in the Pacific Ocean: A multi-model ensemble machine learning approach
	Qi, Jifeng 1,2; Zhang, Linlin 1,2; Yin, Baoshu 1,2,4; Li, Delei 1,2; Xie, Bowen 3; Sun, Guimin 1,2
	2023-12-01
发表期刊	DYNAMICS OF ATMOSPHERES AND OCEANS
ISSN	0377-0265
卷号	104 页码:16
通讯作者	Qi, Jifeng([email protected])
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.
关键词	Ensemble machine learning model Satellite observations Ocean subsurface thermal structure Pacific ocean
DOI	10.1016/j.dynatmoce.2023.101403
收录类别	SCI
语种	英语
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
WOS类目	Geochemistry & Geophysics ; Meteorology & Atmospheric Sciences ; Oceanography
WOS记录号	WOS:001150093200001
出版者	ELSEVIER
WOS关键词	SEA-SURFACE SALINITY ; IN-SITU ; DATA ASSIMILATION ; TEMPERATURE ; CIRCULATION ; IMPACT ; MODEL
引用统计	被引频次：3[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.qdio.ac.cn/handle/337002/184297
专题	海洋环流与波动重点实验室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.Chinese Acad Sci, Inst Oceanol, CAS Key Lab Ocean Circulat & Waves, Qingdao, Peoples R China 2.Univ Chinese Acad Sci, Beijing, Peoples R China 3.Qingdao Univ Sci & Technol, Sch Math & Phys, Qingdao, Peoples R China 4.Chinese Acad Sci, CAS Engn Lab Marine Ranching, Inst Oceanol, Qingdao, Peoples R China
第一作者单位	中国科学院海洋研究所
通讯作者单位	中国科学院海洋研究所
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.

条目包含的文件
文件名称/大小	文献类型	版本类型	开放类型	使用许可
1-s2.0-S037702652300（13138KB）	期刊论文	出版稿	限制开放	CC BY-NC-SA	浏览