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Unveiling the Drivers of Tropical Indian Ocean Warming through Machine Learning-Assisted Surface Wind

文献类型：期刊论文

作者Guo, Weihao1,5,6; Zhang, Rongwang1,5,6; Wang, Xin1,5,6; Wang, Chunzai1,5,6; Li, Xiaofeng3,4; Han, Weiqing2; Zhang, Lei1,5,6
刊名JOURNAL OF CLIMATE
出版日期2025-11-01
卷号38期号:22页码:6763-6779
关键词Indian Ocean Climate change Trends Tropical variability Machine learning
ISSN号0894-8755
DOI10.1175/JCLI-D-25-0003.1
通讯作者Zhang, Rongwang(rwzhang@scsio.ac) ; Wang, Xin(wangxin@scsio.ac.cn)
英文摘要The tropical Indian Ocean (TIO) has experienced pronounced warming trends in recent decades, with dynamical processes recognized as key drivers. However, the role of thermal processes remains uncertain due to discrepancies in surface-wind-induced heat flux across existing datasets. The present study introduces a random forest machine learning algorithm that synergistically integrates the advantages of in situ observations and satellite data, yielding a monthly surface wind [machine learning-assisted wind (MLAWind)] dataset and corresponding air-sea heat flux from 1950 to 2022 with a horizontal resolution of 1 degrees 3 1 degrees. The MLAWind exhibits high accuracy and robust generalization capability based on evaluations using both satellite and buoy observations. Besides, it is capable of effectively representing spatial and temporal characteristics of surface wind. In contrast to the majority of existing reanalysis datasets, MLAWind reveals a decline in surface wind over the TIO since 1950, which is further supported by the west-east asymmetrical variations in sea surface height and thermocline depth. The attenuation of surface wind is more significant in the eastern TIO as compared to the western TIO, leading to a remarkable reduction in evaporative cooling within the eastern TIO. The thermal processes associated with surface-wind-induced heat flux serve as the essential drivers of the warming in the eastern TIO, with a contribution accounting for approximately 45% of that of dynamical processes. The findings of our study challenge existing reanalysis results but are aligned with state-of-the-art models, highlighting that the significance of thermal processes is substantially underestimated in most existing reanalysis datasets.
WOS关键词LATENT-HEAT FLUX ; WALKER CIRCULATION ; BULK PARAMETERIZATION ; REANALYSIS ; VARIABILITY ; BUDGET ; WAVE
资助项目National Natural Science Foundation of China[41925024] ; National Natural Science Foundation of China[42376027] ; National Natural Science Foundation of China[42406197] ; National Natural Science Foundation of China[42330404] ; National Key R&D Program of China[2022YFF0801400] ; Development Fund of South China Sea Institute of Oceanology of the Chinese Academy of Sciences[SCSIO202208] ; Special Fund of South China Sea Institute of Oceanology of the Chinese Academy of Sciences[SCSIO2023QY01]
WOS研究方向Meteorology & Atmospheric Sciences
语种英语
WOS记录号WOS:001604552200001
出版者AMER METEOROLOGICAL SOC
源URL[http://ir.qdio.ac.cn/handle/337002/203707]  
专题海洋研究所_海洋环流与波动重点实验室
通讯作者Zhang, Rongwang; Wang, Xin
作者单位1.Chinese Acad Sci, South China Sea Inst Oceanol, Guangdong Key Lab Ocean Remote Sensing, Guangzhou, Peoples R China
2.Univ Colorado, Dept Atmospher & Ocean Sci, Boulder, CO USA
3.Chinese Acad Sci, Ctr Ocean Mega Sci, Qingdao, Peoples R China
4.Chinese Acad Sci, Inst Oceanol, Key Lab Ocean Circulat & Waves, Qingdao, Peoples R China
5.Chinese Acad Sci, South China Sea Inst Oceanol, Global Ocean & Climate Res Ctr, Guangzhou, Peoples R China
6.Chinese Acad Sci, South China Sea Inst Oceanol, State Key Lab Trop Oceanog, Guangzhou, Peoples R China
推荐引用方式
GB/T 7714		 Guo, Weihao,Zhang, Rongwang,Wang, Xin,et al. Unveiling the Drivers of Tropical Indian Ocean Warming through Machine Learning-Assisted Surface Wind[J]. JOURNAL OF CLIMATE,2025,38(22):6763-6779.
APA Guo, Weihao.,Zhang, Rongwang.,Wang, Xin.,Wang, Chunzai.,Li, Xiaofeng.,...&Zhang, Lei.(2025).Unveiling the Drivers of Tropical Indian Ocean Warming through Machine Learning-Assisted Surface Wind.JOURNAL OF CLIMATE,38(22),6763-6779.
MLA Guo, Weihao,et al."Unveiling the Drivers of Tropical Indian Ocean Warming through Machine Learning-Assisted Surface Wind".JOURNAL OF CLIMATE 38.22(2025):6763-6779.

入库方式： OAI收割

来源：海洋研究所

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