IOCAS-IR  > 海洋环流与波动重点实验室
Impact of surface and subsurface-intensified eddies on sea surface temperature and chlorophyll a in the northern Indian Ocean utilizing deep learning
Liu, Yingjie; Li, Xiaofeng
2023-11-16
发表期刊OCEAN SCIENCE
ISSN1812-0784
卷号19期号:6页码:1579-1593
通讯作者Li, Xiaofeng([email protected])
摘要Mesoscale eddies, including surface-intensified eddies (SEs) and subsurface-intensified eddies (SSEs), significantly influence phytoplankton distribution in the ocean. Nevertheless, due to the sparse in situ data, understanding of the characteristics of SSEs and their influence on chlorophyll a (Chl a ) concentration is still unclear. Consequently, the study utilized a deep learning model to extract SEs and SSEs in the northern Indian Ocean (NIO) from 2000 to 2015, using satellite-derived sea surface height (SSH) and sea surface temperature (SST) data. The analysis revealed that SSEs accounted for 39 % of the total eddies in the NIO, and their SST signatures exhibited opposite behaviour compared to SEs. Furthermore, by integrating ocean colour remote-sensing data, the study investigated the contrasting impacts of SEs and SSEs on Chl a concentration in two basins of the NIO, the Arabian Sea (AS) and the Bay of Bengal (BoB), known for their disparate biological productivity. In the AS, SEs induced Chl a anomalies that were 2 to 3 times higher than those caused by SSEs. Notably, there were no significant differences in Chl a anomalies induced by the same type of eddies between summer and winter. In contrast, the BoB exhibited distinct seasonal variations, where SEs induced slightly higher Chl a anomalies than SSEs during the summer, while substantial differences were observed during the winter. Specifically, subsurface-intensified anticyclonic eddies (SSAEs) led to positive Chl a anomalies, contrasting the negative anomalies induced by surface-intensified anticyclonic eddies (SAEs) with comparable magnitudes. Moreover, while both subsurface-intensified cyclonic eddies (SSCEs) and surface-intensified cyclonic eddies (SCEs) resulted in positive Chl a anomalies during winter in the BoB, the magnitude of SSCEs was only one-third of that induced by SCEs. Besides, subsurface Chl a induced by SSAEs (SSCEs) is similar to 0.1 mg m - 3 greater (less) than that caused by SAEs (SCEs) in the upper 30 (50) m using Biogeochemical Argo profiles. The distinct Chl a between SEs and SSEs can be attributed to their contrasting subsurface structures revealed by Argo profiles. Compared to SAEs (SCEs), SSAEs (SSCEs) enhance (decrease) production via the convex (concave) of the isopycnals that occur around the mixed layer. The study provides a valuable approach to investigating subsurface eddies and contributes to a comprehensive understanding of their influence on chlorophyll concentration.
DOI10.5194/os-19-1579-2023
收录类别SCI
语种英语
资助项目Qingdao National Laboratory for Marine Science and Technology
WOS研究方向Meteorology & Atmospheric Sciences ; Oceanography
WOS类目Meteorology & Atmospheric Sciences ; Oceanography
WOS记录号WOS:001170616600001
出版者COPERNICUS GESELLSCHAFT MBH
WOS关键词MESOSCALE EDDIES ; ARABIAN SEA ; INTRATHERMOCLINE EDDIES ; INTERANNUAL VARIABILITY ; SATELLITE-OBSERVATIONS ; PHYTOPLANKTON BIOMASS ; EDDY ; BAY ; DYNAMICS ; LAYER
引用统计
被引频次:4[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/337002/184518
专题海洋环流与波动重点实验室
通讯作者Li, Xiaofeng
作者单位Chinese Acad Sci, CAS Key Lab Ocean Circulat & Waves, Inst Oceanol, Qingdao 266071, 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
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GB/T 7714
Liu, Yingjie,Li, Xiaofeng. Impact of surface and subsurface-intensified eddies on sea surface temperature and chlorophyll a in the northern Indian Ocean utilizing deep learning[J]. OCEAN SCIENCE,2023,19(6):1579-1593.
APA Liu, Yingjie,&Li, Xiaofeng.(2023).Impact of surface and subsurface-intensified eddies on sea surface temperature and chlorophyll a in the northern Indian Ocean utilizing deep learning.OCEAN SCIENCE,19(6),1579-1593.
MLA Liu, Yingjie,et al."Impact of surface and subsurface-intensified eddies on sea surface temperature and chlorophyll a in the northern Indian Ocean utilizing deep learning".OCEAN SCIENCE 19.6(2023):1579-1593.
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