Estimation of deep-water formation intensity using multi-satellite measurements in the East Sea (Japan sea) SCIE SCOPUS

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Title
Estimation of deep-water formation intensity using multi-satellite measurements in the East Sea (Japan sea)
Author(s)
Kim, Myeong-Hyeon; Kim, Dae-Won; Kim, Deok Su; Li, Feili; Jo, Young-Heon
KIOST Author(s)
Kim, Deok Su(김덕수)
Alternative Author(s)
김덕수
Publication Year
2023-04
Abstract
Deep-water formation (DWF) in the East Sea (Japan Sea) can affect the regional meridional overturning circulation via air–sea interactions. Thus, it is important to monitor long-term changes to understand the future regional climate and, thus, marine ecosystem changes associated with DWF. In this study, we propose a new DWF estimation method based on satellite measurements rather than on sparse in situ measurements. First, we estimated the volume of the mixed water (VMW) as a proxy for the intensity of the DWF based on the HYbrid Coordinate Ocean Model (HYCOM) data and examined the VMW signals in satellite measurements of the chlorophyll-a (Chl-a) concentrations and sea-level anomalies (SLA) from January to March for 21 years (1998–2018). When the DWF was intense in the East Sea, Chl-a and SLA decreased. This inverse relationship between the DWF and sea-surface features was much clearer during strong DWF years than during weak DWF years. The decreasing rates of Chl-a and SLA over strong DWF years were −0.0044 mg m−3 and −0.0020 m day−1, respectively. Furthermore, we estimated the VMW from the SLA and Chl-a using a multi-linear regression. These findings will allow us to understand the intensity of the DWF through VMW using multi-satellite measurements alone.
ISSN
0967-0637
URI
https://sciwatch.kiost.ac.kr/handle/2020.kiost/43909
DOI
10.1016/j.dsr.2023.103969
Bibliographic Citation
Deep-Sea Research Part I: Oceanographic Research Papers, v.194, 2023
Publisher
Pergamon Press Ltd.
Keywords
East sea (Japan Sea); Marginal climate change; Deep-water formation; Satellite sea-level anomaly measurements; Satellite chlorophyll-a measurements; Multi-linear regression
Type
Article
Language
English
Document Type
Article
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