Reconstructed 3-D ocean temperature derived from remotely sensed sea surface measurements for mixed layer depth analysis SCIE SCOPUS

Cited 5 time in WEB OF SCIENCE Cited 5 time in Scopus
Title
Reconstructed 3-D ocean temperature derived from remotely sensed sea surface measurements for mixed layer depth analysis
Author(s)
Jeong Y.; Hwang J.; Park J.; Jang C.J.; Jo Y.-H.
KIOST Author(s)
Jang, Chan Joo(장찬주)
Publication Year
2019-12
Abstract
The mixed layer depth (MLD) is generally estimated using in situ or model data. However, MLD analyses have limitations due to the sparse resolution of the observed data. Therefore, this study reconstructs three-dimensional (3D) ocean thermal structures using only satellite sea surface measurements for a higher spatial and longer temporal resolution than that of Argo and diagnoses the decadal variation of global MLD variability. To simulate the ocean thermal structures, the relationship between the ocean subsurface temperature and the sea surface fields was computed based on gridded Argo data. Based on this relationship, high spatial resolution and extended periods of satellite-derived altimeter, sea surface temperature (SST), and wind stress data were used to estimate the 3D ocean thermal structures with 0.25° spatial resolution and 26 standard depth levels (5-2000 m) for 24 years (1993-2016). Then, the MLD was calculated using a temperature threshold method (DT = 0.2 °C) and correlated reasonably well (>0.9) with other MLD datasets. The extended 24-year data enabled us to analyze the decadal variability of the MLD. The global linear trend of the 24-year MLD is -0.110 m yr-1; however, from 1998 to 2012, the linear trend is -0.003 m yr-1 which is an order of magnitude smaller than that of other periods and corresponds to a global warming hiatus period. Via comparisons between the trends of the SST anomalies and the MLD anomalies, we tracked how the MLD trend changes in response to the global warming hiatus. © 2019 by the authors.
ISSN
2072-4292
URI
https://sciwatch.kiost.ac.kr/handle/2020.kiost/440
DOI
10.3390/rs11243018
Bibliographic Citation
Remote Sensing, v.11, no.24, 2019
Publisher
MDPI AG
Keywords
Ensemble empirical mode decomposition; Hiatus; Mixed layer depth; Multiple linear regression; remote sensing; Subsurface temperature
Type
Article
Language
English
Document Type
Article
Publisher
MDPI AG
Related Researcher
Research Interests

upper ocean dynamics,regional climate modeling,ocean climate change,해양상층역학,지역기후모델링,해양기후변화

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