Remote Sensing of Sea Surface pCO2 in the Bering Sea in Summer Based on a Mechanistic Semi-Analytical Algorithm (MeSAA)

The Bering Sea, one of the largest and most productive marginal seas, is a crucial carbon sink for the marine carbonate system. However, restricted by the tough observation conditions, few underway datasets of sea surface partial pressure of carbon dioxide (pCO2) have been obtained, with most of the...

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Published in:Remote Sensing
Main Authors: Xuelian Song, Yan Bai, Wei-Jun Cai, Chen-Tung Chen, Delu Pan, Xianqiang He, Qiankun Zhu
Format: Text
Language:English
Published: Multidisciplinary Digital Publishing Institute 2016
Subjects:
Online Access:https://doi.org/10.3390/rs8070558
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spelling ftmdpi:oai:mdpi.com:/2072-4292/8/7/558/ 2023-08-20T04:05:35+02:00 Remote Sensing of Sea Surface pCO2 in the Bering Sea in Summer Based on a Mechanistic Semi-Analytical Algorithm (MeSAA) Xuelian Song Yan Bai Wei-Jun Cai Chen-Tung Chen Delu Pan Xianqiang He Qiankun Zhu agris 2016-06-30 application/pdf https://doi.org/10.3390/rs8070558 EN eng Multidisciplinary Digital Publishing Institute https://dx.doi.org/10.3390/rs8070558 https://creativecommons.org/licenses/by/4.0/ Remote Sensing; Volume 8; Issue 7; Pages: 558 sea surface p CO 2 satellite remote sensing semi-analytical algorithm the Bering Sea marine carbonate system Text 2016 ftmdpi https://doi.org/10.3390/rs8070558 2023-07-31T20:54:45Z The Bering Sea, one of the largest and most productive marginal seas, is a crucial carbon sink for the marine carbonate system. However, restricted by the tough observation conditions, few underway datasets of sea surface partial pressure of carbon dioxide (pCO2) have been obtained, with most of them in the eastern areas. Satellite remote sensing data can provide valuable information covered by a large area synchronously with high temporal resolution for assessments of pCO2 that subsequently allow quantification of air-sea carbon dioxide 2 flux. However, pCO2 in the Bering Sea is controlled by multiple factors and thus it is hard to develop a remote sensing algorithm with empirical regression methods. In this paper pCO2 in the Bering Sea from July to September was derived based on a mechanistic semi-analytical algorithm (MeSAA). It was assumed that the observed pCO2 can be analytically expressed as the sum of individual components controlled by major factors. First, a reference water mass that was minimally influenced by biology and mixing was identified in the central basin, and then thermodynamic and biological effects were parameterized for the entire area. Finally, we estimated pCO2 with satellite temperature and chlorophyll data. Satellite results agreed well with the underway observations. Our study suggested that throughout the Bering Sea the biological effect on pCO2 was more than twice as important as temperature, and contributions of other effects were relatively small. Furthermore, satellite observations demonstrate that the spring phytoplankton bloom had a delayed effect on summer pCO2 but that the influence of this biological event varied regionally; it was more significant on the continental slope, with a later bloom, than that on the shelf with an early bloom. Overall, the MeSAA algorithm was not only able to estimate pCO2 in the Bering Sea for the first time, but also provided a quantitative analysis of the contribution of various processes that influence pCO2. Text Bering Sea MDPI Open Access Publishing Bering Sea Remote Sensing 8 7 558
institution Open Polar
collection MDPI Open Access Publishing
op_collection_id ftmdpi
language English
topic sea surface p CO 2
satellite remote sensing
semi-analytical algorithm
the Bering Sea
marine carbonate system
spellingShingle sea surface p CO 2
satellite remote sensing
semi-analytical algorithm
the Bering Sea
marine carbonate system
Xuelian Song
Yan Bai
Wei-Jun Cai
Chen-Tung Chen
Delu Pan
Xianqiang He
Qiankun Zhu
Remote Sensing of Sea Surface pCO2 in the Bering Sea in Summer Based on a Mechanistic Semi-Analytical Algorithm (MeSAA)
topic_facet sea surface p CO 2
satellite remote sensing
semi-analytical algorithm
the Bering Sea
marine carbonate system
description The Bering Sea, one of the largest and most productive marginal seas, is a crucial carbon sink for the marine carbonate system. However, restricted by the tough observation conditions, few underway datasets of sea surface partial pressure of carbon dioxide (pCO2) have been obtained, with most of them in the eastern areas. Satellite remote sensing data can provide valuable information covered by a large area synchronously with high temporal resolution for assessments of pCO2 that subsequently allow quantification of air-sea carbon dioxide 2 flux. However, pCO2 in the Bering Sea is controlled by multiple factors and thus it is hard to develop a remote sensing algorithm with empirical regression methods. In this paper pCO2 in the Bering Sea from July to September was derived based on a mechanistic semi-analytical algorithm (MeSAA). It was assumed that the observed pCO2 can be analytically expressed as the sum of individual components controlled by major factors. First, a reference water mass that was minimally influenced by biology and mixing was identified in the central basin, and then thermodynamic and biological effects were parameterized for the entire area. Finally, we estimated pCO2 with satellite temperature and chlorophyll data. Satellite results agreed well with the underway observations. Our study suggested that throughout the Bering Sea the biological effect on pCO2 was more than twice as important as temperature, and contributions of other effects were relatively small. Furthermore, satellite observations demonstrate that the spring phytoplankton bloom had a delayed effect on summer pCO2 but that the influence of this biological event varied regionally; it was more significant on the continental slope, with a later bloom, than that on the shelf with an early bloom. Overall, the MeSAA algorithm was not only able to estimate pCO2 in the Bering Sea for the first time, but also provided a quantitative analysis of the contribution of various processes that influence pCO2.
format Text
author Xuelian Song
Yan Bai
Wei-Jun Cai
Chen-Tung Chen
Delu Pan
Xianqiang He
Qiankun Zhu
author_facet Xuelian Song
Yan Bai
Wei-Jun Cai
Chen-Tung Chen
Delu Pan
Xianqiang He
Qiankun Zhu
author_sort Xuelian Song
title Remote Sensing of Sea Surface pCO2 in the Bering Sea in Summer Based on a Mechanistic Semi-Analytical Algorithm (MeSAA)
title_short Remote Sensing of Sea Surface pCO2 in the Bering Sea in Summer Based on a Mechanistic Semi-Analytical Algorithm (MeSAA)
title_full Remote Sensing of Sea Surface pCO2 in the Bering Sea in Summer Based on a Mechanistic Semi-Analytical Algorithm (MeSAA)
title_fullStr Remote Sensing of Sea Surface pCO2 in the Bering Sea in Summer Based on a Mechanistic Semi-Analytical Algorithm (MeSAA)
title_full_unstemmed Remote Sensing of Sea Surface pCO2 in the Bering Sea in Summer Based on a Mechanistic Semi-Analytical Algorithm (MeSAA)
title_sort remote sensing of sea surface pco2 in the bering sea in summer based on a mechanistic semi-analytical algorithm (mesaa)
publisher Multidisciplinary Digital Publishing Institute
publishDate 2016
url https://doi.org/10.3390/rs8070558
op_coverage agris
geographic Bering Sea
geographic_facet Bering Sea
genre Bering Sea
genre_facet Bering Sea
op_source Remote Sensing; Volume 8; Issue 7; Pages: 558
op_relation https://dx.doi.org/10.3390/rs8070558
op_rights https://creativecommons.org/licenses/by/4.0/
op_doi https://doi.org/10.3390/rs8070558
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