Remote Sensing of Sea Surface pCO2 in the Bering Sea in Summer Based on a Mechanistic Semi-Analytical Algorithm (MeSAA)
AbstractThe 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. View Full-Text
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Song, X.; Bai, Y.; Cai, W.-J.; Chen, C.-T.A.; Pan, D.; He, X.; Zhu, Q. Remote Sensing of Sea Surface pCO2 in the Bering Sea in Summer Based on a Mechanistic Semi-Analytical Algorithm (MeSAA). Remote Sens. 2016, 8, 558.
Song X, Bai Y, Cai W-J, Chen C-TA, Pan D, He X, Zhu Q. Remote Sensing of Sea Surface pCO2 in the Bering Sea in Summer Based on a Mechanistic Semi-Analytical Algorithm (MeSAA). Remote Sensing. 2016; 8(7):558.Chicago/Turabian Style
Song, Xuelian; Bai, Yan; Cai, Wei-Jun; Chen, Chen-Tung A.; Pan, Delu; He, Xianqiang; Zhu, Qiankun. 2016. "Remote Sensing of Sea Surface pCO2 in the Bering Sea in Summer Based on a Mechanistic Semi-Analytical Algorithm (MeSAA)." Remote Sens. 8, no. 7: 558.