Carbon Sinks and Variations of pCO(2) in the Southern Ocean From 1998 to 2018 Based on a Deep Learning Approach
Wang, Yanjun2,3; Li, Xiaofeng2,3; Song, Jinming1,2; Li, Xuegang1,2; Zhong, Guorong1,2; Zhang, Bin2,3
刊名IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
2021
卷号14页码:3495-3503
关键词Carbon sink feedforward neural network (FFNN) machine learning pCO(2) Southern Ocean
ISSN号1939-1404
DOI10.1109/JSTARS.2021.3066552
通讯作者Li, Xiaofeng(xiaofeng.li@ieee.org)
英文摘要The Southern Ocean comprises 25% of the global ocean surface area, accounts for nearly half of the total carbon sink of the global oceans, and is a place that significantly reduces the impacts of anthropogenic CO2 emissions. Due to the sparsity of observational data, the changes in Southern Ocean carbon sinks over time remain uncertain. In this study, we integrated correlation analysis and a feedforward neural network to improve the accuracy of carbon flux estimations in the Southern Ocean. Based on observation data from 1998-2018, we reconstructed the Southern Ocean's pCO(2) grid data during this period. The root-mean-square error obtained by fitting the observation data was 8.86 mu atm, indicating that the results were better than those of the two primary statistically based models in the Surface Ocean pCO(2) mapping intercomparison. The results also showed that the Southern Ocean's capacity to act as a carbon sink has gradually increased since 2000; it reduced during 2010-2013 but increased significantly after that. The Southern Ocean's seasonality is characterized by minimum carbon uptake in winter due to increased upwelling; this is followed by a rapid increase toward maximum uptake in summer, which is mainly biologically driven. There is an apparent double-ring structure in the Southern Ocean, as noted in other studies. This study confirms that the inner ring (50-70 degrees S) is a carbon source area gradually transforming into a carbon sink, while the outer ring (35-50 degrees S) continues to serve as a carbon sink.
资助项目National Key R&D Program of China[2017YFA0603201] ; 13th FiveYear Informatization Plan of the Chinese Academy of Sciences, Construction of Scientific Data Center System[XXH-13514] ; Big Earth Data Science Engineering Project[XDA19060104]
WOS研究方向Engineering ; Physical Geography ; Remote Sensing ; Imaging Science & Photographic Technology
语种英语
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号WOS:000638400600005
内容类型期刊论文
源URL[http://ir.qdio.ac.cn/handle/337002/170522]  
专题海洋研究所_海洋生态与环境科学重点实验室
通讯作者Li, Xiaofeng
作者单位1.Chinese Acad Sci, Inst Oceanol, Key Lab Marine Ecol & Environm Sci, Qingdao 266071, Peoples R China
2.Chinese Acad Sci, Ctr Ocean Megasci, Qingdao 266071, Peoples R China
3.Chinese Acad Sci, Dept Marine Sci Data Ctr, Inst Oceanol, Qingdao 266071, Peoples R China
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Wang, Yanjun,Li, Xiaofeng,Song, Jinming,et al. Carbon Sinks and Variations of pCO(2) in the Southern Ocean From 1998 to 2018 Based on a Deep Learning Approach[J]. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,2021,14:3495-3503.
APA Wang, Yanjun,Li, Xiaofeng,Song, Jinming,Li, Xuegang,Zhong, Guorong,&Zhang, Bin.(2021).Carbon Sinks and Variations of pCO(2) in the Southern Ocean From 1998 to 2018 Based on a Deep Learning Approach.IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,14,3495-3503.
MLA Wang, Yanjun,et al."Carbon Sinks and Variations of pCO(2) in the Southern Ocean From 1998 to 2018 Based on a Deep Learning Approach".IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 14(2021):3495-3503.
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