Carbon Sinks and Variations of pCO(2) in the Southern Ocean From 1998 to 2018 Based on a Deep Learning Approach

doi:10.1109/JSTARS.2021.3066552

	Carbon Sinks and Variations of pCO(2) in the Southern Ocean From 1998 to 2018 Based on a Deep Learning Approach
	Wang, Yanjun 1,2; Li, Xiaofeng 1,2; Song, Jinming2,3 ; Li, Xuegang 2,3; Zhong, Guorong 2,3; Zhang, Bin 1,2
	2021
发表期刊	IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
ISSN	1939-1404
卷号	14 页码:3495-3503
通讯作者	Li, Xiaofeng(xiaofeng.li@ieee.org)
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.
关键词	Carbon sink feedforward neural network (FFNN) machine learning pCO(2) Southern Ocean
DOI	10.1109/JSTARS.2021.3066552
收录类别	SCI
语种	英语
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
WOS类目	Engineering, Electrical & Electronic ; Geography, Physical ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:000638400600005
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：14[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.qdio.ac.cn/handle/337002/170523
专题	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, Dept Marine Sci Data Ctr, Inst Oceanol, Qingdao 266071, Peoples R China 2.Chinese Acad Sci, Ctr Ocean Megasci, Qingdao 266071, Peoples R China 3.Chinese Acad Sci, Inst Oceanol, Key Lab Marine Ecol & Environm Sci, Qingdao 266071, Peoples R China
第一作者单位	中国科学院海洋研究所; 中国科学院海洋大科学研究中心
通讯作者单位	中国科学院海洋研究所; 中国科学院海洋大科学研究中心
推荐引用方式 GB/T 7714	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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