中国陆架边缘海碳源汇的强度与控制因素

宋金明; 袁华茂; 李学刚; 曲宝晓; 钟国荣; 邢建伟; 马骏; 段丽琴; 王启栋; 戴佳佳; 刘珊珊

中国陆架边缘海碳源汇的强度与控制因素

宋金明^{1, 2, 3, ,},
袁华茂^{1, 2, 3},
李学刚^{1, 2, 3},
曲宝晓^{1, 2, 3},
钟国荣¹,
邢建伟^{1, 2, 3},
马骏^{1, 2, 3},
段丽琴^{1, 2, 3},
王启栋^{1, 2, 3},
戴佳佳^{1, 2, 3},
刘珊珊¹

1.
中国科学院海洋研究所，青岛 266404
2.
青岛海洋科技中心海洋生态与环境科学功能实验室，青岛 266237
3.
中国科学院大学，北京 100049

基金项目: 崂山实验室项目（ LSKJ202204001）;国家重点研发计划项目（2022YFC3104305）。

详细信息

通讯作者:
宋金明（1964—），研究员，博士生导师, 长期从事海洋生源要素的生物地球化学过程研究。E-mail: jmsong@qdio.ac.cn

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出版历程
- 收稿日期: 2024-12-27
- 修回日期: 2025-03-28
- 网络出版日期: 2025-04-24

Strength and control factors of carbon source/sinks in the China margin seas

Song Jinming^{1, 2, 3
, ,},
Yuan Huamao^{1, 2, 3},
Li Xuegang^{1, 2, 3},
Qu Baoxiao^{1, 2, 3},
Zhong Guorong¹,
Xing Jianwei^{1, 2, 3},
Ma Jun^{1, 2, 3},
Duan Liqin^{1, 2, 3},
Wang Qidong^{1, 2, 3},
Dai Jiajia^{1, 2, 3},
Liu Shanshan¹

1.
Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266404, China
2.
Marine Ecology and Environmental Science Laboratory, Qingdao Marine Science and Technology Center, Qingdao 266237, China
3.
University of Chinese Academy of Sciences, Beijing 100049, China

摘要

摘要: 基于观测和大数据机器学习的研究表明，中国陆架边缘海（渤海、黄海、东海和南海）的年均碳汇强度为−10.2±4.4 TgC/a。黄海、东海和南海北部全年吸收大气CO₂，而渤海、南海南部和长江口沿岸向大气释放CO₂。东海碳汇最强，平均通量为−10.5±4.5 TgC/a，黄海碳汇较小，为−2.1±0.9 TgC/a。面积最小的渤海碳源为+0.3±0.1 TgC/a，而面积最大的南海其碳源强度为+2.0±0.9 TgC/a。从季节上看，冬季我国边缘海碳汇强度最大，为−45.7±19.7 TgC/a，春季较弱，为−16.9±7.3 TgC/a。而夏季和秋季我国边缘海整体为碳源，平均分别为+11.9±5.1 TgC/a和+9.9±4.3 TgC/a。中国陆架边缘海碳源汇强度平均不确定度为±43.0%（±4.4 TgC a⁻¹），比以往用局部海域观测数据导致的平均90.5%的不确定度，基于观测数据机器学习构建格点数据的估算结果不确定度降低了47.5%。海-气界面CO₂分压差的差异和由风速引起的CO₂交换速率差异是导致我国边缘海海-气界面碳源汇时空变化的关键，究其根本是源于水文动力、陆源输入、浮游生物群落及大洋跨陆架输送等多种因素和过程对我国边缘海海-气碳交换的控制作用。
- 碳源汇强度 /
- 海气界面二氧化碳通量 /
- 控制因素 /
- 中国陆架边缘海
Abstract: Research based on observation and machine learning shows that the average annual carbon sink intensity of the China marginal seas (Bohai Sea, Yellow Sea, East China Sea and South China Sea) is −10.2±4.4 TgC/a. The Yellow Sea, the East China Sea and the northern region of the South China Sea absorb atmospheric CO₂, while the Bohai Sea, the southern South China Sea and the Yangtze River Estuary release CO₂ into the atmosphere. The East China Sea carbon sinks are the strongest, with an average flux of −10.5±4.5 TgC/a, and the Yellow Sea carbon sinks are relatively small, −2.1±0.9 TgC/a. The smallest carbon source appeared in the Bohai Sea is +0.3±0.1 TgC/a, while the largest carbon source intensity appeared in the South China Sea is +2.0±0.9 TgC/a. According to seasonal change, the carbon sink intensity in winter is the highest in the China margin seas, with −45.7±19.7 TgC/a, and weaker in spring, at −16.9±7.3 TgC/a. In summer and autumn, China's marginal seas as a whole are carbon sources, with an average of +11.9±5.1 TgC/a and +9.9±4.3 TgC/a respectively. The average uncertainty of carbon source sink strength in China marginal seas is ±43.0% (±4.4 TgC a⁻¹) in the estimation results of constructing lattiness data based on observation data and machine learning, which is 47.5% of the reduced uncertainty less than 90.5% of the average uncertainty by only regional observation data. The difference of P_CO2 at the sea-air interface and the difference in the CO₂ exchange rate caused by wind speed are the key controlling factors of carbon source/sink in the China marginal seas. In fact, they are controlled by the basic factors and processes such as hydrodynamics, land-based source input, plankton communities and ocean-shelf transportation.
- carbon source/sink strength /
- carbon dioxide flux across the sea-air interface /
- control factors /
- China marginal seas

HTML全文

图 1 中国陆架边缘海表层海水pCO₂实测数据站位

Fig. 1 PCO₂ measured stations of seawater on the surface of the China margin seas

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图 2 基于现场观测的机器学习构建格点数据的我国陆架边缘海碳源汇估算体系

Fig. 2 Carbon source/sink estimation system based on on-site observation and machine learning construction grid data of the China margin seas

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图 3 集成学习广义回归-前反馈神经网络算法结构

Fig. 3 General regression neural network approach to reconstruct structure

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图 4 我国陆架边缘海季节平均海-气界面CO₂分压差空间分布（春、夏、秋、冬）

Fig. 4 Seasonal average sea-air interface CO₂ pressure difference spatial distributions (spring, summer, autumn, winter)

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图 5 我国陆架边缘海主要海域平均海-气界面CO₂分压差年际变化

Fig. 5 Annual variations of pCO₂ difference in the China margin seas

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图 6 中国陆架边缘海季节平均海-气界面CO₂传输速率空间分布（cm/h）

Fig. 6 Spatial distributions of the average CO₂ transmission rates at sea-air interface of the China margin seas

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图 7 中国陆架边缘海平均海-气界面CO₂交换速率年际变化

Fig. 7 Inter-annual changes in the average CO₂ exchange rates of the sea-air interface on the China margin seas

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图 8 2003−2022年中国陆架边缘海平均碳源汇分布格局（渤黄东南海平均总碳通量）（BS:渤海; YS:黄海; ECS:东海; SCS:南海）

Fig. 8 Distributions of the average carbon source/ sink in China margin seas from 2003 to 2022 (the average CO₂ fluxes of the Bohai Sea, the Yellow Sea, the East China Sea and the South China Sea)

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图 9 中国边缘海碳源汇强度季节变化（mol m⁻² a⁻¹）

Fig. 9 Seasonal changes of the carbon/sinks strength in China margin seas(mol m⁻² a⁻¹)

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图 10 中国边缘海海-气界面碳通量年际变化

Fig. 10 Annual changes in CO₂ fluxes across the sea-air interface on China margin seas

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图 11 近20年来我国陆架边缘海碳源汇分布的演化（mol m⁻² a⁻¹）

Fig. 11 The distribution evolution of CO₂ fluxes of China margin seas in the past 20 years (mol m⁻² a⁻¹)

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图 12 我国陆架边缘海碳源汇强度年际变化趋势的空间分布（mol m⁻² a⁻²）

Fig. 12 Spatial distributions of the annual variation trend of carbon source/sink strength in China margin seas

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表 1 2003-2022年间中国陆架边缘海长期平均碳源汇强度（TgC a⁻¹）

Tab. 1 Average carbon source/sink strength of China margin seas from 2003 to 2022

海域	春季	夏季	秋季	冬季	全年平均
渤海	−0.4±0.2	0.5±0.2	1.2±0.5	0.1±0.0	0.3±0.1
黄海	−4.6±2.0	−1.2±0.5	0.1±0.0	−2.8±1.2	−2.1±0.9
东海	−15.1±6.5	−0.3±0.1	−1.7±0.7	−24.8±10.7	−10.5±4.5
南海	3.2±1.4	12.9±5.6	10.4±4.5	−18.2±7.8	2.1±0.9
中国陆架边缘海	−16.9±7.3	11.9±5.1	9.9±4.3	−45.8±19.7	−10.2±4.4

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表 2 近20年来中国陆架边缘海碳源汇强度年际变化（TgC a⁻¹）

Tab. 2 Annual changes of carbon source/sinks strength of China margin seas in the past 20 years（TgC a⁻¹）

年度	渤海	黄海	东海	南海	中国陆架边缘海
2003	0.7±0.5	0.6±0.4	−4.4±3.0	3.6±2.4	0.5±0.3
2004	0.5±1.8	−0.2±0.9	−4.1±15.4	4.2±15.5	0.3±1.1
2005	0.1±0.1	−1.4±1.2	−4.4±3.8	3.1±2.7	−2.5±2.2
2006	0.4±0.1	−1.5±0.3	−5.3±1.0	3.7±0.7	−2.7±0.5
2007	0.6±0.3	−0.8±0.4	−5.5±2.6	2.8±1.3	−2.9±1.3
2008	0.4±0.2	−0.9±0.5	−4.8±2.7	0.2±0.1	−5.1±2.9
2009	0.5±0.1	−1.3±0.2	−5.9±1.1	2.0±0.4	−4.6±0.8
2010	0.5±0.2	−2.4±1.0	−9.6±4.0	1.8±0.8	−9.6±4.0
2011	0.4±0.1	−2.7±1.1	−8.1±3.3	0.8±0.3	−9.7±3.9
2012	0.3±0.1	−2.5±0.7	−8.5±2.5	3.8±1.1	−6.8±2.0
2013	0.2±0.1	−2.1±0.8	−7.5±2.9	2.9±1.1	−6.5±2.5
2014	0.3±0.1	−1.9±0.6	−11.9±3.5	2.8±0.8	−10.7±3.2
2015	0.4±0.2	−1.9±0.8	−12.5±5.1	3.4±1.4	−10.7±4.4
2016	0.5±0.2	−2.5±0.8	−12.0±3.9	3.3±1.1	−10.7±3.5
2017	0.5±0.2	−1.7±0.6	−12.2±4.3	0.7±0.2	−12.7±4.5
2018	0.3±0.1	−2.4±1.0	−12.9±5.2	−0.1±0.0	−15.2±6.1
2019	0.3±0.1	−2.3±0.6	−12.8±3.6	−0.5±0.2	−15.3±4.4
2020	0.0±0.0	−4.0±2.3	−16.1±9.1	−4.9±2.8	−25.1±14.2
2021	0.0±0.0	−4.5±2.3	−17.0±8.7	−5.5±2.8	−27.0±13.9
2022	−0.1±0.1	−5.3±2.4	−16.9±7.6	−3.8±1.7	−26.3±11.9

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