Satellite-estimated air-sea CO2 fluxes in the Bohai Sea, Yellow Sea, and East China Sea: Patterns and variations during 2003-2019

Shujie Yu; Zigeng Song; Yan Bai; Xianghui Guo; Xianqiang He; Weidong Zhai; Huade Zhao; Minhan Dai

doi:10.1016/j.scitotenv.2023.166804

Satellite-estimated air-sea CO₂ fluxes in the Bohai Sea, Yellow Sea, and East China Sea: Patterns and variations during 2003-2019

Sci Total Environ. 2023 Dec 15:904:166804. doi: 10.1016/j.scitotenv.2023.166804. Epub 2023 Sep 7.

Authors

Shujie Yu¹, Zigeng Song², Yan Bai³, Xianghui Guo⁴, Xianqiang He¹, Weidong Zhai⁵, Huade Zhao⁶, Minhan Dai⁴

Affiliations

¹ State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, PR China; Ocean College, Zhejiang University, Zhoushan 316021, PR China.
² State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, PR China.
³ State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, PR China; Ocean College, Zhejiang University, Zhoushan 316021, PR China. Electronic address: baiyan@sio.org.cn.
⁴ State Key Laboratory of Marine Environmental Science and College of Ocean and Earth Sciences, Xiamen University, Xiamen 361102, PR China.
⁵ Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519080, PR China.
⁶ National Marine Environmental Monitoring Center, Dalian 116023, PR China.

PMID: 37689183
DOI: 10.1016/j.scitotenv.2023.166804

Abstract

The Bohai Sea (BS), Yellow Sea (YS), and East China Sea (ECS) together form one of the largest marginal sea systems in the world, including enclosed and semi-enclosed ocean margins and a wide continental shelf influenced by the Changjiang River and the strong western boundary current (Kuroshio). Based on in situ seawater pCO₂ data collected on 51 cruises/legs over the past two decades, a satellite retrieval algorithm for seawater pCO₂ was developed by combining the semi-mechanistic algorithm and machine learning method (MeSAA-ML-ECS). MeSAA-ML-ECS introduced semi-analytical parameters, including the temperature-dependent seawater pCO₂ (pCO_2,therm) and upwelling index (UI_SST), to characterise the combined effect of atmospheric CO₂ forcing, thermodynamic effects, and multiple mixing processes on seawater pCO₂. The best-selected machine learning algorithm is XGBoost. The satellite-derived pCO₂ achieved excellent performance in this complicated marginal sea, with low root mean square error (RMSE = 20 μatm) and mean absolute percentage deviation (APD = 4.12 %) for independent in situ validation dataset. During 2003-2019, the annual average CO₂ sinks in the BS, YS, ECS, and entire study area were 0.16 ± 0.26, 3.85 ± 0.68, 14.80 ± 3.09, and 18.81 ± 3.81 Tg C/yr, respectively. Under continuously increasing atmospheric CO₂ concentration, the BS changed from a weak source to a weak sink, the YS experienced interannual fluctuations but did not show significant trend, while the ECS acted as a strong sink with CO₂ absorption increased from ∼10 Tg C in 2003 to ∼19 Tg C in 2019. In total, CO₂ uptake in the entire study area increased by 85 % in 17 years. For the first time, we present the most refined variation in the satellite-derived pCO₂ and air-sea CO₂ flux dataset. These complete ocean carbon sink statistics and new insights will benefit further research on carbon fixation and its potential capacity.

Keywords: Air-sea CO(2) flux; Bohai Sea-Yellow Sea-East China Sea; Machine learning; Seawater pCO(2); Semi-analytical algorithm (MeSAA).