Yu Shujie, Song Zigeng, Bai Yan, Guo Xianghui, He Xianqiang, Zhai Weidong, Zhao Huade, Dai Minhan
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.
State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, PR China.
Sci Total Environ. 2023 Dec 15;904:166804. doi: 10.1016/j.scitotenv.2023.166804. Epub 2023 Sep 7.
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 pCO data collected on 51 cruises/legs over the past two decades, a satellite retrieval algorithm for seawater pCO 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 pCO (pCO) and upwelling index (UI), to characterise the combined effect of atmospheric CO forcing, thermodynamic effects, and multiple mixing processes on seawater pCO. The best-selected machine learning algorithm is XGBoost. The satellite-derived pCO 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 CO 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 CO 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 CO absorption increased from ∼10 Tg C in 2003 to ∼19 Tg C in 2019. In total, CO 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 pCO and air-sea CO flux dataset. These complete ocean carbon sink statistics and new insights will benefit further research on carbon fixation and its potential capacity.
渤海（BS）、黄海（YS）和东海（ECS）共同构成了世界上最大的边缘海系统之一，包括封闭和半封闭的海洋边缘以及受长江和强大的西边界流（黑潮）影响的广阔大陆架。基于过去二十年在51个航次/航段上收集的现场海水pCO数据，结合半机制算法和机器学习方法（MeSAA-ML-ECS）开发了一种海水pCO的卫星反演算法。MeSAA-ML-ECS引入了半分析参数，包括温度依赖的海水pCO（pCO）和上升流指数（UI），以表征大气CO强迫、热力学效应和多种混合过程对海水pCO的综合影响。最佳选择的机器学习算法是XGBoost。卫星反演的pCO在这个复杂的边缘海表现出色，独立现场验证数据集的均方根误差较低（RMSE = 20 μatm），平均绝对百分比偏差（APD = 4.12%）。在2003 - 2019年期间，渤海、黄海、东海和整个研究区域的年平均CO汇分别为0.16±0.26、3.85±0.68、14.80±3.09和18.81±3.81 Tg C/yr。在大气CO浓度持续增加的情况下，渤海从弱源变为弱汇，黄海经历年际波动但未显示出显著趋势，而东海作为强汇，CO吸收量从2003年的约10 Tg C增加到2019年的约19 Tg C。总的来说，整个研究区域的CO吸收量在17年内增加了85%。我们首次展示了卫星反演的pCO和海气CO通量数据集中最精细的变化。这些完整的海洋碳汇统计数据和新见解将有助于进一步研究碳固定及其潜在能力。
