Articles | Volume 15, issue 4
https://doi.org/10.5194/essd-15-1711-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-15-1711-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
17 Apr 2023
Data description paper |
| 17 Apr 2023
| 17 Apr 2023
Spatial reconstruction of long-term (2003–2020) sea surface pCO2 in the South China Sea using a machine-learning-based regression method aided by empirical orthogonal function analysis
Zhixuan Wang
State Key Laboratory of Marine Environmental Science, Xiamen University, Xiamen 361102, China
College of Ocean and Earth Sciences, Xiamen University, Xiamen 361102, China
Guizhi Wang
State Key Laboratory of Marine Environmental Science, Xiamen University, Xiamen 361102, China
College of Ocean and Earth Sciences, Xiamen University, Xiamen 361102, China
Fujian Provincial Key Laboratory for Coastal Ecology and Environmental Studies, Xiamen University, Xiamen 361102, China
Xianghui Guo
Fujian Provincial Key Laboratory for Coastal Ecology and Environmental Studies, Xiamen University, Xiamen 361102, China
Yan Bai
State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, State Oceanic Administration, Hangzhou 310012, China
Yi Xu
State Key Laboratory of Marine Environmental Science, Xiamen University, Xiamen 361102, China
College of Ocean and Earth Sciences, Xiamen University, Xiamen 361102, China
State Key Laboratory of Marine Environmental Science, Xiamen University, Xiamen 361102, China
College of Ocean and Earth Sciences, Xiamen University, Xiamen 361102, China
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Short summary
We reconstructed monthly sea surface pCO2 data with a high spatial resolution in the South China Sea (SCS) from 2003 to 2020. We validate our reconstruction with three independent testing datasets and present a new method to assess the uncertainty of the data. The results strongly suggest that our reconstruction effectively captures the main features of the spatiotemporal patterns of pCO2 in the SCS. Using this dataset, we found that the SCS is overall a weak source of atmospheric CO2.
We reconstructed monthly sea surface pCO2 data with a high spatial resolution in the South China...
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