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.
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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Cited
13 citations as recorded by crossref.
- The global daily High Spatial–Temporal Coverage Merged tropospheric NO2 dataset (HSTCM-NO2) from 2007 to 2022 based on OMI and GOME-2 K. Qin et al. https://doi.org/10.5194/essd-16-5287-2024
- Assessing atmospheric CO2 concentrations and contributions from biogenic and anthropogenic sources in the Pearl River Delta region B. Mai et al. https://doi.org/10.1016/j.uclim.2024.101864
- Enhanced North Pacific biological pump contributed to atmospheric carbon dioxide plateau during the Bølling-Allerød period X. Zhu et al. https://doi.org/10.1038/s43247-025-02943-5
- Construction of a High Spatiotemporal Resolution Dataset of Satellite-DerivedpCO2and Air–Sea CO2Flux in the South China Sea (2003–2019) Z. Song et al. https://doi.org/10.1109/TGRS.2023.3306389
- Temporal and spatial variation of pCO2 in the coastal waters of the Pearl river estuary in winter and summer and its influencing factors: A model study G. Guo et al. https://doi.org/10.1016/j.marenvres.2025.107366
- Data mining-based machine learning methods for improving hydrological data: a case study of salinity field in the Western Arctic Ocean S. Tao et al. https://doi.org/10.3389/fmars.2024.1490548
- Construction of long-term (2003–2024) DIN dataset in the Chinese coastal ocean using interpretable ML Z. Song et al. https://doi.org/10.1016/j.ecoinf.2026.103886
- Global ocean surface pCO2 retrieval and the influence of mesoscale eddies on its performance Y. Wang et al. https://doi.org/10.1016/j.scitotenv.2025.179856
- Sub-monthly Processes Contribute Significantly to CO2 Uptake in the South China Sea X. Wang et al. https://doi.org/10.1007/s00376-025-4439-0
- Estimating surface pCO2 and its long-term variability in the East China Sea using machine learning with satellite data D. Kwon et al. https://doi.org/10.3389/fmars.2026.1951665
- Spatial-temporal heterogeneity of social-ecological infrastructures for spatial planning and governance in coastal cities: The case of Macau M. Li et al. https://doi.org/10.1016/j.habitatint.2026.103822
- Impacts on the carbon cycle in the coastal waters of Zhujiang (Pearl) River Estuary from nutrient reduction: scenario simulation and response characteristics G. Guo et al. https://doi.org/10.1007/s13131-025-2635-5
- A machine-learning reconstruction of sea surface pCO2 in the North American Atlantic Coastal Ocean Margin from 1993 to 2021 Z. Wu et al. https://doi.org/10.5194/essd-17-43-2025
13 citations as recorded by crossref.
- The global daily High Spatial–Temporal Coverage Merged tropospheric NO2 dataset (HSTCM-NO2) from 2007 to 2022 based on OMI and GOME-2 K. Qin et al. https://doi.org/10.5194/essd-16-5287-2024
- Assessing atmospheric CO2 concentrations and contributions from biogenic and anthropogenic sources in the Pearl River Delta region B. Mai et al. https://doi.org/10.1016/j.uclim.2024.101864
- Enhanced North Pacific biological pump contributed to atmospheric carbon dioxide plateau during the Bølling-Allerød period X. Zhu et al. https://doi.org/10.1038/s43247-025-02943-5
- Construction of a High Spatiotemporal Resolution Dataset of Satellite-DerivedpCO2and Air–Sea CO2Flux in the South China Sea (2003–2019) Z. Song et al. https://doi.org/10.1109/TGRS.2023.3306389
- Temporal and spatial variation of pCO2 in the coastal waters of the Pearl river estuary in winter and summer and its influencing factors: A model study G. Guo et al. https://doi.org/10.1016/j.marenvres.2025.107366
- Data mining-based machine learning methods for improving hydrological data: a case study of salinity field in the Western Arctic Ocean S. Tao et al. https://doi.org/10.3389/fmars.2024.1490548
- Construction of long-term (2003–2024) DIN dataset in the Chinese coastal ocean using interpretable ML Z. Song et al. https://doi.org/10.1016/j.ecoinf.2026.103886
- Global ocean surface pCO2 retrieval and the influence of mesoscale eddies on its performance Y. Wang et al. https://doi.org/10.1016/j.scitotenv.2025.179856
- Sub-monthly Processes Contribute Significantly to CO2 Uptake in the South China Sea X. Wang et al. https://doi.org/10.1007/s00376-025-4439-0
- Estimating surface pCO2 and its long-term variability in the East China Sea using machine learning with satellite data D. Kwon et al. https://doi.org/10.3389/fmars.2026.1951665
- Spatial-temporal heterogeneity of social-ecological infrastructures for spatial planning and governance in coastal cities: The case of Macau M. Li et al. https://doi.org/10.1016/j.habitatint.2026.103822
- Impacts on the carbon cycle in the coastal waters of Zhujiang (Pearl) River Estuary from nutrient reduction: scenario simulation and response characteristics G. Guo et al. https://doi.org/10.1007/s13131-025-2635-5
- A machine-learning reconstruction of sea surface pCO2 in the North American Atlantic Coastal Ocean Margin from 1993 to 2021 Z. Wu et al. https://doi.org/10.5194/essd-17-43-2025
Saved (final revised paper)
Latest update: 14 Oct 2026
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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