Earth System Science Data
Earth System Science Data
Earth System Science Data
Articles | Volume 15, issue 4
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.
https://doi.org/10.5194/essd-15-1711-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 15, issue 4
Data description paper
 | 
17 Apr 2023
Data description paper |  | 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, Guizhi Wang, Xianghui Guo, Yan Bai, Yi Xu, and Minhan Dai

Viewed

Total article views: 3,560 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
2,544 917 99 3,560 134 150
  • HTML: 2,544
  • PDF: 917
  • XML: 99
  • Total: 3,560
  • BibTeX: 134
  • EndNote: 150
Views and downloads (calculated since 04 Oct 2022)
Cumulative views and downloads (calculated since 04 Oct 2022)

Viewed (geographical distribution)

Total article views: 3,560 (including HTML, PDF, and XML) Thereof 3,439 with geography defined and 121 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 17 Nov 2025
Download
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.
Read more
Share
Altmetrics
Final-revised paper
Preprint