Articles | Volume 15, issue 4
https://doi.org/10.5194/essd-15-1711-2023
https://doi.org/10.5194/essd-15-1711-2023
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

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2022-322', Anonymous Referee #1, 17 Nov 2022
    • AC1: 'Reply on RC1', Minhan Dai, 23 Dec 2022
  • RC2: 'Comment on essd-2022-322', Anonymous Referee #2, 24 Nov 2022
    • AC2: 'Reply on RC2', Minhan Dai, 23 Dec 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Minhan Dai on behalf of the Authors (20 Jan 2023)  Author's response   Author's tracked changes   Manuscript 
ED: Reconsider after major revisions (26 Jan 2023) by Giuseppe M.R. Manzella
AR by Minhan Dai on behalf of the Authors (08 Mar 2023)  Author's response   Author's tracked changes   Manuscript 
ED: Publish as is (13 Mar 2023) by Giuseppe M.R. Manzella
AR by Minhan Dai on behalf of the Authors (22 Mar 2023)
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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.
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