Articles | Volume 21, issue 8
https://doi.org/10.5194/bg-21-2159-2024
https://doi.org/10.5194/bg-21-2159-2024
Research article
 | 
30 Apr 2024
Research article |  | 30 Apr 2024

Assessing improvements in global ocean pCO2 machine learning reconstructions with Southern Ocean autonomous sampling

Thea H. Heimdal, Galen A. McKinley, Adrienne J. Sutton, Amanda R. Fay, and Lucas Gloege

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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 bg-2023-160', Anonymous Referee #1, 09 Nov 2023
    • AC1: 'Reply on RC1', Thea Hatlen Heimdal, 21 Dec 2023
  • RC2: 'Comment on bg-2023-160', Anonymous Referee #2, 15 Nov 2023
    • AC2: 'Reply on RC2', Thea Hatlen Heimdal, 21 Dec 2023

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
ED: Reconsider after major revisions (14 Jan 2024) by Julia Uitz
AR by Thea Hatlen Heimdal on behalf of the Authors (17 Jan 2024)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (21 Jan 2024) by Julia Uitz
RR by Anonymous Referee #1 (23 Jan 2024)
RR by Anonymous Referee #2 (29 Jan 2024)
ED: Publish subject to minor revisions (review by editor) (10 Feb 2024) by Julia Uitz
AR by Thea Hatlen Heimdal on behalf of the Authors (21 Feb 2024)  Author's response   Author's tracked changes   Manuscript 
ED: Publish subject to minor revisions (review by editor) (23 Feb 2024) by Julia Uitz
AR by Thea Hatlen Heimdal on behalf of the Authors (28 Feb 2024)  Author's response   Author's tracked changes   Manuscript 
ED: Publish as is (11 Mar 2024) by Julia Uitz
AR by Thea Hatlen Heimdal on behalf of the Authors (13 Mar 2024)  Manuscript 

Post-review adjustments

AA: Author's adjustment | EA: Editor approval
AA by Thea Hatlen Heimdal on behalf of the Authors (17 Apr 2024)   Author's adjustment   Manuscript
EA: Adjustments approved (26 Apr 2024) by Julia Uitz
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Short summary
Measurements of ocean carbon are limited in time and space. Machine learning algorithms are therefore used to reconstruct ocean carbon where observations do not exist. Improving these reconstructions is important in order to accurately estimate how much carbon the ocean absorbs from the atmosphere. In this study, we find that a small addition of observations from the Southern Ocean, obtained by autonomous sampling platforms, could significantly improve the reconstructions. 
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