Articles | Volume 20, issue 8
Research article
 | Highlight paper
28 Apr 2023
Research article | Highlight paper |  | 28 Apr 2023

Reconstructing ocean carbon storage with CMIP6 Earth system models and synthetic Argo observations

Katherine E. Turner, Doug M. Smith, Anna Katavouta, and Richard G. Williams


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on bg-2022-166', Anonymous Referee #1, 08 Sep 2022
    • AC1: 'Reply on RC1', Katherine Turner, 28 Oct 2022
  • RC2: 'Comment on bg-2022-166', Anonymous Referee #2, 24 Sep 2022
    • AC2: 'Reply on RC2', Katherine Turner, 28 Oct 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
ED: Reconsider after major revisions (04 Nov 2022) by Peter Landschützer
AR by Katherine Turner on behalf of the Authors (15 Jan 2023)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (24 Jan 2023) by Peter Landschützer
RR by Anonymous Referee #1 (09 Feb 2023)
ED: Publish subject to minor revisions (review by editor) (14 Feb 2023) by Peter Landschützer
AR by Katherine Turner on behalf of the Authors (09 Mar 2023)  Author's response   Author's tracked changes   Manuscript 
ED: Publish as is (17 Mar 2023) by Peter Landschützer
AR by Katherine Turner on behalf of the Authors (06 Apr 2023)  Manuscript 
The paper by Turner and co-authors tackles the very timely question on how well we can reconstruct carbon inventories given the sparse observations. Using an Ensemble Optimal Interpolation approach and synthetic observations, the authors show that a large fraction of ocean carbon and its variability can be reconstructed using temperature and salinity measurements in the top 100 meter, however, reconstruction skill decreases when the top 2000 meters are considered. The authors propose a new way to use sparse observations to better understand historical carbon cycle changes, i.e., an important quantity in light of future changes driven by man-made emissions.
Short summary
We present a new method for reconstructing ocean carbon using climate models and temperature and salinity observations. To test this method, we reconstruct modelled carbon using synthetic observations consistent with current sampling programmes. Sensitivity tests show skill in reconstructing carbon trends and variability within the upper 2000 m. Our results indicate that this method can be used for a new global estimate for ocean carbon content.
Final-revised paper