18 Aug 2022
18 Aug 2022
Status: a revised version of this preprint is currently under review for the journal BG.

Reconstructing ocean carbon storage with CMIP6 models and synthetic Argo observations

Katherine E. Turner1, Doug M. Smith2, Anna Katavouta1,3, and Richard G. Williams1 Katherine E. Turner et al.
  • 1Department of Earth, Ocean, and Ecological Sciences, University of Liverpool, Liverpool, United Kingdom
  • 2UK Met Office, Exeter, United Kingdom
  • 3National Oceanography Centre, Liverpool, United Kingdom

Abstract. The ocean carbon store plays a vital role in setting the carbon response to emissions and variability in the carbon cycle. However, due to the ocean’s strong regional and temporal variability, sparse carbon observations limit our understanding of historical carbon changes. We explore how widespread Argo temperature and salinity profiles can provide information to reconstruct ocean carbon inventories with Ensemble Optimal Interpolation. Ensemble Optimal Interpolation draws upon first-order relationships between variables, and high-complexity climate models been used previously to reconstruct interior ocean heat content and salinity changes from Argo profiles. The CMIP6 model ensemble shows coherent relationships between upper-ocean carbon, temperature, salinity, and atmospheric CO2 that result in solutions that reflect the controls of undersaturation, solubility, and alkalinity. The Ensemble Optimal Interpolation method is tested with synthetic reconstructions of upper-ocean carbon fields using perfect information of temperature and salinity changes, along with atmospheric CO2 concentrations. Sensitivity tests of show that, in most regions, the trend in ocean carbon and over 60 % of detrended variability can be reconstructed using local temperature and salinity measurements. Expanding the synthetic reconstructions to include irregular sampling consistent with Argo profile locations results in a similar capacity to reconstruct ocean carbon variability, as the increased information provided from multiple sampling locations compensates for the propagation of errors within the CMIP6 covariance fields. Overall, our work indicates that hydrographic measurements can provide valuable information about changes in the ocean carbon inventory. The flexibility of optimal interpolation indicates a strong potential for new, independent, and mechanistic estimates of the historical carbon inventory that can be used to compare with other estimates of the ocean carbon system.

Katherine E. Turner et al.

Status: final response (author comments only)

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

Katherine E. Turner et al.

Model code and software

DIC-EnOI-Summer2022 Katherine Turner

Katherine E. Turner et al.


Total article views: 588 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
421 156 11 588 0 1
  • HTML: 421
  • PDF: 156
  • XML: 11
  • Total: 588
  • BibTeX: 0
  • EndNote: 1
Views and downloads (calculated since 18 Aug 2022)
Cumulative views and downloads (calculated since 18 Aug 2022)

Viewed (geographical distribution)

Total article views: 560 (including HTML, PDF, and XML) Thereof 560 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
Latest update: 03 Feb 2023
Short summary
We present a new method for reconstructing ocean carbon using climate models and temperature and salinity fields. We test this method by reconstructing modelled carbon fields using synthetic observations consistent with current sampling programmes. Sensitivity tests find that the method can reproduce both trends and variability in upper-ocean carbon. Our results indicate that this method can provide a new estimate for ocean carbon stocks and can be used to compare with existing carbon products.