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

Time varying changes and uncertainties in the CMIP6 ocean carbon sink from global to regional to local scale

Parsa Gooya1, Neil C. Swart2,1, and Roberta C. Hamme1 Parsa Gooya et al.
  • 1School of Earth and Ocean Sciences, University of Victoria, Victoria, BC, V8P 5C2, Canada
  • 2Canadian Centre for Climate Modelling and Analysis, Environment and Climate Change Canada, Victoria, BC, V8W 2P2, Canada

Abstract. As a major sink for anthropogenic carbon, the oceans slow the increase of carbon dioxide in the atmosphere and regulate climate change. Future changes in the ocean carbon sink, and its uncertainty at a global and regional scale, are key to understanding the future evolution of the climate. Here, we conduct a multimodel analysis of the changes and uncertainties in the historical and future ocean carbon sink using output data from the latest phase of the Coupled Model Intercomparison Project: CMIP6, and observations. We show that the ocean carbon sink is concentrated in highly active regions – 70 percent of the total sink occurs in less than 40 percent of the global ocean. High pattern correlations between the historical and projected future carbon sink indicate that future uptake will largely continue to occur in historically important regions. We conduct a detailed breakdown of the sources of uncertainty in the future carbon sink by region. Scenario uncertainty dominates at the global scale, followed by model uncertainty, and then internal variability. We demonstrate how the importance of internal variability increases moving to smaller spatial scales and go on to show how the breakdown between scenario, model, and internal variability changes between different ocean basins, governed by different processes. Moreover, we show that internal variability changes with time based on the scenario. As with the mean sink, we show that uncertainty in the future ocean carbon sink is also concentrated in the known regions of historical uptake. The resulting patterns in the signal-to-noise ratio have strong implications for observational detectability and time of emergence, which varies both in space and with scenario. Our results suggest that to detect human influence on the ocean carbon sink as early as possible and to efficiently reduce uncertainty in future carbon uptake, modeling and observational efforts should be focused in the known regions of high historical uptake, including the Northwest Atlantic and the Southern Ocean.

Parsa Gooya 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 esd-2022-19', Anonymous Referee #1, 27 Jun 2022
    • AC1: 'Reply on RC1', Parsa Gooya, 03 Sep 2022
  • RC2: 'Comment on esd-2022-19', Anonymous Referee #2, 06 Jul 2022
    • AC2: 'Reply on RC2', Parsa Gooya, 04 Sep 2022
      • AC3: 'Reply on AC2', Parsa Gooya, 04 Sep 2022

Parsa Gooya et al.

Parsa Gooya et al.


Total article views: 686 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
553 113 20 686 47 9 9
  • HTML: 553
  • PDF: 113
  • XML: 20
  • Total: 686
  • Supplement: 47
  • BibTeX: 9
  • EndNote: 9
Views and downloads (calculated since 18 May 2022)
Cumulative views and downloads (calculated since 18 May 2022)

Viewed (geographical distribution)

Total article views: 583 (including HTML, PDF, and XML) Thereof 583 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
Latest update: 08 Dec 2022
Short summary
We report on the ocean carbon sink and sources of uptake uncertainty from the latest version of the Coupled Model Intercomparison Project. We show that knowledge about historical regions of uptake will provide us information about future regions of uptake change and uncertainty. We break down the uncertainty to the sources and evaluate the dependence on location and integeration scale. Our results help us make useful suggestions for both modeling community and observational campaign planning.