Preprints
https://doi.org/10.5194/bg-2023-160
https://doi.org/10.5194/bg-2023-160
10 Oct 2023
 | 10 Oct 2023
Status: a revised version of this preprint was accepted for the journal BG and is expected to appear here in due course.

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

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

Abstract. The Southern Ocean plays an important role in the exchange of carbon between the atmosphere and oceans, and is a critical region for the ocean uptake of anthropogenic CO2. However, estimates of the Southern Ocean air-sea CO2 flux are highly uncertain due to limited data coverage. Increased sampling in winter and across meridional gradients in the Southern Ocean may improve machine learning (ML) reconstructions of global surface ocean pCO2. Here, we use a Large Ensemble Testbed (LET) of Earth System Models and the pCO2-Residual reconstruction method to assess improvements in pCO2 reconstruction fidelity that could be achieved with additional autonomous sampling in the Southern Ocean added to existing Surface Ocean CO2 Atlas (SOCAT) observations. The LET allows us to robustly evaluate the skill of pCO2 reconstructions in space and time through comparison to ‘model truth’. With only SOCAT sampling, Southern Ocean and global pCO2 are overestimated, and thus the ocean carbon sink is underestimated. Incorporating Uncrewed Surface Vehicle (USV) sampling increases the spatial and seasonal coverage of observations within the Southern Ocean, leading to a decrease in the overestimation of pCO2. A modest number of additional observations in southern hemisphere winter and across meridional gradients in the Southern Ocean leads to improvement in reconstruction bias and root-mean squared error (RMSE) can be improved by as much as 65 % and 19 %, respectively, as compared to using SOCAT sampling alone. Lastly, the large decadal variability of air-sea CO2 fluxes shown by SOCAT-only sampling, may be partially attributable to undersampling of the Southern Ocean.

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

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

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

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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 that a small addition of observations from the Southern Ocean, obtained by autonomous sampling platforms, could significantly improve the reconstructions.
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