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Articles | Volume 21, issue 8
Articles | Volume 21, issue 8
https://doi.org/10.5194/bg-21-2159-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-21-2159-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 21, issue 8
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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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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