Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 12, issue 12
Articles | Volume 12, issue 12
https://doi.org/10.5194/gmd-12-5113-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-12-5113-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 12, issue 12
Development and technical paper
 | Highlight paper
 | 
10 Dec 2019
Development and technical paper | Highlight paper |  | 10 Dec 2019

A comparative assessment of the uncertainties of global surface ocean CO2 estimates using a machine-learning ensemble (CSIR-ML6 version 2019a) – have we hit the wall?

Luke Gregor, Alice D. Lebehot, Schalk Kok, and Pedro M. Scheel Monteiro

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Short summary
The ocean plays a vital role in mitigating climate change by taking up atmospheric carbon dioxide (CO2). Historically sparse ship-based measurements of surface ocean CO2 make direct estimates of CO2 exchange changes unreliable. We introduce a machine-learning ensemble approach to fill these observational gaps. Our method performs incrementally better relative to past methods, leading to our hypothesis that we are perhaps reaching the limitation of machine-learning algorithms' capability.
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