Articles | Volume 12, issue 12
Geosci. Model Dev., 12, 5113–5136, 2019
https://doi.org/10.5194/gmd-12-5113-2019
Geosci. Model Dev., 12, 5113–5136, 2019
https://doi.org/10.5194/gmd-12-5113-2019

Development and technical paper 10 Dec 2019

Development and technical 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 et al.

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Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Luke Gregor on behalf of the Authors (28 Jul 2019)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (29 Jul 2019) by Andrew Yool
RR by Peter Landschützer (01 Aug 2019)
RR by Jamie Shutler (17 Sep 2019)
ED: Publish subject to minor revisions (review by editor) (19 Sep 2019) by Andrew Yool
AR by Luke Gregor on behalf of the Authors (17 Oct 2019)  Author's response    Manuscript
ED: Publish subject to minor revisions (review by editor) (28 Oct 2019) by Andrew Yool
AR by Luke Gregor on behalf of the Authors (05 Nov 2019)  Author's response    Manuscript
ED: Publish as is (08 Nov 2019) by Andrew Yool
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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.