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.
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.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| Highlight paper
| 
10 Dec 2019
Development and technical paper | Highlight paper |
| 10 Dec 2019
| 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?
SOCCO, Council for Scientific and Industrial Research, Cape Town,
7700, South Africa
MaRe, Marine Research Institute, University of Cape Town, Cape Town,
7700, South Africa
Environmental Physics, Institute of Biogeochemistry and Pollutant
Dynamics, ETH Zürich, 8092 Zürich, Switzerland
Alice D. Lebehot
SOCCO, Council for Scientific and Industrial Research, Cape Town,
7700, South Africa
MaRe, Marine Research Institute, University of Cape Town, Cape Town,
7700, South Africa
Schalk Kok
Department of Mechanical & Aeronautical Engineering, University of Pretoria, Pretoria, 0028, South Africa
Pedro M. Scheel Monteiro
SOCCO, Council for Scientific and Industrial Research, Cape Town,
7700, South Africa
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- Seasonal Variability of the Surface Ocean Carbon Cycle: A Synthesis K. Rodgers et al. https://doi.org/10.1029/2023GB007798
- The Ocean Carbon Cycle T. DeVries https://doi.org/10.1146/annurev-environ-120920-111307
- Sea-surface pCO2 maps for the Bay of Bengal based on advanced machine learning algorithms A. Joshi et al. https://doi.org/10.1038/s41597-024-03236-w
- A seamless ensemble-based reconstruction of surface ocean pCO2 and air–sea CO2 fluxes over the global coastal and open oceans T. Chau et al. https://doi.org/10.5194/bg-19-1087-2022
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Saved (final revised paper)
Latest update: 09 Aug 2026
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.
The ocean plays a vital role in mitigating climate change by taking up atmospheric carbon...
