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.
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. 10.1029/2023GB007798
- The Ocean Carbon Cycle T. DeVries 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. 10.1038/s41597-024-03236-w
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- The Southern Ocean carbon sink has been overestimated in the past three decades G. Zhong et al. 10.1038/s43247-024-01566-6
- Projected poleward migration of the Southern Ocean CO2 sink region under high emissions P. Mongwe et al. 10.1038/s43247-024-01382-y
- Atmospheric CO2 and Sea Surface Temperature Variability Cannot Explain Recent Decadal Variability of the Ocean CO2 Sink T. DeVries 10.1029/2021GL096018
- Variability of North Atlantic CO<sub>2</sub> fluxes for the 2000–2017 period estimated from atmospheric inverse analyses Z. Chen et al. 10.5194/bg-18-4549-2021
- Data-based estimates of interannual sea–air CO<sub>2</sub> flux variations 1957–2020 and their relation to environmental drivers C. Rödenbeck et al. 10.5194/bg-19-2627-2022
- How Well Do We Understand the Land‐Ocean‐Atmosphere Carbon Cycle? D. Crisp et al. 10.1029/2021RG000736
- Large spread in interannual variance of atmospheric CO2 concentration across CMIP6 Earth System Models V. Martín-Gómez et al. 10.1038/s41612-023-00532-x
- Observation-constrained estimates of the global ocean carbon sink from Earth system models J. Terhaar et al. 10.5194/bg-19-4431-2022
Latest update: 20 Nov 2024
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...