Articles | Volume 14, issue 23
https://doi.org/10.5194/bg-14-5551-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/bg-14-5551-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
08 Dec 2017
Research article |
| 08 Dec 2017
Empirical methods for the estimation of Southern Ocean CO2: support vector and random forest regression
Southern Ocean Carbon-Climate Observatory (SOCCO), CSIR, Cape Town, South Africa
University of Cape Town, Department of Oceanography, Cape Town, South Africa
Schalk Kok
University of Pretoria, Department of Mechanical and Aeronautical Engineering, Pretoria, South Africa
Pedro M. S. Monteiro
Southern Ocean Carbon-Climate Observatory (SOCCO), CSIR, Cape Town, South Africa
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Cited
34 citations as recorded by crossref.
- A Multiparametric Nonlinear Regression Approach for the Estimation of Global Surface Ocean pCO2 Using Satellite Oceanographic Data K. Krishna et al. 10.1109/JSTARS.2020.3026363
- Seasonal Surface Eddy Mixing in the Kuroshio Extension: Estimation and Machine Learning Prediction W. Guan et al. 10.1029/2021JC017967
- Simulating the radiative forcing of oceanic dimethylsulfide (DMS) in Asia based on machine learning estimates J. Zhao et al. 10.5194/acp-22-9583-2022
- Modelling global mesozooplankton biomass using machine learning K. Liu et al. 10.1016/j.pocean.2024.103371
- Data‐Driven Modeling of the Distribution of Diazotrophs in the Global Ocean W. Tang & N. Cassar 10.1029/2019GL084376
- The seasonal cycle of <i>p</i>CO<sub>2</sub> and CO<sub>2</sub> fluxes in the Southern Ocean: diagnosing anomalies in CMIP5 Earth system models N. Mongwe et al. 10.5194/bg-15-2851-2018
- Assessment of austral autumn air–sea CO2 exchange in the Pacific sector of the Southern Ocean and dominant controlling factors A. Mo et al. 10.3389/fmars.2023.1192959
- The role of biota in the Southern Ocean carbon cycle P. Boyd et al. 10.1038/s43017-024-00531-3
- A monthly surface <i>p</i>CO<sub>2</sub> product for the California Current Large Marine Ecosystem J. Sharp et al. 10.5194/essd-14-2081-2022
- The sensitivity ofpCO2reconstructions to sampling scales across a Southern Ocean sub-domain: a semi-idealized ocean sampling simulation approach L. Djeutchouang et al. 10.5194/bg-19-4171-2022
- Recent Trends and Variability in the Oceanic Storage of Dissolved Inorganic Carbon L. Keppler et al. 10.1029/2022GB007677
- A seamless ensemble-based reconstruction of surface ocean <i>p</i>CO<sub>2</sub> and air–sea CO<sub>2</sub> fluxes over the global coastal and open oceans T. Chau et al. 10.5194/bg-19-1087-2022
- Reconstruction of monthly fCO2 distribution in the Ross Sea, Antarctica during 1998 -2018 using machine learning technique and observational data sets A. Mo et al. 10.22761/DJ2022.4.3.003
- Increasing the Resolution of Ocean pCO2 Maps in the South Eastern Atlantic Ocean Merging Multifractal Satellite-Derived Ocean Variables I. Hernandez-Carrasco et al. 10.1109/TGRS.2018.2840526
- On the potential of mapping sea level anomalies from satellite altimetry with Random Forest Regression M. Passaro & M. Juhl 10.1007/s10236-023-01540-4
- Winter Air‐Sea CO2 Fluxes Constructed From Summer Observations of the Polar Southern Ocean Suggest Weak Outgassing N. Mackay & A. Watson 10.1029/2020JC016600
- Generalization of Parameter Selection of SVM and LS-SVM for Regression J. Zeng et al. 10.3390/make1020043
- Remote Sensing Estimations of the Seawater Partial Pressure of CO₂ Using Sea Surface Roughness Derived From Synthetic Aperture Radar Y. Wang et al. 10.1109/TGRS.2024.3379984
- The Distribution of pCO2W and Air-Sea CO2 Fluxes Using FFNN at the Continental Shelf Areas of the Arctic Ocean I. Wrobel-Niedzwiecka et al. 10.3390/rs14020312
- Quantifying Errors in Observationally Based Estimates of Ocean Carbon Sink Variability L. Gloege et al. 10.1029/2020GB006788
- Improving Estimates of Dynamic Global Marine DMS and Implications for Aerosol Radiative Effect J. Zhao et al. 10.1029/2023JD039314
- Nonlocality of scale-dependent eddy mixing at the Kuroshio Extension M. Liu et al. 10.3389/fmars.2023.1137216
- Revised estimates of ocean-atmosphere CO2 flux are consistent with ocean carbon inventory A. Watson et al. 10.1038/s41467-020-18203-3
- Carbon Sinks and Variations of pCO2 in the Southern Ocean From 1998 to 2018 Based on a Deep Learning Approach Y. Wang et al. 10.1109/JSTARS.2021.3066552
- A comparative assessment of the uncertainties of global surface ocean CO<sub>2</sub> estimates using a machine-learning ensemble (CSIR-ML6 version 2019a) – have we hit the wall? L. Gregor et al. 10.5194/gmd-12-5113-2019
- Reconstruction of monthly fCO2 distribution in the Ross Sea, Antarctica during 1998 -2018 using machine learning technique and observational data sets A. Mo et al. 10.22761/DJ2022..4.3.003
- Interannual drivers of the seasonal cycle of CO<sub>2</sub> in the Southern Ocean L. Gregor et al. 10.5194/bg-15-2361-2018
- The Southern Ocean Carbon Cycle 1985–2018: Mean, Seasonal Cycle, Trends, and Storage J. Hauck et al. 10.1029/2023GB007848
- Machine Learning Estimates of Global Marine Nitrogen Fixation W. Tang et al. 10.1029/2018JG004828
- Improved Quantification of Ocean Carbon Uptake by Using Machine Learning to Merge Global Models and pCO2 Data L. Gloege et al. 10.1029/2021MS002620
- Estimating Ocean Surface Currents With Machine Learning A. Sinha & R. Abernathey 10.3389/fmars.2021.672477
- Role of Aerosols in Spring Blooms in the Central Yellow Sea During the COVID-19 Lockdown by China J. Baek et al. 10.3389/fmars.2022.911819
- GOBAI-O2: temporally and spatially resolved fields of ocean interior dissolved oxygen over nearly 2 decades J. Sharp et al. 10.5194/essd-15-4481-2023
- Global ocean dimethyl sulfide climatology estimated from observations and an artificial neural network W. Wang et al. 10.5194/bg-17-5335-2020
34 citations as recorded by crossref.
- A Multiparametric Nonlinear Regression Approach for the Estimation of Global Surface Ocean pCO2 Using Satellite Oceanographic Data K. Krishna et al. 10.1109/JSTARS.2020.3026363
- Seasonal Surface Eddy Mixing in the Kuroshio Extension: Estimation and Machine Learning Prediction W. Guan et al. 10.1029/2021JC017967
- Simulating the radiative forcing of oceanic dimethylsulfide (DMS) in Asia based on machine learning estimates J. Zhao et al. 10.5194/acp-22-9583-2022
- Modelling global mesozooplankton biomass using machine learning K. Liu et al. 10.1016/j.pocean.2024.103371
- Data‐Driven Modeling of the Distribution of Diazotrophs in the Global Ocean W. Tang & N. Cassar 10.1029/2019GL084376
- The seasonal cycle of <i>p</i>CO<sub>2</sub> and CO<sub>2</sub> fluxes in the Southern Ocean: diagnosing anomalies in CMIP5 Earth system models N. Mongwe et al. 10.5194/bg-15-2851-2018
- Assessment of austral autumn air–sea CO2 exchange in the Pacific sector of the Southern Ocean and dominant controlling factors A. Mo et al. 10.3389/fmars.2023.1192959
- The role of biota in the Southern Ocean carbon cycle P. Boyd et al. 10.1038/s43017-024-00531-3
- A monthly surface <i>p</i>CO<sub>2</sub> product for the California Current Large Marine Ecosystem J. Sharp et al. 10.5194/essd-14-2081-2022
- The sensitivity ofpCO2reconstructions to sampling scales across a Southern Ocean sub-domain: a semi-idealized ocean sampling simulation approach L. Djeutchouang et al. 10.5194/bg-19-4171-2022
- Recent Trends and Variability in the Oceanic Storage of Dissolved Inorganic Carbon L. Keppler et al. 10.1029/2022GB007677
- A seamless ensemble-based reconstruction of surface ocean <i>p</i>CO<sub>2</sub> and air–sea CO<sub>2</sub> fluxes over the global coastal and open oceans T. Chau et al. 10.5194/bg-19-1087-2022
- Reconstruction of monthly fCO2 distribution in the Ross Sea, Antarctica during 1998 -2018 using machine learning technique and observational data sets A. Mo et al. 10.22761/DJ2022.4.3.003
- Increasing the Resolution of Ocean pCO2 Maps in the South Eastern Atlantic Ocean Merging Multifractal Satellite-Derived Ocean Variables I. Hernandez-Carrasco et al. 10.1109/TGRS.2018.2840526
- On the potential of mapping sea level anomalies from satellite altimetry with Random Forest Regression M. Passaro & M. Juhl 10.1007/s10236-023-01540-4
- Winter Air‐Sea CO2 Fluxes Constructed From Summer Observations of the Polar Southern Ocean Suggest Weak Outgassing N. Mackay & A. Watson 10.1029/2020JC016600
- Generalization of Parameter Selection of SVM and LS-SVM for Regression J. Zeng et al. 10.3390/make1020043
- Remote Sensing Estimations of the Seawater Partial Pressure of CO₂ Using Sea Surface Roughness Derived From Synthetic Aperture Radar Y. Wang et al. 10.1109/TGRS.2024.3379984
- The Distribution of pCO2W and Air-Sea CO2 Fluxes Using FFNN at the Continental Shelf Areas of the Arctic Ocean I. Wrobel-Niedzwiecka et al. 10.3390/rs14020312
- Quantifying Errors in Observationally Based Estimates of Ocean Carbon Sink Variability L. Gloege et al. 10.1029/2020GB006788
- Improving Estimates of Dynamic Global Marine DMS and Implications for Aerosol Radiative Effect J. Zhao et al. 10.1029/2023JD039314
- Nonlocality of scale-dependent eddy mixing at the Kuroshio Extension M. Liu et al. 10.3389/fmars.2023.1137216
- Revised estimates of ocean-atmosphere CO2 flux are consistent with ocean carbon inventory A. Watson et al. 10.1038/s41467-020-18203-3
- Carbon Sinks and Variations of pCO2 in the Southern Ocean From 1998 to 2018 Based on a Deep Learning Approach Y. Wang et al. 10.1109/JSTARS.2021.3066552
- A comparative assessment of the uncertainties of global surface ocean CO<sub>2</sub> estimates using a machine-learning ensemble (CSIR-ML6 version 2019a) – have we hit the wall? L. Gregor et al. 10.5194/gmd-12-5113-2019
- Reconstruction of monthly fCO2 distribution in the Ross Sea, Antarctica during 1998 -2018 using machine learning technique and observational data sets A. Mo et al. 10.22761/DJ2022..4.3.003
- Interannual drivers of the seasonal cycle of CO<sub>2</sub> in the Southern Ocean L. Gregor et al. 10.5194/bg-15-2361-2018
- The Southern Ocean Carbon Cycle 1985–2018: Mean, Seasonal Cycle, Trends, and Storage J. Hauck et al. 10.1029/2023GB007848
- Machine Learning Estimates of Global Marine Nitrogen Fixation W. Tang et al. 10.1029/2018JG004828
- Improved Quantification of Ocean Carbon Uptake by Using Machine Learning to Merge Global Models and pCO2 Data L. Gloege et al. 10.1029/2021MS002620
- Estimating Ocean Surface Currents With Machine Learning A. Sinha & R. Abernathey 10.3389/fmars.2021.672477
- Role of Aerosols in Spring Blooms in the Central Yellow Sea During the COVID-19 Lockdown by China J. Baek et al. 10.3389/fmars.2022.911819
- GOBAI-O2: temporally and spatially resolved fields of ocean interior dissolved oxygen over nearly 2 decades J. Sharp et al. 10.5194/essd-15-4481-2023
- Global ocean dimethyl sulfide climatology estimated from observations and an artificial neural network W. Wang et al. 10.5194/bg-17-5335-2020
Discussed (final revised paper)
Latest update: 22 Nov 2024
Short summary
We use machine learning to extrapolate ship measurements of CO2 using satellite data. We present two ML methods new to this field. These methods perform well in the context of previous work and reproduce the decadal trends of previous estimates. To test the methods, we simulate the exact observed setup in biogeochemical ocean model output. We show that the new methods perform well in synthetic data. Lastly, we show that there is only a weak bias due to undersampling in the SOCAT v3 dataset.
We use machine learning to extrapolate ship measurements of CO2 using satellite data. We present...
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