Biogeosciences
Biogeosciences
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Articles | Volume 14, issue 23
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.
https://doi.org/10.5194/bg-14-5551-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Articles | Volume 14, issue 23
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

Luke Gregor, Schalk Kok, and Pedro M. S. Monteiro

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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.
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