Articles | Volume 14, issue 23
https://doi.org/10.5194/bg-14-5551-2017
https://doi.org/10.5194/bg-14-5551-2017
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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Cited articles

Atlas, R., Hoffman, R. N., Ardizzone, J., Leidner, S. M., Jusem, J. C., Smith, D. K., and Gombos, D.: A Cross-calibrated, Multiplatform Ocean Surface Wind Velocity Product for Meteorological and Oceanographic Applications, B. Am. Meteorol. Soc., 92, 157–174, https://doi.org/10.1175/2010BAMS2946.1, 2011.
Bakker, D. C. E., Hoppema, M., Schröder, M., Geibert, W., and de Baar, H. J. W.: A rapid transition from ice covered CO2–rich waters to a biologically mediated CO2 sink in the eastern Weddell Gyre, Biogeosciences, 5, 1373–1386, https://doi.org/10.5194/bg-5-1373-2008, 2008.
Biastoch, A., Böning, C. W., Getzlaff, J., Molines, J.-M., and Madec, G.: Causes of Interannual–Decadal Variability in the Meridional Overturning Circulation of the Midlatitude North Atlantic Ocean, J. Climate, 21, 6599–6615, https://doi.org/10.1175/2008JCLI2404.1, 2008.
Boyd, P. W. and Ellwood, M. J.: The biogeochemical cycle of iron in the ocean, Nat. Geosci., 3, 675–682, https://doi.org/10.1038/ngeo964, 2010.
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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