Articles | Volume 16, issue 10
https://doi.org/10.5194/gmd-16-2995-2023
https://doi.org/10.5194/gmd-16-2995-2023
Development and technical paper
 | 
31 May 2023
Development and technical paper |  | 31 May 2023

Testing the reconstruction of modelled particulate organic carbon from surface ecosystem components using PlankTOM12 and machine learning

Anna Denvil-Sommer, Erik T. Buitenhuis, Rainer Kiko, Fabien Lombard, Lionel Guidi, and Corinne Le Quéré

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Cited articles

Alldredge, A.: The carbon, nitrogen and mass content of marine snow as a function of aggregate size, Deep-Sea Res. Pt. I, 45, 529–541, https://doi.org/10.1016/S0967-0637(97)00048-4, 1998. 
Biau, G.: Analysis of Random Forest model, J. Mach. Learn. Res., 13, 1063–1095, 2012. 
Buitenhuis, E. T., Hashioka, T., and Le Quéré, C.: Combined constraints on global ocean primary production using observations and models: OCEAN PRIMARY PRODUCTION, Global Biogeochem. Cy., 27, 847–858, https://doi.org/10.1002/gbc.20074, 2013. 
Buitenhuis, E. T., Le Quéré, C., Bednaršek, N., and Schiebel, R.: Large Contribution of Pteropods to Shallow CaCO3 Export, Global Biogeochem. Cy., 33, 458–468, https://doi.org/10.1029/2018GB006110, 2019. 
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Short summary
Using outputs of global biogeochemical ocean model and machine learning methods, we demonstrate that it will be possible to identify linkages between surface environmental and ecosystem structure and the export of carbon to depth by sinking organic particles using real observations. It will be possible to use this knowledge to improve both our understanding of ecosystem dynamics and of their functional representation within models.
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