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An Alternative to Static Climatologies: Robust Estimation of Open Ocean CO2 Variables and Nutrient Concentrations From T, S, and O2 Data Using Bayesian Neural Networks

Henry Bittig 1 Tobias Steinhoff 2 Hervé Claustre 3 Björn Fiedler 2 Nancy Williams Raphaëlle Sauzède Arne Körtzinger 4 Jean-Pierre Gattuso 5
1 IOW - Leibniz Institute for Baltic Sea Research Warnemünde
2 GEOMAR - Helmholtz Centre for Ocean Research [Kiel]
3 LOV - Laboratoire d'océanographie de Villefranche
4 CAU - Christian-Albrechts-Universität zu Kiel
5 LOV - Laboratoire d'océanographie de Villefranche
Abstract : which shows a significant, high latitude-intensified increase between +0.1 and +0.4 units per decade. This shows the utility that such transfer functions with realistic uncertainty estimates provide to ocean biogeochemistry and global climate change research. In addition, CANYON-B provides robust and accurate estimates of nitrate, phosphate, and silicate. Matlab and R code are available at https://github.com/HCBScienceProducts/.
Keywords : carbon cycle GLODAP marine carbonate system surface pCO 2 climatology Revelle buffer factor increase machine learning nutrient estimation
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https://hal.archives-ouvertes.fr/hal-02345747
Contributor : Jean-Pierre Gattuso <>
Submitted on : Thursday, January 7, 2021 - 10:33:47 AM
Last modification on : Friday, January 8, 2021 - 3:08:34 AM

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INSU | SU-TI | LOV | SU-SCIENCES | UMS-829 | SORBONNE-UNIVERSITE | CNRS

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Henry Bittig, Tobias Steinhoff, Hervé Claustre, Björn Fiedler, Nancy Williams, et al.. An Alternative to Static Climatologies: Robust Estimation of Open Ocean CO2 Variables and Nutrient Concentrations From T, S, and O2 Data Using Bayesian Neural Networks. Frontiers in Marine Science, Frontiers Media, 2018, 5, ⟨10.3389/fmars.2018.00328⟩. ⟨hal-02345747⟩

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