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

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

This work presents two new methods to estimate oceanic alkalinity (AT), dissolved inorganic carbon (CT), pH, and pCO2 from temperature, salinity, oxygen, and geolocation data. “CANYON-B” is a Bayesian neural network mapping that accurately reproduces GLODAPv2 bottle data and the biogeochemical relat...

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Bibliographic Details
Main Authors: Henry C. Bittig, Tobias Steinhoff, Hervé Claustre, Björn Fiedler, Nancy L. Williams, Raphaëlle Sauzède, Arne Körtzinger, Jean-Pierre Gattuso
Format: Article
Language:English
Published: Frontiers Media S.A. 2018-09-01
Series:Frontiers in Marine Science
Subjects:
carbon cycle
GLODAP
marine carbonate system
surface pCO2 climatology
Revelle buffer factor increase
machine learning
Online Access:https://www.frontiersin.org/article/10.3389/fmars.2018.00328/full

Internet

https://www.frontiersin.org/article/10.3389/fmars.2018.00328/full

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