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dc.contributor.authorBittig, Henry C.
dc.contributor.authorSteinhoff, Tobias
dc.contributor.authorClaustre, Hervé
dc.contributor.authorFiedler, Björn
dc.contributor.authorWilliams, Nancy L.
dc.contributor.authorSauzède, Raphaëlle
dc.contributor.authorKörtzinger, Arne
dc.contributor.authorGattuso, Jean-Pierre
dc.identifier.citationBittig, H.C., Steinhoff, T., Claustre, H., Fiedler, B., Williams, N.L., Sauzède, R., Körtzinger, A. and Gattuso, J-P. (2018) 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. Frontier in Marine Science, 5:328, 29pp. DOI: 10.3389/fmars.2018.0032en_US
dc.description.abstractThis 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 relations contained therein. “CONTENT” combines and refines the four carbonate system variables to be consistent with carbonate chemistry. Both methods come with a robust uncertainty estimate that incorporates information from the local conditions. They are validated against independent GO-SHIP bottle and sensor data, and compare favorably to other state-of-the-art mapping methods. As “dynamic climatologies” they show comparable performance to classical climatologies on large scales but a much better representation on smaller scales (40–120 d, 500–1,500 km) compared to in situ data. The limits of these mappings are explored with pCO2 estimation in surface waters, i.e., at the edge of the domain with high intrinsic variability. In highly productive areas, there is a tendency for pCO2 overestimation due to decoupling of the O2 and C cycles by air-sea gas exchange, but global surface pCO2 estimates are unbiased compared to a monthly climatology. CANYON-B and CONTENT are highly useful as transfer functions between components of the ocean observing system (GO-SHIP repeat hydrography, BGC-Argo, underway observations) and permit the synergistic use of these highly complementary systems, both in spatial/temporal coverage and number of observations. Through easily and robotically-accessible observations they allow densification of more difficult-to-observe variables (e.g., 15 times denser AT and CT compared to direct measurements). At the same time, they give access to the complete carbonate system. This potential is demonstrated by an observation-based global analysis of the Revelle buffer factor 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
dc.rightsAttribution 4.0*
dc.subject.otherCarbon cycleen_US
dc.subject.otherSurface pCO2 climatologyen_US
dc.subject.otherMachine learningen_US
dc.subject.otherRevelle buffer factor increaseen_US
dc.titleAn Alternative to Static Climatologies: Robust Estimation of Open Ocean CO2 Variables and Nutrient Concentrations From T, S, and O2 Data Using Bayesian Neural Networks.en_US
dc.typeJournal Contributionen_US
dc.subject.parameterDisciplineCarbon, nitrogen and phosphorusen_US
dc.subject.parameterDisciplineCarbonate systemen_US
dc.subject.dmProcessesData analysisen_US
dc.bibliographicCitation.titleFrontiers in Marine Scienceen_US
dc.bibliographicCitation.issueArticle 328en_US
dc.description.eovSea surface temperatureen_US
dc.description.eovSubsurface temperatureen_US
dc.description.eovSea surface salinityen_US
dc.description.eovSubsurface salinityen_US
dc.description.eovInorganic carbonen_US
dc.description.adoptionValidated (tested by third parties)en_US
dc.description.methodologyTypeSpecification of criteriaen_US
dc.description.methodologyTypeReports with methodological relevanceen_US C. Bittig

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Except where otherwise noted, this item's license is described as Attribution 4.0