An Alternative to Static Climatologies: Robust Estimation of Open Ocean CO2 Variables and Nutrient Concentrations From T, S, and O-2 Data Using Bayesian Neural Networks

Type Article
Date 2018-09
Language English
Author(s) Bittig Henry C.1, Steinhoff Tobias2, Claustre Harve1, Fiedler Bjoern2, Williams Nancy L.ORCID3, 4, Sauzede RaphaelleORCID5, Koertzinger Arne2, 6, Gattuso Jean-PierreORCID1, 7
Affiliation(s) 1 : Sorbonne Univ, Lab Oceanog Villefranche, CNRS, UMR 7093, Villefranche Sur Mer, France.
2 : GEOMAR Helmholtz Zentrum Ozeanforsch Kiel, Kiel, Germany.
3 : NOAA, Pacific Marine Environm Lab, 7600 Sand Point Way Ne, Seattle, WA 98115 USA.
4 : Oregon State Univ, Coll Earth Ocean & Atmospher Sci, Corvallis, OR USA.
5 : Univ Polynesie Francaise, Lab Geosci Pacifique Sud, Tahiti, French Polynesi, France.
6 : Christian Albrechts Univ Kiel, Kiel, Germany.
7 : Sci Po, Inst Sustainable Dev & Int Relat, Paris, France.
Source Frontiers In Marine Science (2296-7745) (Frontiers Media Sa), 2018-09 , Vol. 5 , P. 328 (29p.)
DOI 10.3389/fmars.2018.00328
WOS© Times Cited 26
Keyword(s) carbon cycle, GLODAP, marine carbonate system, surface pCO(2) climatology, Revelle buffer factor increase, machine learning, nutrient estimation

This work presents two new methods to estimate oceanic alkalinity (A(T)), dissolved inorganic carbon (C-T), pH, and pCO(2) 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 pCO(2) 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 pCO(2 )overestimation due to decoupling of the O-2 and C cycles by air-sea gas exchange, but global surface pCO(2) 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 A(T) and C-T 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

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Bittig Henry C., Steinhoff Tobias, Claustre Harve, Fiedler Bjoern, Williams Nancy L., Sauzede Raphaelle, Koertzinger Arne, Gattuso Jean-Pierre (2018). An Alternative to Static Climatologies: Robust Estimation of Open Ocean CO2 Variables and Nutrient Concentrations From T, S, and O-2 Data Using Bayesian Neural Networks. Frontiers In Marine Science, 5, 328 (29p.). Publisher's official version : , Open Access version :