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| Bittig, Henry C.; Steinhoff, Tobias; Claustre, Harve; Fiedler, Bjoern; Williams, Nancy L.; Sauzede, Raphaelle; Koertzinger, Arne; Gattuso, Jean-pierre. |
| 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... |
| Tipo: Text |
Palavras-chave: Carbon cycle; GLODAP; Marine carbonate system; Surface pCO(2) climatology; Revelle buffer factor increase; Machine learning; Nutrient estimation. |
| Ano: 2018 |
URL: https://archimer.ifremer.fr/doc/00675/78681/80879.pdf |
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