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A global monthly climatology of oceanic total dissolved inorganic carbon: a neural network approach ArchiMer
Broullon, Daniel; Perez, Iz F; Velo, Anton; Hoppema, Mario; Olsen, Are; Takahashi, Taro; Key, Robert M.; Tanhua, Toste; Magdalena Santana-casiano, J.; Kozyr, Alex.
Anthropogenic emissions of CO2 to the atmosphere have modified the carbon cycle for more than 2 centuries. As the ocean stores most of the carbon on our planet, there is an important task in unraveling the natural and anthropogenic processes that drive the carbon cycle at different spatial and temporal scales. We contribute to this by designing a global monthly climatology of total dissolved inorganic carbon (TCO2), which offers a robust basis in carbon cycle modeling but also for other studies related to this cycle. A feedforward neural network (dubbed NNGv2LDEO) was configured to extract from the Global Ocean Data Analysis Project version 2.2019 (GLODAPv2.2019) and the Lamont-Doherty Earth Observatory (LDEO) datasets the relations between TCO2 and a set...
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Ano: 2020 URL: https://archimer.ifremer.fr/doc/00676/78830/81115.pdf
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