Earth System Science Data
Earth System Science Data
Earth System Science Data
Articles | Volume 11, issue 3
Articles | Volume 11, issue 3
https://doi.org/10.5194/essd-11-1109-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-11-1109-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 11, issue 3
31 Jul 2019
 | 31 Jul 2019

A global monthly climatology of total alkalinity: a neural network approach

Daniel Broullón, Fiz F. Pérez, Antón Velo, Mario Hoppema, Are Olsen, Taro Takahashi, Robert M. Key, Toste Tanhua, Melchor González-Dávila, Emil Jeansson, Alex Kozyr, and Steven M. A. C. van Heuven

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Short summary
In this work, we are contributing to the knowledge of the consequences of climate change in the ocean. We have focused on a variable related to this process: total alkalinity. We have designed a monthly climatology of total alkalinity using artificial intelligence techniques, that is, a representation of the average capacity of the ocean in the last decades to decelerate the consequences of climate change. The climatology is especially useful to infer the evolution of the ocean through models.
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