Articles | Volume 11, issue 3
Earth Syst. Sci. Data, 11, 1109–1127, 2019
https://doi.org/10.5194/essd-11-1109-2019
Earth Syst. Sci. Data, 11, 1109–1127, 2019
https://doi.org/10.5194/essd-11-1109-2019

  31 Jul 2019

31 Jul 2019

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

Daniel Broullón et al.

Data sets

A global monthly climatology of total alkalinity: a neural network approach (2019) [Dataset] D. Broullón, F. F. Pérez, A. Velo, M. Hoppema, A. Olsen, T. Takahashi, R. M. Key, M. González-Dávila, T. Tanhua, E. Jeansson, A. Kozyr, and S. M. A. C. van Heuven https://doi.org/10.20350/digitalCSIC/8644

Model code and software

Neural Network Object D. Broullón, F. F. Pérez, A. Velo, M. Hoppema, A. Olsen, T. Takahashi, R. M. Key, M. González-Dávila, T. Tanhua, E. Jeansson, A. Kozyr, and S. M. A. C. van Heuven https://doi.org/10.20350/digitalCSIC/8644

Video supplement

Video of the monthly climatology of total alkalinity D. Broullón, F. F. Pérez, A. Velo, M. Hoppema, A. Olsen, T. Takahashi, R. M. Key, M. González-Dávila, T. Tanhua, E. Jeansson, A. Kozyr, and S. M. A. C. van Heuven https://doi.org/10.20350/digitalCSIC/8644

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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.