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ESSD | Articles | Volume 11, issue 3
Earth Syst. Sci. Data, 11, 1109–1127, 2019
https://doi.org/10.5194/essd-11-1109-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Earth Syst. Sci. Data, 11, 1109–1127, 2019
https://doi.org/10.5194/essd-11-1109-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  31 Jul 2019

31 Jul 2019

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

Daniel Broullón et al.
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Interactive discussion
Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer review completion
AR: Author's response | RR: Referee report | ED: Editor decision
AR by Daniel Broullón on behalf of the Authors (27 Feb 2019)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (01 Mar 2019) by David Carlson
RR by Anonymous Referee #1 (20 Mar 2019)
RR by Anonymous Referee #3 (05 Apr 2019)
ED: Reconsider after major revisions (08 Apr 2019) by David Carlson
AR by Daniel Broullón on behalf of the Authors (20 Jun 2019)  Author's response    Manuscript
ED: Publish as is (03 Jul 2019) by David Carlson
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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.
In this work, we are contributing to the knowledge of the consequences of climate change in the...
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