Journal cover Journal topic
Earth System Science Data The data publishing journal
Journal topic

Journal metrics

Journal metrics

  • IF value: 9.197 IF 9.197
  • IF 5-year value: 9.612 IF 5-year
    9.612
  • CiteScore value: 12.5 CiteScore
    12.5
  • SNIP value: 3.137 SNIP 3.137
  • IPP value: 9.49 IPP 9.49
  • SJR value: 4.532 SJR 4.532
  • Scimago H <br class='hide-on-tablet hide-on-mobile'>index value: 48 Scimago H
    index 48
  • h5-index value: 35 h5-index 35
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.

Viewed

Total article views: 2,579 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
2,071 470 38 2,579 108 33 43
  • HTML: 2,071
  • PDF: 470
  • XML: 38
  • Total: 2,579
  • Supplement: 108
  • BibTeX: 33
  • EndNote: 43
Views and downloads (calculated since 26 Oct 2018)
Cumulative views and downloads (calculated since 26 Oct 2018)

Viewed (geographical distribution)

Total article views: 2,270 (including HTML, PDF, and XML) Thereof 2,236 with geography defined and 34 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Saved (final revised paper)

No saved metrics found.

Saved (preprint)

No saved metrics found.

Discussed (final revised paper)

No discussed metrics found.

Discussed (preprint)

No discussed metrics found.
Latest update: 12 Aug 2020
Download
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...
Citation