Articles | Volume 12, issue 3
https://doi.org/10.5194/essd-12-1725-2020
https://doi.org/10.5194/essd-12-1725-2020
Data description paper
 | 
05 Aug 2020
Data description paper |  | 05 Aug 2020

A global monthly climatology of oceanic total dissolved inorganic carbon: a neural network approach

Daniel Broullón, Fiz F. Pérez, Antón Velo, Mario Hoppema, Are Olsen, Taro Takahashi, Robert M. Key, Toste Tanhua, J. Magdalena Santana-Casiano, and Alex Kozyr

Viewed

Total article views: 3,962 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
2,876 950 136 3,962 353 136 145
  • HTML: 2,876
  • PDF: 950
  • XML: 136
  • Total: 3,962
  • Supplement: 353
  • BibTeX: 136
  • EndNote: 145
Views and downloads (calculated since 11 Mar 2020)
Cumulative views and downloads (calculated since 11 Mar 2020)

Viewed (geographical distribution)

Total article views: 3,962 (including HTML, PDF, and XML) Thereof 3,493 with geography defined and 469 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 20 Nov 2024
Download
Short summary
This work offers a vision of the global ocean regarding the carbon cycle and the implications of ocean acidification through a climatology of a changing variable in the context of climate change: total dissolved inorganic carbon. The climatology was designed through artificial intelligence techniques to represent the mean state of the present ocean. It is very useful to introduce in models to evaluate the state of the ocean from different perspectives.
Altmetrics
Final-revised paper
Preprint