Articles | Volume 15, issue 9
https://doi.org/10.5194/essd-15-4023-2023
https://doi.org/10.5194/essd-15-4023-2023
Data description paper
 | 
13 Sep 2023
Data description paper |  | 13 Sep 2023

Barium in seawater: dissolved distribution, relationship to silicon, and barite saturation state determined using machine learning

Öykü Z. Mete, Adam V. Subhas, Heather H. Kim, Ann G. Dunlea, Laura M. Whitmore, Alan M. Shiller, Melissa Gilbert, William D. Leavitt, and Tristan J. Horner

Viewed

Total article views: 2,652 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
1,969 590 93 2,652 70 55 66
  • HTML: 1,969
  • PDF: 590
  • XML: 93
  • Total: 2,652
  • Supplement: 70
  • BibTeX: 55
  • EndNote: 66
Views and downloads (calculated since 02 Mar 2023)
Cumulative views and downloads (calculated since 02 Mar 2023)

Viewed (geographical distribution)

Total article views: 2,652 (including HTML, PDF, and XML) Thereof 2,597 with geography defined and 55 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 23 Nov 2024
Download
Short summary
We present results from a machine learning model that accurately predicts dissolved barium concentrations for the global ocean. Our results reveal that the whole-ocean barium inventory is significantly lower than previously thought and that the deep ocean below 1000 m is at equilibrium with respect to barite. The model output can be used for a number of applications, including intercomparison, interpolation, and identification of regions warranting additional investigation.
Altmetrics
Final-revised paper
Preprint