Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 12, issue 5
Articles | Volume 12, issue 5
https://doi.org/10.5194/gmd-12-2091-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-12-2091-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 12, issue 5
Model description paper
 | 
29 May 2019
Model description paper |  | 29 May 2019

LSCE-FFNN-v1: a two-step neural network model for the reconstruction of surface ocean pCO2 over the global ocean

Anna Denvil-Sommer, Marion Gehlen, Mathieu Vrac, and Carlos Mejia

Viewed

Total article views: 6,036 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
4,386 1,524 126 6,036 707 189 242
  • HTML: 4,386
  • PDF: 1,524
  • XML: 126
  • Total: 6,036
  • Supplement: 707
  • BibTeX: 189
  • EndNote: 242
Views and downloads (calculated since 07 Nov 2018)
Cumulative views and downloads (calculated since 07 Nov 2018)

Viewed (geographical distribution)

Total article views: 6,036 (including HTML, PDF, and XML) Thereof 5,604 with geography defined and 432 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 02 Dec 2025
Download
Short summary
This work is dedicated to a new model that reconstructs the surface ocean partial pressure of carbon dioxide (pCO2) over the global ocean on a monthly 1°×1° grid. The model is based on a feed-forward neural network and represents the nonlinear relationships between pCO2 and the ocean drivers. Reconstructed pCO2 has a satisfying accuracy compared to independent observational data and shows a good agreement in seasonal and interannual variability with three existing mapping methods.
Read more
Share