Earth System Dynamics
Earth System Dynamics
ESD
Articles | Volume 17, issue 3
Articles | Volume 17, issue 3
https://doi.org/10.5194/esd-17-651-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/esd-17-651-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 17, issue 3
Research article
 | 
01 Jun 2026
Research article |  | 01 Jun 2026

Improving terrestrial carbon flux simulations with machine learning and global Earth observations

Christian Seiler

Viewed

Total article views: 7,192 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
5,701 1,209 282 7,192 235 334
  • HTML: 5,701
  • PDF: 1,209
  • XML: 282
  • Total: 7,192
  • BibTeX: 235
  • EndNote: 334
Views and downloads (calculated since 16 Jun 2025)
Cumulative views and downloads (calculated since 16 Jun 2025)

Viewed (geographical distribution)

Total article views: 7,192 (including HTML, PDF, and XML) Thereof 7,058 with geography defined and 134 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Saved (final revised paper)

Latest update: 08 Oct 2026
Download
Short summary
This study shows how machine learning combined with global Earth observations can improve simulations of the land carbon cycle. Optimizing key model parameters enhances the accuracy of historical carbon fluxes, while machine-learning tools help assess the robustness of these results in the presence of compensating parameter effects. The findings demonstrate that parameter optimization strongly influences simulated carbon fluxes, highlighting its importance for improving climate models.
Read more
Share
Altmetrics
Final-revised paper
Preprint