Atmospheric Chemistry and Physics
Atmospheric Chemistry and Physics
ACP
Articles | Volume 26, issue 4
Articles | Volume 26, issue 4
https://doi.org/10.5194/acp-26-2561-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-26-2561-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 26, issue 4
Research article
 | 
18 Feb 2026
Research article |  | 18 Feb 2026

Constraining a data-driven CO2 flux model by ecosystem and atmospheric observations using atmospheric transport

Samuel Upton, Markus Reichstein, Wouter Peters, Santiago Botía, Jacob A. Nelson, Sophia Walther, Martin Jung, Fabian Gans, László Haszpra, and Ana Bastos

Viewed

Total article views: 5,740 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
4,643 812 285 5,740 298 347
  • HTML: 4,643
  • PDF: 812
  • XML: 285
  • Total: 5,740
  • BibTeX: 298
  • EndNote: 347
Views and downloads (calculated since 20 May 2025)
Cumulative views and downloads (calculated since 20 May 2025)

Viewed (geographical distribution)

Total article views: 5,740 (including HTML, PDF, and XML) Thereof 5,542 with geography defined and 198 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Saved (final revised paper)

Latest update: 10 Sep 2026
Download
Short summary
We create a hybrid ecosystem-level carbon flux model using both eddy-covariance observations and observations of the atmospheric mole fraction of CO2 at three tall-tower observatories. Our study uses an atmospheric transport model (STILT) to connect the atmospheric signal to the ecosystem-level model. We show that this inclusion of atmospheric information meaningfully improves the model's representation of the interannual variability of the global net flux of CO2.
Read more
Share
Altmetrics
Final-revised paper
Preprint