Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 19, issue 21
Articles | Volume 19, issue 21
https://doi.org/10.5194/gmd-19-10365-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-19-10365-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 19, issue 21
Model description paper
 | 
03 Nov 2026
Model description paper |  | 03 Nov 2026

Leveraging leaf-level optimality processes with explicit acclimation improves global GPP representation in an individual-based DGVM (LPJ-GUESS v4.1.1)

Matthew Forrest, Mateus Dantas de Paula, Filipe Gomes de Almeida, Sandy P. Harrison, I. Colin Prentice, and Thomas Hickler

Viewed

Total article views: 492 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
289 152 51 492 68 42 51
  • HTML: 289
  • PDF: 152
  • XML: 51
  • Total: 492
  • Supplement: 68
  • BibTeX: 42
  • EndNote: 51
Views and downloads (calculated since 25 Jun 2026)
Cumulative views and downloads (calculated since 25 Jun 2026)

Viewed (geographical distribution)

Total article views: 492 (including HTML, PDF, and XML) Thereof 459 with geography defined and 33 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 03 Nov 2026
Download
Short summary
Global photosynthesis on land is a large flow of carbon in the Earth system which underpins food production and ecosystems. It is particularly important to know how it responds to climate change, but current vegetation models do not agree on this. We implemented a new photosynthesis model based on recent theoretical advances into a vegetation model. We found that it improved the representation of photosynthesis when compared both to global data from satellites and detailed site measurements.
Read more
Share
Altmetrics
Final-revised paper
Preprint