Articles | Volume 17, issue 6
https://doi.org/10.5194/gmd-17-2299-2024
https://doi.org/10.5194/gmd-17-2299-2024
Development and technical paper
 | 
20 Mar 2024
Development and technical paper |  | 20 Mar 2024

Optimising CH4 simulations from the LPJ-GUESS model v4.1 using an adaptive Markov chain Monte Carlo algorithm

Jalisha T. Kallingal, Johan Lindström, Paul A. Miller, Janne Rinne, Maarit Raivonen, and Marko Scholze

Viewed

Total article views: 4,639 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
3,395 1,025 219 4,639 434 202 297
  • HTML: 3,395
  • PDF: 1,025
  • XML: 219
  • Total: 4,639
  • Supplement: 434
  • BibTeX: 202
  • EndNote: 297
Views and downloads (calculated since 19 Apr 2023)
Cumulative views and downloads (calculated since 19 Apr 2023)

Viewed (geographical distribution)

Total article views: 4,639 (including HTML, PDF, and XML) Thereof 4,578 with geography defined and 61 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Saved (final revised paper)

Latest update: 19 Oct 2026
Download
Short summary
By unlocking the mysteries of CH4 emissions from wetlands, our work improved the accuracy of the LPJ-GUESS vegetation model using Bayesian statistics. Via assimilation of long-term real data from a wetland, we significantly enhanced CH4 emission predictions. This advancement helps us better understand wetland contributions to atmospheric CH4, which are crucial for addressing climate change. Our method offers a promising tool for refining global climate models and guiding conservation efforts
Share