Articles | Volume 21, issue 22
https://doi.org/10.5194/bg-21-5173-2024
https://doi.org/10.5194/bg-21-5173-2024
Research article
 | 
19 Nov 2024
Research article |  | 19 Nov 2024

Observational benchmarks inform representation of soil organic carbon dynamics in land surface models

Kamal Nyaupane, Umakant Mishra, Feng Tao, Kyongmin Yeo, William J. Riley, Forrest M. Hoffman, and Sagar Gautam

Viewed

Total article views: 1,413 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
1,007 351 55 1,413 138 56 59
  • HTML: 1,007
  • PDF: 351
  • XML: 55
  • Total: 1,413
  • Supplement: 138
  • BibTeX: 56
  • EndNote: 59
Views and downloads (calculated since 21 Mar 2023)
Cumulative views and downloads (calculated since 21 Mar 2023)

Viewed (geographical distribution)

Total article views: 1,413 (including HTML, PDF, and XML) Thereof 1,403 with geography defined and 10 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 31 May 2025
Download
Short summary
Representing soil organic carbon (SOC) dynamics in Earth system models (ESMs) is a key source of uncertainty in predicting carbon–climate feedbacks. Using machine learning, we develop and compare predictive relationships in observations (Obs) and ESMs. We find different relationships between environmental factors and SOC stocks in Obs and ESMs. SOC prediction in ESMs may be improved by representing the functional relationships of environmental controllers in a way consistent with observations.
Share
Altmetrics
Final-revised paper
Preprint