Preprints
https://doi.org/10.5194/bg-2023-50
https://doi.org/10.5194/bg-2023-50
21 Mar 2023
 | 21 Mar 2023
Status: a revised version of this preprint was accepted for the journal BG and is expected to appear here in due course.

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

Abstract. Representing soil organic carbon (SOC) dynamics in Earth system models (ESMs) is a key source of uncertainty in predicting carbon climate feedbacks. Machine learning models can help identify dominant environmental controllers and their functional relationships with SOC stocks. The resulting knowledge can be implemented in ESMs to reduce uncertainty and better predict SOC dynamics over space and time. In this study, we used a large number of SOC field observations (n = 54,000), geospatial datasets of environmental factors (n = 46), and two machine learning approaches (Random Forest (RF) and Generalized Additive Modeling (GAM)) to: (1) identify dominant environmental controllers of global and biome-specific SOC stocks, (2) derive functional relationships between environmental controllers and SOC stocks, and (3) compare the identified environmental controllers and predictive relationships with those in Coupled Model Intercomparison Project phase six (CMIP6) models. Our results showed that diurnal temperature, drought index, cation exchange capacity, and precipitation were important observed environmental controllers of SOC stocks. RF model predictions of global-scale SOC stocks were relatively accurate (R2 = 0.61, RMSE = 0.46 kg m−2). In contrast, precipitation, temperature, and net primary productivity explained > 96 % of ESM-modeled SOC stock variability. We also found very different functional relationships between environmental factors and SOC stocks in observations and ESMs. SOC predictions in ESMs may be improved significantly by including additional environmental controls (e.g., cation exchange capacity) and representing the functional relationships of environmental controllers consistent with observations.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Kamal Nyaupane, Umakant Mishra, Feng Tao, Kyongmin Yeo, William J. Riley, Forrest M. Hoffman, and Sagar Gautam

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on bg-2023-50', Lorenzo Menichetti, 08 May 2023
    • AC1: 'Reply on RC1', Umakant Mishra, 10 Oct 2023
  • RC2: 'Comment on bg-2023-50', Anonymous Referee #2, 24 Sep 2023
    • AC2: 'Reply on RC2', Umakant Mishra, 10 Oct 2023

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on bg-2023-50', Lorenzo Menichetti, 08 May 2023
    • AC1: 'Reply on RC1', Umakant Mishra, 10 Oct 2023
  • RC2: 'Comment on bg-2023-50', Anonymous Referee #2, 24 Sep 2023
    • AC2: 'Reply on RC2', Umakant Mishra, 10 Oct 2023
Kamal Nyaupane, Umakant Mishra, Feng Tao, Kyongmin Yeo, William J. Riley, Forrest M. Hoffman, and Sagar Gautam
Kamal Nyaupane, Umakant Mishra, Feng Tao, Kyongmin Yeo, William J. Riley, Forrest M. Hoffman, and Sagar Gautam

Viewed

Total article views: 806 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
585 176 45 806 79 41 42
  • HTML: 585
  • PDF: 176
  • XML: 45
  • Total: 806
  • Supplement: 79
  • BibTeX: 41
  • EndNote: 42
Views and downloads (calculated since 21 Mar 2023)
Cumulative views and downloads (calculated since 21 Mar 2023)

Viewed (geographical distribution)

Total article views: 802 (including HTML, PDF, and XML) Thereof 802 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 11 Nov 2024
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. We used machine learning to develop and compare predictive relationships in observations and ESMs. We found different relationships between environmental factors and SOC stocks in observations and ESMs. SOC predictions in ESMs may be improved by representing the functional relationships of environmental controllers consistent with observations.
Altmetrics