Observational benchmarks inform representation of soil organic carbon dynamics in land surface models
Journal Article
·
· Biogeosciences (Online)
- University of Texas, El Paso, TX (United States)
- Sandia National Laboratories (SNL-CA), Livermore, CA (United States)
- Cornell University, Ithaca, NY (United States)
- IBM, Yorktown Heights, NY (United States)
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
- Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
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 establish their functional relationships with SOC stocks. The resulting knowledge can be integrated into ESMs to reduce uncertainty and improve predictions of 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 (namely 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 models used in Phase 6 of the Coupled Model Intercomparison Project (CMIP6). Our results showed that the diurnal temperature, drought index, cation exchange capacity, and precipitation were important observed environmental predictors of global SOC stocks. While the RF model identified 14 environmental factors that describe climatic, vegetation, and edaphic conditions as important predictors of global SOC stocks (R2=0.61, RMSE = 0.46 kg m-2), current ESMs oversimplify the relationships between environmental factors and SOC, with precipitation, temperature, and net primary productivity explaining > 96 % of the variability in ESM-modeled SOC stocks. Further, our study revealed notable disparities among the functional relationships between environmental factors and SOC stocks simulated by ESMs compared with observed relationships. To improve SOC representations in ESMs, it is imperative to incorporate additional environmental controls, such as the cation exchange capacity, and refine the functional relationships to align more closely with observations.
- Research Organization:
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States); Sandia National Lab. (SNL-CA), Livermore, CA (United States)
- Sponsoring Organization:
- USDOE; USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE National Nuclear Security Administration (NNSA); USDOE Office of Science (SC), Biological and Environmental Research (BER)
- Grant/Contract Number:
- AC02-05CH11231; AC05-00OR22725; NA0003525
- OSTI ID:
- 2478026
- Alternate ID(s):
- OSTI ID: 2498478
OSTI ID: 2538480
OSTI ID: 2481495
- Report Number(s):
- SAND--2025-00084J; ark:/13030/qt770200t4
- Journal Information:
- Biogeosciences (Online), Journal Name: Biogeosciences (Online) Journal Issue: 22 Vol. 21; ISSN 1726-4189
- Publisher:
- Copernicus Publications, EGUCopyright Statement
- Country of Publication:
- United States
- Language:
- English