Abstract
The storage and cycling of soil organic carbon (SOC) are governed by multiple co-varying factors, including climate, plant productivity, edaphic properties, and disturbance history. Yet, it remains unclear which of these factors are the dominant predictors of observed SOC stocks, globally and within biomes, and how the role of these predictors varies between observations and process-based models. Here we use global observations and an ensemble of soil biogeochemical models to quantify the emergent importance of key state factors – namely, mean annual temperature, net primary productivity, and soil mineralogy – in explaining biome- to global-scale variation in SOC stocks. We use a machine-learning approach to disentangle the role of covariates and elucidate individual relationships with SOC, without imposing expected relationships a priori. While we observe qualitatively similar relationships between SOC and covariates in observations and models, the magnitude and degree of non-linearity vary substantially among the models and observations. Models appear to overemphasize the importance of temperature and primary productivity (especially in forests and herbaceous biomes, respectively), while observations suggest a greater relative importance of soil minerals. This mismatch is also evident globally. However, we observe agreement between observations and model outputs in select individual biomes – namely, temperate deciduous forests and grasslands, which both show stronger relationships of SOC stocks with temperature and productivity, respectively. This approach highlights biomes with the largest uncertainty and mismatch with observations for targeted model improvements. Understanding the role of dominant SOC controls, and the discrepancies between models and observations, globally and across biomes, is essential for improving and validating process representations in soil and ecosystem models for projections under novel future conditions.
| Original language | English |
|---|---|
| Pages (from-to) | 5-17 |
| Number of pages | 13 |
| Journal | Biogeochemistry |
| Volume | 156 |
| Issue number | 1 |
| DOIs | |
| State | Published - Oct 2021 |
Funding
K.G. was supported by a USDA NIFA Postdoctoral Fellowship and as a Lawrence Fellow at Lawrence Livermore National Laboratory (LLNL). Work at LLNL was conducted under the auspices of DOE Contract DE-AC52-07NA27344 and was supported by the LLNL-LDRD Program under Project No. 21-ERD-045. This study stems from a synthesis group Advancing Soil Organic Matter Research: Synthesizing Multi-scale Observations supported through the Long-Term Ecological Research Network Office (LNO; NSF award numbers 1545288 and 1929393) and the National Center for Ecological Analysis and Synthesis (NCEAS) at the University of California, Santa Barbara, and lead by W.R.W and K.L. W.R.W. was supported by the Niwot Ridge LTER program (NSF DEB – 1637686) and USDA-NIFA (2020-67019-31395). J.A.M.M. was supported by Postdoctoral Development funds at Oak Ridge National Laboratory, which is managed by UT-Battelle, LLC, for the US Department of Energy under contract DE-AC05-00OR22725. This manuscript has been authored in part by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan ( http://energy.gov/downloads/doe-public-access-plan ). K.G. was supported by a USDA NIFA Postdoctoral Fellowship and as a Lawrence Fellow at Lawrence Livermore National Laboratory (LLNL). Work at LLNL was conducted under the auspices of DOE Contract DE-AC52-07NA27344 and was supported by the LLNL-LDRD Program under Project No. 21-ERD-045. This study stems from a synthesis group Advancing Soil Organic Matter Research: Synthesizing Multi-scale Observations supported through the Long-Term Ecological Research Network Office (LNO; NSF award numbers 1545288 and 1929393) and the National Center for Ecological Analysis and Synthesis (NCEAS) at the University of California, Santa Barbara, and lead by W.R.W and K.L. W.R.W. was supported by the Niwot Ridge LTER program (NSF DEB – 1637686) and USDA-NIFA (2020-67019-31395). J.A.M.M. was supported by Postdoctoral Development funds at Oak Ridge National Laboratory, which is managed by UT-Battelle, LLC, for the US Department of Energy under contract DE-AC05-00OR22725. This manuscript has been authored in part by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan ).
Keywords
- Earth system models
- Global change
- Global databases
- Machine learning
- Microbial models
- Model benchmarking
- Soil carbon