Microbial models with data‐driven parameters predict stronger soil carbon responses to climate change
Abstract
Abstract Long‐term carbon (C) cycle feedbacks to climate depend on the future dynamics of soil organic carbon ( SOC ). Current models show low predictive accuracy at simulating contemporary SOC pools, which can be improved through parameter estimation. However, major uncertainty remains in global soil responses to climate change, particularly uncertainty in how the activity of soil microbial communities will respond. To date, the role of microbes in SOC dynamics has been implicitly described by decay rate constants in most conventional global carbon cycle models. Explicitly including microbial biomass dynamics into C cycle model formulations has shown potential to improve model predictive performance when assessed against global SOC databases. This study aimed to data‐constrained parameters of two soil microbial models, evaluate the improvements in performance of those calibrated models in predicting contemporary carbon stocks, and compare the SOC responses to climate change and their uncertainties between microbial and conventional models. Microbial models with calibrated parameters explained 51% of variability in the observed total SOC , whereas a calibrated conventional model explained 41%. The microbial models, when forced with climate and soil carbon input predictions from the 5th Coupled Model Intercomparison Project ( CMIP 5), produced stronger soil C responses tomore »
- Authors:
-
- Department of Microbiology and Plant Biology University of Oklahoma Norman OK USA, Computational Science Laboratory Microsoft Research Cambridge UK
- Computational Science Laboratory Microsoft Research Cambridge UK
- Department of Microbiology and Plant Biology University of Oklahoma Norman OK USA, Center for Earth System Science Tsinghua University Beijing China
- Publication Date:
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1400893
- Resource Type:
- Publisher's Accepted Manuscript
- Journal Name:
- Global Change Biology
- Additional Journal Information:
- Journal Name: Global Change Biology Journal Volume: 21 Journal Issue: 6; Journal ID: ISSN 1354-1013
- Publisher:
- Wiley-Blackwell
- Country of Publication:
- United Kingdom
- Language:
- English
Citation Formats
Hararuk, Oleksandra, Smith, Matthew J., and Luo, Yiqi. Microbial models with data‐driven parameters predict stronger soil carbon responses to climate change. United Kingdom: N. p., 2015.
Web. doi:10.1111/gcb.12827.
Hararuk, Oleksandra, Smith, Matthew J., & Luo, Yiqi. Microbial models with data‐driven parameters predict stronger soil carbon responses to climate change. United Kingdom. https://doi.org/10.1111/gcb.12827
Hararuk, Oleksandra, Smith, Matthew J., and Luo, Yiqi. Fri .
"Microbial models with data‐driven parameters predict stronger soil carbon responses to climate change". United Kingdom. https://doi.org/10.1111/gcb.12827.
@article{osti_1400893,
title = {Microbial models with data‐driven parameters predict stronger soil carbon responses to climate change},
author = {Hararuk, Oleksandra and Smith, Matthew J. and Luo, Yiqi},
abstractNote = {Abstract Long‐term carbon (C) cycle feedbacks to climate depend on the future dynamics of soil organic carbon ( SOC ). Current models show low predictive accuracy at simulating contemporary SOC pools, which can be improved through parameter estimation. However, major uncertainty remains in global soil responses to climate change, particularly uncertainty in how the activity of soil microbial communities will respond. To date, the role of microbes in SOC dynamics has been implicitly described by decay rate constants in most conventional global carbon cycle models. Explicitly including microbial biomass dynamics into C cycle model formulations has shown potential to improve model predictive performance when assessed against global SOC databases. This study aimed to data‐constrained parameters of two soil microbial models, evaluate the improvements in performance of those calibrated models in predicting contemporary carbon stocks, and compare the SOC responses to climate change and their uncertainties between microbial and conventional models. Microbial models with calibrated parameters explained 51% of variability in the observed total SOC , whereas a calibrated conventional model explained 41%. The microbial models, when forced with climate and soil carbon input predictions from the 5th Coupled Model Intercomparison Project ( CMIP 5), produced stronger soil C responses to 95 years of climate change than any of the 11 CMIP 5 models. The calibrated microbial models predicted between 8% (2‐pool model) and 11% (4‐pool model) soil C losses compared with CMIP 5 model projections which ranged from a 7% loss to a 22.6% gain. Lastly, we observed unrealistic oscillatory SOC dynamics in the 2‐pool microbial model. The 4‐pool model also produced oscillations, but they were less prominent and could be avoided, depending on the parameter values.},
doi = {10.1111/gcb.12827},
journal = {Global Change Biology},
number = 6,
volume = 21,
place = {United Kingdom},
year = {Fri Mar 06 00:00:00 EST 2015},
month = {Fri Mar 06 00:00:00 EST 2015}
}
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