DOI: 10.1111/gcb.12827
论文题名: Microbial models with data-driven parameters predict stronger soil carbon responses to climate change
作者: Hararuk O. ; Smith M.J. ; Luo Y.
刊名: Global Change Biology
ISSN: 13541013
出版年: 2015
卷: 21, 期: 6 起始页码: 2439
结束页码: 2453
语种: 英语
英文关键词: Carbon cycle
; Carbon-climate feedback
; Data assimilation
; Model calibration
; Soil biogeochemistry
; Soil organic matter
Scopus关键词: carbon cycle
; climate change
; data assimilation
; microbial community
; soil carbon
; soil chemistry
; soil microorganism
; soil organic matter
; carbon
; soil
; calibration
; carbon cycle
; chemistry
; climate change
; metabolism
; microbiology
; soil
; theoretical model
; Calibration
; Carbon
; Carbon Cycle
; Climate Change
; Models, Theoretical
; Soil
; Soil Microbiology
英文摘要: 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 (CMIP5), produced stronger soil C responses to 95 years of climate change than any of the 11 CMIP5 models. The calibrated microbial models predicted between 8% (2-pool model) and 11% (4-pool model) soil C losses compared with CMIP5 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. © 2014 John Wiley & Sons Ltd. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/61641
Appears in Collections: 影响、适应和脆弱性
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作者单位: Department of Microbiology and Plant Biology, University of Oklahoma, Norman, OK, United States; Computational Science Laboratory, Microsoft Research, Cambridge, United Kingdom; Center for Earth System Science, Tsinghua University, Beijing, China
Recommended Citation:
Hararuk O.,Smith M.J.,Luo Y.. Microbial models with data-driven parameters predict stronger soil carbon responses to climate change[J]. Global Change Biology,2015-01-01,21(6)