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Title: Responses of two nonlinear microbial models to warming and increased carbon input

A number of nonlinear microbial models of soil carbon decomposition have been developed. Some of them have been applied globally but have yet to be shown to realistically represent soil carbon dynamics in the field. A thorough analysis of their key differences is needed to inform future model developments. In this paper, we compare two nonlinear microbial models of soil carbon decomposition: one based on reverse Michaelis–Menten kinetics (model A) and the other on regular Michaelis–Menten kinetics (model B). Using analytic approximations and numerical solutions, we find that the oscillatory responses of carbon pools to a small perturbation in their initial pool sizes dampen faster in model A than in model B. Soil warming always decreases carbon storage in model A, but in model B it predominantly decreases carbon storage in cool regions and increases carbon storage in warm regions. For both models, the CO2 efflux from soil carbon decomposition reaches a maximum value some time after increased carbon input (as in priming experiments). This maximum CO2 efflux (Fmax) decreases with an increase in soil temperature in both models. However, the sensitivity of Fmax to the increased amount of carbon input increases with soil temperature in model A but decreasesmore » monotonically with an increase in soil temperature in model B. These differences in the responses to soil warming and carbon input between the two nonlinear models can be used to discern which model is more realistic when compared to results from field or laboratory experiments. Lastly, these insights will contribute to an improved understanding of the significance of soil microbial processes in soil carbon responses to future climate change.« less
 [1] ;  [2] ;  [3] ;  [4] ;  [5] ;  [6] ;  [7] ;  [8] ;  [9] ;  [10] ;  [11] ;  [11]
  1. CSIRO Ocean and Atmosphere (Australia)
  2. Univ. of Tennessee, Knoxville, TN (United States). Department of Ecology and Evolutionary Biology
  3. Univ. of Texas, Arlington, TX (United States). Department of Mathematics
  4. Austin Peay State University, Clarksville, TN (United States). Department of Mathematics and Statistics
  5. Univ. of California, Davis, CA (United States). Department of Environmental Science and Policy
  6. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Computational Earth Sciences Group
  7. Imperial College, London (United Kingdom). Department of Mathematics
  8. Microsoft Research, Cambridge (United Kingdom). Computational Science Laboratory
  9. Univ. of Oklahoma, Norman, OK (United States). Department of Microbiology and Plant Biology; Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
  10. Univ. of Oklahoma, Norman, OK (United States). Department of Mathematics
  11. Univ. of Oklahoma, Norman, OK (United States). Department of Microbiology and Plant Biology
Publication Date:
OSTI Identifier:
Grant/Contract Number:
Accepted Manuscript
Journal Name:
Biogeosciences (Online)
Additional Journal Information:
Journal Name: Biogeosciences (Online); Journal Volume: 13; Journal Issue: 4; Journal ID: ISSN 1726-4189
European Geosciences Union
Research Org:
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
Sponsoring Org:
USDOE Office of Science (SC)
Country of Publication:
United States