OOS 82-7
What determines the sensitivities of the simulated soil carbon to warming and substrate priming in three different soil carbon models?

Friday, August 14, 2015: 10:10 AM
310, Baltimore Convention Center
Yingping Wang, CSIRO Marine and Atmospheric Research, Victoria 3195, Australia
Jiang Jiang, Department of Ecology and Evolutionary Biology, University of Tennessee
Benito Chen-Charpentier, Mathematics Department, University of Texas at Arlington, Arlington, TX
Fola B. Agusto, Department of Mathematics and Statistics, Austin Peay State University, Clarksville, TN
Alan Hastings, Department of Environmental Science and Policy, University of California, Davis, Davis, CA
Forrest M. Hoffman, Computational Earth Sciences Group, Climate Change Science Institute (CCSI), Oak Ridge National Laboratory, Oak Ridge, TN
Martin Rasmussen, Mathematics, Imperial College London
Katherine Todd-Brown, Pacific Northwest National Laboratory, Richland, WA
Matthew Smith, Computational Science Laboratory, Microsoft Research, Cambridge, United Kingdom
Ying Wang, Department of Mathematics, University of Oklahoma, Norman, OK
Xia Xu, Department of Ecology, Evolution & Organismal Biology, Iowa State University of Science and Technology, Ames, IA
Yiqi Luo, Microbiology and Plant Biology, University of Oklahoma, Norman, OK

Soil is the largest pool of organic carbon on land, and plays a critical role in global carbon cycle. However the predicted responses of soil carbon to future climate warming and carbon addition vary significantly among different models. For example, soil carbon is predicted to decrease with warming by conventional linear models, but increase with warming by some nonlinear soil microbial models. Therefore it is important to develop a better understanding what controls the sensitivities of soil carbon to warming and to carbon input in each model, so experiments can be designed to assess which model represents the observed response most accurately. In this study, we compare three models: a conventional linear model, and two nonlinear models: one based on Michaelis-Menten kinetics and the other based on the reverse Michaelis-Menten kinetics. These three models broadly cover the range of soil carbon models used at present.


Using theoretical analysis and numerical simulations, we show that the sensitivity of soil carbon pool size to warming is inversely proportional to the temperature sensitivity of soil carbon turnover rate in the conventional linear model, but vary with temperature dependence of both microbial growth efficiency and turnover rate in the two nonlinear models. We also show how the simulated acclimation to warming occurs in the two nonlinear models.

These three models also have different responses to additional carbon input, as in priming experiments. Priming response as simulated by the nonlinear model using Michaelis-Menten kinetics is much higher that by the nonlinear model using reverse Michaelis-Menten kinetics. The magnitude of maximum priming response (Pmax) that is estimated as the percentage increase in CO2 efflux from the decomposition of soil organic carbon in the primed treatment relative to that in control increases linearly with the amount of carbon addition in both nonlinear models. Also the dependence of Pmax on soil temperature is quite different between the two nonlinear models, particularly at higher soil temperature (>28 oC). These differences may provide a way of distinguishing between these three different types of soil carbon models using observations from experiments, which will improve our understanding how microbial dynamics controls the soil carbon dynamics and eventually the representation of soil carbon dynamics in global land models.