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

Abstract

Abstract.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. Here 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 CO 2 efflux from soil carbon decomposition reaches a maximum value some time after increased carbon input (as in priming experiments). This maximum CO 2 efflux (F max ) decreases with an increase in soil temperature in both models. However, the sensitivity of F max to the increased amount of carbon input increases with soil temperature in model A but decreases 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. These insights will contribute to an improved understanding of the significance of soil microbial processes in soil carbon responses to future climate change.

Higher climatological temperature sensitivity of soil carbon in cold than warm climates

“…S3). However, the reduced sensitivity of tropical soil carbon is also consistent with nonlinear models that 170 predict a temperature optimum for decomposition 29 , though only at tropical temperatures.…”

“…Since our main goal here is to focus on temperature controls to decomposition, we seek to separate and mask out those soils that are most strongly affected by having either too much or too little water. We calculate the curve in figure 2 based on the derivative of the central relationship in 29 figure 1b. Q 10 is an exponential notation that is traditionally defined relative to the instantaneous 30 decomposition parameter k as:…”

Transient dynamics of terrestrial carbon storage: mathematical foundation and its applications

“…To describe the active roles of microbes in organic C decomposition, a suite of nonlinear microbial models has been proposed using Michaelis-Menten or reverse Michaelis-Menten equations (Allison et al, 2010;Wieder et al, 2013). Those nonlinear models exhibit unique behaviors of modeled systems, such as damped oscillatory responses of soil C dynamics to small perturbations and insensitivity of the equilibrium pool sizes of litter or soil carbon to inputs (Li et al, 2014;Wang et al, 2014Wang et al, , 2016. Oscillations have been documented for single enzymes at timescales between 10 −4 and 10 s (English et al, 2006;Goldbeter, 2013;Xie, 2013).…”

Data-mining analysis of the global distribution of soil carbon in observational databases and Earth system models

“…Although we examined key factors from a wide variety of candidate properties, potentially important mechanisms that would improve the reproducibility of SOC distributions by ESMs and process-based ecosystem models may still be missing. For example, including microbial dynamics in SOC models may improve projections of global soil carbon by ESMs (Wieder et al, 2013), although models that include these dynamics are still in development (see Wang et al, 2014Wang et al, , 2016Wieder et al, 2015). Reports indicate that the role of mycorrhizae in soil carbon storage is important (Averill et al, 2014).…”

“…Another uncertainty of this analysis is the issue of scale because analyses applied at much finer resolutions, such as 1 km, might be governed by different influential factors. The potential mechanisms, parameterization, and other modeling issues for next-generation ESMs are not limited to those listed above and have been thoroughly discussed elsewhere Ostle et al, 2009;Wieder et al, 2015).…”