1 Introduction

Soil has always been a focus of climate change studies due to its large carbon (C) stocks—the global soil organic C (SOC) stock is at least four times greater than atmospheric C [Irvine and Law, 2002; Jobbágy and Jackson, 2000], and soil respiration is the second largest flux between the biosphere and the atmosphere following photosynthesis [Raich and Potter, 1995]. Therefore, soil C dynamics plays a key role in net C sequestration of terrestrial ecosystems and is essential to our understanding of biogeochemical cycles and its climate‐C interactions [IPCC, 2013].

Recent comprehensive analyses have shown that there are notable limitations of traditional first‐order decomposition algorithms in current Earth system models. Those decomposition models are not able to capture the spatial distributions of SOC stocks and primary drivers of SOC dynamics [Todd‐Brown et al., 2013], while microbial‐based soil organic matter decomposition models have been increasingly used at both site‐ and global‐scale studies [Allison et al., 2010; He et al., 2014b; Wieder et al., 2013], although more rigorous examination of these models is still needed [Li et al., 2014]. The current generation of microbial‐based decomposition models usually features a common framework where enzyme production and microbial physiology are associated with total microbial biomass (MIC), which has a direct coupling with SOC enzymatic decomposition.

A key microbial life history trait that is usually lacking in these models is microbial dormancy. Dormancy is a common, bet‐hedging strategy used by microorganisms when environmental conditions limit growth and reproduction [Lennon and Jones, 2011]. When microorganisms are confronted with unfavorable conditions, they may enter a reversible state of low metabolic activity and resuscitate when favorable conditions occur. Microorganisms in this state of reduced metabolic activity are not able to drive biogeochemical processes such as soil CO 2 production; therefore, only active microorganisms are involved in utilizing substrates in soils [Blagodatskaya and Kuzyakov, 2013]. Although some studies have explicitly incorporated dormancy into models [Ayati, 2012; Wirtz, 2003], they are mostly confined to incubation experiments, and applications of microbial models in natural environments generally do not consider dormancy.

There are four motivations that led to the inception of this study to represent dormancy in microbial‐based decomposition models. First, the current coupled SOC‐MIC structure leads to oscillatory behavior of soil organic and microbial C pools with unrealistically large amplitudes of interannual variation [Y. Wang et al., 2014; Wieder et al., 2013]; thus, incorporating dormancy may structurally improve model realism. Second, there is a scale mismatch in current measurement procedures of microbial biomass since different portions of microbial biomass are actually measured. For example, substrate‐induced respiration and fumigation techniques measure the total microbial biomass when the conversion factor is used, whereas direct microscopy combined with cell staining such as fluorescence in situ hybridization measures the active portion of total biomass [Blagodatskaya and Kuzyakov, 2013] Along this line, the aforementioned inconsistency may pose challenges in data‐model integration and in microbial model comparisons and evaluation. Finally, the transition between dormant and active states of microbes can be fast (in the order of hours to days) with substantial magnitude change (e.g., an order of magnitude) in the portion of active biomass and the relative abundance of different phylogenetically clustered microbial groups; however, these transitions usually feature little change in total microbial biomass [Hagerty et al., 2014; Placella et al., 2012]. Thus, total microbial biomass may not be a sufficient indicator of microbial activities as opposed to the more responsive active portion of microbial biomass.