Abstract
Abstract. A large amount of soil carbon in northern temperate and boreal regions could be emitted as greenhouse gases in a warming future. However, lacking detailed microbial processes such as microbial dormancy in current biogeochemistry models might have biased the quantification of the regional carbon dynamics. Here the effect of microbial dormancy was incorporated into a biogeochemistry model to improve the quantification for the last century and this century. Compared with the previous model without considering the microbial dormancy, the new model estimated the regional soils stored 75.9 Pg more C in the terrestrial ecosystems during the last century and will store 50.4 and 125.2 Pg more C under the RCP8.5 and RCP2.6 scenarios, respectively, in this century. This study highlights the importance of the representation of microbial dormancy in earth system models to adequately quantify the carbon dynamics in the northern temperate and boreal natural terrestrial ecosystems.
Highlights
The land ecosystems in northern temperate and boreal regions (> 45◦ N) occupy 22 % of the global surface and store over 40 % of the global soil organic carbon (SOC) (McGuire and Hobbie, 1997; Melillo et al, 1993; Tarnocai et al, 2009; Hugelius et al, 2014)
Our previous studies using TEM have demonstrated that equifinality derived from site-level parameterization will affect the uncertainty in the estimation of regional carbon dynamics (Tang and Zhuang, 2008, 2009)
Our regional simulations with two contrasting models (MICTEM, MIC-TEM-dormancy) indicate the regional natural terrestrial ecosystems acted as a carbon sink in past decades, which is consistent with results from other process-based models (White et al, 2000; McGuire et al, 2009; Schimel, 2013)
Summary
The land ecosystems in northern temperate and boreal regions (> 45◦ N) occupy 22 % of the global surface and store over 40 % of the global soil organic carbon (SOC) (McGuire and Hobbie, 1997; Melillo et al, 1993; Tarnocai et al, 2009; Hugelius et al, 2014). An emerging field of research has begun to incorporate microbial ecology into existing process-based models to represent decomposition in ways that include important microbial processes that were previously ignored (Zha and Zhuang, 2018; Schimel and Weintraub, 2003; Allison et al, 2010; German et al, 2012) These microbe-based models tend to better reproduce field and satellite observations than traditional ones that treat soil decomposition as a firstorder decay process without considering microbial activities (Treseder et al, 2011; Wieder et al, 2013; Todd-Brown et al, 2011; Lawrence et al, 2009; Moorhead and Sinsabaugh, 2006). Some vital microbial traits such as microbial dormancy and community shifts are still rarely explicitly considered in large-scale ecosystem models (Wieder et al, 2015), and this may introduce notable uncertainties (Graham et al, 2014, 2016; Wang et al, 2015; Bouskill et al, 2012; Kaiser et al, 2014)
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