Stoichiometrically Coupled Carbon and Nitrogen Cycling in the Microbial-Mineral Carbon Stabilization Model Version 1.0 (MIMICS-CN V1.0)

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Stoichiometrically Coupled Carbon and Nitrogen Cycling in the Microbial-Mineral Carbon Stabilization Model Version 1.0 (MIMICS-CN V1.0) Geosci. Model Dev., 13, 4413–4434, 2020 https://doi.org/10.5194/gmd-13-4413-2020 © Author(s) 2020. This work is distributed under the Creative Commons Attribution 4.0 License. Stoichiometrically coupled carbon and nitrogen cycling in the MIcrobial-MIneral Carbon Stabilization model version 1.0 (MIMICS-CN v1.0) Emily Kyker-Snowman1, William R. Wieder2,3, Serita D. Frey1, and A. Stuart Grandy1 1Department of Natural Resources and the Environment, University of New Hampshire, Durham, NH 03824, USA 2Climate and Global Dynamics Laboratory, National Center for Atmospheric Research, Boulder, CO 80305, USA 3Institute of Arctic and Alpine Research, University of Colorado Boulder, Boulder, CO 80309, USA Correspondence: Emily Kyker-Snowman ([email protected]) Received: 13 November 2019 – Discussion started: 19 December 2019 Revised: 1 June 2020 – Accepted: 24 July 2020 – Published: 22 September 2020 Abstract. Explicit consideration of microbial physiology in chiometric characteristics of soil microbes, especially under soil biogeochemical models that represent coupled carbon– environmental change scenarios. nitrogen dynamics presents opportunities to deepen under- standing of ecosystem responses to environmental change. The MIcrobial-MIneral Carbon Stabilization (MIMICS) model explicitly represents microbial physiology and physic- 1 Introduction ochemical stabilization of soil carbon (C) on regional and global scales. Here we present a new version of MIMICS Soils contain the largest actively cycling terrestrial carbon with coupled C and nitrogen (N) cycling through litter, mi- (C) stocks on earth and also serve as the dominant source of crobial, and soil organic matter (SOM) pools. The model nutrients, like nitrogen (N), that are critical for maintaining was parameterized and validated against C and N data from ecosystem productivity (Gruber and Galloway, 2008; Job- the Long-Term Inter-site Decomposition Experiment Team bágy and Jackson, 2000). Soil C cycle projections and their (LIDET; six litter types, 10 years of observations, and 13 response to global change factors remain highly uncertain sites across North America). The model simulates C and N (Bradford et al., 2016; Todd-Brown et al., 2013), but recent losses from litterbags in the LIDET study with reasonable ac- empirical insights into microbial processing of soil C provide curacy (C: R2 D 0:63; N: R2 D 0:29), which is comparable opportunities to update models and reduce this uncertainty with simulations from the DAYCENT model that implicitly (Cotrufo et al., 2013; Kallenbach et al., 2016; Lehmann and represents microbial activity (C: R2 D 0:67; N: R2 D 0:30). Kleber, 2015; Schmidt et al., 2011; Six et al., 2006). Several Subsequently, we evaluated equilibrium values of stocks (to- models have been developed recently with explicit represen- tal soil C and N, microbial biomass C and N, inorganic tation of nonlinear microbial C processing dynamics, includ- N) and microbial process rates (soil heterotrophic respira- ing the MIcrobial-MIneral Carbon Stabilization (MIMICS) tion, N mineralization) simulated by MIMICS-CN across model (Sulman et al., 2018; Wieder et al., 2014, 2015b) and the 13 simulated LIDET sites against published observations others (Abramoff et al., 2017; Allison, 2014; Fatichi et al., from other continent-wide datasets. We found that MIMICS- 2019; Hararuk et al., 2015; Robertson et al., 2019; Sulman CN produces equilibrium values in line with measured val- et al., 2014; Wang et al., 2013, 2014a, 2017). While these ues, showing that the model generates plausible estimates of models serve different purposes, some can be as good as or ecosystem soil biogeochemical dynamics across continental- better than models without explicit microbial pools at simu- scale gradients. MIMICS-CN provides a platform for cou- lating global soil C stocks and the response of soil C to en- pling C and N projections in a microbially explicit model, but vironmental perturbations (Wieder et al., 2013, 2015b), and experiments still need to identify the physiological and stoi- they also predict very different long-term responses of soil Published by Copernicus Publications on behalf of the European Geosciences Union. 4414 E. Kyker-Snowman et al.: MIMICS-CN v1.0 C to global change (Wieder et al., 2013, 2018). Microbially 2017; Fatichi et al., 2019; Huang et al., 2018; Schimel and explicit models have thus furthered our understanding of C Weintraub, 2003; Sistla et al., 2014; Sulman et al., 2014, cycling in the terrestrial system, but they also provide new 2017, 2018, 2019; Wang et al., 2014a, 2017, 2013). To date, opportunities to explore couplings between C and nutrient the majority of these microbially explicit C–N models have cycles, especially N. been developed to explore soil biogeochemical interactions Terrestrial models that couple C and N cycles reveal im- and microbial community dynamics, while only one has been portant ecosystem feedbacks that are absent from C-only validated for N dynamics across a continental-scale gradient models. For example, across ecosystems, experimental ma- (Fatichi et al., 2019). nipulations consistently indicate that N availability limits Although there is great value in exploring diverse ap- plant productivity (LeBauer and Treseder, 2008). C-only proaches to explicitly representing microbes in purely theo- model configurations in models typically predict that CO2 retical or site-specific applications, implementing these con- fertilization will result in a large increase in both plant pro- ceptual developments within larger-scale models requires ductivity and the land C sink in coming decades, but nutri- convincing evidence that adding them improves model per- ent limitation may constrain the magnitude of this terres- formance against large-scale data. Recent soil model com- trial ecosystem C uptake (Wieder et al., 2015a; Zaehle et al., parisons report divergent responses to simulated global 2015; Zaehle and Dalmonech, 2011). As terrestrial models change experiments among microbially explicit model for- increasingly represent coupled C–N biogeochemistry, accu- mulations, highlighting the large uncertainty in their under- rate model estimates of N release from soil organic matter lying process-level representation and parameterization (Sul- (SOM) will become important to reducing uncertainty in the man et al., 2018; Wieder et al., 2018). The addition of ex- CO2 fertilization response of the terrestrial C cycle. plicit microbial pools may improve the predictive ability of Currently, most biogeochemical models that couple C and landscape-scale models in the long run, but microbial models N cycles have an implicit representation of microbial activ- must be validated against landscape-scale datasets of a vari- ity. These conventional models represent SOM decomposi- ety of pools and process rates before they can reasonably be tion with the assumption that chemical recalcitrance of or- expected to improve model performance and reduce uncer- ganic matter dictates the turnover of litter and SOM pools tainty. (Luo et al., 2016). Carbon and N fluxes represented in these We developed a coupled C–N version of MIMICS models are directly proportional to donor pool sizes, with- (MIMICS-CN) to fill the need at the intersection of micro- out any explicit representation of the microbes that medi- bially explicit models, coupled C–N models, models that ate these fluxes (Schimel, 2001, 2013). Linear decay con- work well enough to be considered for use in ESMs, and stants and transfer coefficients determine the flow of C and models that can be validated against currently available N through a decomposition cascade, and rates of N immo- large-scale data. The C-only iteration of MIMICS considers bilization and mineralization emerge from the interaction of tradeoffs involved with microbial functional traits as well as fixed respiration fractions and the stoichiometry of donor and both physicochemical (i.e., mineral associations) and chemi- receiver SOM pools. The lack of plant–microbe–soil feed- cal (i.e., recalcitrance) mechanisms of C stabilization in soil. backs in these models may limit their predictive capacity, es- Wieder et al. (2014, 2015b) and Sulman et al. (2018) evalu- pecially in the face of environmental change. For example, in ated this C-only version of MIMICS across site, continental, these models increased plant inputs to soil only build soil C and global scales. Here we expand on this work, introducing and N stocks, and plants have no way to stimulate the micro- MIMICS-CN, which incorporates stoichiometrically coupled bial community to mine existing SOM for N without model C and N cycling of all microbial, litter, and SOM pools and modifications (Guenet et al., 2016; Wutzler and Reichstein, stoichiometric constraints on microbial growth. Our core ob- 2013). This “N mining” or “priming” effect, where increased jectives were to (1) formulate a framework and parameteri- plant inputs result in increased microbial activity and decom- zation for coupled C and N cycling in MIMICS; (2) validate position rates, has been demonstrated in experimental studies MIMICS-CN against a continental-scale litter decomposition (Cheng and Kuzyakov, 2005; Dijkstra et al., 2013; Phillips et dataset (LIDET) and compare MIMICS-CN to a microbially- al., 2012) and may be a critical pathway for plants to obtain implicit, linear model (DAYCENT); and (3) evaluate equilib- more N and support increased plant productivity under ele- rium soil and microbial stocks and fluxes (and their param- vated
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