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ABSTRACT Litter decomposition is an important ecosystem process and global carbon flux that has been shown to be controlled by climate, litter quality, and microbial communities. Process‐based ecosystem models are used to predict responses of litter decomposition to climate change. While these models represent climate and litter quality effects on litter decomposition, they have yet to integrate empirical microbial community data into their parameterizations for predicting litter decomposition. To fill this gap, our research used a comprehensive leaf litterbag decomposition experiment at 10 temperate forest U.S. National Ecological Observatory Network (NEON) sites to calibrate (7 sites) and validate (3 sites) the MIcrobial‐MIneral Carbon Stabilization (MIMICS) model. MIMICS was calibrated to empirical decomposition rates and to their empirical drivers, including the microbial community (represented as the copiotroph‐to‐oligotroph ratio). We calibrate to empirical drivers, rather than solely rates or pool sizes, to improve the underlying drivers of modeled leaf litter decomposition. We then validated the calibrated model and evaluated the effects of calibration under climate change using the SSP 3–7.0 climate change scenario. We find that incorporating empirical drivers of litter decomposition provides similar, and sometimes better (in terms of goodness‐of‐fit metrics), predictions of leaf litter decomposition but with different underlying ecological dynamics. For some sites, calibration also increased climate change‐induced leaf litter mass loss by up to 5%, with implications for carbon cycle‐climate feedbacks. Our work also provides an example for integrating data on the relative abundance of bacterial functional groups into an ecosystem model using a novel calibration method to bridge empiricism and process‐based modeling, answering a call for the use of empirical microbial community data in process‐based ecosystem models. We highlight that incorporating mechanistic information into models, as done in this study, is important for improving confidence in model projections of ecological processes like litter decomposition under climate change. 

ABSTRACT Managing soils to increase organic carbon storage presents a potential opportunity to mitigate and adapt to global change challenges, while providing numerous co‐benefits and ecosystem services. However, soils differ widely in their potential for carbon sequestration, and knowledge of biophysical limits to carbon accumulation may aid in informing priority regions. Consequently, there is great interest in assessing whether soils exhibit a maximum capacity for storing organic carbon, particularly within organo–mineral associations given the finite nature of reactive minerals in a soil. While the concept of soil carbon saturation has existed for over 25 years, recent studies have argued for and against its importance. Here, we summarize the conceptual understanding of soil carbon saturation at both micro‐ and macro‐scales, define key terminology, and address common concerns and misconceptions. We review methods used to quantify soil carbon saturation, highlighting the theory and potential caveats of each approach. Critically, we explore the utility of the principles of soil carbon saturation for informing carbon accumulation, vulnerability to loss, and representations in process‐based models. We highlight key knowledge gaps and propose next steps for furthering our mechanistic understanding of soil carbon saturation and its implications for soil management. 

Abstract Nutrient limitation is widespread in terrestrial ecosystems. Accordingly, representations of nitrogen (N) limitation in land models typically dampen rates of terrestrial carbon (C) accrual, compared with C‐only simulations. These previous findings, however, rely on soil biogeochemical models that implicitly represent microbial activity and physiology. Here we present results from a biogeochemical model testbed that allows us to investigate how an explicit versus implicit representation of soil microbial activity, as represented in the MIcrobial‐MIneral Carbon Stabilization (MIMICS) and Carnegie‐Ames‐Stanford Approach (CASA) soil biogeochemical models, respectively, influence plant productivity, and terrestrial C and N fluxes at initialization and over the historical period. When forced with common boundary conditions, larger soil C pools simulated by the MIMICS model reflect longer inferred soil organic matter (SOM) turnover times than those simulated by CASA. At steady state, terrestrial ecosystems experience greater N limitation when using the MIMICS‐CN model, which also increases the inferred SOM turnover time. Over the historical period, however, warming‐induced acceleration of SOM decomposition over high latitude ecosystems increases rates of N mineralization in MIMICS‐CN. This reduces N limitation and results in faster rates of vegetation C accrual. Moreover, as SOM stoichiometry is an emergent property of MIMICS‐CN, we highlight opportunities to deepen understanding of sources of persistent SOM and explore its potential sensitivity to environmental change. Our findings underscore the need to improve understanding and representation of plant and microbial resource allocation and competition in land models that represent coupled biogeochemical cycles under global change scenarios. 

Abstract In the past few decades, there has been an evolution in our understanding of soil organic matter (SOM) dynamics from one of inherent biochemical recalcitrance to one deriving from plant‐microbe‐mineral interactions. This shift in understanding has been driven, in part, by influential conceptual frameworks which put forth hypotheses about SOM dynamics. Here, we summarize several focal conceptual frameworks and derive from them six controls related to SOM formation, (de)stabilization, and loss. These include: (a) physical inaccessibility; (b) organo‐mineral and ‐metal stabilization; (c) biodegradability of plant inputs; (d) abiotic environmental factors; (e) biochemical reactivity and diversity; and (f) microbial physiology and morphology. We then review the empirical evidence for these controls, their model representation, and outstanding knowledge gaps. We find relatively strong empirical support and model representation of abiotic environmental factors but disparities between data and models for biochemical reactivity and diversity, organo‐mineral and ‐metal stabilization, and biodegradability of plant inputs, particularly with respect to SOM destabilization for the latter two controls. More empirical research on physical inaccessibility and microbial physiology and morphology is needed to deepen our understanding of these critical SOM controls and improve their model representation. The SOM controls are highly interactive and also present some inconsistencies which may be reconciled by considering methodological limitations or temporal and spatial variation. Future conceptual frameworks must simultaneously refine our understanding of these six SOM controls at various spatial and temporal scales and within a hierarchical structure, while incorporating emerging insights. This will advance our ability to accurately predict SOM dynamics.