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Abstract. For decades, predominant soil biogeochemical models have used conceptual soil organic matter (SOM) pools and only simulated them to a shallow depthin soil. Efforts to overcome these limitations have prompted the development of the new generation SOM models, including MEMS 1.0, which representsmeasurable biophysical SOM fractions, over the entire root zone, and embodies recent understanding of the processes that govern SOM dynamics. Herewe present the result of continued development of the MEMS model, version 2.0. MEMS 2.0 is a full ecosystem model with modules simulating plantgrowth with above- and belowground inputs, soil water and temperature by layer, decomposition of plant inputs and SOM, and mineralization andimmobilization of nitrogen (N). The model simulates two commonly measured SOM pools – particulate and mineral-associated organic matter (POM andMAOM, respectively). We present results of calibration and validation of the model with several grassland sites in the US. MEMS 2.0 generallycaptured the soil carbon (C) stocks (R2 of 0.89 and 0.6 for calibration and validation, respectively) and their distributions between POM andMAOM throughout the entire soil profile. The simulated soil N matches measurements but with lower accuracy (R2 of 0.73 and 0.31 for calibrationand validation of total N in SOM, respectively) than for soil C. Simulated soil water and temperature were compared with measurements, and theaccuracy is comparable to the other commonly used models. The seasonal variation in gross primary production (GPP; R2 = 0.83), ecosystemrespiration (ER; R2 = 0.89), net ecosystem exchange (NEE; R2 = 0.67), and evapotranspiration (ET; R2 = 0.71) was wellcaptured by the model. We will further develop the model to represent forest and agricultural systems and improve it to incorporate newunderstanding of SOM decomposition. 

Abstract. Soil organic matter (SOM) dynamics in ecosystem-scale biogeochemical modelshave traditionally been simulated as immeasurable fluxes between conceptuallydefined pools. This greatly limits how empirical data can be used to improvemodel performance and reduce the uncertainty associated with theirpredictions of carbon (C) cycling. Recent advances in our understanding ofthe biogeochemical processes that govern SOM formation and persistence demanda new mathematical model with a structure built around key mechanisms andbiogeochemically relevant pools. Here, we present one approach that aims toaddress this need. Our new model (MEMS v1.0) is developed from the MicrobialEfficiency-Matrix Stabilization framework, which emphasizes the importance oflinking the chemistry of organic matter inputs with efficiency of microbialprocessing and ultimately with the soil mineral matrix, when studying SOMformation and stabilization. Building on this framework, MEMS v1.0 is alsocapable of simulating the concept of C saturation and representsdecomposition processes and mechanisms of physico-chemical stabilization todefine SOM formation into four primary fractions. After describing the modelin detail, we optimize four key parameters identified through avariance-based sensitivity analysis. Optimization employed soil fractionationdata from 154 sites with diverse environmental conditions, directly equatingmineral-associated organic matter and particulate organic matter fractionswith corresponding model pools. Finally, model performance was evaluatedusing total topsoil (0–20 cm) C data from 8192 forest and grassland sitesacross Europe. Despite the relative simplicity of the model, it was able toaccurately capture general trends in soil C stocks across extensive gradientsof temperature, precipitation, annual C inputs and soil texture. The novelapproach that MEMS v1.0 takes to simulate SOM dynamics has the potential toimprove our forecasts of how soils respond to management and environmentalperturbation. Ensuring these forecasts are accurate is key to effectivelyinforming policy that can address the sustainability of ecosystem servicesand help mitigate climate change.