Microbial dynamics and organo-mineral interactions strongly regulate soil organic matter (SOM) cycling. Despite the crucial role of microbial dynamics in soil biogeochemical cycles, the explicit representation of microbial processes (e.g., microbial biomass and activity) and their interactions with abiotic processes (e.g., microbial response to environmental factors and substrate availability) in Earth System Models (ESMs) is still limited, which hinders accurate predictions of global carbon cycle dynamics and their responses to climate change. To address this limitation, we integrated a microbe- and mineral-surface-explicit model, ReSOM (the Reaction-network-based model of SOM and microbes), into the Energy Exascale Earth System Model (E3SM) land model (ELM) via the Biogeochemical Transport and Reaction model (BeTRv2).
Here we first describe this new model, ELM-ReSOM, and compare its modeling mechanisms with a microbially implicit model (ELM-ECA) regarding microbial decomposition and respiration response to temperature changes and the critical role of microbe-mineral interactions in subsurface SOM dynamics. Then, we apply both ELM-ReSOM and ELM-ECA to simulate the warming responses of soil carbon dynamics and heterotrophic respiration in a long-term whole-soil warming experiment at the Blodgett Forest Research Station, CA. Results reveal that the ReSOM-simulated warming response of CO2 efflux and soil carbon stocks aligned reasonably well with observations. Additionally, ELM-ReSOM outperformed ELM-ECA in simulating SOC stocks due to its incorporation of microbial processes and organo-mineral interactions. Further, we show that the estimated model uncertainty well encompasses the observed spatial variability (i.e., measurement heterogeneity). These modeling uncertainties arise from ReSOM representations associated with microbial temperature sensitivity, mineral-surface adsorption capacity, and parameters related to microbial and enzyme dynamics (e.g., maximum microbial growth rate, maximum enzyme production rate, and maximum rate of polymer degradation, etc.). Finally, the results also highlight the challenges that microbe-explicit models face in accurately predicting the soil carbon cycle response to future warming across space and time.