Representing Soil Microbial Dynamics and Organo‐Mineral Interactions in the E3SM Land Model (ELM‐ReSOM)

Journal Article · Wed Oct 01 00:00:00 EDT 2025 · Journal of Advances in Modeling Earth Systems

DOI:https://doi.org/10.1029/2024ms004874· OSTI ID:3014160

Tao, Jing; Riley, William J; Tang, Jinyun; Zhu, Qing; Pegoraro, Elaine L; Castanha, Cristina; Abramoff, Rose Z; Torn, Margaret S

Abstract Explicit representation of soil microbial processes and interactions with biotic and abiotic processes in Earth System Models (ESMs) remains limited, despite their importance in biogeochemical cycles. To address this gap, which hinders prediction of global biogeochemial cycling and responses to atmospheric conditions, we integrated a microbe‐ and mineral‐surface‐explicit model, the Reaction‐network‐based model of soil organic matter and Microbes (ReSOM), into the Energy Exascale ESM (E3SM) land model (ELM). Here, we describe ELM‐ReSOM and show a case study at a conifer forest in California. ELM‐ReSOM accurately simulated surface CO 2 fluxes and SOM stocks, demonstrating improved representations of microbial and mineral interactions compared to the default ELM. We examined ELM‐ReSOM sensitivity to microbial traits, enzyme properties, and organo‐mineral interactions. Microbial traits such as the maximum mortality rate, transporter‐density scaling factor, and maximum monomer assimilation rate were strong controllers of heterotrophic respiration, while these microbial traits and enzyme‐related properties collectively influenced SOM stocks. Mineral surfaces primarily affected SOM stocks by adsorbing enzymes, thereby limiting depolymerization. Synergies among processes led to stronger impacts of parameters when evaluated together versus separately (i.e., most parameters had greater indirect than direct effects). For example, due to interactions of microbial necromass with mineral surface adsorption, the indirect effect of the maximum microbial mortality rate was 33% larger than its direct effect on SOM stock. Thus, microbial and enzyme dynamics and their interactions with mineral surfaces play critical roles in SOM cycling. Tackling the challenges of microbe‐explicit models will advance understanding and modeling of SOM dynamics. Plain Language Summary Soils store a vast amount of carbon in organic matter. The activity of soil microbes drives how carbon is released or stored under changing environmental conditions. We introduce E3SM Land Model‐Reaction‐network‐based model of Soil Organic Matter and Microbes (ELM‐ReSOM), a model designed to explicitly represent soil microbial processes and their interactions with soil minerals. By incorporating these detailed mechanisms, ELM‐ReSOM provides accurate predictions of soil carbon and surface CO 2 fluxes at a California forest site. Results showed the critical influence of microbial traits like growth rates and enzyme activity in controlling soil carbon cycling. The study also reveals that interactions among microbial processes and soil minerals have a larger effect than do individual traits on soil carbon storage. These findings improve understanding of the mechanisms driving soil carbon responses to environmental change and provide a model foundation for better global climate predictions. Key Points E3SM Land Model‐Reaction‐network‐based model of Soil Organic Matter and Microbes (ReSOM) accurately simulates CO 2 fluxes and soil organic matter (SOM) stocks by incorporating explicit microbial and mineral‐surface interaction processes Microbial traits strongly influence heterotrophic respiration, and both microbial traits and enzyme properties drive SOM stock dynamics Synergistic interactions among processes dominate parameter total effects, highlighting the role of interactions in SOM cycling dynamics

• Research Organization: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
• Sponsoring Organization: US Department of Energy; USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23), Climate and Environmental Sciences Division (SC-23.1 )
• Grant/Contract Number: AC02-05CH11231
• OSTI ID: 3014160
• Journal Information: Journal of Advances in Modeling Earth Systems, Journal Name: Journal of Advances in Modeling Earth Systems Journal Issue: 10 Vol. 17
• Country of Publication: United States
• Language: English