Explicitly representing soil microbial processes in Earth system models. Issue 10 (26th October 2015) - Wieder, William R.; Allison, Steven D.; Davidson, Eric A.; Georgiou, Katerina; Hararuk, Oleksandra; He, Yujie; Hopkins, Francesca; Luo, Yiqi; Smith, Matthew J.; Sulman, Benjamin; Todd‐Brown, Katherine; Wang, Ying‐Ping; et al.
Abstract: Microbes influence soil organic matter decomposition and the long‐term stabilization of carbon (C) in soils. We contend that by revising the representation of microbial processes and their interactions with the physicochemical soil environment, Earth system models (ESMs) will make more realistic global C cycle projections. Explicit representation of microbial processes presents considerable challenges due to the scale at which these processes occur. Thus, applying microbial theory in ESMs requires a framework to link micro‐scale process‐level understanding and measurements to macro‐scale models used to make decadal‐ to century‐long projections. Here we review the diversity, advantages, and pitfalls of simulating soil biogeochemical cycles using microbial‐explicit modeling approaches. We present a roadmap for how to begin building, applying, and evaluating reliable microbial‐explicit model formulations that can be applied in ESMs. Drawing from experience with traditional decomposition models, we suggest the following: (1) guidelines for common model parameters and output that can facilitate future model intercomparisons; (2) development of benchmarking and model‐data integration frameworks that can be used to effectively guide, inform, and evaluate model parameterizations with data from well‐curated repositories; and (3) the application of scaling methods to integrate microbial‐explicit soil biogeochemistry modules within ESMs. With contributions across scientific disciplines, we feel this roadmap can advance our fundamental understanding of soil biogeochemical dynamics and more realistically project likely soil C response to environmental change at global scales. Key Points: Microbial‐explicit models differ in predictions from conventional models Applying microbial‐explicit models may improve global C cycle projections Spanning microbial to global scales requires integrative model‐data approaches