Dynamics of Fungal and Bacterial Biomass Carbon in Natural Ecosystems: Site‐Level Applications of the CLM‐Microbe Model. (17th February 2021)

• Record Type: Journal Article
• Authors: He, Liyuan; Lipson, David A.; Mazza Rodrigues, Jorge L.; Mayes, Melanie; Björk, Robert G.; Glaser, Bruno; Thornton, Peter; Xu, Xiaofeng
• Is Part Of: Journal of advances in modeling earth systems. Volume 13:Number 2(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-02-17
• Subjects: bacteria -- biomass dynamics -- fungi -- model -- sensitivity; Geological modeling -- Periodicals; Climatology -- Periodicals; Geochemical modeling -- Periodicals; 551.5011
• DOI: 10.1029/2020MS002283
• Languages: English
• ISSNs: 1942-2466

Abstract

Abstract: Explicitly representing microbial processes has been recognized as a key improvement to Earth system models for the realistic projections of soil carbon (C) and climate dynamics. The CLM‐Microbe model builds upon the CLM4.5 and explicitly represents two major soil microbial groups, fungi and bacteria. Based on the compiled time‐series data of fungal (FBC) and bacterial (BBC) biomass C from nine biomes, we parameterized and validated the CLM‐Microbe model, and further conducted sensitivity and uncertainty analysis for simulating C cycling. The model performance was evaluated with mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R 2 ) for relative change in FBC and BBC. The CLM‐Microbe model is able to reasonably capture the seasonal dynamics of FBC and BBC across biomes, particularly for tropical/subtropical forest, temperate broadleaf forest, and grassland, with MAE <0.49 for FBC and <0.36 for BBC and RMSE <0.52 FBC and <0.39 for BBC, while R 2 values are relatively smaller in some biomes (e.g., shrub) due to small sample sizes. We found good consistencies between simulated and observed FBC (R 2 = 0.70, P < 0.001) and BBC (R 2 = 0.26, P < 0.05) on average across biomes, but the model is not able to fully capture the large variation in observed FBC and BBC. Sensitivity analysis shows that the most critical parameters are turnover rate and carbon‐to‐nitrogen ratio of fungi and bacteria and microbial assimilation efficiency. This study confirms that the explicit representation of soil microbial mechanisms enhances model performance in simulating C variables such as heterotrophic respiration and soil organic C density. The further application of the CLM‐Microbe model would deepen our understanding of microbial contributions to global C cycle. Key Points: We carried out the site‐level parameterization and validation of the CLM‐Microbe model The CLM‐Microbe model is able to reasonably capture the seasonal dynamics of fungal and bacterial biomass C Microbial turnover rate, carbon‐to‐nitrogen ratio, and assimilation efficiency are key parameters controlling microbial roles on C cycling