Dynamics of Fungal and Bacterial Biomass Carbon in Natural Ecosystems: Site-level Applications of the CLM-Microbe Model

Abstract:

Explicitly representing microbial mechanisms has been recognized as a key improvement for Earth system models to realistically project soil carbon (C) and climate dynamics. Based on CLM4.5, we developed the CLM-Microbe model by explicitly representing soil processes regulated by fungi and bacteria, two major soil microbial groups, in the soil biogeochemistry cascade. Using observed time-series data of fungal (FBC) and bacterial (BBC) biomass C from nine biomes, we parameterized the CLM-Microbe model, and further conducted sensitivity analysis and uncertainty analysis in simulating C cycling. We evaluated the model performance using mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R²) for relative change in biomass. The CLM-Microbe model is able to capture the seasonal dynamics of FBC and BBC across biomes, particularly for sites in tropical/subtropical forest and grassland, with MAE<0.29 and RMSE<0.37 for FBC and BBC, while R² values were relatively smaller in some biomes, such as shrubs, due to small sample sizes. We found good consistencies between simulated and observed FBC (R²=0.70, P<0.001) and BBC (R²=0.26, P<0.05) across biomes, but the simulated FBC and BBC was in smaller variation. Sensitivity analysis reported the most sensitive parameters as the turnover rate, carbon:nitrogen ratio of fungi and bacteria, and microbial assimilation of soil organic matter. Compared with a global dataset of fungal and bacterial biomass, we found high consistency in FBC, BBC, and FBC:BBC ratio between the annual estimation by the CLM-Microbe model at representative sites and their corresponding biome averages. This modeling study reports the development and testing of the CLM-Microbe model to simulate dynamics of bacterial and fungal biomass, and confirms that the explicit representation of soil microbial mechanisms enhances the model's ability to predict microbial community dynamics and its effects on C cycling processes.