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

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 (R2) 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 R2 values are relatively smaller in some biomes (e.g., shrub) 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) 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.

Microbial seasonality promotes soil respiratory carbon emission in natural ecosystems: A modeling study

“…The CLM-Microbe model was built on the model framework developed by Xu et al (2014) and the default CLM4.5 (Koven et al, 2013), and it has been coupled with a microbial functional groupbased methane module (Wang et al, 2019;Xu et al, 2015) and applied to quantify the fugal and bacterial biomass dynamics in natural ecosystems (He et al, 2021). The CLM4.5 classifies litter into three pools, that is, litter 1 (labile), litter 2 (cellulose) and litter 3 (lignin), and soil organic matter (SOM), materials left during later stages of organic C decay, into four pools, that is, SOM 1, SOM 2, SOM 3, and SOM 4.…”

“…Bacterial and fungal growth is highly sensitive to environmental conditions, such as soil moisture and temperature. Fungi and bacteria are different in turnover time, different lysis rate constants were therefore adopted for fungi and bacteria in the CLM-Microbe model (He et al, 2021). As a result, in the CLM-Microbe model, the fungal and bacterial biomass lysis is mechanistically represented as the interactive effects of their lysis rate constants and environmental factors, that is, r O 2 , r water , r tsoil , and r depth , as described above.…”

“…Since the observational respiratory C fluxes from most sites were reported at hourly intervals, we set the output resolution of transient simulations at an hourly time step. The initial setting for microbial parameters was adopted from He et al (2021), with the parameter set same for sites from the same biome but distinct among biomes.…”

“…Shifts in soil microbial community composition have profound influences on the C cycle (Lipson et al, 2002). Nevertheless, an explicit representation of variations in soil microbial community composition in ESMs is still in infancy and the influence of soil microbial seasonality on C cycle is far from clear (He et al, 2021;Wieder, Allison, et al, 2015;Wieder, Grandy, et al, 2015;Xu et al, 2014).…”

“…To fill the gaps, we investigated the effects of soil microbes (fungi and bacteria) on belowground respiratory fluxes using the CLM-Microbe model. The CLM-Microbe model, mechanistically represented soil microbial community dynamics by differentiating fungal and bacterial physiology, provides a feasible way to investigate the effects of soil microbial variation on C cycling (He et al, 2021). In this study, we aimed to investigate the effects of microbial seasonality on soil respiratory fluxes and annual estimation of…”