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.
| Original language | English |
|---|---|
| Article number | e2020MS002283 |
| Journal | Journal of Advances in Modeling Earth Systems |
| Volume | 13 |
| Issue number | 2 |
| DOIs | |
| State | Published - Feb 2021 |
Funding
The authors are grateful to Dr. Yimei Huang from Northwest A&F University, and Drs. Qian Zhao and Weijun Shen from South China Botanical Garden, Chinese Academy of Sciences for providing the exact sampling date of fungal and bacterial biomass. We thank Dr. Jörg Rinklebe from University of Wuppertal for providing the time-series data of fungal and bacterial biomass data. Liyuan He thanks Dr. Fenghui Yuan for the discussion about sensitivity analysis. This study is partially supported by San Diego State University and the CSU Program for Education & Research in Biotechnology. Partial support for this work was provided by an Early Career Award through the U.S. Department of Energy (DOE) Biological and Environmental Research Program ORNL is managed by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the U.S. DOE. This manuscript has been authored by UT-Battelle, LLC, under contract no. DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-publicaccess-plan). The authors are grateful to Dr. Yimei Huang from Northwest A&F University, and Drs. Qian Zhao and Weijun Shen from South China Botanical Garden, Chinese Academy of Sciences for providing the exact sampling date of fungal and bacterial biomass. We thank Dr. Jörg Rinklebe from University of Wuppertal for providing the time‐series data of fungal and bacterial biomass data. Liyuan He thanks Dr. Fenghui Yuan for the discussion about sensitivity analysis. This study is partially supported by San Diego State University and the CSU Program for Education & Research in Biotechnology. Partial support for this work was provided by an Early Career Award through the U.S. Department of Energy (DOE) Biological and Environmental Research Program ORNL is managed by UT‐Battelle, LLC, under contract DE‐AC05‐00OR22725 with the U.S. DOE. This manuscript has been authored by UT‐Battelle, LLC, under contract no. DE‐AC05‐00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid‐up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan ( http://energy.gov/downloads/doe-publicaccess-plan ).
Keywords
- bacteria
- biomass dynamics
- fungi
- model
- sensitivity