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Global Simulation and Evaluation of Soil Organic Matter and Microbial Carbon and Nitrogen Stocks Using the Microbial Decomposition Model ORCHIMIC v2.0
Author(s) -
Huang Y.,
Guenet B.,
Wang Y. L.,
Ciais P.
Publication year - 2021
Publication title -
global biogeochemical cycles
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.512
H-Index - 187
eISSN - 1944-9224
pISSN - 0886-6236
DOI - 10.1029/2020gb006836
Subject(s) - soil carbon , environmental science , biomass (ecology) , soil organic matter , global change , nitrogen cycle , nitrogen , carbon cycle , soil water , climate change , carbon fibers , soil science , heterotroph , environmental chemistry , atmospheric sciences , ecology , chemistry , ecosystem , mathematics , biology , bacteria , geology , genetics , organic chemistry , algorithm , composite number
Abstract Soils contain the largest amount of land carbon, even a small change of this pool can significantly affect atmospheric CO 2 and climate change. A good representation of soil organic carbon (SOC) dynamics in Earth system models is therefore crucial to predict future climate change. The dynamics of SOC is largely driven by microbial activities and modulated by N cycles. Nevertheless, very few models have explicitly represented soil microorganisms and N cycles integrated at global scale. Here, we present an update of the microbial‐mediated ORCHIMIC model and its application to simulate global gridded SOC stocks, microbial biomass, soil C/N ratio, microbial C/N ratio, and heterotrophic respiration. This is a new attempt to model SOC dynamics with an explicit microbial representation with N dynamics applied at global scale. The model shows relatively good performance in reproducing global SOC and microbial biomass C. The spatial distributions of soil and microbial C/N ratios were not well reproduced because they are sensitive to mineral nitrogen availability controlled by plant uptake, which is not explicitly represented in the model. However, similar relationship between C/N ratios of microbes and soil as observation demonstrated the potential of the model to reproduce global C/N ratios for both microbe and soil pools. Dynamic carbon use efficiency modulated by substrate C/N ratio, consistent with observation, were well represented by mechanistic including microbial dynamics. Modeled suppressed microbial biomass growth under warming climate indicating a weaker positive feedback between soil C pool and climate compared to that predicted by traditional Earth system models.