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Quantifying microbial ecophysiological effects on the carbon fluxes of forest ecosystems over the conterminous United States

机译：量化微生物对美国本土森林生态系统碳通量的影响

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There is a pressing need to develop earth system models (ESMs), in which ecosystem processes are adequately represented, to quantify carbon-climate feedbacks. In particular, explicit representation of the effects of microbial activities on soil organic carbon decomposition has been slow in ESM development. Here we revised an existing Q(10)-based heterotrophic respiration (R-H) algorithm of a large-scale biogeochemical model, the Terrestrial Ecosystem Model (TEM), by incorporating the algorithms of Dual Arrhenius and Michaelis-Menten kinetics and microbial-enzyme interactions. The microbial physiology enabled model (MIC-TEM) was then applied to quantify historical and future carbon dynamics of forest ecosystems in the conterminous United States. Simulations indicate that warming has a weaker positive effect on R-H than that traditional Q(10) model has. Our results demonstrate that MIC-TEM is superior to traditional TEM in reproducing historical carbon dynamics. More importantly, the future trend of soil carbon accumulation simulated with MIC-TEM is more reasonable than TEM did and is generally consistent with soil warming experimental studies. The revised model estimates that regional GPP is 2.48 Pg C year(-1) (2.02 to 3.03 Pg C year(-1)) and NEP is 0.10 Pg C year(-1) (-0.20 to 0.32 Pg C year(-1)) during 2000-2005. Both models predict that the conterminous United States forest ecosystems are carbon sinks under two future climate scenarios during the 21st century. This study suggests that terrestrial ecosystem models should explicitly consider the microbial ecophysiological effects on soil carbon decomposition to adequately quantify forest ecosystem carbon fluxes at regional scales.
机译：迫切需要开发能够充分代表生态系统过程的地球系统模型（ESM），以量化碳气候反馈。特别是，在ESM发展过程中，微生物活性对土壤有机碳分解的影响的显式表示一直很慢。在这里，我们通过合并Dual Arrhenius和Michaelis-Menten动力学和微生物-酶相互作用的算法，修订了现有的基于Q（10）的大规模生物地球化学模型，陆地生态系统模型（TEM）的异养呼吸（RH）算法。然后应用微生物生理学启用模型（MIC-TEM）来量化美国本土森林生态系统的历史和未来碳动态。仿真表明，与传统的Q（10）模型相比，变暖对R-H的积极作用较弱。我们的结果表明，MIC-TEM在重现历史碳动力学方面优于传统TEM。更重要的是，用MIC-TEM模拟的土壤碳积累的未来趋势比TEM更为合理，并且与土壤变暖实验研究基本一致。修订后的模型估计区域GPP为2.48 Pg C年（-1）（2.02至3.03 Pg C年（-1））和NEP为0.10 Pg C年（-1）（-0.20至0.32 Pg C年（-1） ））在2000-2005年期间。两种模型都预测，在21世纪的两种未来气候情景下，美国本土森林生态系统是碳汇。这项研究表明，陆地生态系统模型应明确考虑微生物对土壤碳分解的生态生理影响，以在区域尺度上充分量化森林生态系统的碳通量。

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