Quantifying microbial ecophysiological effects on the carbon fluxes of forest ecosystems over the conterminous United States
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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 Q10-based heterotrophic respiration (RH) 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 RH than that traditional Q10 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.
KeywordsSoil Organic Carbon Soil Temperature Soil Respiration Gross Primary Production Soil Microbial Biomass
We acknowledge the AmeriFlux community to provide the eddy flux data. This research is supported with projects funded to Q.Z., including NSF projects (DEB-#0919331; NSF-0630319), the NASA Land Use and Land Cover Change program (NASA-NNX09AI26G), Department of Energy (DE-FG02-08ER64599), and the NF Division of Information & Intelligent Systems (NSF-1028291).
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