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This presentation will discuss how we have explicitly incorporated the effects of microbial activities on soil organic matter
decomposition into a biogeochemistry model, the Terrestrial Ecosystem Model. Specifically, we will discuss how we have
revised an existing Q10-based heterotrophic respiration algorithm, by incorporating the algorithms of Dual Arrhenius and
Michaelis-Menten kinetics and microbial-enzyme interactions. The microbial physiology enabled model was then applied to
quantify historical and future carbon dynamics of forest ecosystems in the conterminous United States and the Arctic. Our
model simulations for the forest ecosystems in the United States demonstrate that the revised model better simulate historical
ecosystem carbon dynamics. Another research effort has been made to incorporate a microbial dormancy into an explicit
microbial-enzyme decomposition algorithm. The model was then used to examine soil carbon dynamics with and without
representation of microbial dormancy. The model was finally extrapolated to global temperate forest ecosystems. Our study
shows that the dormancy model consistently produced a better match with field-observed heterotrophic soil carbon effluxes
than the no dormancy model. Currently, we are developing more detailed microbial physiologically based soil C and N models
that shall improve the quantification of the land ecosystem C and N dynamics and their feedbacks to the global climate system.
Qianlai Zhuang has completed his PhD from the University of Alaska at Fairbanks and Postdoctoral studies at the Ecosystems Center of the Marine Biological Laboratory at Woods Hole MA. He has published more than 120 peer-reviewed papers. His research focuses on advancing C and N biogeochemistry modeling. To date, he has modeled the impacts of permafrost dynamics, fire disturbances, aerosol and ozone and hydrological dynamics on C and N dynamics of both land and aquatic ecosystems. He has extensively used these models to study the climate change effects on C and N dynamics and their feedbacks to the climate system.