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Register Number: ER65205
Title: Forecasting Carbon Storage as Eastern Forests Age: Joining Experimental and Modeling Approaches at the UMBS Ameriflux Site
Principal Investigator: Curtis, Peter
Institution: OHIO STATE UNIVERSITY
Institution Address: Columbus, OH 43210-1063
Awarded Amount to Date and B&R Code :
FY 2014$0 k
FY 2013$347 kKP170201
FY 2012$336 kKP170201
FY 2011$347 kKP170201
DOE Program Manager: Daniel Stover
BER Division: Climate and Environmental Sciences
Research Area: Terrestrial Ecosystem Science
Abstract Submit Date: 10/09/2013
Project Term: 09/01/2011 - 08/31/2014
Abstract: The future trajectory of the North American carbon sink remains highly uncertain as mixed deciduous forests of the U.S. Midwest and east transition from early to mid and late successional communities. Following heavy disturbance in the 19th and early 20th centuries, dominant early successional canopy species are reaching maturity and beginning to senesce, giving way to a canopy that is more species diverse and structurally heterogeneous. Large-scale mortality is also forcing redistribution of carbon and nitrogen, with the consequences for forest carbon cycling processes largely unknown. In support of DOE BERís mission to improve quantitative forecasts of carbon storage in an important terrestrial ecosystem using scientific data and models, we are incorporating novel physical and biological mechanisms into an earth system atmosphere-biosphere model to improve regional forecasts of future forest carbon storage in response to disturbance and succession, and to current and long-term climate variation. We are using novel structure-function relationships derived from our long-term ecological and meteorological carbon cycling studies at the University of Michigan Biological Station to parameterize the ecosystem model, ED2, for stand to regional simulations of forest carbon storage over a range of forecasted climates. By incorporating the dynamic effects of canopy structure on forest ecosystem function in an earth system model, our results are contributing to continental and global scale modeling efforts that aim to improve the quantitative certainty of predictions of future terrestrial carbon storage, and to advance the formulation of forest management prescriptions for enhancing carbon sequestration in older forests.