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Register Number: ER65199
Title: Supporting Carbon Cycle and Earth Systems Modeling with Measurements and Analysis from the Howland AmeriFlux Site
Principal Investigator: Hollinger, David
Institution: AGRICULTURE, U.S. DEPT. OF, FS
Institution Address: Golden, CO 80401-4720
Awarded Amount to Date and B&R Code :
FY 2014$0 k
FY 2013$349 kKP170201
FY 2012$349 kKP170201
FY 2011$349 kKP170201
DOE Program Manager: James Kuperberg
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 overall goal of the proposed work is to understand how a changing climate affects the functioning of a spruce-fir forest in central Maine. This forest is representative of an important component of the North American boreal forest, and presently taking up increasing quantities of carbon dioxide (CO2) from the atmosphere. By doing this, such forests reduce the rate of build-up of CO2 in the atmosphere and help to slow the progress of the greenhouse effect and climate change. Some models have identified processes within forest ecosystems that will change as climatic conditions change, reducing the rate of CO2 uptake or even causing forests to loose CO2 to the atmosphere, accelerating the greenhouse effect. Presently a range of outputs from such models is possible because we do not have the necessary data to decide if model predictions are plausible. In this work we will make measurements of the isotopic discrimination of CO2 within and above the forest. When combined with our present measurements this will provide a dataset that models will find more challenging to simulate correctly, but if models can do this, they will have achieved an improved ability to predict the future trajectory of atmospheric CO2. The primary methodologies to be used include eddy flux measurements from several forest towers, measurements of carbon isotope discrimination from a new type of field-deployable isotope analyzer, and various modeling activities known as data-model fusion. The outcome of successful completion of this work would be (1) wider adoption of our proposed methods, (2) increased knowledge about how carbon cycles through the environment, (3) an improved capacity to predict how forests will operate in a changing climate, and (4) better prediction of future atmospheric CO2 levels. Benefits include (1) better protection of our forests, and, (2) improved information to inform policy decisions.