Regional carbon budget with a high density concentration tower network: Inversion of CO2 fluxes in the Mid Continental Intensive project
Thomas
Lauvaux, Pennsylvania State University, tul5@meteo.psu.edu
(Presenting)
Liza
Diaz, Pennsylvania State University, lzd120@psu.edu
Natasha
Miles, Pennsylvania State University, nmiles@meteo.psu.edu
Scott
Richardson, Pennsylvania State University, srichardson@psu.edu
Andrew
Schuh, Colorado State University, aschuh@atmos.colostate.edu
Andy
Jacobson, NOAA Boulder, andy.jacobson@noaa.gov
Arlyn
Andrews, NOAA Boulder, arlyn.andrews@noaa.gov
Scott
Denning, Colorado State University, scott.denning@colostate.edu
Ken
Davis, Pennsylvania State University, davis@meteo.psu.edu
Constraining regional carbon budgets from top down or bottom up methods remains a major challenge for a better understanding of the continental carbon balance. Mesoscale inversions have been evaluated against several direct measurements and appear to be a promising tool for linking local eddy flux measurements and calibrated vegetation models to larger scale atmospheric inversion estimates. The MCI project offers the unique opportunity to constrain the regional carbon balance of one of a large, well-characterized and biologically active region of North America, the corn belt, with a sufficiently large number of CO2 concentration observations. We present here the first results of a mesoscale inversion at 20km resolution, using eight concentration towers over a 1000 by 1000 km2 domain. The behavior of the inversion as a function of the structure of the errors and the impact of boundary conditions is explored in order to better characterize uncertainty in the inversion solution. Several eddy flux towers are used to evaluate the regional NEE over the corn dominated region. Preliminary results including daily NEE and uncertainties for summer 2007 will be presented and discussed.
Presentation Type: Poster
Poster Session: Carbon Cycle Science
NASA TE Funded Awards Represented:
Ogle, Stephen
Resolving Net CO2 Exchange in the Mid-Continent Region of North America by Comparing and Reconciling Results from Inverse Modeling and Inventory-Based Approaches