High Resolution Regional CO2 Flux Estimates with the WRF/STILT/VPRM Model and a Bayesian Inversion Framework
Janusz
Eluszkiewicz, Atmospheric and Environmental Research, Inc., jel@aer.com
(Presenting)
Thomas
Nehrkorn, Atmospheric and Environmental Research, Inc., tnehrkor@aer.com
Daniel
Matross, University of California Berkeley, dmatross@nature.berkeley.edu
Steven
Wofsy, Harvard University, swofsy@seas.harvard.edu
We have obtained top-down estimates of CO2 fluxes for Northeast US and southern Canada through a Bayesian inversion of tower and airborne (COBRA-Maine) data collected in the summer of 2004. The inversion has employed the STILT LPDM driven by a customized version of the WRF model as the transport model and the VPRM model as the a priori biosphere model. The availability of aircraft data has proven to be critical in identifying important transport biases, particularly with regard to errors in the advected lateral boundary condition. We will illustrate the impact of removing these biases on the resulting flux estimates and their uncertainties.
Overall, the WRF/STILT/VPRM framework offers a powerful tool for regional flux estimates of CO2 and other greenhouse gases at high spatial and temporal resolution, capable of ingesting data from a variety of measurement platforms, including towers, aircraft, ground-based column, and spaceborne sensors (both passive and active).
NASA Carbon Cycle & Ecosystems Active Awards Represented by this Poster: