Andrew
E
Schuh, Atmospheric Science, Colorado State University, aschuh@atmos.colostate.edu
Scott
Denning, Atmospheric Science, Colorado State University, denning@atmos.colostate.edu
(Presenting)
Marek
Uliasz, Atmospheric Science, Colorado State University, marek@atmos.colostate.edu
Dusanka
Zupanski, Cooperative Institute for Research in the Atmosphere, CSU, zupanski@cira.colostate.edu
In order to facilitate future decision making regarding regional carbon fluxes, it is essential to better quantify uncertainty in inverse carbon flux models. At Colorado State University, research is being performed in order to better quantify sources and sinks and associated uncertainties, on a mesoscale level, through a coupled atmospheric (RAMS and PCTM) and terrestrial carbon flux (Sib3) model. In particular, carbon-dioxide flux and mixing ratio data were collected from the numerous tall towers (30 meters or greater) during the summer of 2004. The fully coupled terrestrial-atmospheric model, SibRAMS, will be forced with 2004 reanalysis data to predict fine scale weather on a 40km grid for the continental United States for the summer of 2004. Relevant portions of this simulated weather, including wind fields and pertinent turbulence components, are extracted and used to create backward in time Lagrangian Particle Dispersion Modeled (LPDM) influence functions. Pseudo spatial carbon-dioxide mixing ratio and flux data created by SibRams is then used as input to estimation routines in order to try and predict pseudo tower data at different heights. Prior information as well as pseudo data will be combined in order to provide a somewhat well constrained problem. Attention will particularly be paid to hierarchical Bayesian regression schemes to measure influence at different factor levels (continental-level, biome-level, and model grid cell level).