Toward constraining regional-scale fluxes of CO₂ with atmospheric observations over a continent: 2. Analysis of COBRA data using a receptor-oriented framework

We present an analysis framework and illustrate its potential to constrain terrestrial carbon fluxes at the regional scale using observations of CO2 and CO over North America acquired during the CO2 Budget and Rectification Airborne (COBRA) study in 2000. The COBRA data set, presented in detail in a companion paper [Gerbig et al., 2003] provides dense spatial coverage and extensive profiling in the lower atmosphere, revealing strong CO2 signatures of land surface fluxes in the active and relic mixed layers of the atmosphere. We introduce a "receptor-oriented" analysis framework designed to quantitatively interpret the atmospheric signatures of surface processes by linking concentrations at measurement locations (receptors) to surface fluxes in upwind regions. The framework incorporates three main components: (1) the Stochastic Time-Inverted Lagrangian Transport (STILT) model, driven with assimilated winds and running backward in time to map out the source-receptor relationship (footprint) at high temporal and spatial resolution; (2) an observation-based lateral boundary condition for CO2, resolving vertical and meridional gradients; and (3) a simple parameterization for biosphere-atmosphere fluxes that uses eddy covariance observations from the AmeriFlux network as prior estimates for fluxes. This framework allows quantitative comparison between the top-down constraint on fluxes from airborne observations of CO2 with the bottom-up constraint of eddy flux measurements in a Bayesian synthesis inversion. The model is used to investigate the observed representation error (mismatch between point measurements and grid-cell-averaged values in models), evaluated in the companion paper, showing that unresolved spatial variability of surface fluxes gives rise to most of the representation error over the continent. Thus the representation error reflects the effect of aggregation errors. Discrepancies between simulated and observed CO2 distributions are assessed to indicate where improvements are needed, including improved empirical representation of biosphere-atmosphere exchange process and better simulation of convective processes in atmospheric transport models.