We are establishing a continuous CO₂ observing network in the Rocky Mountains, building on technological and modeling advances made during the Carbon in the Mountains Experiment (CME), to improve our understanding of regional carbon fluxes and to fill key gaps in the North American Carbon Program (NACP). We will present a description of the Rocky RACCOON network and early results from the first three sites.

Atmospheric observations obtained during intensive field experiments are used to characterize regional sources and test data assimilation techniques. In this study, the STEM-2K1 (Sulfur Transport Eulerian Model, version 2K1) and its adjoint model are applied to the analysis of observations from aircraft platforms made during the summer 2004 ICARTT (International Consortium for Atmospheric Research on Transport) experiment. Observed ratios between CO₂ and tracers and model derived airmass markers are used to identify emission signatures, indicating the influence of different sources. Model derived influence functions along with assimilated transport model results of anthropogenic tracers are used to characterize the anthropogenic CO₂ emissions in the Midwest during the summer 2004 period. This analysis gives an initial look at the Midwest domain which is the focus of the expansion of NOAA Climate Monitoring and Diagnostic Laboratory’s tall tower observation network and the upcoming Mid-Continent NACP Intensive Campaign.

There are two basic approaches for inferring surface-atmosphere exchange for trace gases on regional scales: a bottom-up approach, in which local process knowledge is scaled up, and a top-down approach, in which the larger-scale constraint from atmospheric concentration measurements is applied in combination with transport models. Here we combine the two approaches, and assess the information content added by boundary layer concentration data. More specifically, we analyze the potential for inferring spatially resolved surface fluxes from atmospheric tracer observations within the mixed layer, such as from monitoring towers, using a receptor oriented transport model (Stochastic Time-Inverted Lagrangian Transport [STILT] model, [Lin et al., 2003]) coupled to a simple biosphere in which CO2 fluxes are represented as functional responses to environmental drivers (radiation and temperature, [Gerbig et al., 2003]). Transport and fluxes are coupled on a dynamic grid using a polar projection with high horizontal resolution (~20 km) in near field, and low resolution far away (as coarse as 2000 km), reducing the number of surface pixels without significant loss of information. To test the system, and to evaluate the errors associated with the retrieval of fluxes from atmospheric observations, a pseudo data experiment was performed. A large number of realizations of measurements (pseudo data) and a priori fluxes was generated, and for each case spatially resolved fluxes were retrieved. Results indicate strong potential for high resolution retrievals based on a network of tall towers, subject to the requirement of correctly specifying the a priori uncertainty covariance, especially the off diagonal elements that control spatial correlations.

Upwelling of high-nutrient waters in the equatorial Pacific gives rise to a band of enhanced primary production around the equator that stretches from Peru almost to Indonesia. It has been suggested that this oceanic region accounts for a large part of global net production. The equatorial Pacific is also thought to be the largest oceanic CO₂ source and makes an important contribution to the atmospheric CO₂ budget.

Stable isotope measurements of atmospheric carbon dioxide from the CSIRO global flask sampling program with improved traceability to the international primary reference material VPDB (Vienna Pee Dee Belemnite), and with improved uncertainty estimates, are presented. The measurements have been used with an improved time dependent inversion model to reassess terrestrial and oceanic contributions to the interannual variability in atmospheric CO₂.

Transport-based inversions of atmospheric CO₂ concentration measurements have been used by several groups [e.g., Bousquet, et al., 2000; Rödenbeck, et al., 2003; Baker, et al., 2005] to estimate monthly regional CO₂ fluxes from the 1980s to the present. When compared at the scale of broad latitude bands, the inter-annual variability (IAV) of these results is broadly consistent. This agreement breaks down, however, when the fluxes are partitioned regionally inside these latitude bands, or even into global land/ocean totals. We show here that this disagreement can largely be explained by random estimation errors and transport model errors affecting the estimates.