The Orbiting Carbon Observatory (OCO) mission will deliver space-based observations of atmospheric CO₂ with the potential to resolve many of the uncertainties in the spatial and temporal variability of carbon sources and sinks.  Our assessments of the measurement requirements for space-based remote sensing of atmospheric CO₂ conclude that the data must support retrievals of the column-averaged CO₂ dry air mole fraction, X_CO2, with precisions of 3 to 4 ppm to resolve the annually averaged gradients between the Northern and Southern hemispheres, but higher precision (1 to 2 ppm) will be needed to resolve East-West gradients and questions like the location and spatial extent of the Northern Hemisphere terrestrial carbon sink.  These conclusions are derived from the results of observational system simulation experiments (OSSEs) and synthesis inversion models [Rayner and O’Brien, 2001; O’Brien and Rayner, 2002; Rayner et al., 2002]. The X_CO2 precision requirements also considered the OCO mission design, the amplitude of X_CO2 spatial and temporal gradients, and the relationship between X_CO2 data precision and regional scale surface CO₂ flux uncertainties inferred from X_CO2 data.

We use the theoretically ideal tracer ¹⁴CO₂ to estimate the fossil fuel CO₂ enhancement in boundary layer air at two sites in New England and Colorado. Improved D¹⁴C measurement precision of 1.6-2.6‰ provides fossil fuel CO₂detection capability of 0.8-1.5 ppm. Using the tracers CO and SF₆, we obtain two additional independent estimates of the fossil fuel CO₂ component, and we assess the biases in these methods by comparison with the ¹⁴CO₂-based estimates. Large differences are observed between the SF₆-based estimates and those from the ¹⁴CO₂ and CO methods. The CO-based estimates show seasonally coherent biases, underestimating fossil fuel CO₂ in winter and overestimating in summer.

The effect of rainfall on mass transfer across the air-water interface was investigated through the CO₂ absorption experiments in a turbulent open-channel flow with the free surface. The results show that the rainfall enhances both the turbulent mixing near the free surface on the liquid side and the CO₂ transfer across the interface. The mass transfer coefficient on the liquid side is well correlated by both the mean vertical momentum flux of rainfall, M, and the mean kinetic energy of rain droplets impinging on the unit area of the air-water interface, KEF. However, it was not concluded which of M and KEF is a better parameter for expressing the rainfall effects on the mass transfer. The comparison between the mass transfer coefficient obtained in this study and that obtained in wind-driven turbulence suggests that it is of great importance to consider the rainfall effect on the CO₂ exchange rate between the atmosphere and ocean in precisely estimating the global carbon cycle in a climate model.

Regular multi-year measurements of atmospheric CO₂ mixing ratios at a network of sites (Fig. 1) give quantitative spatial and temporal information on surface sources and sinks [e.g., Conway et al., 1994]. Using a global atmospheric tracer transport model in a high-resolution (daily, 4x5 degree pixels) inversion setup, we estimate surface-atmosphere CO₂ fluxes that give the best match between modelled and observed CO₂ concentrations. Building on an earlier study [Rödenbeck et al., 2003], this contribution (1) presents new CO₂ flux estimates using methodological developments, and (2) provides an update on interannual fluxes over the most recent anomalous time period 2002-2003.

The synoptic scale atmosphere-biosphere interaction can cause anomalies of ~10 ppm with length scale of ~1000 km in the monthly averaged surface CO₂ concentration. These anomalies may contribute to the errors and uncertainties of CO₂ inversion estimates.

We quantify atmosphere-biosphere carbon exchange at the continental scale across North America during the summers of 2003 and 2004. The 2003 campaign features continental transects across the northern portion of North America with significant influence from biomass burning, while the 2004 study focuses on the greater New England and Quebec region. We use a Lagrangian, adjoint atmospheric model [Gerbig et al. 2003a,b; Lin et al. 2003] coupled to a biosphere model derived from the Vegetation Photosynthesis Model [Xiao et al., 2004]. Our analysis of the 2004 airborne data demonstrates the progression of increasing carbon uptake through the boreal zone during the seasonal transition from early spring to late summer. Data from the coast-to-coast transects of the 2003 campaign allow us to quantify large scale carbon exchange across the continent.

We have developed a carbon flux inversion method for using a mesoscale meteorological model (CSU-RAMS) within a Maximum Likelihood Ensemble Filter (MLEF, Zupanski 2005; Zupanski and Zupanski 2005). The MLEF is a variant of the Ensemble Kalman Filter, and is used to optimize model state variables and parameters based on continuous observations of CO₂ mixing ratio. The method does not require the development of a model adjoint, but rather relies on transformation of variables to efficiently obtain estimates of fluxes with uncertainties and dynamical model error from an ensemble of forward model simulations. We demonstrate this method using a mesoscale simulation of weather, transport, and the surface carbon budget over the continental USA during the summer. The estimation procedure decomposes the total surface flux into photosynthesis and respiration (which are assumed to be modeled correctly to first order), plus an unknown but time-invariant fractional error in each.  These residuals are estimated for each model grid cell over a moving window in time, allowing atmospheric observations to be integrated over sufficient time to obtain constraint. Model error can also be estimated by this procedure, and the method can be extended to larger domains and longer integrations.