Abstract:

We derive regional-scale (∼104 km2) CO₂ flux estimates for summer 2004 in the northeast United States and southern Quebec by assimilating extensive data into a receptor-oriented model-data fusion framework. Surface fluxes are specified using the Vegetation Photosynthesis and Respiration Model (VPRM), a simple, readily optimized biosphere model driven by satellite data, AmeriFlux eddy covariance measurements and meteorological fields. The surface flux model is coupled to a Lagrangian atmospheric adjoint model, the Stochastic Time-Inverted Lagrangian Transport Model (STILT) that links point observations to upwind sources with high spatiotemporal resolution. Analysis of CO₂ concentration data from the NOAA-ESRL tall tower at Argyle, ME and from extensive aircraft surveys, shows that the STILT– VPRM framework successfully links model flux fields to regionally representative atmospheric CO₂ data, providing a bridge between ‘bottom-up’ and ‘top-down’ methods for estimating regional CO₂ budgets on timescales from hourly to monthly. The surface flux model, with initial calibration to eddy covariance data, produces an excellent a priori condition for inversion studies constrained by atmospheric concentration data. Exploratory optimization studies show that data from several sites in a region are needed to constrain model parameters for all major vegetation types, because the atmosphere commingles the influence of regional vegetation types, and even high-resolution meteorological analysis cannot disentangle the associated contributions. Airborne data are critical to help define uncertainty within the optimization framework, showing for example, that in summertime CO₂ concentration at Argyle (107 m) is ∼0.6 ppm lower than the mean in the planetary boundary layer.