What have we learned from intensive atmospheric sampling field programmes of CO₂?

The spatial and temporal gradients in atmospheric CO2 contain signatures of carbon fluxes, and as part of inverse studies, these signatures have been combined with atmospheric models to infer carbon sources and sinks. However, such studies have yet to yield finer-scale, regional fluxes over the continent that can be linked to ecosystem processes and ground-based observations. The reasons for this gap are twofold: lack of atmospheric observations over the continent and model deficiencies in interpreting such observations. This paper describes a series of intensive atmospheric sampling field programmes designed as pilot experiments to bridge the observational gap over the continent and to help test and develop models to interpret these observations. We summarize recent results emerging from this work, outlining the role of the intensive atmospheric programmes in collecting CO2 data in both the vertical and horizontal dimensions. These data: (1) quantitatively establish the spatial variability of CO2 and the associated errors from neglecting this variability in models; (2) directly measure regional carbon fluxes from airmass-following experiments and (3) challenge models to reduce and account for uncertainties in atmospheric transport. We conclude with a look towards the future, outlining ways in which intensive atmospheric sampling can contribute towards advancing carbon science. [References: 47

Estimating regional carbon exchange in New England and Quebec by combining atmospheric, ground-based and satellite data

We derive regional-scale (similar to 10(4) km(2)) 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 similar to 0.6 ppm lower than the mean in the planetary boundary layer. [References: 31