Seminar: Samuel Upton

There are large uncertainties in the magnitude and distribution of the net flux of carbon dioxide from the biosphere to the atmosphere, or Net Ecosystem Exchange (NEE). The two major ways of quantifying NEE, top-down approaches that typically use atmospheric inversions, and bottom-up estimates which rely on process-based or data-driven models or inventories, have known strengths and limitations. Atmospheric inversions produce estimates of NEE that are consistent with the atmospheric CO2 growth rate at regional and global scales, but are highly uncertain at smaller scales. Bottom-up data-driven models based on eddy-covariance measurements match local observations of NEE and their spatial variability, but have difficulty in accurately upscaling to a reliable global estimate. Combing these two approaches can produce a data-driven model which inherits the strengths of both bottom-up and top-down systems. We develop two modeling structures that allow us to constrain a bottom-up data-driven flux model, derived from eddy-covariance data, using atmospheric information. The first study uses top-down models directly, using an ensemble of atmospheric inversions as the basis of the atmospheric constraint. The second study uses an atmospheric transport model to translate surface flux densities to mixing ratios, allowing for a constraint from direct observations of atmospheric CO2. Both systems effectively use the dual-constraint to improve the global estimation of NEE.