Combining FLUXNET data with atmospheric CO2 observations in a global data assimilation framework to study variability of carbon sources and sinks

Abstract:

In recent years, observations of the carbon cycle in the atmosphere and on land have all intensified dramatically, including FLUXNET network that measures surface fluxes of water, heat and CO2, and space-based CO2 observations from SCHIAMACHI, AIRS, GOSAT and OCO2, and other remotely sensed observations of terrestrial ecosystems attributes such as vegetation indices and fluorescence. Yet such data are often used independently and in isolated fashion, thus missing their potential complementary features. We describe a novel joint land-atmosphere data assimilation system that will simultaneously handle multiple modeling components and multiple streams of data. This challenging task is achieved using the Local-Ensemble-Transform-Kalman Filter (LETKF) that has been shown to be a powerful tool in a variety of atmosphere, ocean and carbon data assimilation settings. It includes a number of advanced features, including quantification of transport error using ensemble meteorological analysis, variable localization, and up-scaling flux tower via a footprint observation operator. The system offers an opportunity to combine the 'top-down' atmosphere approach that gives best large-scale constraint with the 'bottom-up' ground-based approach that depicts smaller scale processes and their changes. We will present some initial results using the atmospheric models SPEEDY, GEOSChem, and the terrestrial ecosystem model VEGAS.