Abstract

The first overriding question of the North American Carbon Program Implementation Plan deals with diagnosis of North American carbon fluxes. One of the critical aspects of this question focuses on "scaling issues inherent in applying data sets over areas that were not measured" [Denning et al. 2005]. This need highlights the importance of understanding the spatial and temporal scale-dependence of parameters controlling carbon flux variability, and developing methods for using data collected at multiple scales to infer carbon fluxes. Geostatistical kriging and inverse modeling tools offer a unique opportunity to identify, characterize, and quantify relationships between observed or inferred fluxes and a suite of auxiliary environmental variables. This presentation will describe recent work aimed at improving understanding of variables and processes controlling CO2 fluxes at various spatial and temporal scales, by using remote-sensing, in situ, and atmospheric data. The applied methods are based on geostatistical tools for characterizing spatial variability, inferring relationships among environmental variables, and inverse modeling for identifying releases and uptake of CO2. New methods for characterizing remote sensing observations of CO2 will also be discussed. Overall, the presented approaches can be used to identify parameters that explain observed or inferred carbon flux variability at various spatial and temporal scales within a consistent and statistically rigorous framework.