Data-Model Fusion at AmeriFlux Sites: Towards Predictive Understanding of Seasonal and Interannual Variability in Net Ecosystem Exchange

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To understand carbon fluxes between the land and the atmosphere, scientists have widely employed the so-called eddy-flux method to quantify land carbon sequestration. The eddy-flux measurements at hundreds of sites across the world all have showed strong year-to-year variation (i.e., interannual variability) in the strength of the terrestrial carbon sink. This project seeks to determine the underlying causes of the seasonal and interannual variation (SIAV) in net ecosystem exchange of carbon dioxide as measured by many long-term micrometeorological sites that make up the AmeriFlux and the Fluxnet eddy-flux networks. While previous efforts have focused on developing a mechanistic understanding of observed seasonal and interannual variability in net ecosystem exchange of carbon dioxide at single sites, far less work has attempted to identify a more general description or model for seasonal and interannual variability based on data from a broad range of ecosystems. This project will use multiple approaches, including data processing, statistical analysis, process-based modeling, and inverse analysis to examine the direct and indirect effects of climatic variation on ecosystem processes, to elucidate general patterns of variability in net ecosystem exchange of carbon dioxide across ecosystem types, and to develop a process-based model for predicting seasonal and interannual variability. The broader impacts of this research lie in the synthetic nature of the work, given the large investment the world is making in eddy flux data, and given the potential for these data sets to substantially improve our knowledge of the global carbon cycle and to inform policy making.