Novel nonstationary and nonlinear dynamic time series analysis tools are applied to multiyear eddy covariance CO2 flux and micrometeorological data from the Harvard Forest and University of Michigan Biological Station field study sites. Firstly, the utility of these tools for partitioning the gross photosynthesis and bulk respiration signals within these series is demonstrated when employed within a simple model framework. This same framework offers a promising new method for gap filling missing CO2 flux data. Analysing the dominant seasonal components extracted from the CO2 flux data using these tools, models are inferred for daily gross photosynthesis and bulk respiration. Despite their simplicity, these models fit the data well and yet are characterized by well-defined parameter estimates when the models are optimized against calibration data. Predictive validation of the models also demonstrates faithful forecasts of annual net cumulative CO2 fluxes for these sites.
First paper to treat land-surface CO2 flux modeling as an identification problem, highlighting the requirement for synergy between model design and information content of calibration data. It is also one of the first to characterize eddy covariance observation noise. Schulz and Young's contributions were minor, Stauch was Jarvis' RA. RAE_import_type : Journal article RAE_uoa_type : Earth Systems and Environmental Sciences