Novel nonstationary and nonlinear dynamic time series analysis tools are applied to multiyear eddy covariance CO2 flux and micrometeorological data from the Harvard Forest the 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. Analyzing 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.