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Keywords:

  • Bayesian analysis;
  • data assimilation;
  • Markov Chain Monte Carlo (MCMC) methods;
  • net ecosystem exchange (NEE);
  • parameter estimation;
  • terrestrial carbon cycle

Abstract

We performed a synthetic analysis of Harvard Forest net ecosystem exchange of CO2 (NEE) time series and a simple ecosystem carbon flux model, the simplified Photosynthesis and Evapo-Transpiration model (SIPNET). SIPNET runs at a half-daily time step, and has two vegetation carbon pools, a single aggregated soil carbon pool, and a simple soil moisture sub-model. We used a stochastic Bayesian parameter estimation technique that provided posterior distributions of the model parameters, conditioned on the observed fluxes and the model equations. In this analysis, we estimated the values of all quantities that govern model behavior, including both rate constants and initial conditions for carbon pools. The purpose of this analysis was not to calibrate the model to make predictions about future fluxes but rather to understand how much information about process controls can be derived directly from the NEE observations. A wavelet decomposition enabled us to assess model performance at multiple time scales from diurnal to decadal. The model parameters are most highly constrained by eddy flux data at daily to seasonal time scales, suggesting that this approach is not useful for calculating annual integrals. However, the ability of the model to fit both the diurnal and seasonal variability patterns in the data simultaneously, using the same parameter set, indicates the effectiveness of this parameter estimation method. Our results quantify the extent to which the eddy covariance data contain information about the ecosystem process parameters represented in the model, and suggest several next steps in model development and observations for improved synthesis of models with flux observations.