Influences of error distributions of net ecosystem exchange on parameter estimation of a process-based terrestrial model

Accuracy of model predictions can be improved by parameter estimation from measurements. It was assumed that measurement errors of net ecosystem exchange of CO₂(NEE) by the eddy covariance technique follow a normal distribution. However, recent studies have showed that eirors in eddy covariance measurements closely follow a double exponential rather than a normal distribution. In this paper, we compared effects of different distributions of measurement errors of NEE data on estimation of parameters and carbon fluxes components. Daily NEE measurements from 2003 to 2005 at the Changbaishan forest site were assimilated into a process-based terrestrial ecosystem model. The Markov Chain Monte Carlo method was used to derive the probability density functions of estimated parameters. Our results showed the modeled annual total gross primary production (GPP) and ecosystem respiration (Re) using the normal error distribution were higher than those using the double exponential distribution by 61-86 g Cm^-2 a^-1 and 107-116 g Cm^-2 a^-1, respectively. As a result, modeled annual sum of NEE under an assumption of the normal error distribution was lower by 29-47 g C m^-2 a^-1 than that under the double exponential error distribution. Especially, the modeled daily NEE based on the normal distribution underestimated the strong carbon sink in Changbaishan forest during the growing seasons. We concluded that types of measurement error distributions and corresponding cost functions can substantially influence parameter estimation and estimated carbon fluxes with data assimilation.