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Free keywords:
Atmospheric transport
Models
Carbon
Abstract:
[1] For the estimation of surface CO2 fluxes from atmospheric concentration measurements, most often Bayesian approaches have been adopted. As with all Bayesian techniques the definition of prior probability distributions is a critical step in the analysis. However, practical considerations usually guide the definition of prior information rather than objective criterions. In this paper, in situ CO2 flux pointwise measurements made by the eddy-covariance technique are used to estimate the errors of prior fluxes provided by the prognostic carbon-water-energy model ORCHIDEE. The results contradict the usual convenient assumption of a multivariate Gaussian distribution. The errors of ORCHIDEE have a heavier-tail distribution with a linear temporal dependency after the second lag day and no particular spatial structure. Such error distribution significantly complicates the inversion of CO2 surface fluxes. [References: 18]
