On the assignment of prior errors in Bayesian inversions of CO2 surface fluxes

[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 eddycovariance 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. Citation: Chevallier, F., N. Viovy, M. Reichstein, and P. Ciais (2006), On the assignment of prior errors in Bayesian inversions of CO2 surface fluxes, Geophys. Res. Lett., 33, L13802, doi:10.1029/2006GL026496.

[1]  Ian G. Enting,et al.  A synthesis inversion of the concentration and δ 13 C of atmospheric CO 2 , 1995 .

[2]  Taro Takahashi,et al.  Towards robust regional estimates of CO2 sources and sinks using atmospheric transport models , 2002, Nature.

[3]  Sander Houweling,et al.  Inverse modeling of CO2 sources and sinks using satellite data: a synthetic inter-comparison of measurement techniques and their performance as a function of space and time , 2003 .

[4]  E. Jaynes.  Information Theory and Statistical Mechanics , 1957 .

[5]  D. H. Peterson,et al.  Aspects of climate variability in the Pacific and the western Americas , 1989 .

[6]  P. Ciais,et al.  Europe-wide reduction in primary productivity caused by the heat and drought in 2003 , 2005, Nature.

[7]  I. C. Prentice,et al.  A dynamic global vegetation model for studies of the coupled atmosphere‐biosphere system , 2005 .

[8]  W. Oechel,et al.  FLUXNET: A New Tool to Study the Temporal and Spatial Variability of Ecosystem-Scale Carbon Dioxide, Water Vapor, and Energy Flux Densities , 2001 .

[9]  Marc Bocquet,et al.  Grid resolution dependence in the reconstruction of an atmospheric tracer source , 2005 .

[10]  Ü. Rannik,et al.  Estimates of the annual net carbon and water exchange of forests: the EUROFLUX methodology , 2000 .

[11]  Dusanka Zupanski,et al.  An ensemble data assimilation system to estimate CO2 surface fluxes from atmospheric trace gas observations , 2005 .

[12]  D. Hollinger,et al.  Uncertainty in eddy covariance measurements and its application to physiological models. , 2005, Tree physiology.

[13]  C.,et al.  Analysis methods for numerical weather prediction , 2022 .

[14]  I. Fung,et al.  Observational Contrains on the Global Atmospheric Co2 Budget , 1990, Science.

[15]  Philippe Bousquet,et al.  Inferring CO2 sources and sinks from satellite observations: Method and application to TOVS data , 2005 .

[16]  Sander Houweling,et al.  CO 2 flux history 1982–2001 inferred from atmospheric data using a global inversion of atmospheric transport , 2003 .

[17]  Kevin R. Gurney,et al.  Maximum likelihood estimation of covariance parameters for Bayesian atmospheric trace gas surface flux inversions , 2005 .