Uncertainty Estimation and Decomposition based on Monte Carlo and Multimodel Photochemical Simulations

Abstract : This paper investigates (1) the main sources of uncertainties in ground-level ozone simulations, (2) the best method to estimate them, and (3) the decomposition of the errors in measurement, representativeness and modeling errors. It first compares the Monte Carlo approach, solely based on perturbations in the input fields and parameters, with the multimodel approach, which relies on an ensemble of models with different chemical, physical and numerical formulations. Two ensembles of 100 members are generated for the full year 2001 over Europe. Their uncertainty estimations for ground-level ozone are compared. For both ensembles, we select a sub-ensemble that minimizes the variance of the rank histogram, so that it is supposed to better represent the uncertainties. The multimodel (sub-)ensemble shows more variability and seems to better represent the uncertainties (especially for the localization of the covariances) than the Monte Carlo (sub-)ensemble. The main sources of the uncertainties originating in the input fields and parameters are then identified with a linear regression of the output ozone concentrations on the applied perturbations. The uncertainty ranges due to the different input fields and parameters are computed at urban, rural and background observation stations. For both the multimodel ensemble and the Monte Carlo ensemble, ozone boundary conditions play an important role, even at continental scale; but many other fields or parameters appear to be a significant source of uncertainty. The discrepancies between observations and model simulations are due to measurement errors, representativeness errors and modeling errors (i.e., shortcomings in the model formulation or in its input data). Using two independent methods, we estimate the variance of the representativeness errors. We conclude that the measurement errors are comparatively low, and that the representativeness errors can explain at least a third of the variance of the discrepancies.
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Damien Garaud, Vivien Mallet. Uncertainty Estimation and Decomposition based on Monte Carlo and Multimodel Photochemical Simulations. [Research Report] RR-7903, 2012, pp.33. 〈hal-00678306〉

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