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An ensemble Kalman filter (EnKF) assimilation method is applied to the tracertransport using the same stratospheric transport model as in the four-dimensional variational(4D-Var) assimilation system BASCOE (Belgian AssimilationSystem for Chemical ObsErvations). This EnKF version of BASCOE was built primarilyto avoid the large costs associated with the maintenance of an adjoint model.The EnKF developed in BASCOE accounts for two adjustable parameters: aparameter α controlling the model error term and a parameter <i>r</i>controlling the observational error. The EnKF system is shown to be markedlysensitive to these two parameters, which are adjusted based on the monitoringof a χ<sup>2</sup> test measuring the misfit between the control variable and theobservations. The performance of the EnKF and 4D-Var versions was estimatedthrough the assimilation of Aura-MLS (microwave limb sounder) ozone observations during an 8-monthperiod which includes the formation of the 2008 Antarctic ozone hole. Toensure a proper comparison, despite the fundamental differences between thetwo assimilation methods, both systems use identical and carefully calibratedinput error statistics. We provide the detailed procedure for thesecalibrations, and compare the two sets of analyses with a focus on the lowerand middle stratosphere where the ozone lifetime is much larger than theobservational update frequency. Based on the observation-minus-forecaststatistics, we show that the analyses provided by the two systems aremarkedly similar, with biases less than 5% and standard deviationerrors less than 10% in most of the stratosphere. Since the biases aremarkedly similar, they most probably have the same causes: these can bedeficiencies in the model and in the observation data set, but not in theassimilation algorithm nor in the error calibration. The remarkably similarperformance also shows that in the context of stratospheric transport, thechoice of the assimilation method can be based on application-dependentfactors, such as CPU cost or the ability to generate an ensemble offorecasts

Comparison of the ensemble Kalman filter and 4D-Var assimilation methods using a stratospheric tracer transport model