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We compare two optimized chemical data assimilation systems, one based on theensemble Kalman filter (EnKF) and the other based on four-dimensionalvariational (4D-Var) data assimilation, using a comprehensive stratosphericchemistry transport model (CTM). This work is an extension of the BelgianAssimilation System for Chemical ObsErvations (BASCOE), initially designed towork with a 4D-Var data assimilation. A strict comparison of both methods inthe case of chemical tracer transport was done in a previous study andindicated that both methods provide essentially similar results. In thepresent work, we assimilate observations of ozone, HCl, HNO3, H2O andN2O from EOS Aura-MLS data into the BASCOE CTM with a full description ofstratospheric chemistry. Two new issues related to the use of the fullchemistry model with EnKF are taken into account. One issue is a large numberof error variance parameters that need to be optimized. We estimate anobservation error variance parameter as a function of pressure level for eachobserved species using the Desroziers method. For comparison purposes, weapply the same estimate procedure in the 4D-Var data assimilation, where bothscale factors of the background and observation error covariance matrices areestimated using the Desroziers method. However, in EnKF the background errorcovariance is modelled using the full chemistry model and a model error termwhich is tuned using an adjustable parameter. We found that it is adequate tohave the same value of this parameter based on the chemical tracerformulation that is applied for all observed species. This is an indicationthat the main source of model error in chemical transport model is due to thetransport. The second issue in EnKF with comprehensive atmospheric chemistrymodels is the noise in the cross-covariance between species that occurs whenspecies are weakly chemically related at the same location. These errors needto be filtered out in addition to a localization based on distance. Theperformance of two data assimilation methods was assessed through an 8-monthlong assimilation of limb sounding observations from EOS Aura MLS. This paperdiscusses the differences in results and their relation to stratosphericchemical processes. Generally speaking, EnKF and 4D-Var provide results ofcomparable quality but differ substantially in the presence of model error orobservation biases. If the erroneous chemical modelling is associated withmoderately fast chemical processes, but whose lifetimes are longer than themodel time step, then EnKF performs better, while 4D-Var develops spuriousincrements in the chemically related species. If, however, the observationbiases are significant, then 4D-Var is more robust and is able to rejecterroneous observations while EnKF does not

EnKF and 4D-Var data assimilation with chemical transport model BASCOE (version 05.06)