Ensemble assimilation of stratospheric temperature and ozone observations in a chemistry-climate model
It is found that our ensemble Kalman filter constrains well the stratospheric chemistry and dynamics, given proper localization of the background error covariances, even with a limited amount of observations. The cross-species background error covariances are critical in this achievement, particularly the ones representing the advection of the dynamical and chemical variables. However, significant but non-deleterious noise is observed in the analysis ensembles, in contrary to other ensemble data assimilation systems, where an under-variability is typically observed in the ensembles. An interpretation of these results will be presented.