Reducing systematic errors by empirically correcting model errors

A methodology for the correction of systematic errors in a simplified atmospheric generalcirculation model is proposed. First, a method for estimating initial tendency model errors is developed, based on a 4-dimensional variational assimilation of a long-analysed dataset of observations in a simple quasi-geostrophic baroclinic model. Then, a time variable potential vorticity source term is added as a forcing to the same model, in order to parameterize subgridscale processes and unrepresented physical phenomena. This forcing term consists in a (largescale) flow dependent parametrization of the initial tendency model error computed by the variational assimilation. The flow dependency is given by an analogues technique which relies on the analysis dataset. Such empirical driving causes a substantial improvement of the model climatology, reducing its systematic error and improving its high frequency variability. Lowfrequency variability is also more realistic and the model shows a better reproduction of Euro- Atlantic weather regimes. A link between the large-scale flow and the model error is found only in the Euro-Atlantic sector, other mechanisms being probably the origin of model error in other areas of the globe.