Abstract

The long term mean flow simulated in General Circulation Models (GCMs) of the atmosphere exhibits certain systematic errors compared to climatology. These errors are due to errors in the tendencies of the prognostic variables computed from the model equations; i.e. forcing errors. Because of energy dispersion and feedback's, the spatial distributions of the long term mean forcing errors and the long term mean errors in the prognostic variables themselves are generally quite different. Therefore it is almost impossible to use the long term mean errors in the prognostic variables to deduce where the forcing errors are located in time and space and to deduce their nature. But forcing errors can be obtained as the difference between observed tendencies and model tendencies and they will constitute a space-time map of all the model deficits. In this study the long term mean tendency of forcing errors in both January and July are calculated for all the dynamical prognostic variables in the Arpege/IFS climate model. This is done via assimilation of ERA data into the model using a very simple four dimensional data assimilation (nudging). The identified forcing errors are next used as additional forcing in long perpetual Jan. and Jul. simulations. It is seen that the model long term systematic errors are generally reduced by using the additional constant forcing.