Cloud physics in large scale models - subgrid scale processes:

For large scale models (e.g. global climate models) clouds are usually subgrid phenomenon; they are not resolved on the grid (neither horizontally nor vertically). Thus, clouds must be parametrised in large scale models. In many parametrisations clouds are mainly represented in using grid mean values of variables temperature, relative humidity etc., while cloud inhomogeneities are either not taken into account or are prescribed by probability density functions for mimicking the variations of different parameters. However, it is very difficult to prescribe such subgrid variations in a consistent and physically correct matter. Generally, such cloud parametrisations should react on changes in the large scale dynamics, i.e. they should rely on the "good" model features (and large scale models definitely are good in representing large scale dynamics). Especially, this is a key issue for climate models because for predicting a future climate cloud parametrisation should preferably not rely on empirical relations as derived from actual measurements but on physical principles.

Recently, a new parametrisation for orographic cirrus clouds in the global climate model ECHAM5 was developed (Joos et al., 2008). For representing these subgrid clouds, we coupled the already existing gravity wave drag scheme (e.g. Lott and Miller, 1997) with a physically based ice microphysics (Kärcher & Lohmann, 2002). The representation of cirrus clouds was drastically improved by using this ansatz. This also makes clear that more physically based cloud parametrisations in large scale models can really help us to improve our understanding of clouds in the atmosphere and to get more reliable estimation for the radiative impact of cirrus clouds.