Abstract. Cloud properties are usually assumed to be homogeneous within the cloudy part of the grid-box, i.e. subgrid-scale inhomogeneities in cloud cover and/or microphysical properties are often neglected. However, precipitation formation is initiated by large particles. Thus mean values are not representative and could lead to a delayed onset of precipitation.

For a more physical description of the subgrid-scale structure of clouds we introduce a new statistical sub-column algorithm to study the impact of cloud inhomogeneities on stratiform precipitation. Each model column is divided into N independent sub-columns with sub-boxes in each layer, which are completely clear or cloudy. The cloud cover is distributed over the sub-columns depending on the diagnosed cloud fraction. Mass and number concentrations of cloud droplets and ice crystals are distributed randomly over the cloudy sub-columns according to prescribed probability distributions. Shapes and standard deviations of the distributions are obtained from aircraft observations.

We have implemented this sub-column algorithm into the ECHAM5 global climate model to take subgrid variability of cloud cover and microphysical properties into account. Simulations with the Single Column Model version of ECHAM5 were carried out for one period of the Mixed-Phase Polar Arctic Cloud Experiment (MPACE) campaign as well as for the Eastern Pacific Investigation of climate Processes (EPIC) campaign. Results with the new algorithm show an earlier onset of precipitation for the EPIC campaign and a higher conversion of liquid to ice for the MPACE campaign, which reduces the liquid water path in better agreement with the observations than the original version of the ECHAM5 model.