| description abstract | Of great importance for the simulation of climate using general circulation models is their ability to represent accurately the vertical distribution of fractional cloud amount. In this paper, a technique to derive cloud fraction as a function of height using ground-based radar and lidar is described. The relatively unattenuated radar detects clouds and precipitation throughout the whole depth of the troposphere, whereas the lidar is able to locate cloud base accurately in the presence of rain or drizzle. From a direct comparison of 3 months of cloud fraction observed at Chilbolton, England, with the values held at the nearest grid box of the European Centre for Medium-Range Forecasts (ECMWF) model it is found that, on average, the model tends to underpredict cloud fraction below 7 km and considerably overpredict it above. The difference below 7 km can in large part be explained by the fact that the model treats snow and ice cloud separately, with snow not contributing to cloud fraction. Modifying the model cloud fraction to include the contribution from snow (already present in the form of fluxes between levels) results in much better agreement in mean cloud fraction, frequency of occurrence, and amount when present between 1 and 7 km. This, together with the fact that both the lidar and the radar echoes tend to be stronger in the regions of ice clouds that the model regards as snow, indicates that snow should not be treated as radiatively inert by the model radiation scheme. Above 7 km, the difference between the model and the observations is partly due to some of the high clouds in the model being associated with very low values of ice water content that one would not expect the radar to detect. However, removal of these from the model still leaves an apparent overestimate of cloud fraction by up to a factor of 2. A tendency in the lowest kilometer for the model to simulate cloud features up to 3 h before they are observed is also found. Overall, this study demonstrates the considerable potential of active instruments for validating the representation of clouds in models. | |
