SUBGRID-SCALE CLOUDS · Improving subgrid-scale cloud parameterization in global climate models using remote sensing data

Climate change is probably the most important challenge facing mankind. Our understanding of the climate system and ability to make projections about its future evolution are based almost exclusively on global climate models. Although climate models have shown remarkable improvement in recent years, their projections are still subject to large uncertainties. It is well established that clouds are the weakest link in climate simulations. The poor representation of the many cloud-related processes such as precipitation, aerosol indirect effects, and cloud-radiation interactions is the major source of uncertainty in climate models. Fortunately, a treasure trove of measurements from a new generation of earth observing satellites has become available in recent years. The aim of this proposal is to significantly improve the representation of clouds in the global climate model of the Max Planck Institute for Meteorology by tapping into this large remote sensing data set. In particular, we propose to develop a statistical approach based on probability density functions to better describe subgrid-scale cloud processes such as horizontal and vertical cloud variability, and convection. It is expected that this effort will have resulted in substantially improved climate change projections by the time of the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, currently scheduled to be prepared in the 2010-2013 period.