Testing and developing cloud parameters in the LMDZ GCM by using POLDER and METEOSAT satellite data.
As they cover the earth's surface by 60% on average, and have a very strong effect on radiation in both the terrestrial and the solar spectrum, clouds impact strongly on earth's climate. Unfortunately, the representation of clouds in climate models of global scale is very disappointing. As almost no cloud properties can be resolved by a global-scale model, they have to be parameterized in terms of the model's variables. Cloud parameterization is complicated by the fact that clouds cover a large range of scales (from microphysics to mesoscale systems) and that they vary strongly in space and time. Observations by satellites may help to improve cloud parameterizations. Satellites observe the whole globe and cover approximately the same spatial and temporal scales as GCMs. We use the POLDER-1 instrument’s data which is suited to derive cloud particle properties as well as larger-scale quantities. The additional use of the geostationary instrument METEOSAT data helps us to overcome possible shortcomings due to the orbiting POLDER instrument’s coarser time-resolution and are useful as a second independent data source for observations. In the atmospheric GCM of the Laboratoire de Météorologie Dynamique (LMDZ), we test three different types of parameterizations: First, the standard cloud parameterizations as a ``control'' case. Secondly, we introduce a rather simple cloud microphysics scheme including liquid water microphysical processes as autoconversion and accretion and able to caluclate cloud droplet radius deduced from cloud liquid water and sulfate aerosol concentration. Finally, we implement a more sophisticated microphysics scheme which also takes into account ice-phase processes and activation of additional aerosol species. In the latter two parameterizations, we focus on some particular parameters to examine. To compare the model's results with the satellite data, we nudge the model to ECMWF reanalysis’ winds and temperature data to have the right meteorological conditions. Then we simulate the cloud properties seen by the orbiting POLDER instrument by sampling the satellite's swath path and calculating the cloud properties seen form the space using different cloud overlap assumptions. To compare the model to METEOSAT data, we calculate the Infrared brigthness temperature seen by METEOSAT using the model's radiation parameterization. We use statistical relationships in both model and observations data to evaluate the different parameterizations.
EGS - AGU - EUG Joint Assembly
- Pub Date:
- April 2003