2.5 Evaluation and Improvement of a statistical Cloud Parametrization in ECHAM Using Ground-based Remote Sensing Observations

Monday, 28 June 2010: 12:00 PM
Cascade Ballroom (DoubleTree by Hilton Portland)
Verena Grützun, Max Plack Institute for Meteorology, Hamburg, Germany; and J. Quaas and F. Ament

Cloud feedbacks are appreciated to be one of the reasons for the large scatter of Global Climate Model (GCM) simulations (Randall et al., 2007). A major challenge is the representation of the variability and heterogeneity of clouds in the comparably large grid boxes of GCMs. While simple approaches use uniform distributions of the total water content with arbitrary distribution widths (Le Treut and Li, 1991), more sophisticated schemes employ statistical distributions and predict higher order distribution moments. These schemes offer more detailed information about the distribution of water in the grid box, which can be used for calculation of precipitation formation, radiative properties, and the fractional cloud cover. One way to reduce the uncertainties in the representation of clouds and their feedback to the climate is a thorough evaluation of the respective cloud process parametrizations in GCMs. We chose the promising statistical cloud scheme by Tompkins (2002), which is already implemented in ECHAM6 (Roeckner, 2003), for a detailed evaluation with ground based remote sensing data. The scheme has been developed at the hand of cloud resolving simulations and evaluated with satellite data. However, satellite data unfortunately lack important information such as accurate water vapor retrievals, as well as high vertical resolution. The combination of measurements from lidar, radar and radiometers, e.g. using the Integrated Profiling Technique (IPT, Löhnert et al., 2004), yields a rich and comprehensive data set for model evaluation: it is possible to retrieve accurate vertical profiles of humidity, liquid water, temperature, and also the hydrometeor distribution within the clouds along with the corresponding uncertainty in the data.

We will make use of long-term ground-based remote sensing data by University of Hamburg and by the Richard-Aßmann-Observatory of the Deutscher Wetterdienst (DWD) to evaluate and improve the statistical cloud scheme by Tompkins (2002) in ECHAM6. Methods are developed to enable the comparison of column based real data with grid box based distribution moments in the model. Depending on the results, the scheme will be further developed such that variance and skewness are simulated realistically and can be further used for cloud microphysical processes like precipitation formation or in the radiation calculations. For first time, the evaluation of a statistical scheme is tackled by detailed high resolution ground based remote sensing data from long term measurements, which will offer much insight into the relevant processes and possibly lead to a further advancement of statistical cloud schemes.

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