Extended Abstract (136K)4.24
Statistical Analyses of Satellite Cloud Object Data to Study Climate Sensitivities
Kuan-Man Xu, NASA/LRC, Hampton, VA; and B. A. Wielicki and T. Wong
Recently, Xu et al. (2004; submitted to J. Climate) presented an objective classification methodology which uses Earth Observing System (EOS) satellite data to classify distinct "cloud objects" defined by cloud-system types, sizes, geographic locations, and matched large-scale environments. This analysis method identifies a cloud object as a contiguous region of the Earth with a single dominant cloud-system type. It determines the shape and size of the cloud object from the satellite data and the cloud-system selection criteria. The statistical properties of the identified cloud objects are analyzed in terms of probability density functions (PDFs) based upon the footprint information. The grand mean PDFs of cloud microphysical, macrophysical and optical properties and radiative fluxes can be used to study climate sensitivities. This approach offers two advantages: it reduces cloud variability by grouping data from the same cloud-system type and it reduces sampling noises by combining results from a wide range of geographic regions.
This study will present a statistical validation of the fixed anvil temperature hypothesis of Hartmann and Larson (2002), who proposed that the emission temperature of anvil clouds remains unchanged during climate change. We use the EOS (Earth Observing System) data from January to August 1998. We have found that the PDFs of the outgoing longwave radiation fluxes are very similar from one month to another while the cloud top heights are higher during those months with higher sea surface temperatures. Other cloud optical and microphysical properties are rather similar during this eight month period. Therefore, this hypothesis is basically valid. We will perform further analysis of the satellite footprint information by examining the joint PDFs between two variables to better understand the physical insight into this hypothesis. New results will be presented at the meeting.
Session 4, Observed Seasonal to Interannual Climate Variability (parallel with Sessions 3 and 5)
Tuesday, 11 January 2005, 8:30 AM-5:30 PM
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