PNNL

Abstract

In this paper an atmospheric classification scheme based on fields that are resolved by global climate models (and numerical weather prediction models) is investigated as a mechanism to map the large-scale (synoptic-scale) atmospheric state to distributions of local-scale cloud properties. Using a bootstrap resampling technique, the temporal stability and distinctness of vertical profiles of cloud occurrence (obtained from a vertically pointing millimeter wavelength cloud-radar) are analyzed as a function of the atmospheric state. A stable class-based map from the large-scale to local-scale cloud properties could be of great utility in the analysis of GCM-predicted cloud properties, by providing a physical context from which to understand any differences between the model output and observations, as well as to separate differences (in total distribution) that are caused by having different weather regimes (or synoptic scale activity) rather than problems in the representation of clouds for a particular regime. Furthermore, if sufficiently robust mappings can be established, it could form the basis of a statistical GCM cloud parameterization.