Establishing the True Nature of Cloud Overlap with ARM Data

Di Girolamo, L.(a), Astin, I.(b), and McFarquhar, G.M.(a), University of Illinois at Urbana-Champaign (a), University of Reading (b)
Fourteenth Atmospheric Radiation Measurement (ARM) Science Team Meeting

In recent years, many studies have stressed the importance of establishing the true nature of cloud overlap in order to improve the general circulation models used to produce climate forecasts. The long term meteorological data gathered by the DOE ARM program offers a unique dataset needed for cloud overlap studies. However, the current difficulty in determining the true nature of cloud overlap from ARM data is the lack of a proper statistical approach. We will demonstrate the current limitations used thus far in determining cloud overlap from observations, and point to a new statistical approach to derive the true nature of cloud overlap. This new approach is completely general, allowing for significance testing to be performed, and includes the horizontal and vertical resolutions of the climate model as dependent variables for which cloud overlap must depend on. We will also discuss how the new cloud overlap formulation can be included in climate models.

Note: This is the poster abstract presented at the meeting; an extended version was not provided by the author(s).