2A.2 On the relationship between large scale dynamics and clouds: a decadal climatology of cloud properties for the U.S. Southern Great Plains

Monday, 24 January 2011: 1:45 PM
608 (Washington State Convention Center)
Stuart M. Evans, University of Washington, Seattle, WA; and R. T. Marchand and T. P. Ackerman

Improving cloud parameterizations in large scale models hinges on understanding the statistical connection between large scale dynamics and the cloud fields they produce. We use an atmospheric classification technique developed and applied by Marchand and coauthors (2009, J. Climate) to investigate the relationship between synoptic scale dynamic patterns and cloud properties. Our technique uses a neural net classifier acting on reanalysis data to identify atmospheric states and then uses independent cloud radar observations of vertical cloud occurrence to test the statistical significance of each state. Preliminary results using data centered on the U. S. Southern Great Plains (SGP) site operated by the Atmospheric Radiation Measurement Program identified a dozen meteorological states each producing statistically distinct cloud fields.

Here we extend our preliminary study using 13 years of ERA-Interim reanalysis data and associated vertically pointing millimeter wavelength cloud radar observations from the SGP site in Lamont, Oklahoma. This longer record allows us to achieve increased statistical confidence in our state classifications and associated cloud patters, as well as providing an extended analysis period. Given simultaneous time series of our state classification and other observables, we can determine the distribution of observables associated with each dynamical state. Observables include ground-based quantities such as cloud occurrence, precipitation, liquid water path, cloud optical depth and surface fluxes, and satellite quantities such as fractional cloud cover, top-of-atmosphere fluxes and cloud effect, and retrieved cloud properties. The multi-year record allows us to investigate both the seasonal and interannual variability of the dynamic patterns. In addition, we can examine the duration of particular patters (since we classify the atmosphere pattern every 6 hours) and the transition probability from any state to any other state.

Our analysis approach is, to the best or our knowledge, unique in the way it statistically links large-scale dynamics to cloud occurrence and, subsequently, other physical observables. It provides new opportunities for analysis of the behavior of the atmosphere. Our near term goal is to apply this technique to climate model output to determine to what extent climate models can duplicate this observed linkage between dynamical states and associated hydrological and radiative properties.

- Indicates paper has been withdrawn from meeting
- Indicates an Award Winner