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A Parameterization of the Statistical Moments of Total Water for Large-Scale Models

Klein, S.A. (a), Tompkins, A. (b), and Pincus, R. (c), NOAA/GFDL (a), ECMWF (b), NOAA-CIRES Climate Diagnostics Center (c)
Eleventh Atmospheric Radiation Measurement (ARM) Science Team Meeting

The physical realism of cloud simulations in large scale models is severely compromised by the unphysical tuning of model parameters. Tuning is required so that simulations of the current climate are realistic, but this implies that con- fidence in the potentially large cloud feedbacks to climate change must remain quite low. Remedying this situation will require, at least, a more accurate and explicit assessment of the effects of unresolved non-linear processes. We describe a new statistically-based parameterization based on the work of Adrian Tompkins (ECMWF). In this formulation the amount of total water (including vapor, cloud liquid, and ice) is assumed to follow a specified probability distribution function, and prognostic variables are carried for the mean, variance, and skewness of the distribution. Cloud fraction and the amount of condensed water are di- agnosed from the distribution and the cell-mean thermo- dynamic properties. Sources and sinks of the distribution moments are tied directly to the processes that influence them, including convection, turbulence, and microphysics. We test the new parameterization against cloud resolving model simulations of the July 1997 ARM Intensive Observing Period.

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