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A New Statistically based Autoconversion rate Parameterization for use in Large-Scale ModelsThe autoconversion rate is a key process for the formation of precipitation in warm clouds. In climate models, physical processes such as autoconversion rate, which are calculated from grid mean values, are biased, because they do not take subgrid variability into account. Recently, statistical cloud schemes have been introduced in large-scale models to account for partially cloud-covered grid boxes. However, these schemes do not include the in-cloud variability in their parameterizations. In this paper, a new statistically based autoconversion rate considering the in-cloud variability is introduced and tested in three cases using the Canadian Single Column Model (SCM) of the global climate model. The results show that the new autoconversion rate improves the model simulation, especially in terms of liquid water path in all three case studies.
Document ID
20030063148
Acquisition Source
Langley Research Center
Document Type
Reprint (Version printed in journal)
Authors
Lin, Bing
(NASA Langley Research Center Hampton, VA, United States)
Zhang, Junhua
(Dalhousie Univ. Halifax, Nova Scotia, Canada)
Lohmann, Ulrike
(Dalhousie Univ. Halifax, Nova Scotia, Canada)
Date Acquired
August 21, 2013
Publication Date
January 1, 2002
Publication Information
Publication: Journal of Geophysical Research
Volume: 107
Issue: D24, 4750
Subject Category
Meteorology And Climatology
Distribution Limits
Public
Copyright
Public Use Permitted.
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