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Incorporating the subgrid-scale variability of clouds in the autoconversion parameterization using a PDF-scheme

MPS-Authors
http://pubman.mpdl.mpg.de/cone/persons/resource/persons37375

Weber,  T.
The Atmosphere in the Earth System, MPI for Meteorology, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons37298

Quaas,  J.
The Atmosphere in the Earth System, MPI for Meteorology, Max Planck Society;

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2012MS000156.pdf
(Publisher version), 446KB

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Citation

Weber, T., & Quaas, J. (2012). Incorporating the subgrid-scale variability of clouds in the autoconversion parameterization using a PDF-scheme. Journal of Advances in Modeling Earth Systems, 4: M11003. doi:10.1029/2012MS000156.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0010-505F-3
Abstract
An investigation of the impact of the subgrid-scale variability of cloud liquid water on the autoconversion process as parameterized in a general circulation model is presented in this paper. For this purpose, a prognostic statistical probability density distribution (PDF) of the subgrid scale variability of cloud water is incorporated in a continuous autoconversion parameterization. Thus, the revised autoconversion rate is calculated by an integral of the autoconversion equation over the PDF of total water mixing ratio from the saturation vapor mixing ratio to the maximum of total water mixing ratio. An evaluation of the new autoconversion parameterization is carried out by means of one year simulations with the ECHAM5 climate model. The results indicate that the new autoconversion scheme causes an increase of the frequency of occurrence of high autoconversion rates and a decrease of low ones compared to the original scheme. This expected result is due to the emphasis on areas of high cloud liquid water in the new approach, and the non-linearity of the autoconversion with respect to liquid water mixing ratio. A similar trend as in the autoconversion is observed in the accretion process resulting from the coupling of both processes. As a consequence of the altered autoconversion, large-scale surface precipitation also shows a shift of occurrence from lower to higher rates. The vertically integrated cloud liquid water estimated by the model shows slight improvements compared to satellite data. Most importantly, the artificial tuning factor for autoconversion in the continuous parameterization could be reduced by almost an order of magnitude using the revised parameterization.