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Title: A unified parameterization of clouds and turbulence using CLUBB and subcolumns in the Community Atmosphere Model

Journal Article · · Geoscientific Model Development Discussions (Online)
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  1. National Center for Atmospheric Research, Boulder, CO (United States); Univ. of Wisconsin-Milwaukee, Milwaukee, WI (United States)
  2. National Center for Atmospheric Research, Boulder, CO (United States)
  3. Univ. of Wisconsin-Milwaukee, Milwaukee, WI (United States)
  4. Univ. of Washington, Seattle, WA (United States)
  5. Nanjing Univ., Nanjing (China); Collaborative Innovation Center of Climate Change, Jiangsu Province (China); Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
  6. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
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Most global climate models parameterize separate cloud types using separate parameterizations. This approach has several disadvantages, including obscure interactions between parameterizations and inaccurate triggering of cumulus parameterizations. Alternatively, a unified cloud parameterization uses one equation set to represent all cloud types. Such cloud types include stratiform liquid and ice cloud, shallow convective cloud, and deep convective cloud. Vital to the success of a unified parameterization is a general interface between clouds and microphysics. One such interface involves drawing Monte Carlo samples of subgrid variability of temperature, water vapor, cloud liquid, and cloud ice, and feeding the sample points into a microphysics scheme.This study evaluates a unified cloud parameterization and a Monte Carlo microphysics interface that has been implemented in the Community Atmosphere Model (CAM) version 5.3. Results describing the mean climate and tropical variability from global simulations are presented. The new model shows a degradation in precipitation skill but improvements in short-wave cloud forcing, liquid water path, long-wave cloud forcing, precipitable water, and tropical wave simulation. Also presented are estimations of computational expense and investigation of sensitivity to number of subcolumns.

Research Organization:
Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
Sponsoring Organization:
USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR). Scientific Discovery through Advanced Computing (SciDAC)
Grant/Contract Number:
SC0008668; SC0008323; AGS-0968640; AGS-0968657; AC06-76RL01830
OSTI ID:
1213586
Journal Information:
Geoscientific Model Development Discussions (Online), Vol. 8, Issue 6; ISSN 1991-962X
Publisher:
European Geosciences UnionCopyright Statement
Country of Publication:
United States
Language:
English

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