On the Sensitivity of Atmospheric Ensembles to Cloud Microphysics in Long-Term Cloud-Resolving Model Simulations

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  • ZENG Xiping
    Goddard Earth Sciences and Technology Center, University of Maryland at Baltimore County Laboratory for Atmospheres, NASA Goddard Space Flight Center
  • TAO Wei-Kuo
    Laboratory for Atmospheres, NASA Goddard Space Flight Center
  • LANG Stephen
    Laboratory for Atmospheres, NASA Goddard Space Flight Center Science Systems and Applications Inc.
  • Y. HOU Arthur
    Laboratory for Atmospheres, NASA Goddard Space Flight Center
  • ZHANG Minghua
    School of Marine and Atmospheric Sciences, Stony Brook University
  • SIMPSON Joanne
    Laboratory for Atmospheres, NASA Goddard Space Flight Center

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Month-long large-scale forcing data from two field campaigns are used to drive a cloud-resolving model (CRM) and produce ensemble simulations of clouds and precipitation. Observational data are then used to evaluate the model results. To improve the model results, a new parameterization of the Bergeron process is proposed that incorporates the number concentration of ice nuclei (IN). Numerical simulations reveal that atmospheric ensembles are sensitive to IN concentration and ice crystal multiplication.<br>Two- (2D) and three-dimensional (3D) simulations are carried out to address the sensitivity of atmospheric ensembles to model dimensionality. It is found that the ensembles with high IN concentration are more sensitive to dimensionality than those with low IN concentration. Both the analytic solutions of linear dry models and the CRM output show that there are more convective cores with stronger updrafts in 3D simulations than in 2D, which explains the differing sensitivity of the ensembles to dimensionality at different IN concentrations.

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