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Title: Influences of an entrainment–mixing parameterization on numerical simulations of cumulus and stratocumulus clouds

Abstract

Different entrainment–mixing processes can occur in clouds; however, a homogeneous mixing mechanism is often implicitly assumed in most commonly used microphysics schemes. Here, we first present a new entrainment–mixing parameterization that uses the grid mean relative humidity without requiring the relative humidity of the entrained air. Then, the parameterization is implemented in a microphysics scheme in a large eddy simulation model, and sensitivity experiments are conducted to compare the new parameterization with the default homogeneous entrainment–mixing parameterization. The results indicate that the new entrainment–mixing parameterization has a larger impact on the number concentration, volume mean radius, and cloud optical depth in the stratocumulus case than in the cumulus case. This is because inhomogeneous and homogeneous mixing mechanisms dominate in the stratocumulus and cumulus cases, respectively, which is mainly due to the larger turbulence dissipation rate in the cumulus case. Because stratocumulus clouds break up during the dissipation stage to form cumulus clouds, the effects of this new entrainment–mixing parameterization during the stratocumulus dissipation stage are between those during the stratocumulus mature stage and the cumulus case. A large aerosol concentration can enhance the effects of this new entrainment–mixing parameterization by decreasing the cloud droplet size and evaporation timescale. The resultsmore » of this new entrainment–mixing parameterization with grid mean relative humidity are validated by the use of a different entrainment–mixing parameterization that uses parameterized entrained air properties. This study sheds new light on the improvement of entrainment–mixing parameterizations in models.« less

Authors:
ORCiD logo; ; ORCiD logo; ; ; ; ;
Publication Date:
Research Org.:
Brookhaven National Laboratory (BNL), Upton, NY (United States); Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Atmospheric Radiation Measurement (ARM) Data Center
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER); USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office; National Natural Science Foundation of China (NSFC)
Contributing Org.:
Pacific Northwest National Laboratory (PNNL); Argonne National Laboratory (ANL)
OSTI Identifier:
1864910
Alternate Identifier(s):
OSTI ID: 1863096; OSTI ID: 1866757
Report Number(s):
BNL-222907-2022-JAAM
Journal ID: ISSN 1680-7324
Grant/Contract Number:  
33504; SC00112704; SC0012704; SC0112704; 41822504; 42175099; 42027804; 41975181; 42075073
Resource Type:
Published Article
Journal Name:
Atmospheric Chemistry and Physics (Online)
Additional Journal Information:
Journal Name: Atmospheric Chemistry and Physics (Online) Journal Volume: 22 Journal Issue: 8; Journal ID: ISSN 1680-7324
Publisher:
Copernicus GmbH
Country of Publication:
Germany
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES

Citation Formats

Xu, Xiaoqi, Lu, Chunsong, Liu, Yangang, Luo, Shi, Zhou, Xin, Endo, Satoshi, Zhu, Lei, and Wang, Yuan. Influences of an entrainment–mixing parameterization on numerical simulations of cumulus and stratocumulus clouds. Germany: N. p., 2022. Web. doi:10.5194/acp-22-5459-2022.
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Xu, Xiaoqi, Lu, Chunsong, Liu, Yangang, Luo, Shi, Zhou, Xin, Endo, Satoshi, Zhu, Lei, & Wang, Yuan. Influences of an entrainment–mixing parameterization on numerical simulations of cumulus and stratocumulus clouds. Germany. https://doi.org/10.5194/acp-22-5459-2022
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Xu, Xiaoqi, Lu, Chunsong, Liu, Yangang, Luo, Shi, Zhou, Xin, Endo, Satoshi, Zhu, Lei, and Wang, Yuan. Mon . "Influences of an entrainment–mixing parameterization on numerical simulations of cumulus and stratocumulus clouds". Germany. https://doi.org/10.5194/acp-22-5459-2022.
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@article{osti_1864910,
title = {Influences of an entrainment–mixing parameterization on numerical simulations of cumulus and stratocumulus clouds},
author = {Xu, Xiaoqi and Lu, Chunsong and Liu, Yangang and Luo, Shi and Zhou, Xin and Endo, Satoshi and Zhu, Lei and Wang, Yuan},
abstractNote = {Different entrainment–mixing processes can occur in clouds; however, a homogeneous mixing mechanism is often implicitly assumed in most commonly used microphysics schemes. Here, we first present a new entrainment–mixing parameterization that uses the grid mean relative humidity without requiring the relative humidity of the entrained air. Then, the parameterization is implemented in a microphysics scheme in a large eddy simulation model, and sensitivity experiments are conducted to compare the new parameterization with the default homogeneous entrainment–mixing parameterization. The results indicate that the new entrainment–mixing parameterization has a larger impact on the number concentration, volume mean radius, and cloud optical depth in the stratocumulus case than in the cumulus case. This is because inhomogeneous and homogeneous mixing mechanisms dominate in the stratocumulus and cumulus cases, respectively, which is mainly due to the larger turbulence dissipation rate in the cumulus case. Because stratocumulus clouds break up during the dissipation stage to form cumulus clouds, the effects of this new entrainment–mixing parameterization during the stratocumulus dissipation stage are between those during the stratocumulus mature stage and the cumulus case. A large aerosol concentration can enhance the effects of this new entrainment–mixing parameterization by decreasing the cloud droplet size and evaporation timescale. The results of this new entrainment–mixing parameterization with grid mean relative humidity are validated by the use of a different entrainment–mixing parameterization that uses parameterized entrained air properties. This study sheds new light on the improvement of entrainment–mixing parameterizations in models.},
doi = {10.5194/acp-22-5459-2022},
journal = {Atmospheric Chemistry and Physics (Online)},
number = 8,
volume = 22,
place = {Germany},
year = {Mon Apr 25 00:00:00 EDT 2022},
month = {Mon Apr 25 00:00:00 EDT 2022}
}
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