On Which Microphysical Time Scales to Use in Studies of Entrainment-Mixing Mechanisms in Clouds

Chunsong Lu, Yangang Liu, Bin Zhu, Seong Soo Yum, Steven K. Krueger, Yujun Qiu, Shengjie Niu, Shi Luo

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9 Citations (Scopus)

Abstract

The commonly used time scales in entrainment-mixing studies are examined to seek the most appropriate one, based on aircraft observations of cumulus clouds from the RACORO campaign and numerical simulations with the Explicit Mixing Parcel Model. The time scales include the following: τevap, the time for droplet complete evaporation; τphase, the time for saturation ratio deficit (S) to reach 1/e of its initial value; τsatu, the time for S to reach −0.5%; and τreact, the time for complete droplet evaporation or S to reach −0.5%. It is found that the proper time scale to use depends on the specific objectives of entrainment-mixing studies. First, if the focus is on the variations of liquid water content (LWC) and S, then τreact for saturation, τsatu and τphase are almost equivalently appropriate, because they all represent the rate of dry air reaching saturation or of LWC decrease. Second, if one focuses on the variations of droplet size and number concentration, τreact for complete evaporation and τevap are proper because they characterize how fast droplets evaporate and whether number concentration decreases. Moreover, τreact for complete evaporation and τevap are always positively correlated with homogeneous mixing degree (ψ); thus, the two time scales, especially τevap, are recommended for developing parameterizations. However, ψ and the other time scales can be negatively, positively, or not correlated, depending on the dominant factors of the entrained air (i.e., relative humidity or aerosols). Third, all time scales are proportional to each other under certain microphysical and thermodynamic conditions.

Original languageEnglish
Pages (from-to)3740-3756
Number of pages17
JournalJournal of Geophysical Research: Atmospheres
Volume123
Issue number7
DOIs
Publication statusPublished - 2018 Apr 16

Bibliographical note

Funding Information:
This research is supported by the National Key Research and Development Program of China (2017YFA0604000), the National Natural Science Foundation of China (NSFC) (91537108), the China Meteorological Administration Special Public Welfare Research Fund (GYHY201406001), the Natural Science Foundation of Jiangsu Province, China (BK20160041), NSFC(41475035 and 41305120), the Six Talent Peak Project in Jiangsu, China (2015-JY-011), and the 333 High-level Talents Training Project in Jiangsu (BRA2016424). Liu is supported by the U.S. Department of Energy’s BER Atmospheric System Research (ASR) Program (DE-SC00112704). Yum is sup ported by KMA Research and Development Program under grant KMIPA2015-2061. We appreciate the helpful discussions with Dr. Zefeng Zhang in NUIST. The RACORO data are available from https://www.arm.gov/. The model results are available from https://pan.baidu.com/s/1ggWkkd9.

Funding Information:
1Key Laboratory of Meteorological Disaster, Ministry of Education (KLME)/Joint International Research Laboratory of Climate and Environment Change (ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD)/Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, Nanjing University of Information Science and Technology, Nanjing, China, 2Ministry of Education Key Laboratory for Earth System Modeling and Department for Earth System Science, Tsinghua University, Beijing, China, 3Environmental and Climate Sciences Department, Brookhaven National Laboratory, Upton, NY, USA, 4Department of Atmospheric Sciences, Yonsei University, Seoul, South Korea, 5Department of Atmospheric Sciences, University of Utah, Salt Lake City, UT, USA

Publisher Copyright:
©2018. American Geophysical Union. All Rights Reserved.

All Science Journal Classification (ASJC) codes

  • Atmospheric Science
  • Geophysics
  • Earth and Planetary Sciences (miscellaneous)
  • Space and Planetary Science

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