Consideration of Initial Cloud Droplet Size Distribution Shapes in Quantifying Different Entrainment-Mixing Mechanisms
Abstract
Entrainment-mixing process significantly affects cloud micro/macrophysics and the evaluation of aerosol indirect effects. However, it remains an open question as to how to quantify entrainment-mixing mechanism for broad cloud droplet size distributions (CDSDs). Here, CDSDs with different spectral widths are used to initialize the Explicit Mixing Parcel Model to address this problem, and microphysical properties are compared for different CDSD widths. For relatively broad CDSDs, as the number concentration and liquid water content decrease, the volume-mean radius and mean radius increase, which is opposite to the scenario for relatively narrow CDSDs and the homogeneous/inhomogeneous conceptual model. For the relatively broad CDSDs, such relationships could be mistaken as inhomogeneous mixing with subsequent ascent in analysis of the conventional microphysical mixing diagram. This then causes traditional homogenous mixing degrees to be unrealistically negative and not applicable for relatively broad CDSDs. New measures are introduced to explicitly account for the effect of CDSD spectral shapes on quantifying entrainment-mixing mechanism. The new measures yield reasonable ranges of values that conform to the dynamical measures such as the Damköhler and transitional scale numbers, and their temporal variation can be explained physically. This study extends the measures of homogeneous mixing degrees from narrow to broad CDSDs,more »
- Authors:
-
- Nanjing University of Information Science & Technology (China)
- Brookhaven National Lab. (BNL), Upton, NY (United States)
- Chinese Academy of Meteorological Sciences, Beijing (China). State Key Laboratory of Severe Weather
- Suzhou Meteorological Bureau (China)
- Publication Date:
- Research Org.:
- Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Atmospheric Radiation Measurement (ARM) Data Center; Brookhaven National Lab. (BNL), Upton, NY (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Biological and Environmental Research (BER); National Key Research and Development Program of China; Second Tibetan Plateau Scientific Expedition and Research (STEP) Program; National Natural Science Foundation of China (NSFC); National Center of Meteorology; USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office
- Contributing Org.:
- Pacific Northwest National Laboratory (PNNL); Brookhaven National Laboratory (BNL); Argonne National Laboratory (ANL); Oak Ridge National Laboratory (ORNL)
- OSTI Identifier:
- 1787983
- Alternate Identifier(s):
- OSTI ID: 1798473
- Report Number(s):
- BNL-221644-2021-JAAM
Journal ID: ISSN 2169-897X
- Grant/Contract Number:
- SC0011270; 33504; SC0012704
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Journal of Geophysical Research: Atmospheres
- Additional Journal Information:
- Journal Volume: 126; Journal Issue: 13; Journal ID: ISSN 2169-897X
- Publisher:
- American Geophysical Union
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 54 ENVIRONMENTAL SCIENCES
Citation Formats
Luo, Shi, Lu, Chunsong, Liu, Yangang, Gao, Wenhua, Zhu, Lei, Xu, Xiaoqi, Li, Junjun, and Guo, Xiaohao. Consideration of Initial Cloud Droplet Size Distribution Shapes in Quantifying Different Entrainment-Mixing Mechanisms. United States: N. p., 2021.
Web. doi:10.1029/2020jd034455.
Luo, Shi, Lu, Chunsong, Liu, Yangang, Gao, Wenhua, Zhu, Lei, Xu, Xiaoqi, Li, Junjun, & Guo, Xiaohao. Consideration of Initial Cloud Droplet Size Distribution Shapes in Quantifying Different Entrainment-Mixing Mechanisms. United States. https://doi.org/10.1029/2020jd034455
Luo, Shi, Lu, Chunsong, Liu, Yangang, Gao, Wenhua, Zhu, Lei, Xu, Xiaoqi, Li, Junjun, and Guo, Xiaohao. Fri .
"Consideration of Initial Cloud Droplet Size Distribution Shapes in Quantifying Different Entrainment-Mixing Mechanisms". United States. https://doi.org/10.1029/2020jd034455. https://www.osti.gov/servlets/purl/1787983.
@article{osti_1787983,
title = {Consideration of Initial Cloud Droplet Size Distribution Shapes in Quantifying Different Entrainment-Mixing Mechanisms},
author = {Luo, Shi and Lu, Chunsong and Liu, Yangang and Gao, Wenhua and Zhu, Lei and Xu, Xiaoqi and Li, Junjun and Guo, Xiaohao},
abstractNote = {Entrainment-mixing process significantly affects cloud micro/macrophysics and the evaluation of aerosol indirect effects. However, it remains an open question as to how to quantify entrainment-mixing mechanism for broad cloud droplet size distributions (CDSDs). Here, CDSDs with different spectral widths are used to initialize the Explicit Mixing Parcel Model to address this problem, and microphysical properties are compared for different CDSD widths. For relatively broad CDSDs, as the number concentration and liquid water content decrease, the volume-mean radius and mean radius increase, which is opposite to the scenario for relatively narrow CDSDs and the homogeneous/inhomogeneous conceptual model. For the relatively broad CDSDs, such relationships could be mistaken as inhomogeneous mixing with subsequent ascent in analysis of the conventional microphysical mixing diagram. This then causes traditional homogenous mixing degrees to be unrealistically negative and not applicable for relatively broad CDSDs. New measures are introduced to explicitly account for the effect of CDSD spectral shapes on quantifying entrainment-mixing mechanism. The new measures yield reasonable ranges of values that conform to the dynamical measures such as the Damköhler and transitional scale numbers, and their temporal variation can be explained physically. This study extends the measures of homogeneous mixing degrees from narrow to broad CDSDs, and sheds new light on the consideration of CDSD shapes in parameterizations of entrainment-mixing mechanism.},
doi = {10.1029/2020jd034455},
journal = {Journal of Geophysical Research: Atmospheres},
number = 13,
volume = 126,
place = {United States},
year = {Fri Jun 11 00:00:00 EDT 2021},
month = {Fri Jun 11 00:00:00 EDT 2021}
}
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