Editorial Type:
Article
Article Type:
Research Article

Effect of Aerosol on the Susceptibility and Efficiency of Precipitation in Warm Trade Cumulus Clouds

Hongli Jiang Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, and NOAA/Earth System Research Laboratory, Boulder, Colorado

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Graham Feingold NOAA/Earth System Research Laboratory, Boulder, Colorado

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Armin Sorooshian Department of Chemical and Environmental Engineering, University of Arizona, Tucson, Arizona

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Print Publication:
01 Nov 2010
DOI:
https://doi.org/10.1175/2010JAS3484.1
Page(s):
3525–3540
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Abstract

Large-eddy simulations of warm, trade wind cumulus clouds are conducted for a range of aerosol conditions with a focus on precipitating clouds. Individual clouds are tracked over the course of their lifetimes. Precipitation rate decreases progressively as aerosol increases. For larger, precipitating clouds, the polluted clouds have longer lifetimes because of precipitation suppression. For clean aerosol conditions, there is good agreement between the average model precipitation rate and that calculated based on observed radar reflectivity Z and precipitation rate R relationships. Precipitation rate can be expressed as a power-law function of liquid water path (LWP) and Nd, to reasonable accuracy. The respective powers for LWP and Nd are of similar magnitude compared to those based on observational studies of stratocumulus clouds. The time-integrated precipitation rate represented by a power-law function of LWP, Nd, and cloud lifetime is much more reliably predicted than is R expressed in terms of LWP and Nd alone. The precipitation susceptibility (So = −dlnR/dlnNd) that quantifies the sensitivity of precipitation to changes in Nd depends strongly on LWP and exhibits nonmonotonic behavior with a maximum at intermediate LWP values. The relationship between So and precipitation efficiency is explored and the importance of including dependence on Nd in the latter is highlighted. The results provide trade cumulus cloud population statistics, as well as relationships between microphysical/macrophysical properties and precipitation, that are amenable for use in larger-scale models.

Corresponding author address: Hongli Jiang, NOAA/Earth System Research Laboratory, 325 Broadway, Boulder, CO 80305. Email: hongli.jiang@noaa.gov

Abstract

Large-eddy simulations of warm, trade wind cumulus clouds are conducted for a range of aerosol conditions with a focus on precipitating clouds. Individual clouds are tracked over the course of their lifetimes. Precipitation rate decreases progressively as aerosol increases. For larger, precipitating clouds, the polluted clouds have longer lifetimes because of precipitation suppression. For clean aerosol conditions, there is good agreement between the average model precipitation rate and that calculated based on observed radar reflectivity Z and precipitation rate R relationships. Precipitation rate can be expressed as a power-law function of liquid water path (LWP) and Nd, to reasonable accuracy. The respective powers for LWP and Nd are of similar magnitude compared to those based on observational studies of stratocumulus clouds. The time-integrated precipitation rate represented by a power-law function of LWP, Nd, and cloud lifetime is much more reliably predicted than is R expressed in terms of LWP and Nd alone. The precipitation susceptibility (So = −dlnR/dlnNd) that quantifies the sensitivity of precipitation to changes in Nd depends strongly on LWP and exhibits nonmonotonic behavior with a maximum at intermediate LWP values. The relationship between So and precipitation efficiency is explored and the importance of including dependence on Nd in the latter is highlighted. The results provide trade cumulus cloud population statistics, as well as relationships between microphysical/macrophysical properties and precipitation, that are amenable for use in larger-scale models.

Corresponding author address: Hongli Jiang, NOAA/Earth System Research Laboratory, 325 Broadway, Boulder, CO 80305. Email: hongli.jiang@noaa.gov

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