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Deconstructing the precipitation susceptibility construct: Improving methodology for aerosol‐cloud precipitation studies
Author(s) -
Sorooshian Armin,
Feingold Graham,
Lebsock Matthew D.,
Jiang Hongli,
Stephens Graeme L.
Publication year - 2010
Publication title -
journal of geophysical research: atmospheres
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.67
H-Index - 298
eISSN - 2156-2202
pISSN - 0148-0227
DOI - 10.1029/2009jd013426
Subject(s) - aerosol , precipitation , environmental science , cloud computing , construct (python library) , atmospheric sciences , meteorology , climatology , metric (unit) , computer science , geology , geography , operating system , operations management , economics , programming language
It is generally thought that an increase in aerosol particles suppresses precipitation in warm clouds. The nature and magnitude of this effect are highly uncertain owing to numerous microphysical and macrophysical processes that influence clouds over a wide range of spatial and temporal scales. This work addresses the need to improve the evidence for and quantification of aerosol effects on precipitation by using observational data. Previous work introduced the concept of precipitation susceptibility as a metric for changes in precipitation that result from aerosol perturbations. Motivated by the difficulty in obtaining statistically significant aerosol measurements in the vicinity of clouds, this study explores breaking up the precipitation susceptibility construct into separate components: an aerosol‐cloud interaction component and a cloud‐precipitation component. These are used to quantify precipitation susceptibility, while also accounting for meteorological factors that could obfuscate the response of clouds to aerosol perturbations. The utility of this technique is demonstrated using a diverse set of tools, including data from NASA's A‐Train constellation of satellites, aircraft measurements, and models of various complexities. Employing this method results in increased confidence in causal relationships between aerosol perturbations and precipitation.

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