The role of turbulent fluctuations in aerosol activation and cloud formation

Prabhakaran, Prasanth; Shawon, Abu Sayeed; Kinney, Gregory; Thomas, Subin; Cantrell, Will; Shaw, Raymond A.

doi:10.1073/pnas.2006426117

Cited by 49 publications

(60 citation statements)

References 41 publications

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“…As argued in Liu et al (1995), Chandrakar et al (2020), and Wu and McFarquhar (2018), the shape of CDSD is critical for understanding cloud processes. In fact, the CDSDs in adiabatic clouds are not necessarily narrow as they can be broadened due to processes such as turbulent fluctuation and/or giant cloud condensation nuclei (Chandrakar et al, 2016;Desai et al, 2018;Feingold et al, 1999;Johnson, 1982;Lu, Liu, Niu, & Xue, 2018;McGraw & Liu, 2006;Prabhakaran et al, 2020;Yin et al, 2000). The measurements from cloud-penetrating airplane observations indicate that the CDSDs are usually broad (Gerber et al, 2008;Lu, Liu, Niu, & Xue, 2018;Small & Chuang, 2008;Yum et al, 2015).…”

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confidence: 99%

Consideration of Initial Cloud Droplet Size Distribution Shapes in Quantifying Different Entrainment‐Mixing Mechanisms

Luo

Liu

et al. 2021

JGR Atmospheres

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Entrainment‐mixing process significantly affects cloud micro/macrophysics and the evaluation of aerosol indirect effects. However, it is still 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.

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confidence: 99%

Consideration of Initial Cloud Droplet Size Distribution Shapes in Quantifying Different Entrainment‐Mixing Mechanisms

Luo

Liu

et al. 2021

JGR Atmospheres

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“…(2009) (see also Chen et al., 2016, 2018) and those introduced by Prabhakaran et al. (2020) and explored more completely here. In Section 4.2, these two perspectives were introduced in the context of our experiments.…”

Section: Implications For the Atmosphere And Summarymentioning

confidence: 77%

“…(2009) considered polydisperse particles exposed to a single peak supersaturation in a parcel (vertical lines in the figure) while in Prabhakaran et al. (2020) and here we have examined particles of a single size and composition (vertical lines in the figure) which are exposed to a distribution of saturation ratios in the environment.…”

Section: Implications For the Atmosphere And Summarymentioning

confidence: 99%

“…Note that the abscissas for panels a and b are the aerosol critical saturation ratio, c S , while in a' and b', the abscissas are the saturation ratio in the environment, env S . The variable considered changes because Reutter et al (2009) considered polydisperse particles exposed to a single peak supersaturation in a parcel (vertical lines in the figure) while in Prabhakaran et al (2020) and here we have examined particles of a single size and composition (vertical lines in the figure) which are exposed to a distribution of saturation ratios in the environment.…”

Section: 1029/2020jd033799mentioning

confidence: 99%

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Dependence of Aerosol‐Droplet Partitioning on Turbulence in a Laboratory Cloud

et al. 2021

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Activation is the first step in aerosol‐cloud interactions, which have been identified as one of the principal uncertainties in Earth's climate system. Aerosol particles become cloud droplets, or activate, when the ambient saturation ratio exceeds a threshold, which depends on the particle's size and hygroscopicity. In the traditional formulation of the process, only average, uniform saturation ratios are considered. However, turbulent environments like clouds intrinsically have fluctuations around mean values in the scalar fields of temperature and water vapor concentration, which determine the saturation ratio. Through laboratory measurements, we show that these fluctuations are an important parameter that needs to be addressed to fully describe activation. Our results show, even for single‐sized, chemically homogeneous aerosols, that fluctuations blur the correspondence between activation and a particle's size and chemical composition, that turbulence can increase the fraction of aerosol particles which activate, and that the activated fraction decreases monotonically as the concentration of aerosol increases. Taken together, our data demonstrate that fluctuations can have effects equivalent to the aerosol limited and updraft limited regimes, known from adiabatic parcel theory.

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“…However, in the atmosphere it is difficult to quantify the fluctuations in S. (See Siebert and Shaw (2017) for one example.) Our goal, in a laboratory setting, is to quantify the effects of fluctuations in S on activation and the drop size distribution (Chandrakar et al, 2016(Chandrakar et al, , 2017(Chandrakar et al, , 2020bPrabhakaran et al, 2020). (Laboratory investigations of the effect of turbulence on collision-coalescence would likely require facilities with greater vertical extents than are currently available (Shaw et al, 2020).)…”

Section: Introductionmentioning

confidence: 99%

Effects of the Large-Scale Circulation on Temperature and Water Vapor Distributions in the Π Chamber

Anderson¹,

Thomas²,

Prabhakaran³

et al. 2021

Preprint

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Abstract. Microphysical processes are important for the development of clouds and thus Earth's climate. For example, turbulent fluctuations in the water vapor concentration, r, and temperature, T, cause fluctuations in the saturation ratio, S. Because S is the driving factor in the condensational growth of droplets, fluctuations may broaden the cloud droplet size distribution due to individual droplets experiencing different growth rates. The small scale turbulent fluctuations in the atmosphere that are relevant to cloud droplets are difficult to quantify through field measurements. We investigate these processes in the laboratory, using Michigan Tech's Π Chamber. The Π Chamber utilizes Rayleigh-Benard convection (RBC) to create the turbulent conditions inherent in clouds. In RBC it is common for a large scale circulation (LSC) to form. As a consequence of the LSC, the temperature field of the chamber is not spatially uniform. In this paper, we characterize the LSC in the Π chamber and show how it affects the shape of the distributions of r, T and S. The LSC was found to follow a single roll with an updraft and downdraft along opposing walls of the chamber. Near the updraft (downdraft), the distributions of T and r were positively (negatively) skewed. S consistently had a negatively skewed distribution, with the downdraft being the most negative.

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The role of turbulent fluctuations in aerosol activation and cloud formation

Cited by 49 publications

References 41 publications

Consideration of Initial Cloud Droplet Size Distribution Shapes in Quantifying Different Entrainment‐Mixing Mechanisms

Consideration of Initial Cloud Droplet Size Distribution Shapes in Quantifying Different Entrainment‐Mixing Mechanisms

Dependence of Aerosol‐Droplet Partitioning on Turbulence in a Laboratory Cloud

Effects of the Large-Scale Circulation on Temperature and Water Vapor Distributions in the Π Chamber

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