Aerosols-cloud microphysics-thermodynamics-turbulence: evaluating supersaturation in a marine stratocumulus cloud

Ditas, Florian; Shaw, Raymond A.; Siebert, Holger; Simmel, Martin; Wehner, B.; Wiedensohler, A.

doi:10.5194/acp-12-2459-2012

Cited by 66 publications

(74 citation statements)

References 22 publications

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“…The two measurement locations discussed here are interesting with regard to the ratio of presumed cloud droplet number concentration (CDNC) to the total aerosol particle number concentration. It has been reported that, although under the clean and convective conditions ambient S c may reach as high as 1.0 %, in the polluted boundary layer S c usually remains below 0.3 % (Ditas et al, 2012;Hammer et al, 2014;Hudson and Noble, 2014). If one assumes this value, a comparatively small fraction of aerosol in northern Finland and central Netherlands would potentially activate into cloud droplets if exposed to this S c .…”

Section: Ccn Concentrationsmentioning

confidence: 99%

A synthesis of cloud condensation nuclei counter (CCNC) measurements within the EUCAARI network

et al. 2015

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Published by Copernicus Publications on behalf of the European Geosciences Union. M. Paramonov et al.: A synthesis of CCNC measurements within the EUCAARI networkAbstract. Cloud condensation nuclei counter (CCNC) measurements performed at 14 locations around the world within the European Integrated project on Aerosol Cloud Climate and Air Quality interactions (EUCAARI) framework have been analysed and discussed with respect to the cloud condensation nuclei (CCN) activation and hygroscopic properties of the atmospheric aerosol. The annual mean ratio of activated cloud condensation nuclei (N CCN ) to the total number concentration of particles (N CN ), known as the activated fraction A, shows a similar functional dependence on supersaturation S at many locations -exceptions to this being certain marine locations, a free troposphere site and background sites in south-west Germany and northern Finland. The use of total number concentration of particles above 50 and 100 nm diameter when calculating the activated fractions (A 50 and A 100 , respectively) renders a much more stable dependence of A on S; A 50 and A 100 also reveal the effect of the size distribution on CCN activation. With respect to chemical composition, it was found that the hygroscopicity of aerosol particles as a function of size differs among locations. The hygroscopicity parameter κ decreased with an increasing size at a continental site in south-west Germany and fluctuated without any particular size dependence across the observed size range in the remote tropical North Atlantic and rural central Hungary. At all other locations κ increased with size. In fact, in Hyytiälä, Vavihill, Jungfraujoch and Pallas the difference in hygroscopicity between Aitken and accumulation mode aerosol was statistically significant at the 5 % significance level. In a boreal environment the assumption of a size-independent κ can lead to a potentially substantial overestimation of N CCN at S levels above 0.6 %. The same is true for other locations where κ was found to increase with size. While detailed information about aerosol hygroscopicity can significantly improve the prediction of N CCN , total aerosol number concentration and aerosol size distribution remain more important parameters. The seasonal and diurnal patterns of CCN activation and hygroscopic properties vary among three long-term locations, highlighting the spatial and temporal variability of potential aerosol-cloud interactions in various environments.

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Section: Ccn Concentrationsmentioning

confidence: 99%

A synthesis of cloud condensation nuclei counter (CCNC) measurements within the EUCAARI network

et al. 2015

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“…Another method to derive the supersaturation of a cloud was used by, for example, Hammer et al (2014), Ditas et al (2012), Asmi et al (2012), and Anttila et al (2009). In these studies the fraction of activated particles in a cloud was deduced from the comparison of the number size distribution of interstitial particles (i.e., particles not taken up into cloud droplets) and total aerosol particles (i.e., cloud residuals plus interstitial particles).…”

Section: L Krüger Et Al: Assessment Of Cloud Supersaturationmentioning

confidence: 99%

“…A common approach to derive an effective average peak supersaturation at which ambient clouds are formed is to compare the particle number or CN size distributions of total and interstitial aerosol (e.g., Anttila et al, 2009; al., 2012; Ditas et al, 2012;Hammer et al, 2014). We also used this method, which will be referred to as the "SMPS method", to calculate S avg from our aerosol size distribution measurement results.…”

Section: Average Peak Supersaturation Based On 50 % Activation S Avgmentioning

confidence: 99%

Assessment of cloud supersaturation by size-resolved aerosol particle and cloud condensation nuclei (CCN) measurements

et al. 2014

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Abstract. In this study we show how size-resolved measurements of aerosol particles and cloud condensation nuclei (CCN) can be used to characterize the supersaturation of water vapor in a cloud. The method was developed and applied during the ACRIDICON-Zugspitze campaign (17 September to 4 October 2012) at the high-Alpine research station Schneefernerhaus (German Alps, 2650 m a.s.l.). Number size distributions of total and interstitial aerosol particles were measured with a scanning mobility particle sizer (SMPS), and size-resolved CCN efficiency spectra were recorded with a CCN counter system operated at different supersaturation levels.During the evolution of a cloud, aerosol particles are exposed to different supersaturation levels. We outline and compare different estimates for the lower and upper bounds (S low , S high ) and the average value (S avg ) of peak supersaturation encountered by the particles in the cloud. A major advantage of the derivation of S low and S avg from size-resolved CCN efficiency spectra is that it does not require the specific knowledge or assumptions about aerosol hygroscopicity that are needed to derive estimates of S low , S high , and S avg from aerosol size distribution data. For the investigated cloud event, we derived S low ≈ 0.07-0.25 %, S high ≈ 0.86-1.31 % and S avg ≈ 0.42-0.68 %.

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“…These values are chosen based on observations that updrafts in stratocumulus clouds are in the order of 0.1 m s −1 and in cumulus clouds are in the order of 1.0 m s −1 (Ditas et al, 2012;Katzwinkel et al, 2014). Results also show that they correspond to the aerosollimited regime (the 1.0 m s −1 case leads to a w/n ratio of 10 −3 m s −1 cm 3 ) and the transitional regime (the 0.1 m s −1 case leads to a w/n ratio of 5 × 10 −4 m s −1 cm 3 ).…”

Section: Effect Of Vertical Velocitymentioning

confidence: 99%

“…Turbulence induces vertical oscillations of air parcels and causes fluctuations in temperature, water vapor concentration and supersaturation (e.g., Ditas et al, 2012;Hammer et al, 2015). The effects of supersaturation fluctuations on droplet condensational growth in turbulent environments have been studied for several decades (e.g., Cooper, 1989;Khvorostyanov and Curry, 1999).…”

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confidence: 99%

Cloud droplet size distribution broadening during diffusional growth: ripening amplified by deactivation and reactivation

et al. 2018

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Abstract. Cloud droplet size distributions (CDSDs), which are related to cloud albedo and rain formation, are usually broader in warm clouds than predicted from adiabatic parcel calculations. We investigate a mechanism for the CDSD broadening using a moving-size-grid cloud parcel model that considers the condensational growth of cloud droplets formed on polydisperse, submicrometer aerosols in an adiabatic cloud parcel that undergoes vertical oscillations, such as those due to cloud circulations or turbulence. Results show that the CDSD can be broadened during condensational growth as a result of Ostwald ripening amplified by droplet deactivation and reactivation, which is consistent with early work. The relative roles of the solute effect, curvature effect, deactivation and reactivation on CDSD broadening are investigated. Deactivation of smaller cloud droplets, which is due to the combination of curvature and solute effects in the downdraft region, enhances the growth of larger cloud droplets and thus contributes particles to the larger size end of the CDSD. Droplet reactivation, which occurs in the updraft region, contributes particles to the smaller size end of the CDSD. In addition, we find that growth of the largest cloud droplets strongly depends on the residence time of cloud droplet in the cloud rather than the magnitude of local variability in the supersaturation fluctuation. This is because the environmental saturation ratio is strongly buffered by numerous smaller cloud droplets. Two necessary conditions for this CDSD broadening, which generally occur in the atmosphere, are as follows: (1) droplets form on aerosols of different sizes, and (2) the cloud parcel experiences upwards and downwards motions. Therefore we expect that this mechanism for CDSD broadening is possible in real clouds. Our results also suggest it is important to consider both curvature and solute effects before and after cloud droplet activation in a cloud model. The importance of this mechanism compared with other mechanisms on cloud properties should be investigated through in situ measurements and 3-D dynamic models.

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Aerosols-cloud microphysics-thermodynamics-turbulence: evaluating supersaturation in a marine stratocumulus cloud

Cited by 66 publications

References 22 publications

A synthesis of cloud condensation nuclei counter (CCNC) measurements within the EUCAARI network

A synthesis of cloud condensation nuclei counter (CCNC) measurements within the EUCAARI network

Assessment of cloud supersaturation by size-resolved aerosol particle and cloud condensation nuclei (CCN) measurements

Cloud droplet size distribution broadening during diffusional growth: ripening amplified by deactivation and reactivation

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