Strong aerosol–cloud interaction in altocumulus during updraft periods: lidar observations over central Europe

Schmidt, Jörg; Ansmann, Albert; Bühl, Johannes; Wandinger, Ulla

doi:10.5194/acp-15-10687-2015

Cited by 48 publications

(56 citation statements)

References 64 publications

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“…In contrast, liquid water content or path was more sensitive to adjustments in cloud base or updraft velocity. Collocated Raman and Doppler lidar measurements also confirm that turbulence and entrainment convolute the aerosol−droplet number correlation above the cloud base within altocumulus (26). Along with studies like these, the attribution metrics presented here are useful in determining if models capture the correct source of N d and N i variability.…”

Section: Discussionmentioning

confidence: 80%

Role of updraft velocity in temporal variability of global cloud hydrometeor number

Sullivan

Lee

Oreopoulos

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

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Understanding how dynamical and aerosol inputs affect the temporal variability of hydrometeor formation in climate models will help to explain sources of model diversity in cloud forcing, to provide robust comparisons with data, and, ultimately, to reduce the uncertainty in estimates of the aerosol indirect effect. This variability attribution can be done at various spatial and temporal resolutions with metrics derived from online adjoint sensitivities of droplet and crystal number to relevant inputs. Such metrics are defined and calculated from simulations using the NASA Goddard Earth Observing System Model, Version 5 (GEOS-5) and the National Center for Atmospheric Research Community Atmosphere Model Version 5.1 (CAM5.1). Input updraft velocity fluctuations can explain as much as 48% of temporal variability in output ice crystal number and 61% in droplet number in GEOS-5 and up to 89% of temporal variability in output ice crystal number in CAM5.1. In both models, this vertical velocity attribution depends strongly on altitude. Despite its importance for hydrometeor formation, simulated vertical velocity distributions are rarely evaluated against observations due to the sparsity of relevant data. Coordinated effort by the atmospheric community to develop more consistent, observationally based updraft treatments will help to close this knowledge gap.global models | clouds | vertical velocity | sensitivity | attribution C loud radiative forcing remains one of the largest sources of uncertainty in the overall terrestrial radiative budget (1). Incloud phase partitioning, or the fraction of liquid versus ice hydrometeors, can be as important as cloud cover in the calculation of cloud radiative forcing (2). Cloud long-wave emissivity depends on cloud water path and hydrometeor sizes, along with cloud height and temperature. Cloud short-wave albedo also depends on particle size, because more and smaller hydrometeors yield a higher optical depth for the same water path (1). Global climate models (GCMs) predict a diversity of liquid and ice water paths (3), as well as cloud hydrometeor sizes, and the treatment of initial hydrometeor formation, i.e., droplet activation or ice nucleation, contributes to this spread for all cloud types (4, 5).The available supersaturation of a cloudy air parcel determines how many hydrometeors can form therein. Supersaturation strongly depends on aerosol and dynamical parameters. Updraft, or vertical velocity, is especially important because it is the driver of supersaturation generation, owing to the induced expansion cooling during air mass ascent. Aerosol particle surfaces upon which vapor can condense or deposit, called cloud condensation nuclei (CCN) or ice nucleating particles (INP), respectively, act as a sink of supersaturation. The balance between supersaturation generation and loss eventually determines the number of hydrometeors that form in the cloud. As part of the increasing trend to track both cloud hydrometeor mass and number density in GCM cloud modules (6-9), most state o...

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Section: Discussionmentioning

confidence: 80%

Role of updraft velocity in temporal variability of global cloud hydrometeor number

Sullivan

Lee

Oreopoulos

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

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“…Conversely, in situ airborne measurements are closer to the process scale and may result in more accurate estimates of the aerosol effect (Werner et al, 2014). However, the studies reviewed in Pandithurai et al (2012) and Schmidt et al (2015) are mostly for stratus or cumulus clouds over ocean. Additionally, measurements of w were either not available or were not differentiated in most of the previous analyses, while the results are often integrated in altitude or limited to a specific cloud layer (e.g., cloud top in satellite retrievals).…”

Section: Comparing the Main Drivers Of Bulk Microphysical Properties mentioning

confidence: 99%

“…Pandithurai et al (2012) provide a compilation of this type of calculation (see their Table 2), showing a high variability among the sources. According to Schmidt et al (2015), the differences are due to not only the measurement setup but also to the region (ocean or land), the types of clouds, and the differences in the methodologies. Remote sensing techniques often retrieve vertically integrated quantities at relatively rough horizontal resolution, which can smooth the results, meaning lower sensitivities.…”

Section: Comparing the Main Drivers Of Bulk Microphysical Properties mentioning

confidence: 99%

Sensitivities of Amazonian clouds to aerosols and updraft speed

et al. 2017

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Abstract. The effects of aerosol particles and updraft speed on warm-phase cloud microphysical properties are studied in the Amazon region as part of the ACRIDICON-CHUVA experiment. Here we expand the sensitivity analysis usually found in the literature by concomitantly considering cloud evolution, putting the sensitivity quantifications into perspective in relation to in-cloud processing, and by considering the effects on droplet size distribution (DSD) shape. Our in situ aircraft measurements over the Amazon Basin cover a wide range of particle concentration and thermodynamic conditions, from the pristine regions over coastal and forested areas to the southern Amazon, which is highly polluted from biomass burning. The quantitative results show that particle concentration is the primary driver for the vertical profiles of effective diameter and droplet concentration in the warm phase of Amazonian convective clouds, while updraft speeds have a modulating role in the latter and in total condensed water. The cloud microphysical properties were found to be highly variable with altitude above cloud base, which we used as a proxy for cloud evolution since it is a measure of the time droplets that were subject to cloud processing. We show that DSD shape is crucial in understanding cloud sensitivities. The aerosol effect on DSD shape was found to vary with altitude, which can help models to better constrain the indirect aerosol effect on climate.

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“…This is implicated by the increase of both the ACI metrics as well as the correlation coefficients. The invigoration of ACI processes in the updraught regime was also reported in previous studies (Schmidt et al, 2015). It is important to note that the number of available profiles is greatly diminished by the selection of updraught areas only.…”

Section: Impact Of the Updraughtmentioning

confidence: 88%

“…Feingold et al, 2003;Garrett et al, 2004;Pandithurai et al, 2009;Schmidt et al, 2015). The scope of instruments and measured parameters still differs among them.…”

Section: Introductionmentioning

confidence: 99%

Monitoring aerosol–cloud interactions at the CESAR Observatory in the Netherlands

Sarna

Russchenberg

2017

Atmos. Meas. Tech.

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Abstract. The representation of aerosol-cloud interaction (ACI) processes in climate models, although long studied, still remains the source of high uncertainty. Very often there is a mismatch between the scale of observations used for ACI quantification and the ACI process itself. This can be mitigated by using the observations from groundbased remote sensing instruments. In this paper we presented a direct application of the aerosol-cloud interaction monitoring technique (ACI monitoring). ACI monitoring is based on the standardised Cloudnet data stream, which provides measurements from ground-based remote sensing instruments working in synergy. For the data set collected at the CESAR Observatory in the Netherlands we calculate ACI metrics. We specifically use attenuated backscatter coefficient (ATB) for the characterisation of the aerosol properties and cloud droplet effective radius (r e ) and number concentration (N d ) for the characterisation of the cloud properties. We calculate two metrics: ACI r = ln(r e )/ln(ATB) and ACI N = ln(N d )/ln(ATB). The calculated values of ACI r range from 0.001 to 0.085, which correspond to the values reported in previous studies. We also evaluated the impact of the vertical Doppler velocity and liquid water path (LWP) on ACI metrics. The values of ACI r were highest for LWP values between 60 and 105 g m −2 . For higher LWP other processes, such as collision and coalescence, seem to be dominant and obscure the ACI processes. We also saw that the values of ACI r are higher when only data points located in the updraught regime are considered. The method presented in this study allow for monitoring ACI daily and further aggregating daily data into bigger data sets.

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Strong aerosol–cloud interaction in altocumulus during updraft periods: lidar observations over central Europe

Cited by 48 publications

References 64 publications

Role of updraft velocity in temporal variability of global cloud hydrometeor number

Role of updraft velocity in temporal variability of global cloud hydrometeor number

Sensitivities of Amazonian clouds to aerosols and updraft speed

Monitoring aerosol–cloud interactions at the CESAR Observatory in the Netherlands

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