scholarly journals Impact of hygroscopic CCN and turbulence on cloud droplet growth: A parcel-DNS approach

2019 ◽  
Author(s):  
Sisi Chen ◽  
Lulin Xue ◽  
Man-Kong Yau
Keyword(s):  
Cloud Condensation Nuclei ◽  
Hybrid Approach ◽  
Cloud Droplet ◽  
Droplet Size Distribution ◽  
Relative Dispersion ◽  
Droplet Growth ◽  
Cloud Droplets ◽  
Cloud Development ◽  
Condensational Growth ◽  
Opposing Trend

Abstract. This paper investigates the relative importance of turbulence, hygroscopicity of cloud condensation nuclei (CCN), and aerosol loading on early cloud development. A parcel-DNS hybrid approach is developed to seamlessly simulate the evolution of cloud droplets in warm clouds. The results show that turbulence and CCN hygroscopicity have a dominant effect on the formation of large droplets. When CCN hygroscopicity is considered, condensational growth has a strong effect in the first minute, providing sufficient collector droplets. In the meantime, turbulence effectively accelerates the collisions among the collector droplets and the small droplets and continues to broaden the droplet size distribution (DSD). In contrast, seeding of extra aerosols modulates the growth of small droplets by inhibiting condensational growth while the growth of large droplets remains unaffected, resulting in a similar tail of the DSD. Overall, seeding reduces the LWC and effective radius but increases the relative dispersion. This opposing trend of the bulk properties suggests that the traditional Kessler-type or Sundqvist-type autoconversion parameterizations which mainly depend on the LWC or mean radius might not represent the drizzle formation process well. Properties related to the width or the shape of the DSD are also needed, suggesting that the Berry-and-Reinhardt scheme is conceptually better.

scholarly journals Broadening of Modeled Cloud Droplet Spectra Using Bin Microphysics in an Eulerian Spatial Domain

2018 ◽  
Vol 75 (11) ◽  
pp. 4005-4030 ◽  
Author(s):  
Hugh Morrison ◽  
Mikael Witte ◽  
George H. Bryan ◽  
Jerry Y. Harrington ◽  
Zachary J. Lebo
Keyword(s):  
Cloud Condensation Nuclei ◽  
Cloud Droplet ◽  
Droplet Size Distribution ◽  
Dynamical Models ◽  
Mixing Processes ◽  
Numerical Diffusion ◽  
Vertical Advection ◽  
1D Model ◽  
Bin Microphysics ◽  
Condensational Growth

Abstract This study investigates droplet size distribution (DSD) characteristics from condensational growth and transport in Eulerian dynamical models with bin microphysics. A hierarchy of modeling frameworks is utilized, including parcel, one-dimensional (1D), and three-dimensional large-eddy simulation (LES). The bin DSDs from the 1D model, which includes only vertical advection and condensational growth, are nearly as broad as those from the LES and in line with observed DSD widths for stratocumulus clouds. These DSDs are much broader than those from Lagrangian microphysical calculations within a parcel framework that serve as a numerical benchmark for the 1D tests. In contrast, the bin-modeled DSDs are similar to the Lagrangian microphysical benchmark for a rising parcel in which Eulerian transport is not considered. These results indicate that numerical diffusion associated with vertical advection is a key contributor to broadening DSDs in the 1D model and LES. This DSD broadening from vertical numerical diffusion is unphysical, in contrast to the physical mixing processes that previous studies have indicated broaden DSDs in real clouds. It is proposed that artificial DSD broadening from vertical numerical diffusion compensates for underrepresented horizontal variability and mixing of different droplet populations in typical LES configurations with bin microphysics, or the neglect of other mechanisms that broaden DSDs such as growth of giant cloud condensation nuclei. These results call into question the ability of Eulerian dynamical models with bin microphysics to investigate the physical mechanisms for DSD broadening, even though they may reasonably simulate overall DSD characteristics.

scholarly journals An Observational Study on Cloud Spectral Width in North China

Atmosphere ◽  
2019 ◽  
Vol 10 (3) ◽  
pp. 109 ◽  
Author(s):  
Yuan Wang ◽  
Shengjie Niu ◽  
Chunsong Lu ◽  
Yangang Liu ◽  
Jingyi Chen ◽  
...  
Keyword(s):  
Size Range ◽  
Cloud Droplet ◽  
Droplet Size Distribution ◽  
Spectral Width ◽  
Relative Dispersion ◽  
Cloud Base ◽  
Cloud Droplets ◽  
Factors Affecting ◽  
Cloud Droplet Size Distribution ◽  
Stratiform Clouds

Cloud droplet size distribution (CDSD) is a critical characteristic for a number of processes related to clouds, considering that cloud droplets are formed in different sizes above the cloud-base. This paper analyzes the in-situ aircraft measurements of CDSDs and aerosol concentration ( N a ) performed in stratiform clouds in Hebei, China, in 2015 to reveal the characteristics of cloud spectral width, commonly known as relative dispersion ( ε , ratio of standard deviation (σ) to mean radius (r) of the CDSD). A new algorithm is developed to calculate the contributions of droplets of different sizes to ε . It is found that small droplets with the size range of 1 to 5.5 μm and medium droplets with the size range of 5.5 to 10 μm are the major contributors to ε, and the medium droplets generally dominate the change of ε. The variation of ε with N a can be well explained by comparing the normalized changes of σ and r ( k σ / σ and k r / r ), rather than k σ and k r only ( k σ is Δσ/Δ N a and k r is Δr/Δ N a ). From the perspective of external factors affecting ε change, the effects of N a and condensation are examined. It is found that ε increases initially and decreases afterward as N a increases, and “condensational broadening” occurs up to 1 km above cloud-base, potentially providing observational evidence for recent numerical simulations in the literature.

scholarly journals Impact of aerosols and turbulence on cloud droplet growth: an in-cloud seeding case study using a parcel–DNS (direct numerical simulation) approach

2020 ◽  
Vol 20 (17) ◽  
pp. 10111-10124
Author(s):  
Sisi Chen ◽  
Lulin Xue ◽  
Man-Kong Yau
Keyword(s):  
Numerical Simulation ◽  
Direct Numerical Simulation ◽  
Early Stage ◽  
Cloud Condensation Nuclei ◽  
Hybrid Approach ◽  
Onset Time ◽  
Relative Dispersion ◽  
Droplet Radius ◽  
Droplet Growth ◽  
Cloud Seeding

Abstract. This paper investigates the relative importance of turbulence and aerosol effects on the broadening of the droplet size distribution (DSD) during the early stage of cloud and raindrop formation. A parcel–DNS (direct numerical simulation) hybrid approach is developed to seamlessly simulate the evolution of cloud droplets in an ascending cloud parcel. The results show that turbulence and cloud condensation nuclei (CCN) hygroscopicity are key to the efficient formation of large droplets. The ultragiant aerosols can quickly form embryonic drizzle drops and thus determine the onset time of autoconversion. However, due to their scarcity in natural clouds, their contribution to the total mass of drizzle drops is insignificant. In the meantime, turbulence sustains the formation of large droplets by effectively accelerating the collisions of small droplets. The DSD broadening through turbulent collisions is significant and therefore yields a higher autoconversion rate compared to that in a nonturbulent case. It is argued that the level of autoconversion is heavily determined by turbulence intensity. This paper also presents an in-cloud seeding scenario designed to scrutinize the effect of aerosols in terms of number concentration and size. It is found that seeding more aerosols leads to higher competition for water vapor, reduces the mean droplet radius, and therefore slows down the autoconversion rate. On the other hand, increasing the seeding particle size can buffer such a negative feedback. Despite the fact that the autoconversion rate is prominently altered by turbulence and seeding, bulk variables such as liquid water content (LWC) stays nearly identical among all cases. Additionally, the lowest autoconversion rate is not co-located with the smallest mean droplet radius. The finding indicates that the traditional Kessler-type or Sundqvist-type autoconversion parameterizations, which depend on the LWC or mean radius, cannot capture the drizzle formation process very well. Properties related to the width or the shape of the DSD are also needed, suggesting that the scheme of Berry and Reinhardt (1974) is conceptually better. It is also suggested that a turbulence-dependent relative-dispersion parameter should be considered.

scholarly journals Bridging the condensation-collision size gap: a direct numerical simulation of continuous droplet growth in turbulent cloud

2018 ◽  
Author(s):  
Sisi Chen ◽  
Man-Kong Yau ◽  
Peter Bartello ◽  
Lulin Xue
Keyword(s):  
Positive Impact ◽  
Cloud Droplet ◽  
Droplet Size Distribution ◽  
Droplet Growth ◽  
Small Scale ◽  
Combined Processes ◽  
The Individual ◽  
Condensational Growth ◽  
The Impact ◽  
Rain Formation

Abstract. In most previous DNS studies on droplet growth in turbulence, condensational growth and collisional growth were treated separately. Studies in recent decades have postulated that small-scale turbulence may accelerate droplet collisions when droplets are still small when condensational growth is effective. This implies that both processes should be considered simultaneously to unveil the full history of droplet growth and rain formation. This paper introduces the first DNS approach to explicitly study the continuous droplet growth by condensation and collisions inside an adiabatic ascending cloud parcel. Results from the condensation-only, collision-only, and condensation-collision experiments are compared to examine the contribution to the broadening of droplet size distribution by the individual process and by the combined processes. Simulations of different turbulent intensities are conducted to investigate the impact of turbulence on each process and on the condensation-induced collisions. The results show that the condensational process promotes the collisions in a turbulent environment and reduces the collisions when in still air, indicating a positive impact of condensation on turbulent collisions. This work suggests the necessity to include both processes simultaneously when studying droplet-turbulence interaction to quantify the turbulence effect on the evolution of cloud droplet spectrum and rain formation.

Turbulence Effects of Collision Efficiency and Broadening of Droplet Size Distribution in Cumulus Clouds

2018 ◽  
Vol 75 (1) ◽  
pp. 203-217 ◽  
Author(s):  
Sisi Chen ◽  
M. K. Yau ◽  
Peter Bartello
Keyword(s):  
Size Distribution ◽  
Droplet Size ◽  
Droplet Size Distribution ◽  
Droplet Growth ◽  
Small Scale ◽  
Cumulus Clouds ◽  
Collision Efficiency ◽  
Cloud Droplets ◽  
R Ratio ◽  
Condensational Growth

This paper aims to investigate and quantify the turbulence effect on droplet collision efficiency and explore the broadening mechanism of the droplet size distribution (DSD) in cumulus clouds. The sophisticated model employed in this study individually traces droplet motions affected by gravity, droplet disturbance flows, and turbulence in a Lagrangian frame. Direct numerical simulation (DNS) techniques are implemented to resolve the small-scale turbulence. Collision statistics for cloud droplets of radii between 5 and 25 μm at five different turbulence dissipation rates (20–500 cm2 s−3) are computed and compared with pure-gravity cases. The results show that the turbulence enhancement of collision efficiency highly depends on the r ratio (defined as the radius ratio of collected and collector droplets r/ R) but is less sensitive to the size of the collector droplet investigated in this study. Particularly, the enhancement is strongest among comparable-sized collisions, indicating that turbulence can significantly broaden the narrow DSD resulting from condensational growth. Finally, DNS experiments of droplet growth by collision–coalescence in turbulence are performed for the first time in the literature to further illustrate this hypothesis and to monitor the appearance of drizzle in the early rain-formation stage. By comparing the resulting DSDs at different turbulence intensities, it is found that broadening is most pronounced when turbulence is strongest and similar-sized collisions account for 21%–24% of total collisions in turbulent cases compared with only 9% in the gravitational case.

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

2017 ◽  
Author(s):  
Fan Yang ◽  
Pavlos Kollias ◽  
Raymond A. Shaw ◽  
Andrew M. Vogelmann
Keyword(s):  
Droplet Size ◽  
Dynamic Models ◽  
Ostwald Ripening ◽  
Cloud Model ◽  
Cloud Droplet ◽  
Droplet Size Distribution ◽  
Diffusional Growth ◽  
Cloud Droplets ◽  
Cloud Properties ◽  
Condensational Growth

Abstract. Cloud droplet size distributions (CDSDs), which are related to cloud albedo and lifetime, are usually broader in warm clouds than predicted from adiabatic parcel calculations. We investigate a mechanism for the CDSD broadening using a Lagrangian bin-microphysics cloud parcel model that considers the condensational growth of cloud droplets formed on polydisperse, sub-micrometer 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 Korolev (1995). 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 smaller cloud droplets. Two necessary conditions for this CDSD broadening, which generally occur in the atmosphere, are: (1) droplets form on polydisperse aerosols of varying hygroscopicity 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.

scholarly journals Regional New Particle Formation as Modulators of Cloud Condensation Nuclei and Cloud Droplet Number in the Eastern Mediterranean

2018 ◽  
Author(s):  
Panayiotis Kalkavouras ◽  
Aikaterini Bougiatioti ◽  
Nikos Kalivitis ◽  
Maria Tombrou ◽  
Athanasios Nenes ◽  
...  
Keyword(s):  
Gas Phase ◽  
Particle Number ◽  
Eastern Mediterranean ◽  
Cloud Condensation Nuclei ◽  
Cloud Droplet ◽  
Significant Fraction ◽  
Size Distributions ◽  
Condensation Nuclei ◽  
Cloud Droplets ◽  
Condensational Growth

Abstract. A significant fraction of atmospheric particles that serve as cloud condensation nuclei (CCN), and furthermore as cloud droplets are thought to originate from the condensational growth of new particles formed from the gas phase. Here, particle number size distributions (

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

2018 ◽  
Vol 18 (10) ◽  
pp. 7313-7328 ◽  
Author(s):  
Fan Yang ◽  
Pavlos Kollias ◽  
Raymond A. Shaw ◽  
Andrew M. Vogelmann
Keyword(s):  
Droplet Size ◽  
Dynamic Models ◽  
Ostwald Ripening ◽  
Cloud Model ◽  
Cloud Droplet ◽  
Droplet Size Distribution ◽  
Diffusional Growth ◽  
Cloud Droplets ◽  
Cloud Properties ◽  
Condensational Growth

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.

scholarly journals Cloud-droplet growth due to supersaturation fluctuations in stratiform clouds

2018 ◽  
Author(s):  
Xiang-Yu Li ◽  
Gunilla Svensson ◽  
Axel Brandenburg ◽  
Nils E. L. Haugen
Keyword(s):  
Reynolds Number ◽  
Size Distribution ◽  
Cloud Droplet ◽  
Energy Dissipation Rate ◽  
Droplet Growth ◽  
Size Distributions ◽  
Cloud Droplets ◽  
Stratiform Clouds ◽  
Water Vapor Mixing Ratio ◽  
Condensational Growth

Abstract. Condensational growth of cloud droplets due to supersaturation fluctuations is investigated by solving the hydrodynamic and thermodynamic equations using direct numerical simulations with droplets being modeled as Lagrangian particles. We find that the width of droplet size distributions increases with time, which is contrary to the classical theory without supersaturation fluctuations, where condensational growth leads to progressively narrower size distributions. Nevertheless, in agreement with earlier Lagrangian stochastic models of the condensational growth, the standard deviation of the surface area of droplets increases as t1/2. Also, we numerically confirm that the time evolution of the size distribution depends strongly on the Reynolds number and only weakly on the mean energy dissipation rate. This is shown to be due to the fact that temperature fluctuations and water vapor mixing ratio fluctuations increases with increasing Reynolds number, therefore the resulting supersaturation fluctuations are enhanced with increasing Reynolds number. Our simulations may explain the broadening of the size distribution in stratiform clouds qualitatively, where the updraft velocity is almost zero.

scholarly journals Broadening of Cloud Droplet Spectra through Eddy Hopping: Turbulent Entraining Parcel Simulations

2018 ◽  
Vol 75 (10) ◽  
pp. 3365-3379 ◽  
Author(s):  
Gustavo C. Abade ◽  
Wojciech W. Grabowski ◽  
Hanna Pawlowska
Keyword(s):  
Droplet Size ◽  
Turbulent Mixing ◽  
Cloud Condensation Nuclei ◽  
Cloud Droplet ◽  
Cloud Microphysics ◽  
Droplet Growth ◽  
Size Spectrum ◽  
Entrainment Rate ◽  
The Mean ◽  
The Impact

This paper discusses the effects of cloud turbulence, turbulent entrainment, and entrained cloud condensation nuclei (CCN) activation on the evolution of the cloud droplet size spectrum. We simulate an ensemble of idealized turbulent cloud parcels that are subject to entrainment events modeled as a random process. Entrainment events, subsequent turbulent mixing inside the parcel, supersaturation fluctuations, and the resulting stochastic droplet activation and growth by condensation are simulated using a Monte Carlo scheme. Quantities characterizing the turbulence intensity, entrainment rate, CCN concentration, and the mean fraction of environmental air entrained in an event are all specified as independent external parameters. Cloud microphysics is described by applying Lagrangian particles, the so-called superdroplets. These are either unactivated CCN or cloud droplets that grow from activated CCN. The model accounts for the addition of environmental CCN into the cloud by entraining eddies at the cloud edge. Turbulent mixing of the entrained dry air with cloudy air is described using the classical linear relaxation to the mean model. We show that turbulence plays an important role in aiding entrained CCN to activate, and thus broadening the droplet size distribution. These findings are consistent with previous large-eddy simulations (LESs) that consider the impact of variable droplet growth histories on the droplet size spectra in small cumuli. The scheme developed in this work is ready to be used as a stochastic subgrid-scale scheme in LESs of natural clouds.

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