scholarly journals Predicting the morphology of ice particles in deep convection using the super-droplet method: development and evaluation of SCALE-SDM 0.2.5-2.2.0, -2.2.1, and -2.2.2

2020 ◽  
Vol 13 (9) ◽  
pp. 4107-4157 ◽  
Author(s):  
Shin-ichiro Shima ◽  
Yousuke Sato ◽  
Akihiro Hashimoto ◽  
Ryohei Misumi
Keyword(s):  
Method Development ◽  
Cloud Condensation Nuclei ◽  
Deep Convection ◽  
Cloud Microphysics ◽  
Mixed Phase ◽  
Computational Time ◽  
Ice Particles ◽  
Mass Dimension ◽  
Mixed Phase Clouds ◽  
Droplet Method

Abstract. The super-droplet method (SDM) is a particle-based numerical scheme that enables accurate cloud microphysics simulation with lower computational demand than multi-dimensional bin schemes. Using SDM, a detailed numerical model of mixed-phase clouds is developed in which ice morphologies are explicitly predicted without assuming ice categories or mass–dimension relationships. Ice particles are approximated using porous spheroids. The elementary cloud microphysics processes considered are advection and sedimentation; immersion/condensation and homogeneous freezing; melting; condensation and evaporation including cloud condensation nuclei activation and deactivation; deposition and sublimation; and coalescence, riming, and aggregation. To evaluate the model's performance, a 2-D large-eddy simulation of a cumulonimbus was conducted, and the life cycle of a cumulonimbus typically observed in nature was successfully reproduced. The mass–dimension and velocity–dimension relationships the model predicted show a reasonable agreement with existing formulas. Numerical convergence is achieved at a super-particle number concentration as low as 128 per cell, which consumes 30 times more computational time than a two-moment bulk model. Although the model still has room for improvement, these results strongly support the efficacy of the particle-based modeling methodology to simulate mixed-phase clouds.

scholarly journals Predicting the morphology of ice particles in deep convection using the super-droplet method: development and evaluation of SCALE-SDM 0.2.5-2.2.0/2.2.1

2019 ◽  
Author(s):  
Shin-ichiro Shima ◽  
Yousuke Sato ◽  
Akihiro Hashimoto ◽  
Ryohei Misumi
Keyword(s):  
Method Development ◽  
Cloud Condensation Nuclei ◽  
Deep Convection ◽  
Cloud Microphysics ◽  
Mixed Phase ◽  
Computational Time ◽  
Ice Particles ◽  
Mass Dimension ◽  
Mixed Phase Clouds ◽  
Droplet Method

Abstract. The super-droplet method (SDM) is a particle-based numerical scheme that enables accurate cloud microphysics simulation with lower computational demand than multi-dimensional bin schemes. Using SDM, a detailed numerical model of mixed-phase clouds is developed in which ice morphologies are explicitly predicted without assuming ice categories or mass-dimension relationships. Ice particles are approximated using porous spheroids. The elementary cloud microphysics processes considered are advection and sedimentation; immersion/condensation and homogeneous freezing; melting; condensation and evaporation including cloud condensation nuclei activation and deactivation; deposition and sublimation; collision-coalescence, -riming, and -aggregation. To evaluate the model's performance, a 2D large-eddy simulation of a cumulonimbus was conducted, and the results well capture characteristics of a real cumulonimbus. The mass-dimension and velocity-dimension relationships the model predicted show a reasonable agreement with existing formulas. Numerical convergence is achieved at a super-particle number concentration as low as 128/cell, which consumes 30 times more computational time than a two-moment bulk model. Although the model still has room for improvement, these results strongly support the efficacy of the particle-based modeling methodology to simulate mixed-phase clouds.

Predicting the morphology of ice particles in deep convection using the super-droplet method

2020 ◽  
Author(s):  
Shin-ichiro Shima ◽  
Yousuke Sato ◽  
Akihiro Hashimoto ◽  
Ryohei Misumi
Keyword(s):  
Method Development ◽  
Cloud Condensation Nuclei ◽  
Deep Convection ◽  
Cloud Microphysics ◽  
Mixed Phase ◽  
Computational Time ◽  
Ice Particles ◽  
Mass Dimension ◽  
Mixed Phase Clouds ◽  
Droplet Method

<p>In this presentation, we summarize the main results of Shima et al. (2019). The super-droplet method (SDM) is a particle-based numerical algorithm that enables accurate cloud microphysics simulation with lower computational demand than multi-dimensional bin schemes. Using SDM, we developed a detailed numerical model of mixed-phase clouds in which ice morphologies are explicitly predicted without assuming ice categories or mass-dimension relationships. Ice particles are approximated as porous spheroids. The elementary cloud microphysics processes considered are advection and sedimentation; immersion/condensation and homogeneous freezing; melting; condensation and evaporation including cloud condensation nuclei activation and deactivation; deposition and sublimation; collision-coalescence, -riming, and -aggregation. To evaluate the model's performance, we conducted a 2D large-eddy simulation of a cumulonimbus. The results well capture characteristics of a real cumulonimbus. The mass-dimension and velocity-dimension relationships the model predicted show a reasonable agreement with existing formulas. Numerical convergence is achieved at a super-particle number concentration as low as 128/cell, which consumes 30 times more computational time than a two-moment bulk model. Although the model still has room for improvement, these results strongly support the efficacy of the particle-based modeling methodology to simulate mixed-phase clouds. </p><p>Shima, S., Sato, Y., Hashimoto, A., and Misumi, R.: Predicting the morphology of ice particles in deep convection using the super-droplet method: development and evaluation of SCALE-SDM 0.2.5-2.2.0/2.2.1, Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2019-294, 1-83, 2019.</p>

scholarly journals Cloud condensation nuclei as a modulator of ice processes in Arctic mixed-phase clouds

2011 ◽  
Vol 11 (15) ◽  
pp. 8003-8015 ◽  
Author(s):  
S. Lance ◽  
M. D. Shupe ◽  
G. Feingold ◽  
C. A. Brock ◽  
J. Cozic ◽  
...  
Keyword(s):  
Droplet Size ◽  
Indirect Effect ◽  
Cloud Condensation Nuclei ◽  
Droplet Size Distribution ◽  
Mixed Phase ◽  
Condensation Nuclei ◽  
Microphysical Properties ◽  
Ice Particles ◽  
Northern Alaska ◽  
Mixed Phase Clouds

Abstract. We propose that cloud condensation nuclei (CCN) concentrations are important for modulating ice formation of Arctic mixed-phase clouds, through modification of the droplet size distribution. Aircraft observations from the Aerosol, Radiation, and Cloud Processes affecting Arctic Climate (ARCPAC) study in northern Alaska in April 2008 allow for identification and characterization of both aerosol and trace gas pollutants, which are then compared with cloud microphysical properties. Consistent with previous studies, we find that the concentration of precipitating ice particles (>400 μm) is correlated with the concentration of large droplets (>30 μm). We are further able to link the observed microphysical conditions to aerosol pollution, originating mainly from long range transport of biomass burning emissions. The case studies demonstrate that polluted mixed-phase clouds have narrower droplet size distributions and contain 1–2 orders of magnitude fewer precipitating ice particles than clean clouds at the same temperature. This suggests an aerosol indirect effect leading to greater cloud lifetime, greater cloud emissivity, and reduced precipitation. This result is opposite to the glaciation indirect effect, whereby polluted clouds are expected to precipitate more readily due to an increase in the concentration of particles acting as ice nuclei.

scholarly journals Cloud condensation nuclei as a modulator of ice processes in Arctic mixed-phase clouds

2011 ◽  
Vol 11 (2) ◽  
pp. 6737-6770 ◽  
Author(s):  
S. Lance ◽  
M. D. Shupe ◽  
G. Feingold ◽  
C. A. Brock ◽  
J. Cozic ◽  
...  
Keyword(s):  
Droplet Size ◽  
Indirect Effect ◽  
Cloud Condensation Nuclei ◽  
Droplet Size Distribution ◽  
Mixed Phase ◽  
Condensation Nuclei ◽  
Microphysical Properties ◽  
Ice Particles ◽  
Northern Alaska ◽  
Mixed Phase Clouds

Abstract. We propose that cloud condensation nuclei (CCN) concentrations are important for modulating ice formation of Arctic mixed-phase clouds, through modification of the droplet size distribution. Aircraft observations from the Aerosol, Radiation, and Cloud Processes affecting Arctic Climate (ARCPAC) study in northern Alaska in April 2008 allow for identification and characterization of both aerosol and trace gas pollutants, which are then compared with cloud microphysical properties. Consistent with previous studies, we find that the concentration of precipitating ice particles (>400 μm) is correlated with the concentration of large droplets (>30 μm). We are further able to link the observed microphysical conditions to aerosol pollution, originating mainly from long range transport of biomass burning emissions. The case studies demonstrate that polluted mixed-phase clouds have narrower droplet size distributions and contain 1–2 orders of magnitude fewer precipitating ice particles than clean clouds at the same temperature. This suggests an aerosol indirect effect leading to greater cloud lifetime, greater cloud emissivity, and reduced precipitation. This result is opposite to the glaciation indirect effect, whereby polluted clouds are expected to precipitate more readily due to an increase in the concentration of particles acting as IN.

scholarly journals Effects of Cloud Condensation Nuclei and Ice Nucleating Particles on Precipitation Processes and Supercooled Liquid in Mixed-phase Orographic Clouds

2016 ◽  
Author(s):  
Jiwen Fan ◽  
L. Ruby Leung ◽  
Daniel Rosenfeld ◽  
Paul J. DeMott
Keyword(s):  
Water Content ◽  
Sierra Nevada ◽  
Cloud Condensation Nuclei ◽  
Supercooled Water ◽  
Mixed Phase ◽  
Condensation Nuclei ◽  
Local Circulation ◽  
Orographic Clouds ◽  
Snow Precipitation ◽  
Mixed Phase Clouds

Abstract. How orographic mixed-phase clouds respond to the change of cloud condensation nuclei (CCN) and ice nucleating particles (INPs) are highly uncertain. The main snow production mechanism in warm and cold mixed-phase orographic clouds (referred to as WMOC and CMOC, respectively, distinguished here as those having cloud tops warmer and colder than −20 °C) could be very different. We quantify the CCN and INP impacts on supercooled water content, cloud phases and precipitation for a WMOC and a CMOC case with a set of sensitivity tests. It is found that deposition plays a more important role than riming for forming snow in the CMOC, while the role of riming is dominant in the WMOC case. As expected, adding CCN suppresses precipitation especially in WMOC and low INP. However, this reverses strongly for CCN > 1000 cm−3. We find a new mechanism through which CCN can invigorate mixed-phase clouds over the Sierra Nevada Mountains and drastically intensify snow precipitation when CCN concentrations are high (1000 cm−3 or higher). In this situation, more widespread shallow clouds with greater amount of cloud water form in the valley and foothills, which changes the local circulation through more latent heat release that transports more moisture to the windward slope, leading to much more invigorated mixed-phase clouds over the mountains that produce higher amounts of snow precipitation. Increasing INPs leads to decreased riming and mixed-phase fraction in the CMOC but has the opposite effects in the WMOC, as a result of liquid-limited and ice-limited conditions, respectively. However, it increases precipitation in both cases due to an increase of deposition for the CMOC but enhanced riming and deposition in the WMOC. Increasing INPs dramatically reduces supercooled water content and increases the cloud glaciation temperature, while increasing CCN has the opposite effects with much smaller significance.

scholarly journals The relative impact of cloud condensation nuclei and ice nucleating particle concentrations on phase partitioning in Arctic mixed-phase stratocumulus clouds

2018 ◽  
Vol 18 (23) ◽  
pp. 17047-17059 ◽  
Author(s):  
Amy Solomon ◽  
Gijs de Boer ◽  
Jessie M. Creamean ◽  
Allison McComiskey ◽  
Matthew D. Shupe ◽  
...  
Keyword(s):  
Cloud Condensation Nuclei ◽  
Mixed Phase ◽  
Radiative Properties ◽  
Condensation Nuclei ◽  
Phase Partitioning ◽  
Cloud Dynamics ◽  
Stratocumulus Clouds ◽  
Cloud Ice ◽  
Mixed Phase Clouds ◽  
The Impact

Abstract. This study investigates the interactions between cloud dynamics and aerosols in idealized large-eddy simulations (LES) of Arctic mixed-phase stratocumulus clouds (AMPS) observed at Oliktok Point, Alaska, in April 2015. This case was chosen because it allows the cloud to form in response to radiative cooling starting from a cloud-free state, rather than requiring the cloud ice and liquid to adjust to an initial cloudy state. Sensitivity studies are used to identify whether there are buffering feedbacks that limit the impact of aerosol perturbations. The results of this study indicate that perturbations in ice nucleating particles (INPs) dominate over cloud condensation nuclei (CCN) perturbations; i.e., an equivalent fractional decrease in CCN and INPs results in an increase in the cloud-top longwave cooling rate, even though the droplet effective radius increases and the cloud emissivity decreases. The dominant effect of ice in the simulated mixed-phase cloud is a thinning rather than a glaciation, causing the mixed-phase clouds to radiate as a grey body and the radiative properties of the cloud to be more sensitive to aerosol perturbations. It is demonstrated that allowing prognostic CCN and INPs causes a layering of the aerosols, with increased concentrations of CCN above cloud top and increased concentrations of INPs at the base of the cloud-driven mixed layer. This layering contributes to the maintenance of the cloud liquid, which drives the dynamics of the cloud system.

Beyond Cloud Microphysics: Representing Subgrid-Scale Processes in Lagrangian Cloud Models

2020 ◽  
Author(s):  
Fabian Hoffmann
Keyword(s):  
Three Dimensional ◽  
Cloud Microphysics ◽  
Mixed Phase ◽  
Droplet Growth ◽  
Subgrid Scale ◽  
Modeling Framework ◽  
Mixing Processes ◽  
New Approach ◽  
Cloud Models ◽  
Mixed Phase Clouds

<p>While the use of Lagrangian cloud microphysical models dates back as far as the 1950s, the integration of this framework into fully-coupled, three-dimensional dynamical models is only possible for about 10 years. In addition to the highly accurate and detailed representation of cloud microphysical processes, these so-called Lagrangian Cloud Models (LCMs) also allow for new ways of representing subgrid-scale dynamical processes and their effects on the microphysical development of clouds, typically neglected or only crudely parameterized due to computational constraints.</p><p>In this talk, I will present a new approach in which supersaturation fluctuations on the subgrid-scale of a large-eddy simulation (LES) model are represented by an economical, one-dimensional model that represents turbulent compression and folding. With a resolution comparable to direct numerical simulation (DNS), inhomogeneous and finite rate mixing processes are explicitly resolved. Applications of this modeling approach for warm-phase shallow cumuli and stratocumuli, and first applications for mixed-phase clouds will be discussed. Generally, clouds susceptible to inhomogeneous mixing show a reduction in the droplet number concentration and stronger droplet growth, in agreement with theory. Stratocumulus entrainment rates tend to be lower using the new approach compared to simulations without it, indicating a more appropriate representation of the entrainment-mixing process. Finally, the Wegner-Bergeron-Findeisen-Process, leading to a rapid ice formation in mixed-phase clouds, is decelerated.</p><p>All in all, this new modeling framework is capable of bridging the gap between LES and DNS, i.e., it enables representing all scales relevant to cloud physics, from entire cloud fields to the smallest turbulent fluctuations, in a single model, allowing to study their interactions explicitly and granting new insights.</p>

scholarly journals Some effects of ice crystals on the FSSP measurements in mixed phase clouds

2012 ◽  
Vol 12 (19) ◽  
pp. 8963-8977 ◽  
Author(s):  
G. Febvre ◽  
J.-F. Gayet ◽  
V. Shcherbakov ◽  
C. Gourbeyre ◽  
O. Jourdan
Keyword(s):  
Particle Size ◽  
Mixed Phase ◽  
Ice Crystals ◽  
Crystal Surfaces ◽  
Hexagonal Ice ◽  
Ice Particles ◽  
Second Mode ◽  
Ice Production ◽  
Bulk Parameters ◽  
Mixed Phase Clouds

Abstract. In this paper, we show that in mixed phase clouds, the presence of ice crystals may induce wrong FSSP 100 measurements interpretation especially in terms of particle size and subsequent bulk parameters. The presence of ice crystals is generally revealed by a bimodal feature of the particle size distribution (PSD). The combined measurements of the FSSP-100 and the Polar Nephelometer give a coherent description of the effect of the ice crystals on the FSSP-100 response. The FSSP-100 particle size distributions are characterized by a bimodal shape with a second mode peaked between 25 and 35 μm related to ice crystals. This feature is observed with the FSSP-100 at airspeed up to 200 m s−1 and with the FSSP-300 series. In order to assess the size calibration for clouds of ice crystals the response of the FSSP-100 probe has been numerically simulated using a light scattering model of randomly oriented hexagonal ice particles and assuming both smooth and rough crystal surfaces. The results suggest that the second mode, measured between 25 μm and 35 μm, does not necessarily represent true size responses but corresponds to bigger aspherical ice particles. According to simulation results, the sizing understatement would be neglected in the rough case but would be significant with the smooth case. Qualitatively, the Polar Nephelometer phase function suggests that the rough case is the more suitable to describe real crystals. Quantitatively, however, it is difficult to conclude. A review is made to explore different hypotheses explaining the occurrence of the second mode. However, previous cloud in situ measurements suggest that the FSSP-100 secondary mode, peaked in the range 25–35 μm, is likely to be due to the shattering of large ice crystals on the probe inlet. This finding is supported by the rather good relationship between the concentration of particles larger than 20 μm (hypothesized to be ice shattered-fragments measured by the FSSP) and the concentration of (natural) ice particles (CPI data). In mixed cloud, a simple estimation of the number of ice crystals impacting the FSSP inlet shows that the ice crystal shattering effect is the main factor in observed ice production.

scholarly journals Hemispheric contrasts in ice formation in stratiform mixed-phase clouds: disentangling the role of aerosol and dynamics with ground-based remote sensing

2021 ◽  
Vol 21 (23) ◽  
pp. 17969-17994
Author(s):  
Martin Radenz ◽  
Johannes Bühl ◽  
Patric Seifert ◽  
Holger Baars ◽  
Ronny Engelmann ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Boundary Layer ◽  
Gravity Wave ◽  
Detection Threshold ◽  
Cloud Microphysics ◽  
Mixed Phase ◽  
Ice Formation ◽  
Absolute Difference ◽  
Optical Parameters ◽  
Mixed Phase Clouds

Abstract. Multi-year ground-based remote-sensing datasets were acquired with the Leipzig Aerosol and Cloud Remote Observations System (LACROS) at three sites. A highly polluted central European site (Leipzig, Germany), a polluted and strongly dust-influenced eastern Mediterranean site (Limassol, Cyprus), and a clean marine site in the southern midlatitudes (Punta Arenas, Chile) are used to contrast ice formation in shallow stratiform liquid clouds. These unique, long-term datasets in key regions of aerosol–cloud interaction provide a deeper insight into cloud microphysics. The influence of temperature, aerosol load, boundary layer coupling, and gravity wave motion on ice formation is investigated. With respect to previous studies of regional contrasts in the properties of mixed-phase clouds, our study contributes the following new aspects: (1) sampling aerosol optical parameters as a function of temperature, the average backscatter coefficient at supercooled conditions is within a factor of 3 at all three sites. (2) Ice formation was found to be more frequent for cloud layers with cloud top temperatures above -15∘C than indicated by prior lidar-only studies at all sites. A virtual lidar detection threshold of ice water content (IWC) needs to be considered in order to bring radar–lidar-based studies in agreement with lidar-only studies. (3) At similar temperatures, cloud layers which are coupled to the aerosol-laden boundary layer show more intense ice formation than decoupled clouds. (4) Liquid layers formed by gravity waves were found to bias the phase occurrence statistics below -15∘C. By applying a novel gravity wave detection approach using vertical velocity observations within the liquid-dominated cloud top, wave clouds can be classified and excluded from the statistics. After considering boundary layer and gravity wave influences, Punta Arenas shows lower fractions of ice-containing clouds by 0.1 to 0.4 absolute difference at temperatures between −24 and -8∘C. These differences are potentially caused by the contrast in the ice-nucleating particle (INP) reservoir between the different sites.

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