scholarly journals Aerosol–Cloud Interaction in Deep Convective Clouds over the Indian Peninsula Using Spectral (Bin) Microphysics

2017 ◽  
Vol 74 (10) ◽  
pp. 3145-3166 ◽  
Author(s):  
K. Gayatri ◽  
S. Patade ◽  
T. V. Prabha
Keyword(s):  
Size Distribution ◽  
Cloud Condensation Nuclei ◽  
Wrf Model ◽  
Cloud Droplet ◽  
Liquid Water Content ◽  
Cloud Microphysics ◽  
Large Area ◽  
Surface Precipitation ◽  
Aerosol Cloud ◽  
Bin Microphysics

Abstract The Weather Research and Forecasting (WRF) Model coupled with a spectral bin microphysics (SBM) scheme is used to investigate aerosol effects on cloud microphysics and precipitation over the Indian peninsular region. The main emphasis of the study is in comparing simulated cloud microphysical structure with in situ aircraft observations from the Cloud Aerosol Interaction and Precipitation Enhancement Experiment (CAIPEEX). Aerosol–cloud interaction over the rain-shadow region is investigated with observed and simulated size distribution spectra of cloud droplets and ice particles in monsoon clouds. It is shown that size distributions as well as other microphysical characteristics obtained from simulations such as liquid water content, cloud droplet effective radius, cloud droplet number concentration, and thermodynamic parameters are in good agreement with the observations. It is seen that in clouds with high cloud condensation nuclei (CCN) concentrations, snow and graupel size distribution spectra were broader compared to clouds with low concentrations of CCN, mainly because of enhanced riming in the presence of a large number of droplets with a diameter of 10–30 μm. The Hallett–Mossop ice multiplication process is illustrated to have an impact on snow and graupel mass. The changes in CCN concentrations have a strong effect on cloud properties over the domain, amounts of cloud water, and the glaciation of the clouds, but the effects on surface precipitation are small when averaged over a large area. Overall enhancement of cold-phase cloud processes in the high-CCN case contributed to slight enhancement (5%) in domain-averaged surface precipitation.

scholarly journals A Comparative Study of Bin and Bulk Cloud Microphysics Schemes in Simulating a Heavy Precipitation Case

Atmosphere ◽  
2018 ◽  
Vol 9 (12) ◽  
pp. 475 ◽  
Author(s):  
Hyunho Lee ◽  
Jong-Jin Baik
Keyword(s):  
Heat Release ◽  
Latent Heat ◽  
Wrf Model ◽  
Heavy Precipitation ◽  
Cloud Microphysics ◽  
Latent Heat Release ◽  
Microphysics Scheme ◽  
Surface Precipitation ◽  
Double Moment ◽  
Bin Microphysics

Comparisons between bin and bulk cloud microphysics schemes are conducted by simulating a heavy precipitation case using a bin microphysics scheme and four double-moment bulk microphysics schemes in the Weather Research and Forecasting (WRF) model. For this, we implemented an updated bin microphysics scheme in the WRF model. All of the microphysics schemes underestimate observed strong precipitation, but the bin microphysics scheme yields the result that is closest to observations. The differences among the schemes are more pronounced in terms of hydrometeor number concentration than in terms of hydrometeor mixing ratio. In this case, the bin scheme exhibits remarkably more latent heat release by deposition and riming than the bulk schemes. This causes stronger updrafts and more upward transport of water vapor, which leads to more deposition, and again, increases the latent heat release. An additional simulation using the bin scheme but excluding the riming of cloud droplets on ice crystals, which is not or poorly treated in the examined bulk schemes, shows that surface precipitation is slightly weakened and moved farther downwind compared to that of the control simulation. This implies that the more appropriate representation of microphysical processes in the bin microphysics scheme contributes to the more accurate prediction of precipitation in this case.

scholarly journals Understanding aerosol–cloud interactions through modeling the development of orographic cumulus congestus during IPHEx

2019 ◽  
Vol 19 (3) ◽  
pp. 1413-1437 ◽  
Author(s):  
Yajuan Duan ◽  
Markus D. Petters ◽  
Ana P. Barros
Keyword(s):  
Water Content ◽  
Liquid Water ◽  
Cloud Droplet ◽  
Liquid Water Content ◽  
Cloud Microphysics ◽  
Turbulent Dispersion ◽  
Cloud Base ◽  
Droplet Growth ◽  
Aerosol Cloud ◽  
Aerosol Cloud Interactions

Abstract. A new cloud parcel model (CPM) including activation, condensation, collision–coalescence, and lateral entrainment processes is used to investigate aerosol–cloud interactions (ACIs) in cumulus development prior to rainfall onset. The CPM was applied with surface aerosol measurements to predict the vertical structure of cloud development at early stages, and the model results were evaluated against airborne observations of cloud microphysics and thermodynamic conditions collected during the Integrated Precipitation and Hydrology Experiment (IPHEx) in the inner region of the southern Appalachian Mountains (SAM). Sensitivity analysis was conducted to examine the model response to variations in key ACI physiochemical parameters and initial conditions. The CPM sensitivities mirror those found in parcel models without entrainment and collision–coalescence, except for the evolution of the droplet spectrum and liquid water content with height. Simulated cloud droplet number concentrations (CDNCs) exhibit high sensitivity to variations in the initial aerosol concentration at cloud base, but weak sensitivity to bulk aerosol hygroscopicity. The condensation coefficient ac plays a governing role in determining the evolution of CDNC, liquid water content (LWC), and cloud droplet spectra (CDS) in time and with height. Lower values of ac lead to higher CDNCs and broader CDS above cloud base, and higher maximum supersaturation near cloud base. Analysis of model simulations reveals that competitive interference among turbulent dispersion, activation, and droplet growth processes modulates spectral width and explains the emergence of bimodal CDS and CDNC heterogeneity in aircraft measurements from different cloud regions and at different heights. Parameterization of nonlinear interactions among entrainment, condensational growth, and collision–coalescence processes is therefore necessary to simulate the vertical structures of CDNCs and CDSs in convective clouds. Comparisons of model predictions with data suggest that the representation of lateral entrainment remains challenging due to the spatial heterogeneity of the convective boundary layer and the intricate 3-D circulations in mountainous regions.

scholarly journals Comparisons of CCN with Supercooled Clouds

2010 ◽  
Vol 67 (9) ◽  
pp. 3006-3018 ◽  
Author(s):  
James G. Hudson ◽  
Stephen Noble ◽  
Vandana Jha
Keyword(s):  
Droplet Size ◽  
Cloud Condensation Nuclei ◽  
Correlation Coefficients ◽  
Cloud Droplet ◽  
Liquid Water Content ◽  
Cloud Microphysics ◽  
Cloud Base ◽  
Cloud Droplets ◽  
Lower Cloud ◽  
Droplet Sizes

Abstract More than 140 supercooled clouds were compared with corresponding out-of-cloud cloud condensation nuclei (CCN) measurements. In spite of significant differences in altitude, temperature, distances from cloud base, updraft velocity (W), entrainment, and so on, the correlation coefficients (R) between droplet and CCN concentrations were substantial although not as high as those obtained in warm clouds with less variability of nonaerosol influences. CCN at slightly lower altitudes than the clouds had higher R values than CCN measured at the same altitude. Ice particle concentrations appeared to reduce droplet concentrations and reduce R between CCN and droplet concentrations, but only above 6-km altitude and for temperatures below −20°C. Although higher CCN concentrations generally resulted in higher droplet concentrations, increases in droplet concentrations were generally less than the increases in CCN concentrations. This was apparently due to the expected lower cloud supersaturations (S) when CCN concentrations are higher as was usually the case at lower altitudes. Cloud supersaturations showed more variability at higher altitudes and often very high values at higher altitudes. The use of liquid water content rather than droplet concentrations for cloud threshold resulted in higher R between CCN and droplet concentrations. The same R pattern for cumulative droplet–CCN concentrations as a function of threshold droplet sizes as that recently uncovered in warm clouds was found. This showed R changing rapidly from positive values when all cloud droplets were considered to negative values for slightly larger droplet size thresholds. After reaching a maximum negative value at intermediate droplet sizes, R then reversed direction to smaller negative or even positive values for larger cloud droplet size thresholds. This R pattern of CCN concentrations versus cumulative droplet concentrations for increasing size thresholds is consistent with adiabatic model predictions and thus suggests even greater CCN influence on cloud microphysics.

Role of dimethyl sulfide on the formation and growth of aerosols, and its impact on liquid clouds in the Arctic summer

2020 ◽  
Author(s):  
Roya Ghahreman ◽  
Wanmin Gong ◽  
Ann-Lise Norman ◽  
Stephen R. Beagley ◽  
Ayodeji Akingunola ◽  
...  
Keyword(s):  
Dimethyl Sulfide ◽  
Model Simulation ◽  
Cloud Condensation Nuclei ◽  
Cloud Droplet ◽  
Liquid Water Content ◽  
High Arctic ◽  
Cloud Microphysics ◽  
The Arctic ◽  
The Impact ◽  
Arctic Summer

<p>Atmospheric dimethyl sulfide, DMS, is the main biogenic source of sulfate particles in the Arctic atmosphere. Sulfate particles have a net cooling effect, which can partially offset Arctic warming from absorbing aerosols, such as black carbon. As efficient cloud condensation nuclei (CCN), sulfate particles are also able to influence the cloud’s microphysical properties. </p><p>DMS production and emission to the atmosphere increase during the Arctic summer, due to a greater ice-free sea surface area and higher biological activity. In the model simulation of a field campaign conducted over the Canadian high Arctic during the summer of 2014 (NETCARE; Abbatt et al. 2019), the inclusion of DMS in the model, GEM-MACH, resulted in a significant increase, up to 100%, in the modelled atmospheric SO<sub>2</sub> in some regions of the Canadian Arctic. Analysis of the modelled size-segregated aerosol sulfate indicated that DMS has the most significant impact on particles in the size range of 50 – 200 nm in this case. Simulations have shown that localized regions of high seawater DMS can have a significant impact on atmospheric concentrations.</p><p>Further investigation of DMS impact on the Arctic summer cloud microphysics was carried out by using a fully coupled version of GEM-MACH. Overall, the model simulations show that the inclusion of DMS in model leads to an increase in cloud droplet number concentrations (CDNC) and a decrease in droplet mean mass diameters (MMD), and has no significant effects on liquid water content (LWC). The impact of DMS on Canadian weather forecasts will be evaluated using operational forecast tools.</p>

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.

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 Implementation of aerosol–cloud interactions in the regional atmosphere–aerosol model COSMO-MUSCAT(5.0) and evaluation using satellite data

2017 ◽  
Vol 10 (6) ◽  
pp. 2231-2246 ◽  
Author(s):  
Sudhakar Dipu ◽  
Johannes Quaas ◽  
Ralf Wolke ◽  
Jens Stoll ◽  
Andreas Mühlbauer ◽  
...  
Keyword(s):  
Satellite Data ◽  
Transport Model ◽  
Cloud Condensation Nuclei ◽  
Cloud Droplet ◽  
Atmospheric Model ◽  
Horizontal Resolution ◽  
Small Scale ◽  
Aerosol Model ◽  
Aerosol Cloud ◽  
Aerosol Cloud Interactions

Abstract. The regional atmospheric model Consortium for Small-scale Modeling (COSMO) coupled to the Multi-Scale Chemistry Aerosol Transport model (MUSCAT) is extended in this work to represent aerosol–cloud interactions. Previously, only one-way interactions (scavenging of aerosol and in-cloud chemistry) and aerosol–radiation interactions were included in this model. The new version allows for a microphysical aerosol effect on clouds. For this, we use the optional two-moment cloud microphysical scheme in COSMO and the online-computed aerosol information for cloud condensation nuclei concentrations (Cccn), replacing the constant Cccn profile. In the radiation scheme, we have implemented a droplet-size-dependent cloud optical depth, allowing now for aerosol–cloud–radiation interactions. To evaluate the models with satellite data, the Cloud Feedback Model Intercomparison Project Observation Simulator Package (COSP) has been implemented. A case study has been carried out to understand the effects of the modifications, where the modified modeling system is applied over the European domain with a horizontal resolution of 0.25°  ×  0.25°. To reduce the complexity in aerosol–cloud interactions, only warm-phase clouds are considered. We found that the online-coupled aerosol introduces significant changes for some cloud microphysical properties. The cloud effective radius shows an increase of 9.5 %, and the cloud droplet number concentration is reduced by 21.5 %.

scholarly journals Effects of cosmic ray decreases on cloud microphysics

2012 ◽  
Vol 12 (2) ◽  
pp. 3595-3617 ◽  
Author(s):  
J. Svensmark ◽  
M. B. Enghoff ◽  
H. Svensmark
Keyword(s):  
Cloud Condensation Nuclei ◽  
Cosmic Ray ◽  
Liquid Water Content ◽  
Total Solar Irradiance ◽  
Cloud Microphysics ◽  
Forbush Decreases ◽  
Cloud Data ◽  
Sigma Level ◽  
Cloud Properties ◽  
Simple Equations

Abstract. Using cloud data from MODIS we investigate the response of cloud microphysics to sudden decreases in galactic cosmic radiation – Forbush decreases – and find responses in effective emissivity, cloud fraction, liquid water content, and optical thickness above the 2–3 sigma level 6–9 days after the minimum in atmospheric ionization and less significant responses for effective radius and cloud condensation nuclei (<2 sigma). The magnitude of the signals agree with derived values, based on simple equations for atmospheric parameters. Furthermore principal components analysis gives a total significance of the signal of 3.1 sigma. We also see a correlation between total solar irradiance and strong Forbush decreases but a clear mechanism connecting this to cloud properties is lacking. There is no signal in the UV radiation. The responses of the parameters correlate linearly with the reduction in the cosmic ray ionization. These results support the suggestion that ions play a significant role in the life-cycle of clouds.

scholarly journals An intercomparison of radar-based liquid cloud microphysics retrievals and implication for model evaluation studies

2011 ◽  
Vol 4 (6) ◽  
pp. 7109-7158 ◽  
Author(s):  
D. Huang ◽  
C. Zhao ◽  
M. Dunn ◽  
X. Dong ◽  
G. G. Mace ◽  
...  
Keyword(s):  
Great Plains ◽  
Liquid Water ◽  
Model Evaluation ◽  
Evaluation Studies ◽  
Cloud Droplet ◽  
Liquid Water Content ◽  
Cloud Microphysics ◽  
Effective Radius ◽  
High Altitudes ◽  
Liquid Water Path

Abstract. To assess if current radar-based liquid cloud microphysical retrievals of the Atmospheric Radiation Measurement (ARM) program can provide useful constraints for modeling studies, this paper presents intercomparison results of three cloud products at the Southern Great Plains (SGP) site: the ARM MICROBASE, University of Utah (UU), and University of North Dakota (UND) products over the nine-year period from 1998 to 2006. The probability density and spatial autocorrelation functions of the three cloud Liquid Water Content (LWC) retrievals appear to be consistent with each other, while large differences are found in the droplet effective radius retrievals. The differences in the vertical distribution of both cloud LWC and droplet effective radius retrievals are found to be alarmingly large, with the relative difference between nine-year mean cloud LWC retrievals ranging from 20% at low altitudes to 100% at high altitudes. Nevertheless, the spread in LWC retrievals is much smaller than that in cloud simulations by climate and cloud resolving models. The MICROBASE effective radius ranges from 2.0 at high altitudes to 6.0 μm at low altitudes and the UU and UND droplet effective radius is 6 μm larger. Further analysis through a suite of retrieval experiments shows that the difference between MICROBASE and UU LWC retrievals stems primarily from the partition total Liquid Water path (LWP) into supercooled and warm liquid, and from the input cloud boundaries and LWP. The large differences between MICROBASE and UU droplet effective radius retrievals are mainly due to rain/drizzle contamination and the assumptions of cloud droplet concentration used in the retrieval algorithms. The large discrepancy between different products suggests caution in model evaluation with these observational products, and calls for improved retrievals in general.

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