Cloud Droplet Size Distribution Research Articles

Life Cycle Evolution of Mixing in Shallow Cumulus Clouds

AbstractUnderstanding how entrainment and mixing shape the cloud droplet size distribution (DSD) is crucial for understanding the optical properties and precipitation efficiency of clouds. Different mixing scenarios, mainly homogeneous and inhomogeneous, shape the DSD in a distinct way and alter the cloud's impact on climate. However, the prevalence of these mixing scenarios and how they vary in space and time is still uncertain, as underlying processes are commonly unresolved by conventional numerical models. To overcome this challenge, we employ the L3 model, which considers supersaturation fluctuations and turbulent mixing down to the finest relevant lengthscales, making it possible to represent different mixing scenarios realistically. We investigate the spatial and temporal evolution of mixing scenarios over the life cycle of shallow cumulus clouds for varying boundary layer humidities and aerosol concentrations. Our findings suggest homogeneous mixing is generally predominant in cumulus clouds, while different mixing scenarios occur concurrently in the same cloud. Notably, inhomogeneous mixing increases over the cloud life cycle across all analyzed cases. The mean and standard deviation of supersaturation are found to be the most capable indicators of this evolution, providing a comprehensive insight into the characteristics of mixing scenarios. Finally, we show inhomogeneous mixing is more prevalent in drier boundary layers and for higher aerosol concentrations, underscoring the need for a more comprehensive investigation of how these mixing dynamics evolve in a changing climate.

Locally narrow droplet size distributions are ubiquitous in stratocumulus clouds.

Marine stratocumulus clouds are the "global reflectors," sharply contrasting with the underlying dark ocean surface and exerting a net cooling on Earth's climate. The magnitude of this cooling remains uncertain in part owing to the averaged representation of microphysical processes, such as the droplet-to-drizzle transition in global climate models (GCMs). Current GCMs parameterize cloud droplet size distributions as broad, cloud-averaged gammas. Using digital holographic measurements of discrete stratocumulus cloud volumes, we found cloud droplet size distributions to be narrower at the centimeter scale, never resembling the cloud average. These local distributions tended to form pockets of similar-looking cloud regions, each characterized by a size distribution shape that is diluted to varying degrees. These observations open the way for new modeling representations of microphysical processes.

Cloud microphysical response to entrainment and mixing is locally inhomogeneous and globally homogeneous: Evidence from the lab

Entrainment of dry air into clouds strongly influences cloud optical and precipitation properties and the response of clouds to aerosol perturbations. The response of cloud droplet size distributions to entrainment-mixing is examined in the Pi convection-cloud chamber that creates a turbulent, steady-state cloud. The experiments are conducted by injecting dry air with temperature (Te) and flow rate (Qe) through a flange in the top boundary, into the otherwise well-mixed cloud, to mimic the entrainment-mixing process. Due to the large-scale circulation, the downwind region is directly affected by entrained dry air, whereas the upwind region is representative of the background conditions. Droplet concentration (Cn) and liquid water content (L) decrease in the downwind region, but the difference in the mean diameter of droplets (Dm) is small. The shape of cloud droplet size distributions relative to the injection point is unchanged, to within statistical uncertainty, resulting in a signature of inhomogeneous mixing, as expected for droplet evaporation times small compared to mixing time scales. As Te and Qe of entrained air increase, however, Cn, L, and Dm of the whole cloud system decrease, resulting in a signature of homogeneous mixing. The apparent contradiction is understood as the cloud microphysical responses to entrainment and mixing differing on local and global scales: locally inhomogeneous and globally homogeneous. This implies that global versus local sampling of clouds can lead to seemingly contradictory results for mixing, which informs the long-standing debate about the microphysical response to entrainment and the parameterization of this process for coarse-resolution models.

Paths From Aerosol Particles to Activation and Cloud Droplets in Shallow Cumulus Clouds: The Roles of Entrainment and Supersaturation Fluctuations

AbstractThe activation of aerosol particles and its contribution to cloud droplet size distributions in shallow cumulus clouds are investigated by analyzing the cloud field simulation results using a Lagrangian cloud model coupled with large eddy simulation, which allows explicit simulations of the activation and tracking of each super‐droplet. Analysis is focused on the differences in activations for aerosol particles following central updraft from a cloud base (CB) and particles entrained above the cloud base (EA). Time series of various variables following individual particles confirm the distinctive nature of CB and EA activation. Strong supersaturation fluctuations induced by entrainment and turbulent mixing at cloud edges cause most EA particles to be deactivated soon after activation, substantially limiting their lifetime and growth relative to CB particles. Most net activation—the difference between activation and deactivation—occurs at the cloud base and vanishes aloft. Cloud droplet activation spectra follow the theoretical prediction at high supersaturation conditions, although the proportional constant is smaller because of particle deactivation. The spectra deviate from the theoretical prediction at low supersaturation conditions since they are affected by the transport of the CB particles activated elsewhere.

Influence of natural and anthropogenic aerosols on cloud base droplet size distributions in clouds over the South China Sea and West Pacific

Abstract. Cumulus clouds are common over maritime regions. They are important regulators of the global radiative energy budget and global hydrologic cycle, as well as a key contributor to the uncertainty in anthropogenic climate change projections due to uncertainty in aerosol–cloud interactions. These interactions are regionally specific owing to their strong influences on aerosol sources and meteorology. Here, our analysis focuses on the statistical properties of marine boundary layer (MBL) aerosol chemistry and the relationships of MBL aerosol to cumulus cloud properties just above cloud base as sampled in 2019 during the NASA Cloud, Aerosol and Monsoon Processes Philippines Experiment (CAMP2Ex). The aerosol and clouds were sampled by instruments on the NASA P-3 aircraft over three distinct maritime regions around the Philippines: the West Pacific, the South China Sea, and the Sulu Sea. Our analysis shows three primary sources influenced the aerosol chemical composition: clean marine (ocean source), industrial (Southeast Asia, Manila, and cargo and tanker ship emissions), and biomass burning (Borneo and Indonesia). The clean marine aerosol chemical composition had low values of all sampled chemical signatures, specifically median values of 2.2 µg m−3 of organics (ORG), 2.3 µg m−3 of SO4, 0.3 µg m−3 of NO3, 1.4 µg m−3 of NH4, 0.04 µg m−3 of Cl, and 0.0074 µg m−3 of refractory black carbon (BC). Chemical signatures of the other two aerosol source regions were industrial, with elevated SO4 having a median value of 6.1 µg m−3, and biomass burning, with elevated median concentrations of ORG 21.2 µg m−3 and BC 0.1351 µg m−3. Based on chemical signatures, the industrial component was primarily from ship emissions, which were sampled within 60 km of ships and within projected ship plumes. Normalized cloud droplet size distributions in clouds sampled near the MBL passes of the P-3 showed that clouds impacted by industrial and biomass burning contained higher concentrations of cloud droplets, by as much as 1.5 orders of magnitude for diameters < 13 µm compared to clean marine clouds, while at size ranges between 13.0–34.5 µm the median concentrations of cloud droplets in all aerosol categories were nearly an order of magnitude less than the clean marine category. In the droplet size bins centered at diameters > 34.5 µm concentrations were equal to, or slightly exceeded, the concentrations of the clean marine clouds. These analyses show that anthropogenic aerosols generated from industrial and biomass burning sources significantly influenced cloud base microphysical structure in the Philippine region enhancing the small droplet concentration and reducing the concentration of mid-sized droplets.

Use of multiplatform in-situ observation to study vertical structure and microphysics of clouds during southwest monsoon over Western Ghats, India

Combination of radiosonde profiles with collocated in-situ ground and aircraft measurements is used for the first time to study the vertical structure and microphysics of clouds during southwest monsoon over the Western Ghats, India. The morphology of clouds is detailed with the help of radiosonde observations and classified as low, mid, and high-level clouds depending on the cloud base height. Radiosonde sounding profiles indicated occurrences of both single and multi-layered clouds with higher occurrences of single-layered (∼35%) clouds during monsoon transition period (June and September) and two-layered (∼42%) during core monsoon period (July and August). Dominance of low (∼30%) and high-level (∼60%) clouds were noticed compared to mid-level clouds over the observational site during the southwest monsoon.Warm cloud microphysics was investigated using collocated ground and airborne in situ measurements. Irrespective of the cloud type, the cloud liquid water content and the effective droplet diameter increased with altitude. One of the key results is the rapid broadening of the cloud droplet size distribution with height. The number concentration of droplets above 25 μm diameters showed a steep decrease at altitudes above 1800 m, suggesting active collision-coalescence.

Comparison of Cloud Properties between SGLI Aboard GCOM-C Satellite and MODIS Aboard Terra Satellite

This study presents a comprehensive comparison of Level 2.0 cloud properties between a Second-generation Global Imager (SGLI) aboard the GCOM-C satellite and a Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Terra satellite, to better understand the qualities of cloud properties obtained from SGLI/GCOM-C launched on 23 December 2017. The cloud pixels identified as water phase by both satellite sensors are highly consistent to each other by more than 90%, although the consistency is only ~60% for ice phase cloud pixels. A comparison of cloud properties—cloud optical thickness (COT) and cloud particle effective radius (CER)—between these two satellite sensors reveals that water and ice cloud properties can have different degrees of agreement depending on underlying surface. The relative difference (RD) values of 22% (18%) and 37% (24%) for water cloud COT (CER) comparison over ocean and land surfaces and respective values of 35% (42%) and 35% (62%) for comparisons of ice cloud properties, and also other comparison metrics, suggest better agreements for water cloud properties than for ice cloud properties, and for ocean surface than for land surface. Though cloud properties differences between MODIS and SGLI can arise from inherent features of cloud retrieval algorithms, such as differences in ancillary data, surface reflectance, cloud droplet size distribution function, model for ice particle habit, etc., this study further identifies the important roles of cloud thickness and Sun and satellite positions for differences in cloud properties between SGLI and MODIS: the differences in cloud properties are found to increase for thinner clouds, higher solar zenith angle, and higher differences in viewing zenith and azimuth angles between these satellite sensors, and such differences are more distinct for water cloud properties than for ice cloud properties.

High-spatial-resolution retrieval of cloud droplet size distribution from polarized observations of the cloudbow

Abstract. The cloud droplet size distribution is often described by a gamma distribution defined by the effective radius and the effective variance. The effective radius is directly related to the cloud's optical thickness, which influences the radiative properties of a cloud. The effective variance affects, among other things, the evolution of precipitation. Both parameters can be retrieved from measurements of the cloudbow. The cloudbow (or rainbow) is an optical phenomenon that forms due to the single scattering of radiation by liquid cloud droplets at the cloud edge. The polarized radiance of the cloudbow crucially depends on the cloud droplet size distribution. The effective radius and the effective variance can be retrieved by fitting model simulations (stored in a lookup table) to polarized cloudbow observations. This study uses measurements from the wide-field polarization-sensitive camera of the spectrometer of the Munich Aerosol Cloud Scanner (specMACS) aboard the German “High Altitude and LOng range research aircraft” (HALO). Along with precise cloud geometry data derived by a stereographic method, a geolocalization of the observed clouds is possible. Observations of the same cloud from consecutive images are combined into one radiance measurement from multiple angles. Two case studies of trade-wind cumulus clouds measured during the EUREC4A (ElUcidating the RolE of Cloud-Circulation Coupling in ClimAte) field campaign are presented, and the cloudbow technique is demonstrated. The results are combined into maps of the effective radius and the effective variance with a 100 m × 100 m spatial resolution and large coverage (across-track swath width of 8 km). The first case study shows a stratiform cloud deck with distinct patches of large effective radii up to 40 µm and a median effective variance of 0.11. specMACS measures at a very high angular resolution (binned to 0.3∘) which is necessary when large droplets are present. The second case study consists of small cumulus clouds (diameters of approximately 2 km). The retrieved effective radius is 7.0 µm, and the effective variance is 0.08 (both median values). This study demonstrates that specMACS is able to determine the droplet size distribution of liquid water clouds even for small cumulus clouds, which are a problem for traditional droplet size retrievals based on total reflectances.

Scaling of Turbulence and Microphysics in a Convection–Cloud Chamber of Varying Height

AbstractThe convection–cloud chamber enables measurement of aerosol and cloud microphysics, as well as their interactions, within a turbulent environment under steady‐state conditions. Increasing the size of a convection–cloud chamber, while holding the imposed temperature difference constant, leads to increased Rayleigh, Reynolds and Nusselt numbers. Large–eddy simulation coupled with a bin microphysics model allows the influence of increased velocity, time, and spatial scales on cloud microphysical properties to be explored. Simulations of a convection–cloud chamber, with fixed aspect ratio and increasing heights of H = 1, 2, 4, and (for dry conditions only) 8 m are performed. The key findings are: Velocity fluctuations scale as H1/3, consistent with the Deardorff expression for convective velocity, and implying that the turbulence correlation time scales as H2/3. Temperature and other scalar fluctuations scale as H−3/7. Droplet size distributions from chambers of different sizes can be matched by adjusting the total aerosol injection rate as the horizontal cross‐sectional area (i.e., as H2 for constant aspect ratio). Injection of aerosols at a point versus distributed throughout the volume makes no difference for polluted conditions, but can lead to cloud droplet size distribution broadening in clean conditions. Cloud droplet growth by collision and coalescence leads to a broader right tail of the distribution compared to condensation growth alone, and this tail increases in magnitude and extent monotonically as the increase of chamber height. These results also have implications for scaling within turbulent, cloudy mixed‐layers in the atmosphere, such as fog layers.

Impact of the Microphysics in HARMONIE-AROME on Fog

This study concerns the impact of microphysics on the HARMONIE-AROME NWP model. In particular, the representation of cloud droplets in the single-moment bulk microphysics scheme is examined in relation to fog forecasting. We focus on the shape parameters of the cloud droplet size distribution and recent changes to the representation of the cloud droplet number concentration (CDNC). Two configurations of CDNC are considered: a profile that varies with height and a constant one. These aspects are examined together since few studies have considered their combined impact during fog situations. We present a set of six experiments performed for two non-idealised three-dimensional case studies over the Iberian Peninsula and the North Sea. One case displays both low clouds and fog, and the other shows a persistent fog field above sea. The experiments highlight the importance of the considered parameters that affect droplet sedimentation, which plays a key role in modelled fog. We show that none of the considered configurations can simultaneously represent all aspects of both cases. Hence, continued efforts are needed to introduce relationships between the governing parameters and the relevant atmospheric conditions.

The impact of microphysical uncertainty conditional on initial and boundary condition uncertainty under varying synoptic control

Abstract. The relative impact of individual and combined uncertainties of cloud condensation nuclei (CCN) concentration and the shape parameter of the cloud droplet size distribution (CDSD) in the presence of initial and boundary condition uncertainty (IBC) on convection forecasts is quantified using the convection-permitting model ICON-D2 (ICOsahedral Non-hydrostatic). We performed 180-member ensemble simulations for five real case studies representing different synoptic forcing situations over Germany and inspected the precipitation variability on different spatial and temporal scales. During weak synoptic control, the relative impact of combined microphysical uncertainty on daily area-averaged precipitation accounts for about one-third of the variability caused by operational IBC uncertainty. The effect of combined microphysical perturbations exceeds the impact of individual CCN or CDSD perturbations and is twice as large during weak control. The combination of IBC and microphysical uncertainty affects the extremes of daily spatially averaged rainfall of individual members by extending the tails of the forecast distribution by 5 % in weakly forced conditions. The responses are relatively insensitive in strong forcing situations. Visual inspection and objective analysis of the spatial variability in hourly rainfall rates reveal that IBC and microphysical uncertainties alter the spatial variability in precipitation forecasts differently. Microphysical perturbations slightly shift convective cells but affect precipitation intensities, while IBC perturbations scramble the location of convection during weak control. Cloud and rainwater contents are more sensitive to microphysical uncertainty than precipitation and less dependent on synoptic control.

Impacts of combined microphysical and land-surface uncertainties on convective clouds and precipitation in different weather regimes

Abstract. To reduce the underdispersion of precipitation in convective-scale ensemble prediction systems, we investigate the relevance of microphysical and land-surface uncertainties for convective-scale predictability. We use three different initial soil moisture fields and study the response of convective precipitation to varying cloud condensation nuclei (CCN) concentrations and different shape parameters of the cloud droplet size distribution (CDSD) by applying a novel combined-perturbation strategy. Using the new ICOsahedral Non-hydrostatic (ICON) model, we construct a 60-member ensemble for cases with summertime convection under weak and strong synoptic-scale forcing over central Europe. We find a systematic positive soil moisture–precipitation feedback for all cases, regardless of the type of synoptic forcing, and a stronger response of precipitation to different CCN concentrations and shape parameters for weak forcing than for strong forcing. While the days with weak forcing show a systematic decrease in precipitation with increasing aerosol loading, days with strong forcing also show nonsystematic responses for some values of the shape parameters. The large magnitudes of precipitation deviations compared to a reference simulation ranging between −23 % and +18 % demonstrate that the uncertainties investigated here and, in particular, their collective effect are highly relevant for quantitative precipitation forecasting of summertime convection in central Europe. A rainwater budget analysis is used to identify the dominating source and sink terms and their response to the uncertainties applied in this study. Results also show a dominating cold-rain process for all cases and a strong but mostly nonsystematic impact on the release of latent heat, which is considered to be the prime mechanism for the upscale growth of small errors affecting the predictability of convective systems. The combined ensemble spread when accounting for all three uncertainties lies in the same range as the ones from an operational convective-scale ensemble prediction system with 20 members determined in previous studies. This indicates that the combination of different perturbations used in our study may be suitable for ensemble forecasting and that this method should be evaluated against other sources of uncertainty.

Sensitivity analysis of an aerosol-aware microphysics scheme in Weather Research and Forecasting (WRF) during case studies of fog in Namibia

Abstract. Aerosol-aware microphysics parameterisation schemes are increasingly being introduced into numerical weather prediction models, allowing for regional and case-specific parameterisation of cloud condensation nuclei (CCN) and cloud droplet interactions. In this paper, the Thompson aerosol-aware microphysics scheme, within the Weather Research and Forecasting (WRF) model, is used for two fog cases during September 2017 over Namibia. Measurements of CCN and fog microphysics were undertaken during the AErosols, RadiatiOn and CLOuds in southern Africa (AEROCLO-sA) field campaign at Henties Bay on the coast of Namibia during September 2017. A key concept of the microphysics scheme is the conversion of water-friendly aerosols to cloud droplets (hereafter referred to as CCN activation), which could be estimated from the observations. A fog monitor 100 (FM-100) provided cloud droplet size distribution, number concentration (Nt), liquid water content (LWC), and mean volumetric diameter (MVD). These measurements are used to evaluate and parameterise WRF model simulations of Nt, LWC, and MVD. A sensitivity analysis was conducted through variations to the initial CCN concentration, CCN radius, and the minimum updraft speed, which are important factors that influence droplet activation in the microphysics scheme of the model. The first model scenario made use of the default settings with a constant initial CCN number concentration of 300 cm−3 and underestimated the cloud droplet number concentration, while the LWC was in good agreement with the observations. This resulted in droplet size being larger than the observations. Another scenario used modelled data as CCN initial conditions, which were an order of magnitude higher than other scenarios. However, these provided the most realistic values of Nt, LWC, MVD, and droplet size distribution. From this, it was concluded that CCN activation of around 10 % in the simulations is too low, while the observed appears to be higher reaching between 20 % and 80 %, with a mean (median) of 0.55 (0.56) during fog events. To achieve this level of activation in the model, the minimum updraft speed for CCN activation was increased from 0.01 to 0.1 m s−1. This scenario provided Nt, LWC, MVD, and droplet size distribution in the range of the observations, with the added benefit of a realistic initial CCN concentration. These results demonstrate the benefits of a dynamic aerosol-aware scheme when parameterised with observations.

Automated Identification of Characteristic Droplet Size Distributions in Stratocumulus Clouds Utilizing a Data Clustering Algorithm

Abstract Droplet-level interactions in clouds are often parameterized by a modified gamma fitted to a “global” droplet size distribution. Do “local” droplet size distributions of relevance to microphysical processes look like these average distributions? This paper describes an algorithm to search and classify characteristic size distributions within a cloud. The approach combines hypothesis testing, specifically, the Kolmogorov–Smirnov (KS) test, and a widely used class of machine learning algorithms for identifying clusters of samples with similar properties: density-based spatial clustering of applications with noise (DBSCAN) is used as the specific example for illustration. The two-sample KS test does not presume any specific distribution, is parameter free, and avoids biases from binning. Importantly, the number of clusters is not an input parameter of the DBSCAN-type algorithms but is independently determined in an unsupervised fashion. As implemented, it works on an abstract space from the KS test results, and hence spatial correlation is not required for a cluster. The method is explored using data obtained from the Holographic Detector for Clouds (HOLODEC) deployed during the Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA) field campaign. The algorithm identifies evidence of the existence of clusters of nearly identical local size distributions. It is found that cloud segments have as few as one and as many as seven characteristic size distributions. To validate the algorithm’s robustness, it is tested on a synthetic dataset and successfully identifies the predefined distributions at plausible noise levels. The algorithm is general and is expected to be useful in other applications, such as remote sensing of cloud and rain properties. Significance Statement A typical cloud can have billions of drops spread over tens or hundreds of kilometers in space. Keeping track of the sizes, positions, and interactions of all of these droplets is impractical, and, as such, information about the relative abundance of large and small drops is typically quantified with a “size distribution.” Droplets in a cloud interact locally, however, so this work is motivated by the question of whether the cloud droplet size distribution is different in different parts of a cloud. A new method, based on hypothesis testing and machine learning, determines how many different size distributions are contained in a given cloud. This is important because the size distribution describes processes such as cloud droplet growth and light transmission through clouds.

Combined effects of soil moisture and microphysical perturbations on convective clouds and precipitation for a locally forced case over Central Europe

AbstractTo study the combined impact of soil moisture and microphysical perturbations on convective clouds and precipitation over Central Europe, an ensemble of five dozen real‐world weather prediction forecasts was conducted with the COnsortium for Small‐scale MOdeling (COSMO) model at convection‐permitting resolution for a case with weak large‐scale forcing (6 June 2016). We find a large sensitivity of precipitation, ranging from +10% to 23% in 12‐hr precipitation totals. While the homogeneous soil‐moisture bias of 25% primarily controls the timing of convection initiation and the amount of surface rainfall, the number of cloud condensation nuclei and width of the cloud droplet size distribution mainly control the number, size, and lifetime of convective clouds. In moisture‐limited conditions, mainly positive couplings are acting. Drier soils, cleaner air, and a broader cloud droplet size distribution result in less rainfall. Wetter soils and more polluted conditions lead to fewer, but larger, cloud clusters. Since microphysical process rates depend systematically on the sign of the perturbations, but rainfall does not, there are compensating effects at work that buffer microphysical perturbations directly and impact the cloud condensate amount and the rainfall at the ground.

Observation on the Droplet Ranging from 2 to 16 μm in Cloud Droplet Size Distribution Based on Digital Holography

Cloud droplets size distribution (DSD) is one of the significant characteristics for liquid clouds. It plays an important role for the aerosol–droplet–cloud mechanism and variation in cloud microphysics. However, the minuscule sampling space is insufficient for the observation of whole DSD when using high-magnification optical systems. In this paper, we propose an observation method for cloud droplets ranging from 2 to 16 μm, by which the balance relationship between sampling space and optical magnification is realized. The method combines an in-line digital holographic interferometer (DHI) with the optical magnification of 5.89× and spatial stitching technique. The minimum size in DSD is extended to 2 μm, which improves the integrity of size distribution. Simultaneously, the stability of DSD is enhanced by increasing the tenfold sampling volume of cloud droplets. The comparative experiment between the in-line DHI and fog monitor demonstrates that the DSD obtained by this method is reliable, which can be used for the analysis of microphysical parameters. In the Beijing Aerosol and Cloud Interaction Chamber (BACIC), the observation results show that the size of cloud droplets follows the Gamma distribution, which is consistent with the theoretical DSD. The results of cloud microphysical parameters indicate that each pair of parameters has a positive correlation, and then the validity of observation method is confirmed. Additionally, the high-concentration aerosol condition significantly mitigates the effect of random turbulence and enhances the robustness of the microphysical parameter data.

Large‐Eddy Simulations of a Convection Cloud Chamber: Sensitivity to Bin Microphysics and Advection

AbstractBin microphysics schemes are useful tools for cloud simulations and are often considered to provide a benchmark for model intercomparison. However, they may experience issues with numerical diffusion, which are not well quantified, and the transport of hydrometeors depends on the choice of advection scheme, which can also change cloud simulation results. Here, an atmospheric large‐eddy simulation model is adapted to simulate a statistically steady‐state cloud in a convection cloud chamber under well‐constrained conditions. Two bin microphysics schemes, a spectral bin method and the method of moments, as well as several advection methods for the transport of the microphysical variables are employed for model intercomparison. Results show that different combinations of microphysics and advection schemes can lead to considerable differences in simulated cloud properties, such as cloud droplet number concentration. We find that simulations using the advection scheme that suffers more from numerical diffusion tends to have a smaller droplet number concentration and liquid water content, while simulation with the microphysics scheme that suffers more from numerical diffusion tends to have a broader size distribution and thus larger mean droplet sizes. Sensitivities of simulations to bin resolution, spatial resolution, and temporal resolution are also tested. We find that refining the microphysical bin resolution leads to a broader cloud droplet size distribution due to the advection of hydrometeors. Our results provide insight for using different advection and microphysics schemes in cloud chamber simulations, which might also help understand the uncertainties of the schemes used in atmospheric cloud simulations.

Influence of aerosol physico-chemical properties on cloud microphysical parameters perceived using in-situ high altitude observations

Variability in cloud microphysical parameters, aerosol physicochemical properties and their relationship were studied using in-situ ground observations from a high altitude site in Western Ghats, India, during monsoon season. Cloud microphysical parameters, such as cloud droplet number concentration (CDNC), liquid water content (LWC), and effective diameter (ED), have been analyzed. Aerosol–CCN (cloud condensation nuclei) number concentration and mass concentration of non-refractory particulate matter (NR-PM1) during summer monsoon also showed day to day and monthly variation. Cloud droplet size distribution (CDSD) showed a bimodal spectrum with two prominent peaks i.e. in 3–10 μm and 12–22 μm size ranges. The estimated spectral width of CDSD (σ) broadly varied from 0 to 4 μm and the effective radius ratio (β) from 0.6–1.8. Further, the effect of aerosol on cloud microphysical parameters and CDSD has been investigated. There was an apparent variation in CCN super saturation spectrum, aerosol chemical composition, and cloud microphysics during high and low aerosol loading daysThe aerosol chemical composition showed a higher sulphate and ammonium (organics) contribution during low (high) aerosol loading days. CCN concentration and CDNC (ED) were higher (less) during high aerosol days and vice versa during low aerosol days. A review of microphysical characteristics of monsoon clouds over various locations of the Indian region has shown that this is the first such report that provides observational evidence of how variation in aerosol physical and chemical properties affects the CCN and cloud properties derived explicitly from long-term ground-based observations.

Importance of aerosols and shape of the cloud droplet size distribution for convective clouds and precipitation

Abstract. The predictability of deep moist convection is subject to large uncertainties resulting from inaccurate initial and boundary data, the incomplete description of physical processes, or microphysical uncertainties. In this study, we investigate the response of convective clouds and precipitation over central Europe to varying cloud condensation nuclei (CCN) concentrations and different shape parameters of the cloud droplet size distribution (CDSD), both of which are not well constrained by observations. We systematically evaluate the relative impact of these uncertainties in realistic convection-resolving simulations for multiple cases with different synoptic controls using the new icosahedral non-hydrostatic ICON model. The results show a large systematic increase in total cloud water content with increasing CCN concentrations and narrower CDSDs, together with a reduction in the total rain water content. This is related to a suppressed warm-rain formation due to a less efficient collision–coalescence process. It is shown that the evaporation at lower levels is responsible for diminishing these impacts on surface precipitation, which lies between +13 % and −16 % compared to a reference run with continental aerosol assumption. In general, the precipitation response was larger for weakly forced cases. We also find that the overall timing of convection is not sensitive to the microphysical uncertainties applied, indicating that different rain intensities are responsible for changing precipitation totals at the ground. Furthermore, weaker rain intensities in the developing phase of convective clouds can allow for a higher convective instability at later times, which can lead to a turning point with larger rain intensities later on. The existence of such a turning point and its location in time can have a major impact on precipitation totals. In general, we find that an increase in the shape parameter can produce almost as large a variation in precipitation as a CCN increase from maritime to polluted conditions. The narrowing of the CDSD not only decreases the absolute values of autoconversion and accretion but also decreases the relative role of the warm-rain formation in general, independent of the prevailing weather regime. We further find that increasing CCN concentrations reduce the effective radius of cloud droplets in a stronger manner than larger shape parameters. The cloud optical depth, however, reveals a similarly large increase with larger shape parameters when changing the aerosol load from maritime to polluted. By the frequency of updrafts as a function of height, we show a negative aerosol effect on updraft strength, leading to an enervation of deep convection. These findings demonstrate that both the CCN assumptions and the CDSD shape parameter are important for quantitative precipitation forecasting and should be carefully chosen if double-moment schemes are used for modeling aerosol–cloud interactions.

An extensive data set for in situ microphysical characterization of low-level clouds in a Finnish sub-Arctic site

Abstract. Continuous, semi-long-term, ground-based in situ cloud measurements were conducted during eight Pallas Cloud Experiments (PaCEs) held in autumn between 2004 and 2019. Those campaigns were carried out in the Finnish sub-Arctic region at the Sammaltunturi station (67∘58′24′′ N, 24∘06′58′′ E; 560 m a.m.s.l.), the part of the Pallas Atmosphere–Ecosystem Supersite and Global Atmosphere Watch (GAW) program. Two cloud spectrometer ground setups and a weather station were installed on the roof of the station to measure in situ cloud properties and several meteorological variables. Thus, the obtained data sets include the size distribution of cloud droplets as a measured cloud parameter along with the air temperature, dew point temperature, humidity, pressure, horizontal wind speed and direction, (global solar) sun radiation, and visibility at the station. Additionally, the number concentration, effective diameter, median volume diameter, and liquid water content from each instrument were derived. The presented data sets provide a insight into microphysics of low-level clouds in sub-Arctic conditions over a wide range of temperatures (−25.8 to 8.8 ∘C). The data are available in the Finnish Meteorological Institute (FMI) open data repository for each campaign and each cloud spectrometer ground setup individually: https://doi.org/10.23728/FMI-B2SHARE.988739D21B824C709084E88ED6C6D54B (Doulgeris et al., 2021).