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Cloud processes relevant to radiative and precipitation properties depend on the shape of the cloud droplet size distribution. Recent holographic observations revealed that cloud droplet populations do not have the same size distribution shapes throughout but form regions of characteristic distributions with similar microphysical properties. We investigate the existence and properties of these characteristic distributions within Large‐Eddy Simulations of stratocumulus clouds using Lagrangian and bin microphysics schemes. Distribution types are identified, revealing localized characteristic distributions that vary on the scale of the largest convective cell for simulations with bin microphysics. The results from the Lagrangian microphysics scheme hint at similar behavior. Compared to observations, the simulated clouds are much more uniform. Analysis of the LES results suggests a connection to the local entrainment rate, so the poorly resolved entrainment interface in LES may be a cause of the uniformity. The uniformity of the large‐scale forcing could also be a factor.

Cloud microphysical properties are significantly affected by entrainment and mixing processes. However, it is unclear how the entrainment rate affects the relative dispersion of cloud droplet size distribution. Previously, the relationship between relative dispersion and entrainment rate was found to be positive or negative. To reconcile the contrasting relationships, the Explicit Mixing Parcel Model is used to determine the underlying mechanisms. When evaporation is dominated by small droplets, and the entrained environmental air is further saturated during mixing, the relationship is negative. However, when the evaporation of big droplets is dominant, the relationship is positive. Whether or not the cloud condensation nuclei are considered in the entrained environmental air is a key factor as condensation on the entrained condensation nuclei is the main source of small droplets. However, if cloud condensation nuclei are not entrained, the relationship is positive. If cloud condensation nuclei are entrained, the relationship is dependent on many other factors. High values of vertical velocity, relative humidity of environmental air, and liquid water content, and low values of droplet number concentration, are more likely to cause the negative relationship since new saturation is easier to achieve by evaporation of small droplets. Further, the signs of the relationship are not strongly affected by the turbulence dissipation rate, but the higher dissipation rate causes the positive relationship to be more significant for a larger entrainment rate. A conceptual model is proposed to reconcile the contrasting relationships. This work enhances the understanding of relative dispersion and lays a foundation for the quantification of entrainment-mixing mechanisms.

Relative dispersion (ε), as a parameter characterizing droplet spectral shape, exerts a considerable impact on cloud radiation and precipitation processes, and its accurate parameterization is urgently needed in models. Current ε parameterizations, which are based on droplet number concentration or simply set as constants, are inadequate to satisfy the demand. This study shows, utilizing in‐situ cloud and fog observations from five underlying surface regions (urban, suburban, mountainous, coastal and rainforest) of China, that ε uniformly and stably manifests as initially increasing then decreasing as volume‐mean diameter increases across these regions. Based on this relationship, a ε parameterization is established, which exhibits improved predictive capabilities in evaluating both cloud albedo effect and cloud lifetime effect. The parameterization is expected to enhance cloud simulation accuracy and minimize discrepancy between observed and simulated cloud radiation and precipitation, particularly for weather and climate models that commonly use the double‐moment cloud microphysical schemes. Plain Language Summary Clouds play a crucial role in the Earth's weather and climate. One key factor in understanding cloud behavior is the width of cloud droplet size distribution, quantified by relative dispersion. Accurately representing relative dispersion in weather and climate models is essential, yet current methods are often overly simplistic. Many models either rely on fixed values or use empirical monotonic equations based solely on droplet number concentration. However, the relationship between relative dispersion and droplet number concentration varies significantly across regions and can even be contradictory. In this study, we analyzed cloud and fog observations from five different regions, encompassing urban, suburban, mountainous, coastal, and rainforest environments. Our analysis revealed a consistent pattern: relative dispersion first increases and then decreases as the volume‐mean droplet diameter grows. Based on this insight, we developed a new method to predict relative dispersion. This approach has the potential to improve the accuracy of estimating cloud albedo and lifetime effects, enhancing the representation of aerosol‐cloud interactions in weather and climate models. Key Points Correlation between droplet spectral dispersion and volume‐mean diameter remains consistent across different regions Compared to previous dispersion parameterizations, the parameterization with volume‐mean diameter provides better predictions for dispersion The dispersion parameterization with volume‐mean diameter could reduce the uncertainty in simulating aerosol indirect effects

Atmospheric aerosols not only cause severe haze pollution, but also affect climate through changes in cloud properties. However, during the haze pollution, aerosol‐cloud interactions are not well understood due to a lack of in situ observations. In this study, we conducted simultaneous observations of cloud droplet and particle number size distribution, together with supporting atmospheric parameters, from ground to cloud base in East China using a high‐payload tethered airship. We found that high concentrations of aerosols and cloud condensation nuclei were constrained below cloud, leading to the pronounced “Twomey effect” near the cloud base. The cloud inhibited the pollutants dispersion by reducing surface heat flux and thus deteriorated the near‐surface haze pollution. Satellite retrievals matched well with the in situ observations for low stratus clouds, while were insufficient to quantify aerosol‐cloud interactions for other cases. Our results highlight the importance to combine in situ vertical and satellite observations to quantify the aerosol‐cloud interactions. Plain Language Summary Atmospheric aerosols, one of the major pollutants contributing to air pollution, also play an important role in climate through their interactions with clouds. The impact of aerosols on cloud properties remains the largest uncertainty in climate projections, partly due to a lack of in situ observations. Here, we conducted simultaneous observations on atmospheric aerosols and clouds from ground to 1,200 m above ground level in East China using a high‐payload tethered airship. We found aerosols number concentration was high below the clouds, which increased the cloud droplet concentration and decreased the cloud droplet diameter near cloud base. The clouds deteriorated the near‐surface air pollution, thus increasing exposure to hazardous levels. For low stratiform clouds, the satellite retrievals matched well with the observations, suggesting the satellite observation is a powerful tool to investigate clouds. However, the aerosol‐cloud interactions can still be underestimated by satellite measurements as the satellites record cloud properties near cloud top. We emphasize the need for direct in situ observations from the ground to high altitudes to quantify the effects of aerosols on cloud properties. Key Points The pronounced Twomey effect near the cloud base was directly observed during the haze pollution by the tethered airship measurement The observed Twomey effect at the cloud base in East China contradicts the satellite‐detected anti‐Twomey effect at the top of clouds Satellite retrieved cloud effective radius is comparable to observation near cloud base of low stratus clouds, while is biased for others

Changes in aerosol amount and size distribution significantly impact cloud droplet size distribution, as aerosols act as cloud condensation nuclei (CCNs) and influence the relative dispersion (ε) of cloud droplet spectra. Relative dispersion plays a key role in parameterizing cloud processes in general circulation models (GCMs) and microphysical schemes, affecting precipitation estimates and climate predictions. However, the effects of varying aerosol modes on cloud microphysics remain debated, depending on thermodynamic conditions and cloud type. This study simulates a warm stratiform cloud in Jiangxi, China, using the Weather Research and Forecasting (WRF) Spectra–Bin Microphysics scheme (SBM-FAST) from 18:00 on 24 December 2014 to 06:00 on 25 December 2014 (UTC). Satellite and aircraft observations were used to validate the simulation, showing good agreement in cloud structure. Sensitivity experiments were conducted by increasing nucleation, accumulation, and coarse-mode aerosols 5-fold and by reducing the total aerosol concentration to 1/5 of the control. Results show that higher aerosol concentrations enhance cloud formation and broaden droplet spectra, while lower concentrations suppress cloud development. Accumulation-mode aerosols increase small-droplet concentrations, while nucleation- and coarse-mode aerosols favor larger droplets. The correlation between ε and volume-weighted radius (Rv) shifts from positive to negative as Rv increases. This transition is driven by cloud droplet collision–coalescence, condensation, and activation. Increased accumulation-mode aerosol concentrations shift the ε–Rv correlation from negative to positive in the Rv range of 4.5–8 µm, while reduced aerosol concentrations strengthen the negative correlation. Regardless of different coalescence intensities, ε converges with the increase in number concentration of cloud droplets (Nc).

An intercomparison between 10 single-column (SCM) and 5 large-eddy simulation (LES) models is presented for a radiation fog case study inspired by the Local and Non-local Fog Experiment (LANFEX) field campaign. Seven of the SCMs represent single-column equivalents of operational numerical weather prediction (NWP) models, whilst three are research-grade SCMs designed for fog simulation, and the LESs are designed to reproduce in the best manner currently possible the underlying physical processes governing fog formation. The LES model results are of variable quality and do not provide a consistent baseline against which to compare the NWP models, particularly under high aerosol or cloud droplet number concentration (CDNC) conditions. The main SCM bias appears to be toward the overdevelopment of fog, i.e. fog which is too thick, although the inter-model variability is large. In reality there is a subtle balance between water lost to the surface and water condensed into fog, and the ability of a model to accurately simulate this process strongly determines the quality of its forecast. Some NWP SCMs do not represent fundamental components of this process (e.g. cloud droplet sedimentation) and therefore are naturally hampered in their ability to deliver accurate simulations. Finally, we show that modelled fog development is as sensitive to the shape of the cloud droplet size distribution, a rarely studied or modified part of the microphysical parameterisation, as it is to the underlying aerosol or CDNC.

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.

We perform a model intercomparison of summertime high Arctic (> 80∘ N) clouds observed during the 2008 Arctic Summer Cloud Ocean Study (ASCOS) campaign, when observed cloud condensation nuclei (CCN) concentrations fell below 1 cm−3. Previous analyses have suggested that at these low CCN concentrations the liquid water content (LWC) and radiative properties of the clouds are determined primarily by the CCN concentrations, conditions that have previously been referred to as the tenuous cloud regime. The intercomparison includes results from three large eddy simulation models (UCLALES-SALSA, COSMO-LES, and MIMICA) and three numerical weather prediction models (COSMO-NWP, WRF, and UM-CASIM). We test the sensitivities of the model results to different treatments of cloud droplet activation, including prescribed cloud droplet number concentrations (CDNCs) and diagnostic CCN activation based on either fixed aerosol concentrations or prognostic aerosol with in-cloud processing. There remains considerable diversity even in experiments with prescribed CDNCs and prescribed ice crystal number concentrations (ICNC). The sensitivity of mixed-phase Arctic cloud properties to changes in CDNC depends on the representation of the cloud droplet size distribution within each model, which impacts autoconversion rates. Our results therefore suggest that properly estimating aerosol–cloud interactions requires an appropriate treatment of the cloud droplet size distribution within models, as well as in situ observations of hydrometeor size distributions to constrain them. The results strongly support the hypothesis that the liquid water content of these clouds is CCN limited. For the observed meteorological conditions, the cloud generally did not collapse when the CCN concentration was held constant at the relatively high CCN concentrations measured during the cloudy period, but the cloud thins or collapses as the CCN concentration is reduced. The CCN concentration at which collapse occurs varies substantially between models. Only one model predicts complete dissipation of the cloud due to glaciation, and this occurs only for the largest prescribed ICNC tested in this study. Global and regional models with either prescribed CDNCs or prescribed aerosol concentrations would not reproduce these dissipation events. Additionally, future increases in Arctic aerosol concentrations would be expected to decrease the frequency of occurrence of such cloud dissipation events, with implications for the radiative balance at the surface. Our results also show that cooling of the sea-ice surface following cloud dissipation increases atmospheric stability near the surface, further suppressing cloud formation. Therefore, this suggests that linkages between aerosol and clouds, as well as linkages between clouds, surface temperatures, and atmospheric stability need to be considered for weather and climate predictions in this region.

Cloud droplet size distribution (DSD) is essential for quantifying the roles of clouds in earth system, including cloud albedo, precipitation formation, and cloud lifetime. The response of cloud droplet spectral relative dispersion (ε) to aerosol number concentration (Na) as well as the role of turbulence in this response are yet puzzling. This study uses large eddy simulation to examine the ε–Na relationship and derives an expression for ε from a minimal model to elucidate this relationship. Our findings indicate that as Na increases, ε initially decreases because aerosols weaken turbulence‐induced broadening more than condensational narrowing. However, as Na continues to rise, ε increases as aerosols weaken condensational narrowing more significantly than turbulence‐induced broadening. These findings improve the understanding of the aerosol effects on cloud DSD and address the challenge of quantifying aerosol indirect effects considering turbulence, potentially leading to new cloud microphysics parameterizations. Plain Language Summary The width of cloud droplet size distribution (DSD) significantly impacts cloud radiative properties and rain formations. However, its observed response to varying aerosol concentration is contrasting, and theoretical explanations are lacking. Using an advanced high‐resolution model, this study reveals that cloud DSD first narrows and then broadens as aerosol concentration increases. Furthermore, we propose a mathematical expression which explains the contrasting relationships by considering turbulence. These findings provide a comprehensive insight into the interplay between aerosol loading, cloud DSD, and turbulence within clouds. Key Points Cloud droplet size distribution first narrows and then broadens with increasing aerosol loading in large eddy simulations A minimal model is proposed to explain the non‐monotonic dependence of cloud droplet spectral relative dispersion on aerosol loading The results reconcile both non‐turbulent and turbulent scenarios regarding relationships between relative dispersion and aerosol loading

The glory, a striking optical phenomenon seen from space in unpolarized satellite images can be mapped onto the cloud's droplet sizes with a characteristic scale of 10μm$\\mu m$ . Such a mapping allows us to infer the mean and variance of the cloud droplets' radius, an important property that has remained elusive and inaccessible to passive unpolarized satellite sensing. Here, we propose a simple and robust polarization‐like differential approach to map the glory's spectral properties to the desired moments of the droplet size distribution. By taking the differences between two spectrally close channels, we reduce multiple scattering contributions and amplify the single‐scattering signal, thus allowing for a simple and rapidly converging map from glory to droplet size distribution. Moreover, the droplet information reflects the upper part of the cloud, adding another sample to the traditional multiple scattering‐based retrievals that reflect droplet properties deeper in the cloud. Plain Language Summary Glories appear as colored rings around the perfect backscatter angle. The pattern can often be seen in mountains where the sun is behind the observer, projecting the observer's shadow onto a cloud. Faint glories can be found in non‐polarized satellite images, forming over marine Stratocumulus clouds. We can translate the optical properties of the glories that were collected by satellites for more than 20 years to the cloud's droplet size‐distribution (DSD) mean and variance. So far, the cloud's DSD variance, which is an important climate property, was not retrieved from non‐polarized satellite data. To do so in a simple and robust way, we propose a differential approach that enhances the glory's signal relative to the background reflectance. The DSD information that is retrieved from the glory reflects the uppermost part of the cloud. An area that reflects the interactions between the cloud and the cloud‐free atmosphere above it. Moreover, for thick clouds, it provides information from a higher point along the cloud profile in addition to the information that is retrieved by the multi‐scattering approaches that represent deeper areas within the cloud. Key Points A simple and robust method is proposed to map the glory's properties to the droplets' mean and variance The method uses unpolarized reflectance images in a few spectral bands, enabling the analysis of 20 years of moderate resolution imaging spectroradiometer (MODIS) data Differences between two spectral images enhance the glory's single scattering signal relative to the background