The Impact of Cumulus Clouds and CCN Regeneration on Aerosol Vertical Distribution and Size

Yael Arieli Department of Earth and Planetary Science, Weizmann Institute of Science, Rehovot, Israel

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Alexander Khain Institute of Earth Science, Hebrew University, Jerusalem, Israel

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Ehud Gavze Institute of Earth Science, Hebrew University, Jerusalem, Israel

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Orit Altaratz Department of Earth and Planetary Science, Weizmann Institute of Science, Rehovot, Israel

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Eshkol Eytan Department of Earth and Planetary Science, Weizmann Institute of Science, Rehovot, Israel

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Ilan Koren Department of Earth and Planetary Science, Weizmann Institute of Science, Rehovot, Israel

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Abstract

This study employs a high-resolution (10 m) System for Atmospheric Modeling (SAM) coupled with the spectral bin microphysical (SBM) scheme to thoroughly investigate the processes governing the evolution of aerosol properties within and outside a shallow cumulus cloud. The model encompasses the complete life cycle of cloud droplets, starting from their formation through their evolution until their complete evaporation or sedimentation to the ground. Additionally, the model tracks the aerosols’ evolution both within the droplets and in the air. Aerosols are transported within the droplets, grow by droplet coalescence, and are released into the atmosphere after droplet evaporation (regeneration process). The aerosol concentration increases by droplet evaporation and decreases along with falling drops. So, the effects of clouds on the surrounding aerosols depend on the microphysical and dynamic processes, which in turn depend on the amount of background aerosols; here, we compare clean and polluted conditions. It is shown that the regeneration process is highly important and that shallow trade cumulus clouds significantly impact the vertical profile of aerosol concentration in the lower troposphere, as well as their size distribution, and can serve as a source of large cloud condensation nuclei. Furthermore, it is shown that both precipitating and nonprecipitating boundary layer clouds contribute to a substantial increase in aerosol concentration within the inversion layer due to intense evaporation.

© 2025 American Meteorological Society. This published article is licensed under the terms of a Creative Commons Attribution 4.0 International (CC BY 4.0) License .

Corresponding authors: Alexander Khain, alexander.khain@mail.huji.ac.il; Ilan Koren, ilan.koren@weizmann.ac.il

Abstract

This study employs a high-resolution (10 m) System for Atmospheric Modeling (SAM) coupled with the spectral bin microphysical (SBM) scheme to thoroughly investigate the processes governing the evolution of aerosol properties within and outside a shallow cumulus cloud. The model encompasses the complete life cycle of cloud droplets, starting from their formation through their evolution until their complete evaporation or sedimentation to the ground. Additionally, the model tracks the aerosols’ evolution both within the droplets and in the air. Aerosols are transported within the droplets, grow by droplet coalescence, and are released into the atmosphere after droplet evaporation (regeneration process). The aerosol concentration increases by droplet evaporation and decreases along with falling drops. So, the effects of clouds on the surrounding aerosols depend on the microphysical and dynamic processes, which in turn depend on the amount of background aerosols; here, we compare clean and polluted conditions. It is shown that the regeneration process is highly important and that shallow trade cumulus clouds significantly impact the vertical profile of aerosol concentration in the lower troposphere, as well as their size distribution, and can serve as a source of large cloud condensation nuclei. Furthermore, it is shown that both precipitating and nonprecipitating boundary layer clouds contribute to a substantial increase in aerosol concentration within the inversion layer due to intense evaporation.

© 2025 American Meteorological Society. This published article is licensed under the terms of a Creative Commons Attribution 4.0 International (CC BY 4.0) License .

Corresponding authors: Alexander Khain, alexander.khain@mail.huji.ac.il; Ilan Koren, ilan.koren@weizmann.ac.il

1. Introduction

Clouds are a major component in the climate system, generally covering half to two-thirds of the globe (Stubenrauch et al. 2013; King et al. 2013). Clouds reflect and absorb both incoming solar radiation and longwave radiation, thus exerting both cooling and warming effects on our planet (Hartmann et al. 1992; Ramanathan et al. 1989; Stephens and Webster 1979; Wetherald and Manabe 1988; Berg et al. 2009). Moreover, they are one of the controlling factors in regulating the troposphere’s water vapor content, an important greenhouse gas (Cess 1975; Ramanathan and Inamdar 2006; Sun and Lindzen 1993; Rauber et al. 2007).

Aerosol particles also have a major climatic radiative effect. They interact with solar radiation through absorption and scattering and, to a lesser extent, with terrestrial radiation through absorption, scattering, and emission. In addition, aerosol particles play a fundamental role in cloud formation and evolution as they act as cloud condensation nuclei (CCNs), whose activation leads to the formation of cloud droplets. Therefore, CCNs’ concentration, size, and properties are key parameters in cloud microscale and macroscale processes and properties, such as cloud radiative properties (Albrecht 1989; Twomey 1977), precipitation development (Squires 1958), and the turbulent mixing of clouds with their environment (Bretherton et al. 2007). Many studies explored the topic of aerosol–cloud interactions, focusing on how changes in the aerosol amount and properties affect clouds’ microphysics and dynamics (Altaratz et al. 2014; Khain 2009; Khain and Pinsky 2018).

Understanding how clouds modify the aerosol size distribution is crucial due to its implications for the next clouds that develop in the field. The region around clouds, which is the transition between clouds and clear skies, known as the twilight zone, contributes significantly to the atmospheric radiative properties (Eytan et al. 2020; Koren et al. 2009; Jahani et al. 2022). This zone is occupied by liquid droplets and haze (humidified aerosols). Observations from satellites and ground-based sources have indicated its existence up to approximately 30 km away from cumulus clouds, exhibiting an e-fold behavior over a 10-km distance (Koren et al. 2007).

The efficiency of an aerosol particle in acting as a CCN is determined by its size and hygroscopicity (chemical composition). Particles with larger sizes and higher hygroscopicity are more efficient in forming cloud droplets, thereby requiring lower supersaturation conditions. Understanding the temporal and spatial variability of aerosol particle concentration is crucial due to the multifaceted roles these particles play, including their impact on cloud formation and processes.

Soluble aerosols appear as haze particles in humid air (RH > ∼70%). The size of a haze particle can be several times larger than that of a dry aerosol. Complete droplet evaporation would result in haze particle formation (according to the environmental relative humidity), and the particle size plays a major part in the radiative properties. In large-scale models, the optical properties of aerosols depend on air humidity, meaning they are treated as haze (Morcrette et al. 2009; Tang 1997; Tang and Munkelwitz 1994).

Regeneration is a mechanism of aerosol formation due to complete drop evaporation. Previous studies have considered this mechanism, but in a simplified manner, often utilizing 2D models or moment-based calculations of aerosol size distributions (Flossmann et al. 1985; Feingold et al. 1996; Hatzianastassiou et al. 1998; Xue et al. 2010; Yin et al. 2005). Fan et al. (2009) used a 3D model to simulate single-layer mixed-phase clouds. They assumed that drop evaporation leads to a release of aerosols with a size distribution proportional to the initial background aerosol distribution. A similar approach was employed by Cui and Carslaw (2006) who used an axisymmetric cloud model with bin microphysics. Their study demonstrated that moderately deep mixed-phase convective clouds can significantly alter aerosol distribution in the upper troposphere. Shpund et al. (2019) simulated a mesoscale convective system using the WRF–SBM model. In that study, the parameterization of aerosol return was similar to that in Fan et al. (2009), but the drop evaporation led to the release of aerosol distribution proportional to the distribution of activated aerosols at each grid point. In Magaritz et al.’s (2010) study, the effects of a stratocumulus cloud on aerosols were simulated using a 2D Lagrangian–Eulerian bin microphysical model with aerosols within droplets. It was shown that droplet collisions increase the aerosol size, but the drizzle formation largely eliminates the largest aerosols. Lebo and Seinfeld (2011) examined the marine stratocumulus deck in a small domain. They developed two-dimensional aerosol–cloud microphysical models that predict the simultaneous development of the discretized aerosols and drop size distributions. This was achieved by integrating a bin aerosol model with a bin microphysical model and computing the transfer of aerosol solute mass between drops, relying on water mass transfer principles. They showed that this treatment predicts increased LWP with increased aerosol loading. Leung et al. (2023) modeled aerosol regeneration in shallow cumulus clouds using two-moment bulk microphysics. Their study demonstrated that increased aerosol loading enhances aerosol regeneration and detrainment aloft while reducing aerosol removal via rainout. Hoffmann and Feingold (2023) analyzed 3D simulated stratocumulus using a Lagrangian cloud model coupled with large-eddy simulation (LES) (Hoffmann et al. 2015) and showed that the aerosol size distribution is shifted toward larger sizes due to collision–coalescence processing.

Our study focuses on shallow marine cumulus clouds and their effect on aerosol profiles. These clouds cover about 12% over the ocean (Warren et al. 1988), and they have an important role in transporting moisture, aerosols, heat, and momentum within the boundary layer and into the free troposphere (Johnson and Lin 1997). Those vertical fluxes have an important role in preconditioning deep convection (Lenderink et al. 2004; Tiedtke 1989). Hence, a better understanding of shallow cumulus processes is also important for understanding deep convective clouds.

In this study, we aim to investigate the effects of aerosol regeneration and shallow cumulus clouds on the aerosol vertical profile and size distribution. To achieve this, we conducted high-resolution simulations of single-trade cumulus clouds forming at different aerosol concentration conditions. By tracking the aerosol evolution within the droplets and in the air, we calculate the changes in the aerosol size distribution in the domain, within the clouds, and around them.

The rest of the paper is organized as follows. Section 2 describes the model and the newly implemented part (section 2a), as well as the simulation setup (section 2b). Section 3 presents the results; section 3a shows the effects of clouds on the amount of aerosols along the vertical profile, while section 3b discusses the effect on the aerosol sizes. Finally, section 4 provides concluding remarks.

2. Methods

a. Model description

In this study, we employed the System for Atmospheric Modeling (SAM; Khairoutdinov and Randall 2003) coupled with the spectral bin microphysical (SBM) scheme (Khain et al. 2004, 2008). The microphysical scheme solves equations for two size (number) distribution functions of water droplets and dry aerosols (serving as CCNs). They are calculated on two different logarithmic doubling mass grids, each containing 33 bins. The minimum radius size of the dry aerosol particles is 0.0012 μm, and the maximal one is 2 μm.

Based on the Köhler theory and using the supersaturation values for water, the critical dry aerosol radius is calculated, and dry aerosols larger than this size are nucleated into droplets. The corresponding bins in the dry aerosol size distribution are emptied. Dry aerosols smaller than 0.33 μm are nucleated into the smallest droplet size bin (2 μm), whereas the size of droplets forming on larger aerosols is determined by multiplying the dry aerosol size by a factor of 6 (following Kogan 1991). This factor is estimated based on detailed calculations of ascending parcels while considering that large CCNs cannot reach the haze equilibrium size. The smaller dry aerosols are transported in the air through both advection and convection and can be activated at later times if supersaturation increases.

The diffusional growth and evaporation are calculated using a semianalytical approach, solving a coupled system of differential equations to determine the droplet growth and the corresponding decrease in supersaturation simultaneously. To decrease the droplet size distribution (DSD) artificial broadening, a method analogous to a movable mass grid (Kogan 1991), as well as modified remapping techniques, is employed. These approaches for decreasing the numerical diffusion allow the model to be sensitive to aerosol concentration and predict well the DSD width and the height of the first radar echo (Benmoshe et al. 2012; Khain et al. 2019).

Collision–coalescence is solved by the stochastic collision equation with minimal diffusivity based on the exponential flux method following Bott (1998). The collision kernels are calculated using an exact superposition method (considering the flow fields around colliding droplets) described by Pinsky et al. (2001). Sedimentation is calculated using fall velocities determined by Beard (1976). Currently, the model does not include aerosol sedimentation, wet scavenging, and drop breakup. Moreover, the presented simulations do not account for radiative effects.

The new component we have implemented into the model is the tracking of aerosol evolution within the cloud droplets. This enables a description of the return of the dry aerosols to the atmosphere after droplets’ complete evaporation. In the droplet nucleation process, the activated dry aerosols are transferred from the size distribution of the dry aerosol to the DSD grid. In case dry aerosols are soluble, it actually means that the droplets represent a weak solution. However, the aerosol mass within the droplet is considered for the tracking. The aerosols in the droplets are described by a new (third) size distribution grid (containing 33 bins), reflecting the distribution of dry aerosol mass fraction within a drop. We calculate the total aerosol mass in each drop size bin, and knowing the drops’ concentration, we obtain the aerosol mass per drop. The aerosol mass within the droplets is tracked and calculated according to the microphysical processes. During droplet diffusional growth (or partial evaporation), the droplet water mass increases (decreases), while the aerosol mass does not change. Drop–drop collisions and coalescence form drops whose mass equals the sum of the two drop masses. The aerosol mass in the resulting drop is the sum of aerosol masses in the colliding drop. Aerosols within drops are advected, mixed, and sedimented with the corresponding drops.

Once a droplet completely evaporates, the aerosol shifts into the size distribution of dry aerosols. It moves into the bin that corresponds to its mass. It is assumed that the evaporation of one droplet results in the release of one aerosol particle. This phenomenon is referred to as “regeneration.” Subsequently, if a regenerated aerosol particle re-enters the cloud, it can reactivate and become a droplet (depending on the supersaturation conditions).

It should be noted that the humidity conditions around the cloud support haze formation. At subsaturation conditions, all haze particles can be assumed to be in equilibrium with the surroundings. The radius of the haze particles (req) is calculated using the Köhler theory:
S=AreqBrN3req3rN3,
where S is the environmental saturation (as a fraction), rN is the aerosol radius, req is the haze radius at equilibrium, and A and B are coefficients weakly depending on temperature [Eq. (5.1.11) from Khain and Pinsky (2018)].

b. Simulation setup

Four single marine cumulus cloud (Cu) simulations were conducted using the BOMEX (Holland and Rasmusson 1973; Siebesma et al. 2003) thermodynamic conditions. The setup was taken from Siebesma et al. (2003), including the large-scale forcing, vertical profiles of the water vapor mixing ratio, and potential temperature, with an inversion layer located at 1500–2000 m. The surface fluxes were kept constant, and the background wind was set to zero in order to keep the cloud in the center of the domain. Following Jaenicke (1988) and Altaratz et al. (2008), the size distribution of the aerosols was prescribed as the sum of three lognormal distributions describing ultrafine, accumulation, and coarse aerosol modes, where in these simulations, the ultrafine mode was set to zero (Fig. S1 in the online supplemental material); hence, the smallest occupied aerosol size bin was 0.0062 μm. The clouds were initiated by a bubble thermal perturbation, with a maximal magnitude of 0.1 K, at the center of the domain. Its horizontal radius was set to 500 m and depth was set to 100 m above the surface. The perturbation decays to zero as a cosine square function, and random noise is added on top of it (Ovtchinnikov and Kogan 2000). The clouds were initiated under two different aerosol concentration conditions. The first cloud developed in a clean environment (clean cloud, dry aerosol concentration of 50 cm−3), and the second cloud developed in polluted conditions (polluted cloud, dry aerosol concentration of 500 cm−3). The initial dry aerosol concentration was set as a constant number (50 or 500 cm−3) below the cloud base (600 m) and zero above this altitude since this study aims to investigate the effect of the regeneration process on the aerosol field around the cloud. The changes in aerosol concentration occurred solely through physical processes, as there were no external sources of aerosols introduced during later stages of the simulation. All dry aerosol particles were assumed to be ammonium sulfate and to serve as CCNs. Two simulations were conducted for each type of cloud (clean or polluted; a total of four simulations). The first simulation included the regeneration process, while the second did not.

The domain size was 5.12 km × 5.12 km × 4 km (which is substantially larger than the cloud size), with lateral cyclic boundary conditions. The horizontal resolution was dx = dy = 10 m, and the vertical resolution was dz = 10 m up to 3 km and 50 m for the uppermost kilometer. The simulation’s time step was dt = 0.5 s.

3. Results

a. The clouds’ effect on the aerosol vertical profiles

The evolution of all clouds simulated in this work can be described, in general, in a similar way. The growing stage lasts up to approximately 33 min when the clouds reach their maximum depth, followed by dissipation. More precisely, the clean cloud attains its maximum depth between 33 and 34 min, while the polluted cloud reaches this point slightly earlier, between 32 and 33 min. Figure 1 (clean case) and Fig. 2 (polluted case) present the cross sections of the two simulated clouds and the dry aerosol concentration around them at different times along their dissipation stage. During the dissipation stage, there is intensive evaporation, and the regeneration of aerosols enriches their concentration around the clouds.

Fig. 1.
Fig. 1.

Dry aerosol concentration (cm−3) for the clean cloud case at different times. The magenta contour denotes the cloud boundary (defined by a threshold of LWC > 0.01 g kg−1), and the horizontal black dashed line represents the inversion layer base. The upper (lower) row represents the results of the simulation with (without) the regeneration scheme. The initial dry aerosol number concentration was 50 cm−3, distributed below 600 m.

Citation: Journal of the Atmospheric Sciences 82, 1; 10.1175/JAS-D-24-0112.1

Fig. 2.
Fig. 2.

As in Fig. 1, but for the polluted cloud. Note the different color scales.

Citation: Journal of the Atmospheric Sciences 82, 1; 10.1175/JAS-D-24-0112.1

There is a significant difference in the concentration and altitude of the aerosols between the simulated results with (Fig. 1, upper row) and without (bottom row) aerosol regeneration in the clean condition simulations. In the simulation without the regeneration scheme, the aerosols are confined to an altitude of 1500 m, in contrast to the regeneration simulation, where there are more aerosols and in higher altitudes. The reasons for these differences lie in the microphysical and dynamic cloud processes. In both cases, the large aerosols were activated in the lower part of the cloud and the nonactivated aerosols were carried upward by the updrafts. The supersaturation conditions that developed at elevated levels (above 1500 m) enabled the activation of small dry aerosols in both cases (with and without aerosol regeneration; Figs. S2a,f). As a result, the concentration of nonactivated dry aerosols approached zero in the simulation without regeneration. In contrast, dry aerosols are observed at high altitudes in the simulation with aerosol regeneration (Figs. 1b–e), as they were created through droplet evaporation. Figures S4b and S4c show the presence of downdrafts at high levels, which leads to this droplet evaporation.

In the polluted cloud simulations (Fig. 2), there is a large concentration of drops, resulting in lower supersaturation values (Figs. S3a,f) compared to the clean case. Consequently, there is less activation of the smallest dry aerosols; instead, they are being transported upward with the cloud updraft to the cloud top (lower row of Fig. 2) to altitudes of approximately 2000 m, both with and without considering the regeneration process. At the same time, drop evaporation results in a much larger dry aerosol concentration in the regeneration case (upper row of Fig. 2). It is important to note that the polluted cloud does not precipitate, allowing for a high concentration of dry aerosols, reaching several hundred per cubic centimeter after droplet evaporation.

Precipitation is formed in the clean cloud simulations (Fig. 1). The simulation without regeneration produces a larger amount of surface rain (a total amount of ∼38.9 × 103 kg) compared to the simulation with regeneration, which produces a total of ∼18.6 × 103 kg. This difference can be explained by the entrainment of dry aerosols into the cloud in the regeneration simulation. This increases the droplet concentration and intensifies the competition for available water vapor, resulting in smaller droplets. This reduces the collision–coalescence efficiency, ultimately translating into a reduced rainfall amount. In the clean cloud case, the reduced dry aerosol amount below the cloud base (600 m) can be attributed to the strong downdrafts caused by the rain (Figs. 1c–e,h–j and S4c,h). This significant difference in precipitation amount will also have an impact on the vertical redistribution of water vapor.

While the top of the growing cloud is moving inside the inversion layer (1500–2000 m), the buoyancy in its upper part is negative (Eytan et al. 2022), causing the development of in-cloud downdrafts (see in Figs. S4b,g and S5b,g). In parallel, there is a strong detrainment at the upper part of the cloud. The detrainment supports drop evaporation, which returns dry aerosols to the environment. This detrainment zone can be seen both in Figs. 1 and 2 in panels (c)–(e) in the anvil-like shape of the dry aerosol concentration. The most intense droplet evaporation occurs in the inversion zone, where vigorous cloud-environment mixing results in cloud dilution and subsaturation conditions.

Figures 3 and 4 present the vertical profiles of the mean and maximum (per specific altitude) dry aerosol number concentration (NCCN) at different times, along the dissipation stage in the clean (Fig. 3) and polluted (Fig. 4) cloud cases. Both the maximum and mean values of NCCN are larger in the regeneration simulations. As dissipation progresses, the curve depicting the mean NCCN values for the regeneration simulation (dashed light blue line) diverges further from the curve for the simulation without regeneration (dashed orange line). This indicates a significant return of dry aerosols to the atmosphere following droplet evaporation.

Fig. 3.
Fig. 3.

Vertical profiles of the maximum and average dry aerosol concentration in the clean cloud case at different times. The dark blue (red) lines represent the vertical profile of the maximal NCCN for the simulation with (without) the aerosol regeneration scheme. The dashed light blue (orange) lines represent the average NCCN profile for each level for the simulation with (without) the aerosol regeneration. Taken for an area of 1 km2 around the cloud’s center.

Citation: Journal of the Atmospheric Sciences 82, 1; 10.1175/JAS-D-24-0112.1

Fig. 4.
Fig. 4.

As in Fig. 3, but for the polluted cloud case. Note the different scales of the axes compared to Fig. 3.

Citation: Journal of the Atmospheric Sciences 82, 1; 10.1175/JAS-D-24-0112.1

In the regeneration simulations, the values of maximum NCCN below the cloud base exceed the initial ones due to the addition of aerosols released by drop evaporation on top of the background values.

In summary, the simulations that treated the aerosol regeneration showed a significantly larger dry aerosol concentration compared to the simulation that did not consider it. This is especially true for nonprecipitating clouds. These results demonstrate the significant impact of cloud processes on the vertical profiles of aerosol concentration in the convective boundary layer.

b. Effect of clouds on aerosol size

Next, we will examine the effect of the regeneration process on the aerosol size distributions. Figures 5 (clean cloud) and 6 (polluted cloud) present the size distribution of the dry aerosols at different heights at 48 min of simulation (advanced dissipation stage). Figures 5a and 6a show the results of the simulations that did not consider the regeneration scheme; hence, they present the dry aerosols that were not activated and were transported with the air. Figures 5b and 6b present the simulated results with the regeneration scheme. These panels show a significant enhancement in the concentration of large aerosols compared to the size distribution in the initial stage. These large aerosols formed through the collision–coalescence of droplets that eventually evaporated. This is part of the significant effect of the regeneration process on aerosol size distribution that can be easily recognized. The regeneration process leads to a bimodal aerosol spectrum. The first mode consists of the smallest nonactivated aerosols, which ascend within the cloud’s updrafts. The width of this mode decreases with height, which indicates in-cloud nucleation, meaning the transformation of the largest dry aerosols into drops. The second mode results from the regeneration of aerosols through droplet evaporation, which occurs after collisions and coalescence.

Fig. 5.
Fig. 5.

Dry aerosol size distribution (cm−3), at different heights, for the clean cloud, at 48 min of simulation. Each line represents the mean of 30 size distributions taken from randomly chosen 30 places at a certain height, outside the cloud, up to ∼1000 m around the cloud center. The black line denotes the initial dry aerosol size distribution below the cloud base. The upper (bottom) panel represents the simulation without (with) aerosol regeneration.

Citation: Journal of the Atmospheric Sciences 82, 1; 10.1175/JAS-D-24-0112.1

Fig. 6.
Fig. 6.

As in Fig. 5, but for a polluted cloud.

Citation: Journal of the Atmospheric Sciences 82, 1; 10.1175/JAS-D-24-0112.1

It can be seen that the first mode of the polluted cloud (Fig. 6) is wider than the one of the clean cloud (Fig. 5). This is due to the larger supersaturation values in the clean cloud case that caused the activation of more small-mode aerosols into droplets. In both the polluted and clean cases, the concentration of the first aerosol mode is larger when considering regeneration. This increase can be attributed to the efficient evaporation of small droplets, which release small dry aerosols back into the atmosphere.

In the clean simulation case (Fig. 5b), the concentration of dry aerosols increases with their size, and there are no median-size aerosols. This is a result of the collision–coalescence of drops. In addition, the concentration of the large dry aerosols decreases with height in the clean cloud case since they efficiently precipitate and most of the raindrops fall down in the cloud (they do not evaporate). The picture changes substantially when examining the dry aerosol within and around the polluted cloud (Fig. 6b). First, the second mode in the size distribution has a maximum at 0.4–0.7 μm, and then the number decreases for larger sizes. This is due to the small sizes of drops in the polluted case that collide and coalesce, forming smaller drops compared to the drops resulting from collisions in the clean cloud case. Second, the concentration of large dry aerosols tends to increase with altitude, reaching a maximum around z = 1500 m. This can be explained by the increased size of the droplets with height due to collisions. Unlike in clean clouds, these large droplets do not reach raindrop size and, therefore, do not precipitate. Instead, they evaporate, releasing larger aerosols into the surrounding atmosphere.

Observation of two modes in the dry aerosol size distribution was previously reported from in situ measurements and referred to as the “Hoppel minima” (Hoppel et al. 1986; Hoppel and Frick 1990; Hudson et al. 2015). This minimum in the dry aerosol size distribution between the two modes is closely related to the bimodal distribution of cloud droplets and raindrops and attributed to collisions between them. Aerosols with sizes that are smaller than the minimum are mostly unactivated aerosols. The larger aerosols are the regenerated aerosols, where, in our case, they grew by drop coalescence and subsequent evaporation.

Figures 7 and 8 present horizontal cross sections (at 1500 m) of the dry aerosol number concentration for the clean and polluted clouds. They correspond to a simulation time of 48 min, which is similar to the results presented in the previous figures (Figs. 5 and 6).

Fig. 7.
Fig. 7.

Horizontal cross sections of dry aerosol concentration (cm−3) at 1500-m height and 48 min of simulation for the clean cloud case, from the simulation with the regeneration of aerosols. The magenta contour marks the cloud boundary. (a) The total dry aerosol concentration, including all bins. (b) Only bins with a radius equal to 2 μm (the biggest bin size in the grid). (c) Only bins with a radius lesser than 2 μm.

Citation: Journal of the Atmospheric Sciences 82, 1; 10.1175/JAS-D-24-0112.1

Fig. 8.
Fig. 8.

Horizontal cross sections of dry aerosol concentration (cm−3) at 1500-m height and 48 min of simulation for the polluted cloud case, from the simulation with the regeneration of aerosols. The magenta contour marks the cloud boundary. (a) The total dry aerosol concentration, including all bins. (b) Only bins with a radius greater than 0.0197 μm. (c) Only bins with a radius equal to or greater than 1 μm. (d) Only bins with a radius equal to or lesser than 0.0197 μm. The value of ra = 0.0197 μm was chosen because it is the largest dry aerosol that was not activated at that snapshot and at that height.

Citation: Journal of the Atmospheric Sciences 82, 1; 10.1175/JAS-D-24-0112.1

Note that the simulated cumulus cloud creates a large zone of enhanced dry aerosol concentration around it, with approximate linear dimensions of 1.5–2 km in the clean case (Fig. 7) and 2–2.5 km in the polluted case (Fig. 8). These aerosol regions around the clouds are several times larger than the volume of the clouds themselves at their maximal size (t ∼ 33 min; see Figs. 1a and 2a).

In the clean case simulation (Figs. 7a,b), most of the dry aerosols around the cloud have a radius of ra = 2 μm. Note that, in our model, ra = 2 μm is the maximum dry aerosol bin size; hence, the actual size of the aerosol might have been even larger (due to drop collisions). Contrary, in the polluted case (Fig. 8c), only a small number of dry aerosols (out of the total) with ra ≥ 1 μm were detected in this height. Figures 8b and 8c show that inside the cloud (marked by a magenta counter), there are only small dry aerosols, with ra < 0.0197 μm. These small dry aerosols were not activated and were carried to this height by the cloud’s updraft. This agrees with the discussion in section 3a.

While the simulations were initialized by locating the dry aerosols only below the cloud base (up to 600 m) and mainly within the accumulation mode size (0.006–0.5 μm aerosol radius size), at the dissipation stage, the aerosols are situated up to ∼2200 m, with a shift of the size distribution toward larger sizes. This shift due to the collision–coalescence process agrees with previous studies (Flossmann et al. 1985; Feingold et al. 1996; Hoffmann and Feingold 2023). These large aerosols, that are part of the coarse mode, are formed by the complete evaporation of coalesced drops and can significantly impact cloud dynamics, microphysics, and the radiative properties of the atmosphere.

As shown in Figs. S2 and S3, the drop evaporation increases the relative humidity in the environment surrounding the cloud. These humidity levels exceed the delinquency level, i.e., soluble aerosol particles produce haze with a size that can be calculated using Eq. (1). Figure 9 presents the vertical profiles of the mean size (per height) of the dry aerosol and of the haze at 52 min of simulation (Fig. 9a—polluted; Fig. 9b—clean). For both cases, the profile of the mean radius in the simulations that included the regeneration grows exponentially from the cloud base to about 1000-m height. It presents a similar mean radius at higher altitudes. In the polluted case, the maximum mean dry radius is ∼0.6 μm (at 1500–1700 m) while the maximum mean haze radius is ∼1.2 μm (at 1100–1500 m); in the clean case, the maximum mean dry radius is ∼1.9 μm (around 1600 m) while the maximum mean haze radius is ∼4 μm (at 1100–1500 m).

Fig. 9.
Fig. 9.

Vertical profiles of the mean dry aerosol radius and mean haze radius (per height) at 52 min of simulation. The blue (red) line is the mean dry aerosol radius for the simulation without (with) the regeneration of aerosols. The yellow line represents the mean haze radius in the simulation with the regeneration. (a) The polluted cloud. (b) The clean cloud case. The calculation of the mean includes the voxels in subsaturation conditions in a square of 2 km2 centered around the cloud center.

Citation: Journal of the Atmospheric Sciences 82, 1; 10.1175/JAS-D-24-0112.1

Our findings show a much larger mean dry aerosol radius for the simulations that considered the regeneration process. This occurs as some of the big activated aerosols and the merged aerosols after drop collisions return to the free atmosphere after drop evaporation. Notably, the haze size is larger than that of the dry aerosol, highlighting the regeneration mechanism’s crucial role in influencing the troposphere’s radiation properties.

4. Conclusions

In this study, we used the System for Atmospheric Modeling (SAM), coupled with the spectral bin microphysical (SBM) scheme. The detailed representation of the microphysical process by the SBM model, along with the high-resolution simulations that capture important scales of turbulent flow, allows for an accurate representation of the life cycle of aerosol particles within cloud droplets. To track the aerosols and their changes due to the microphysical processes experienced by the cloud droplets in which they are embedded, we added another size distribution grid to the model: the total dry aerosol mass per droplet size bin. This enables us to investigate the effects of clouds on aerosol concentrations and properties. We simulate shallow trade cumulus clouds under different aerosol conditions (initialized below the cloud base): 50 cm−3 (clean, precipitating) and 500 cm−3 (polluted, nonprecipitating). For each type of cloud, two simulations were conducted, with and without the regeneration scheme that brings aerosols back to the atmosphere after drop evaporation. This study examines the effects of shallow cumulus clouds on atmospheric aerosols, specifically focusing on the cloud dissipation stage since drop evaporation and aerosol regeneration occur mainly at this stage in the cloud’s life cycle.

Variations in dry aerosol concentrations and sizes impact cloud processes and properties, potentially leading to changes in cloud size (including cloud-top height), radiative properties, and precipitation processes. These changes can impact cloud processes and evolution through entrainment processes, as well as the formation and processes of consequent clouds within the field, which will meet modified environmental conditions. The interplay between dry aerosol concentration in the boundary layer and cloud microphysics warrants investigation to improve our understanding of boundary layer–aerosol–cloud interactions.

Our findings demonstrate significant differences in aerosol vertical distribution and size distribution between the simulations with and without the regeneration process. We found a significant concentration of dry aerosols within the inversion layer of both precipitating and nonprecipitating clouds, attributed to aerosol regeneration resulting from intense evaporation. One key finding is the production of large aerosols, classified within the coarse mode, through aerosol regeneration after collision–coalescence of drops within the cloud. Notably, we observed the presence of these large aerosols at altitudes extending up to the inversion layer. These large aerosols can later re-enter the cloud, influencing its dynamic and microphysical properties. Previous studies have demonstrated the impact of large aerosols on precipitation processes and on the radiative properties of the atmosphere (Yin et al. 2000; Cheng et al. 2007, 2009).

Our results are supported by observational evidence regarding the cloud’s impact on aerosol concentration and size. For instance, Dipu et al. (2013) identified a significant concentration of large aerosols at high altitudes within specific atmospheric layers, namely, at 2 and 5 km, which we believe to be inversion layers. The presence of large CCNs and haze at altitudes of a few kilometers was also observed in the Cloud Aerosol Interaction and Precipitation Enhancement Experiment (T. Prabhakaran 2023, personal communication). Moreover, our findings align with previous numerical studies that have demonstrated how shallow cumulus clouds can significantly enhance the upward transport of surface-emitted pollutants (Chen et al. 2012).

Additionally, our study showed a bimodal distribution of the dry aerosol when considering the regeneration process. The phenomenon of two modes with minima in the aerosol size distribution, as depicted in Figs. 5 and 6, is consistent with previous in situ measurements, known as the “Hoppel minima” (Hoppel et al. 1986; Hoppel and Frick 1990; Liu et al. 2021). This phenomenon is attributed to collision–coalescence of cloud droplets, a mechanism supported by both theoretical (Flossmann et al. 1985) and numerical (Hoffmann and Feingold 2023) studies.

We found that, in the clean case, including aerosol regeneration substantially decreases precipitation amount. This significant difference in precipitation has a considerable impact on the vertical redistribution of water vapor. Given the abundance of shallow cumulus clouds in the atmosphere, this result may have important implications for their representation in global models.

The results of our study highlight the profound influence that even a single cumulus cloud can have on the concentration of dry aerosols in the boundary layer. We demonstrated that a cloud with a scale of approximately 1 km creates a surrounding region with altered aerosol concentrations that extend several times farther than the cloud’s horizontal extent. The components comprising the cloud twilight zone are typically considered a combination of humidified aerosols (Bar-Or et al. 2012; Twohy et al. 2009), undetected clouds (Eytan et al. 2020; Hirsch et al. 2014), and larger processed aerosols (Marshak et al. 2021; Eck et al. 2012). However, our findings suggest an additional contributing factor: increased aerosol concentrations around clouds due to aerosol transport facilitated by the clouds themselves, thereby substantially shaping Earth’s radiative properties. Further consideration of cumulus cloud ensembles would enhance the accuracy of calculations related to Earth’s radiative characteristics.

We demonstrated that aerosol regeneration transports aerosols to the lower troposphere, increasing concentrations at the inversion layer, where they would otherwise be minimal. These regenerated aerosols are also larger, and we expect that both the increased concentration and size will enhance the scattering of solar radiation. However, the influence on cloud radiative properties may differ due to the effects of aerosol regeneration on cloud microphysics, which will be explored in future studies.

Recent airborne measurements of aerosols around clouds in the Amazon region reported by Braga et al. (2022) revealed the generation of large CCNs by clouds. We assume that advancing the study of cloud–aerosol interactions requires detailed measurements of aerosol concentration and size distribution as a function of their distance from clouds. The impact of aerosol regeneration on surrounding aerosol fields should be investigated for both small cumulus and deep convective clouds, with a clear distinction between polluted and clean cloud conditions.

It is important to note the limitations of our model. Currently, it does not account for aerosol sedimentation or wet deposition. We believe the effects of those processes are minor in our single shallow cloud simulations but can have a significant effect in deeper clouds or cloud field simulations, where larger aerosols with nonnegligible sedimentation rates are present. Additionally, our study considers a single type of aerosol, which limits the generalizability of our conclusions. The microphysical scheme does not treat the haze stage in the aerosol growth. It affects the description of the first stage of the cloud formation, which can impact the drop size distribution.

Our focus on simulating an isolated cumulus cloud aimed at gaining a process-level understanding of the dynamics and microphysics involved. This approach allows us to utilize high resolution in both time and space, coupled with an accurate description of the microphysical processes. We acknowledge the limitations of analyzing one cloud, as it does not fully represent the complexity of cumulus cloud fields in nature. Although we studied a marine trade cumulus cloud, we believe that the conclusions of our study may apply to continental and tropical shallow cumulus clouds as well. However, this broader application requires detailed investigation in future studies.

In conclusion, our study sheds more light on the complex relationship between aerosol dynamics, concentration, and cloud processes. It highlights the importance of integrating these factors into atmospheric modeling and emphasizes the significant role of dry aerosol regeneration in cloud microphysics and vertical aerosol transport. Understanding the changes in the vertical profile of aerosols and its size distribution is vital for improving our predictions of cloud processes and properties in various environmental contexts. Further research in this area is essential for advancing our understanding of cloud–aerosol interactions and their broader implications for weather and climate. The subsequent stage of our research will focus on examining how the regeneration of aerosols affects the cloud microphysical processes.

Acknowledgments.

This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (CloudCT, Grant Agreement 810370) and by the Israel Science Foundation (Grants 2635/20; 1449/22).

Data availability statement.

The SAM codes are available on the website of Professor Marat Khairoutdinov (Khairoutdinov 2004). The codes to reproduce the figures of the manuscript are publicly available at https://doi.org/10.34933/871eae61-de6f-4274-9515-b829611e0141.

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