The effects of aerosol on warm convective cloud cores are evaluated using
single cloud and cloud field simulations. Three core definitions are
examined: positive vertical velocity (
It is shown that a
The aerosol effect on the diffusion efficiencies plays a crucial role in the
development of the cloud and its partition to core and margin. Using the
RH
Aerosols remain one of the largest sources of uncertainty in climate predictions, mainly via their effects on clouds (IPCC, 2013). Here we focus on the aerosol effects on warm clouds. Aerosols act as cloud condensation nuclei (CCN) during heterogeneous nucleation of cloud droplets (Köhler, 1936; Mason and Chien, 1962). The number, size, and composition of an aerosol distribution determine the initial cloud droplet size distribution (DSD). Polluted clouds (i.e., more aerosols) have more but smaller droplets and a narrower DSD compared to clean clouds (Andreae et al., 2004; Twomey, 1977). Changes in the initial DSD drive various effects and feedbacks on the cloud's evolution and key processes, such as droplet mobility, condensation–evaporation budgets, collision–coalescence, and entrainment (Jiang et al., 2006; Koren et al., 2015; Small et al., 2009; Xue and Feingold, 2006).
It is well known that an abundance of small droplets in a cloud (a narrow
DSD) reduces the efficiency of the collision–coalescence process
(Squires, 1958; Twomey, 1977; Warner, 1968),
prolongs the diffusional growth time (Khain et al., 2005;
Wang, 2005), and delays or even completely suppresses the initiation of
precipitation (Albrecht, 1989; Hudson
and Mishra, 2007; Hudson and Yum, 2001; L'Ecuyer et al., 2009). Moreover,
in-cloud condensational growth is more efficient in consuming
supersaturation because of the larger surface-area-to-volume ratio of
droplets (Dagan et
al., 2015a, b; Mordy, 1959; Pinsky et al., 2013; Reutter et al., 2009;
Seiki and Nakajima, 2014). We note that throughout this work the word
However, the increase in aerosol amount yields suppressing effects as well. An opposite effect should take place in the subsaturated regions of the cloud, where more numerous and smaller droplets increase the evaporation rate and loss of cloud mass (Grant and van den Heever, 2015; Saleeby et al., 2015; Storer and van den Heever, 2013). Henceforth evaporation will be referred to as a process (i.e., change of mass per unit of time) rather than complete evaporation of a water drop. Increased evaporation can promote entrainment mixing, which in turn mixes more subsaturated air into the cloud and further promotes evaporation (Jiang et al., 2006; Small et al., 2009; Xue and Feingold, 2006), effectively initiating a positive feedback between evaporation and mixing with the eventual suppression of cloud growth. This effect may also be accompanied by a suppressing effect of the larger water loading in polluted clouds, which contain more liquid water mass.
The competition between those opposing processes that are driven by enhanced aerosol loading determines the net aerosol effect on cloud properties such as cloud fraction, lifetime, albedo, mass, size, and precipitation amount. However, the sign and magnitude of such effects are non-trivial (Jiang and Feingold, 2006). Previous studies report opposing findings regarding the total aerosol effects on warm clouds (Altaratz et al., 2014). Some studies suggest cloud invigoration by aerosols (bigger and deeper clouds; Dey et al., 2011; Kaufman et al., 2005; Koren et al., 2014; Yuan et al., 2011), while some suggest cloud suppression or no effect at all (Jiang and Feingold, 2006; Li et al., 2011; Savane et al., 2015; Xue et al., 2008). Moreover, other work has shown that the precipitation susceptibility (i.e., quantifies the sensitivity of precipitation to the aerosol increase) has a non-monotonic behavior that reaches its maximum at intermediate liquid water path (LWP) values (Sorooshian et al., 2009), implying that the resultant aerosol effects are heavily dependent on cloud type and environmental conditions (Khain et al., 2008).
A different approach to aerosol effects suggests that cloud systems can be buffered to microphysical effects (Stevens and Feingold, 2009). Several studies have shown that given enough time for the cloud system to reach steady state, cloud macrophysical parameters (e.g., cloud fraction and rain yield) show similar results for various aerosol concentrations (Carrió and Cotton, 2014; Glassmeier and Lohmann, 2018; Seifert et al., 2015). Based on the idea that clouds can be partitioned to aerosol-limited, updraft-limited, or aerosol- and updraft-sensitive regimes (Reutter et al., 2009), a unified theory for the contradicting results regarding aerosol effects was suggested (Dagan et al., 2015b). Given an aerosol range that covers all three regimes, the competition between opposite processes leads to an optimum value of aerosol concentration regarding various cloud properties like total mass, cloud top, or rain (Dagan et al., 2015b). A cloud that develops under low aerosol concentration is aerosol limited, as it does not have enough collective droplet surface area to consume the available water vapor. On the other side of the trend, a cloud that develops in polluted environment (with more aerosols than the optimum) is influenced significantly by enhanced entrainment and larger water loading, causing suppression of cloud development. The optimal concentration is a function of the thermodynamic conditions (temperature and humidity profiles) and cloud size.
Environments that support larger cloud development will have larger cloud cores that are positively affected by aerosol increase and can be regarded as aerosol limited (i.e., on the ascending branch of the aerosol trend) up to a higher optimal aerosol concentration. Environmental conditions that support small clouds are more strongly affected by cloud suppression processes at the cloud margins (due to higher cloud surface-area-to-volume ratio) and would have a lower optimal aerosol concentration. This can explain why studies biased to smaller clouds (mostly numerical modeling studies) report cloud suppression and studies biased to larger clouds (mostly observational studies) report cloud invigoration. Similar conclusions were reached for the cloud field scale as well (Dagan et al., 2017).
In addition, it was shown that clouds impact differently the environmental thermodynamics according to the aerosol level in the field (Dagan et al., 2016; Seifert and Heus, 2013; Seifert et al., 2015). For example changes in aerosol loading impact the amount of precipitation reaching the surface and subsequently the evaporative cooling below cloud base and the organization patterns (Seifert and Heus, 2013; Seigel, 2014; Xue et al., 2008). Moreover, an increase in aerosol loading may increase evaporation rates around the margins and tops of clouds (Seigel, 2014; Stevens, 2007; Xue and Feingold, 2006), cooling the upper cloudy layer and increasing the convective instability. Therefore aerosol effects on phase changes and precipitation result in vertical redistribution of heat and moisture, which may either stabilize or destabilize the environment in which subsequent clouds grow (Seifert and Heus, 2013).
Irrespective of the definition chosen, the cloud's core and margin are dominated by different processes (Dagan et al., 2015b). These processes often compete with each other, with the dominant one changing along the cloud's evolution. For example, at the initial stage of cloud formation, a cloud is more adiabatic and is controlled by the core's processes (condensation), and when it dissipates the margin processes are more dominant (entrainment and evaporation). Aerosols affect each of these processes and thus each stage in the cloud's lifetime. As a continuation of Part 1 (Heiblum et al., 2019) of this work (hereafter PT1), in this part we analyze aerosol effects on the cloud's partition to core and margin throughout the lifetime of a cloud. We report the consequences that these effects have on evolution of a cloud in terms of volume, mass, and lifetime. As opposed to other works that typically focus on a single cloud core definition, here three different definitions are used (see Sect. 2), with emphasis placed on the sensitivity of each core definition to aerosol concentration. Moreover, the combination of single cloud with large-eddy simulations enables us to gain process-level understanding and test the robustness of our findings.
The analyses performed here are to the most part identical to those
described in PT1 of this work. In this section we shall thus only give a
brief review of the methods used. For single cloud simulations we use the
Tel Aviv University axisymmetric cloud model (TAU-CM; Reisin et
al., 1996), and for cloud field simulations we use the System for
Atmospheric Modeling (SAM) model (version 6.10.3; for details see the following web page:
Both models utilize explicit bin-microphysics schemes (Khain et al., 2004; Tzivion et al., 1987), solving nucleation, diffusion (i.e., condensation and evaporation), collisional coalescence, breakup, and sedimentation microphysical processes. The single cloud model is initialized using a Hawaiian thermodynamic profile based on the 91285 PHTO Hilo radiosonde at 00:00 Z on 21 August 2007. The cloud field model is set up based on the BOMEX case study, including an initialization setup (sounding, surface fluxes, and surface roughness) and large-scale forcing setup (Siebesma et al., 2003). More details on the model setups and definitions can be found in PT1.
To study the effects of aerosols on the cloud cores we run each model setup
with three different aerosol concentrations: clean – 25 cm
In order to reduce the problem's dimensionality and distill signals in a cloud field system governed by high variance, we use the center of gravity vs. mass (CvM) phase space in combination with an automated 3-D cloud tracking algorithm (Heiblum et al., 2016a). The CvM phase space enables a compact view of all clouds in the simulation by projecting only their center-of-gravity (COG) height and mass at each output time step. Using the cloud tracking, it was shown that the lifetime of a cloud can be described by a trajectory on this phase space. Hence, the different locations in the CvM space are associated with different stages in a cloud's lifetime (i.e., growing, precipitating, and dissipating). For an in-depth explanation of the CvM space, the reader is referred to Sect. 2.4 in PT1 (see schematic illustration – Fig. 1, PT1).
Figure 1 presents time series of single cloud core volume fractions
(
Lower-aerosol-concentration simulations produce more rain and at earlier
stages of cloud lifetime due to efficient collision coalescence. The
increase in the
The theoretical arguments in PT1 showed that
Figure 2 shows the values of the temperature (
Scatterplots of pixel total buoyancy (m s
The clean and intermediate simulations show a similar dependence of buoyancy
on vertical velocity; however, it is apparent that these simulations also
include an outlier scatter region of pixels with positive buoyancy and weak
negative vertical velocity which is absent in the polluted simulation (see
white arrows; Fig. 2). Consistent with the rest of the cloudy pixels, these
outlier pixels have positive
Although not usually the focus of studies, the existence of positively buoyant downdrafts in convective clouds has been reported in both observations (Igau et al., 1999; Wei et al., 1998) and simulations (Xu and Randall, 2001; Zhao and Austin, 2005a, b). A possible explanation for this can be deduced from previous theoretical studies predicting mixing-induced downdrafts in cumulus clouds (Betts and Silva Dias, 1979; Betts, 1982). It was shown that in some cases cloud–environment mixtures are negatively buoyant (while still containing liquid water), and the consequent downdrafts can sometimes descend only partway down to the cloud base before reaching neutral buoyancy. Similar to convective overshooting, parcels with negative vertical momentum may then undershoot the downdraft equilibrium level and turn positively buoyant while the downdraft weakens. One can therefore expect the magnitude of positive buoyancy within the downdraft to reach a maximum when the velocity approaches zero. Hereafter we refer to positive buoyancy production within downdrafts as downdraft buoyancy.
Downdraft buoyancy production occurs frequently in cumulus fields because the negatively buoyant downdrafts follow a warming lapse rate which is more unstable than the environmental one, which is typically between the dry adiabat and moist adiabat (as is the case for the Hawaiian and BOMEX profiles simulated in this work). On one extreme, a descending parcel is least buoyant (i.e., coolest) when evaporation (after mixing) keeps it just barely saturated (Paluch and Breed, 1984, also PT1) so that the lapse rate of descent tends to be moist adiabatic and may remain negatively buoyant. On the other extreme, if little to no evaporation of liquid water occurs, the descent will follow the dry adiabat and switch to neutral (and then positive) buoyancy rapidly. Thus, the ability of a negatively buoyant cloudy downdraft to sustain itself depends on continuous inflow of liquid water (by mixing) and its consequent evaporation (Knupp and Cotton, 1985).
Indeed, the results in Fig. 2 match the hypothesis explained above, where
positively buoyant downdrafts are warmer than the environment and tend to
show larger buoyancy values for weaker downdraft velocities (especially for
the intermediate case). Further analysis also shows that the more
unsaturated the downdrafts (indicated also by low
Here we evaluate how aerosol effects within the core and margin (using the
three core definitions) affect the cloud characteristics, focusing on two
main parameters: size (or volume) and mass. In Fig. 3 we follow the
evolution of cloud, core, and margin mass and volume for different aerosol
concentrations, using only the RH
Time series of cloud mass (kg;
One can generally expect an increase in diffusion and decrease in
collision–coalescence processes efficiency with an increase in aerosol
concentration (Hudson and Yum, 2001;
Jiang et al., 2009; L'Ecuyer et al., 2009; Pinsky et al., 2013), affecting
both condensation and evaporation processes. The intermediate concentration
shows the highest total mass as a result of being an optimal case, with
higher condensation efficiency than the clean case and lower evaporation
efficiency than the polluted case. It is convenient to represent the
condensation and evaporation efficiencies by the RH
With respect to cloud total volume, the lower the concentration, the larger the total cloud volume. We note that the cloud volume here excludes regions of precipitation below the initial cloud base height. By separating to core and margin regions, one can see that the total cloud volume is primarily dependent on the volume of the margin, which increases significantly with decreasing concentration. This is especially true during the dissipating stages of cloud lifetime, when the cloud is margin dominated. Although increasing the aerosol concentration does initially yield an increase in core volume (as was seen for the mass), the extents of the core size are typically smaller than those of the margin. There are large differences in the relative core volume percentage for the different clouds. The clean (polluted) cloud is margin (core) dominated with respect to volume for most of its lifetime. Excluding time of formation, the clean cloud shows the lowest core volume fractions but manages to maintain its core for the longest time span.
These results with respect to cloud volume can be attributed to the smaller drop sizes and higher diffusion efficiencies with an increase in aerosol concentration. Additionally, lower collision–coalescence efficiencies also maintain a narrow droplet spectrum of small droplets in the polluted cloud. During the growing stage a higher aerosol concentration may permit the cloud to condense more water, release more latent heat, and promote cloud growth. This explains the larger core volume sizes. However, after the cloud exhausts its convective potential (i.e., the growth of the convective core terminates and reaches its peak in mass), its main method of expansion is by mixing with the environment (i.e., detrainment and dilution). We note that precipitation can also be considered a method of expansion; however our choice to focus on volume above initial cloud base excludes this effect. Detrainment and mixing with the environment result in subsaturation conditions and evaporation of LWC. A clear indication for dilution is seen in Fig. 3, where between 30 and 35 min of simulation time, both the clean and polluted clouds lose total mass but only the clean cloud increases in total volume. The polluted cloud is composed of small drops, evaporates its margin regions efficiently, and is thus limited in horizontal growth by detrainment. The clean cloud is composed of larger drops, less efficient in evaporating its margins, and hence can grow by dilution of its LWC upon a larger volume. This large margin “shields” the core during dissipation stages and enables it to the live for a longer time.
The mechanism behind the results in Fig. 3 is demonstrated in Fig. 4, where horizontal cross-sections of mean (taken in the vertical dimension) cloud RH are shown for different stages during the clouds' lifetimes. For the polluted cloud, supersaturated or subsaturated conditions are rare. The RH throughout the cloud is near 100 % (almost always between 99.8 % and 100.2 %) except for a few pixels at its far edges which are a bit below 99 %. The polluted cloud resembles what one would expect to see using a moist adiabatic approximation (i.e., saturation adjustment), where all excess water vapor above saturation is converted to liquid water, mimicking infinitely efficient condensation (and evaporation).
Four snapshots of horizontal cross-sections of RH (%; see panel titles for times). Panels include the results of different aerosol concentrations (see legend). Cross-sections are obtained by taking the mean RH of all vertical levels for each horizontal distance from the cloud center axis.
The clean cloud shows opposite behavior, with extremes of large supersaturation during cloud growth (initial stages) and large subsaturation during cloud dissipation (final stages). The large supersaturation can be explained by slow diffusional growth, but the large subsaturation also takes into consideration the larger drop sizes, which take more time to evaporate. This enables the clean cloud to expand to larger horizontal extents (by dilution and mixing with the environment without fully evaporating) and live for longer times. The intermediate aerosol concentration shows a midway scenario, where the supersaturation is consumed more efficiently than the clean case and at the same time much larger values of subsaturation may exist than those seen for the polluted case.
In the following section we expand our analyses of aerosol effects on cloud core and margin from the single cloud scale to the cloud field scale. A cloud field can be considered to be composed of many individual clouds and thus can serve to test the robustness of the aerosol effects seen for a single cloud. Moreover, cloud fields include the added complexity of interactions between clouds and the clouds' effects on their thermodynamic environment.
Here CvM space representations (see Sect. 2) are used to observe the core
volume fractions of all clouds in BOMEX cloud field simulations. The rows in
Fig. 5 represent different aerosol concentrations, while the columns
represent different core type definitions. Different aerosol concentrations
produce a vastly different scatter of clouds in the CvM space, as was
previously discussed in depth (Heiblum et al., 2016b). The
clean simulation (25 cm
CvM phase-space diagrams of
The polluted simulation (2000 cm
The results in Fig. 5 show a consistent behavior of the core volume
fractions for all aerosol concentrations, where the
Average core mass fraction
These results are consistent with PT1 and the single cloud simulations in
Sect. 3.1. Nevertheless, some significant aerosol effects on the partition
to core types can be seen. Focusing on the growing branch first (i.e., clouds
located near the adiabat), we note the following:
For the RH The The core volume fractions of the largest mass clouds increase with increasing aerosol concentration for all core types. The mean area of large-mass clouds increases significantly with a decrease in aerosol concentration. Higher RH The likelihood of finding dissipating cloud fragments with a
We also note a general increase in the fraction of clouds that are
As opposed to the single cloud simulations (Sect. 3) where cloud lifetime
can be easily defined, in cloud field simulations (especially the cleaner
cases) many clouds do not live as individual clouds from formation to
dissipation but rather split and merge with other clouds continuously
(Heiblum et al., 2016b). Thus, in order to evaluate the
lifetime evolution of cores in cloud fields, we focus on the growing branch
and use cloud mass (kg) as a proxy for the cloud lifetime during its initial
and mature stages. We assume that in the vicinity of the growing branch a
larger mass corresponds to a later stage in its lifetime.
CvM space diagrams showing the pixel fractions (
In Fig. 6 the core mass and volume fractions (
The core volume fractions show lower values than the mass fractions. The clean clouds are margin dominated for most masses, and the polluted clouds are core dominated for all masses. The intermediate case is generally confined to values between the other two cases. Figure 6 can be considered comparable with the lower panels in Fig. 3g and h, but excluding the dissipating part of those time series. The similar findings in both figures indicate the robustness of the aerosol effects on core properties in clouds.
Following the analyses of Sect. 3.1, we next test how aerosol concentration
affects the subset properties of one core type within another for all clouds
in a field (Fig. 7). We focus only on the typically smaller-sized cores
(
For the dissipating clouds in general, the lower the mass and the higher the
COG, the smaller the overlap. The dissipating branches do include a scatter
of small cloud for which RH
The other two permutations (i.e., The lower the concentration, the lower the chance that The lower the concentration, the more prevalent the independent dissipating branch
For the case of
Analyses of dominant buoyancy term within
In Sect. 3.2 it was seen that for single clouds, positive buoyancy results
from two main mechanisms: (i) convective updrafts – where updrafts promote
supersaturation and latent heat release, and thus always positive
Analysis of the buoyancy components in the CvM space (Fig. 8c, f, and i)
shows that the large majority of clouds are
Thus, we hypothesize that the polluted simulation only permits buoyancy
cores of the updraft type which intersect with the other core types (i.e.,
The polluted case is populated with dependent cores (white scatter) and
shows a classic pre-precipitation convective growth scenario, where relative
dominance of the
The independent cores span all the range of possibilities of
The intermediate simulation shows an additional scatter area of dependent
core clouds with increasing
From Fig. 3 it was demonstrated that a large part of the differences in single cloud characteristics (such as mass, volume, and the partition of these to core and margin regions) due to aerosols can be attributed to differences in vapor diffusion efficiencies. In Fig. 9 we check how these aerosol effects are manifested in the cloud field scale (using the CvM space) by observing the mean RH in the cloud core and margin of all clouds, where the core (margin) mean RH can be taken as a proxy for condensation (evaporation) efficiency. To gain additional intuition regarding the distribution of RH values within the clouds, vertical cross-sections (parallel to the prevailing wind direction) of the most massive clouds from each simulation are shown.
The vertical cross-sections demonstrate the large differences in the massive
clouds for each of the simulations. In addition to the increase in
precipitation production, lower aerosol concentrations yield much larger
horizontal extents of clouds. The clean, intermediate, and polluted most
massive clouds have a maximum radius of
The intermediate-case cloud shows lower maximal and minimal RH values and an increased dominance of the margin region. This cloud penetrates the inversion layer and entrains dry air into the cloud. In addition, the cloud produces significant precipitation which initiates downdrafts of dry entrained air through the cloud center. It can be seen that the increased vertical development of the intermediate-case cloud in comparison with the clean case increases the mixing with the environment. Thus, the dynamic effect of increased mixing and reduction in cloud RH overcomes the microphysical effect of increased evaporation and increase in cloud RH. The polluted-case cloud, on the other hand, shows a homogeneous RH pattern, with most of the cloud showing around 100 % RH and only a thin layer at the cloud edges (mainly at the upper regions) showing lower RH values. The polluted cloud penetrates the inversion layer as well, but this case lacks precipitation, and the microphysical effect of evaporation overcomes the dynamical effect of mixing.
Keeping in mind the insights obtained from comparisons of individual cloud, we move on to compare the RH characteristics of all clouds within the field. Looking first at core mean RH, a robust decrease is seen with increase in aerosol concentration. This decrease is seen for all cloud types and locations within the CvM space. The polluted case displays the most homogeneous pattern, with all clouds showing core mean RH values around 100 %, indicating efficient consumption of the supersaturation. The intermediate case displays a slightly less homogeneous pattern, with values ranging from 100 % to 101 % and with the higher values occurring along the growing cloud branch, especially for the largest clouds. The clean case shows the largest variance in core mean RH, ranging from 100 % for some cloud fragments that soon start to dissipate to 103 % in the cores of the large cloud entities. In addition to the low efficiency in consuming supersaturation, the high RH values in clean large clouds are due to the “protection” by large margin regions surrounding the core region.
The CvM patterns of mean margin RH show significant differences between the
polluted case and the other two. The mean margin RH values of the polluted
case are only marginally lower than 100 %, since subsaturated conditions
within the cloud are quickly adjusted by efficient evaporation. Only the
largest clouds in the polluted case permit lower mean margin RH values
(
As seen in the vertical cross-section examples, the largest clouds in the intermediate case have even lower margin RH values than for the clean case. This can be explained by the increased development of the large intermediate clouds to heights with lower RH and by more intense downdrafts for these large clouds. The lowest RH values in the domain are seen for the precipitating fragments (i.e., located below the adiabat). These fragments typically contain low concentrations of large drop sizes (precipitation drops) which are slow to evaporate and capable of surviving in low-RH conditions within the sub-cloudy layer.
In this work we explored how the aerosol effects on warm convective clouds
are reflected in their partition to core and margin regions. Following Part 1 of this work (PT1), we evaluated three types of core definitions: positive
buoyancy (
During dissipation stages cores frequently cease to be subsets of one
another and may either increase or decrease in their volume fractions. In
cloud fields we also observe small cloud fragments which shed off larger
cloud entities. This shedding increases for the lower-concentration
simulations which produce long-lived large cloud entities due to cloud
merging. These fragments show large variance in volume fraction (for all
core types) magnitudes without any consistent behavior. This is due to the
fact that they shed off various locations of the cloud. The polluted,
non-precipitating cases are unique in that can one expect the
For low aerosol concentrations, a
We show that the
The updraft buoyancy type is seen for all aerosol concentrations, while the
dissipation buoyancy type is only seen for lower aerosol concentrations. The
fact that the downdraft
Focusing on cores using the RH definition, a cloud's mass (volume) is dependent primarily on the processes in its core (margin). The core increases cloud mass by condensation while the margin increases the cloud's volume by mixing with the environment or dilution. The magnitude of the effects in each region of the cloud is strongly dependent on the aerosol concentration. Polluted clouds are core dominated both in terms of mass and volume, since they can hardly maintain their margins. Clean clouds are also core dominated in terms of mass but to a lesser degree. Clean clouds tend to be margin dominated in terms of volume for most their lifetimes. Thus, despite weaker convection in the clean clouds, their large, slow evaporating margins enable their cores (and the entire cloud) to exist for longer time spans by applying a large protecting shield around the core.
The different diffusion efficiencies are demonstrated by observing the relative humidity (RH) values in clouds. Cleaner clouds show larger variance in RH values. During their growing stages large supersaturation in the core and subsaturation in the margin can be seen. During their dissipation stages clouds may exist for minutes without any cloud core, with the entire cloud at subsaturation. Polluted clouds show the opposite, with RH values nearing 100 % throughout the cloud at all stages. Hence, above a certain aerosol concentration, the saturation adjustment approximation (i.e., instant condensation of all supersaturation) can be considered valid. However, the transition from clean to polluted is not always linear. For example, the largest clouds in the intermediate case have a lower margin RH value than both the clean and polluted cases. This is due to the fact that the intermediate case manages to develop taller (than the clean case) clouds with stronger updrafts and downdrafts, which entrain drier air from above the inversion layer base, but at the same time is less efficient in evaporating (than the polluted case) water and adjusting the RH to 100 %.
The microphysical and thermodynamical profiles used to initialize the single cloud and cloud field numerical simulations can be obtained upon request from the corresponding author.
RHH ran the cloud field simulations, conducted the analyses, and wrote the final draft of paper. LP participated in writing the first draft and performed single cloud simulations and relevant analyses. OA, GD, and IK participated in paper editing and discussions.
The authors declare that they have no conflict of interest.
The authors would like to acknowledge the Weizmann Institute High Performance Computing (HPC) team for their support.
The research leading to these results was supported by the Ministry of Science and Technology, Israel (grant no. 3-14444).
This paper was edited by Eric Jensen and reviewed by two anonymous referees.