Introduction
Despite the extensive research conducted in the last few decades and the
fact that clouds have an important role in the Earth's energy balance
(Trenberth et al., 2009), clouds are still considered to be one of the
largest sources of uncertainty in the study of climate and climate change
(Forster et al., 2007; Boucher et al., 2013).
Warm-cloud (containing liquid water only) formation depends on the
availability of water vapor and aerosols acting as cloud condensation nuclei
(CCN). Changes in aerosol concentration modulate the cloud droplet size
distribution and total number. Polluted clouds (forming under high aerosol
loading) initially have smaller and more numerous droplets, with a narrower
size distribution compared to clean clouds (Squires, 1958; Squires and
Twomey, 1960; Warner and Twomey, 1967; Fitzgerald and Spyers-Duran, 1973).
The initial droplet size distribution affects key cloud processes such as
condensation–evaporation, collision–coalescence and sedimentation. The
condensation–evaporation process is proportional to the total droplet
surface area, which increases with the droplet number concentration (for a
given total liquid water mass). Under given supersaturation conditions, the
condensation in polluted clouds is more efficient (higher condensation rate
or shorter consumption time of the supersaturation – Pinsky et al., 2013;
Seiki and Nakajima, 2014; Koren et al., 2014; Kogan and Martin, 1994; Dagan
et al., 2015a). However, under subsaturation conditions, for the same reason,
it implies higher evaporation efficiency. The evaporation induces downdrafts
and stronger vorticity and hence can lead to stronger mixing of the cloud
with its environment in polluted conditions (Xue and Feingold, 2006; Jiang et
al., 2006; Small et al., 2009).
The initiation of collision–coalescence is delayed in polluted clouds (Gunn
and Phillips, 1957; Squires, 1958; Albrecht, 1989). This drives a delay in
rain formation and can affect the amount of surface rain (Rosenfeld, 1999,
2000; Cheng et al., 2007; Khain, 2009; Levin and Cotton, 2009; Koren et al.,
2012; Hazra et al., 2013a, b; Dagan et al., 2015b).
Aerosol effects on single warm convective clouds were shown to have an
optimal value with respect to maximal water mass, cloud depth and rain yield
(Dagan et al., 2015a, b), which depends on the environmental conditions. For
aerosol concentrations lower than the optimum, the positive relationship
between aerosol concentration and cloud development is a result of two main
processes: (1) a larger latent heat release driven by the increase in the
condensation efficiency causing stronger updrafts and (2) a decrease in the
effective terminal velocity (|η|, i.e. mass-weighted terminal velocity
of the hydrometeors) (Koren et al., 2015) due to initial smaller droplets
and the delay in the collision–coalescence process. The smaller droplets
have higher mobility (the water mass moves up better with surrounding
updraft), reaching higher into the atmosphere and prolonging the cloud growth.
For aerosol concentration values above the optimum, the suppressing aerosol
effects take over, namely (1) stronger mixing of the cloud with its
environment driven by the increased evaporation efficiency (Small et al.,
2009) and (2) an increased water-loading effect due to the rain suppression.
Understanding the overall aerosol effect is even more complex when
considering processes on the cloud field scale. Clouds affect the
surrounding thermodynamic conditions by changing the humidity and
temperature profiles (Lee et al., 2014; Seifert et al., 2015; Stevens and
Feingold, 2009; Saleeby et al., 2015). In addition, clouds affect the solar
and longwave radiation budgets in the field. Over land the radiation effects
change the surface temperature and therefore can significantly affect heat
and moisture fluxes and as a result the cloud properties (Koren et al., 2004,
2008a; Feingold et al., 2005).
The invigoration mechanism, which refers to deeper and larger clouds with
larger mass that develop under polluted conditions, was studied mainly in
deep convective clouds (Andreae et al., 2004; Koren et al., 2005; Rosenfeld
et al., 2008; Tao et al., 2012; Fan et al., 2013; Hazra et al., 2013a;
Altaratz et al., 2014). Our focus here is on warm-cloud fields for which
previous observational studies reported an invigoration effect or a
non-monotonic response of the clouds to an increase in aerosol loading. For
example, Kaufman et al. (2005) found an increase in cloud fraction (CF) of
warm-cloud fields with increasing aerosol loading over the tropical Atlantic
Ocean. Yuan et al. (2011) reported that an increase in volcanic aerosols
near Hawaii led to increased trade cumulus CF and cloud top height.
Dey et al. (2011) have shown that an increase in aerosol optical depth (AOD) from
clean to slightly polluted resulted in an increase in CF in warm clouds over
the Indian Ocean. An additional increase in the AOD resulted in a decrease in CF, explained by the semi-direct effect of absorbing aerosols. Costantino
and Bréon (2013) reported higher CF over the southeastern Atlantic
under high aerosol loading conditions. From convective stability
considerations, deeper clouds tend to have a larger area (larger CF). It was
shown that warm convective cloud area correlates positively with cloud depth (Benner
and Curry, 1998; Koren et al., 2008b).
Koren et al. (2014) have shown that warm convective clouds over the southern
oceans can be considered as aerosol limited up to moderate aerosol loading
conditions. As the AOD increases, the clouds were shown to be deeper and
larger and to produce stronger rain rates. A reversal in the trend of the liquid
water path (LWP) as a function of increasing AOD was reported using
observations of warm convective clouds under a large range of meteorological
conditions (Savane et al., 2015). Li et al. (2011) studied warm clouds over
the southern great plains of the United States and reported no aerosol
effect on cloud top height.
On the other hand, numerical studies of the aerosol effect on warm cumulus
cloud fields show either no effect or cloud suppression (meaning shallower
and smaller clouds under higher aerosol loading conditions). Jiang and
Feingold (2006) found that the LWP, CF and cloud depth of warm shallow
convective clouds are insensitive to an increase in aerosol loading.
However, they demonstrated rain suppression by aerosols. Xue et al. (2008)
showed smaller clouds and suppression of precipitation in an increased
aerosol loading environment. Jiang et al. (2010) found a non-monotonic
change in the derivative of the surface rain rate with aerosol loading
(susceptibility) for higher maximal LWP clouds but a monotonic decrease in
the total precipitation with aerosol loading. Seigel (2014) showed that cloud size decreases with aerosol loading due to enhanced entrainment at
the cloud margins.
Some previous studies have demonstrated clouds' alteration of their
environment (Zhao and Austin, 2005; Heus and Jonker, 2008; Malkus, 1954; Lee
et al., 2014; Zuidema et al., 2012; Roesner et al., 1990). One example of
such an effect is the “preconditioning” or “cloud-deepening” effect (Nitta and
Esbensen, 1974; Roesner et al., 1990; Stevens, 2007; Stevens and Seifert,
2008), where clouds cool and moisten the upper cloudy and inversion layers
and in this way encourage the development of the next generation of clouds that
encounter improved environmental conditions. This effect is influenced by
the clouds' microphysical properties (Stevens and Feingold, 2009; Saleeby et
al., 2015). The role of warm convective clouds in moistening the free
troposphere was studied intensively using both observations and cloud field
numerical models (Brown and Zhang, 1997; Johnson et al., 1999; Takemi et
al., 2004; Kuang and Bretherton, 2006; Holloway and Neelin, 2009; Waite and
Khouider, 2010).
Albrecht (1993) used a theoretical single-column model to study the effect
of precipitation on the thermodynamic structure of the trade wind boundary layer
and found that even low rain rates can dramatically affect the profiles.
Under precipitating conditions, the cloudy layer is warmer, drier and more
stable than under non-precipitation conditions. Albrecht (1993) also showed that under
non-precipitating conditions the inversion height is greater than under
precipitating conditions, due to the larger amount of liquid water
evaporating at those elevations.
Another way clouds affect their environment is by the evaporation of rain below
the cloud base, which induces cooling of the sub-cloud layer (Zuidema et
al., 2012; Heiblum et al., 2016a). Lee et al. (2014) demonstrated the
aerosol effects on the field's CAPE (convective available potential energy) (as distributed above cloud base or
below it). The organization of the field is influenced by cloud processes as
well. Enhanced evaporative cooling in the sub-cloud layer, for example, can
produce cold pools which enhance the generation of clouds only at their
boundaries and hence change the organization of the field (Seigel, 2014;
Seifert and Heus, 2013; Heiblum et al., 2016a).
A recent paper (Dagan et al., 2016) showed that polluted clouds act to
increase the thermodynamic instability with time, while clean clouds consume
the atmospheric instability. The trend of the pollution-driven increase in
the instability is halted once the clouds are thick enough to develop
significant precipitation. Indeed, studies of long simulation times
(> 30 h) showed that the initial differences between clean and
polluted cases are reduced by negative feedbacks of the clouds on the
thermodynamic conditions (Lee et al., 2012; Seifert et al., 2015).
In this work we explore the coupled microphysical-dynamic system of warm
marine cloud fields using a bin-microphysics scheme under a large range of
aerosol concentrations. We study the aerosol–cloud–environmental
thermodynamic system by examining how changes in aerosol concentrations
affect clouds properties and the related modifications of the thermodynamic
conditions over time, which also drive feedbacks on the evolution of cloud properties.
Methodology
The SAM (System for Atmospheric Modeling), non-hydrostatic, anelastic large eddy simulation
(LES) model version 6.10.3 (Khairoutdinov and Randall, 2003) was used to simulate
the well-studied trade cumulus case of the Barbados Oceanographic and Meteorological EXperiment
(BOMEX; Holland and Rasmusson, 1973;
Siebesma et al., 2003). The BOMEX case is an idealized trade cumulus cloud
field that is based on observations made near Barbados during June 1969.
This case was initialized using the setup specified in Siebesma et al. (2003).
The setup includes surface fluxes and large-scale forcing (see
details in Heiblum et al., 2016b). The horizontal resolution was set to 100 m, while the vertical resolution was set to 40 m. The domain size was 12.8 × 12.8 × 4.0 km3
and the time step was 1 s. Due to computational
limitations, we had to restrict the domain size to a scale that has a
limited capability for capturing large-scale organization (Seifert and Heus,
2013). The model ran for 16 h and the statistical analysis included
all but the first 2 h (total of 14 h). After 2 h of simulations
the initial increase in the total liquid water mass in the domain
stopped and the differences between the simulations (difference in relation to the aerosol loading)
became significant. Therefore, 2 h is determined as spin-up time (similar to
the spin-up time in Xue and Feingold, 2006).
A bin microphysical scheme (Khain and Pokrovsky, 2004) was used. The scheme
solves warm microphysical processes, including droplet nucleation,
diffusional growth, collision coalescence, sedimentation and breakup.
In order to focus on the aerosol effect on the thermodynamic properties of
the field, the radiative effects (as included in the large-scale forcing –
see details in Dagan et al., 2016) were prescribed in all simulations. The
aerosol distribution adopts a marine size distribution (see details in
Jaenicke, 1988, and Altaratz et al., 2008). Eight different simulations were
conducted simulating a wide range of aerosol loading conditions from
extremely pristine to polluted (total concentration of 5, 25, 50, 100, 250,
500, 2000 and 5000 cm-3 near ground level; Dagan et al., 2015a). To
reduce the results' sensitivity to the shape of the aerosol size distribution
and to focus on the aerosol number concentration effect, the different
aerosol concentrations are calculated by the multiplication of all bins by a
constant factor and maintaining a constant shape of the size distribution.
The aerosol is assumed to be composed of ammonium sulfate and initialized
with a constant mixing ratio with height. A prognostic equation is solved for
the aerosol mass, including regeneration upon evaporation and removal by
surface rain. Regeneration upon evaporation of cloud drops was shown to be a
very important source of aerosols, especially in polluted conditions (Yin et
al., 2005). The aerosol serves as potential cloud condensation nuclei (CCN), and it is activated based on the Köhler theory (the scheme is described in
Khain et al., 2000). The aerosol (water drop) size distribution is
calculated between 5 nm and 2 µm (2 µm–3.2 mm). For both aerosol
and drops, successive bins represent doubling of the mass.
The effects of changes in aerosol concentration on the drop concentration
and its mean size for the different simulations can be found in Fig. S1 in
the Supplement.
Results and discussion
Mean cloud field properties under different aerosol loading
conditions
The aerosol effects on the mean field properties during the entire run are
examined first, followed by a more detailed examination of the time
evolution in the next section. Figure 1 presents mean values of key
properties of cloud fields as a function of the aerosol loading for the
entire (14 h) simulation time.
Mean properties (over domain and time) of the simulated cloud
fields as a function of the aerosol concentration used in the
simulation: (a) total liquid water mass in the domain, (b) cloudy LWP, (c) CF for columns with τ > 0.3, (d) maximum cloud top, (e) mean
cloud top and (f) surface rain rate. Each of these mean properties are
calculated for the last 14 h of the 16 h of simulation. The
error bars present the standard errors. For details about the different
properties see the text.
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The total water mass (calculated as mean over time in each domain) as a
function of aerosol concentration shows a clear reversal in the trend (Fig. 1a).
For the given environmental conditions simulated here, it increases
when aerosol loading is increased from 5 to 50 cm-3. An additional increase
in the aerosol loading results in a decrease in the total water mass in the
domain.
The LWP (Fig. 1b) calculated as a mean over time over
all cloudy columns in each domain, which is strongly correlated with the
total water mass, also shows the same non-monotonic general trend. The
maximum in the curve of the cloudy LWP is at a slightly higher aerosol
concentration compared to the total mass (100 cm-3). This difference
can be explained by the link to the CF (calculated as the
area covered by clouds with optical path τ > 0.3; Fig. 1c)
that decreases above an aerosol loading of 25 cm-3. Thus, for the more
polluted simulations, the mass is distributed on smaller horizontal cloud
areas, as shown in previous studies (Seigel, 2014).
There is also a significant difference in the way the water mass is
distributed along the atmospheric column in the different simulations. The
maximum cloud top height (Fig. 1d), calculated as a mean over time of the
altitude of the highest grid box in the domain that contains liquid water
content (LWC > 0.01 g kg-1), increases significantly when
aerosol loading increases up to 500 cm-3 (an increase from 1692 to
2120 m when aerosol loading increases from 5 to 500 cm-3). An
additional increase in the aerosol loading results in a minor decrease in the
maximum cloud top height (down to 2030 m for an aerosol loading of
5000 cm-3). The minor decrease seen for this range of aerosol
concentration (compared with the larger decrease in the mean LWP for example)
can be explained by the location of the maximal cloud top height above the
cloud core, which is affected mainly by the invigoration processes (enhanced
condensation and latent heat release) and less by margin-oriented processes
(enhanced entrainment and evaporation) that significantly impact the total
cloud mass (Dagan et al., 2015a). Another reason is the cloud-deepening
effect under polluted conditions (Stevens, 2007; Seifert et al., 2015) that
will be described later. As for the mean cloud top height calculated as a
mean of all cloudy columns along the whole run (Fig. 1e), the trend shows a
monotonic increase with aerosol loading. The trend is approaching a
saturation level for high aerosol concentration values. The mean cloud top
value over the simulation is 810 and 1010 m for the simulations with aerosol
loading of 5 to 5000 cm-3, respectively.
Presenting together the mean over time of the maximum and the mean cloud top
height captures, in a compact yet informative, way the response of the cloud
top height distribution to changes in aerosol loading and reduces the
sensitivity to outliers. Moreover, by averaging over time, the significance
of the outliers is decreased as well.
The trend in the domain's average rain rate, as a function of the aerosol
loading (Fig. 1f), shows a peak at a relatively low aerosol loading (similar
to the optimal value of the CF) of 25 cm-3.
Figure 2 presents the vertical profiles of the total condensed and evaporated
mass during the simulations for four different simulations. We note that as
the aerosol loading increases, both the condensed and evaporated mass
increased (this is due to the increase in the diffusion rates – see Fig. S2
– and despite the decrease in cloud fraction – see Fig. 1c; Dagan et al.,
2015a; Koren et al., 2014; Pinsky et al., 2013; Seiki and Nakajima, 2014).
Below the cloud base (located around 550 m), the clean simulations have
small rain evaporation values which are absent in the polluted simulations.
Domain's total condensed (solid lines) and evaporated mass (dashed
lines) for 14 h of simulation along four different simulations conducted
with different aerosol concentration levels (5 cm-3 – blue; 50 cm-3 – green; 250 cm-3 – red; and 2000 cm-3 – cyan).
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Effective terminal velocity (η) is defined as the mass-weighted average
terminal velocity of all the hydrometeors within a given volume of air (Koren
et al., 2015). By definition, η measures the terminal velocity of the
water mass's center of gravity (COG), i.e. the COG's movement with respect to
the surrounding air's vertical velocity (W). Small absolute values η imply that the droplets' COG will move better with the
surrounding air; i.e. the droplets will have better mobility (Koren et al.,
2015). The sum VCOG=W+η (η always negative) reflects
the water mass COG vertical velocity relative to the surface. Positive
VCOG implies a rise in the COG, and a negative value means a
fall.
The mean updraft (in both space and time, weighted by the liquid water mass
in each grid box to be consistent with the COG point of view – Fig. 3a)
increases with the increase in aerosol loading, in agreement with previous
studies (Saleeby et al., 2015; Seigel, 2014). This indicates an increase in
the latent heat contribution to the cloud buoyancy, driven by an increase in
the condensation efficiency (Dagan et al., 2015a, b; Koren et al., 2014;
Pinsky et al., 2013; Seiki and Nakajima, 2014) (Figs. 2 and S2). At the same
time, η decreases as the aerosol concentration increases
(Fig. 3b) indicating better mobility of the smaller droplets, allowing them
to move more easily with the air's updraft. The outcome of these two effects
is an increased VCOG for a higher aerosol concentration
(Fig. 3c), indicating that the polluted clouds' liquid water is pushed higher within the atmosphere (Koren et al., 2015) as shown by higher COG (Fig. 3d).
Mean (over time and space) of (a) updraft (W), (b) effective
terminal velocity (η), (c) the center of gravity velocity VCOG and (d) COG
(center of gravity) height as a function of the aerosol concentration.
All calculated for the last 14 h of the 16 h of simulation.
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The mean COG height of the water mass (Grabowski et al., 2006; Koren et al.,
2009) (Fig. 3d), increases with the aerosol loading up to a relatively high
concentration (500 cm-3). Note that while the trend in the system's
characteristic velocities (η and W) is a monotonic increase, the COG
has an optimal aerosol concentration for which it reaches its maximum height
(500 cm-3). For aerosol concentrations above 500 cm-3, a minor
decrease is shown. As described above, the COG height increase with aerosol
loading, between extremely clean and polluted conditions, can be explained by
increased VCOG, which is a product of both lower η and increased updraft on the cloud scale and larger thermodynamic
instability induced by the polluted clouds on the field scale as will be
shown in the next section (Dagan et al., 2016; Heiblum et al., 2016a). The
reduction in the mean COG height in the most polluted simulations is caused
by cloud-suppressing processes, including an enhanced entrainment (see the
enhanced evaporation efficiency with aerosol loading – Figs. 2, S2) and
larger water loading (Dagan et al., 2015a – shown also in Fig. 4a below).
(a) Mean LWC vertical profiles. (b) Vertical
profiles of the mean (over time) total liquid water mass per height for four
different simulations (5 cm-3 – blue; 50 cm-3 – green;
250 cm-3 – red; and 2000 cm-3 – cyan). The mean profiles are
calculated for the last 14 h of the 16 h of simulation. Note that dotted
parts of the curves in (a) represents heights at which the total
liquid water mass was less then 1 % of the maximum total mass (Fig. 4b).
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The trend in COG height can be also viewed (in more detail) in Fig. 4a, which
presents profiles of mean LWC for cloudy voxels only.
We show that both the height and the magnitude of the maximum LWC increase
with the aerosol loading. This is due to both rain suppression (Fig. 1f) and
an increased VCOG (Fig. 3c) with aerosol loading. There is a
reduction in the mean LWP (for > 100 cm-3 – Fig. 1b)
although there is an increase in the LWC with aerosol loading due to the
differences in cloud fraction (Fig. 1c) and in the vertical distribution of
the liquid water (Fig. 4b). At the upper part of the clouds
(H > 2000 m), in the polluted case, a small amount of cloudy
pixels have a large mean LWC (and hence a large water-loading effect), but
the total amount of liquid water is small (Fig. 4b). Below the cloud base
(H < ∼ 550 m) the LWC trend is reversed due to the
enhancement of rain in the clean runs (Fig. 1f). The increase in LWC with
aerosol loading implies a larger water-loading negative component in the
clouds' buoyancy.
All the evidence presented in Figs. 2–4 explains the non-monotonic trends in
the cloud properties' response to changes in aerosol loading (Fig. 1). For
clean conditions (below the optimal aerosol concentration value), an increase
in aerosol loading would enhance the cloud development (larger mass, LWP,
cloud top, CF, rain rate) because of two main factors: (1) an increase in the
condensation efficiency (due to the larger total droplet surface area for
condensation and longer time – Figs. 2, S2) and (2) smaller effective
terminal velocity (|η|) values, which, per given updraft, allow a cloud's
hydrometeors to be pushed higher within the atmosphere (Koren et al., 2015)
(Fig. 3b).
The higher condensation efficiency in polluted clouds (Fig. 2) results in a
larger latent heat release that enhances the updraft (Fig. 3a) and cloud
development. The increased VCOG reflects the two cloud-enhancing
processes (decrease in η and larger mean updraft). We
note that the increase in the mean updraft values with aerosol loading is
seen despite the negative effect of water loading (see Fig. 4a). For aerosol
concentrations above the optimum, cloud development is suppressed by the
increase in evaporation efficiency (Fig. 2) and hence stronger mixing of the
cloud with its environment (i.e. Small et al., 2009) and larger water loading
due to rain suppression (Dagan et al., 2015a, Fig. 4a).
The time evolution of the mean cloud field properties under different
aerosol loading conditions
All the aerosol effects that were discussed up to this point
(condensation–evaporation efficiencies, η and water loading) are
applicable both on the single-cloud scale as well as on the cloud field
scale. However, on the cloud field scale, another aspect needs to be
considered, namely the time evolution of the effect of clouds on the field's
thermodynamic conditions (which was the focus of a recent study by Dagan et
al., 2016).
Figure 5 presents the changes (final value minus initial one) in the
temperature (T) and water vapor content (qv) vertical profiles
as a function of aerosol concentration used in the simulation. The initial
profiles were identical in all simulations. Figure S3 presents the full
temporal evolution of those parameters. In low aerosol concentration runs
(100 cm-3 and below), the sub-cloud layer becomes cooler and wetter
with time and the cloudy layer warmer and drier. Meanwhile, under higher
aerosol concentrations conditions (250 cm-3 and above), the sub-cloud
layer becomes warmer and drier while the cloudy and inversion layers become
colder and wetter. This trend is driven by the condensation–evaporation
tendencies along the vertical profile (see Fig. 2; Dagan et al., 2016). Under
low aerosol concentration conditions, water condenses at the cloudy layer and
is advected downward to the sub-cloud layer where it partially evaporates.
Under polluted conditions, on the other hand, the condensed water from the
lower part of the cloudy layer is advected up to the upper cloudy and
inversion layers (driven by larger VCOG – Fig. 3) and evaporates
there (Dagan et al., 2016).
Total change, during 16 h of simulation in the temperature (K; a) and water vapor content (g kg-1; b) domain
mean vertical profiles as a function of the aerosol concentration used in the
simulation.
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Change (in %) in key variables between the mean values in the
last period of the simulations and the first period. Negative values are
presented in bold.
Total mass
LWP
COG
Max top
Mean top
W max
CF
Rain rate
(%)
(%)
(%)
(%)
(%)
(%)
(%)
(%)
5 cm-3
-57.0
-61.4
-43.1
-32.9
-39.7
-28.2
-19.7
-53.5
25 cm-3
-45.2
-58.3
-39.6
-17.8
-37.4
-38.8
-0.6
-32.9
50 cm-3
-43.8
-53.1
-33.7
-15.6
-31.6
-47.9
-7.5
-40.1
100 cm-3
-20.1
-13.0
-16.1
-3.2
-13.0
-32.8
-19.0
19.6
250 cm-3
17.5
48.6
5.0
12.4
5.0
-4.3
-40.7
598.1
500 cm-3
37.4
64.2
19.9
19.2
10.7
9.4
-30.9
841.5
2000 cm-3
-3.7
10.6
14.8
10.1
17.9
6.0
-17.8
–
5000 cm-3
-10.1
5.7
13.7
9.9
17.5
2.9
-20.7
–
Such trends in the environmental thermodynamic conditions are likely to
affect the formation of clouds. In Fig. 6 the time evolution of some of the
key cloud field properties is considered (the same properties that were shown
in Fig. 1). The blue, green and red curves represent the mean values over the
first, second and third periods of the simulations, respectively (each one
covers 4 h and 40 min). Table 1 presents change (in percentage) in the mean
values of key variables between the third period of the eight simulations
(11 h 20 min to 16 h into the simulation; red curves in Fig. 6) and the
first period (2 h to 6 h 40 min into the simulation; blue curves in
Fig. 6).
Mean properties (over time and domain) of the simulated cloud fields
as a function of the aerosol concentration used in the
simulation: (a) total liquid water mass in the
domain, (b) cloudy LWP, (c) CF for columns with τ > 0.3, (d) maximum cloud top, (e) mean cloud
top and (f) surface rain rate. Each property is calculated
separately for each period of one-third of the simulations (blue, green and
red for the first, second and third periods, respectively). The error bars
present the standard error. For details about the different properties, see
the text.
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The examination of the evolution of the mean total water mass along the
simulations (Fig. 6a; blue, green and red curves) presents a different trend
between the clean and the polluted simulations. In the clean simulations
(5–100 cm-3) the total water mass decreases significantly with time (a
decrease of 57, 45, 44 and 20 % in total mass for the cases of 5, 25, 50
and 100 cm-3, respectively – see Table 1). On the other hand, in the
more polluted simulations, (with an aerosol loading of 250 and
500 cm-3), there is an increase in the total water mass with time (of
17 and 37 % between the first and the last periods of the simulations,
respectively). Under extremely polluted conditions of 2000 and
5000 cm-3, the total water mass in the domain is small and there is
little change with time. These changes in time push the optimum aerosol
concentration to higher values along the simulation time. This trend is also
shown for the optimum aerosol concentration with regard to the mean cloudy
LWP (Fig. 6b), maximum top (Fig. 6d) and mean top (Fig. 6e).
Trends in the mean rain rate show that in the cleanest simulations (5, 25 and
50 cm-3), the rate decreases with time (Fig. 6f; 53.3, 32.9 and
40.1 %, respectively). In the regime of medium to fairly high aerosol
loading (100, 250 and 500 cm-3), the rain rate increases (19.6, 598.1
and 841.5 %, respectively). And in the most polluted simulations (2000
and 5000 cm-3), the surface rain is negligible throughout the
simulation time. These trends are explained below.
The time evolution of the thermodynamic conditions (Fig. 5) shows a
reduction (enhancement) in the thermodynamic instability with time in the
clean (polluted) simulations. Figure 6 and Table 1 indicate that under clean
conditions the decrease in the thermodynamic instability with time leads to
a decrease in the mean cloud field properties such as total mass, cloud top
height and rain rate. Under polluted conditions the trends are opposite and
the mean cloud field properties increase with time due to the increase in
thermodynamic instability (Dagan et al., 2016) and due to the cloud
deepening (Stevens and Seifert, 2008; Stevens, 2007; Seifert et al., 2015).
These differences between the clean and polluted simulations drive changes
in the optimum aerosol concentration with time. For example, for the LWP
(Fig. 1b) the optimum aerosol concentration is 50, 100 and 250 cm-3 for the first, second and third parts of the simulation, respectively.
Summary
Cloud processes can be divided in a simplistic manner into two characteristic
scales – the cloud scale and the field scale. Here, using LES model with a
bin microphysical scheme we studied the outcome of the processes of the two
scales acting together. We first presented domain-averaged properties over
the whole simulation time (Sect. 3.1) to indicate the general aerosol effects
in a first-order manner, and then we followed the time evolution of the
effects (Sect. 3.2).
A non-monotonic aerosol effect was reported recently for a single-cloud scale
(Dagan et al., 2015a, b). Here we show that these trends “survived” the
domain and time averaging. We argue that the trend of an enhanced development is driven by two main processes of enhanced condensation and reduced
effective terminal velocity (which improves the droplets' mobility). These
processes are mainly related to the core of the clouds and to the early
stages of cloud development. We show that the cloud systems characteristic
velocities can capture these effects. The effective terminal velocity (η) inversely measures the mobility. Smaller droplets with smaller variance
will have smaller |η| and therefore will be pushed higher in a given
updraft, whereas larger droplets with larger |η| will move downward with respect to the surrounding air. An increase in condensation efficiency
drives more latent heat release, which enhances the cloud updraft. We showed
that VCOG is a product of the two velocities.
The descending branch in which an increase in aerosol loading suppresses
cloud development is governed by an increase in the evaporation efficiency of
the subsaturated parts of the clouds and by an increase in water loading.
Since clouds change the atmospheric thermodynamic conditions in which they
form, different initial clouds would cause a different impact on the
environment. Therefore, the cloud field is a continuously evolving system for
which aerosol properties determine an important part of the temporal trends.
Figure 5 shows striking differences between the evolution of the
thermodynamic profiles in clean and polluted cases. For the polluted clouds
(mostly non-precipitating), the upper cloudy layer turns wetter and cooler
due to enhanced evaporation and the sub-cloud layer becomes warmer and
drier; together, these developments act to increase the instability. On the
other hand, clean precipitating clouds consume the initial instability with
time by warming the cloudy layer (due to latent heat release) and cooling the
sub-cloud layer by the evaporation of rain.
The polluted cloud feedbacks on the thermodynamic conditions act to deepen
the clouds. Since clouds that form in a more unstable environment are
expected to be aerosol limited up to higher aerosol concentrations (Koren et
al., 2014; Dagan et al., 2015a), an increase in the domain's instability for
the polluted cases drives an increase in the optimal aerosol concentration
with time.
We note that such an increase in the instability cannot last forever. A
deepened cloud will eventually produce larger precipitation rates that may
weaken the overall effect on the field (Stevens and Feingold, 2009; Seifert
et al., 2015). These results pose an interesting question on the dynamical
state of cloud fields in nature. Do the cloud fields “manage” to reach a
“near-equilibrium” state (Seifert et al., 2015), for which the deepening
effect balances the aerosol effect fast enough that the effects are buffered
most of the time (Stevens and Feingold, 2009). Or maybe, the characteristic
lifetime of a trade cumulus cloud field is shorter than the time it takes to
significantly balance the aerosol effects. In this case the cloud fields
could be regarded as “transient”, and therefore, as shown here, aerosol
might have a strong effect on the clouds, both through affecting the
microphysics, initiating many feedbacks on the cloud scale, and by affecting
the field thermodynamic evolution over time.