Introduction
Atmospheric aerosols, formed naturally and anthropogenically, influence the
radiative energy budget of the Earth–atmosphere system in many ways. They
scatter or absorb a fraction of the incoming solar radiation to cool or warm
the atmosphere, decreasing surface temperature and altering atmospheric
stability (e.g., Jacobson, 2002; Wang et al., 2013). They also serve as
cloud condensation nuclei (CCN) and ice nuclei (IN), modifying optical
properties and lifetime of clouds (e.g., Penner et al., 2001; Zhang et al.,
2007). The aerosol indirect effect, generally referred to as the aerosol
impact on cloud reflective properties and lifetime (Twomey, 1977; Houghton,
2001), has constituted one of the largest uncertainties in climate
prediction (IPCC, 2013). In addition, the aerosol effects on precipitation
have been regarded as an important but poorly understood process that could
have major implications to climate and water supplies (Levin and Cotton,
2007; Wang et al., 2014a, b).
For a given amount of condensable water vapor, elevated aerosol
concentrations increase the number of cloud droplets and reduce their sizes,
enhancing not only the reflective properties but also the lifetime of clouds
through suppressing warm-rain processes (Twomey, 1977; Albrecht, 1989).
Accumulative observational and modeling evidence has shown that reduced cloud
droplet size, due to increasing CCN, inhibits collision and coalescence
processes, suppressing warm rain and delaying the onset of precipitation.
Therefore, more droplets are further allowed to be transported above the
0 ∘C isotherm, triggering the efficient mixed-phase process to
release more latent heat and intensify the convection (e.g., Rosenfeld and
Lensky, 1998; Rosenfeld and Woodley, 2000; Kaufman and Nakajima, 1993;
Andreae et al., 2004; Kaufman et al., 2005; Fan et al., 2007a; Khain et al.,
2008; Koren et al., 2010; Li et al., 2013; Loftus and Cotton, 2014). However,
recent studies have shown that an optimal aerosol loading exists to
invigorate convection (Rosenfeld et al., 2008; Koren et al., 2014; Dagan et
al., 2015). Additionally, the aerosol impacts on cloud developments are also
proposed to be dependent on the environmental conditions, such as relative
humidity and vertical wind shear (Van Den Heever et al., 2007; Lee et al.,
2008; Fan et al., 2009, 2016; Tao et al., 2012).
The observational and model-derived evidence on how aerosols influence
rainfall remains elusive due to the complexity of cloud processes, which are
determined by intricate thermodynamic, dynamical, and microphysical processes
and their interactions (Levin and Cotton, 2007; McComiskey and Feingold,
2012; Lin et al., 2016). Observations have demonstrated that the aerosol
effect on precipitation depends on both the type of aerosols and
precipitating environments. Rainfall reduction has been observed in polluted
industrial and urban regions in shallow clouds or clouds with the top
temperature exceeding -10 ∘C (e.g., Rosenfeld, 2000; Ramanathan et
al., 2001; Andreae et al., 2004; Yang et al., 2013). However, documented
rainfall increase has also been observed around heavily polluted coastal
areas or over oceans influenced by anthropogenic aerosols (e.g., Cerveny and
Balling Jr., 1998; Shepherd and Burian, 2003; Zhang et al., 2007; Li et al.,
2008b; Koren et al., 2012, 2014). Model results tend to support the argument
that increasing aerosol concentrations enhances precipitation under a moist,
unstable atmosphere (e.g., Khain et al., 2005; Fan et al., 2007b; Li et al.,
2008a, 2009; Wang et al., 2011; Fan et al., 2013).
The Tibetan Plateau (TP), located in central eastern Eurasia and with an
average elevation of more than 4000 m, significantly affects the formation
and variability of the Asian summer monsoon through mechanical and thermal
dynamical effects (Wu et al., 2007). Due to its strong surface heating, the
cumulus clouds are active over the TP and can be organized to form
convective systems, contributing substantially to the precipitation over TP
and adjacent areas. The TP is surrounded by several important natural and
anthropogenic aerosol sources, and the in situ and satellite measurements
have shown that anthropogenic aerosols and dust have been lofted to the TP,
directly influencing the regional climate (Engling et al., 2011). Soot
aerosols deposited on the TP glaciers have been confirmed to contribute
significantly to observed glacier retreat (Xu et al., 2009). Absorbing
aerosols over the TP have been proposed to directly affect monsoon rainfall
through the elevated-heat-pump mechanism (Lau et al., 2008; D'Errico et al.,
2015; Li et al., 2016).
However, to date few studies have been performed to investigate the aerosol
indirect effect or the aerosol–cloud interaction over the TP. In the present
study, we report an investigation of the aerosol effect on the cumulus cloud
development and precipitation over the TP. Two types of cumulus clouds
occurring over the TP and the North China Plain (NCP) are simulated using a
cloud-resolving weather research and forecasting (CR-WRF) model for comparisons. The
model configuration is described in Sect. 2. The results and discussions
are presented in Sect. 3, and summary and conclusions are given in Sect. 4.
Models and design of numerical experiments
Model configuration
A CR-WRF model (Skamarock, 2004) is used in the study to simulate cumulus
clouds. A two-moment bulk microphysical scheme developed by Li et al. (2008a)
is utilized to account for the aerosol–cloud interactions in the
simulations. The mass mixing ratio and number concentration of five
hydrometeors are predicted in the bulk microphysical scheme, including cloud
water, rain water, ice crystal, snow flake, and graupel. The gamma function
is used to represent the size distribution of the five hydrometeors. Detailed
information is provided in Li et al. (2008a).
In order to consider the aerosol activation to CCN and IN, the Community
Multiscale Air Quality (CMAQ)/Model-3 aerosol
module (Binkowski and Roselle, 2003) is implemented into the CR-WRF model.
Aerosols are simulated in the CMAQ using a modal approach assuming that
particles are represented by three superimposed lognormal size distributions.
The aerosol species – including sulfate, nitrate, ammonium, organic and
black carbon, and other unidentified species (dust-like) – are predicted in
the module.
Atmospheric sounding (a) over the Tibetan Plateau (87.08∘ E,
28.63∘ N, 4302 m a.s.l.) at 08:00 UTC on 12 August 2014 and
(b) over North China Plain (114.35∘ E, 37.17∘ N,
181 m a.s.l.) at 08:00 UTC on 24 August 2014. The black line corresponds to the
temperature, and the blue line represents the dew point temperature.
For the CCN nucleation, the critical radius of dry aerosols is calculated
from the k-Köhler theory developed by Petters and Kreidenweis (2007,
2008, 2013) using water supersaturation predicted by the CR-WRF model (Rogers
and Yau, 1989; Pruppacher and Klett, 1997). If the activated CCN radius is
less than 0.03 µm, the mass of water condensation on CCN is
calculated under the equilibrium assumption; otherwise, the mass of water
condensing on CCN is calculated by
mw = K43πra3ρw
at zero supersaturation, where 3 < K < 8 (Khain, 2000).
Additionally, a novel, flexible approach proposed by Philips et al. (2008,
2013) has been used to parameterize the ice heterogeneous nucleation within
clouds. The method has empirically derived dependencies on the chemistry and
surface area of multiple species of IN aerosols, mainly including dust and
black and organic carbon aerosols. Three kinds of ice nucleation mechanisms
are considered in the method: contact, immersion, and condensation freezing.
Design of numerical experiments and statistical method in data analysis
The spatial resolution used in the cloud simulations is 1 km in the
horizontal direction and about 250 m in the vertical direction. The model
domain of 200 × 200 × 80 grid boxes along the x, y, and
z directions, respectively, has been used to provide 200 km × 200 km
horizontal and 20 km vertical coverage in this study. The initial and
boundary conditions of water vapor are from the sounding data. The
simulations use the open boundary conditions under which variables of all
horizontal gradients are zero at the lateral boundary.
Two types of cumulus clouds are simulated using the CR-WRF model. The
cumulus cloud over the TP (hereafter referred to as Cu-TP) is initialized
using the sounding data (87.08∘ E, 28.63∘ N, 4302 m a.s.l.)
at 08:00 UTC on 24 August 2014 (Fig. 1a). The cumulus cloud over
the NCP (hereafter referred to as Cu-NCP) is initialized using the sounding
data (114.35∘ E, 37.17∘ N, 181 m a.s.l.) at 08:00 UTC on
12 August 2014 (Fig. 1b). The selected sounding profiles over the TP and
NCP reveal a moderate instability in the atmosphere, with similar convective
available potential energy (CAPE) for comparison, i.e., 675 J kg-1 for
Cu-TP and 651 J kg-1 for Cu-NCP. Although Cu-TP and Cu-NCP have a
similar CAPE, the remarkable difference of the initialization elevation
between Cu-TP and Cu-NCP causes their distinct development processes. The
0 ∘C isotherm is generally at a level of around 5 km a.s.l. in
the summer. Therefore, when an air parcel perturbed in the boundary layer
ascends to form a cloud, the rising distance to the 0 ∘C isotherm
is around 1 km over the TP and about 4 km over the NCP. Therefore, the
occurrence of the efficient mixed-phase process is much earlier for the
cumulus cloud over the TP than the NCP, which substantially advances the
development of the cloud over the TP.
The cumulus development is triggered by a warm bubble 15 km wide and a
maximum temperature anomaly of 4 ∘C at the height of 1.5 km a.g.l. (Li
et al., 2008a), and the integration time is 2 h. Observed aerosol
concentrations over the TP exhibit a large variation during the monsoon
season; i.e., the observed sulfate concentrations range from 0.1 µg m-3
to several micrograms per square meter (Decesari et al., 2010). Therefore, a set of 28 initial
aerosol size distributions with the aerosol number concentration ranging
from 20 to 9000 cm-3 and the sulfate mass concentration ranging from
0.02 to 9.0 µg cm-3 at the surface level is used. Other aerosol
species are scaled using the measurement at the Nepal Climate
Observatory – Pyramid (NCO-P) (Decesari et al., 2010). These aerosol
distributions are designated for environments ranging from very clean
background air mass to polluted urban plumes over the TP and NCP. Although
the observed organic aerosol dominates the aerosol composition at NCO-P
(Decesari et al., 2010), considering the large uncertainties in the
hygroscopicity of organic aerosols, the hygroscopicity parameter for the
secondary organic aerosol is set to 0.05 in the study (Petters and
Kreidenweis, 2007, 2008). Hence, sulfate aerosols (or inorganic aerosols)
still play a dominant role in the CCN activation. The aerosol concentration
is assumed to decrease exponentially with height in the model simulations
(Li et al., 2008a).
We have adopted several assumptions and simplifications for the processes
associated with aerosols. In the simulations, only the accumulation mode of
aerosols is used for the CCN and IN activation, and the aerosol spatial
distributions are determined by the initial and boundary conditions, without
consideration of chemistry, emissions, and release from cloud droplet
evaporation or ice crystal sublimation. The sulfate, nitrate, ammonium, organic and black carbon, and
dust-like aerosols in the accumulation mode are included to consider the
aerosol CCN and IN effects. Therefore, the surface-level aerosol number
concentration ([Na]) is used to represent all types of aerosols,
and the CCN concentration at a certain supersaturation (SS) is not used in
the study. It is worth noting that the simple aerosol assumption is liable to
cause rather large uncertainties in the aerosol activation to CCN and IN.
Aerosol chemistry in clouds plays a considerable role in the aerosol
nucleation and growth. Direct emissions from anthropogenic sources contribute
substantially to the CCN and IN, even over the TP with increasing human
activities. Furthermore, mineral dust from the natural source frequently
dominates the TP throughout the year. Therefore, future studies need to be
conducted to include all the aerosol modes, chemistry, and emissions.
In order to evaluate the overall response of simulated cumulus clouds to
changes in aerosol concentrations, the population mean (p mean) of a given
variable over all qualified grid points and for a given integration interval
is used in the study (Wang, 2005), defined as
C‾p=1∑t=T1T2N(t)∑t=T1T2∑n>nminq>qminc(x,y,t),
where c represents a given quantity. The calculation only applies to the grid
points where both the mass concentration q and number concentration n of a
hydrometeor or the summation of several hydrometeors exceed the given
minima. The total number of the grid points at a given output time step t is
represented by N(t). T1 and T2 are the start and end output time steps, respectively.
Modeled p mean of (a) cloud droplet number concentration and
(b) effective radius as a function of the initial [Na] in Cu-TP and Cu-NCP.
Results and discussions
Response of cloud properties to changes in aerosol concentrations
Figure 2a depicts the dependence of the p mean of the cloud droplet number
concentration (CDNC) on the [Na]. Increasing [Na] provides more CCN to activate,
and, although more activated droplets compete for the available water vapor,
the water vapor condensation efficiency is enhanced due to the increased
bulk droplet surface area, accelerating the latent heat release and the
updraft to provide more supersaturated water vapor. Therefore, the
increasing CDNC is very consistent with increasing [Na] in Cu-TP and Cu-NCP,
in good agreement with previous studies (e.g., Fan et al., 2007a, b; Li et
al., 2008a). When the [Na] increases from about 20 to 9000 cm-3,
the p mean of the CDNC increases from 0.56 to 218 cm-3
for Cu-NCP. However, more aerosols are activated in Cu-TP
compared to Cu-NCP, and the p mean of the CDNC increases from 0.80 to
415 cm-3 for Cu-TP. Although the CAPE is similar for Cu-TP and
Cu-NCP, the p mean of CDNC in Cu-TP is higher than that in Cu-NCP with the
same [Na].
Modeled p mean of (a) cloud water mass concentration and
(b) supercooled cloud water mass concentration as a function of the
initial [Na] in Cu-TP and Cu-NCP in Cu-TP and Cu-NCP.
Response of cloud properties in Cu-TP and Cu-NCP under three aerosol
conditions*.
Clouds
Cu-TP
Cu-NCP
Background
Clean
Polluted
Background
Clean
Polluted
Initial formation time of hydrometeors (min)
Rain
10
14
20
8
10
14
Ice crystal
12
10
8
24
24
26
Graupel
12
14
16
18
18
16
P mean of effective radius of hydrometeors (µm)
Rain
119
132
647
110
151
223
Graupel
559
665
917
221
303
447
* The aerosol concentrations are 90, 900, and 9000 cm-3 for the
background, clean, and polluted conditions, respectively.
With the [Na] increasing from 20 to 9000 cm-3, the effective radius of
cloud droplet (Reff) in Cu-TP is reduced from about 18.5 to
4.1 µm, and the Reff in Cu-NCP is also consistently reduced from 14.3 to
6.6 µm (Fig. 2b). Interestingly, when the [Na] is less than about
240 cm-3, the Reff in Cu-TP is larger than that in Cu-NCP with the same
[Na], although the CDNC in Cu-TP is higher than that in Cu-NCP, showing more
cloud water condensed in Cu-TP. Figure 3a presents the dependence of the
cloud water content (CWC) on the [Na] in Cu-TP and Cu-NCP, showing that the
CWC increases with increasing [Na]. This positive relationship is caused by
the combined effects of the increase in CDNC and the decrease in
Reff, which inhibit the collision/coalescence of cloud droplets and also
enhance the water vapor condensation efficiency and the updraft to generate
more available condensable water vapor. The CWC in Cu-TP is higher than that
in Cu-NCP for the same [Na], due to higher CDNC and likely stronger updrafts
in Cu-TP. The Cu-TP is triggered at an elevation of more than 4000 m a.s.l.
Therefore, considering that the 0 ∘C isotherm is at a level of
around 5000 m a.s.l., the cloud water formed in the cumulus tends to be
transported above the 0 ∘C isotherm to become supercooled,
initiating the efficient mixed-phase process to release more latent heat and
enhance the updraft. Therefore, there exists more supercooled cloud water in
Cu-TP than Cu-NCP when [Na] is the same (Fig. 3b).
Figure 4 provides the vertical profiles of the hydrometeors mass
concentrations (summed over the horizontal domain and then averaged during
the simulation period) under three aerosol scenarios: a very low
[Na] of 90 cm-3, a low [Na] of 900 cm-3,
and a high [Na] of 9000 cm-3, corresponding to the
background, clean, and polluted atmosphere, respectively. In Cu-TP and
Cu-NCP, the CWC achieves the highest level under the high [Na] case and the lowest under the very
low [Na] case (Fig. 4a and b). A higher [Na] enhances
CDNC and reduces Reff, suppressing the conversion from cloud
water to rain water and sustaining more CWC in the cloud. In Table 1, the
initial formation time of rain water is delayed with the [Na]
increase in Cu-TP and Cu-NCP. The height of the maximum CWC slightly
increases from the very low to high-[Na] conditions in Cu-TP and
Cu-NCP, but the maximum CWC occurs at 6–8 km a.s.l. in Cu-TP and
2–4 km a.s.l. in Cu-NCP. Therefore, for Cu-TP, most of cloud droplets are
above the 0 ∘C isotherm (about 5 km a.s.l.) and supercooled.
Vertical profiles of time-averaged masses of hydrometeors under
background (90 cm-3, blue), clean (900 cm-3, green), and polluted
(9000 cm-3, red) [Na] conditions for (a, b) cloud water, (c, d) rain
water, (e, f) ice particles (ice + snow), and (g, h) graupel in
Cu-TP and Cu-NCP, respectively. The brown solid and dotted lines represent
the surface level and the 0 ∘C isotherm, respectively.
The ice particles (ice + snow) generally reach the
highest level in the high
[Na] and lowest in the very low [Na], which is
consistent with those of the CWC in Cu-TP and Cu-NCP (Fig. 4e and f). In the
present study, the homogeneous freezing and rime-splintering mechanisms
(DeMott et al., 1994; Hallett and Mossop, 1974) are included for the ice
nucleation. In addition, the heterogeneous ice nucleation – including the
contact, immersion, and condensation freezing – is all parameterized using
the method proposed by Philips et al. (2008, 2013) and considering the IN
effect, depending not only on temperature and ice supersaturation but also on
the chemistry and surface area of multiple species of IN aerosols. The
[Na] enhancement generally suppresses the warm-rain process to
reduce the rain water but provides more IN and supercooled CWC to accelerate
the ice nucleation process. In addition, the rime-splintering mechanism also
affects the ice particle profiles at the height with temperature ranging from
-8 to -3 ∘C (Hallet and Mosssop, 1974). At the height of
6–8 km a.s.l. in Cu-TP and 4–6 km a.s.l. in Cu-NCP, the ice particle
profiles are similar in the very low and low [Na] cases, which is
caused by the rime-splintering mechanism. The ice crystal production from the
rime-splintering mechanism is related to the graupel particles and the cloud
droplets with radii exceeding 24 µm. Large cloud droplets in the
very low [Na] facilitate the ice crystal productions from the
rime-splintering mechanism, increasing the ice particle mass concentrations
at the height of 6–8 km a.s.l. in Cu-TP and 4–6 km a.s.l. in Cu-NCP.
Furthermore, there are more ice particles in Cu-TP than Cu-NCP with the same
[Na] conditions. The initial formation time of ice crystals is
advanced by at least 12 min in Cu-TP compared to Cu-NCP (Table 1). The
0 ∘C isotherm is at a level of around 5 km a.s.l. for the Cu-TP
and Cu-NCP. However, the occurrence heights for the Cu-TP and Cu-NCP are more
than 4 km and about 0.2 km a.s.l., respectively; when an air parcel
perturbed in the boundary layer ascends to form a cloud, the rising distance
to the 0 ∘C isotherm is about 1 km over the TP and around 4 km
over the NCP. Therefore, the ice crystal formation time is significantly
shortened in the Cu-TP compared to the Cu-NCP. The early formation of ice
crystals not only facilitates their growth but also advances the glaciation
process to intensify convections, further enhancing the growth process.
The rain water in Cu-TP achieves the highest level in the very low
[Na] and lowest in the high [Na], and vice versa in
Cu-NCP (Fig. 4c and d). Not considering the contribution of graupel melting
to the rain water, enhancement of [Na] suppresses the warm-rain
process to reduce the rain water but enhances the raindrop size, which
conversely accelerates the raindrop falling (Table 1). In Cu-TP, due to
relatively low temperature below the freezing level and short falling
distance (about 1 km), graupel dominates the precipitating particles, melting less to rain
water. So early occurrence of the warm-rain process in the very low
[Na] case causes the most rain water formation (Fig. 4c).
However, in Cu-NCP, graupel falling below the freezing level tends to melt
due to high temperature and long falling distance (about 4–5 km), enhancing
the rain water formation. More ice particles and supercooled CWC in the high
[Na] case are favorable for the ice growth through deposition,
aggregation among ice crystals, and riming of supercooled droplets (Wang and
Change, 1993a, b; Lou et al., 2003), and heavily rimed ice crystals are
transferred to graupel, enhancing the graupel formation. Therefore, in
Cu-NCP, the high [Na] corresponds to the maximum graupel content
and also rain water content (Fig. 4d and h). However, in Cu-TP, below 12 km,
the low [Na] corresponds to the largest amounts of graupel. Early
occurrence of the glaciation process in Cu-TP causes most raindrops to be
frozen to form graupel. The freezing rate of raindrops depends on the
temperature, the raindrop size and number, and their corresponding variations
with time (Lou et al., 2003). Generally, the raindrops with the larger size
are easier to be frozen under the lower temperature. The [Na]
enhancement decreases the raindrop number but increases its size and updraft
to lower the temperature, causing the maximum raindrop freezing efficiency
under the low-[Na] conditions. In addition, increasing the
[Na] invigorates the convection and produce larger graupel, and
then the melting of the graupel causes the formation of larger raindrops
(Table 1).
It is worth noting that ice particles and graupel are transported above
12 km a.s.l. or even exceeding 16 km a.s.l. (near tropopause) in Cu-TP, showing
intensified convection and also contributing to moistening the upper troposphere.
Response of convective strength to changes in aerosol concentrations
The p mean of the updraft and downdraft in a core area is used to measure
the convective strength of the simulated cumulus clouds, which is defined by
the absolute vertical wind speed exceeding 1 m s-1 and total condensed
water mixing ratio being more than 10-2 g kg-1 (Wang, 2005). When the [Na]
increases from 20 to 9000 cm-3, the p mean of the core updraft
increases from 2.0 to 4.3 m s-1 in Cu-TP and from 1.5 to 2.7 m s-1
in Cu-NCP (Fig. 5a). The enhancement of the core updraft with
increasing [Na] is caused by the suppression of the warm-rain process to
induce the more efficient mixed-phase process, releasing more latent heat to
intensify the convection. With the same [Na], the p mean of the core updraft
is larger in Cu-TP than in Cu-NCP, showing the significant impact of the
early occurrence of the glaciation process on the cloud development.
Simulated p mean of (a) updraft and (b) downdraft in the core area
(defined as an area where the absolute vertical velocity of wind is greater
than 1 m s-1 and the total condensed water content exceeds 10-2 g kg-1) as a function of the initial [Na] in Cu-TP and Cu-NCP.
In Cu-TP, with the [Na] increase, the p mean of the downdraft increases when
the [Na] is less than 90 cm-3, but it becomes insensitive to the changes
in [Na] when the [Na] is between 90 and 1800 cm-3, and it commences to
decrease when the [Na] exceeds 1800 cm-3 (Fig. 5b). The complex
nonlinear variation of the p mean of the downdraft with the [Na] reflects the
change in the vertical distribution of ice particles and graupel caused by
the enhancement of [Na] in Cu-TP. The enhancement of the convective strength
with increasing [Na] not only intensifies the convection to facilitate
precipitation, producing more precipitable particles, but also transports
more ice particles and graupel to the upper troposphere due to the specific
topography and further suppresses the occurrence of the downdraft. However,
the p mean of the downdraft in Cu-NCP increases steadily with [Na]. Such an
increase in the core downdraft with [Na] might be caused by the formation of a
large mass loading of precipitable particles to reduce buoyancy and increase
downdrafts. Interestingly, when the [Na] is less than about 450 cm-3, the
p mean of downdraft in Cu-TP is greater than that in Cu-NCP, but opposite
when [Na] exceeding 450 cm-3, indicating the influence of the early
occurrence of the glaciation process due to the specific topography in Cu-TP.
The maximum updraft, representing the largest local latent heat release,
generally increases with [Na] in Cu-TP and Cu-NCP (Fig. 6a). The maximum
updraft in Cu-TP is much higher than that in Cu-NCP with the same [Na]. In
Cu-TP, when the [Na] exceeds 750 cm-3, the maximum updraft becomes
insensitive to changes in the [Na]. In Cu-NCP, the maximum updraft is not very
sensitive to changes in the [Na] when the [Na] exceeds 2400 cm-3. The
maximum downdraft, or the largest drag speed, indicating the largest
strength to inhibit the development of the convection, also increases
generally with the [Na] in Cu-TP and Cu-NCP (Fig. 6b), but Cu-TP produces
a more intensive maximum downdraft than Cu-NCP.
Modeled (a) maximum updraft and (b) minimum downdraft as a function
of the initial [Na] in Cu-TP and Cu-NCP.
Response of precipitation to changes in aerosol concentrations
Figure 7 shows the variation of the accumulated precipitation with
[Na] in Cu-TP and Cu-NCP. Generally, the precipitation increases
with [Na], which is consistent with previous modeling studies
(e.g., Khain et al., 2005, 2008; Fan et al., 2007a; Li et al., 2008a, 2009).
Since Cu-TP and Cu-NCP occur under humid conditions, the precipitation
enhancement with [Na] is also in good agreement with
measurements. Observations have shown the precipitation enhancement around
heavily polluted coastal urban areas (Shepherd and Burian, 2003; Ohashi and
kida, 2002) or over oceans influenced by pollution aerosols (Cerveny and
Balling Jr., 1998; Li et al., 2008b; Koren et al., 2012, 2014).
Modeled cumulative precipitation inside the model domain (mm) as a
function of the initial [Na] in Cu-TP and Cu-NCP.
When the [Na] is increased from about 20 to 9000 cm-3, the
precipitation of Cu-TP increases from 0.13 to 0.23 mm; when the [Na]
exceeds 300 cm-3, the precipitation becomes insensitive to the
variation in [Na]. In contrast, the precipitation of Cu-NCP consistently
increases from 0.03 to 0.37 mm, with [Na] ranging from 20 to
9000 cm-3. In addition, when the [Na] is less than 500 cm-3, Cu-TP
produces more precipitation than Cu-NCP, which can be explained by the early
occurrence of the glaciation process causing less warm rain but more
efficient mixed-phase processes. However, when the [Na] exceeds 500 cm-3,
the precipitation efficiency of Cu-NCP is higher than that of Cu-TP,
although the convective strength is larger in Cu-TP than Cu-NCP. The
increasing convective strength with [Na] not only enhances the precipitation
but also transports more ice and graupel particles above 12 km to form the
anvil. The ice particles and graupel in the anvil are subject to
sublimation and evaporation to moisten the upper troposphere, and they decrease
the precipitation efficiency in Cu-TP.
The water content and precipitation in the Cu-TP respond well monotonically to the changes in the [Na].
Numerous studies have shown the reduced liquid water path (LWP) by increasing
aerosols under relatively dry conditions (e.g., Khain et al., 2005). During
the summer monsoon season, the atmosphere over the TP is humid due to the
water vapor transport by the monsoon (Fig. 1a). The ambient humidity in the
simulations of the Cu-TP exceeds 80 % in the low-level atmosphere, causing
the good monotonicity in the responses of water content and precipitation to
aerosols.
Response of (a) the p mean of core updraft and
(b) cumulative precipitation inside the model domain to the change
in the maximum perturbation temperature of the warm bubble under various
aerosol conditions in Cu-TP and Cu-NCP.
Sensitivity studies
Recent studies have demonstrated that convection is more active and stronger
during summertime over the Tibetan Plateau due to its unique thermodynamic
forcing (Hu et al., 2016). We have further performed sensitivity studies to
explore the impact of the maximum perturbation temperature (MPT) in the warm
bubble on the development of cumulus clouds. The MPTs of 2.0 and
0.5 ∘C are used to trigger Cu-TP and Cu-NCP, with the [Na]
ranging from 20 to 9000 cm-3.
For Cu-TP, the core updraft decreases slightly when the MPT is reduced from
4.0 to 2.0 ∘C, and particularly when the [Na] exceeds
100 cm-3 the decrease of the core updraft is
indiscernible (Fig. 8a). When the MPT is reduced from 2.0 to 0.5 ∘C,
the core updraft decreases considerably. However, for Cu-NCP, the core
updraft decreases substantially when the MPT is reduced from 4.0 to
0.5 ∘C. When the MPT is 0.5 ∘C and the [Na] is
less than 80 cm-3, the updraft core area is not formed in Cu-NCP. When
the MPT is the same, the core updraft is much larger in Cu-TP than Cu-NCP
with the same [Na]; even the core updraft in Cu-TP with a MPT of
0.5 ∘C is larger than that in Cu-NCP with a MPT of 4.0 ∘C
when the [Na] is more than 80 cm-3. Therefore, under the
unstable conditions over the Tibetan Plateau, a small perturbation can induce
strong convections, which is primarily caused by early occurrence of the
glaciation process due to the specific topography, as discussed in Sect. 3.1.
The accumulated precipitation generally decreases with the MPT in Cu-TP and
Cu-NCP with the same [Na] (Fig. 8b). When the MPT is
4.0 ∘C, Cu-NCP produces more precipitation than Cu-TP, with the
[Na] exceeding 500 cm-3, but Cu-TP produces much more
precipitation than Cu-NCP, with a MPT of 0.5 ∘C under all aerosol
conditions. In addition, the precipitation generally increases with
increasing [Na] in Cu-TP and Cu-NCP with various MPTs, and it
does not exhibit a nonlinear variation with the [Na], which is
not consistent with the results in Li et al. (2008a). The possible reason is
that in this study the maximum p mean of CDNC is about 410 cm-3,
which is much less than that in Li et al. (2008a). If the [Na] is
further increased, the precipitation might be suppressed.
Summary and conclusions
The aerosol–cloud interaction over the TP has been examined using the CR-WRF
model with a two-moment microphysical scheme considering the aerosol effects
on CCN and IN. For comparisons, two types of cumulus clouds, occurring over
the TP and NCP in August 2014, are modeled to examine the response of the
cumulus cloud development to the change in aerosol concentrations. A set of
28 aerosol profiles is utilized in simulations, with the surface aerosol
number concentration varying from 20 to 9000 cm-3 and the sulfate mass
concentration varying from 0.02 to 9.0 µg cm-3. Multiple aerosol
species are considered to provide CCN and IN, including sulfate, nitrate,
ammonium, organic and black carbon, and dust-like aerosols.
In general, with varying aerosol concentrations from very clean background
conditions to polluted conditions, more aerosols are activated,
significantly increasing the CDNC and decreasing the droplet size in Cu-TP
and Cu-NCP. Formation of a large amount of cloud droplets with small sizes
suppresses the warm-rain process and enhances water vapor condensation
efficiency and updraft to generate more available condensable water vapor.
When more cloud droplets are transported above the 0 ∘C isotherm,
occurrence of the mixed-phase process releases more latent heat to further
enhance the cloud core updraft and increase precipitation, intensifying the
convections in Cu-TP and Cu-NCP.
However, early occurrence of the glaciation process in Cu-TP, which is
triggered at an elevation of more than 4000 m, causes large differences
between Cu-TP and Cu-NCP. Many more supercooled cloud droplets are formed in
Cu-TP than Cu-NCP with the same aerosol concentration, facilitating the
mixed-phase process and significantly enhancing the core updraft and maximum
updraft in Cu-TP compared to Cu-NCP. Nevertheless, the intensified
convection induced by the increase of aerosol concentrations in Cu-TP not
only facilitates the precipitation but also delivers more ice-phase
hydrometeors into the upper troposphere to form the anvil, decreasing the
precipitation efficiency. Therefore, in Cu-TP, when aerosol concentrations
are high, the precipitation enhancement becomes insignificant with
increasing aerosol concentrations, but a considerable amount of ice-phase
hydrometeors are lofted above 12 km or even exceeding 16 km. Additionally,
sensitivity studies have also shown that under the unstable conditions over
the TP a small perturbation in temperature can induce strong convections,
which is primarily caused by early occurrence of the glaciation process due
to the specific topography.
In the present study, both CCN and IN effects are considered in the cloud
simulations. However, there are still difficulties in quantitatively
distinguishing those two effects on the ice-phase cloud development using
sensitivity studies. Obviously, the CCN play a dominant role in the
mixed-phase cloud development. Even when the IN are scare in the atmosphere,
the mixed-phase cloud development is not hindered with sufficient CCN,
because freezing of raindrops, the subsequent splinter-riming process, and
homogeneous freezing of cloud droplets still initialize the glaciation
process to facilitate the development of the mixed-phase cloud.
It is worth noting that, although the CAPE is similar for the Cu-TP and
Cu-NCP, it might not be fair to compare aerosol impacts on the cloud
development over the TP with the NCP, considering the difference of the
water vapor profile, wind shear, topography, and anthropogenic and natural
aerosol sources between the two regions. However, the comparisons have
highlighted that the topography plays a large role in the development of
cumulus over the TP.
Rapid growth of industrialization, urbanization, and transportation in Asia
has caused severe air pollution, progressively increasing aerosol
concentrations in the regions surrounding the TP. Pollution aerosols from
surrounding areas have been observed to be transported to the TP.
Considering the very clean atmosphere over the TP, elevated aerosol
concentrations can considerably enhance the convections due to its specific
topography. Numerous studies have shown that the TP significantly influences
the formation and variability of the Asian summer monsoon through mechanical
and thermal dynamical effects (e.g., Wu et al., 2007). In addition, Fu et
al. (2006) have reported that convection over the TP provides the main
pathway for cross-tropopause transport in the Asian monsoon/TP region.
Hence, intensification of convections due to the increase of aerosol
concentrations over the TP not only enhances the latent heat release to warm
the middle troposphere, influencing the Asian summer monsoon, but also delivers
more hydrometeors into the upper troposphere, allowing more water vapor to
travel into the lower stratosphere. Further studies are needed to evaluate
the aerosol indirect effect on the Asian summer monsoon and the
troposphere–stratosphere exchange over the TP.