The Tibetan Plateau (TP) is of great importance for weather and climate due
to its role as a heat and water resource. Relatively clean aerosol conditions
over the Plateau make the study on the aerosol–cloud–precipitation
interactions in this pristine continental region distinctive. In order to
investigate the impacts of aerosols on small-scale convection processes over
the TP, a convective event with precipitation observed on 24 July 2014 in
Naqu was selected to explore the influence of aerosols on the onset and
intensity of precipitation. We use the Modern-Era Retrospective analysis for
Research and Applications Version 2 (MERRA-2) reanalysis to derive the cloud
condensation nuclei (CCN) number concentration, which can be regarded as the
real-time background. These values are adopted to initialize the regional Weather Research Forecast (WRF) 4.0 meteorological model and to simulate the onset of convective events
and the formation of precipitation. Four sets of experiments, named clean
(1/10 CCN), control (default setting), Tibetan Plateau (CCN calculated
from MERRA-2 reanalysis), and polluted (10 times CCN), were adopted for our
simulations. A detailed analysis of the microphysical processes shows that
the conversion of cloud water into rain is enhanced by small increases in
aerosol concentration, while it is suppressed by larger increases in
concentration. However, the transformation of cloud water to graupel and the
development of convective clouds are favored under a polluted situation. As a
result, the onset of the precipitation is delayed and cold-rain intensity
increases.
Ministry of Science and Technology of the People's Republic of China2018YFC1505704National Natural Science Foundation of China41905025China Scholarship Counciln/aIntroduction
The role of aerosol particles on the formation of convective clouds and
related precipitation remains a matter of extensive scientific
investigations (Andreae et al., 2004; Fan et al., 2013; Freud and Rosenfeld, 2012; Li et al., 2011; Rosenfeld et al., 2008; Sun and Zhao, 2021; Tao et
al., 2012; Zhao et al., 2020). Due to the complexity of the processes
involved, the treatment of convective cloud formation in weather forecast
models remains uncertain, especially for the regions with insufficient
observational data (Ma et al., 2018). The Tibetan Plateau (TP) represents a
clean region, in which the aerosol optical depth baseline value is
comparable to that in the Arctic and remote ocean areas (Pokharel et al.,
2019; Yang et al., 2021b). However, even though the TP is regarded as a
pristine continent, it is occasionally perturbed by the intrusion of dust
particles originating in the surrounding deserts and by black carbon
particles produced by biomass burning in the regions of South Asia and part
of Africa (Zhu et al., 2019; Zhao et al., 2020; Yang et al., 2021b). The
analysis presented here in the climate-sensitive and environmentally fragile
continental TP characterized by frequent convective events will hopefully
be of interest for similar investigations to be conducted in other areas of
the world, which is about the aerosol perturbations on a pristine continent.
The Tibetan Plateau, with an average elevation of more than 4000 m,
covers approximately a quarter of the Chinese territory (Wu et al., 2007;
Yao et al., 2012). It greatly influences weather and climate in East Asia
and even globally due to its unique geographical location and
topography-induced thermal and dynamical effects (Pokharel et al., 2019).
The water vapor balance on the TP directly affects the water cycle over a
large area of the plateau and the surrounding areas due to high sensible
heat and low air density (Duan et al., 2012; Fu et al., 2006; Zhao et al.,
2018). Convection on the Tibetan Plateau is characterized by high-frequency
but low-intensity activity (Fu et al., 2006; Gao et al., 2016; Ye, 1981).
Aerosols can act as cloud condensation nuclei (CCN) and ice nuclei (IN) that
affect cloud microphysical processes and thermal and dynamical conditions
(Intergovernmental Panel on Climate Change (IPCC), 2013; Redemann et al., 2021; Stevens et al., 2017; Yang et al.,
2021a). Relatively clean conditions with low levels of background aerosols,
frequent convection, and induced precipitation make the study of aerosols'
impact on convective precipitation over the TP distinctive.
Aerosol observational sites over the TP are sparse. Ground-based
observations include (1) the two stations of the Automated Aerosol
Observation Network (AERONET) in Nam Co and Qomolangma (QOMS); (2) the
stations of PM2.5 and PM10 concentrations from the China Air Quality Online Monitoring and Analysis Platform of the Ministry of Environmental Protection
at the seven stations of Linzhi, Ali, Lhasa, Changdu, Naqu, Shannan, and
Shigatse; and (3) the concentrations of PM1 at four stations from China
Meteorological Administration (CMA) Atmosphere Watch Network (CAWNET) at
Gongga, Lhasa, Xining, and Shangri-La. The monitoring of PM2.5 and
PM10 on the TP were initiated in January 2013 at Lhasa, in January 2015
at Ali and Naqu, and in January 2017 at Changdu, Shannan, Shigatse, and
Linzhi. The CMA recorded PM1 data at Gonga, Lhasa, and Shangri-La
from January 2014, and at Xining starting in 2018. CMA used a GRIMM model
1.180 aerosol spectrometer with observations every 5 min at
wavelengths ranging from 1 to 10 µm. A decade of measurements
of aerosol optical properties at two AERONET stations, Nam Co and QOMS on
the Tibetan Plateau, shows that aerosol optical depth (AOD) values were
maximum in spring and minimum in autumn (Pokharel et al., 2019). Due to the
anisotropic reflection of the unique geographical surface in TP, the
satellite retrieval of aerosol properties is difficult (Zhao et al., 2020).
The main aerosol types on the Tibetan Plateau were further identified as
continental background, biomass burning, and dust (Pokharel et al., 2019;
Yang et al., 2021b; Zhu et al., 2019; Zhao et al., 2020). Satellite
observations from March to June indicate that aerosols are transported from
South Asia to the region close to the Himalayas (Liu et al., 2008). In
summer, aerosols from Northwest China and Central Asia are transported to
the northern Tibetan Plateau (Huang et al., 2007). In general, aerosol
conditions over the TP correspond mainly to a background situation. However,
incoming pollution from South/East Asia under the influence of the summer
monsoon can cause relatively high disturbances in the area of the Tibetan
Plateau.
Among the studies conducted in development over the TP are the Third
Tibetan Plateau Atmospheric Scientific Experiment (TIPEX-II and TIPEX-III),
initiated jointly by the China Meteorological Administration (CMA), the
Chinese Academy of Sciences (CAS), and the National Natural Scientific
Foundation of China (NSFC) (Zhao et al., 2018), and the Third Pole
Environment (TPE) Program, which was initially proposed and agreed upon by
several participants from China, India, Germany, Japan, Italy, Nepal, the
Netherlands, Norway, Pakistan, US, Canada, Tajikistan, and Switzerland (Yao
et al., 2012). These studies highlighted the role of aerosol characteristics
and related impact on cloud and precipitation in the TP in relation to
weather and climate modification due to East Asia and South Asia
anthropogenic emissions, dust mobilization in the Taklamakan Desert
(Kang et al., 2019; Liu et al., 2019; Xu et al., 2015), and also in relation
to further impacts on the weather system in the downstream regions, e.g.,
Yangtze Delta region, or/and Sichuan Basin (Lau and Kim, 2018; Liu et al.,
2019, 2020; Zhao et al., 2018, 2020). It has been
shown that cloud cover and radiation effects in pristine regions are
particularly sensitive to aerosols (Garrett and Zhao, 2006). Further, aerosols
on the Tibetan Plateau can affect weather and climate directly by absorbing
and scattering solar radiation and indirectly by modifying the nature of
the clouds. Using a cloud-resolving Weather Research and Forecasting (WRF) model,
Zhou et al. (2017) found that the increase in the aerosol load over the
plateau not only contributes to enhanced updrafts in clouds but also
transports a larger number of ice phase particles to the upper troposphere.
Based on satellite observations and the reanalysis of the dataset, Liu et
al. (2019) studied the effect of aerosols on clouds over the Tibetan Plateau
and the effect of dust-contaminated convective clouds on precipitation in
downstream areas. They identified an effect of Taklamakan dust on convective
clouds, which in turn causes heavy rainfall in downstream areas. However,
one should highlight that there are still some uncertainties in the
satellite retrievals. The findings of aerosol-related studies require
situation-specific analyses, since the northern and southern parts of the
Tibetan Plateau are characterized by different aerosol backgrounds and
composition with different climate systems and meteorological conditions.
Using the aerosol spectral radiative transfer model (SPRINTARS) and the
non-hydrostatic icosahedral atmospheric model (NICAM), Liu et al. (2020)
found that dust aerosol transported from the Taklamakan Desert delayed the
onset of heavy rainfall in the northern Tibetan Plateau by 12 h through
the indirect aerosol–cloud interaction and enhanced the precipitation in
the northern region. Aerosols may also influence the Asian monsoon by
affecting snow melting trends and TP surface temperature, which in turn
affects precipitation (Lee et al., 2013). The role of aerosols in the
teleconnections between the heat pump and the stronger convection and
precipitation in the TP or downstream regions (Wu et al., 2016) also needs
to be accounted for in the weather forecasting models (Liu et al., 2019;
Zhao et al., 2020).
Although after decades of efforts, our awareness of Tibetan Plateau aerosols
and related weather impact gradually increased, the confidence of current
knowledge on aerosols over the TP still needs further observational
evidence, more in-depth physical analysis, and model investigation. In order
to gain understanding on the formation of small-scale convection and related
precipitation, we analyze here a particular event that took place in Naqu
(31.483∘ N, 92.067∘ E) on 24 July 2014. As observational
data are sparse, we use the Modern-Era Retrospective analysis for Research
and Applications Version 2 (MERRA-2) reanalysis to derive the cloud
condensation nuclei, which can be regarded as the real-time background.
These values are adopted to initialize the regional Weather Research Forecast (WRF) 4.0 meteorological
model and to simulate the onset of convective events and the formation of
precipitation. Vertical soundings provide data on the state of the
background atmosphere. The purpose of the present study is to use available
information in this region of the Tibetan Plateau to assess the dependence
of the evolution of convective events on the pristine continent under
different background atmospheric aerosol burden. Since data in the region of
the Tibetan Plateau are sparse, the study relies heavily on model
simulations, and the outcome should therefore be regarded as a preliminary
and partial attempt to investigate a possible relationship between aerosol
and convective precipitation in this region. This methodology could then be
applied in other regions of the world with similar background environments.
The paper is organized as follows: Sect. 2 introduces the data and the
methodology that are adopted in the study; it also describes the convection
event under investigation and presents the experimental design for the
numerical simulations. Section 3 compares the microphysical processes that
characterize the different model experiments. Section 4 presents a summary
and the conclusions.
Data and methodsDataMERRA-2 data
MERRA-2, a long-term global reanalysis that assimilates space-based
observations of aerosols (Randles et al., 2017), is an upgrade of the
offline aerosol analysis data MERRAero based on the GOCART model (Chin et
al., 2002). GOCART emission sources include aerosols and gases from biomass
burning, fossil fuel combustion, natural emission sources (ocean, volcanic
eruptions, dust), etc. (Chin et al., 2014). The bias-revised AOD is obtained
from the observations by the Moderate Resolution Imaging Spectroradiometer
(MODIS). Cloud-filtered Aerosol Robotic Network (AERONET) AOD data are used
as input in a neural network to integrate MODIS radiances into the bias-corrected AOD. The MERRA-2
aerosol reanalysis data are additionally included in the NASA Earth
Observing System (EOS), NOAA Polar Operational Environmental Satellites
(POES), and ground-based observations (Randles et al., 2017). Note that
uncertainties are incurred when satellite retrievals are used over the TP,
due to the complicated reflection of the land surface (Yang et al., 2020;
Zhao et al., 2020; Jiang et al., 2022). The dataset used in the present
paper is the MERRA-2 aerosol mixing ratio data MERRA-2 inst3_3d_aer_Nv for 23 July 2014, with a spatial
resolution of 0.625∘×0.5∘ (longitude, latitude) on 72
vertical layers and with a temporal resolution of 3 h.
Precipitation and sounding data
The station–satellite combined 0.1∘×0.1∘ hourly
precipitation data (Shen et al., 2014) are provided by the China Meteorological Data
Network National Meteorological Science Data Center, while the ground
precipitation observations are obtained from the Naqu automatic station.
Note that some unrealistic rainfall centers are depicted over western China
due to the sparse automatic weather station network (Shen et al., 2014). The
sounding data are taken from the China Meteorological Data Network National
Meteorological Science Data Center (http://data.cma.cn, last access: 8 April 2023).
MethodThe calculation for cloud condensation nuclei (CCN)
In the Thompson aerosol-aware scheme (Thompson and Eidhammer, 2014), the
number concentration of cloud droplets is not fixed but is derived from a
series of calculations and look-up tables of the CCN and IN input calculated
from the mixing ratio of different aerosol species. This scheme takes into
account the activation of cloud condensation nuclei to form cloud droplets.
Further, the aerosol background mixing ratios are used to calculate the
cloud droplet number concentration. The input MERRA-2 inst3_3d_aer_Nv data contain the following
variables: mass mixing ratios of sea salt (SS; five bins), sulfate
(SO4), organic carbon (OC), black carbon (BC), and dust (DU; five
bins). The characteristic particle sizes, density parameters, and particle
size ranges were obtained with reference to the aerosol radius distribution
file of MERRA-2 (Chin et al., 2002). We assume that dust particles larger
than 0.5 µm are ice-friendly aerosols and that all remaining aerosol
species except black carbon are water-friendly aerosols. The aerosol number
concentrations are calculated at the WRF preprocessing stage by assuming a
log-normal distribution with characteristic diameter and geometric standard
deviation in the concentration (Thompson and Eidhammer, 2014). Since the
aerosol radius distribution file of MERRA-2 provides the particle size
intervals for different bins of sea salt and dust particles, the integration
of the probability density function is determined between the lower and the
upper limits of the radius. The details of the aerosol parameters are shown
in Table 1.
Aerosol particle radius, standard deviation, and density.
The total mass density calculation equation is derived by
∫rlowerrupper10-18Nrlnσg2πexp-12ln2σg(lnr-lnrg)243πr3×ρdr=M,
where N is the number concentration (unit: #kg-1), r is the
integral radius (unit: µm), σg is the geometric standard
deviation (unit: µm), rg is the median radius (unit: µm), and
ρ is the particle density (unit: kgm-3). The probability
density integral for selected bin needs to be multiplied to the probability
P in the corresponding bin, and it is calculated as
P=∫rlowerrupper1rlnσg2πexp-12ln2σg(lnr-lnrg)2dr∫01rlnσg2πexp-12ln2σg(lnr-lnrg)2dr.
Since only dust aerosol particles with a
radius greater than 0.5 µm are considered to be ice-friendly aerosols in the model, the percentage of particles with a
radius greater than 0.5 µm of the total number of particles in the
interval is also calculated after the number concentration is derived for
the first dust bin. The number concentration of ice-friendly
aerosol Ni and of water-friendly aerosol concentration Nw are
calculated by Eqs. (3) and (4), respectively, as follows:
3Ni=Ndust1×P(rlower=0.5µm)+∑i=25Ndusti4Nw=NSO4+NOC+∑i=15Nssi.
Here, Ndusti is the number concentration of dust aerosol particles for
five specific bins, NSO4 is the sulfate number concentration,
NOC is the organic carbon number concentration, and Nssi is the
number concentration of sea salt particles for five specific bins. The data
are interpolated to the simulation area and finally written to the WRF
Pre-Processing System (WPS).
Case selection
The typical convective precipitation event in Naqu, a city in the relatively
flat plateau with a simple meadow surface, on 24 July 2014 is selected for
simulation. A mesoscale precipitation event with a large-scale impact
occurred in the central plateau, while the center of the precipitation area
was concentrated in the southern part of the central plateau. The elevation
of the central plateau ranges from 4600 to 5200 m. As shown in Fig. 1,
Naqu is located at the northern edge of this precipitation, and the 24 h
accumulated precipitation amount in Naqu reaches 5.8 mm. On 24 July, the
hourly precipitation amount at 07:00 UTC at Naqu station reached 4.7 mm,
which is of medium intensity.
Accumulated precipitation of 24 h in Tibet and the hourly
precipitation in Naqu on 24
July 2014.
From the sounding data map at 00:00 UTC (08:00 Beijing time (BT)) on 24 July
2014 (Fig. 2a), the temperature dew point difference in Naqu (solid red line
minus solid green line) was less than 4 ∘C, which means that a wet
layer was formed between 400–500 hPa. A relatively dry area was present
above 300 hPa, and the whole layer formed an “inverted trumpet” with a dry
upper layer superimposed on a wet lower layer, which is conducive of
producing an unstable development of convection. In Fig. 2b, which
corresponds to 12:00 UTC (20:00 BT) on the same day, the
relative humidity of the air in the upper troposphere increased
significantly and the relative dry layer disappeared; the whole atmosphere
was in a near-saturated state and gradually became stable. This suggests
that the convection developed during 00:00 to 12:00 UTC on 24 July 2014.
T-logP sounding data from Naqu station at (a) 00:00 and (b)
12:00 UTC on 24 July 2014 (solid black line: temperature–pressure curve
(laminar curve); solid green line: dew point pressure curve; solid red line:
state curve; solid gray line (diagonal): isotherm; solid gray line
(horizontal): isobaric line; dashed blue line: adiabatic wet line;
dashed red line: adiabatic dry line; dashed green line: saturation mixing ratio; and
dashed light blue line: 0 ∘C isotherm).
Model setup
The Weather Research Forecast (WRF) model is one of the most commonly used
meteorological research and numerical weather forecasting systems. It
provides users with a wide choice of formulations for atmospheric processes
and can run on a variety of computer platforms
(http://www2.mmm.ucar.edu/wrf/users/, last access: 8 April 2023). The model version used in this paper
is WRF 4.0, and the basic model settings are shown in Table 2. The
integration of 24 h starts at 00:00 UTC on 24 July 2014. A triple
nesting grid with spacing of 25, 5, and 1 km, respectively, and an
integration step of 60 s for the outer layer is applied, as shown in
Fig. 3. The precipitation in the 0.1∘×0.1∘ area around
Naqu (31.4–31.5∘ N, 92.0–92.1∘ E; area A) and the
distribution of the aerosol number concentration in the 1∘×1∘ area around Naqu (31–32∘ N, 91.5–92.5∘ E;
area B) are examined in our detailed analysis.
Model's basic settings.
Model's basic settingsModel versionWRF 4.0Initial fieldFNLSimulation period24 July 2014 00:00–25 July 2014 00:00 UTCStep length60 sNumber of nesting levelsThree levelsGrid size25:5:1Center pointLatitude: 28.0∘ N,Longitude: 92.0∘ E
Color-filled map of the height field for simulated region (area A
is marked with black rectangle, and area B is marked with red rectangle).
The simulation uses the rapid radiative transfer model for general circulation models (RRTMG) longwave and shortwave radiation scheme
(Iacono et al., 2008), the Mellor–Yamada–Janjic–Niino (MYNN) surface layer
scheme (Dyer and Hicks, 1970), the MYNN 2.5 level turbulent kinetic energy (TKE)-based planetary boundary layer scheme (Nakanishi and Niino, 2009)
and the Noah-MP land surface scheme (Niu et al., 2011). The Grell–Freitas
cumulus convective parameterization scheme (Grell and Freitas, 2013) is adopted
for the outer two grids, while the cumulus scheme is turned off in the inner
grid. The physical parameter schemes are shown in Table 3. The microphysical
scheme selected in this paper is the Thompson aerosol-aware scheme (Thompson and Eidhammer, 2014), in which the default is set as the control run (control), the
clean and polluted schemes multiply the default cloud condensation nuclei
number by 1/10 and 10 times, respectively, and the TP uses the MERRA-2 aerosols
on 23 July 2014. The experimental settings are described in Table 4.
Figure 4 compares the spatial distribution of the vertically averaged
water-friendly aerosol number concentration from (a) clean, (b) control, (c)
TP, and (d) polluted cases at 00:00 UTC on 24 July 2014. It shows that, at the
simulation start time, the number concentration of the water-friendly
aerosols in TP simulation (Fig. 4c) is almost 2 times than that of default
simulation (Fig. 4b), which can be regarded as slightly polluted situation.
In this way, the dependence of the evolution of the convective event that
took place in Naqu (31.483∘ N, 92.067∘ E) on 24 July
2014, is examined under different background atmospheric aerosol burden,
which are almost 1/10, 1 time, 2 times, 10 times of the default CCN setting
for clean, control, TP (slightly polluted), and polluted, respectively.
Experimental settings.
MarkerMicrophysical settingsSettingsControl“use_aero_icbc” is set to falseDefault NaCCN, NaIN settingClean“use_aero_icbc” is set to false1/10×NaCCN, NaINPolluted“use_aero_icbc” is set to false10×NaCCN, NaINTP (slightly polluted)“use_aero_icbc” is set to trueMERRA-2 aerosol reanalysis
Vertically averaged water-friendly aerosol number concentration
from (a) clean, (b) control, (c) TP, and (d) polluted cases at 00:00 UTC on 24
July 2014 in the 1∘×1∘ area around Naqu
(31–32∘ N, 91.5–92.5∘ E; area B). The dot represents the
position of Naqu.
Since the precipitation is interrupted at 11:00 UTC (Fig. 1), the analysis
focuses on the vertical distribution of the hydrometeor categories from
00:00 to 11:00 UTC on 24 July 2014. The mean precipitation in the
0.1∘×0.1∘ area surrounding Naqu (31.4–31.5∘ N,
92.0–92.1∘ E; area A) is selected for a time series analysis.
Figure 5 shows that the precipitation starts at 06:00 and the hourly maximum
precipitation occurs at 08:00 UTC. Afterwards, the precipitation intensity
gradually decreases and ends up at 11:00 UTC. All four simulations show a
decreasing precipitation rate occurring after 09:00 UTC. The maximum
precipitation intensity is predicted to happen at 07:00 in the clean and TP
simulations; it occurs at 08:00 and at 09:00 UTC in the control and the polluted
simulations, respectively. The timing of the maximum precipitation rate is
delayed, and the precipitation intensity is enhanced as air pollution heavily
increases. Comparing the simulation results for clean and polluted
conditions, we find that the time at which precipitation starts is later in
polluted air than in a clean situation. However, the amount of precipitation
was significantly enhanced. This suggests that an increase in atmospheric
aerosol load leads to a delayed onset but an increased intensity of the
precipitation.
Time series of hourly precipitation rate (mm) in area A
(31.4–31.5∘ N, 92.0–92.1∘ E) from 00:00 to 11:00 UTC on
24 July 2014.
Hydrometeor categories and microphysical processes analysis
In order to analyze the influence of aerosols on water condensate at
different heights, the time series of the vertical distribution of liquid
phase water condensate and ice phase water condensate from clean, control,
TP, and polluted are shown in Fig. 6a–d, respectively. Note that,
compared to urban areas, the baseline aerosol burden in TP is pristine, and
the clean simulation here represents an extremely clean condition. From 01:00
to 03:00 UTC, the liquid phase water condensate existed in all four simulated
cases and were mainly distributed between the pressure levels of 350 and
450 hPa. During this time, no precipitation was produced or the amount of
precipitation was small. The analysis of the vertically pointing Ka-band
cloud radar observation at Naqu also shows that only scattered clouds
existed at the height between 5 and 7 km before 05:00 UTC (Cheng et al.,
2022).
Time series of the vertical distribution of the mean liquid phase
and ice phase water condensate mixing ratio in (a) clean, (b) control, (c)
TP, and (d) polluted simulations in area A (31.4–31.5∘ N, 92.0–92.1∘ E), in gkg-1, with dashed red lines as
isotherms.
From 05:00 UTC, in the clean simulation (Fig. 6a), the liquid phase water
condensate is mainly distributed in the lower layers and its abundance
starts to increase, which indicates the warm-based convective cloud formed,
while less ice phase water condensate is presented. Compared to the
clean simulation, in the control scenario (Fig. 6b), the amount of liquid
phase water condensate formed in the control case is higher and the maximum
value is located at a higher altitude. At the same time, the ice phase water
condensate increases. It indicates shifting from the clean to control scenario,
the convective cloud invigorates and precipitation increases with increasing
aerosol number concentration. In the TP simulation (Fig. 6c), in which the
water-friendly aerosols background is 2 times of that in the control
simulation (Fig. 6b) but not in the polluted simulation, the amount of
liquid phase water condensate decreases sharply. This indicates the rain
already started (Fig. 5). It also suggests that the precipitation intensity
increases, and the precipitation starts earlier with the increase of aerosol
loading when the atmosphere is slightly polluted. This may be explained by
aerosol-limited environment and the higher coalescence efficiency due to the
secondary droplet activation in convective clouds, especially in relatively
clean areas (Efraim et al., 2022). In the polluted scenario (Fig. 6d), the
liquid phase water condensate in the polluted case does not change
substantially; however, the onset time is delayed. Under the polluted situation,
the warm cloud precipitation does not occur easily, and the cloud
development is more vigorous. As a result, the onset time of the
precipitation is delayed. The ice phase water condensate increased
substantially. In the polluted case, more ice phase water condensate is
formed in both upper and lower layers (Fig. 6d), while in the TP case (Fig. 6c), there is more ice phase water condensate only in the upper layers. This
suggests that, with the increase of aerosol loading, the ice cloud
precipitation increases. As a result, the onset time of the precipitation is
delayed, but the precipitation intensity increases. This is consistent with
the impact of aerosols on convective precipitation as derived from
observations in Southeast China (Jiang et al., 2016; Wu et al., 2016; Yang
et al., 2018).
In order to analyze the evolution of microphysical quantities and processes,
considering that precipitation mainly occurs between 06:00 and 11:00 UTC,
various water condensate particles in area A are averaged during this
period. Five water condensate mixing ratios obtained for cloud water, cloud
ice, rain, snow, and graupel as a function of pressure for clean, control,
TP, and polluted simulations are shown in Fig. 7a–d,
respectively. At 150–300 hPa, snowfall occurs in all four scenarios, and the
proportion of snowfall increases as pollution increases. At 300–500 hPa,
compared with the clean simulation (Fig. 7a), the five water condensate
mixing ratios increase with the increased aerosol burden in the control
simulation (Fig. 7b). Compared with the control simulation (Fig. 7b), the
mixing ratio of rain increases, while that of both cloud water and graupel
decrease in the TP simulation (Fig. 7c). This suggests that, as aerosol
loading increases, the conversion process of cloud water to rain
invigorates. In the polluted scenario (Fig. 7d), the mixing ratios of cloud
water, graupel, and snow are characterized by larger values than in the
other three scenarios, while the mixing ratio of rain has the smallest
value. It indicates that the conversion process of cloud water to rain is
suppressed, but the conversion of cloud water to graupel is favored. At
500–600 hPa, which is near the surface, rainfall is dominant in the clean
case (Fig. 7a), while graupel in addition to rainfall are visible in other
simulations (Fig. 7b–d). This suggests that, with the increase of
aerosol burden, the conversion process of cloud water to rain in clouds is
suppressed, but the generation of ice phase particles is favored. The
proportion of surface graupel to the total precipitation increases from
6.913 %, 7.833 %, 14.004 %, and 26.376 % in clean, control, TP, and
polluted, respectively. It also indicates that the development of convective
clouds is more vigorous under the polluted scenario.
Mean water condensate mixing ratio as a function of height for (a)
clean, (b) control, (c) TP, and (d) polluted cases in area A
(31.4–31.5∘ N, 92.0–92.1∘ E) from 06:00 to 11:00 UTC on
24 July 2014; units: gkg-1.
The vertical distributions of the number concentration of cloud water, rain,
and snow for the four scenarios (which are not shown here) show similar
results, which indicates the increase of aerosol number concentration tends
to increase the cloud droplet number concentration, but tends to decrease the cloud
droplet scale, suppresses the warm cloud rainfall, and invigorates cloud
development (Fig. 8), producing more ice phase substances. The melting of
ice phase particles increases the cold-rain precipitation, which delays the
onset of the precipitation and increases precipitation intensity. It is
consistent with the findings that in the polluted scenario the increase in
aerosols suppresses the warm-rain process but enhances the growth of graupel
and increases the cold rain (Rosenfeld and Woodley, 2000; Tao et al., 2012).
Updrafts in clouds in area A (31.4–31.5∘ N, 92.0–92.1∘ E) averaged from 06:00 to 11:00 UTC on 24 July 2014,
in units of ms-1.
Summary and discussion
Aerosol studies on the Tibetan Plateau are constrained by a small number of
stations and observations and by a limited amount of satellite data. The
aerosol optical thickness in this region is generally smaller than in other
regions, with only a few cases exceeding 0.1, which also explains the low
availability of aerosol satellite data in the region. Although the region
can be considered as a region with a background aerosol situation, air
masses transported by summer winds from South Asia can cause relatively
strong local disturbances. The unique topography and the relatively pristine
aerosol background levels above the Tibetan Plateau motivate us to explore
the impact of aerosols on the formation of local convective precipitation
events.
The Weather Research and Forecasting (WRF) model 4.0 version with Thompson
aerosol-aware microphysical scheme was used to explore the influence of
aerosols on convective precipitation processes. A specific convective
precipitation event in Naqu in the central Tibetan Plateau that occurred on
24 July 2014 was selected in our study. Four sets of experiments, named
clean (1/10 CCN), control (default setting), Tibetan Plateau (CCN
calculated from MERRA-2 reanalysis), and polluted (10 times CCN), were
retained for our simulations. A detailed analysis of the microphysical
processes shows that the conversion of cloud water into rain is enhanced by
small increases in aerosol concentration, while it is suppressed by larger
increases in concentration. At the same time, the generation of ice phase
particles and the development of convective clouds are enhanced under the
polluted situation. As a result, the onset of the precipitation is delayed;
however, rainfall occurs with higher intensity.
Since the air in the plateau area is relatively clean, the response of
precipitation could be sensitive to aerosol perturbation. However, the
errors associated with the observations over the Tibetan Plateau are large
and sensitive to convective precipitation during the initial phase of the
event. Under such circumstances, our study has adopted a compromise approach
to discuss the effect of aerosols on convective precipitation in the
relatively clean highlands.
The treatment of aerosols in the model can be chosen according to the air
quality situation at a particular time. If the air is clean, initial
conditions for the simulated aerosol concentrations can be chosen to be
close to the actual observations; in a polluted situation, the background
field for the WRF simulation can be generated according to the real-time
aerosol reanalysis method as described in the paper, especially before year
2015. More sustained and comprehensive observations over the Tibetan Plateau
are a prerequisite for better understanding the aerosol impact on
precipitation formation in this region. More factors, such as latent heat,
sensible heat, surface topography, aerosol types, etc., should be carried out
as comprehensive analysis in this region. At the same time, approaches to
determine the measurement representation error (Asher et al., 2022) for model
evaluation should be established in the pristine region.
Data availability
The data used in this study can be accessed on request from the China Meteorological Data Network National Meteorological Science Data Center (http://data.cma.cn, last access: 8 April 2023) or downloaded from 10.5281/zenodo.7816832 (Jiang, 2023).
Author contributions
MJ: conceptualization, investigation, writing and editing, and funding acquisition. YL: visualization and editing.
WH: investigation and simulation. YY: editing. GB: conceptualization, supervision, and editing. XZ: revision.
Competing interests
The contact author has declared that none of the authors has any competing interests.
Disclaimer
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
This study was supported by the National Key Research and Development
Program of China (2018YFC1505704), the National Natural Science Foundation
of China (41905025), Chengdu University of Information Technology Research
Fund (KYTZ202217), and the China Scholarship Council. We would like to
thank the China Meteorological Data Network National Meteorological Science Data Center (http://data.cma.cn, last access: 8 April 2023), Wenhua
Gao from the State Key Laboratory of Severe Weather, Chinese Academy of
Meteorological Sciences, and Xiaolong Cheng from the Institute of
Plateau Meteorology, China Meteorological Administration, Minhong Song,
Xianyu Yang, and Xiaoling Zhang from Chengdu Plain Urban Meteorology
and Environment Observation and Research Station of Sichuan Province and
Chengdu University of Information Technology, for their suggestions that
have benefited this study. We also greatly appreciate the valuable comments
from the anonymous reviewers.
Financial support
This research has been supported by the Ministry of Science and Technology of the People's Republic of China (grant no. 2018YFC1505704), the National Natural Science Foundation of China (grant no. 41905025), Chengdu University of Information Technology
Research Fund (grant no. KYTZ202217), and the China Scholarship Council.The article processing charges for this open-access publication were covered by the Max Planck Society.
Review statement
This paper was edited by Yuan Wang and reviewed by three anonymous referees.
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