This study examines the role played by aerosol in torrential rain that occurred in the Seoul area, which is a conurbation area where urbanization has been rapid in the last few decades, using cloud-system-resolving model (CSRM) simulations. The model results show that the spatial variability in aerosol concentrations causes the inhomogeneity of the spatial distribution of evaporative cooling and the intensity of associated outflow around the surface. This inhomogeneity generates a strong convergence field in which torrential rain forms. With the increases in the variability in aerosol concentrations, the occurrence of torrential rain increases. This study finds that the effects of the increases in the variability play a much more important role in the increases in torrential rain than the much-studied effects of the increases in aerosol loading. Results in this study demonstrate that for a better understanding of extreme weather events such as torrential rain in urban areas, not only changing aerosol loading but also changing aerosol spatial distribution since industrialization should be considered in aerosol–precipitation interactions.
It has been reported that there has been an increase in the frequency of torrential rain in urban areas over the last decades (Bouvette et al., 1982; Diem and Brown, 2003; Fujibe, 2003; Takahashi, 2003; Burian and Shepherd, 2005; Shepherd, 2005; Chen et al., 2015). Over the last decades, population in urban areas has increased significantly. In 1950, 30 % of the whole population in the world lived in urban areas; however, in 2010, 54 % of the whole population lived in urban areas. It is predicted that in 2050, 66 % of the whole population will live in urban areas (United Nations, 2015). In addition, urban areas are the centers of economic activity and play a key role in economic productivity (United Nations, 2015). Hence, the increase in the frequency of torrential rain, which has substantial negative impacts on human life and properties by causing events such as flooding and landslide, particularly in urban areas has important social and economic implications.
Torrential rain in urban areas frequently involves highly inhomogeneous spatial distributions of precipitation (Dhar and Nandergi, 1993; Mannan et al., 2013). While some places in a metropolitan area experience light precipitation, others in the area experience extremely heavy precipitation or torrential rain for an identical mesoscale convective system (MCS) that covers the whole area (e.g., Sauer et al., 1984; Korea Meteorological Administration, 2011). Note that this type of MCS is forced by synoptic-scale temperature and humidity forcings. These synoptic-scale forcings tend to be spatially homogeneous in the MCS, which is on a mesoscale and thus much smaller than that of the forcings. Hence, these forcings tend to intensify all cloud cells in the MCS in an approximately homogeneous fashion, which tends to produce cloud cells with a similar intensity. These cloud cells with similar intensity are likely to result in a homogeneous distribution of precipitation over a domain of interest since cloud cells with similar intensity are likely to produce similar precipitation. This indicates that the consideration of the synoptic-scale forcings alone is not able to explain the occurrence of torrential rain, which is associated with inhomogeneous spatial distributions of precipitation. Note that numerous numerical weather prediction studies have utilized the concept of the synoptic-scale forcings to identify mechanisms that control the inhomogeneity of precipitation distributions and associated torrential rain. This is one of the reasons these studies have shown low forecast accuracy for torrential rain and not been able to provide a clear picture of the mechanisms (Mladek et al., 2000; Yeh and Chen, 2004; Mannan et al., 2013). The highly inhomogeneous distribution of precipitation means that there are highly inhomogeneous variables, processes, and forcings which disrupt the synoptic-forcing-induced homogeneity of MCSs in urban areas. Some of those forcings are mesoscale forcings that show mesoscale variability and, for example, are related to phenomena such as sea breeze fronts and lake breezes. In particular, in urban areas, due to strong heat fluxes at the surface, there is the urban heat island (UHI) effect, as another example of these phenomena. Examples of these variables and processes are cold pool, rear inflow, wind shear, and mesoscale vorticity. Aerosol is also one of the variables that has large spatial variability. In particular, urban aerosol particles are produced by randomly distributed sources (e.g., traffic), which enables aerosol to have large variability in urban areas.
It is well known that increasing aerosol loading alters cloud microphysical properties such as cloud particle size and autoconversion. Cloud liquid particles, which are droplets, collide and collect to grow into raindrops and this growth process is referred to as autoconversion. Collision and collection are more efficient when particle sizes are larger. Hence, increasing aerosol loading, which is known to reduce the particle size, reduces the efficiency of the growth of cloud liquid particles to raindrops via autoconversion. This results in more cloud liquid, which is not converted to raindrops, and thus in more cloud liquid mass as a source of evaporation and freezing. It has been shown that aerosol-induced increases in cloud liquid mass and associated increases in freezing of cloud liquid can enhance parcel buoyancy and thus invigorate convection (Khain et al., 2005; Rosenfeld et al., 2008; Li et al., 2011; Wang et al., 2014). Invigorated convection can enhance precipitation. Studies (e.g., van den Heever et al., 2006; Fan et al., 2009; Lebo and Seinfeld, 2011; Lebo, 2017) have shown that aerosol-induced invigoration of convection and enhancement of precipitation depend on competition between aerosol-induced increases in buoyancy and those in hydrometeor loading, aerosol-induced increases in condensational heating, and associated invigoration in the warm sector of a cloud system. Other studies (e.g., Khain et al., 2008; Lee et al., 2008b; Fan et al., 2009) have shown that the invigoration-related enhancement of precipitation also depends on environmental conditions that are represented by wind shear, relative humidity, and instability.
Aerosol-induced increases in cloud liquid mass and associated increases in evaporation can intensify gust fronts, which in turn intensify subsequently developing convective clouds and enhance precipitation (Khain et al., 2005; Seifert and Beheng, 2006; Tao et al., 2007, 2012; van den Heever and Cotton, 2007; Storer et al., 2010; Lee and Feingold, 2013; Lee et al., 2017). Aerosol-induced invigoration and intensification of convection and associated convective clouds raise a hypothesis that the large spatial variability in aerosol in tandem with increasing aerosol loading can generate and enhance torrential rain, which can involve the inhomogeneity of precipitation and associated cloud intensity in urban areas. For example, cloud cells (in an MCS) sitting on a significant portion of a metropolitan area with a higher aerosol concentration can be invigorated more than those cells on the rest of the area with a lower aerosol concentration. This can lead to enhanced precipitation and possibly torrential rain at the portion with the higher aerosol concentration, while in the rest there can be less precipitation. This creates an inhomogeneity of precipitation distributions that can accompany torrential rain in the specific portion of the area. A further increase in aerosol concentration in the portion with the higher aerosol concentration will further enhance precipitation and torrential rain there and thus create a greater inhomogeneity of precipitation distributions. Motivated by the hypothesis and associated argument here, among the forcings, processes, and variables which have spatial variability, this study focuses on aerosol. To examine aerosol effects on clouds and precipitation, numerical simulations are performed by using a cloud-system-resolving model (CSRM) that resolves cloud-scale microphysical and dynamic processes and simulates the effect of the variability and loading of aerosol on precipitation.
Using the CSRM, an observed MCS that involves deep convective clouds and
torrential rain is simulated. Here, deep convective clouds reach the
tropopause. For the simulations, we select an MCS over the Seoul area (in
South Korea) that has a population of
The 850 hPa wind (m s
The MCS was observed in the Seoul area, South Korea, over a period between 09:00 LST (local solar time)
27 July and 09:00 LST 28 July 2011. A significant amount of precipitation is
recorded during this period, with a local maximum value of
As a CSRM, we use the Advanced Research Weather Research and Forecasting (ARW) model (version 3.3.1), which is a nonhydrostatic compressible model. Prognostic microphysical variables are transported with a fifth-order monotonic advection scheme (Wang et al., 2009). Shortwave and longwave radiation parameterizations have been included in all simulations by adopting the Rapid Radiation Transfer Model (RRTM; Mlawer et al., 1997; Fouquart and Bonnel, 1980). The effective sizes of hydrometeors are calculated in a microphysics scheme that is adopted by this study and the calculated sizes are transferred to the RRTM. Then, the effects of the effective sizes of hydrometeors on radiation are calculated in the RRTM.
Triple-nested domains used in the CSRM simulations. The boundary of the figure itself is that of Domain 1, while the rectangles marked by “d02” and “d03” represent the boundary of Domain 2 and Domain 3, respectively. The dotted line represents the boundary of Seoul and terrain heights are contoured every 250 m.
To represent microphysical processes, the CSRM employs a bin scheme. The bin
scheme employed is based on the Hebrew University Cloud Model (HUCM)
described by Khain et al. (2011). The bin scheme solves a system of kinetic
equations for size distribution functions for water drops, ice crystals
(plate, columnar, and branch types), snow aggregates, graupel, hail, and cloud
condensation nuclei (CCN). Each size distribution is represented by 33 mass
doubling bins, i.e., the mass of a particle
For a three-dimensional simulation of the observed MCS, i.e., the control
run, two-way interactive triple-nested domains with a Lambert conformal map
projection as shown in Fig. 2 are adopted. A domain with a 500 m resolution
covering the Seoul area (Domain 3) is nested in a domain with a 1.5 km
resolution (Domain 2), which in turn is nested in a domain with a 4.5 km
resolution (Domain 1). The length of Domain 3 in the east–west direction is
220 km, while the length in the north–south direction is 180 km. The
lengths of Domain 2 and Domain 3 in the east–west direction are 390 and 990
km, respectively, and those in the north–south direction are 350 and
1100 km, respectively. The Seoul area is a conurbation area that is centered in
Seoul and includes Seoul and surrounding highly populated cities. Hence, the
Seoul area is composed of multiple cities whose total population
is
Reanalysis data, which are produced by the Met Office Unified Model (Brown et
al., 2012) and recorded continuously every 6 h on a
The current version of the ARW model assumes horizontally homogeneous aerosol
properties. For the control run that focuses on the effect of aerosol on
torrential rain in an urban area (i.e., Seoul area) where aerosol properties
such as composition and number concentration vary significantly in terms of
time and space, we abandon this assumption of homogeneity and consider the
spatiotemporal variability in aerosol properties over the urban area. For
this, we develop an aerosol preprocessor that is able to represent the
variability in aerosol properties. This aerosol preprocessor interpolates
observed background aerosol properties such as aerosol mass (e.g., PM
The variability in aerosol properties is observed by surface sites that
measure PM
Aerosol size distribution at the surface.
AERONET measurements indicate that overall, aerosol particles in the Seoul
area during the MCS period follow a trimodal lognormal distribution and
aerosol particles, on average, are an internal mixture of 60 % ammonium
sulfate and 40 % organic compound. This organic compound is assumed to be
water soluble and composed of (by mass) 18 % levoglucosan
(
In clouds, aerosol size distributions evolve with sinks and sources, which include advection and droplet nucleation (Fan et al., 2009). Aerosol activation is calculated according to the Köhler theory, i.e., aerosol particles with radii exceeding a critical value at a grid point are activated to become droplets based on predicted supersaturation, and the corresponding bins of the aerosol spectra are emptied. After activation, aerosol mass is transported within hydrometeors by collision–coalescence and removed from the atmosphere once hydrometeors that contain aerosols reach the surface. It is assumed that in the planetary boundary layer (PBL), background aerosol concentrations do not vary with height but above the PBL background aerosol concentrations reduce exponentially with height. It is also assumed that in non-cloudy areas, aerosol size and spatial distributions are set to follow background counterparts. In other words, once clouds disappear completely at any grid point, aerosol size distributions and number concentrations at those points recover to background counterparts. This assumption has been used by numerous CSRM studies and proven to simulate overall aerosol properties and their impacts on clouds and precipitation reasonably well (Morrison and Grabowski, 2011; Lebo and Morrison, 2014; Lee et al., 2016). This assumption indicates that we do not consider the effects of clouds and associated convective and turbulent mixing on the properties of background aerosol. Also, the prescription of those properties (e.g., number concentration, size distribution, and chemical composition) explained above indicates that this study does not take aerosol physical and chemical processes into account. This enables the confident isolation of the sole effects of given background aerosol on clouds and precipitation in the Seoul area, which has not been understood well, by excluding those aerosol processes and cloud effects on background aerosol.
Spatial distributions of background aerosol number concentrations at
the surface (black contours; in
As seen in Fig. 4a and b at 19:00 and 20:00 LST 27 July 2011, there is a
large variability in background aerosol concentrations in the Seoul area.
This variability is generated by contrast between the high aerosol
concentrations in the western part of the domain where aerosol concentration
is greater than 1500 cm
Summary of simulations.
In addition to the control run and the low-aerosol run, there are more simulations that are performed to better understand the effect of aerosol on precipitation here. To isolate the effects of aerosol concentrations on precipitation from those of aerosol spatial variability or vice versa, the control run and the low-aerosol run are repeated with homogeneous spatial distributions of aerosol. These homogeneous spatial distributions mean that there is no contrast in aerosol number concentrations between the western part of the domain and the eastern part, and aerosol number concentrations do not vary over the domain. The repeated simulations are referred to as the “control-homoge” run and the “low-aerosol-homoge” run. The analyses of model results below indicate that differences in precipitation between the control run and the low-aerosol run are closely linked to cloud liquid evaporative cooling and to elucidate this linkage, the control run and the low-aerosol run are repeated again by turning off cooling from cloud liquid evaporation. These repeated simulations are referred to as the “control-noevp” run and the “low-aerosol-noevp” run. While a detailed description of those repeated simulations is given in Sect. 4.3, a brief description is given in Table 1.
Vertical distributions of the averaged
In this study, analyses of results are performed only in the Seoul area (or Domain 3) where the 500 m resolution is applied. Hence, in the following, the description of the simulation results and their analyses is only over Domain 3, unless otherwise stated.
Time series of the area-mean precipitation rates at the surface
smoothed over 3 h for the control run, the low-aerosol run, and
observations in Domain 3. In panel
Figure 5 shows the observed and simulated vertical profiles of potential
temperature, water vapor mass density,
Frequency distributions of the precipitation rates at the surface,
which are collected over the whole domain, for
The area-mean precipitation rate at the surface smoothed over 3 h for the
control run and the low-aerosol run is depicted by solid lines in Fig. 6.
Dotted lines in Fig. 6 depict the precipitation rate for the repeated control
run and low-aerosol run and will be discussed in Sect. 4.3. The simulated
precipitation rate in the control run follows the observed counterpart well,
which demonstrates that simulations perform reasonably well. Here, observed
precipitation is obtained from measurement by rain gauges that are parts of
the automatic weather station (AWS) at the surface. The AWS has a spatial
resolution of
Figure 7a, b, and c show frequency distributions of precipitation rates that are collected over all time steps and all grid points at the surface in the simulations. In Fig. 7, solid lines represent frequency distributions for the control run and the low-aerosol run, while dashed lines represent those for the repeated control run and low-aerosol run, which will be described in Sect. 4.3. Figure 7a, d, g, j, and m show frequency distributions only for the control run and the low-aerosol run. The other panels in Fig. 7 are supposed to show distributions only for the repeated control run and low aerosol run; however, for comparisons among the control run, the low-aerosol run, and the repeated runs, the control run and the low-aerosol run are displayed as well in those panels.
In Fig. 7a, b, and c, frequency distributions of observed precipitation rates
that are interpolated to grid points and time steps in the simulations are
also shown. The observed maximum precipitation rate is
Spatial distributions of precipitation rates at the surface. Green
rectangles mark areas with heavy precipitation and are described in detail in
text. Purple lines mark the eastern part of where there is substantial
transition from high-value aerosol concentrations to low-value aerosol
concentrations as in Fig. 4. Panels
While we do not see a large difference in cumulative precipitation between
the control run (154.7 mm) and the low-aerosol run (150.2 mm), the
frequency distribution of precipitation rates shows distinctively different
features between the control run and the low-aerosol run (Fig. 7a). For
precipitation with rates above 60 mm h
Boundary of each area which has the observed surface precipitation
rate of 60 mm h
Figure 8 shows spatial distributions of precipitation rates at the surface.
Purple lines in Fig. 8 mark the eastern part of where there is substantial
transition from high-value aerosol concentrations to low-value aerosol
concentrations as in Fig. 4. In this transition part, as explained in Fig. 4,
there is reduction in aerosol concentrations by more than a factor of 10.
Figure 8a and b show those distributions at 17:00 LST 27 July 2011
corresponding to initial stages of the precipitating system in the control run
and the low-aerosol run, respectively. At 17:00 LST, there is a small area
of precipitation around the northwest corner of the domain in both the
control run and the low-aerosol run. This implies that a small cloud system
develops around the northwest corner of the domain at 17:00 LST. The size of
the system and its precipitation area grow with time and at 19:00 LST, the
size is much larger (Fig. 8c and d). The maximum precipitation rate reaches
By 20:00 LST, the maximum rate of torrential rain reaches
The system propagates eastwards after 20:00 LST in a way that its
easternmost part is closer to the east boundary of the domain as seen in
comparisons between Fig. 8e (Fig. 8f) and Fig. 8g (Fig. 8h) for the control
(low-aerosol) run. As seen in Fig. 8g and in the previous hours, for the
control run more than 90 % of heavy precipitation events are
concentrated in a specific area (surrounded by the green rectangle) at
23:00 LST. However, in the low-aerosol run, heavy precipitation is not
concentrated in a specific area at 23:00 LST. Unlike the green rectangle in
the control run at 23:00 LST, the green rectangle at 23:00 LST in the
low-aerosol run surrounds an area where
Of interest is that the green rectangle is included in an area which is
surrounded by the purple line in all panels with different times in Fig. 8
and further discussion for this matter is provided in Sect. 4.2. After
23:00 LST 27 July 2011, the precipitating system enters its decaying stage.
Figure 7m shows precipitation-rate frequency in the control run and the
low-aerosol run for a period between 04:00 and 05:00 LST 28 July 2011. As
seen in Fig. 7m, with the progress of the decaying stage, the maximum
precipitation rate reduces down to
Same as Fig. 8 but with convergence at the surface (white contours)
and the column-averaged condensation rates (yellow contours) which are
superimposed on the precipitation field. In panels
For the examination of condensation which is the main source of
precipitation, convergence fields at the surface, where updrafts that produce
condensation originate, are obtained and the column-averaged
condensation rates are superimposed on them. Other processes such as
deposition and freezing produce the mass of solid hydrometeors and act as
sources of precipitation; however, their contribution to precipitation
is
Same as in Fig. 10 but with wind vector fields (arrows), which are superimposed on the precipitation, convergence, and condensation fields.
Figure 11 shows horizontal distributions of wind vector field (arrows)
superimposed upon fields of convergence, condensation, and precipitation. In
general, particularly from 19:00 LST on, in the area with high-value aerosol
concentrations to the west of the strong convergence field (surrounded by the
green rectangle), there are greater horizontal wind speeds than in the area
with low-value aerosol concentrations to the east of the strong convergence
field in the control run. As seen in comparisons between the location of the
rectangle and that of the purple line, which mark the transition zone for
aerosol concentrations, the area to the west of the rectangle has higher
aerosol concentrations than that to the east. In the area with high-value
aerosol concentrations, there is greater cloud liquid evaporation occurring
than in the area with low-value aerosol concentrations in the control run as
shown in Fig. 12a. Figure 12a shows the vertical distribution of the time-
and domain-averaged cloud liquid and rain evaporation rates over each of the
areas to the west and east of the strong convergence field, which is
surrounded by the green rectangle, and over the period between 17:00 and
19:00 LST for the control run and the low-aerosol run. For the calculation
of the averaged values in Fig. 12, the area to the west (east) of the strong
convergence field is set to include all parts of the north–south direction,
which is the
Vertical distributions of the time- and domain-averaged
High-value aerosol concentrations reduce autoconversion and in turn increase
cloud liquid as a source of evaporation and thus increase cloud liquid
evaporation compared to low-value aerosol concentrations. In addition,
high-value aerosol concentrations produce high-value cloud droplet number
concentration and the associated high-value surface areas of droplets. The
surface of droplets is where condensation occurs and as shown by Lee et
al. (2009) and a recent study by Fan et al. (2018), the high-value surface
areas cause higher-value condensation compared to the situation with
low-value aerosol concentrations that lead to lower-value condensation. The
averaged condensation rate over the abovementioned area to the west (east)
of the strong convergence field and over the period between 17:00 and
19:00 LST is 1.28 (0.97) g m
After reaching the near-surface altitudes below
Note that, associated with aerosol concentrations in the western part of the domain, which are 2 times greater in the control run than in the low-aerosol run, there are differences in aerosol concentrations 2 times greater between the area with high-value aerosol concentrations and that with low-value aerosol concentrations in the control run than in the low-aerosol run. This leads to a transition in aerosol concentrations 2 times greater , particularly in the transition zone surrounded by the purple line in the control run than in the low-aerosol run (Fig. 4). Associated with this, there is a greater reduction in autoconversion and increases in cloud liquid and surface-to-volume ratio of cloud droplets in the area with high-value aerosol concentrations in the control run than in the low-aerosol run. Then, there is greater evaporation, intensity of downdrafts, and associated outflow and its acceleration during its southeastward movement around the surface in that area in the control run than in the low-aerosol run (Figs. 11 and 12). This means that there is stronger collision between outflow and the surrounding air in the control run than in the low-aerosol run, and stronger collision forms the strong convergence field (in the green rectangle), which is much more intense in the control run than in the low-aerosol run as seen in Figs. 10 and 11. Over this much more intense convergence field, there is the formation of stronger updrafts that are able to form stronger convection, which is in turn able to produce more events of heavy precipitation in the control run than in the low-aerosol run (Fig. 7). The more intense strong convergence field in the green rectangle establishes stronger feedbacks between the convergence field, condensation, heavy precipitation, and evaporation in the control run than in the low-aerosol run. Hence, differences in intensity of the convergence field shown in the green rectangle and in the heavy precipitation between the runs become greater as time progresses (Figs. 7, 10, and 11).
It is discussed that cloud liquid evaporative cooling plays an important role in the formation of the strong convergence field where most of heavy precipitation occurs (surrounded by the green rectangle) in the control run. To confirm this role, we repeat the control run and the low-aerosol run with cooling from cloud liquid evaporation turned off and cooling from rain evaporation left on. The repeated control run and the low-aerosol run are referred to as the control-noevp run and the low-aerosol-noevp run, respectively. In these repeated runs, cloud liquid mass reduces due to cloud liquid evaporation, although cloud liquid evaporation does not affect temperature.
The temporal evolution of precipitation rates in the control-noevp run and
the low-aerosol-noevp run is similar to that in the control run and the
low-aerosol run (Fig. 6a). However, due to the absence of cloud liquid
evaporative cooling, there is no formation of strong outflow and convergence
field (as seen in wind field and the green rectangle in the control run and
the low-aerosol run) in these repeated runs as shown in Fig. 13a and b.
Figure 13a and b show wind vector and convergence fields at the surface over
the whole domain in the control-noevp run and the low-aerosol-noevp run,
respectively, at 23:00 LST, which corresponds to the mature stage of the
system. Note that the strong convergence field is clearly distinguishable in
its intensity and length from any other convergence lines in each of the
control run and the low-aerosol run as seen in Figs. 10 and 11. However,
there is no field in each of the repeated runs that is distinguishable in its
intensity and length from other lines as seen in Fig. 13a and b. This leads
to the situation in which there is no particular convergence field in the
control-noevp run that produces many more events of heavy precipitation than
in the low-aerosol-noevp run. As seen in Fig. 7h and k, associated with this,
differences in the frequency of heavy precipitation with rates above
60 mm h
Note that between the control run and the low-aerosol run, there are changes not only in the spatial variability in aerosol concentrations but also in aerosol concentrations. This means that differences between those runs are caused not only by changes in the variability but also by changes in aerosol concentrations. Although there have been many studies on the effects of changes in aerosol concentrations on heavy precipitation, studies on those effects of changes in the variability have been rare. Motivated by this, as a preliminary step to the understanding of those effects of changes in the variability, here, we attempt to isolate the effects of changes in the variability on heavy precipitation from those in aerosol concentrations or vice versa. For this purpose, the control run and the low-aerosol run are repeated with homogeneous spatial distributions of background aerosol concentrations. These repeated runs are referred to as the control-homoge run and the low-aerosol-homoge run. In the control-homoge run (low-aerosol-homoge run), aerosol concentrations over the domain are fixed at one value, which is the domain-averaged concentration of the background aerosol in the control run (the low-aerosol run), at each time step. Hence, in the control-homoge run and the low-aerosol-homoge run, the variability (or contrast) in the spatial distribution of aerosol concentrations between the area with high-value aerosol concentrations and that with low-value aerosol concentrations is removed, which achieves homogeneous spatial distributions.
The temporal evolution of precipitation rates in the control-homoge run and the low-aerosol-homoge run is similar to that in the control run and the low-aerosol run (Fig. 6b). However, with the homogeneity in the spatial distribution of aerosol concentrations, there is no formation of strong outflow and thus strong convergence field that is distinguishable from any other convergence lines in the control-homoge run and low-aerosol-homoge run as seen in Fig. 13c and d. Figure 13c and d show wind vector and convergence fields over the whole domain at 23:00 LST in the control-homoge run and the low-aerosol-homoge run, respectively. In the absence of the variability between the area with high-value aerosol concentrations and that with low-value aerosol concentrations, there are no differences in evaporative cooling between those areas and thus there is no strong outflow or convergence field which is distinguishable from any other lines.
Spatial distributions of convergence (red contours) and wind vector
(arrows) at the surface at 23:00 LST. Panels
Comparisons between the control run and the control-homoge run (the
low-aerosol run and the low-aerosol-homoge run) isolate the effects of the
variability on heavy precipitation from those of aerosol concentrations whose
averaged value is set at an identical value at each time step in the runs.
Due to the absence of the variability in the spatial distribution of aerosol
concentrations and the associated strong convergence field, the frequency of
heavy precipitation in the control-homoge run and in the low-aerosol-homoge
run is, on average, just
Remember that there is an identical domain-averaged background aerosol
concentration at each time step between the control run and the
control-homoge run and between the low-aerosol run and the low-aerosol-homoge
run. Hence, changes in the averaged aerosol concentration between the
control-homoge run and the low-aerosol-homoge run are identical to those
between the control run and the low-aerosol run. With these identical changes
in the averaged aerosol concentration, between the control run and the
low-aerosol run, there are additional changes in the variability in aerosol
distributions. There is a larger frequency of heavy precipitation in the
control-homoge run than in the low-aerosol-homoge run (Fig. 7c). However, as
mentioned above, there is no strong convergence field which is
distinguishable from any other lines in the control-homoge run, as seen in
Fig. 13c. Associated with this, differences in the frequency of heavy
precipitation between the control-homoge run and the low-aerosol-homoge run
are much smaller than those between the control run and the low-aerosol run,
particularly during the period between 19:00 and 23:00 LST, as seen in
Fig. 7i and l. This results in a situation in which differences in the frequency
of heavy precipitation between the control-homoge run and the
low-aerosol-homoge run are, on average, just
This study examines how aerosol affects heavy precipitation in an urban conurbation area. For this examination, a case that involves an MCS and torrential rain over the conurbation area which is centered in Seoul, South Korea, is simulated. This case has large spatial variability in aerosol concentrations, which involves high-value aerosol concentrations in the western part of the domain and low-value aerosol concentrations in the eastern part of the domain.
It is well-known that increases in aerosol concentrations reduce autoconversion and increase cloud liquid as a source of evaporation, which enhances evaporation and associated cooling. Hence, high-value aerosol concentrations in the western part of the domain cause high-value evaporative cooling rates, while low-value aerosol concentrations in the eastern part of the domain cause low-value evaporative cooling rates. Greater evaporative cooling produces greater negative buoyancy and more intense downdrafts in the western part than in the eastern part. More intense downdrafts then turn into stronger outflow over the western part that collides with surrounding air over the eastern part to form a strong convergence field along the boundary between those parts. Over this strong convergence field, most heavy precipitation forms. When contrast in aerosol concentrations between the western and eastern parts, which represents the spatial variability in aerosol concentrations, is reduced together with aerosol concentrations over the western part, differences in evaporative cooling and outflow between those parts decrease substantially. This results in a much weaker convergence field along the boundary, which is followed by much fewer occurrences of heavy precipitation events compared to those with greater contrast. It is found that the changing variability has many more impacts on heavy precipitation than the changing aerosol loading.
Studies (e.g., Niyogi et al., 2006; Thielen et al., 2000) have shown that at the edge of a metropolitan area, due to stark contrast in the surface roughness (representing the surface property) between the area and surrounding rural areas, there are enhanced convergence and updrafts. The urban heat island (UHI) effect, which is associated with the surface property in metropolitan areas, also results in enhanced convergence and updrafts at the edge of the area (Ryu et al., 2013; Schmid and Niyogi, 2017). In addition, a metropolitan area has stronger and more aerosol sources than surrounding rural areas; hence, contrast in aerosol concentrations at the edge of a metropolitan area or at the urban–rural boundary, which is characterized by contrast in the surface property between the urban and rural areas, is unlikely to be rare. This study suggests that in case there is this type of contrast in aerosol properties such as aerosol concentration at the boundary, there can be enhanced convergence and updrafts at the edge of a metropolitan area. Hence, this study suggests that urban–rural contrast in aerosol should be considered as an additional factor (in addition to contrast in the surface roughness and the UHI effect) to understand the enhancement of convergence and updrafts at the edge of a metropolitan area.
It should be noted that urban surface properties, which are represented by the roughness and control the UHI effect, and their contrast with the rural surface properties do not vary significantly with respect to time and space compared to the variation in aerosol properties. Hence, the location of the urban–rural boundary does not change significantly with time and space. However, in contrast to this, aerosol properties vary substantially with respect to time and space and thus the location of boundary between high aerosol concentrations and low aerosol concentrations substantially vary with respect to time and space. For example, in a place such as a large-scale industrial complex within an urban area away from an urban boundary, there can be an increase in aerosol concentrations and thus high aerosol concentrations. These high aerosol concentrations can advect, as exemplified in the case adopted in this study, and a boundary between a place with low aerosol concentrations and a place with high aerosol concentrations can vary spatiotemporally within the urban area. This indicates that the boundary between the place with high aerosol concentrations and that with low aerosol concentrations does not necessarily have to be co-located with the urban–rural boundary, which is characterized by contrast in the surface property between urban and rural areas and whose location does not change much with respect to time and space. Demonstrating this, in this study, the high aerosol–low aerosol boundary, which is, for example, outlined by the purple line in Fig. 4a and b, is not co-located with the urban–rural boundary but located in the middle of the Seoul area. Considering that at the high aerosol–low aerosol boundary, heavy precipitation is concentrated in this study, a spatiotemporal variation in the boundary leads to a spatiotemporal variation in heavy precipitation within an urban area as shown in this study. Hence, while previous theories on urban heavy precipitation can explain heavy precipitation at urban–rural boundaries (characterized by the surface property contrast) and are not able to explain heavy precipitation in various locations within an urban area, the findings in this study elucidate a mechanism behind heavy precipitation in various locations in an urban area and thus give a more comprehensive understanding of torrential rain in urban areas.
There are numerous factors that control the spatial distribution of updrafts and associated condensation. Note that changes in this distribution induce those in the spatial distribution of precipitation that may involve the generation and the enhancement of torrential rain. One of the factors is found to be increasing aerosol concentrations by previous studies (e.g., Khain et al., 2005; Seifert and Beheng, 2006; van den Heever and Contton, 2007; Tao et al., 2007, 2012; Storer et al., 2010; Lee and Feingold, 2013; Lee et al., 2017). These previous studies have found that increasing aerosol concentrations can alter the vertical and horizontal gradient of latent heating and cooling by altering the spatial distributions of freezing, evaporation, and condensation. This alteration leads to that in updrafts, cloud cells, and precipitation, which involves the generation and the enhancement of torrential rain. However, these studies have focused only on increasing aerosol concentrations and assumed that background aerosol concentrations are spatially distributed in a homogeneous fashion and, hence, have not considered the effect of the spatial variability in aerosol on the spatial distribution of latent heat processes, cloud dynamics, and precipitation. For example, previous studies have found that aerosol-induced localized changes in evaporation for individual cloud cells can create subsequent localized changes in the horizontal gradient of latent cooling and temperature in and around individual cloud cells. Note that each of these individual localized changes is limited to each individual localized area in and around each individual cloud cell. These changes lead to the generation and the enhancement of torrential rain in and around individual cloud cells. It is found that increasing spatial variability in aerosol concentrations also increases the gradient of evaporation and temperature. These changes lead to increases in the occurrence of heavy precipitation in a specific area which is along the high aerosol–low aerosol boundary and is not limited to a localized area in and around a cloud cell. It is demonstrated that increasing variability plays a much more important role in aerosol-induced increases in the occurrence of heavy precipitation than increases in aerosol concentrations with their homogeneous spatial distributions.
As mentioned, observed aerosol particles include components which do not absorb radiation significantly; hence, the aerosol absorption of radiation is not considered in this study. However, ammonium sulfate and organic compounds, which are observed to comprise aerosol here, reflect and scatter radiation, although this reflection and scattering is not considered in this study. The reflection and scattering of solar radiation by aerosol decreases solar radiation that reaches the surface and thus surface fluxes. Higher aerosol concentrations in the western part of the domain can cause more reflection and scattering of solar radiation by aerosol than in the eastern part. This can reduce surface fluxes, the associated convection intensity, condensation, and transportation of cloud liquid to unsaturated areas by convective motion in the western part more than in the eastern part. As a result, there can be reduction in the contrast in evaporative cooling between the parts compared to the contrast with no consideration of the reflection and scattering. This can lower the intensity and frequency of heavy precipitation by diminishing the contrast in wind field between the parts. However, the simulated intensity and frequency of heavy precipitation with no consideration of the reflection and scattering by aerosol are not that different from observed counterparts. This indicates that the effect of the reflection and scattering by aerosol, and associated changes in surface fluxes on heavy precipitation, is likely to be insignificant in reality. This is likely to be due to the fact that once deep clouds with a high-value cloud fraction and cloud optical depth form, the effect of aerosol on radiation is taken over by that of clouds on radiation, which leads to a situation in which aerosol effects on radiation become negligible compared to cloud effects on radiation.
The data used are currently private and stored in our private computer system. Opening the data to the public requires approval from funding sources. Since funding projects associated with this work are still going on, these sources do not allow the data to be open to the public; 2–3 years after these project ends, the data can be open to the public. However, if there is any inquiry about the data, contact the corresponding author Seoung Soo Lee (slee1247@umd.edu).
SSL, BGK, and ZL established essential initiative ideas to start this work. While SSL worked on the analysis of simulation data, BGK and ZL worked on the analysis of observation data. YSC, CHJ, JU, JM, and KHS provided observation data from Korea and participated in their preliminary analysis.
The authors declare that they have no conflict of interest.
This research is supported by the Korea Environmental Industry and Technology Institute funded by the South Korean Ministry of Environment as “Climate Change Correspondence Program”, the US National Oceanic and Atmospheric Administration (grant NOAA-NWS-NWSPO-2015-2004117), and the National Strategic Project-Fine particle of the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (MSIT), the Ministry of Environment (ME), and the Ministry of Health and Welfare (MOHW) (NRF-2017M3D8A1092022). This research is also supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2018R1A6A1A08025520). Edited by: Johannes Quaas Reviewed by: Annette Miltenberger and one anonymous referee