Climate is critically affected by aerosols, which alter cloud lifecycles and precipitation distribution through radiative and microphysical effects. In this study, aerosol and cloud property datasets from MODIS (Moderate Resolution Imaging
Spectroradiometer), onboard the Aqua satellite, and surface observations, including aerosol concentrations, raindrop size distribution, and meteorological parameters, were used to statistically quantify the effects of aerosols on
low-level warm-cloud microphysics and drizzle over northern Taiwan during
multiple fall seasons (from 15 October to 30 November of 2005–2017). Our results
indicated that northwestern Taiwan, which has several densely populated
cities, is dominated by low-level clouds (e.g., warm, thin, and broken
clouds) during the fall season. The observed effects of aerosols on warm
clouds indicated aerosol indirect effects (i.e., increased aerosol loading
caused a decrease in cloud effective radius (CER)), an increase in cloud
optical thickness, an increase in cloud fraction, and a decrease in cloud-top temperature under a fixed cloud water path. Quantitatively,
aerosol–cloud interactions (ACI=-∂lnCER∂lnα|CWP, changes in CER relative to changes in aerosol amounts) were 0.07 for our research domain and varied
between 0.09 and 0.06 in the surrounding remote (i.e., ocean) and polluted
(i.e., land) areas, respectively, indicating aerosol indirect effects were
stronger in the remote area. From the raindrop size distribution analysis,
high aerosol loading resulted in a decreased frequency of drizzle events,
redistribution of cloud water to more numerous and smaller droplets, and
reduced collision–coalescence rates. However, during light rain (≤1 mm h-1), high aerosol concentrations drove raindrops towards smaller droplet sizes and increased the appearance of drizzle drops. This study used long-term surface and satellite data to determine aerosol variations in northern Taiwan, effects on clouds and precipitation, and observational strategies for future research on aerosol–cloud–precipitation interactions.
Introduction
Since the industrial revolution, the quantity of aerosols produced by human activities has increased significantly, with the strongest aerosol emissions from areas with frequent industrial activities or high biomass burning (Textor et al., 2006). The effect of aerosols on climate is recognized as significant (Charlson et al., 1992; Kiehl and Briegleb, 1993; Penner et al., 2001; Ramanathan et al., 2001; Ramaswamy et al., 2001) albeit
complex. Aerosols can alter cloud properties with subsequent impacts on
climate, i.e., aerosol indirect effect (Warner and Twomey, 1967; Twomey,
1974; Albrecht, 1989; Lohmann and Feichter, 2005). The responses of
convective and boundary layer clouds contribute to the spread of global
cloud feedbacks in general circulation models (GCMs), which dominate the
inter-model differences (Bony et al., 2006). Furthermore, studies have
demonstrated that GCMs significantly overestimate the frequency of drizzle (Stephens et al., 2010), which brings into question the accuracy of
aerosol–cloud interactions (ACIs) in models. Therefore, observational
studies of aerosol and cloud microphysical properties are crucial for
clarifying the relationship between aerosols and the microphysical process
of clouds and evaluating the accuracy of model simulations.
Jones et al. (2009) emphasized that ACIs should be explored at the
regional scale because the aerosol type, concentration, and meteorological
conditions differ depending on the area. Numerous studies have used the
aerosol concentration and cloud droplet size to investigate ACIs at global
or regional scales. A negative correlation between aerosols and cloud drop
size has been observed in global- (Bréon et al., 2002; Myhre et al., 2007; Nakajima et al., 2001) and regional-scale (Costantino and Bréon, 2010; Ou et al., 2012) studies. Sekiguchi et al. (2003) and Grandey and Stier (2010) have used global satellite data and identified different correlations (positive, negative, or weak) between aerosol optical depth (AOD) and cloud effective radius (CER) depending on the location of the observation. Likewise, Twohy et al. (2009) and Christensen et al. (2017) reported spurious correlations between AOD and cloud properties using in situ aircraft and satellite data. Despite advances in
satellite-based retrievals in recent decades, obtaining robust statistical
relationships between aerosols and clouds is difficult using only
satellite-based observations (Christensen et al., 2017).
Nevertheless, some effects from aerosols on cloud microphysics can be
observed using satellite data (Krüger and Graßl, 2002; Menon et
al., 2008; Rosenfeld et al., 2014; Saponaro et al., 2017; Sporre et al., 2014). With satellite-based precipitation observations from the Tropical
Rainfall Measuring Mission (TRMM), Rosenfeld (1999) demonstrated that
aerosols derived from biomass burning suppress warm rain processes. Aircraft
observations over the Amazon basin demonstrated decreased in-cloud droplet
sizes and a delay in precipitation onset when a large quantity of aerosols
entered the cloud (Andreae et al., 2004). The effects of aerosols in
suppressing drizzle have been identified in field experiments on stratocumulus clouds over the northeastern Atlantic Ocean (Albrecht et
al., 1995; Wood, 2005), northeastern Pacific Ocean (Lu et al., 2007, 2009; Stevens et al., 2003; VanZanten et al., 2005), and
southeastern Pacific Ocean (Bretherton et al., 2010; Comstock et al., 2004; Wood et al., 2011). Moreover, model simulations have revealed that polluted environments could suppress drizzle in warm clouds (Ackerman et
al., 2004; Guo et al., 2011; H. Wang et al., 2011; M. Wang et al., 2011).
Although numerous studies have used observations and model simulations to
discuss the indirect effects of aerosols, the interaction mechanism between
aerosols and clouds remains weakly constrained (Bellouin et al., 2020)
in the global climate system.
Huang et al. (2007) used a regional coupled climate–chemical–aerosol
model for East Asia and determined that the aerosol indirect effect
significantly reduced precipitation in fall and winter. Menon et al. (2002) used a global climate model to study the effects of aerosols in China
and India and reported that anthropogenic aerosols increased precipitation
in southeastern China but inhibited precipitation in northeastern China.
Furthermore, Giorgi et al. (2003), using a coupled regional
chemistry–climate model, found that aerosol indirect effects were largely
dominant over direct effects in inhibiting precipitation in East Asian
climates. Takemura et al. (2005) used a global aerosol
transport-radiation model coupled to a general circulation model and
determined that the indirect effect had a strong signal in regions with
large quantities of anthropogenic aerosols and cloud water.
These studies have demonstrated a significant correlation between aerosols
and cloud microphysics and the indirect effect of aerosols on regional
precipitation. However, the aerosol type, concentration, and characteristics
vary by region. Moreover, the uncertainty on radiative forcing, especially
via the impact from clouds, remains large in Earth's radiation budget
(Bellouin et al., 2020). Taiwan is an island with a high population
density, a complicated topography, and a climate that ranges from tropical
in the south to subtropical in the north. These characteristics result in
substantially complex microphysical processes between aerosols and clouds.
In this study, we aimed to systematically analyze aerosols, cloud optical
properties, and precipitation characteristics by integrating satellite and
surface observation data over northern Taiwan to investigate the following
questions. (1) How do aerosols affect cloud microphysical properties in
response to different pollution conditions? (2) How do aerosols affect
the frequency of drizzle and the change in precipitation distribution? In
Sect. 2, we describe the data and methodology. In Sect. 3, we present results
and the discussion. Findings are summarized in Sect. 4.
Data and methodologyStudy area and time period
Our study domain, northern Taiwan, covers the area 24.5–25.8∘ N and 120.8–122.2∘ E (Fig. 1) and has a population of approximately 10 million. The emissions of this area are considered a combination of urban and industrial activities. For this area, air quality worsens in fall when precipitation is less and air masses stagnate. Moreover, the results of Huang et al. (2007) indicated that aerosol indirect effects frequently occur in fall. Therefore, we chose the data period from 15 October to 30 November between 2005 and 2017 (611 d in total) to explore aerosol effects on cloud microphysics and drizzle. To
remove the effect of typhoons from the analysis, typhoon alarm days (21–23 October 2010, Typhoon Megi) issued by the Central Weather Bureau were
excluded in this study.
Surface measurement data
Hourly meteorological (i.e., temperature, relative humidity, rainfall, wind
direction, and wind speed) and PM2.5 concentration data collected from
the Taiwan EPA (Environmental Protection Administration) Pingzhen site (24.95∘ N, 121.20∘ E) and
1 min raindrop size distribution Joss–Waldvogel disdrometer (JWD) data
obtained from the National Central University (NCU) (24.968∘ N,
121.185∘ E) observatory were used. The NCU and EPA Pingzhen sites
are located near each other at the center of the study domain. The
PM2.5 concentration was measured using the MetOne BAM-1020 beta
attenuation monitor. The JWD measures the number of rain droplets every
minute by using 20 bin sizes of 0.359–5.373 mm (n1–n20: 0.359, 0.455,
0.551, 0.656, 0.771, 0.913, 1.116, 1.331, 1.506, 1.665, 1.912, 2.259, 2.584,
2.869, 3.198, 3.544, 3.916, 4.350, 4.859, and 5.373 mm). To ensure data
quality, observations were discarded when the rain rate was lower than 0.1 mm h-1 (Greenberg, 2001; Seela et al., 2017).
Satellite data
Cloud and aerosol data from the NASA Aqua satellite, Moderate Resolution Imaging
Spectroradiometer (MODIS) collection 6 level 2 products (MYD06 for clouds
and MYD04 for aerosols), were used in this study. Data were downloaded from
https://modis.gsfc.nasa.gov/data/ (last access: 22 February 2021). Data on cloud properties included cloud
optical thickness (COT), CER, and cloud water path (CWP), all of which had a
resolution of 1 km, as well as cloud fraction (CF), cloud-top pressure
(CTP), cloud-top temperature (CTT), and cloud phase infrared (CPI), all of
which had a resolution of 5 km. CWP included the liquid water path and ice water
path (CWP=LWP+IWP). For aerosol data, AOD with a resolution of 10 km was used. Descriptions of parameters and products are presented in Table 1. To ensure spatial resolution consistency
between datasets, data were interpolated to a coarse resolution of
0.1∘×0.1∘.
MODIS aerosol and cloud products used in this study.
Satellite aerosol data were not retrieved when conditions were overcast,
except when aerosols were above clouds. To compensate for this limitation,
densely available surface PM2.5 data in the study domain were used. The
composition of PM2.5 in East Asia is usually dominated by carbonaceous
species and water soluble ions, including SO42-, NH4+,
and NO3- (Xu et al., 2012), which are important in
determining the hygroscopicity of aerosols (Shen et al., 2009). Thus,
based on these suitable characteristics and the lack of measured CCN (cloud condensation nuclei) in this
study, we used PM2.5 as a proxy for CCN concentrations. The spatial
homogeneity of PM2.5 concentrations was examined based on the
correlation of concentrations between the Pingzhen site and the 30 air
quality monitoring sites in the northern Taiwan. Results indicated that
correlation coefficients were higher than 0.6 and 0.8 for northern Taiwan
and the research area (24.6–25.2∘ N and
120.9–121.5∘ E), respectively, demonstrating that
PM2.5 data from the Pingzhen site accurately represented the aerosol
concentration over our research domain (Fig. 1).
Spatial correlation coefficient of the PM2.5 concentration
between the Pingzhen station and other stations. The main research area
(24.6–25.2∘ N, 120.9–121.5∘ E) is indicated with a magenta box.
Fine particles were assumed well-mixed throughout the planetary boundary layer (PBL) during daytime
(Maletto et al., 2003). PM2.5 data between 10:00 and 14:00 were
averaged as a measure of daily PM2.5 concentrations for comparison with
Aqua satellite data (overpass time is approximately 13:30 local time).
Furthermore, the 20th percentile of daily average PM2.5 data (≤11.2µgm-3) was defined as clean days (n=123 d). The 80th percentile of daily average PM2.5 data (≥34.6µgm-3) was defined as polluted days (n=121 d). Polluted days were further divided into three groups: slightly polluted (40 d), moderately polluted (40 d), and heavily polluted (41 d) with PM2.5 concentrations of 34.6–39.9, 39.9–52.3, and 52.3–110 µgm-3, respectively.
A previous study reported that the vertical aerosol distribution for the
study region in fall primarily resided within 2 km (Wang et al., 2010). For ACI at a local scale, clouds that occurred below 2 km were
targeted. Therefore, only clouds with CTP≥800 hPa and CPI=1 (water cloud) were included, thereby ensuring that only warm clouds were analyzed.
To quantify ACI, the commonly used formula proposed by Feingold et al. (2001) was employed, as illustrated in Eq. (1). This equation calculates how
a change in aerosols affects CER at a constant CWP.
ACI=-∂lnCER∂lnα|CWP,
where α represents the proxy for the quantity of aerosols, using
either PM2.5 or AOD values. Positive ACI values indicate that a change
in CER depends on increased aerosols and vice versa. An ACI value
approaching 0 indicates that the relationship between CER and aerosols (i.e.,
aerosol indirect effect) is not significant. The ACI calculation should be
performed under a fixed range of CWP in Eq. (1). Therefore, the CWP
population density distribution was divided into 10 groups (Fig. 2), with
each group representing 10 % of CWP data.
Histogram of cloud water path (CWP) values over northern Taiwan
from 15 October to 30 November 2005–2017. CWP is divided into 10 bins (10 % for each bin) indicated by dashed lines. The key CWP group 9 (150≤CWP<297) is marked in the figure. num: number; mean: mean; std: standard deviation.
Data on the raindrop size distribution obtained from JWD were further
processed. The daily rainfall amount was defined as the sum of precipitation
from 10:00 to the next day at 10:00. The American Meteorological Society's Glossary of Meteorology (Huschke, 1959) defines drizzle as very small, numerous, and uniformly dispersed water drops that may appear to float in currents. In contrast to fog droplets, drizzle falls to the ground. In weather observations, drizzle is classified as (a) “very light”, comprised of scattered drops that do not entirely wet an exposed surface regardless of the duration; (b) “light”, the rate of fall
being traced to 0.25 mm h-1; (c) “moderate”, the rate of fall being
0.25–0.50 mm h-1; and (d) “heavy”, the rate of fall exceeding 0.5 mm h-1. When the precipitation equals or exceeds 1 mm h-1, all or part of the precipitation is considered rain. The threshold for rain
intensity was set at 1 mm h-1 to focus on the effect of aerosols on
drizzle. Drizzle drops are conventionally 0.5 mm or less in diameter;
therefore, JWD data in the n1 (0.359 mm) and n2 (0.455 mm) channels were
summarized as drizzle precipitation.
Results and discussionOverall aerosol, cloud, and meteorological characteristics
To explore the effect of aerosols on cloud microphysics and the subsequent
precipitation, a general understanding of aerosol quantities, cloud
microphysics, and precipitation characteristics over the study region is
crucial. Figure 3 illustrates the spatial distribution of mean aerosol and cloud parameter values (including AOD, COT, CWP, CF, CER, and CTP) over northern Taiwan from 15 October to 30 November 2005–2017. The mean AOD reached 0.6 in northwestern Taiwan because of the high density of human activities, whereas lower AOD values (less than 0.2) were observed over the Xueshan mountain range (the green triangle in Fig. 3a).
Average (a) aerosol optical depth (AOD), (b) cloud optical
thickness (COT), (c) cloud water path (CWP), (d) cloud fraction (CF), (e) cloud effective radius (CER), and (f) cloud-top pressure (CTP) in warm clouds from 15 October to 30 November 2005–2017. The magenta box represents the main study area (24.6–25.2∘ N, 120.9–121.5∘ E), and the blue box in (a) is the remote area (25.2–25.8∘ N, 120.9–121.5∘ E). The green triangles in (a) mark the location of the Xueshan mountain range. The topography of northern Taiwan is depicted with brown-colored contour lines (in meter) in (b–f).
Clouds were affected by the prevailing northeastern wind and topography,
resulting in higher top heights and more significant coverage for clouds
over northeastern Taiwan compared with northwestern Taiwan. The mean CWP, CF,
and CER in our study area ranged from 60 to 120 g m-2, 0.6 to 0.7, and
13 to 14.5 µm, respectively. COT was usually around 10, and most of the CTP was higher than 850 hPa, suggesting low-level clouds (e.g., warm, thin, and broken clouds).
Surface PM2.5 concentrations and meteorological parameters for clean
and polluted days were also analyzed. We collected 1189 h of rainfall
data out of approximately 14 000 total hours of meteorological data. The
mean values of temperature, relative humidity, rainfall, wind speed, and
PM2.5 concentrations were 22.3∘, 74.9 %, 1.4 mm h-1,
3.2 m s-1, and 23.4 µgm-3, respectively (illustrated in Fig. 4). The prevailing wind direction was northeastern. During clean days, the aforementioned mean values were 22.2∘, 79.3 %, 1.5 mm h-1, 3.6 m s-1, and 9.9 µgm-3, respectively, compared with the mean values of 22.5∘, 72.5 %, 1.4 mm h-1, 2.7 m s-1, and 43.3 µgm-3, respectively, on polluted days. Overall,
compared with clean days, meteorological conditions on polluted days had
lower relative humidity, less rainfall, more wind direction in addition
to the northeastern wind, and lower wind speed. However, differences were not
observed in mean rainfall rates between clean and polluted days. The number
of rainfall hours differed significantly with 384 h during clean days
and 115 h during polluted days. A weaker and more disorderly direction
of the wind was observed on polluted days, which suggests that pollution may
be associated with more stagnant conditions.
The distribution of (a) temperature, (b) relative humidity, (c) rainfall, (d) wind direction, (e) wind speed, and (f) PM2.5 hourly data from the Pingzhen station from 15 October to 30 November 2005–2017. The gray bars are the distribution of all valid observations; the blue lines represent the clean days; and the red lines represent the polluted days. num: number; mean: mean; std: standard deviation.
CWP is a constraint factor for the ACI index calculation as illustrated in
Eq. (1). We further examined CWP variability in response to main
meteorological parameters (temperature, relative humidity, and rainfall) and
PM2.5 concentrations from the Pingzhen site and CER from MODIS. We
calculated the daily mean value of CWP and CER by averaging grids over the
main research area (24.6–25.2∘ N, 120.9–121.5∘ E). Daily meteorological parameters and PM2.5 concentration data, described in Sect. 2.2, were used. Figure 5 illustrates the mean and standard deviation
of PM2.5 and CER in 10 CWP groups. As CWP increased, the average
temperature and relative humidity gradually decreased and increased,
respectively. No significant correlation was identified between rainfall and
CWP. The complicated relationship between PM2.5 and CWP is illustrated
in Fig. 5. PM2.5 increased with an increase in CWP up to 50 g m-2
and then decreased, whereas CER increased at first before decreasing and
then increasing again. The standard deviation of CWP in group 9 (150≤CWP<297) was smaller than in other groups, indicating that group 9 was
a more stable community; thus, much of the subsequent analysis focused on
group 9 to reduce uncertainties caused by the variability of environmental
conditions.
Multiyear (2005–2017) mean and standard deviation of temperature,
relative humidity (RH), rainfall, PM2.5, and cloud effective radius
(CER) in different cloud water path (CWP) bins. The CWP group numbers are
marked in the top panel.
Aerosol effect on warm-cloud properties
The effects of aerosols on warm-cloud microphysics in different CWP groups
for the main research domain were studied using the ACI index (Eq. 1). Figure 6 illustrates the ACI values and correlation
coefficient (r(ACI)) of the PM2.5 mass concentration and CER under different CWP groups. ACI was 0.07 in CWP group 9 (150≤CWP<297) and had the lowest root mean square error (RMSE=0.23) compared with other groups. The correlation coefficient between PM2.5 and CER in group 9 was -0.19. Positive ACI values were observed when CWP was larger than CWP group 7 (i.e., CWP groups 8–10), and a higher value of ACI was
associated with higher CWP groups. The negative correlation for these groups
indicates an aerosol indirect effect (i.e., an increase in aerosols causes
cloud droplet radii to become smaller under a fixed water content). Negative
ACI values were associated with low-CWP groups (i.e., groups 1–7), which may
be caused by the large standard deviation of CER data in CWP groups with
lower values. However, these low-CWP groups may reduce the effects of
aerosols on warm-cloud microphysics. We compared our results with values
from the literature (Table 2). Feingold et al. (2003) analyzed ACIs by using ground-based remote sensors in Oklahoma, United States, focusing on ice-free, single-layered, nonprecipitating, and airborne-insect-free clouds. Their results indicated that under the same LWP, the ACI values of seven cases were 0.02–0.16. Kim et al. (2008) conducted a 3-year experiment by using ground-based remote sensors to
investigate the aerosol indirect effect. Their results suggested that the
ACI values of continental stratus clouds ranged from 0.04 to 0.17 in
north-central Oklahoma. McComiskey et al. (2009) observed the ACI values
of coastal stratiform clouds between 0.04 and 0.15 by using ground-based
remote sensing data from the Atmospheric Radiation Measurement (ARM) program
at Point Reyes, California, United States. Our findings were on the lower end
of these ranges, likely due to the more polluted conditions in our East Asia
study area.
(a) Aerosol–cloud interaction (ACI) estimated values, computed for
the cloud effective radius (CER) in the different CWP groups by applying
PM2.5 concentrations as aerosol proxies. The shading in (a) represents the RMSE. (b) The correlation coefficients between PM2.5 and CER are illustrated.
ACI values from the literature in comparison to this study.
StudyACI valuesSourcesRegionFeingold et al. (2003)0.02–0.16Ground-based remote sensorsOklahoma, United StatesKim et al. (2008)0.04–0.17Ground-based remote sensorsOklahoma, United StatesMcComiskey et al. (2009)0.04–0.15Ground-based remote sensorsCalifornia, United StatesThis study0.07 in CWP group 9 (150≤CWP<297)Satellite and surface observationsNorthern Taiwan
Because of the distinct ACI signal in CWP group 9, we further explored the
effect of aerosols on cloud microphysical parameters by analyzing their
differences between polluted days and clean days over the main research area
(24.6–25.2∘ N, 120.9–121.5∘ E). Compared with clean days, COT, CER, CF, and CTT exhibited changes of +9.53, -2.77µm, +0.07, and -1.28 K on polluted days (Fig. 7).
While the positive CF value difference may have been due to higher aerosol
loading, the atmospheric condition may have contributed as well. For
instance, Saponaro et al. (2017) showed that CF is more sensitive to
lower-troposphere stability (LTS) than other cloud variables (i.e., CER, CTT,
and COT). Also from Fig. 7, higher PM2.5 concentrations corresponded to
smaller CER and CTT values and higher COT, in agreement with the aerosol
indirect effect.
Difference in (a) COT, (b) CER, (c) CF, and (d) CTT between polluted days and clean days in group 9 (150≤CWP<297).
The relationship between CTT and CER and aerosols was studied in further
detail. Figure 8 displays CWP group 9 (150≤CWP<297) results of the corresponding CTT–CER relationship and the occurrence frequency (%) of CTT on clean and polluted days. On clean days, the mean CER increased from 10.7 to 12.7 µm as CTT decreased from 291 to 279 K, indicating an inverse relationship over much of the CTT range. This phenomenon could be caused by the onset of water cloud generation during strong updrafts; i.e., droplet size increases during air parcel expansion in an adiabatic process (Saito et al., 2019). However, on polluted days, as CTT lowered, the mean CER decreased; at a CTT of 291 to 279 K, CER decreased from 10.8 to 9.1 µm. Figure 8b shows that CTT exhibited a higher
occurrence frequency between 288 and 285 K on polluted days, whereas clean
days had a higher frequency of CTT between 285 and 282 K. These results
suggest that abundant aerosols activated higher concentrations of CCN near
the surface, which tends to form more low-level clouds with smaller cloud
droplet size.
Multiyear (2005–2017) (a) relationship of cloud-top temperature (CTT) and cloud effective radius (CER). Plotted are the mean (solid line) and
1 standard deviation (dashed line) of CER for each 3 K interval and
(b) frequency of occurrence of CTT. Clean and polluted days are depicted with blue and red lines, respectively. Both (a, b) are constrained to CWP group 9 (150≤CWP<297).
Effect of different polluted conditions on ACI
We further explored the effect of aerosols on cloud microphysics under
different polluted conditions. We investigated ACI from two perspectives,
considering different polluted levels and considering different polluted
areas. First, we divided polluted days into three equal groups: slightly,
moderately, and heavily polluted days. We then calculated ACI values by
using RMSE and correlation coefficients (denoted with r(ACI)) of PM2.5 and CER under different CWP groups and at different polluted levels for the
main research domain. As illustrated in Fig. 9a, the three polluted levels
exhibited similar trends, but stronger ACI signals (larger ACI slope and
absolute r(ACI) values) were observed for heavily polluted cases compared with moderately and slightly polluted days. On heavily polluted days (red line), when CWP was larger than group 5, the ACI value increased with increasing CWP, and from group 8, the ACI value was positive, whereas ACI values for slightly and moderately polluted days continued to increase in
groups 7 to 9 but decreased in group 10 and were not consistently at
positive ACI values past a particular CWP range. For CWP groups 7–10, the
ACI values of heavily polluted days were consistently higher than the ACI
values of slightly and moderately polluted days, especially in group 10.
Notably, the differences in ACI values for the three polluted levels (0.08,
0.07, and 0.06 for heavily, moderately, and slightly, respectively)
associated with CWP group 9 (150≤CWP<297) were apparently small; thus the effects on cloud properties may prove insignificant.
Multiyear (2005–2017) ACI values with the RMSE (shaded) and the
correlation coefficient among (a) different polluted levels, (b) different aerosol proxies, and (c) different polluted condition areas.
The effects of aerosols on cloud microphysics over the land and ocean
(denoted with magenta and blue square boxes, respectively, in Fig. 3) are
discussed. Because of the lack of PM2.5 surface observations over the
ocean, we used AOD from MODIS/Aqua as the aerosol proxy in Eq. (1) for the
ACI calculation. To ensure the reliability of calculations, we computed ACI
in the primary research area (24.6–25.2∘ N, 120.9–121.5∘ E) based on different aerosol proxies
(i.e., AOD and PM2.5 concentration). As illustrated in Fig. 9b, in CWP
groups 1–8, ACI values evaluated with AOD had larger values than those
evaluated with PM2.5; the difference was the largest in CWP group 2
(0.22). For positive ACI ranges, ACIs estimated with AOD were positive for
CWP groups 7–10, whereas ACIs computed with PM2.5 were positive after
CWP group 8. In CWP groups 8–10, differences in ACI values became smaller,
especially in group 9. We focused on group 9, which had an ACI value using
PM2.5 of 0.07 and an ACI value using AOD of 0.06, a difference of only
0.01.
The effects of aerosols on cloud microphysics in polluted (i.e., land) and
remote (i.e., ocean, mean AOD of 0.31) areas can be assessed further by using
the ACI value with AOD as an aerosol proxy. We defined the main research
area of 24.6–25.2∘ N and 120.9–121.5∘ E as the polluted area (Fig. 3a magenta box) and
25.2–5.8∘ N and 120.9–121.5∘ E
as the remote area (Fig. 3a blue box). As illustrated in Fig. 9c, ACI values
and correlation coefficients between mean AOD and CER were calculated in
remote and polluted areas. Comparing ACI values between polluted and remote
areas demonstrated that ACI values were higher in the polluted area in CWP
groups 1–5. In this CWP interval, the ACI values of the remote area
increased with an increase in CWP, whereas the ACI values of the polluted
area changed significantly. In CWP groups 6–10, the ACI values of the
remote area became more pronounced than the polluted area. The positive and
increasing tendency of ACI values was observed in larger CWP groups
(>7) in two areas, suggesting that the environmental condition
(i.e., water vapor) was critical to aerosol indirect effects. In CWP group 9,
ACI values were 0.09 and 0.06 for remote and polluted areas, respectively,
indicating that aerosol indirect effects were stronger in remote areas (i.e.,
lower aerosols). These results are consistent with a study (Saponaro et al., 2017) that found large aerosol concentrations can saturate the effect
of ACI, causing a lower ACI value.
Multiyear (2005–2017) (a) JWD sample number of days in each
raindrop size bin, (b) mean droplet number per minute for clean and polluted days, and (c) the differences in the mean droplet number between polluted and clean days. The droplet size for each bin is, in order, 0.359, 0.455, 0.551, 0.656, 0.771, 0.913, 1.116, 1.331, 1.506, 1.665, 1.912, 2.259, 2.584, 2.869, and 3.198 mm.
Aerosol effect on precipitation
Aerosol effects on warm-cloud properties were discussed in Sect. 3.2; these
effects may subsequently alter the cloud lifetime and the precipitation
process. This section further explores their consequential influence on
precipitation. High-time-resolution (1 min) JWD and PM2.5 datasets
were used to investigate the effects of aerosols on the raindrop size
distribution, rainfall, and cloud lifetime. Figure 10a shows the number of sample occurrences under different raindrop size
classifications for clean and polluted days. The sample number (days) was
significantly higher for clean conditions, suggesting rainfall was more
common on clean days than on polluted days. We further calculated the
minute-averaged droplet number in each raindrop size classification for
polluted and clean days. Higher populations of raindrops were observed from
0.359 to 0.656 mm (bins n1–n4), with the peak in 0.455 mm (bin n2) for both
clean and polluted days (Fig. 10b). The difference is plotted in Fig. 10c.
The results illustrate (Fig. 10c) that during polluted days, the droplet
numbers appear lower for the smaller raindrop size (<1.5 mm)
compared to clean days and higher for the larger raindrop size (≥1.5 mm). A significant reduction in droplet number (decreased from 68 min-1 on clean days to 56 min-1 on polluted days) was observed in the 0.455 mm size (bin n2), corresponding to a reduction in drizzle. Our preliminary findings suggest that CCN may have competing effects (Ghan et al., 1998) on water uptake under aerosol-laden air and cloud-water-content-limited conditions, which would alter the precipitation processes.
To investigate the aerosol impacts on the change in droplet size, the
cumulative number distribution of each raindrop size for clean and polluted
days was calculated. We then normalized the data by computing the percentage
of droplet numbers in each raindrop size class to the total number, and the
difference between polluted and clean days was defined by Eq. (2).
nX Difference%min-1=∑i=1dpnXi∑X=1b∑i=1dpnXi×100%-∑i=1dcnXi∑X=1b∑i=1dcnXi×100%,
where nX represents different raindrop size bins, b reflects the number
of bins (b=1–20), and dp and dc represent the number of polluted and clean days, respectively. The results are similar to Fig. 10c; the droplet numbers, on polluted days compared to clean days, appear lower for the smaller raindrop size (≤0.771 mm, bin n5) and higher for the larger raindrop size (>0.771 mm) (Fig. 11a). To investigate the aerosol impacts on light rain, we created a plot similar to Fig. 11a but only considered precipitation less than or equal to 1 mm h-1, as shown in Fig. 11b. Our statistics for the droplet number concentration indicated that raindrop occurrence at n1 and n2 (i.e., drizzle) accounted for over 50 % on both polluted and clean days (not shown here), indicating that drizzle drops were a common raindrop type when rainfall was ≤1 mm h-1. We determined that when rainfall was ≤1 mm h-1, polluted days accounted for a more significant proportion when raindrop size was ≤0.5 mm, as compared with clean days (especially in the raindrop size distribution n1, which accounted for 2.3 %) (Fig. 11b). On the other hand, a decreased proportion when raindrop size was >0.5 mm was observed during polluted days, as compared with clean days. These results indicate that if precipitation is lower than or equal to 1 mm h-1 (i.e., light rain), an abundant amount of CCN drives raindrops to move towards smaller drop sizes, which increases the appearance of drizzle drops.
Multiyear (2005–2017) differences between polluted and clean days as percentages of the cumulative droplet number distribution for (a) all data and (b) the data with precipitation less than or equal to 1 mm h-1. The droplet size bin information of the x axis is the same as Fig. 10.
A modeling study (Huang et al., 2007) revealed that the second indirect
effect of aerosols (a large number of small droplets are generated by
enhanced aerosols and reduce the precipitation efficiency) significantly
reduces fall and winter precipitation from 3 % to 20 % across East
Asia, although it was dependent on the auto-conversion scheme assumed. In
this study, we used observational data (i.e., JWD) to analyze the difference
between the average daily rainfall of polluted and clean days in different
CWP groups and explored whether the increase in aerosol loading inhibits
precipitation. Figure 12a demonstrates that the daily rainfall difference between polluted and clean days varies greatly for CWP groups 1–7, which may be small sample numbers in those CWP groups. However, the average daily rainfall on clean days consistently exhibited higher values in CWP groups 8–10 compared with polluted days. In CWP group 9 (150≤CWP<297), the daily average rainfall on polluted days (1.4 mm) decreased by 6.8 mm compared with clean days (8.2 mm).
Multiyear (2005–2017) (a) mean rainfall in different CWP groups
calculated for clean and polluted days, and (b) average hourly rainfall rate calculated for clean and polluted days according to CWP group 9 (150≤CWP<297) only. Rainfall analyses were performed from 10:00, and the PM2.5 data were averaged from 10:00 to 14:00 as daily PM2.5.
Furthermore, we analyzed the hourly rainfall rate of CWP group 9 (150≤CWP<297) for clean and polluted days to explore the effect of
aerosol on cloud lifetime. Figure 12b illustrates the rainfall rate trends for clean and polluted days. On clean days, rainfall was randomly distributed throughout the entire day with a notably larger rainfall rate observed after 04:00, whereas no rainfall was observed during daytime on polluted days, and a relatively weak rainfall rate started early in the night. Although the existence of an aerosol effect on cloud lifetime is still widely disputed (Small et al., 2009; Stocker, 2014), our preliminary results show that precipitation might be suppressed and delayed under high aerosol loading. Combined with the results from Sect. 3.2, the process in the aerosol–cloud–precipitation interactions is consistent with
the cloud lifetime effect. The presence of aerosols enhances the
concentration of condensation nuclei under a fixed water content, which
increases the cloud droplet number and redistributes cloud water to more
numerous and smaller droplets, reducing collision–coalescence rates, which
in turn suppresses precipitation and delays rainfall occurrence (i.e., the
cloud lifetime effect; Albrecht, 1989; Pincus and Baker, 1994; Lohmann
and Feichter, 2005). Our results provide evidence of this and other aerosol
indirect effects over a highly populated island in the western Pacific.
Conclusions
Numerous studies have explored aerosol–cloud–precipitation interactions in marine stratocumulus clouds based on in situ observations, satellite observations, and models; however, few studies have investigated clouds over a dense population and complex topography area. In this study, we integrated numerous aerosol, cloud, and precipitation data from satellite and surface observations to quantify the effects of aerosols on low-level warm-cloud
microphysics and precipitation over northern Taiwan, an urban area in the
northwestern Pacific Ocean. A 13-year (2005–2017) dataset with a selected timeframe (15 October to 30 November) was used in this study. In contrast to
previous studies that have focused on the rainfall rate, we investigated
changes in raindrop size distribution as the key variable in the effect of
aerosols on precipitation.
We used surface PM2.5 mass concentration data as an aerosol proxy to study
the aerosol impacts on clouds and precipitation. According to the PM2.5
concentration level, the data were split into clean and polluted days. The
analysis of aerosol effects on clouds indicated that in CWP group 9 (150≤CWP<297), the average COT in the main research area
increased by 9.53; CER decreased by 2.77 µm; CF increased by 0.07; and CTT decreased by 1.28 K on polluted days compared with clean days. According to the aerosol indirect effect, polluted atmospheric conditions are
connected with clouds characterized by lower CER and CTP and larger CF and
COT, which our results further support. Regarding the vertical distribution,
our evidence shows that excess aerosols produced more liquid particles at
lower altitude and inhibited the cloud droplet size under polluted
conditions. Moreover, the effects of aerosol on cloud microphysics in
polluted (i.e., land) and remote (i.e., ocean, less polluted) areas were
investigated in CWP group 9: the ACI value of the remote area was 0.09, and
the polluted area was 0.06. The ACI value in the remote area was larger than
in the polluted area, indicating that clouds in the remote area were more
sensitive to aerosol indirect effects.
Our analysis shows that precipitation might be suppressed and delayed under
high aerosol loading. The observational data show higher aerosol
concentration redistributed cloud water to more numerous and smaller
droplets under a constant liquid water content, reducing
collision–coalescence rates, which further suppressed the precipitation and
delayed rainfall duration. Our results are consistent with the cloud
lifetime effect. Finally, we combined the observation of raindrop size
distribution to complete the story of aerosol–cloud–precipitation
interactions. As a result, on polluted days compared to clean days, droplet
numbers decreased for smaller droplets bins but increased for larger
droplets. However, when we looked into the category of light rain (≤1 mm h-1), high concentration of aerosols drove raindrops towards smaller
droplet sizes and increased the appearance of drizzle drops.
Our observational results from northern Taiwan in fall show agreement with
the aerosol indirect effects. However, we did not consider the aerosol
direct radiative effects or long-term variations caused by different weather
systems. Overall, this study used long-term surface and satellite data for a
preliminary understanding of aerosol variations in northern Taiwan, the
effects of aerosol on the environment, and the effects of aerosols on
precipitation. We suggest that further research on
aerosol–cloud–precipitation interactions over this area should be
conducted to fully understand these processes.
Data availability
The satellite data from the MODIS instrument used in this study were obtained from https://ladsweb.modaps.eosdis.nasa.gov/search/ (last access: 22 February 2021; Levy and Hsu, 2015, 10.5067/MODIS/MYD04_L2.061; Platnick et al., 2015,
10.5067/MODIS/MYD06_L2.061).
The meteorological and PM2.5 observation data were available from
the Taiwan EPA (2021) at https://data.epa.gov.tw/dataset/aqx_p_02 (last access: 2 March 2021; data are available on request by contacting Wei-Wen Hsieh at weiwen.hsieh@epa.gov.tw). JWD
data in this study were provided by the Planetary Boundary Layer and Air
Pollution Lab of the Department of Atmospheric Sciences, National Central
University, Taiwan (PBLAP, 2021: http://pblap.atm.ncu.edu.tw/weather10.asp, last access: 22 February 2021; data are available on request by contacting PBLAP group at office@pblap.atm.ncu.edu.tw).
Author contributions
SW, QM, and SL provided guidance on the data processing. YC performed the data analysis. YC, SW, QM, SL, PL, NL, KC, and EJ interpreted and discussed the data results. YC wrote the paper with input from all coauthors. All authors contributed to the final paper.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
This research was supported by the US–Taiwan PIRE program, which itself is supported
by the Ministry of Science and Technology (grant no. MOST
104-2923-M-008-003-MY5) and US National Science Foundation (contract no. PIRE-1545917, managed by Everette Joseph and Pay-Liam Lin), and the Ministry of Science and Technology (grant no. MOST 108-2111-M-008-025). We thank NASA for providing the MODIS data and Taiwan
EPA for providing the air quality and meteorological data.
Financial support
This research has been supported by the Ministry of Science and Technology, Taiwan (grant nos. 104-2923-M-008-003-MY5 and MOST 108-2111-M-008-025), and US National Science Foundation (contract no. PIRE-1545917).
Review statement
This paper was edited by Philip Stier and reviewed by two anonymous referees.
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