Air pollution is a major issue in China and one of the
largest threats to public health. We investigated future changes in
atmospheric circulation patterns associated with haze events in the Beijing
region and the severity of haze events during these circulation conditions from 2015 to 2049 under two different aerosol scenarios: a maximum
technically feasible aerosol reduction (MTFR) and a current legislation
aerosol scenario (CLE). In both cases greenhouse gas emissions follow the
Representative Concentration Pathway 4.5 (RCP4.5). Under RCP4.5 with CLE
aerosol the frequency of circulation patterns associated with haze events
increases due to a weakening of the East Asian winter monsoon via increased
sea level pressure over the North Pacific. The rapid reduction in
anthropogenic aerosol and precursor emissions in MTFR further increases the
frequency of circulation patterns associated with haze events, due to
further increases in the sea level pressure over the North Pacific and a
reduction in the intensity of the Siberian high. Even with the aggressive
aerosol reductions in MTFR periods of poor visibility, represented by above-normal aerosol optical depth (AOD), still occur in conjunction with
haze-favorable atmospheric circulation. However, the winter mean intensity
of poor visibility decreases in MTFR, so that haze events are less dangerous
in this scenario by 2050 compared to CLE and relative to the current
baseline. This study reveals the competing effects of aerosol emission
reductions on future haze events through their direct contribution to
pollutant source and their influence on the atmospheric circulation. A
compound consideration of these two impacts should be taken in future policy
making.
Introduction
The increases in aerosol and precursor emissions in China due to the rapid
economic development and urbanization in recent decades have caused more
frequent and severe haze events. Beijing and the surrounding area is the
most polluted region in China (Niu et al., 2010; Ding and Liu, 2014; An et
al., 2019; Chen and Wang, 2015). Air pollution has become one of the major
issues in China and the greatest threat to public health. Since the
implementation of the “Atmospheric Pollution Prevention and Control Action
Plan” in 2013 (China State Council, 2013), aerosol emissions have
dramatically decreased, with sulfur dioxide (SO2) reduced by 59 % in
2017 compared to 2013 (Zheng et al., 2018). However, haze events have still
occurred regularly in recent years, as, in addition to being influenced by
aerosol emissions, meteorological conditions, including limited scavenging,
dispersion and ventilation, have been found to play important roles in the
variation in air quality in northern China (An et al., 2019; Pei et al.,
2018; Cai et al., 2017). Such events are typically associated with the
occurrence of large-scale atmospheric circulation patterns favoring the
accumulation of pollutants (Chen and Wang, 2015; Zhang et al., 2014).
Locally, a strong temperature inversion in the lower troposphere, weak
surface winds, and subsiding air in the planetary boundary layer are
favorable for the development and persistence of haze events (Wu et al.,
2017; Feng et al., 2018). As anthropogenic aerosol has the potential to
induce changes in the atmospheric circulation, in addition to making a
direct contribution to the chemical composition of haze, it is crucial to
understand how changes in aerosol emissions might contribute to the
frequency and intensity of haze events in future.
On interannual timescales, the East Asian winter monsoon (EAWM) is
significantly negatively correlated with aerosol concentrations in Beijing,
due to the associated high frequency of extreme anomalous southerly episodes
in North China, a weakened East Asian trough in the mid-troposphere and a
northward shift of the East Asian jet stream in the upper troposphere (Jeong
and Park, 2017; Li et al., 2016; Pei et al., 2018). The cold air process
over Beijing is favorable for pollutant dispersion and transport outside
because of the accompanying large near-surface wind speed and deep mixing
layer. A low occurrence of cold air processes in the recent winters of 2013,
2014 and 2017 has resulted in severe pollution (He et al., 2018). In the
past decades, the weakening of the EAWM was found to contribute to the
increased frequency of haze events over North China (Chen and Wang, 2015; An
et al., 2019). Arctic sea ice extent also has been linked to increased
stability over eastern China, explaining 45 %–67 % of the
interannual to interdecadal variability of winter haze days over eastern
China (Wang et al., 2015). Overall, around half of the variability in the
frequency of haze events in Beijing is controlled by meteorological
conditions, while both meteorological conditions and aerosol emissions
contribute to the intensity (Pei et al., 2020). Internal climate variability
has contributed to the rapid increase in early winter haze days in North
China since 2010 (Zhang et al., 2020).
Anthropogenic forcing, estimated by using large-ensemble runs with and
without anthropogenic forcings, has also increased the probability of the
atmospheric patterns conducive to severe haze in Beijing by weakening the
EAWM (Li et al., 2018). Projections based on Coupled Model Intercomparison
Project Phase 5 (CMIP5) models showed that weather conditions conducive to
haze events in Beijing or eastern China will increase with global warming
(Horton et al., 2012, 2014), due to an increased occurrence of stagnation
days in response to both accelerated Arctic ice melting (Cai et al., 2017;
Liu et al., 2019a) and a continued weakening of EAWM (Pei and Yan, 2018; Liu
et al., 2019a). If there is no change in aerosol emission in future,
increased stagnation days and decreased light-precipitation days associated
with global warming would also cause an increase in air pollution days in
eastern China (Chen et al., 2019). Regional climate model simulations under
the Representative Concentration Pathway 4.5 (RCP4.5) scenario showed that the air environment carrying capacity, a
combined metric measuring the capacity of the atmosphere to transport and
dilute pollutants, tends to decrease in the 21st century across China
(Han et al., 2017). However, there is a large uncertainty in future aerosol
emission pathways, with uncertainty around the sign of the change in global
emission rate as well as choice of haze index and internal climate
variability (Scannell et al., 2019; Callahan et al., 2019; Callahan and
Mankin, 2020). Furthermore, changes in aerosol emission may influence the
haze-favorable atmospheric circulation, in addition to their role in haze
composition.
The interplay between the role of aerosol as a constituent of haze and as a
potential driver of changes in the circulation patterns conducive to haze has yet to be explored. If the rapid reductions in aerosol and precursor
emissions currently underway in China continue in future, understanding the
balance between the different influences of anthropogenic aerosol forcing on
haze events is a key question. Typically, anthropogenic aerosol (AA) and
greenhouse gases (GHGs) both vary in the future (e.g., those following the
RCPs or shared socioeconomic pathways), which can make their relative
contributions difficult to determine. In this work, we examine future
scenarios with the same GHGs emission pathway but different aerosol pathways
in order to separate the role of AA forcing. We address the following two
questions. (1) Do the atmospheric conditions conducive to haze events change
differently under different AA scenarios? (2) If so, how does AA forcing modulate
the frequency of haze-favorable circulation and how does the severity of the haze
events change?
The remainder of the paper is organized as follows: we briefly introduce the
experiment design and methods in Sect. 2 and show the atmospheric
circulation patterns conducive to Beijing haze events in Sect. 3.
Projected Beijing haze events under two different aerosol emissions and the
underlying mechanism of projected circulation changes will be given in
Sect. 4. We will finally provide the summary and discussion in Sect. 5.
Experiments and methodsData and experiment design
We use observed daily visibility, relative humidity and wind speed from 1974
to 2013 from the National Climatic Data Center (NCDC) Global Surface Summary
of the Day (GSOD) database (Fig. S1a). Haze days are defined as days with
daily visibility less than 10 km, relative humidity less than 90 % and
surface wind speed less than 7 m s-1 (Chen and Wang, 2015). The
observed haze occurrence is the number of haze days, and observed haze
intensity is defined as the minimum 3 d consecutive visibility (VN3day).
Spatial distributions of winter mean haze occurrence and VN3day are shown in
Fig. S1b and c. Data from the Japanese 55-year Reanalysis (JRA55; Kobayashi et
al., 2015) dataset for the period 1958–2013 are used in this study to
evaluate the model representations of the present-day climate. The
variations in haze index derived from JRA-55 are highly consistent with
those from NCEP–NCAR reanalysis (not shown). We only use JRA-55 in this
study.
Simulations with the Met Office Unified Model (Global Coupled configuration
2) HadGEM3-GC2 (Williams et al., 2015) and the NOAA Geophysical Fluid
Dynamics Laboratory (GFDL) Climate Model version 3 (GFDL-CM3; Donner et al.,
2011; Griffies et al., 2011) are used to investigate the impact of different
aerosol forcing scenarios. HadGEM3-GC2 is run with a horizontal resolution
of N216 (∼60 km) in the atmosphere and 1/4∘ in the ocean. GFDL-CM3 has a horizontal resolution of
∼200 km in the atmosphere and 1∘ in the
ocean. Both models include a representation of aerosol–cloud interactions
(Ming et al., 2006; Bellouin et al., 2011).
Three sets of experiments were carried out with each model (Table S1): a
historical experiment from 1965 to 2014 and two experiments for the future
(2015–2050). In the historical experiment, greenhouse gases and
anthropogenic aerosol and precursor emissions are taken from CMIP5 (Lamarque
et al., 2010; Taylor et al., 2012). The future experiments have common GHG
emissions following the RCP4.5 scenario but different aerosol emission
pathways. The aerosol pathways are the current legislation emissions (CLEs)
and the maximum technically feasible reduction (MTFR) taken from the ECLIPSE
V5a global emission dataset (Amann et al., 2015, https://iiasa.ac.at/web/home/research/researchPrograms/air/ECLIPSEv5a.html, last access: 9 May 2021).
In CLE, anthropogenic aerosol emissions are assumed to evolve following the
current legislation, resulting in a moderate global increase by 2050. In
contrast, MTFR assumes a full implementation of the most advanced technology
presently available to reduce aerosol emissions by 2030, which results in
their rapid global decrease over this period. The regional changes in AA for
the baseline (His), CLE and MTFR can be found in Scannell et al. (2019) and Luo et al. (2020).
We use 1984–2013 as His and 2015–2049 as the future period and
display anomalies between the two. Compared with His, CLE shows a dramatic
increase in SO2 over Asia, with peak values over India (not shown) and
eastern China (Fig. S2a). MTFR has similar changes over Europe to CLE,
negligible changes over India (not shown) and a dipole over China, with a
weak increase to the north and a decrease to the south (Fig. S2b). Thus, a
dramatic decrease in SO2 in MTFR relative to CLE is seen over the whole
Asian continent, particularly over the Beijing region (Fig. S2c).
Haze weather index and East Asian winter monsoon index
We focus on haze events during the winter (December–February) around Beijing, where Chinese haze events are most frequent and severe (Niu et al., 2010;
Chen and Wang, 2015). In this study, we use the haze weather index (HWI)
proposed by Cai et al. (2017) as it has also been shown to have a strong
relationship with PM2.5 concentrations in Beijing.
The HWI comprises three constituent terms representing the vertical
temperature gradient in the troposphere (ΔT), the 850 hPa meridional
wind (V850) and the north–south shear in the 500 hPa zonal wind (U500)
(see boxes and lines in Fig. 1). ΔT is calculated as the difference
between the 850 hPa temperature averaged over 32.5∘–45∘ N, 112.5∘–132.5∘ E and the 250 hPa temperature averaged over 37.5∘–45∘ N,
122.5∘–137.5∘ E. V850 is the 850 hPa meridional wind
averaged over the broader Beijing region (30∘–47.5∘ N,
115∘–130∘ E), and U500 is a latitudinal difference
between the 500 hPa zonal wind averaged over a region to the north of
Beijing (42.5∘–52.5∘ N, 110∘–137.5∘ E) and a region to the south (27.5∘–37.5∘ N, 110∘–137.5∘ E). Each of the three
terms is normalized by their standard deviation over the reference period
(here 1984–2013). The three variables are added together to create the HWI,
which is then normalized again by its standard deviation over the reference
period. A positive HWI represents conditions that are unfavorable to
air-pollutant dispersion, and days with HWI >0 are regarded as
“haze events”. The HWI defined by Cai et al. (2017) made use of daily
data. Due to the unavailability of model data at daily resolution, we instead
used monthly data. The reliability of using HWI calculated from monthly mean
variables will be discussed in Sect. 3 based on reanalysis.
Composite circulation anomalies from JRA-55 with
HWI-daily >0(a, c, e) and HWI-month ≥1 (b, d, f) for 1958–2013.
(a, b) Temperature (K) along 40∘ N; (c, d) 500 hPa winds (vector, m s-1) and their zonal component (shading, m s-1). Panels (e, f): 850 hPa
winds (vector, m s-1) and their meridional component (shading, m s-1). The green boxes and lines indicate the regions used to calculate the
three components of HWI.
The strength of the EAWM is quantified using the index defined by Wang and
Chen (2014). This index takes into account both the east–west and the
north–south pressure gradients and is defined as
EAWM=(2×SLP1-SLP2-SLP3)/2,
where SLP1, SLP2 and SLP3 represent normalized sea level
pressure (SLP) averaged over Siberia (40–60∘ N, 70–120∘ E), the North Pacific (30–50∘ N, 140∘ E–170∘ W) and the Maritime Continent (20∘ S–10∘ N,
110–160∘ E), respectively (see the boxes in Fig. S3). The three
components are converted to anomalies and normalized by their standard
deviation over the reference period (here 1984–2013). As the EAWM is
directly linked to the occurrence of favorable conditions for haze in
Beijing (Pei et al., 2018; Liu et al., 2019b; Hori et al., 2006), we
therefore use this index as an additional metric to assess the potential
changes in future haze events under the CLE and MTFR scenarios and confirm
the robustness of the changes indicated by HWI.
Significance test
To test whether projected winter mean HWI change and frequency of month with
HWI ≥1 are statistically significant, we estimated internal variability
by performing a Monte Carlo approach (Zhang and Delworth, 2018). We first
randomly select a 90-month (to mimic the December–January–February, DJF, months for 1984–2013) period
from all simulations of the baseline and calculate the time-mean HWI and
frequency of months with HWI ≥1 of this sample. Then, we calculate
differences between this sample and the ensemble mean of the baseline. The
differences result only from internal climate variability. We repeat the
first step 5000 times, and the 5000 bootstrapped samples can be viewed as
internal variability of the baseline. For the future projections, we did a similar calculation as for the baseline but by randomly selecting a 105-month
period (to mimic DJF months for 2015–2049) from the projection and calculate its
difference with the baseline. We then compare the medium anomalies of the future
projection with the ranges of the bootstrapped samples. When the median from the future projection falls outside the interquartile range of the baseline, we then
claim that the projected changes are statistically significant (Wilcox et
al., 2020). We also employed a two-sample Kolmogorov–Smirnov test to
determine if the probability density function (PDF) distributions are
significantly different (Chakravarti et al., 1967).
Favorable climatic conditions for Beijing haze events in reanalysis
The circulation anomalies averaged over the days with daily HWI >0 are shown in Fig. 1a, c and e. The vertical temperature profile shows warmer
air at the lower to mid-levels, centered around 850 hPa, and cold anomalies
aloft 250 hPa (Fig. 1a). Thus, the atmosphere is stable and unfavorable for the
vertical dispersion of pollutants. In the midlatitudes (500 hPa), we see
northward-shifted mid-level westerly jets (Fig. 1c). The weakened westerly
winds along 30∘ N inhibit the horizontal dispersion of pollutants
in Beijing. At the lower-level, the anomalous southerly winds at 850 hPa
along the East Asian coast lead to a reduction in the prevailing surface
cold northerlies in winter (Fig. 1e). This reduction favors warmer conditions
at lower levels and increased moisture over Beijing, thus increasing the
likelihood of haze formation and maintenance.
The HWI was defined based on daily data. Due to limitations in data
availability, we instead used monthly data to calculate HWI. To determine
the reliability of this approach, we first examined the relationship between
the magnitude of HWI calculated from monthly data (HWI-month) and the number
of days with daily HWI (HWI-daily) >0 in the JRA-55 reanalysis
during the period 1958–2013 (Fig. 2a, b). The variability of HWI-month is
highly consistent with that of the number of days with HWI-daily >0
(r=0.97). When HWI-month is greater than 0, about 50 % days in that
month are recognized with HWI-daily >0 and up to 62 % days
with HWI-daily >0 when HWI-month ≥1. In this study, we
define favorable climatic conditions of haze events around Beijing as a
month where HWI-month ≥1.
Changes in winter HWI from 1958 to 2013 in JRA-55 reanalysis
relative to 1958–2013 winter mean. (a) DJF mean monthly-based HWI
(HWI-month, black line) and the anomalous days with daily based
HWI >0 (HWI-daily; red line; unit: day); (b) scatterplot of
HWI-month of December, January and February (y axis) and the ratio of days
with HWI-daily >0 (x axis) in each winter month. HWI-month and
HWI-daily are the HWI calculated from monthly data and daily data,
respectively. Panels (c–d) show the anomalies of haze occurrence and the VN3day
when HWI ≥1, where VN3day is the minimum 3 d consecutive visibility.
Hatched area in (c–d) is statistically significant at the 10 % level using
a Student's t test.
We also checked the observed winter haze occurrence and intensity (VN3day)
anomalies when HWI-month ≥1. More haze occurrence and reduced
visibility are observed over North China, indicating the reliability of
using HWI-month ≥1 as a proxy of the favorable climatic conditions for
the haze events in Beijing and the surrounding region. The selection of a
higher threshold of HWI-month (e.g., 1.5) does not make a great difference to
our results (not shown). The circulation anomalies averaged over HWI-month ≥1 (Fig. 1b, d, f) and HWI-daily >0 (Fig. 1a, c, e) are
also consistent with each other, except that the anomalies for HWI-month ≥1 are weaker, as would be expected. The spatial and temporal consistency of
HWI anomalies calculated from monthly and daily data confirms the
suitability of our use of monthly data to explore changes in the frequency
of Beijing haze events associated circulation. In the following sections, we
will use the term HWI to indicate HWI-month for brevity.
Changes in Beijing haze events under two AA emission scenariosChanges in the frequency of haze-favorable circulation
patterns
Both HadGEM3-GC2 and GFDL-CM3 simulate well the key spatial features of the
large-scale atmospheric circulation in winter when compared to JRA-55 for
1984–2013 (Fig. S4). Key features include the westerly jet along
30∘ N, the East Asian trough and northerly winds along the East
Asian coast, which are caused by the zonal thermal contrast and subsequent
pressure gradient between the North Pacific and the Eurasian continent. The
models can also reliably capture the vertical temperature difference, the
weaker East Asian trough and the anomalous 850 hPa southerly winds
associated with haze events (Figs. S5 and 1). The good performance of
HadGEM3-GC2 and GFDL-CM3 in simulating the winter monsoon and haze-favorable
circulation justifies the use of these two models to estimate HWI changes.
There is a large interannual variability in HWI and no significant trend in
HWI either in His, CLE or MTFR (not shown). However, the two models both
show an increase in the mean HWI with no consistent change in the standard
deviation (Fig. 3a, c). The mean HWI in His (1984–2013), CLE (2015–2049) and
MTFR (2015–2049) is 0.00, 0.26 and 0.50 in HadGEM3-GC2. In GFDL-CM3 it is
0, 0.32 and 0.41. There is a slight increase in the standard deviation of
HWI in HadGEM3-GC2 from His (1.0) and CLE (1.0) to MTFR (1.06), while no
change is seen in GFDL-CM3. The occurrence of positive HWI in CLE and MTFR
increases relative to His in both models. In both models, the PDF
distributions of HWI in His and CLE are significantly different at the 1 %
level using a Kolmogorov–Smirnov test. For the distributions of HWI in CLE
and MTFR, they are also significantly different at the 1 % level in
HadGEM3-GC2 but not in GFDL-CM3. The changes in the frequency of different
HWI can be found from the cumulative distribution function (CDF) of HWI
(Fig.3b, d). The frequency of HWI ≥1 for His, CLE and MTFR is
∼16 % (16 %), 22 % (25 %) and 30 % (29 %) in
HadGEM3-GC2 (GFDL-CM3), respectively. If AA emissions follow the CLE
scenario, the frequency of month with HWI ≥1 will increase by 6 %
and 9 % in HadGEM3-GC2 and GFDL-CM3, respectively. The rapid reduction in
AA emissions in MTFR contributes to an extra 4 %–8 % increase
in HWI relative to CLE in both models.
(a) Probability density function (PDF) via a non-parametric
density estimation, kernel density estimation and (b) cumulative
distribution function (CDF) distributions of HWI in winters of His
(1984–2013, grey), CLE (2015–2049, blue) and MTFR (2015–2049, pink)
simulated by HadGEM3-GC2. Panels (c–d) show results for GFDL-CM3. The numbers in
(a) and (c) are the climate mean of HWI, and in (b) and (d) they are the
frequency of month with HWI ≥1, respectively.
We used a Monte Carlo approach to test whether the changes in winter mean
HWI and frequency of months with HWI ≥1 among His, CLE and MTFR are
significantly different from each other (Fig. 4). The time-mean HWI and
frequency (HWI ≥1) in CLE and MTFR are both statistically different from
that in His in the two models. We also see samples in CLE and MTFR change
beyond the range of His in both models, although only in HadGEM3-GC2
simulations is the time-mean HWI in MTFR statistically significant from that
in CLE (Fig. 4a). An examination of the future changes in each component of
the HWI is shown in Fig. S6. Similar changes with HWI are found in all three
components except in V850 in GFDL-CM3. The PDF distributions of all the
component terms of the His are statistically different from CLE and from
MTFR at the 5 % level in both models by using a two-sample
Kolmogorov–Smirnov test, while the distributions in CLE and MTFR are
significantly different in HadGEM3-GC2 only, consistent with our conclusion
based on the Monte Carlo approach (figures not shown). The changes in the
three components of HWI demonstrate the atmospheric conditions favoring haze
events all become more likely with global warming and that future AA
reductions may further increase their likelihood.
Box plots for the 5000 bootstrapped samples of (a) changes
in winter mean HWI and (b) frequency of month with HWI ≥1 in
HadGEM3-GC2 and GFDL-CM3. The grey, blue and pink boxes are results
estimated from His, CLE and MTFR, respectively. Boxes show the interquartile
ranges of the 5000 bootstrapped samples, and black lines show the median.
End points are the 5th and 95th percentiles. A significant difference is seen
when the median from one experiment falls outside the interquartile range of
another.
Possible mechanism for atmospheric circulation changes
To investigate the mechanism underlying these changes in Beijing
haze-favorable circulation frequency, we present the changes in the vertical
temperature profile, and spatial patterns of 850 and 500 hPa winds in
Figs. 5–7. The lower- and mid-troposphere displays an incremental warming
from His to MTFR compared to the upper levels in both models. The peak
warming is at 700 hPa and over 120∘–130∘ E. Conversely,
both models simulate an upper-tropospheric cooling at 250 hPa in CLE
compared to His, albeit of smaller magnitude than the warming below
(Fig. S7). However, the 250 hPa temperature changes between MTFR and CLE
differ in the two models (Figs. 5b, d and S7g, h). Thus, the increase in
tropospheric stability in MTFR relative to CLE is mainly driven by low-level
warming.
The difference in winter mean temperature (K) along
40∘ N (a, c) between CLE (2015–2049) and His (1984–2013) and
(b, d) between MTFR (2015–2049) and CLE (2015–2049). The dotted areas are
statistically significant at the 10 % level using a Student's t test. The
green lines indicate the level and longitude used in the calculation of
ΔT.
Spatial distribution for the difference in winter mean 850 hPa winds (vector, m s-1) and 850 hPa meridional component (shading, m s-1) (a, c) between CLE (2015–2049) and His (1984–2013) and (b, d)
between MTFR (2015–2049) and CLE (2015–2049). The dotted areas denote the
850 hPa meridional winds statistically significant at the 10 % level using
a Student's t test. The black box indicates the region used in the
calculation of V850.
Same as Fig. 6 but for the difference in 500 hPa winds
(vector, m s-1) and 500 hPa zonal component (shading, m s-1). The
black boxes indicate the regions used in the calculation of U500.
Following the CLE aerosol pathway, both HadGEM3-GC2 and GFDL-CM3 project an
anomalous 850 hPa cyclonic circulation over the northwestern Pacific
(0-20∘ N, 120–180∘ E) relative to His and an
anticyclonic anomaly to its north (20–50∘ N, 120–180∘ E)
(Fig. 6a, b). This pattern bears some resemblance to the anomalous circulation
associated with a positive phase of the Arctic Oscillation, which may be due
to melting Arctic sea ice (Shindell et al., 1999; Fyfe et al., 1999; Wang et
al., 2020). The southerly wind anomalies over eastern China, on the western
flank of the anomalous anticyclone, act to weaken the East Asian winter
monsoon and reduce its low-level winds, making conditions favorable for
air-pollutant transport from south to north and air-pollutant accumulation
more likely. With the addition of rapid AA reductions following MTFR, the
850 hPa circulation anomalies are reinforced further (Fig. 6c, d), especially
in HadGEM3-GC2, which simulates much stronger southerly wind anomalies along
the East Asian coast. GFDL-CM3 shows similar anomalies over the North
Pacific in CLE vs. His and MTFR vs. His but distinct responses over China
(Fig. 6d), which likely explains why GFDL-CM3 does not simulate the further
shift in HWI seen in HadGEM3-GC2 between CLE and MTFR (Fig. S6c, f). A
northeasterly anomaly is seen over southeast China in GFDL-CM3 in both CLE
relative to His and MTFR relative to CLE. However, the onshore flow over
Beijing seen in CLE relative to His, which is likely to be a key contributor
to an increase in haze weather events, is not enhanced further by the rapid
aerosol reductions in MTFR (Fig. 6d).
At 500 hPa, a northward shift of the westerly jet stream is projected in CLE
relative to the current baseline, with significant positive zonal wind
anomalies along 50∘ N and negative anomalies along 30∘ N
in both models (Fig. 7a, b). This shift is consistent with the increase in the
meridional temperature gradient over the North Pacific (Fig. S7). Thus, the
East Asian winter trough is weakened, bringing less cold and dry air to the
Beijing area and favoring the formation and maintenance of haze events. The
reductions in AA emissions in MTFR relative to CLE significantly strengthen
the abovementioned circulation anomalies at 500 hPa in both models (Fig. 7c, d) and further increase the frequency of positive U500 differences in
the regions used to calculate the HWI, as seen in Fig. 7c and d. The changes in
500 hPa zonal winds are consistent between the two models, demonstrating the
robustness of the results.
The changes in the three components of HWI in CLE relative to His indicate a
weakened EAWM with increased GHGs, with reductions in AA emissions further
amplifying this effect and increasing the frequency of large-scale
circulation conditions conducive to Beijing haze events. To explore how the
EAWM circulation responds to reductions in AA emissions, we show surface
temperature and sea level pressure changes in MTFR relative to CLE (Fig. 8).
Reduced AA emissions generally amplify the impact of greenhouse gases, with
more warming over the Arctic, the Eurasian continent and the northwestern
Pacific. Thus, the Aleutian low is further weakened in MTFR. In addition,
more warming over the Eurasian continent and northwestern Pacific leads to an SLP decrease over Siberia and the northwestern Pacific, respectively. The
main difference between the two models is found from the SLP changes over
the Eurasian continent in the midlatitudes, where large negative SLP
anomalies are presented in HadGEM3-GC2, while there are no changes in
GFDL-CM3. This may lead to the less westward shift of the North Pacific
anomalous anticyclonic circulation in GFDL-CM3 in Fig. 6d.
The difference of the climate mean surface temperature (a, c, e,
K) and sea level pressure (b, d, f, hPa) between MTFR and CLE simulated by
(a, b) HadGEM3-GC2 and (c, d) GFDL-CM3. The dotted areas in (a–d) are
statistically significant at the 10 % level using a Student's t test.
Panels (e, f) are same as Fig. 4 but for changes in the climate mean EAWM and the
frequency of EAWM ≤-1 in His (1984–2013, grey), CLE (2015–2049, blue)
and MTFR (2015–2049, pink).
The changes in EAWM, using the Wang and Chen (2014) index, in His, CLE and
MTFR are shown in Fig. 8e and f. The EAWM weakens in CLE compared to His (blue
and grey boxes in Fig. 8e, f), mainly due to increased SLP over the North
Pacific (SLP2, Fig. S8b), with no systematic or significant changes in
SLP over Siberia (SLP1) and the Maritime Continent (SLP3)
(Fig. S8a, c). The rapid AA reductions in MTFR cause the SLP over Siberia to
decrease consistently in both models alongside a further increase in
SLP2. The changes in SLP2 (SLP1) are statistically
significant at the 5 % (10 %) level in both models tested by performing
bootstrapped samples (Fig. S8a, b). This further weakens the east–west
contrast, leading to a weaker EAWM in MTFR relative to CLE, consistent with
the differences between CLE and His and between MTFR and CLE seen in the
HWI. The response of SLP over the Maritime Continent (SLP3) to AA
reductions differs between the two models, indicating a large uncertainty in
the SLP3 changes. Thus, the AA forcing reduction predominantly weakens
the EAWM through reducing the zonal thermal contrast.
Changes in haze intensity associated with favoring
circulation
Occurrence of a haze event requires stagnant atmospheric conditions and
also a pollution source. Although future aerosol reductions may cause
further increases in the frequency of atmospheric circulation patterns
currently linked with haze events, such events may become less severe in the
absence of large aerosol emissions. In this section, we will examine the
projected changes in the intensity of Beijing haze events using the aerosol
optical depth (AOD) at 550 nm as a metric for aerosol-induced poor
visibility. The simulated baseline winter mean AOD around the Beijing area is
shown in Fig. 9a and c. To account for model differences in historical AOD, we
used the ratio of AOD at 550 nm (hereafter AOD_ratio) relative
to a baseline winter mean to represent the air-pollution severity. When
AOD_ratio is greater than 1.0, the air-pollution intensity is
higher than the baseline climate mean. HadGEM3-GC2 and GFDL-CM3 both simulate
elevated AOD around Beijing when circulation conditions are favorable
(HWI ≥1) (Fig. 9b, d): 1.5 and 1.3 times the baseline climate mean in
HadGEM3-GC2 and GFDL-CM3, respectively. Aerosol and precursor emission
increases under CLE (Fig. S1) result in a significant increase in climate
winter mean AOD around Beijing in HadGEM3-GC2 (1.1 times) but no significant
change in GFDL-CM3, and climate mean AOD in MTFR decreases to 0.84 and 0.90
of the baseline climate mean around Beijing in HadGEM3-GC2 and GFDL-CM3,
respectively, due to aerosol emissions reduction (Fig. S9).
Winter mean (a, c) AOD at 550 nm in (a) HadGEM3-GC2 and (c)
GFDL-CM3 averaged over 1984–2013. Panels (b, d) are the same as (a, c) but for the mean
AOD_ratio in the winter months with HWI ≥1 (hereafter
AOD_ratio(HWI ≥1)) in His. Blue and red shadings in (b)
and (d) are decreased and elevated AOD relative to the climate winter mean
of His, respectively.
To check whether poor-air-quality events still occur even with reduced
future aerosol emissions, we show the projected AOD_ratio
with HWI ≥1 in Fig. 10. In CLE, when HWI ≥1, AOD_ratio
is elevated compared to the baseline climatology, to 1.5 times the
baseline winter mean in HadGEM3-GC2 and 1.1 times that in GFDL-CM3 (Fig. 10a, c). It is consistent with the increase in aerosol loadings and climate
mean AOD in CLE (Figs. S2a and S9a, b). However, in MTFR, when HWI ≥1,
AOD is slightly higher (AOD_ratio is around 1.1) or
comparable with that of the baseline climatology, albeit with a decrease in
climate mean AOD in MTFR (Fig. 10b, d). So, even with the aggressive aerosol
reductions in MTFR, periods of poor visibility still occur in conjunction
with atmospheric circulation patterns associated with haze in the current
climate.
Same as Fig. 9b and d but for the results projected in CLE
and MTFR. The dotted areas are statistically significant at the 10 % level
using a Student's t test.
(a) PDF and (b) CDF distributions of AOD_ratio(HWI ≥1) over North China (33–45∘ N, 105–122∘ E, box in Fig. 2) in HadGEM3-GC2. Panels (c, d) are the results from GFDL-CM3. The
grey, blue and pink vertical lines and numbers in (a) and (c) are the winter
mean AOD_ratio(HWI ≥1) of His, CLE and MTFR,
respectively. The numbers in (b) and (d) are the cumulative probability of
AOD_ratio(HWI ≥1) higher than the winter mean
AOD_ratio(HWI ≥1) of His.
We calculated the PDF distributions of AOD_ratio surrounding
the Beijing region (box region in Fig. 2) in the months with HWI ≥1 in
His, CLE and MTFR (Fig. 11). In His, the area-averaged AOD_ratio around the Beijing region when HWI ≥1 is elevated to 1.40 (1.24)
times the baseline climate mean in HadGEM-GC2 (GFDL-CM3) (Fig. 11a, b). The
change in AOD_ratio with HWI ≥1 under CLE relative to His
is different between the two models. It increases to 1.45 in HadGEM3-GC2 but
decreases to 1.06 in GFDL-GC3. As expected, the AOD_ratio
with HWI ≥1 in MTFR reduces in both models due to the dramatic reduction
in anthropogenic aerosols. Thus, the mean air-pollution intensity with the
favorable circulation conditions for haze under MTFR will be greatly
relieved. This reduction in GFDL-CM3 under CLE relative to His may be a
reflection of the model's bias. In JRA-55 when HWI ≥1, there are
southerly anomalies over southern China. However, in the baseline in
GFDL-CM3 there is an anomalous cyclonic circulation, which may act to reduce
pollutant accumulation in Beijing (Fig. S5). As shown in Fig. 6b and d, this
anomaly is strengthened in both CLE and MTFR.
To check whether extreme air pollution events would still occur, the
probability of AOD_ratio when HWI ≥1 in the three
scenarios are examined (Fig. 11b, d). In this study, the mean
AOD_ratio across all months when HWI ≥1 in His is
regarded as the winter mean intensity of baseline haze events, i.e., the
grey vertical lines in Fig.11a and c. The probability of haze event intensity
exceeding this threshold is about 44 % and 39 % in HadGEM3-GC2 and
GFDL-CM3, respectively (Fig. 11b, d). Under CLE, it increases to 44 % in
HadGEM3-GC2, while decreases to 23 % in GFDL-CM3, consistent with Fig. 10a and c. In MTFR, a lower probability is projected in both models: 18 % in
HadGEM3-GC2, and 19 % in GFDL-CM3. This demonstrates that severe events
(i.e., higher AOD_ratio) would still happen in MTFR, albeit
with a dramatic reduction in anthropogenic aerosol, even though the mean
intensity of haze events themselves will become less dangerous if aerosol
emissions are reduced.
Summary and discussion
During recent decades, with rapid increases in aerosol and precursor
emissions in China, air pollution has become one of the greatest threats to
public health. Anthropogenic aerosol contributes not only to the chemical
composition of haze but also has the potential to modulate atmospheric
circulation changes. Thus, this paper aims to quantify the incidences of
haze events in a future climate and the influence of aerosol mitigation
efforts. In this study, we examined the changes in the frequency of
atmospheric conditions conducive to haze events around the Beijing region, and
the changes in aerosol optical depth (AOD) during these circulation
conditions through the mid-21st century under two different
anthropogenic aerosol scenarios using two climate models: HadGEM3-GC2 and
GFDL-CM3. We also investigated the mechanism for the changes in the
large-scale atmospheric circulation.
We found that future greenhouse gases (GHG) increases and anthropogenic
aerosol (AA) increases following a current legislation aerosol scenario
(CLE) will increase the frequency of haze-favorable atmospheric circulation
conditions surrounding the Beijing region. The frequency of the haze weather
index (HWI)≥1 derived from monthly data in HadGEM3-GC2 (GFDL-GCM3)
increases from ∼16 % (16 %) at the baseline to
∼22 % (25 %) for 2015–2049 under the CLE scenario. By
comparing the scenario with a maximum technically feasible aerosol reduction
(MTFR), which has the same GHG increases but rapid aerosol reductions, we
show that future aerosol reductions may further amplify the increase in the
frequency of such circulation patterns. Rapid reductions in AA emissions in
MTFR contribute to an extra increase in HWI ≥1 in two models.
The increase in haze frequency in CLE is mainly due to a weakening of the
East Asian winter monsoon, warming of the lower troposphere and weakening
of the East Asian trough, which is likely to be predominantly driven by the
GHG increases. Reduced AA forcing in MTFR could further enhance the above
circulation anomalies and amplify the impact of greenhouse gases. Because
the AA emission reductions in MTFR relative to CLE mainly occur over
continental Asia, the Asian landmass receives more shortwave radiation,
leading to a warmer surface temperature there. This leads to a weaker
Siberian high and further contributes to the weakening of the East Asian
winter monsoon in MTFR.
The analysis of haze intensity based on AOD at 550 nm shows that visibility
with HWI ≥1 is always lower than the baseline winter mean under both CLE
and MTFR. With more reduction in aerosol emissions following the MTFR, the
mean intensity of haze events in the haze-favorable atmospheric circulation
will become less dangerous compared to that in His and CLE in both models.
Meanwhile, the probability of haze event with intensity exceeding the
baseline mean also decreases in MTFR, demonstrating that severe haze events
would also occur in MTFR.
This paper reveals the competing impacts of AA emission reductions on
haze-favorable circulation and haze intensity surrounding Beijing. AA
reductions cause an increased frequency of atmospheric circulation patterns
conducive to haze events but a reduction in the haze intensity when these
circulation patterns do occur. Internal variability may not be fully sampled
because of the limited number of realizations and models used in this study. In
addition, the role of single forcing is not discussed here due to both
changes in AA and GHGs in the CLE and MTFR experiments. We thus further tested the roles of AA forcing in driving the HWI changes during 2015–2050 using
“all-but-one-forcing” initial-condition large ensembles (LEs) with the Community Earth System Model (CESM1)
(Deser et al., 2020; Kay et al., 2015, Table S2 and Fig. S10 in
the Supplement). The large number of ensemble members enables an estimation
of internal variability and an estimation of the signals of regional
response to AA or GHG forcing from the noise of model's internal
variability. Comparing the winter mean HWI of the baseline, it increases
under RCP8.5, and both the decrease in AA and the increase in GHG contribute to the
projected higher HWI and more frequent HWI ≥1.0 (Fig. S10). The response
to the decrease in AA is significant, as seen from the medium of changes in the
projected winter-mean HWI and frequency of month, with HWI ≥1 falling
outside the upper quartile of internal variability (Fig. S10). The signal-to-noise ratio (SNR), defined as the ratio of changes in the multi-member ensemble (MME) mean relative to spread
across the changes in ensemble members, is higher than 1.0 (1.44) for HWI
change when only AA forcing changes in the future (XGHG: ensemble without time-evolving greenhouse gases), consistent with
the results derived from HadGEM3-GC2 and GFDL-CM3. The results from CESM LEs
give additional support for the main findings of this study, highlighting
the substantial impacts of aerosol forcing for future changes in the
atmospheric conditions favoring haze events. A detailed examination of the
role of single anthropogenic forcing and of the impact of internal
variability is needed in the future.
We revealed that the capability of the models in representing haze-favorable
large-scale circulations may impact the simulation of AOD, which introduces
further uncertainties in the future projection of AOD. Model evaluation on
haze-favorable circulation and associated AOD is necessary for future
projections. Our results are consistent with previous studies that global
warming and more reduction in aerosol forcing causing extra warming will
make haze-favorable conditions around the Beijing area more frequent (Callahan
and Markin, 2020). Large uncertainty also exists in the projection of AOD
and pollutants associated with haze events. Better representation in aerosol
parameters and processes could provide a more reliable way to project haze event.
Code availability
The National Climatic Data Center (NCDC)
Global Surface Summary of the Day (GSOD) database can be downloaded from the
GSOD website (https://catalog.data.gov/dataset/global-surface-summary-of-the-day-gsod; GSOD, 2021).
The JRA-55 reanalysis data can be freely downloaded from the rda.ucar.edu
website (https://rda.ucar.edu/datasets/ds628.0/; JRA-55, 2021). Requests for
outputs of the His, CLE and MTFR experiments, or any questions regarding the
data, can be directed to the corresponding author, Lixia Zhang (lixiazhang@mail.iap.ac.cn).
The supplement related to this article is available online at: https://doi.org/10.5194/acp-21-7499-2021-supplement.
Author contributions
LZ designed and wrote the paper with
support from all authors. LJW and MAB helped design the analysis and
supervised the work. NJD and DJP ran the simulations. SH analyzed the
reanalysis data. DL and LZ contributed to the validation of
observational metrics.
Competing interests
The authors declare that they have no conflict
of interest.
Acknowledgements
This work was jointly supported by the Ministry of
Science and Technology of China under grant 2018YFA0606501 and the National
Natural Science Foundation of China under grant no. 41675076. Laura J. Wilcox, Massimo Bollasina and
Jonathan K. P. Shonk were supported by the UK-China Research & Innovation Partnership
Fund through the Met Office Climate Science for Service Partnership (CSSP)
China as part of the Newton Fund. Liwei Zou is supported by National Natural
Science Foundation of China under grant no. 41830966.
Financial support
This research has been supported by the Ministry of Science and Technology of China under grant 2018YFA0606501, the National Natural Science Foundation of China (grant nos. 41675076 and 41830966) and the Newton Fund (UK–China Research & Innovation Partnership Fund through the Met Office Climate Science for Service Partnership – CSSP, China).
Review statement
This paper was edited by Toshihiko Takemura and reviewed by two anonymous referees.
References
Amann, M., Bertok, I., Borken-Kleefeld, J., Cofala, J., Heyes, C.,
Hoglund-Isaksson, L., Kiesewetter, G., Klimont, Z., Schöpp, W.,
Vellinga, N., and Winiwarter, W.: Adjusted historic emission data, projections,
and optimized emission reduction targets for 2030 – A comparison with COM
data 2013, Part A: Results for EU-28, TSAP Report #16A, version 1.1.,
IIASA, Laxenburg, Austria, 2015.An, Z., Huang, R., Zhang, R., Tie, X., Li, G., Cao, J., Zhou, W., Shi, Z.,
Han, Y., Gu, Z., and Ji, Y.: Severe haze in northern China: A synergy of
anthropogenic emissions and atmospheric processes, P. Natl. Acad. Sci. USA, 116,
8657–8666, 10.1073/pnas.1900125116, 2019.Bellouin, N., Rae, J., Jones, A., Johnson, C., Haywood, J., and Boucher, O.:
Aerosol forcing in the Climate Model Intercomparison Project (CMIP5)
simulations by HadGEM2-ES and the role of ammonium nitrate, J. Geophys.
Res., 116, D20206, 10.1029/2011JD016074, 2011.
Cai, W., Li, K., Liao, H., Wang, H., and Wu, L.: Weather conditions conducive to
Beijing severe haze more frequent under climate change, Nat. Clim. Change., 7, 257–62, 2017.Callahan, C. W. and Mankin, J. S.: The influence of internal climate
variability on projections of synoptically driven Beijing haze, Geophys. Res. Lett., 46, e2020GL088548. 10.1029/2020GL088548,
2020.Callahan, C. W., Schnell, J. L., and Horton, D. E.: Multi-index attribution
of extreme winter air quality in Beijing, China, J. Geophys. Res.-Atmos., 124, 4567–4583.
10.1029/2018JD029738, 2019.
Chakravarti, I. M., Laha, R. G., and Roy, J.: Handbook of Methods of Applied Statistics,
Volume I, John Wiley and Sons, New York, 392–394, 1967.Chen, H. and Wang H.: Haze Days in North China and the associated atmospheric
circulations based on daily visibility data from 1960 to 2012. J. Geophys.
Res.-Atmos., 120, 5895–5909, 10.1002/2015JD023225, 2015.Chen, H., Wang, H., Sun, J., Xu, Y., and Yin, Z.: Anthropogenic fine particulate matter pollution will be exacerbated in eastern China due to 21st century GHG warming, Atmos. Chem. Phys., 19, 233–243, 10.5194/acp-19-233-2019, 2019.China State Council: Action Plan on Prevention and Control of Air Pollution,
China State Council, Beijing, China,
available at: http://www.gov.cn/zwgk/2013-09/12/content_2486773.htm (last access: 17 January 2021), 2013.
Deser, C., Phillips, A., Simpson, I., Rosenbloom, N., Coleman D., Lehner, F., and Pendeergrass, A.: Isolating the Evolving Contributions
of Anthropogenic Aerosols and Greenhouse Gases: A New CESM1 Large Ensemble
Community Resource, J. Clim., 33, 7835–7858, 2020.
Ding, Y. and Liu, Y.: Analysis of long-term variations of fog and haze in
China in recent 50 years and their relations with atmospheric humidity, Sci.
China Earth Sci., 57, 36–46, 2014.Donner, L., Wyman, B., Hemler, R., Horowitz, L., Ming, Y., Zhao, M., Golaz, J., Ginoux, P., Lin, S.-J., Schwarzkopf, M., Austin, J., Alaka, G., Cooke, W., Delworth, T., Freidenreich, S., Gordon, C., Griffies, S., Held, I., Hurlin, W., Klein, S., Knutson, T., Langenhorst, A., Lee,H.-C., Lin, Y., Magi, B., Malyshev, S., Milly, P., Naik, V., Nath, M., Pincus, R., Ploshay, J., Ramaswamy, V., Seman, C., Shevliakova, E., Sirutis, J., Stern, W., Stouffer, R., Wilson, R., Winton, M., Wittenberg, A., and Zeng, F.: The
dynamical core, physical parameterizations, and basic simulation
characteristics of the atmospheric component of the GFDL global coupled
model CM3, J. Clim., 24, 3484–3519, 10.1175/2011JCLI3955.1, 2011.Feng, J., Quan, J., Liao, H., Li, Y., and Zhao, X.: An Air Stagnation Index
to Qualify Extreme Haze Events in Northern China, J. Atmos. Sci., 75, 3489–3505, 10.1175/JAS-D-17-0354.1, 2018.
Fyfe, J., Boer, G., and Flato, G.: The Arctic and Antarctic oscillations and
their projected changes under global warming, J. Geophys. Res., 26, 1601–1604, 1999.
Griffies, S., Winton, M., Donner, L., Horowitz, L., Downes, S., Farneti, R., Gnanadesikan, A., Hurlin, W., Lee, H.-C., Liang, Z., Palter, J., Samuels, B., Wittenberg, A., Wyman, B., Yin, J., and Zadeh, N.: The GFDL CM3 Coupled Climate
Model: Characteristics of the Ocean and Sea Ice Simulations, J. Clim., 24, 3520–3544, 2011.GSOD: The National Climatic Data Center (NCDC)
Global Surface Summary of the Day (GSOD) database, available at: https://catalog.data.gov/dataset/global-surface-summary-of-the-day-gsod, last access: 9 May 2021.Han, Z., Zhou, B., Xu, Y., Wu, J., and Shi, Y.: Projected changes in haze pollution potential in China: an ensemble of regional climate model simulations, Atmos. Chem. Phys., 17, 10109–10123, 10.5194/acp-17-10109-2017, 2017.
He, J., Gong, S., Zhou, C., Lu, S., Wu, L., Chen, Y., Yu, Y., Zhao, S., Yu, L., and Yin, C.: Analyses of winter circulation types and
their impacts on haze pollution in Beijing, Atmos. Environ., 192,
94–103, 2018.Horton, D., Harshvardhan, and Diffenbaugh, N.: Response of air stagnation
frequency to anthropogenically enhanced radiative forcing, Environ. Res.
Lett., 7, 044034, 10.1088/1748-9326/7/4/044034, 2012.Horton, D., Skinner, C. B., Singh, D., and Diffenbaugh, N.: Occurrence and
persistence of future atmospheric stagnation events, Nat. Clim. Change,
4, 698–703, 10.1038/NCLIMATE2272, 2014.
Hori, M. E. and Ueda, H.: Impact of global warming on the East Asian winter
monsoon as revealed by nine coupled atmosphere-ocean GCMs, Geophys. Res. Lett., 33, L03713, 2006.Jeong, J. and Park, R.: Winter monsoon variability and its impact on
aerosol concentrations in East Asia, Environ. Pollut., 221, 285–292, 10.1016/j.envpol.2016.11.075, 2017.JRA-55: JRA-55 reanalysis data, available at: https://rda.ucar.edu/datasets/ds628.0/, last access: 9 May 2021.Kay, J. E., Deser, C., Phillips, A., Mai, A., Hannay, C., Strand, G., Arblaster, J. M., Bates, S.C., Danabasoglu, G., Edwards, J., Holland, M., Kushner, P., Lamarque, J.-F., Lawrence, D., Lindsay, K., Middleton, A., Munoz, E., Neale, R., Oleson, K., Polvani, L., and Vertenstein, M.: The community Earth system model (CESM) large ensemble project: a community resource for studying climate change in the presence of internal climate variability, B. Am. Meteorol. Soc., 10.1175/BAMS-D-13-00255.1, 2015.Kobayashi, S., Ota, Y., Harada, Y., Ebita, A., Moriya, M., Onoda, H., Onogi, K., Kamahori, H., Kobayashi, C., Endo, H., Miyaoka, K., and Takahashi, K.: The JRA-55 reanalysis: general specifications
and basic characteristics, J. Meteorol. Soc. Jpn.,
93, 5–48, 10.2151/jmsj.2015-001, 2015.Lamarque, J.-F., Bond, T. C., Eyring, V., Granier, C., Heil, A., Klimont, Z., Lee, D., Liousse, C., Mieville, A., Owen, B., Schultz, M. G., Shindell, D., Smith, S. J., Stehfest, E., Van Aardenne, J., Cooper, O. R., Kainuma, M., Mahowald, N., McConnell, J. R., Naik, V., Riahi, K., and van Vuuren, D. P.: Historical (1850–2000) gridded anthropogenic and biomass burning emissions of reactive gases and aerosols: methodology and application, Atmos. Chem. Phys., 10, 7017–7039, 10.5194/acp-10-7017-2010, 2010.
Li, Q., Zhang, R., and Wang, Y.: Interannual variation of the wintertime
fog-haze days across central and eastern China and its relation with East
Asian winter monsoon, Int. J. Climatol., 36, 346–354,
2016.Li, K., Liao, H., Cai, W., and Yang, Y.: Attribution of anthropogenic
influence on atmospheric patterns conducive to recent most severe haze over
eastern China, Geophys. Res. Lett., 45, 2072–2081.
10.1002/2017GL076570, 2018.Liu, C., Zhang, F., Miao, L., Lei, Y., and Yang, Q: Future haze events in
Beijing, China: When climate warms by 1.5 and 2.0 ∘C, Int. J. Climatol., 40,
3689–3700, 2019a.
Liu, Z., Ming, Y., Wang, L., Bollasina, M., Luo, M., Lau, N.-C., and Yim, S.: A Model Investigation of Aerosol Induced Changes in the East
Asian Winter Monsoon, Geophys. Res. Lett., 46, 10186–10195, 2019b.Luo, F., Wilcox, L., Dong, B., Su, Q., Chen, W., Dunstone, N., Li, S., and Gao, Y.: Projected near-term changes of
temperature extremes in Europe and China under different aerosol emissions,
Environ. Res. Lett., 15, 034013, 10.1088/1748-9326/ab6b34, 2020.
Ming, Y., Ramaswamy, V., Donner, L., and Phillips, V.: A robust
parameterization of cloud droplet activation, J. Atmos. Sci., 63,
1348–1356, 2006.Niu, F., Li Z., Li, C., Lee, K.-H., and Wang, M.: Increase of wintertime fog
in China: Potential impacts of weakening of the eastern Asian monsoon
circulation and increasing aerosol loading, J. Geophys. Res., 115, D00K20,
10.1029/2009JD013484, 2010.Pei, L. and Yan, Z.: Diminishing clear winter skies in Beijing towards a
possible future, Environ. Res. Lett., 13, 124029, 10.1088/1748-9326/aaf032, 2018.Pei, L., Yan, Z., Sun, Z., Miao, S., and Yao, Y.: Increasing persistent haze in Beijing: potential impacts of weakening East Asian winter monsoons associated with northwestern Pacific sea surface temperature trends, Atmos. Chem. Phys., 18, 3173–3183, 10.5194/acp-18-3173-2018, 2018.Pei, L., Yan, Z., Chen, D., and Miao, S.: Climate variability or anthropogenic
emissions: which caused Beijing Haze?, Environ. Res. Lett., 15,
034004, 10.1088/1748-9326/ab6f11, 2020.Scannell, C., Booth, B., Dunstone, N., Rowell, D., Bernie, D., Kasoar, M., Voulgarakis, A., Wilcox, L., Navarro, J., Selan, Ø., and Paynter, D.: The Influence of Remote Aerosol Forcing from
Industrialized Economies on the Future Evolution of East and West African
Rainfall, J. Clim., 32, 8335–8354,
10.1175/JCLI-D-18-0716.1, 2019.
Shindell, D., Miller, R., Schmidt, G., and Pandolfo, L.: Simulation of recent
northern winter climate trends by greenhouse-gas forcing. Nature, 399,
452–455, 1999.
Taylor, K., Stouffer, B., and Meehl, G.: An overview of CMIP5 and the
experiment design, B. Am. Meteorol. Soc., 93, 485–498, 2012.Wang, H., Chen, H., and Liu, J.: Arctic sea ice decline intensified haze
pollution in eastern China, Atmospheric and Oceanic Science Letters, 8, 1–9, 10.3878/AOSL20140081, 2015.
2015.Wang, L. and Chen, W.: An intensity index for the east Asian winter monsoon, J. Clim., 27, 2361, 10.1175/JCLI-D-13-00086.1,
2014.
Wang, Y., Le, T., Chen, G., Yung, Y., Su, H., Seinfeld, J., and Jiang, J.: Reduced European aerosol emissions
suppress winter extremes over northern Eurasia, Nat. Clim. Change, 10,
225–230, 2020.Wilcox, L. J., Liu, Z., Samset, B. H., Hawkins, E., Lund, M. T., Nordling, K., Undorf, S., Bollasina, M., Ekman, A. M. L., Krishnan, S., Merikanto, J., and Turner, A. G.: Accelerated increases in global and Asian summer monsoon precipitation from future aerosol reductions, Atmos. Chem. Phys., 20, 11955–11977, 10.5194/acp-20-11955-2020, 2020.
Williams, K. D., Harris, C. M., Bodas-Salcedo, A., Camp, J., Comer, R. E., Copsey, D., Fereday, D., Graham, T., Hill, R., Hinton, T., Hyder, P., Ineson, S., Masato, G., Milton, S. F., Roberts, M. J., Rowell, D. P., Sanchez, C., Shelly, A., Sinha, B., Walters, D. N., West, A., Woollings, T., and Xavier, P. K.: The Met Office Global Coupled model 2.0 (GC2) configuration, Geosci. Model Dev., 8, 1509–1524, 10.5194/gmd-8-1509-2015, 2015.Wu, P., Ding, Y., and Liu, Y.: Atmospheric circulation and dynamic mechanism for
persistent haze events in the Beijing–Tianjin–Hebei region, Adv. Atmos. Sci., 34, 429–40, 10.1007/s00376-016-6158-z, 2017.Zhang, H. and Delworth, T. L.: Robustness of anthropogenically forced
decadal precipitation changes projected for the 21st century, Nat. Commun., 9,
1150, 10.1038/s41467-018-03611-3, 2018.Zhang, R., Li, Q., and Zhang, R.: Meteorological conditions for the persistent
severe fog and haze event over eastern China in January, Science China Earth
Sciences, 57, 26–35, 10.1007/s11430-013-4774-3, 2014.Zhang, Y., Yin, Z., and Wang, H.: Roles of climate variability on the rapid increases of early winter haze pollution in North China after 2010, Atmos. Chem. Phys., 20, 12211–12221, 10.5194/acp-20-12211-2020, 2020.Zheng, B., Tong, D., Li, M., Liu, F., Hong, C., Geng, G., Li, H., Li, X., Peng, L., Qi, J., Yan, L., Zhang, Y., Zhao, H., Zheng, Y., He, K., and Zhang, Q.: Trends in China's anthropogenic emissions since 2010 as the consequence of clean air actions, Atmos. Chem. Phys., 18, 14095–14111, 10.5194/acp-18-14095-2018, 2018.