Summer surface O3 pollution has rapidly
intensified in China in the recent decade, damaging human and ecosystem
health. In 2017, the summer mean maximum daily average 8 h concentration of
ozone was greater than 150 µg m-3 in North
China. Based on the close relationships between the O3
concentration and the meteorological conditions, a daily surface O3
weather index was constructed, which extends the study period to the
historical period before 2007 and the projected future. Here, we show that in
addition to anthropogenic emissions, the Eurasian teleconnection
pattern (EU), a major globally atmospheric teleconnection pattern, influences
surface O3 pollution in North China on a timescale of climate. The
local meteorological conditions associated with the EU positive phase
supported intense and efficient photochemical reactions to produce more
surface O3. The associated southerlies over North China transported
surrounding O3 precursors to superpose local emissions. Increased
solar radiation and high temperatures during the positive EU phase
dramatically enhanced O3 production. Furthermore, due to the close
connection between the preceding May Arctic sea ice (SI) and summer EU
pattern, approximately 60 % of the interannual variability in
O3-related weather conditions was attributed to Arctic sea ice to
the north of Eurasia. This finding will aid in understanding the interannual
variation in O3 pollution, specifically the related meteorological
conditions.
Introduction
Over the past several decades, due to fast economic development, air
pollution has been increasing in China (Chen, 2013; Watts et al., 2018). The
major air pollution types in China are haze pollution (i.e., high-level fine
particulate matter) in winter (Yin et al., 2015; Wang, 2018) and surface
ozone (O3) pollution in summer (Ma et al., 2016; Tang et al., 2018). Due
to drastic air pollution control in China since 2013, haze pollution has been
controlled in recent years (the environmental statistics unit of a statistics center
in Peking University, 2018), appearing as a sharp decrease in fine
particulate matter. However, surface O3 pollution, which always
occurs on clear and sunny days (Wang et al., 2017), has not improved (Li et
al., 2018). The negative effects of surface O3 pollution, such as
corroding human lungs and destroying agricultural crops and forest
vegetation, were not weaker than those of haze (Liu et al., 2018), but the
impacts of climate variability on surface O3 pollution in China
(Yang et al., 2014) have not been sufficiently studied. In the major urban
agglomerations in China, such as Beijing-Tianjin-Hebei, the Yangtze River Delta
and the Pearl River Delta, the surface O3 concentrations exceeded
the ambient air quality standard of China (100 µg m-3) by
100 %–200 % (Wang et al., 2017). In the Yangtze River Delta, the
interannual variations in NO and O3 levels generally presented
decreasing and increasing trends, respectively, from 2012 to 2015, at both
urban and suburban sites (Tong et al., 2017). Furthermore, the concentration
of O3 and its precursors, e.g., nitrogen oxides
(NOx) and volatile organic compounds (VOCs), in
Beijing-Tianjin-Hebei was significantly larger than that in other regions of
China (Wang et al., 2006; Shi et al., 2015). Revealed by the datasets from
Shangdianzi Station, the long-term trend of O3 concentrations in
North China indicated that the O3 pollution underwent a
significant increase in the period 2005–2015, with an average rate of
1.13±0.01 ppb yr-1 (Ma et al., 2016).
Surface O3 is a secondary pollutant. The precursors of O3
photochemically react with sunlight to generate O3 under suitable
weather conditions, i.e., hot-day and sunny environments (An et al., 2009).
Surface deposition, dynamic transport and dispersion of O3 are also
closely related to atmospheric circulations. For example, the prevailing
positive phase of the North Atlantic Oscillation contributed to the
increasing ozone concentration in western and northern Europe through the
anomalous atmospheric circulations that influence regional photochemical
processes (Christoudias et al., 2012; Pausata et al., 2012). The summer
surface O3 variability in North America is significantly modulated
by the position of the jet stream (Lin et al., 2014). Barnes and Fiore (2013)
pointed out jet position may dynamically modulate surface ozone variability
in eastern North America and other northern midlatitude regions. A strong
positive correlation between the East Asian summer monsoon and summer mean
ozone were found by model simulations (Yang et al., 2014), illustrating that
the changes in meteorological parameters, associated with East Asian summer
monsoon, lead to 2 %–5 % interannual variations in surface
O3 concentrations over central eastern China. Focusing on the
dataset in 2014, a significantly strong western Pacific subtropical high resulted in higher relative humidity, more clouds,
more rainfall, less ultraviolet radiation and lower air temperatures, which
were unfavorable for the formation of O3 (Zhao and Wang, 2017). The
photochemical reaction was the main local source of O3 (Sun et al.,
2019). The hot and dry environments and the intense solar radiation could
accelerate the chemical conversion from the precursor to O3 (An et
al., 2009; Tong et al., 2017). In 2013, a severe heatwave with a highest
temperature of 41.1 ∘C, contributed to the high O3
concentration in the Yangtze River Delta (Pu et al., 2017). The frequency of
large-scale, extreme heat events is closely related to atmospheric patterns,
such as the Eurasian teleconnection pattern (EU; Pu et al., 2017; Li and Sun,
2018) and aerosol effective radiative forcing (Liu and Liao, 2017). The winds
from a polluted area also transport O3 and its precursors downwind
(Doherty et al., 2013). Due to the close relationship between surface
O3 and meteorological conditions, the impacts of climate change on
O3 have been projected by various numerical models (Doherty et al.,
2013; Melkonyan and Wagner, 2013; Zhu and Liao, 2016; Gaudel et al., 2018).
Over eastern China, the surface ozone concentration and possibility of severe
ozone pollution may both increase in the future (Wang et al., 2013).
However, previous studies of O3 pollution in China mainly focused
on observational analyses of several synoptic processes (e.g., Zhao and Wang,
2017), rather than long-term climate diagnostics, because of the lack of
long-term surface O3 observations. The goal of this study is to
examine the large-scale atmospheric circulations associated with the
interannual variation in summer surface O3 pollution in North China
based on long-term meteorological observations. The role of May Arctic sea
ice (ASI), as a preceding and effective driver, is also analyzed. The
outcomes of our research, in terms of climate variability, may provide a
basis for understanding the interannual variation in O3 pollution,
specifically the related meteorological conditions.
Data and method
The hourly O3 concentration data from 2014 to 2017 in China were
provided by the Ministry of Environmental Protection of China. As one of the
three regional background air-monitoring stations in China, the hourly
O3 concentration data at the Shangdianzi station (SDZ; located at
40.7∘ N, 117.1∘ E; and 293.3 m a.m.s.l.) were
continuously observed from 2006 to 2017 and were controlled by the National
Meteorological Information Center, China Meteorological Administration.
According to the Technical Regulation on Ambient Air Quality Index of China
(the Ministry of Environmental Protection of China, 2012), the maximum daily
average 8 h concentration of ozone (MDA8) was used to represent the daily
O3 conditions. The MDA8 was calculated as the maximum of the
running 8 h mean O3 concentrations during 24 h in the day.
However, the systematic observation duration of the surface O3
concentration was much shorter than the meteorological measurements and could
not support the climate analysis.
The monthly sea ice (SI) concentrations (1∘×1∘)
were downloaded from the Met Office Hadley Centre (Rayner et al., 2003), which
are widely used in sea ice-related analysis. The sea ice fields are made more
homogeneous by compensating satellite microwave-based sea ice concentrations
for the impact of surface melt effects on retrievals in the Arctic and by
making the historical in situ concentrations consistent with the satellite
data. The gridded sea ice data were available from 1870 to date, and those
from 1979 to 2018 were extracted here.
The 1∘×1∘ ERA-Interim data used here included
the geopotential height (Z), zonal and meridional wind, relative humidity,
vertical velocity, air temperature from 1000 to 100 hPa, boundary layer
height (BLH), surface air temperature (SAT) and wind, downward UV radiation,
downward solar radiation, low and medium cloud cover, and precipitation (Dee
et al., 2011). The daily mean and monthly mean ERA-Interim data from 1979 to
present were directly downloaded from the ERA-Interim website in this study.
Furthermore, the daily mean and monthly reanalysis datasets supported by the
National Oceanic and Atmospheric Administration were also employed and
denoted as NCEP/NCAR (National Centers for Environmental Prediction and the
National Center for Atmospheric Research) data. The 2.5∘×2.5∘ geopotential height (Z), zonal and meridional wind, relative
humidity, vertical velocity, air temperature from 1000 to 100 hPa, SAT
and wind, downward UV radiation, downward solar radiation, and low and medium
cloud cover were downloaded, which were available from 1948 to present (Kalnay
et al., 1996). The BLH dataset was only available from 1979 to 2014 in the
website of the NARR Monthly Averages (Giese et al.,
2016). The daily precipitation data were from the CPC global analysis of the
daily precipitation dataset (Chen et al., 2008).
The EU pattern is a major teleconnection pattern in the Northern Hemisphere
and appears in all seasons. Wang and Zhang (2015) used the method defined by
Wallace and Gutzler (1981) to calculate the EU pattern index in winter and
pointed out that the positive EU phase is associated with a cold and dry climate
in East China, and vice versa. Meanwhile, Wang and He (2015)
regarded the summer EU pattern as the main reason for the severe summer
drought in North China in 2014. Considering the seasonal change of the EU
pattern's location, the calculation procedure for the summertime EU index was
consistent with that in Wang and He (2015), i.e., Eq. (1):
EUindex=-1×H500‾70-80∘N,60-90∘E+2×H500‾45-55∘N,90-110∘E-1×H500‾35-45∘N,120-140∘E/4,
where H500 represents the geopotential height at 500 hPa, and overbars
denote the area average.
The generalized additive model (GAM), a data-driven method, is particularly
effective at handling the complex nonlinear and non-monotonous relationships
between the dependent variable and the independent variables (Hastie and
Tibshirani, 1990). This approach used a smoothing function, determined by the
independent variables themselves, to transform the expressions and addressed
the dependent variable with different probability distributions by the link
function. To verify the connection between the Arctic sea ice and the
O3 pollution, the Community Atmosphere Model version 5.3 (CAM5;
Meehl et al., 2013) was employed to design numerical experiments. The spatial
resolution employed was 0.9∘×1.25∘, with 30 vertical
hybrid sigma-pressure levels. CAM5.3 uses vertical hybrid delta-pressure
coordinates, including 26 layers with the top located at about 3.5 hPa. The
climatological mean sea surface temperature and sea ice taken from the Hadley
Centre were used to force the control run.
Summer ozone pollution and associated weather conditions
Due to increased surface O3 pollution in China, the number of
O3 measurement stations has dramatically increased since 2014
(Fig. 1a, c, e, g). During 2006–2014, O3 concentrations were only
observed in the most developed regions in China. Since 2015, O3
concentrations have been measured in most areas in eastern China.
O3 concentrations in the high latitudes to midlatitudes were higher than those
in the lower latitudes, which appeared to be separated by the Yangtze River.
The O3 concentrations in North China were already high in 2014; the
summer mean MDA8 in North China was higher than 120 µg m-3.
Observations with a maximum MDA8 higher than 265 µg m-3 (i.e.,
the threshold of the severe surface O3 pollution in China) existed
in the south of Hebei province and in the north of Shandong province (Fig. 1a).
Since that time, the O3-polluted region has expanded. In 2017, the
areas with summer mean MDA8 > 120 µg m-3 were
visibly enlarged. In North China, the summer mean MDA8 observations were
larger than 150 µg m-3, and the maximum MDA8 was nearly
265 µg m-3. South of the Yangtze River, the O3
concentrations were distinctly lower and decreased progressively towards the
Pearl River Delta.
The distribution of the JJA mean MDA8 (a, c, e, g) and the
correlation coefficients (b, d, f, h) between the daily MDA8 and SDZ
MDA8 from 2014 to 2017. The black crosses in (a, c, e, g) indicate
that the maximum daily MDA8 was larger than 265 µg m-3. The
black crosses (b, d, f, h) indicate that the correlation coefficient (CC) was above the
95 % confidence level. The green triangle in (b, d, f, h) illustrates the
location of the Shangdianzi site. The black box in (h) is the location of North China.
Composite of the meteorological conditions associated with different
O3 events during 2007–2017. Results for MOP (a, c, e, g)
and NOP (b, d, f, h) events included (a, b) surface wind
(m s-1; arrow) and v wind (m s-1; shading), (c–d) BLH
(m), (e, f) precipitation (mm), (g–h) SAT (∘C,
shading) and temperature at 200 hPa (∘C; contour). The black dots
denote the composite results that passed the 95 % confidence level. The
boxes represent the area used to calculate OWI. These composites were
calculated using the ERA-Interim dataset. The green triangle in (a, b) illustrates the location of the Shangdianzi site. The composite results
were calculated as the differences between MOP or NOP events and the rest of
the events (i.e., all events, excluding MOP and NOP events).
The variation in the daily observational SDZ MDA8 (black), fitting SDZ MDA8 (red), and OWI (blue)
from June to August during 2007–2017. The numbers are the correlation
coefficients between the observational SDZ MDA8 and fitting SDZ MDA8 (red)
and OWI (blue).
The OWI for MOP (red) and NOP (blue) events during 2007–2017.
The correlation coefficients between the daily MDA8 and OWI from
2014 to 2017. The black crosses indicate that the CC was above the 95 %
confidence level.
The time span of O3 observations (i.e., 2015–2017 for most of the
sites) limited the possibility of determining the role of climate variability
in the interannual O3 variations in North China. Thus, we examined
the representativeness of the O3 measurements at SDZ (one of the
three regional background air-monitoring stations in China, with observations
from 2006–2017). The correlation coefficients between the SDZ MDA8 and the
observed MDA8 at the other sites were calculated and are shown in Fig. 1b, d,
f and h. The distribution of correlation coefficients is similar to the MDA8
in Fig. 1a, c, e and g. The SDZ MDA8 significantly covaried with the MDA8 in
North China in summer. Along with the increasing of the surface O3
pollution, the covariation of SDZ MDA8 and MDA8 in North China strengthens
the representativeness of SDZ for North China. However, the correlation
coefficients between SDZ MDA8 and MDA8 in the south of China were negative,
indicating opposite variation (Zhao and Wang, 2017). The variation in summer
SDZ MDA8 is presented in Fig. S1 in the Supplement. According to the
Technical Regulation on Ambient Air Quality Index in China (The Ministry of
Environmental Protection of China, 2012), we defined the non-O3-polluted (NOP) level at the surface as the O3 concentration
< 100 µg m-3 and the moderate-O3-polluted (MOP) level with O3 concentration
> 215 µg m-3, respectively. The upper and lower
quartile of SDZ MDA8 was 188 and
114 µg m-3, indicating that more than 75 % of summer days
exceeded the NOP threshold even at the regional background air-monitoring
station. During the years 2007–2017, there were 126 NOP days and 155 MOP
days in summer at the SDZ station. The maximum number of MOP days was 26 days in
2015, and the mean number of MOP days was 14 days (Table S1 in the
Supplement). Both the interannual variation in MOP and that in NOP days was
significant at the 95 % confidence level, without an obvious trend.
Due to the significant covariation between the SDZ MDA8 to the MDA8 in North
China, the meteorological conditions were composited for the MOP and NOP days
in SDZ (Fig. 2), and the results were also appropriate for those in North
China. The local and surrounding weather conditions were significantly
different (t test). The anomalous southerlies (Fig. 2a), higher BLH
(Fig. 2c), less rainfall (Fig. 2e), warmer surface air temperature and
cooler temperature in the high troposphere (Fig. 2g) favored surface
O3 pollution. Near the surface, for the polluted conditions, the winds
are northward in North China due to cyclonic anomalies to the west and
anticyclonic flow to the east (Fig. S2a in the Supplement). Anomalous
southerlies from the Yangtze River transported O3 precursors (that
were emitted in the economically developed Yangtze River Delta) and
superposed them with the local high emissions in North China (Fig. 2a). When
the anomalous winds reversed, i.e., northerlies, the O3 precursors
in North China were dispersed, and the surface O3 concentration in
North China was reduced (Fig. 2b). On the upper level, significant
anticyclonic anomalies (Fig. S2c in the Supplement) resulted in sunny days in
summer. A day without rain represents efficient solar radiation, in favor of
the occurrence of surface O3 pollution (Fig. 2e). In contrast, the
cloudy skies and precipitation weakened the photochemistry by influencing
exposure to ultraviolet rays. In addition, precipitation was also an
important indicator of the wet removal efficiency (Fig. 2f). High SAT
enhanced the photochemical reactions and resulted in higher surface
O3 concentrations (Fig. 2g). Differently from the SAT, the
temperature at 200 hPa above North China was significantly negative
(Fig. 2g), dynamically associated with the upper-level anticyclone.
Furthermore, due to the strengthening of solar radiation, the near-surface
turbulence was enhanced, and the boundary layer was lifted (Fig. 2c). The
entrainment of atmospheric ozone from the upper air into the boundary layer
enhanced the surface O3 concentration (An et al., 2009). To confirm
the robustness of the link between meteorological conditions and the MOP and
NOP days over North China, the above composite analysis was repeated with
NCEP/NCAR reanalysis data, and identical results were obtained (Figs. S3 and
S4 in the Supplement).
The variation in the JJA
mean observed SDZ MDA8 (green) from 2006 to 2017, OWI calculated from
ERA-interim datasets during 1979–2017 (blue) and OWI calculated from NOAA
datasets during 1979–2014 (red).
To assess the interannual variation in surface O3 pollution and its
relationship with climate variability (Cai et al., 2017), we fitted an
O3 weather index (OWI) based on long-term meteorological
observations. Firstly, the regional average meteorological elements were
calculated as meteorological indices (Is), and here the selected regions
were determined the most significantly different areas in the composites of MOP
and NOP events in Fig. 2. Then, we defined the OWI as Eq. (2):
OWI=normalizedV10mI+normalizedBI-normalizedPI+normalizedDTI,
where the V10mI is the area-averaged meridional wind at 10 m
(35–50∘ N, 110–122.5∘ E; black box in Fig. 2a), and its
correlation coefficient with the SDZ O3 concentration was 0.39. BI
is an area-averaged
BLH (37.5–47.5∘ N,
112.5–120∘ E; black box in Fig. 2c), and the correlation
coefficient with the SDZ O3 was 0.40. The PI is defined as
area-averaged precipitation (37.5–42.5∘ N, 112–127.5∘ E;
black box in Fig. 2e), whose correlation coefficient with the SDZ
O3 concentration was -0.35 (above the 99 % confidence level).
DTI represents the area-averaged difference in the temperature at the surface
and 200 hPa (SAT minus temperature at 200 hPa; 37.5–47.5∘ N,
110–122.5∘ E; black box in Fig. 2g), and the correlation
coefficient with SDZ O3 concentration was 0.49.
The associated atmospheric circulation. (a) The correlation
coefficients between the JJA mean OWI and surface air temperature (shading),
wind at 200 hPa (arrow) and geopotential height at 500 hPa (contour) from
1979 to 2017. The black dots indicate that the CC with surface air
temperature was above the 95 % confidence level. The cross-section
(110–125∘ E mean) correlation coefficients between JJA mean
OWI (b), EU pattern index (c) and relative humidity
(shading), temperature (contour), and wind (arrow, vertical speed multiplied
by 100) from 1979 to 2017. The black dots indicate that the CC with relative
humidity exceeded the 95 % confidence level (t test). The data used
here are from ERA-Interim datasets.
The variation in the JJA mean observational SDZ MDA8
(µg m-3; blue) and EU index (geopotential metre – gpm; red) from 2007 to 2017.
For comparison, the multiple regression equation was built between the MDA8
and associated weather indices (Fig. 3). Our analysis indicated that the
observed MDA8 was fit well by the multiple regression equation (Fig. 3). The
correlation coefficient was 0.61 between the fit and daily measured MDA8
during 2007–2017 (i.e., 92 days×11 years). The correlation
coefficient between the observed MDA8 and daily OWI was also 0.61 for the
11-year period. Thus, the OWI was easily constructed by accumulating the
normalized weather index and was selected to represent the variation in
surface O3 pollution. A total of 90.3 % of the MOP events were
in the range of OWI > 0, and correspondingly, 90.5 % of the
NOP events were linked with OWI < 0 (Fig. 4). The correlation
coefficients between the OWI and observed MDA8 at the other sites were
calculated (Fig. 5). The significantly positive correlations were distributed
in North China (Fig. 5b–d). Thus, it is reasonable to analyze the variation
in surface O3-related atmospheric circulations in North China using
the OWI, which also extends the study period to the historical period before
2007 and the projected future.
The associated meteorological conditions. (a) The
correlation coefficients between the JJA mean OWI and v wind at 10 m
(shading), surface wind (arrow), (b) relative humidity near the
surface (shading), boundary layer height (contour),
(c) precipitation (shading), downward UV radiation at the surface
(contour), (d) downward solar radiation at the surface (shading),
and sum of low and medium cloud cover (contour) from 1979 to 2017. The black dots
indicate that the CC with temperature was above the 95 % confidence
level. The contours plotted in (b–d) exceeded the 95 %
confidence level. The data used here are from ERA-Interim datasets.
Impacts of EU pattern on the interannual variation in surface ozone
After the assimilation of satellite data, possible in 1979, the quality of
reanalysis data improved. Here, the daily OWI was calculated with both
ERA-Interim and NCEP/NCAR reanalysis data from 1979. According to the above
analysis, the daily OWI could largely represent the variation in MDA8 in
North China. The monthly OWI was computed as the monthly mean of the daily
OWI. During 2007–2017, the constructed JJA (June–July–August) mean OWI
varied similarly with the observed MDA8 and captured the extremes (Fig. 6).
Although the range of the SDZ MDA8 was from 2006 to 2017, only the data from 2007 to
2017 were used in the above OWI construction processes. Thus, the datasets in
2006 were independent samples (i.e., test set) and could verify the
performance of the OWI. The JJA mean OWI in 2006 successfully reflected the
variation in observed MDA8, confirming the robustness of the OWI. Derived
from two different reanalysis datasets, the OWI ERA and OWI NCEP varied
consistently. The above independent verifications proved that the performance
of the summer OWI did not depend on the specific reanalysis data. In the
following study, the monthly OWI from ERA-interim data and associated
physical mechanisms were analyzed. From the mid-1980s to the mid-1990s, the OWI
was below zero, with a slightly decreasing trend and insignificant
interannual variation. Since then, the OWI has increased; furthermore, the
intensity of interannual variation has strengthened. The emissions of
O3 precursors increased persistently and linearly due to the steady
economic development after 1978 in China (Wang, 2017). The strong interannual
variation in the OWI after mid-1990s, representing the impacts of
meteorological conditions on O3 concentrations, contributed to the
interannual fluctuations of the surface O3 pollution. Thus, the
impacts of the large-scale atmospheric circulations on the summer
O3 pollution, especially the related OWI, were analyzed.
The role of the Arctic sea ice. (a) The correlation
coefficients between the JJA mean OWI and May sea ice. (b) The
variation in the May SI index (red bar, area-averaged sea ice of the green
boxes in a), JJA mean EU pattern index (blue bar) and JJA mean
observational SDZ MDA8 (black bar) from 2007 to 2017. (c) The
correlation coefficients between the May SI index and surface air temperature
(shading), and geopotential height at 500 hPa (contour) from 1979 to 2017. The
black dots indicate that the CC with surface air temperature was above the
95 % confidence level. (d) The correlation coefficients between
the May SI index and precipitation (shading), surface wind (arrow),
(e) downward UV radiation at the surface (shading), and sum of low
and medium cloud cover (contour) from 1979 to 2017. The black dots indicate
that the shading CC with precipitation (d) and downward UV radiation
(e) was above the 95 % confidence level. The data used here are
from ERA-Interim datasets.
The variation in the observational OWI (black) and the fitted OWI by
the generalized additive model (GAM; red) from 1979 to 2017.
Composite results of the LowASI experiments (LowASI minus CTRL) by
the CAM5 model: (a) geopotential height at 500 hPa,
(b) preciptation, (c) net radiative flux at the top of the
atmosphere (shading) and temperature at 925 hPa (contour), and
(d) sum of low and medium cloud fraction (shading) and relative
humidity at 925 hPa (contour). The black hatching denotes the differences
with shading that was above the 95 % confidence level (t test).
The atmospheric circulations associated with summer mean OWI, indicated by
the correlation coefficients, are displayed in Fig. 7. In the mid-troposphere to upper troposphere, cyclonic and anticyclonic anomalies were alternately distributed
over the northern Central Siberian Plateau (-), North China and Mongolia
(+), and the Yellow Sea and Sea of Japan (-) (Fig. 7a). These three
atmospheric centers, propagated from the polar region to the midlatitudes,
appeared to be the positive phase of EU pattern (Wang and He, 2015). This
Rossby wave-like train, i.e., the EU pattern, could also be recognized in the
surface air temperature. The correlation coefficient between the EU pattern
index and OWI was 0.44 (after detrending and above the 99 % confidence
level), indicating that the strengthening of the EU positive phase
contributed to the severe surface O3 pollution in North China. More
precisely, the positive phase of EU pattern could modulate the local
meteorological conditions to enhance the photochemical reactions. The EU
pattern is considered to be the main reason for the variability in the severe
drought in North China, i.e., resulting in hot and dry climate extremes (Wang
and He, 2015). To a certain extent, the severe drought environment promoted
the formation of surface ozone. After 2007, the EU index and the
observational SDZ MDA8 showed good agreement (Fig. 8). More than 80 % of
the SDZ MDA8 anomalies showed the same mathematical sign as the anomalous EU
pattern index. Furthermore, the large EU pattern anomalies (i.e., the
EUpatternindex>0.8×its
standard deviation) always induced in-phase surface ozone pollution.
Under barotropic anticyclonic circulation over North China, i.e., one of the
active centers of the positive EU pattern, the significant descending air
flows indicated efficient adiabatic heating (resulting in high temperatures
near the surface) and dry air (i.e., less cloud cover) below 300 hPa
(Fig. 7c). Furthermore, over North China, the air temperature anomalies were negative at
200 hPa but positive below 300 hPa, and the relative humidity anomalies
were positive at 200 hPa but negative below 300 hPa
(Fig. 7c). The barotropic anticyclonic
circulation associated with surface ozone pollution (Fig. 7b) was similar to
the positive EU pattern (Fig. 7c) and led to sunny days, i.e., hot
temperatures (Fig. 7a), strong downward solar radiation and UV radiation
(Fig. 9c, d), less low and medium cloud cover (Fig. 9d), and dry conditions
(Fig. 9b, c). Without the cover of low and medium clouds, the shortwave solar
radiation, especially the UV radiation, penetrated straight to the land
surface. The photochemical reaction of the O3 precursor was
enhanced, generating more O3 near the surface. The dry atmosphere
near the surface, i.e., less precipitation and lower relative humidity,
accelerated the photochemical reaction but restricted the wet clearing of the
stocked O3 in the atmosphere. A higher BLH (Fig. 9b), resulting
from the strengthening of solar radiation, likely facilitated the downward
transportation of O3 aloft. Near the surface, the western part of
these anticyclonic anomalies manifested as significant southerlies (Fig. 9a),
which transported the O3 precursors from the economically developed
Yangtze River Delta. The extraneous O3 precursor, superposed with
local emissions, supported efficient photochemical production of
O3. To confirm the robustness of the atmospheric circulations and
associated physical mechanisms, the above analysis was repeated with the
NCEP/NCAR data, and identical results were obtained (Figs. S5–S6 in the
Supplement). The correspondence between large-scale EU teleconnection and
anticyclonic circulations were clear. Local meteorological conditions, such
as hot land surface (Fig. S5 in the Supplement), violet solar radiation
(Fig. S6c, d in the Supplement), a clear sky (Fig. S6d in the Supplement),
less precipitation (Fig. S6c) and lower relative humidity (Fig. S6b in the
Supplement) were also clearly recognized. Thus, the impacts of the
atmospheric circulations were confirmed by both the ERA-Interim and NCEP/NCAR
data, i.e., the analyses and conclusions were independent of datasets.
Roles of the Arctic sea ice
The positive EU pattern enhanced the local anticyclonic circulation over
North China and facilitated the photochemical processes leading to the
formation of surface ozone. The EU pattern originated from the Arctic region.
The preceding sea ice anomalies could stimulate atmospheric responses like
the EU pattern in summer (Wang and He, 2015) Thus, the role of Arctic sea ice
on the OWI was also explored in this study. The correlation between the sea
ice and JJA OWI was evaluated each month (figure omitted), and we found that
the interannual variation in OWI was significantly correlated with May sea
ice conditions to the north of Eurasia, especially near the Gakkel Ridge, the
Canada Basin and the Beaufort Sea (Fig. 10a). The averaged (green boxes in
Fig. 10a) SI area in May was calculated as the SI index, whose linear
correlation coefficient with JJA OWI was 0.67 (after detrending) from 1979 to
2017. During 2007–2017, 73 % of the May SI anomalies are followed by
observational SDZ MDA8 anomalies with the same mathematical sign (Fig. 10b).
Furthermore, the linear and nonlinear relationships were both introduced
using the generalized additive model (Fig. 11), and the contribution of May
sea ice to the interannual variability in OWI was approximately 60 %.
These positive sea ice anomalies could induce EU pattern responses in the
subsequent summer (Fig. 10c). The excited atmospheric and thermal centers
were located over the central Siberian Plateau, North China and Mongolia, and
the Yellow Sea. Similarly, the local meteorological responses, such as
anomalous southerlies and less precipitation (Fig. 10d), less cloud, and
strong solar radiation (Fig. 10e) were also closely connected with the
positive sea ice anomalies in May. Thus, the preceding May sea ice positively
modulated the EU pattern, and then, this Rossby wave train transported the
impacts from the polar region and strengthened the anticyclonic anomalies
over North China. Finally, suitable meteorological conditions, including
hot and dry air, anomalous southerlies, and intense sunshine, were induced to
intensify the photochemical production of surface ozone pollution. To confirm
the roles of Arctic sea ice and associated physical mechanisms, the above
analysis was repeated with the NCEP/NCAR data, and identical results were
obtained (Fig. S7 in the Supplement).
The causality, i.e., the preceding May sea ice anomalies contributing to the
subsequent JJA OWI in North China, was also confirmed by CAM5. During the
control experiment (CTRL), the CAM5 model was first integrated for 20 years
with climate mean initial and boundary conditions. Next, the data from
1 September of the last 5 years (i.e., 16–20 years) were designated as five
slightly different initial conditions. With each initial condition, the CAM5
model integrated for 10 years. The JJA mean results of the last 6 years
(i.e., 6 years×5groups=30 ensembles) were
employed as the output of the CTRL. On the basis of CTRL, the May sea ice
concentration in the two boxes of Fig. 10a was separately reduced by 10 %
(denoted as LowASI experiments), i.e., a total of 30 sensitivity runs.
Similarly, the JJA mean results of the 30 sensitive runs were employed as the
output of the LowASI. The differences (LowASI minus CTRL) represent the
responses of atmospheric circulations and meteorological conditions to the
declining May sea ice.
It was evident that an EU Rossby wave train was induced on the
mid-troposphere (Fig. 12a), which propagated from the Taymyr Peninsula (-),
northeastern China (+), to the east of China and the Western Pacific (+). Under
such large-scale atmospheric anomalies, the anomalies of relative humidity
were significantly positive and resulted in denser low cloud cover in
North China (Fig. 12d). Furthermore, the cover of cloud efficiently prevented
the solar radiation from reaching the land surface, meanwhile cooling the air
in the boundary layer (Fig. 12c). Without hot and dry air and intense sunshine,
the photochemical production was significantly decelerated, and the generation
of surface O3 was rather weak. Additionally, sufficient moisture
and clouds caused more rainfall (Fig. 12c). The wet deposition effect might
be enhanced. Thus, corresponding to less Arctic sea ice in May, the
photochemical process to generate O3 was weakened, and the wet
deposition effect to decrease O3 was enhanced. That is, the
positive relationship and associated physical mechanisms (i.e., climate links
in ASI, EU pattern and summer surface ozone pollution in North China) were
causally verified.
Conclusions and discussions
Recently, the summer surface O3 concentrations and the number of
O3 observation stations have steadily increased in China. In
general, the O3 concentrations in North China were substantially
higher than those in southern China. To reveal the climatic driver of summer
surface O3 pollution in North China, a daily OWI (i.e., surface
O3 weather index) was constructed based on meteorological and ozone
observations. The robustness of this index (i.e., OWI) was verified by the
ERA-Interim and NCEP/NCAR reanalysis datasets and surface O3
measurements. May Arctic sea ice was found to be a preceding and efficient
climatic driver, which may help with seasonal forecasting. In the historical
period, variation in Arctic sea ice can explain approximately 60 % of the
interannual variability in the summer OWI in North China, which was closely
associated with the surface O3 pollution. Currently, the Arctic
region has been warming approximately twice as much as the global average
(Huang et al., 2017; Zhou, 2017), indicating accelerated change in the sea
ice. Thus, understanding the role of Arctic sea ice may contribute to the
understanding of seasonal variability in O3 pollution.
The EU pattern acted as an atmospheric bridge to link May Arctic sea ice and
the summer surface O3 pollution in North China. The accumulated sea
ice in May could induce the positive EU phase. The anticyclonic circulation
over North China, i.e., one of the active centers of the EU pattern, was
connected with high surface temperature, strong downward solar radiation,
less low- and medium-altitude cloud cover, and drought over North China.
Under such local meteorological conditions, the photochemical reactions to
produce surface O3 were supported. Generally, these anticyclonic
anomalies over North China were barotropic and could persist for a long time;
thus, the processes that produce surface O3 were continuous to
achieve a high concentration. The connections revealed in this study were
based on long-term meteorological measurements and were causally verified by
well-designed numerical experiments.
In order to extend the time range of this study, the OWI was constructed in
North China. Although the feasibility of the construction approach was
strictly examined, the OWI was still a substitution focusing on the impacts
of the weather conditions. When discussing the impacts of atmospheric
circulations, the linear trend was removed to weaken the signal of
anthropogenic emissions. Thus, the results in this study concentrated on and
emphasized the meteorological and climate factors. However, there is no doubt
that the polluted emissions are the fundamental inducement of the surface
O3 pollution. The joint effects of the climate anomalies and the
historical emissions should be studied using the numerical models in the
future. The EU pattern was a well-known continental Rossby wave train and
could link the midlatitude–high-latitude climate with the change in the Arctic.
Although the connection between the Arctic sea ice and the ozone pollution
was revealed, the separate roles of the sea ice near the Gakkel Ridge,
the Canada Basin and Beaufort Sea should be intensively studied in the
future.
Data availability
Hourly O3 concentration data can be downloaded from
http://beijingair.sinaapp.com/ (Ministry of Environmental Protection of
China, last access: 20 March 2018). Hourly O3 concentration data at
the Shangdianzi station are available from
http://beijingair.sinaapp.com/ (National Meteorological Information
Center, China Meteorological Administration, last access: 20 March 2018). Sea
ice concentration data are from the following website:
https://www.metoffice.gov.uk/hadobs/hadisst/data/download.html (Met
Office Hadley Centre, last access: 8 April 2018). Daily mean and monthly mean
ERA-Interim reanalysis datasets can be downloaded from the ERA-Interim
website:
http://www.ecmwf.int/en/research/climate-reanalysis/era-interim
(ERA-Interim, last access: 8 April 2018). The daily mean and monthly
reanalysis data archive supported by the National Oceanic and Atmospheric
Administration are available from
http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.html
(NCEP/NCAR, last access: 12 April 2018). The BLH dataset can be obtained from
https://www.esrl.noaa.gov/psd/data/gridded/data.narr.html (NARR, last
access: 12 April 2018). Daily precipitation datasets are from
https://www.esrl.noaa.gov/psd/cgibin/db_search/DBSearch.pl?Variable=Precipitation&group=0&submit=Search
(CPC, last access: 12 April 2018).
The supplement related to this article is available online at: https://doi.org/10.5194/acp-19-3857-2019-supplement.
Author contributions
ZY and HW designed the research. ZY, YL and
XM performed research. ZY and XZ analyzed data. ZY prepared the paper
with contributions from all co-authors.
Competing interests
The authors declare that they have no conflict of
interest.
Acknowledgements
This research was supported by the National Key Research and Development Plan
(2016YFA0600703), the National Natural Science Foundation of China (91744311
and 41705058), and the Jiangsu Innovation and Entrepreneurship team.
Review statement
This paper was edited by Bryan N. Duncan and reviewed by two
anonymous referees.
References
An, J. L., Wang, Y. S., and Sun, Y.: Assessment of ozone variations and
meteorological effects in Beijing, Ecol. Environ. Sci., 18, 944–951, 2009.
Barnes, E. A. and Fiore, A. M.: Surface ozone variability and the jet
position: Implications for projecting future air quality, Geophys. Res.
Lett., 40, 2839–2844, 2013.Cai, W. J., Li, K., Liao, H., Wang, H. J., and Wu, L. X.: Weather Conditions
Conducive to Beijing Severe Haze More Frequent under Climate Change, Nat.
Clim. Change, 7, 257–262, 10.1038/nclimate3249, 2017.
Chen, M., Shi, W., Xie, P., Silva, V. B. S., Kousky, V. E., Higgins, R. W.,
and Janowiak, J. E.: Assessing objective techniques for gauge-based analyses
of global daily precipitation, J. Geophys. Res., 113, D04110, 2008.Chen, Y., Ebenstein, A., Greenstone, M., and Li, H.: Evidence on the impact
of sustained exposure to air pollution on life expectancy from China's Huai
River policy, P. Natl. Acad. Sci. USA, 110, 12936–12941,
10.1073/pnas.1300018110, 2013.Christoudias, T., Pozzer, A., and Lelieveld, J.: Influence of the North
Atlantic Oscillation on air pollution transport, Atmos. Chem. Phys., 12,
869–877, 10.5194/acp-12-869-2012, 2012.Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P.,
Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P.,
Bechtold, P., and Beljaars, A. C. M.: The ERA-Interim reanalysis:
configuration and performance of the data assimilation system, Q. J. Roy.
Meteorol. Soc., 137, 553–597, 10.1002/qj.828, 2011.
Doherty, R. M., Wild, O., and Shindell, D. T., Zeng, G., MacKenzie, I. A.,
Collins, W. J., Fiore, A. M., Stevenson, D. S., Dentener, F. J., Schultz, M.
G., Hess, P., Derwent, R. G., and Keating, T. J.: Impacts of climate change
on surface ozone and intercontinental ozone pollution: A multi-model study,
J. Geophys. Res.-Atmos., 118, 3744–3763, 2013.
Gaudel, A., Cooper, O. R., Ancellet, G., Barret, B., Boynard, A., Burrows, J.
P., Clerbaux, C., Coheur, P.-F., Cuesta, J., Cuevas, E., Doniki, S., Dufour,
G., Ebojie, F., Foret, G., Garcia, O., Granados Muños, M. J., Hannigan, J.
W., Hase, F., Huang, G., Hassler, B., Hurtmans, D., Jaffe, D., Jones, N.,
Kalabokas, P., Kerridge, B., Kulawik, S. S., Latter, B., Leblanc, T., Le
Flochmoën, E., Lin, W., Liu, J., Liu, X., Mahieu, E., McClure-Begley, A.,
Neu, J. L., Osman, M., Palm, M., Petetin, H., Petropavlovskikh, I., Querel,
R., Rahpoe, N., Rozanov, A., Schultz, M. G., Schwab, J., Siddans, R., Smale,
D., Steinbacher, M., Tanimoto, H., Tarasick, D. W., Thouret, V., Thompson, A.
M., Trickl, T., Weatherhead, E., Wespes, C., Worden, H. M., Vigouroux, C.,
Xu, X., and Zeng, G.: Tropospheric Ozone Assessment Report: Present-day
distribution and trends of tropospheric ozone relevant to climate and global
atmospheric chemistry model evaluation, Elem. Sci. Anth., 6, 39 pp., 2018.Giese, B. S., Seidel, H. F., Compo, G. P., and Sardeshmukh, P. D.: An ensemble of
ocean reanalyses for 1815–2013 with sparse observational input, J. Geophys.
Res.-Ocean., 121, 6891–6910, 10.1002/2016JC012079, 2016.
Hastie, T. J. and Tibshirani, R. J.: Generalized Additive Models, Chapman
& Hall, London, UK, 1–10, 1990.Huang, X. T., Diao, Y. N., and Luo, D. H.: Amplified winter Arctic
tropospheric warming and its link to atmospheric circulation changes, Atmos.
Ocean. Sci. Lett., 10, 435–445, 10.1080/16742834.2017.1394159, 2017.Kalnay, E., Kanamitsu, M., Kistler, R., Collins, W., Deaven, D., Gandin, L.,
Iredell, M., Saha, S., White, G., Woollen, J., Zhu, Y., Leetmaa, A.,
Reynolds, R., Chelliah, M., Ebisuzaki, W., Higgins, W., Janowiak, J., Mo, K.
C., Ropelewski, C., Wang, J., Jenne, R., and Joseph, D.: The NCEP/NCAR
40-year reanalysis project, B. Am. Meteor. Soc., 77, 437–471,
10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2, 1996.Li, K., Jacob, D. J., Liao, H., Shen, L., Zhang, Q., and Bates, K. H.:
Anthropogenic drivers of 2013–2017 trends in summer surface ozone in China,
P. Natl. Acad. Sci. USA, 116, 422–427, 10.1073/pnas.1812168116, 2018.Li, R. X. and Sun, J. Q.: Interdecadal variability of the large-scale
extreme hot event frequency over the middle and lower reaches of the Yangtze
River basin and its related atmospheric patterns, Atmos. Ocean. Sci. Lett.,
11, 63–70, 10.1080/16742834.2017.1335580, 2018.
Lin, M. Y., Horowitz, L. W., Oltmans, S. J., Fiore, A. M., and Fan, S. M.:
Tropospheric ozone trends at Mauna Loa Observatory tied to decadal climate
variability, Nat. Geosci., 7, 136–143, 2014.
Liu, H., Liu, S., Xue, B. R., Lv, Z. F., Meng, Z. H., Yang, X. F., Xue, T.,
Yu, Q., and He, K. B.: Ground-level ozone pollution and its health impacts in
China, Atmos. Environ., 173, 223–230, 2018.Liu, R. J. and Liao, H.: Assessment of aerosol effective radiative forcing
and surface air temperature response over eastern China in CMIP5 models,
Atmos. Ocean. Sci. Lett., 10, 228–234,
10.1080/16742834.2017.1301188, 2017.Ma, Z., Xu, J., Quan, W., Zhang, Z., Lin, W., and Xu, X.: Significant increase
of surface ozone at a rural site, north of eastern China, Atmos. Chem. Phys.,
16, 3969–3977, 10.5194/acp-16-3969-2016, 2016.
Meehl, G. A., Washington, W. M., Arblaster, J. M., Hu, A., Teng, H., Kay, J.
E., Gettelman, A., Lawrence, D. M., Sanderson, B. M., and Strand, W. G.:
Climate change projections in CESM1 (CAM5) compared to CCSM4, J.
Climate, 26, 6287–6308, 2013.
Melkonyan, A. and Wagner, P.: Ozone and its projection in regard to climate
change, Atmos. Environ., 67, 287–295, 2013.Pausata, F. S. R., Pozzoli, L., Vignati, E., and Dentener, F. J.: North
Atlantic Oscillation and tropospheric ozone variability in Europe: model
analysis and measurements intercomparison, Atmos. Chem. Phys., 12,
6357–6376, 10.5194/acp-12-6357-2012, 2012.
Pu, X., Wang, T. J., and Huang, X., Melas, D., Zanis, P., Papanastasiou, D.
K., and Poupkou, A.: Enhanced surface ozone during the heat wave of 2013 in
Yangtze River Delta region, China, Sci. Total Environ., 603–604, 807–816,
2017.Rayner, N. A., Parker, D. E., Horton, E. B., Folland, C. K., Alexander, L. V., Rowell, D.
P., Kent, E. C., and Kaplan, A.: Global analyses of sea surface
temperature, sea ice, and night marine air temperature since the late
nineteenth century, J. Geophys. Res., 108, 4407, 10.1029/2002JD002670,
2003.
Shi, C., Wang, S., Liu, R., Zhou, R., Li, D., and Wang, W.: A study
of aerosol optical properties during ozone pollution episodes in 2013 over
Shanghai, China, Atmos. Res., 153, 235–249, 2015.Sun, L., Xue, L., Wang, Y., Li, L., Lin, J., Ni, R., Yan, Y., Chen, L., Li,
J., Zhang, Q., and Wang, W.: Impacts of meteorology and emissions on
summertime surface ozone increases over central eastern China between 2003
and 2015, Atmos. Chem. Phys., 19, 1455–1469,
10.5194/acp-19-1455-2019, 2019.Tang, B. Y., Xin, J. Y., Gao, W. K., Shao, P., Su, H. J., Wen, T. X., Song,
T., Fan, G. Z., Wang, S. G., and Wang, Y. S.: Characteristics of complex air
pollution in typical cities of North China, Atmos. Ocean. Sci. Lett.,
11, 29–36, 10.1080/16742834.2018.1394158, 2018.The environmental statistics unit of stat-center in Perking University, Air
quality Assessment Report (5): the assessments of the regional pollutions in
“2+31” cities during 2013–2017, available at:
http://songxichen.gsm.pku.edu.cn/index.php/Research/#tab_34 (last
access: 23 March 2018), 2018.The Ministry of Environmental Protection of China: Technical Regulation on
Ambient Air Quality Index, 1 pp.,
http://www.gov.cn/zwgk/2012-03/02/content_2081374.htm (last access: 3
April 2018), 2012.
Tong, L., Zhang, H. L., Yu, J., He, M. M., Xu, N. B., Zhang, J. J., Qian, F.,
Z., Feng, J. Y., and Xiao, H.: Characteristics of surface ozone and nitrogen
oxides at urban, suburban and rural sites in Ningbo, China, Atmos.
Res., 187, 57–68, 2017.
Wallace, J. M. and Gutzler, D. S.: Teleconnections in the geopotential height
field during the Northern Hemisphere winter, Mon. Weather Rev., 109,
784–812, 1981.Wang, H. J.: On assessing haze attribution and control measures in China,
Atmos. Ocean. Sci. Lett., 11, 120–122, 10.1080/16742834.2018.1409067, 2018.
Wang, H. J. and He, S. P.: The North China/Northeastern Asia Severe Summer
Drought in 2014, J. Climate, 28, 6667–6681, 2015.
Wang, N. and Zhang, Y.: Evolution of Eurasian teleconnection pattern and its
relationship to climate anomalies in China, Clim. Dynam.,
44, 1017–1028, 2015.
Wang, T.: Ozone pollution in China: A review of concentrations,
meteorological influences, chemical precursors, and effects, Sci.
Total Environ., 575, 1582–1596, 2017.
Wang, T., Ding, A., Gao, J., and Wu, W. S.: Strong ozone production in urban
plumes from Beijing, China, Geophys. Res. Lett., 33, 320–337, 2006.
Wang, Y. X., Shen, L. L., Wu, S. L., Mlckley, L., He, J. W., and Hao, J. M.:
Sensitivity of surface ozone over China to 2000–2050 global changes of
climate and emissions, Atmos. Environ., 75, 372–382, 2013.Watts, N., Amann, M., Ayeb-Karlsson, S., Belesova, K., Bouley, T., Boykoff,
M., Byass, P., Cai, W. J., Campbell-Lendrum, D., Chambers, J., Cox, P. M.,
Daly, M., Dasandi, N., Davies, M., Depledge, M., Depoux, A., Dominguez-Salas,
P., Drummond, P., Ekins, P., Flahault, A., Frumkin, H., Georgeson, L.,
Ghanei, M., Grace, D., Graham, H., Grojsman, R., Haines, A., Hamilton, I.,
Hartinger, S., Johnson, A., Kelman, I., Kiesewetter, G., Kniveton, D., Liang,
L., Lott, M., Lowe, R., Mace, G., Sewe, M. O., Maslin, M., Mikhaylov, S.,
Milner, J., Latifi, A. M., Moradi-Lakeh, M., Morrissey, K., Murray, K.,
Neville, T., Nilsson, M., Oreszczyn, T., Owfi, F., Pencheon, D., Pye, S.,
Rabbaniha, M., Robinson, E., Rocklov, J., Schutte, S., Shumake-Guillemot, J.,
Steinbach, R., Tabatabaei, M., Wheeler, N., Wilkinson, P., Gong, P.,
Montgomery, H., and Costello, A.: The Lancet Countdown on health and climate
change: From 25 years of inaction to a global transformation for public
health, Lancet, 391, 10–16, 2018.
Yang, Y., Liao, H., and Li, J.: Impacts of the East Asian summer monsoon on
interannual variations of summertime surface-layer ozone concentrations over
China, Atmos. Chem. Phys., 14, 6867–6879,
10.5194/acp-14-6867-2014, 2014.
Yin, Z. C., Wang, H. J., and Guo, W. L.: Climatic change features of fog and
haze in winter over North China and Huang-Huai Area, Sci. China Earth Sci.,
58, 1370–1376, 2015.
Zhao, Z. J. and Wang, Y. X.: Influence of the West Pacific subtropical high
on surface ozone daily variability in summertime over eastern China, Atmos.
Environ., 180, 197–204, 2017.Zhou, W.: Impact of Arctic amplification on East Asian winter climate, Atmos.
Ocean. Sci. Lett., 10, 385–388, 10.1080/16742834.2017.1350093, 2017.
Zhu, J. and Liao, H.: Future ozone air quality and radiative forcing over
China owing to future changes in emissions under the Representative
Concentration Pathways (RCPs), J. Geophys. Res.-Atmos., 121, 1978–2001,
2016.