Inter-annual variability and long-term trends in tropospheric ozone are both
environmental and climate concerns. Ozone measured at Mt Waliguan
Observatory (WLG, 3816 m a.s.l.) on the Tibetan Plateau over the period of
1994–2013 has increased significantly by 0.2–0.3 ppbv yr
Ozone in the troposphere is a potent greenhouse gas, an air pollutant
detrimental to human health and vegetation, and the primary source of
hydroxyl radicals, which play a critical role in atmospheric chemistry. The
long-term variation in ozone is of both environmental and climate concern.
Therefore, it is important to trace the long-term variations in ozone at
different locations and understand the causes of such variations. Continuous
long-term observations of surface ozone have only been made at a few
representative sites in China. The Mt Waliguan (WLG) station
(36
With rapid economic development in eastern China, anthropogenic emissions of
ozone precursors have been increasing during the past 2 decades (van der A
et al., 2006; Kurokawa et al., 2013; Itahashi et al., 2014). Specifically,
emissions of NO
Aside from regional precursor emissions and long-range horizontal transport (Wang et al., 2006a; Lal et al., 2014), the concentration of surface ozone has many other influencing factors. For instance, surface ozone concentrations at high-elevation sites can also be increased by the downward transport of ozone-rich air from the stratosphere during deep convection and stratosphere-to-troposphere exchange (STE) events (Bonasoni et al., 2000; Stohl et al., 2000; Lefohn et al., 2012; Jia et al., 2015; Ma et al., 2014; Langford et al., 2009, 2015; Lin et al., 2012a, 2015a). Studies based on short-term measurements suggested that surface ozone at WLG is influenced by STE events in spring (Zheng et al., 2011) and sometimes during summer (Ding and Wang, 2006). The extent to which STE influences observed year-to-year variability and the decadal trend of ozone at WLG has not been previously investigated.
Changes in large-scale atmospheric circulation patterns can modulate long-range transport of ozone pollution in the troposphere as well as STE. Large-scale physical and dynamical processes including the El Niño–Southern Oscillation (ENSO), the North Atlantic Oscillation (NAO), the quasi-biannual oscillation (QBO), and the solar cycle (Creilson et al., 2003; Ziemke et al., 2005; Oman et al., 2013; Sioris et al., 2014) have been found to significantly affect stratospheric and tropospheric ozone variability. Based on the good correlation between the ENSO index and tropospheric column ozone (TCO) over tropical latitudes, Ziemke et al. (2010) created a so-called “Ozone ENSO Index” (OEI). Over northern mid-latitudes, strong El Niño events enhance long-range transport of Asian ozone pollution towards the eastern North Pacific and the southwestern US by modulating the strength and position of the subtropical jet stream (Lin et al., 2014). Some studies (Zeng and Pyle, 2005; Langford, 1999; Koumoutsaris et al., 2008; Voulgarakis et al., 2011) suggest that the change in dynamics after El Niño events can promote cross-tropopause ozone exchange and lead to a rise in global mean tropospheric ozone burden. However, Lin et al. (2015a) find that El Niño events lead to enhancements in upper tropospheric ozone but this influence does not reach surface air. Over high-elevation regions prone to deep stratospheric intrusions in the western US, Lin et al. (2015b) find that the increased frequency of deep tropopause folds that form in upper-level frontal zones following strong La Niña winters exerts a stronger influence on springtime ozone levels at the surface than the El Niño-related increase in lower stratospheric to upper tropospheric ozone burden.
Similar to the western US, the Tibetan Plateau has been identified as a preferred region for deep stratospheric intrusions (Škerlak et al., 2014). Prior studies show that the QBO, ENSO, and sunspot cycle influence total ozone over the Tibetan Plateau (Ji et al., 2001; Huang et al., 2009; Ningombam, 2011; Zou et al., 2001). The mechanisms controlling the inter-annual variability in jet stream characteristics, STE, and their influences on lower tropospheric ozone measured at WLG are poorly characterised. In addition, China is strongly influenced by the East Asian summer monsoon (EASM). Past studies have pointed out that the EASM influences ozone concentrations in this region through altering the transport of anthropogenic pollution (Derong et al., 2013; Liu et al., 2009, 2011). Using the GEOS-Chem global chemical transport model, Yang et al. (2014) report a positive inter-annual correlation between the EASM and the Tibetan surface ozone concentration. Their results were not evaluated with actual observations of surface ozone.
In this work, we aim to advance knowledge on the factors driving inter-annual variability and long-term trends in ozone at WLG over the past 3 decades. Section 2 briefly describes observational data, model simulations, and the analysis approach. In Sect. 3, we first discuss the links of surface ozone at WLG to air-mass origin, including their seasonal to inter-annual variability (Sect. 3.1). We then use the GFDL AM3 model hindcast simulations to interpret the influences of changes in meteorology, STE, and anthropogenic emissions on ozone measured at WLG in winter, spring, summer, and autumn (Sect. 3.2) and the impact of direct ozone transport versus precursor transport (Sect. 3.3). Section 4 examines the relationship between atmospheric dynamics and surface ozone at WLG, including the influence of STE, EASM, and the sunspot cycle. An empirical model is obtained for the normalised monthly level of surface ozone at WLG using the multivariate regression technique and is used to explain the observed ozone trends.
Ozone concentration and meteorological parameters were measured at the
WLG site (36
The HYSPLIT model (version 4) from NOAA Air Resources Laboratory (Draxler and
Hess, 1997, 1998; Draxler, 1999) is used for the trajectory
analyses, using three different meteorological data sets from NCEP. The NCEP
Global Reanalysis Data with a spatial resolution of 2.5
To study the overall air-mass origin and to determine whether the air mass
collected pollutants from nearby cities, the average direction of each
trajectory relative to the WLG station is calculated for both the 168 h and 24 h trajectories (Fig. S1 in the Supplement). The 168 and 24 h
average directions relative to WLG are clustered into bins of 45
Since ozone is a trace gas with a distinct vertical distribution, it is not
enough to just determine the direction from which the air mass came. The
height of the air mass is also crucial for interpreting the measured ozone
concentrations. As discussed in previous studies (Ma et al., 2002; Xu et al.,
2016), the WLG site is predominantly influenced by air from the planetary
boundary layer (PBL) during daytime and from the free troposphere (FT) during
nighttime, with ozone concentrations showing a daytime minimum and a
nighttime maximum. Daytime and nighttime ozone at WLG show different trends,
particularly in summer (0.07
The potential sources of high ozone are studied using the potential source contribution function (PSCF) analysis method, which has been widely applied to detect possible source regions (Ara Begum et al., 2005; Lucey et al., 2001; Zhou et al., 2004). The PSCF analysis is performed both on PBL and on FT trajectories to detect differences in source region distributions in the PBL and in the FT or above.
The PSCF on the grid (
Abnormally high PSCF values may be produced for certain grid cells with small
A Trajectory-mapped Ozonesonde data set for the Stratosphere and Troposphere
(TOST) was generated from the ozone sounding records with trajectory mapping by
Liu et al. (2013). The data set has a spatial resolution of
5
To obtain the monthly time series of total direct tropospheric ozone
transport contribution to ozone at WLG, the 3-D ozone contribution
climatology of all the grid cells is summed up (Eq. 4). The bottom layer of
the grid cell in which WLG resides is excluded from the summation.
The GFDL AM3 global chemistry–climate model was used to make hindcast
simulations of ozone and related tracers at
Multivariate regression is applied to obtain an empirical model to explain
the relationship and contribution of the various influencing factors to the
surface ozone concentration at WLG. The regression model takes on the
following form:
The 1994–2013 climatology of air-mass origins at WLG in the
PBL
Based on past studies, which were mostly focused on summertime ozone at WLG, high ozone concentrations were mostly linked to downward transport, instead of horizontal transport of anthropogenic pollution (Zhu et al., 2004; Ma et al., 2005; Wang et al., 2006b; Xue et al., 2011). Westerly trajectories were commonly associated with downward transport events and high ozone concentrations, whereas easterly trajectories carried air masses with signals of anthropogenic pollution and lower ozone concentrations. Anthropogenic impact was attributed mostly to the two big cities, Xining and Lanzhou, that are both located to the east of WLG; however, central and eastern China could also have potential impacts (Wang et al., 2006b; Xue et al., 2011).
The average trajectory direction occurrence frequencies
in
Since ozone and its precursors are usually inhomogeneously distributed, both
the horizontal direction and vertical height of the air-mass origin may influence the
local concentration of ozone at WLG. As already pointed out in previous
studies (Ma et al., 2002; Xu et al., 2016), during daytime and nighttime the
WLG site is mainly influenced by air from the PBL and FT, respectively,
causing a daytime minimum and a nighttime maximum of the ozone concentration.
Furthermore, ozone in daytime and nighttime showed different trends, particularly in
summer when daytime and nighttime ozone showed respective trends of
0.07
During spring, Sichuan Province, which is southeast of WLG, displays a significantly high ozone PSCF both in the PBL and FT trajectories, which is possibly evidence for long-range transport of ozone and/or its precursors from Sichuan to WLG. During summer, when air masses from the east occur most frequently (as will be shown in Fig. 2), the entire eastern sector reveals low values of high ozone PSCF, hardly showing signs of anthropogenic influence on WLG. In other words, most air masses from the east in summer are not associated with high ozone. High ozone PSCF occurs dominantly with trajectories from the NW or N. In autumn, in addition to NW or N, significant contributions of trajectories from the E, SE, and S can also be discerned in the PBL trajectories, which suggests that high ozone is linked to air masses coming from western China, central China, the northeastern part of the Tibetan Plateau, the southwestern part of Gansu Province, and north of China (east Mongolia). In the FT trajectories, high ozone concentrations were mainly linked to air masses from western and central China. In addition, air masses over Gansu Province, part of Sichuan Province, and some parts of Russia also show a high PSCF. In winter, the PBL trajectories show a high ozone PSCF mainly in the NW sectors; however, the SW and N-NE sectors also revealed scattered high PSCF values (over some parts of Nepal, northern India, Mongolia, and Inner Mongolia). Aside from the NW sector, the FT trajectories display a significantly high PSCF in the NE sector in the western half of Inner Mongolia.
To evaluate the impact of different air masses, we need to find out which air masses are influencing WLG and evaluate the relative importance of the different air masses during different seasons. Figure 2 depicts the 24 and 168 h average trajectory direction occurrence frequencies for spring, summer, autumn, and winter, respectively. The 168 h average trajectory direction provides us information on the overall origin of the air mass, while the 24 h average trajectory direction should be able to reveal if the air mass passed over nearby polluted regions before arriving at the station.
From the 168 h average trajectory direction occurrence frequencies (Fig. 2b,
d, f, h), it can be seen that WLG is
under the major influence of western and northwestern air masses throughout
the year, with air masses from the east only playing a significant role
during summer, which is in accordance with previous studies (e.g. Zhang et
al., 2011). Hence, the WLG site is overall very clean and highly
representative of a background state. Trajectories from the east (including
the
NE, E, and SE) take up on average 20, 65, 31, and 6 % of all the trajectory
directions during spring, summer, autumn, and winter, respectively. For
The linear variation slope (
From the 168 h average trajectory direction frequencies, it can be seen that the anthropogenic influence is strongest in summer, followed by autumn, and is almost negligible in winter. However, the 24 h average trajectory direction frequencies show significantly larger portions of eastern trajectories and smaller percentages of northwestern trajectories (Fig. 2a, c, e, g), implying that a significant part of air masses originating from the northwest of WLG often bend over to the east 24 h before arriving at WLG. On average 24 h trajectories from the east (including the NE, E, and SE) take up 40, 76, 30, and 15 % of all the trajectory directions during spring, summer, autumn, and winter, respectively. Air-mass trajectories originating from the far northwest bending to the east before their arrival at WLG may entrain pollutants if they travel over the large cities. The large occurrence frequency of eastern trajectories in the endpoints within the last 24 h suggests that anthropogenic influences on WLG during spring and autumn should not be neglected.
It is also worth noting from Fig. 2 that the trajectory direction frequencies were far from constant throughout the 2 decades from 1994 to 2013. There was large inter-annual variability. Some directions show significant variation trends in their occurrence frequencies, which will be discussed later in this section.
The air-mass direction analysis shows that the WLG site is under the influence of different air masses from different horizontal directions. Apart from that, the WLG site is also under the control of distinct air masses from different layers throughout the day, with PBL air masses dominating during the day and FT air masses during the night, which led to a clear diurnal variation with high nighttime and low daytime ozone concentrations (Ma et al., 2002; Xu et al., 2016). STE events were also held responsible for the injection of stratospheric ozone into the troposphere, leading to elevated surface ozone concentrations (Bonasoni et al., 2000; Ding and Wang, 2006; Stohl et al., 2000; Tang et al., 2011; Lefohn et al., 2012; Jia et al., 2015; Ma et al., 2014; Lee et al., 2007; Liang et al., 2008).
Changes in atmospheric circulations might lead to variations in the
occurrence frequencies of air masses from different directions shown in
Fig. 2. Due to the high dependence of local surface ozone concentrations on
the air-mass origin, a significant change in atmospheric circulation may lead
to changing local concentrations of surface ozone at WLG. To investigate this
kind of impact, average 24 h and 168 h trajectory directions are used to
uncover whether there were secular changes in the occurrence frequency of
different directions. Table 1 lists the variation trends (
Table 1 shows an increasing trend in the occurrence frequency of E trajectories in the 24 h average directions but not in the 168 h average directions. This indicates that more trajectories from other directions were turning over to the east of WLG 24 h before their arrival at the site. During spring and autumn, only an increase in the 168 h NW trajectories was found, while during summer, increases were found in both the 168 h W and NE trajectories; these increases are highly possibly linked to the increase in 24 h E trajectories. The SE 24 h trajectories, however, show significant decreasing trends in spring, summer, and autumn, with the strongest decrease in summer. This is consistent with the 168 h results, suggesting that the entire SE air-mass transport pathway decreased in frequency.
Since the NW direction is often linked to high ozone concentrations according
to the PSCF and a significant increase in 168 h trajectory occurrences from
that direction has been detected, a more detailed examination on that part of
the trajectories is necessary. Figure 3 displays the occurrence frequency of
168 h average trajectories in the NW sector that turned to the E, NW, and W
sectors in the last 24 h, with the lines indicating the according decadal
linear variation trends. It can be seen that more and more NW trajectories
bent to the E sector before arriving at WLG, with significant trends in all
seasons and the largest increasing slope in spring. The trajectories
originating from the NW and staying on the NW path throughout the transport
process take up a relatively smaller proportion compared to the other two
pathways and do not show any significant variation trends throughout the 2 decades. Trajectories turning to the W sector are most common and they are
gaining in frequency in spring, autumn, and winter, with autumn showing the
largest increasing slope (0.79 % yr
The occurrence frequency of trajectories whose average 168 h
direction is NW and 24 h direction is E
We next examine a suite of GFDL AM3 simulations designed to isolate the
response of ozone to changes in meteorology, stratospheric exchange, and
anthropogenic emission trends. Figure 4 shows year-to-year variation and
long-term trends in observed and modelled ozone at WLG, as well as the
modelled stratospheric contribution (O
Comparison of seasonal median ozone anomalies at Mt Waliguan over
the period of 1980–2014 from available observations (black), GFDL AM3 BASE
simulations (red), and AM3 stratospheric ozone tracer (O
Comparison of seasonal median ozone anomalies at Mt Waliguan over
the period of 1980–2014 from available observations (black), GFDL AM3 BASE
simulations (red), and under conditions with strong transport from East Asia
(EACOt
A stratospheric ozone tracer implemented in GFDL AM3 (O
To evaluate the effect of pollution transport from South East and East Asia,
we filter ozone in the AM3 BASE simulation with the East Asian CO tracer
(EACOt; see Sect. 2.5). Following the approach of Lin et al. (2015b, 2017)
for western US sites, we use EACOt to identify days when WLG is strongly
influenced by polluted airflow from South East Asia (including China) (i.e.
EACOt greater than its 67th value during each season). Figure 5 shows the
trends of ozone from observations, the BASE simulations, and the simulated
ozone trends under conditions with strong transport from South East Asia
(O
To separate the influences of changes in transport patterns and anthropogenic
emission trends, we compare trends of seasonal mean ozone at 700 hPa
simulated by GFDL AM3 with time-varying (BASE) and constant anthropogenic
emissions (FIXEMIS) over 1995–2014 (Fig. 6). With both emissions and
meteorology varying, AM3 BASE simulates increasing free tropospheric ozone
trends of as large as 1 ppbv yr
In summary, the AM3 modelling results clearly show that the spring and autumn
increases in WLG surface ozone are governed by different processes. Observed
increases in springtime ozone at WLG over the 1994–2013 period are linked to
decadal variability in stratospheric ozone input in the northwest airflow,
while the autumnal increase in ozone at WLG results from pollution transport
from South East Asia, where NO
Trend of total contribution of direct ozone transport to WLG ozone
concentration (ppbv yr
We next examine the impacts of local photochemical production versus large-scale background ozone transport to WLG. The impact of direct ozone transport on ozone trends at WLG are studied by combining the 3-D TOST data from Liu et al. (2013) with the back trajectory analysis (Sect. 2.4). Different from the GFDL AM3 FIXEMIS simulation discussed in Sect. 3.2, the TOST approach discussed in this section does not eliminate the impacts from increases in Asian anthropogenic emissions. The TOST data set is based on trajectory-mapped ozone soundings (Liu et al., 2013). The monthly averages of ozone in each grid should contain signals of background ozone and ozone produced within the grid from precursors emitted by anthropogenic and natural sources. Therefore, mean values in the TOST data set account for not only ozone changes due to transport but also ozone changes associated with varying global to regional anthropogenic and natural emissions.
Average seasonal distributions of ozone contribution to WLG through direct transport of ozone during 1994–2013.
The seasonal average distribution of ozone contribution to WLG through direct tropospheric transport during 1994–2013 is shown in Fig. 8. It can be noted that the distribution varies with season. For winter and spring, the TOST analysis indicates major contributions from the western edge of the Tibetan Plateau and a small contribution from central China to the east of WLG (Fig. 8a). Large contributions from the northwestern to eastern sectors are found in summer, including contributions from Mongolia, Inner Mongolia, and central and eastern China, where high ozone levels can be observed during summertime. During autumn, WLG is strongly influenced by transport from central and eastern China and less influenced by the NW sector. This finding is consistent with the AM3 attribution results that increases in East Asian anthropogenic emissions contribute to raising autumnal ozone measured at WLG.
Figure 9 shows the trends of the ozone transport contribution in different
seasons based on the TOST analysis. Spring shows a significantly increasing
contribution from the north to northwest of WLG and a significantly decreasing
contribution from the western sector, where the average contribution in
spring is largest (see Fig. 8a). Statistically significant increases with
small slopes were found in central Asia and eastern Europe during summer and
winter (Fig. 9b, d). Significant increasing trends in ozone contribution with
relatively large slopes (
Table 2 summarises the total contribution of direct ozone transport to WLG
ozone concentration for each season for the annual mean. The rate of ozone
change derived from the TOST data set for spring
(0.27
Average seasonal distributions of the trend of ozone contribution to
WLG through direct transport of ozone during 1994–2013; grey dots stand for
the grid cells with
Mann–Kendall and linear trends of seasonal average CO observed
during
One of the key issues in producing the TOST data set was the impact of ozone production along the trajectories, which might cause errors in the mapped ozone data. A careful assessment indicates that the errors are mostly small and insignificant (Liu et al., 2013). Our approach of using TOST data is similar to the forward mapping in Liu et al. (2013). Therefore, it is likely that the impact of ozone production along the trajectories during their residence time on our results is small, as in the case of Liu et al. (2013). As the bottom layer of the grid in which WLG resides is excluded in our calculations, direct impacts on our results from regional emissions in the grid containing WLG can be ruled out.
The Kendall's variation slope (
Temporal variations in
Local photochemical production can enhance O
To investigate the relative importance of the influence from the growing
emissions east of WLG, the trajectories and the associated ozone
concentrations were grouped into four groups (SW, NW, NE, and SE) according to
the
In all, the increase in ozone at WLG is influenced by both direct tropospheric ozone transport and rising precursor emissions in the eastern sector. The increase in direct transport of ozone to WLG only led to a significant rise in autumnal ozone, which supports the conclusions from the modelling study in Sect. 3.2.
Due to the transient, localised nature of stratospheric intrusions, diagnosing the presence of stratospheric influence in near-surface ozone requires precise, high-frequency (a few minutes), and co-located measurements of ozone, CO, water vapour, and surface wind gust at remote sites (see Langford et al., 2015). These measurements are not available at WLG. Thus, we rely on a global model that has been previously shown to be able to represent deep stratospheric intrusions (Lin et al., 2012a, 2015a).
The highest ozone concentrations at WLG during spring were observed in 1999
and 2012, coinciding with the largest stratospheric influence simulated in
the GFDL AM3 model (Fig. 4a). In contrast, the springs of 1998 and 2007
experienced lower observed ozone and simulated stratospheric influence. In
this section we investigate the links of these ozone anomalies to changes in
the structure of the jet stream. The top panels of Fig. 11 show time series
of observed daily surface ozone and modelled O
Comparison between the normalised nighttime (dashed black line) third IMF and the normalised 30 hPa QBO index (solid blue line) during 1994–2013.
Correlation coefficients between the QBO index and zonal
Analysis of 200 hPa zonal wind anomalies from the NCEP reanalysis indicates
strengthening of the mid-latitude jet stream across the Tibetan Plateau during
the springs of 1999 and 2012, with the centre of the jet stream shifted to
the north towards WLG compared to the 1994–2013 mean state (Fig. 11b). These
circulation anomalies facilitate the formation of tropopause folding and
transport of stratospheric ozone into the FT above WLG, consistent with
frequent STT events as identified by GFDL AM3 O
Correlation between the surface ozone (1994–2013), NCEP reanalysis precipitation (1990–2015), and EASMI (1990–2015).
The time series of surface ozone at WLG was decomposed into five intrinsic mode functions (IMFs) with different periodicities using the HHT analysis in combination with the empirical mode decomposition (EMD) (Xu et al., 2016). The first IMF shows high frequency, representing variations associated with synoptic systems. The second IMF, with a periodicity of 1 year, represents seasonal variation and made the largest contribution to the variability in ozone. The other IMFs played minor roles in the variations in ozone. However, these IMFs are interesting because they are related to the 2–4-, 7-, and 11-year periodicities found in the ozone data and contribute to the inter-annual variability in ozone at WLG. There are many oscillations within the atmospheric circulation with different periodicities, e.g. QBO with a quasi-2-year periodicity and ENSO with a 2- to 7-year periodicity (Xu et al., 2016). Here, we explore potential links of some atmospheric circulation oscillations to the variations in surface ozone at WLG (Xu et al., 2016). Since nighttime ozone concentrations at WLG are more representative of the free-tropospheric air condition, IMFs of the nighttime ozone data are applied in the following analysis.
Previous studies concluded that column ozone over the Tibetan Plateau bears a
QBO signal with the same phase as the tropical stratospheric wind QBO, which
is caused by the increase and decrease in tropopause height over the Tibetan Plateau
region, as the tropical stratospheric winds shift from easterly to westerly
(Ji et al., 2001). The third IMF of the nighttime surface ozone data
reveals a periodicity closest to that of the QBO index. The comparison
between the QBO index and the third IMF is displayed in Fig. 13. It can be
discerned that the normalised third IMF and the QBO index show a positive
correlation during nighttime (
Although the QBO is an atmospheric oscillation in the stratosphere, its
dynamical and chemical effects are not limited to the stratosphere but can
propagate downward to the Earth's surface and upward to the mesosphere
(Baldwin et al., 2001). Some mechanisms have been proposed to show how the
QBO can change the large-scale circulations and exert impacts on
tropospheric winds, temperature, etc. (e.g. Collimore et al., 2003; Kwan and
Samah, 2003). To see the possibility of a QBO influence on surface ozone at
WLG, correlations between the QBO index and zonal as well as meridional wind
were calculated for different pressure levels. Figure 14 shows the
correlation coefficients for the 500 and 700 hPa levels. As can be seen in
Fig. 14, there is a large zone of positive correlation between the annual QBO
index and zonal winds at both levels, extending from western Asia to central
Asia to the middle of Russia. There is also a large zone of positive
correlation between the annual QBO index and meridional winds at both levels,
extending from the north of the Indian Ocean to central Asia to Russia. The
results suggest that when the QBO is in its positive phase, westerly and
southerly winds over large areas west, northwest, and north of China are
increased. Similar zones of positive correlations also exist between the
annual QBO index and air temperatures at different pressure levels, with a
warming of 0.01–0.04
The EASM, ENSO, and other circulation-related factors might influence surface ozone at WLG through the change of the precipitation or via STE processes. However, these influencing factors are often coupled with each other and a direct relationship between surface ozone observations and these factors might be hard to determine.
Correlation coefficients between the QBO index and the 3–8 km TOST
ozone columns. Correlations for the grids with grey dots indicating those
that are significant (
The correlation coefficients between surface ozone concentrations,
precipitation, and the EASM index (EASMI) for June, July, and August is listed
in Table 4. Only during July could a significant negative correlation (
To investigate the possible impact of the EASM on the STT processes at WLG,
the average location of the subtropical jet stream for the top and bottom
15th
percentile EASMI cases and the correlation coefficients between the 200hPa
zonal wind and the EASMI in June, July, and August from 1990 to 2015 were
calculated and are displayed in Fig. 16a, c, e. The jet stream location (dashed and solid black lines in Fig. 16a)
near WLG does not change in June for strong (EASMI
The EASM can also change the atmospheric circulation and thus change
transport processes over WLG. The average 500 hPa geopotential height
distribution is shown in Fig. 16b, d, f and the locations where the geopotential height is significantly
correlated to the EASMI are marked by
The changes in stratospheric ozone input and in ozone concentration
associated with East Asian, European, and North American transport (%)
introduced by the East Asian summer monsoon. The average ozone values associated
with the EACOt
In summary, weak monsoon years favour STT ozone transport, especially during July and August, and the circulation pattern during weak monsoon years favours the horizontal transport of ozone to WLG during July, which results in a strong negative correlation between the EASMI and the ozone concentrations in July. These results are in contrast with the modelling study by Yang et al. (2014), which suggested that summer ozone concentrations over the Tibetan Plateau were positively correlated to the EASMI.
It is well known that changes in incoming solar ultraviolet radiation can
cause solar cycle signals of ozone in the stratosphere (Maycock et al.,
2016). A solar cycle signal was also found in the tropospheric ozone column
data over the Tibetan Plateau (Huang et al., 2009), with an increase of
4 % in tropospheric ozone from the solar minimum to solar maximum. Here, we
investigate the impact of solar activities on surface ozone trends at WLG by
comparing the normalised 1-year running average SSN with the normalised
daytime and nighttime fifth IMFs of monthly average ozone that were obtained in
our previous study (Xu et al., 2016). Results are displayed in Fig. 17, which
shows that both the daytime and nighttime fifth IMFs are positively correlated
to the SSN, with daytime (
Comparison between the fifth IMF and the 1-year running average SSN during 1994–2013.
The positive correlation between the fifth IMF and the SSN explains the 11-year
periodicity found in the ozone data. Solar activity led to surface ozone
variations within the range of
Multivariate regression coefficients (Eqs. 6–7) of the surface ozone at WLG.
The analysis above suggests that surface ozone at WLG can be influenced by
various factors. Some of these factors mainly disturbed the seasonal
variation in ozone and contributed to the inter-annual differences, others
also contributed to the observed long-term trends. To quantify the
contributions of different factors to surface ozone at WLG, a multivariate
regression was performed, with normalised monthly ozone concentration being a
dependent variable and time and the potential influencing factors being independent
variables. All candidate independent variables, e.g. the QBO index, the NAO
index, the SSN, the modelled O
The regression was conducted stepwise to avoid overfitting. The coefficient
of determination (
Figure 18 shows a comparison between the calculated and observed ozone,
together with the calculated contributions of the influencing factors to the
normalised monthly ozone at the site. It can be seen that the calculated
normalised ozone reproduces the observed one well (
Temporal variation in the
The regression found an annual variation in the normalised ozone with an
amplitude of about 0.67 and no trend. The modelled O
Through an observational and modelling analysis, we have discussed the key drivers of various periodicities and long-term trends of ozone measured at WLG for the four seasons over the past 2 decades, previously reported in the companion paper (Xu et al., 2016). The impact of air-mass origin is investigated using backward trajectory analysis combined with PSCF analysis; the influence of STE and increasing anthropogenic emissions in Asia is evaluated using chemistry–climate model hindcasts driven by reanalysis winds (GFDL AM3; Lin et al., 2017). The impact of direct tropospheric ozone transport on ozone at WLG is examined using 3-D tropospheric ozone climatology data (a subset of TOST) combined with the trajectory analysis results.
Our results show that different processes have contributed to the observed
increasing ozone trends at WLG during spring versus autumn. Analysis of a
stratospheric ozone tracer in GFDL AM3 indicates that STT can explain
The periodicities detected in the HHT analysis of ozone data previously reported by Xu et al. (2016) are linked to various climate indices including EASMI, QBO, and sunspot cycle. The 2–3-year periodicity is linked to the QBO and the 3–7-year periodicity could be partly explained by the EASMI, while the 11-year periodicity is well connected to the sunspot cycle. An empirical model is obtained for normalised monthly level of surface ozone at WLG using the multivariate regression technique and is used to explain the observed ozone trends. Based on these relationships, an empirical model has been established for normalised monthly ozone through multivariate regression. The regression model reproduces the observation well and can capture about one-third of the observed ozone trend.
The results obtained in this work clearly show the complexity of surface ozone in terms of influencing factors. Comprehensive investigations are recommended to understand variations in surface ozone, in particular the long-term trends, at any site. Our work in this paper and the companion paper shows an example of de-convoluting the ozone variations and interpreting those using related dynamical and chemical factors of different scales, which can hopefully inspire similar studies.
The ozone data analysed in this work are partly available
at the World Data Center for Greenhouse Gases (WDCGG)
(
The supplement related to this article is available online at:
The authors declare that they have no conflict of interest.
This article is part of the special issue “Study of ozone, aerosols and radiation over the Tibetan Plateau (SOAR-TP) (ACP/AMT inter-journal SI)”. It is not associated with a conference.
This work is supported by the Natural Science Foundation of China (nos. 41330422 and 41505107), China Special Fund for Meteorological Research in the Public Interest (no. GYHY201106023), Environmental Protection Public Welfare Scientific Research Project, Ministry of Environmental Protection of the People's Republic of China (no. 201509002), the Key research and development program of the Ministry of Science and Technology (no. 2016YFC0202300), and the Basic Research Fund of CAMS (no. 2016Y006). We thank CMA, NOAA/ESRL, and WDCGG for making the monthly CO data available. Edited by: Tao Wang Reviewed by: three anonymous referees