Various observation-based datasets have confirmed positive zonal mean column ozone trends at midlatitudes as a result of the successful implementation of the Montreal Protocol. However, there is still uncertainty about the longitudinal variation of these trends and the direction and magnitude of ozone changes at low latitudes. Here, we use the extended Copernicus Climate Change Service (C3S) dataset (1979–2017) to investigate the long-term variations in total column ozone (TCO) over the Tibetan Plateau (TP) for different seasons. We use piecewise linear trend (PWLT) and equivalent effective stratospheric chlorine loading (EESC)-based multivariate regression models with various proxies to attribute the influence of dynamical and chemical processes on the TCO variability. We also compare the seasonal behaviour of the relative total ozone low (TOL) over the TP with the zonal mean at the same latitude.
Both regression models show that the TP column ozone trends change from negative trends from 1979 to 1996 to small positive trends from 1997 to 2017, although the later positive trend based on PWLT is not statistically significant. The wintertime positive trend starting from 1997 is larger than that in summer, but both seasonal TP recovery rates are smaller than the zonal means over the same latitude band. For TP column ozone, both regression models suggest that the geopotential height at 150 hPa (GH150) is a more suitable and realistic dynamical proxy compared to a surface temperature proxy used in some previous studies. Our analysis also shows that the wintertime GH150 plays an important role in determining summertime TCO over the TP through persistence of the ozone signal. For the zonal mean column ozone at this latitude, the quasi-biennial oscillation (QBO) is nonetheless the dominant dynamical proxy.
We also use a 3-D chemical transport model to diagnose the contributions of different proxies for the TP region. The role of GH150 variability is illustrated by using two sensitivity experiments with repeating dynamics of 2004 and 2008. The simulated ozone profiles clearly show that wintertime TP ozone concentrations are largely controlled by tropics to midlatitude pathways, whereas in summer variations associated with tropical processes play an important role. These model results confirm that the long-term trends of TCO over the TP are dominated by different processes in winter and summer. The different TP recovery rates relative to the zonal means at the same latitude band are largely determined by wintertime dynamical processes.
The Tibetan Plateau (TP), also known as the third pole, is an area very sensitive to global climate change. It exerts important thermal and dynamical effects on the general circulation and climate (Yanai et al., 1992; Ye and Wu, 1998). Furthermore, climate changes over the TP have a significant impact on the distribution of stratospheric ozone.
There is well-established observational evidence of a persistent total column ozone low (TOL) centred over the TP (e.g. Zhou et al., 1995; Zheng et al., 2004; Bian et al., 2006; Tobo et al., 2008). Zou (1996) found that the largest ozone deficit over TP occurs in May, while the smallest deficit occurs in wintertime. Ye and Xu (2003) proposed that the high topography and the elevated heating source associated with thermally forced circulations are the two main reasons for its occurrence. Other studies have also suggested that the thermal–dynamical forcing of the TP, for example, by air expansion, uplifting of the tropopause, thermal convection and monsoon circulation, makes a dominant contribution to the TOL, especially in summer (e.g. Tian et al., 2008; Bian et al., 2011; Guo et al., 2012, 2015). However, the exact coupling pathways between the thermal–dynamical forcing and long-term total column ozone (TCO) changes during different seasons are still not well established.
TP column ozone trends can be significantly affected by internal variability. Zou (1996) reported strong negative TCO trends over Tibet for the 1979–1991 time period and, in subsequent studies, analysed the effects of the quasi-biennial oscillation (QBO) and the El Ninõ–Southern Oscillation (ENSO) (e.g. Zou et al., 2000, 2001). Zhou and Zhang (2005) presented decadal ozone trends over the TP using the merged Total Ozone Mapping Spectrometer/solar backscatter ultraviolet (TOMS/SBUV) ozone data for the period 1979–2002 and found that the downward trends are closely related to the long-term changes of temperature and geopotential height. Zhou et al. (2013) found substantial downward ozone trends over the TP in the longer 1979–2010 TOMS/SBUV record during the winter–spring seasons. They also showed that long-term ozone variations are largely correlated with thermal–dynamical proxies such as lower stratospheric temperature, with its contribution reaching around 10 % of the total ozone change. Zhang et al. (2014) indicated that the TOL over the TP in winter deepened during the period 1979–2009 and that thermal–dynamical processes associated with the TP warming may account for more than 50 % of the TCO decline in this region.
While previous studies have demonstrated the contributions of dynamical processes to the long-term ozone variation over the TP (e.g. Zhou et al., 2013; Zhang et al., 2014), a better proxy is needed to explain the dynamical influence for this region. The geopotential height in the free atmosphere is an important thermal–dynamical proxy that not only conveys information about the thermal structure of the atmosphere but also serves as an indicator of synoptic circulation changes (Christidis and Stott, 2015). The natural and anthropogenic contributions to the changes in geopotential height (GH) establish the coherent thermal–dynamical nature of externally forced changes in the regional climate system, which provides the basis for the validation of climate models. In this study, the GH at 150 hPa over the TP is used as a new thermal–dynamical proxy which incorporates coupling between the local TP circulation and various tropospheric teleconnection patterns and represents the tropospheric dynamical influence more realistically.
With the extended Copernicus Climate Change Service (C3S) TCO time series available from 1979 to early 2018, the aim of this paper is to study the long-term trend and variability in ozone over the Tibetan region. Based on statistical regression analysis of C3S ozone data and TOMCAT/SLIMCAT three-dimensional (3-D) chemical transport model (CTM) simulations, the contributions of different influencing variables, including the local thermal–dynamical proxy (GH), are diagnosed to help understand the long-term ozone variability in different seasons and over different areas.
The layout of the paper is as follows. Section 2 introduces the C3S ozone dataset and TOMCAT/SLIMCAT model used for the analysis of the total ozone variability. The long-term TCO time series and TOL over the TP region are presented in Sect. 3. Regression models as well as analysis of the contribution of different proxies to the total ozone variations in different seasons are given in Sect. 4. Section 5 discusses our 3-D model sensitivity experiments and is followed by our summary and conclusions in Sect. 6.
High-quality observational-based datasets are necessary for better
quantification of decadal TCO trends. This is because interannual
variability can cause variations of up to 20 %, whereas ozone trends are
generally less than half a percent. As the lifetime of most satellite
instruments is less than two decades, merged satellite datasets are widely
used to determine long-term ozone trends. These datasets are created by
combining total ozone measurements from different individual instruments to
provide global coverage over several decades (e.g. Frith et al., 2014).
SBUV provides nearly continuous
satellite-based measurements of total ozone to analyse trends. The
variations from all the instruments are within 2 % relative to the
ground-based data at all latitudes (Labow et al., 2013). SBUV-merged data are
obtained from
Hence, here we use the total column ozone from the C3S which is produced by
the European Centre for Medium-Range Weather Forecasts (ECMWF). For a
detailed description and data availability, see
We use four different area-weighted total ozone time series during
1979–2017: TP (27.5–37.5
Chemistry–transport models are important tools to investigate how past and
present-day ozone-depleting substance (ODS) and greenhouse gas (GHG) concentrations have influenced the
ozone layer (e.g. Shepherd et al., 2014; Zvyagintsev et al., 2015). In
combination with observed ozone time series, simulations allow the
attribution of ozone changes, thus encapsulating our understanding of the
fundamental physics and chemistry that controls ozone and its variations
(e.g. Chipperfield et al., 2017). TOMCAT/SLIMCAT (hereafter SLIMCAT) is a
3-D offline chemical transport model (Chipperfield et al., 2006), which
uses winds and temperatures from meteorological analyses (usually ECMWF) to
specify the atmospheric transport and temperatures and calculates the
abundances of chemical species in the troposphere and stratosphere. The
model has the option of detailed chemical schemes for various scenarios with
different assumptions of factors affecting ozone (e.g. Feng et al., 2011;
Grooß et al., 2018), including the concentrations of major ozone-depleting
substances, aerosol effects from volcanic eruptions (e.g. Dhomse et al.,
2015), and variations in solar forcing (e.g. Dhomse et al., 2013, 2016) and
surface conditions. For this study, the model has been forced by ECMWF
ERA-Interim reanalysis (Dee et al., 2010) and run from 1979 to 2017 at a
horizontal resolution of
We perform control and sensitivity simulations based on the SLIMCAT CTM to elucidate the impact of dynamical changes on the total ozone variations over the TP region. The control experiment R1 uses standard chemical and dynamical parameters for the time period of 1979–2017, which is identical to the control run of Chipperfield et al. (2017). To understand the special dynamical influences (e.g. GH) on ozone variations over the TP, two sensitivity experiments (R2 and R3) were performed with all configurations the same as R1 except the simulations used annually repeating meteorology for the years 2004 and 2008, respectively. These years were chosen because the 150 hPa GH in wintertime is substantially different, while other dynamical proxies are almost the same for the two years. We also take a 5-year average from model dates in 2004–2008 for each sensitivity experiment to exclude the influence from other time-dependent changes (e.g. chemical processes).
Figure 1 shows the TCO time series averaged for
December–January–February (DJF) and June–July–August (JJA) seasons during
1979–2017 over the north-TP (40–50
C3S-based total column ozone (TCO) time series averaged
for December–January–February (DJF) and June–July–August (JJA) seasons
during 1979–2017 over
To illustrate TOL characteristics, we calculate the zonal deviations by subtracting the zonal mean total ozone for each latitude band from the TCO at each grid point (Fig. 2). The negative zonal deviations suggest that the TOL centred over the TP exists for all the seasons. As shown in Fig. 1c and d, TOL over the TP is most discernible in summer (JJA), followed by spring (March–April–May, MAM) and autumn (September–October–November, SON), while it is weakest in winter (DJF). The TOL centre also moves from the northwest in spring (MAM) to the south in winter (DJF). The mechanisms for these seasonal differences over the TP are very different in winter and summer. In wintertime, the plateau geographic effect is somewhat less effective in modifying the lower stratospheric circulation as the subtropical jet moves southwards (e.g. Luo et al., 2019). During summertime, the TP is an elevated heating source causing thermally forced anticyclonic circulation. The upper-level Asian summer monsoon anticyclone coupling with deep convection over the TP can potentially transport ozone-poor air from the boundary layer upward into the upper troposphere and lower stratosphere (Liu et al., 2003; Gettelman et al., 2004; Randel and Park, 2006; Bian et al., 2011). Seasonal variations in TCO over the TP and zonal-TP region are shown in Fig. S2. The wintertime ozone buildup and steady summertime ozone decline are evident over both regions. However, the high topography of the TP causes an earlier phase (about 1 month) and smaller amplitudes in TCO variability over the TP. The different TOL magnitudes in different seasons could be associated with the fact that wintertime ozone concentrations are largely controlled by large-scale dynamical processes, while photochemical loss is the only dominant process in summer. Thus, it is necessary to analyse the influences of the chemical and dynamical processes (e.g. EESC, solar, QBO and the local thermal–dynamical proxy) on the total ozone variability under different atmospheric conditions.
Latitude–longitude cross section of the zonal ozone
deviations for
Multivariate linear regression models are widely used to assess the long-term total ozone trends. In these models, proxies are included to separate the influences of important short- and long-term processes on trend determination. Typically, multivariate linear regression models use equivalent effective stratospheric chlorine (EESC) or piecewise linear trend (PWLT) terms for long-term ozone trends (e.g. Reinsel et al., 2002; Nair et al., 2013; Chehade et al., 2014). EESC is a measure of the total inorganic chlorine and bromine amounts in the stratosphere, which drive chemical ozone depletion. Previous studies have indicated that EESC is a main contributor to the long-term global ozone decline and the trend changes after the end of 1990s (e.g. Newman et al., 2004; Fioletov and Shepherd, 2005; Randel and Wu, 2007). We use this method to study the effect of EESC on the long-term ozone variations over the TP and the other zonal regions. A PWLT-based regression method is used to statistically analyse robustness of decreasing and recovery trends in the total ozone before and after the EESC peak in 1997. Our aim is to clarify statistical significance of the key processes responsible for the total column ozone variations over the TP in different seasons using two different regression models.
Traditional explanatory proxies to account for influence of chemical and
dynamical processes, include the F10.7 solar flux for the 11-year solar
cycle, quasi-biennial oscillation (QBO) at 30 and 10 hPa (QBO30 and
QBO10), and ENSO (e.g. Baldwin et al., 2001;
Camp and Tung, 2007). Some studies also include aerosol optical
depth at 550 nm, to account for ozone loss due to volcanically enhanced stratospheric
aerosol loading after El Chichón (1982) and Mt. Pinatubo (1991)
eruptions. To account for dynamical variability typical indices are wind
near vortex, Arctic oscillation (AO) index, Eliassen–Palm flux or eddy heat
flux (e.g. Chehade et al., 2014 and references therein). Due to unique
nature of TP orography, the local thermal–dynamical forcing, e.g. the
geopotential height at 150 hPa (GH150) and the surface temperature (ST), are
also considered as dynamical proxies. We calculate the GH150 and ST over the
TP and zonal latitude bands from the ECMWF ERA-Interim reanalysis dataset
obtained via
Correlation coefficients for the DJF mean TCO and explanatory variables over the TP during 1979–2017.
Due to the large differences in scales and units of the explanatory
variables, we have standardized all the time series to ensure each factor
contributes approximately proportionately to the final ozone variations. The
transformation does not change the correlation and fitting results. Another
important criterion for multivariate regression model is that explanatory
variables should not be highly correlated with each other. Table 1
shows the correlation values for the DJF mean TCO (over the TP region) and
explanatory variables during 1979–2017 (a similar analysis for JJA is
presented in Table S1 in the Supplement). The local
thermal–dynamical proxy (GH150 or ST over the TP) is de-trended before being
used in the regression models. As shown from the correlation analysis, the
DJF mean TCO has significant negative correlations with EESC, QBO and GH150.
The solar variability proxy (F10.7 index) is strongly correlated with the AO
(0.398) time series. Also, the GH150 time series shows relatively stronger
correlation with the ENSO (
We apply the multivariate linear regression models to the seasonal mean TCO
time series to determine long-term ozone changes over the TP, zonal-TP,
south-TP and north-TP zonal bands, respectively. Table 2 lists the
adjusted determination coefficients (adj.
Adjusted determination coefficients of the PWLT-based regression model for DJF mean TCO over different regions with different proxies.
Using the PWLT-based regression model, we analyse the TCO trends for
1979–1996 and 1997–2017. The fitted signals of the TCO anomalies and
explanatory terms in Eq. (3) for DJF and JJA are shown in Fig. 3,
and corresponding regression coefficients along with 2
Except for the linear trends, all the other explanatory proxies (solar
cycle, QBO and GH150) contribute significantly to the ozone variations in
DJF (above the 99 % confidence level), especially combined contribution
from three dynamical proxies (QBO30, QBO10 and GH150) which adds up to 40 DU.
As shown later JJA ozone concentration are largely controlled by
photochemical ozone loss, contribution from GH150 drops sharply
(
PWLT-based regression coefficients and standard deviations for the DJF and JJA mean TCO over the TP during 1979–2017.
To describe quantitatively the contributions of different explanatory
proxies to the DJF and JJA mean total ozone variability over different
regions, we calculate the percentage ozone change for comparison, as shown
in Fig. 4. These contributions using percentage ozone change are
represented by Eq. (4):
Peak contributions of various explanatory variables to
variability in the total ozone column (in %) in
Previous studies have found that changes in GH150 associated with an
enhanced South Asian high (SAH) results in significant TCO deviations at
150–50 hPa over the TP (Tian et al., 2008; Bian et al., 2011; Guo et al.
2012). From April onwards, as the SAH advances over the TP, summertime GH150
starts increasing (Fig. S4). Between the TP and zonal-TP region,
the GH150 contribution shows a maximum difference in May when the negative
TOL is also strongest (Fig. S2), with a correlation coefficient
of
The seasonal variability in TCO over the TP (Fig. S2) indicates a marked seasonal cycle with a buildup of total ozone through the winter and a decline through the summer. The correlation of the DJF mean TCO with the subsequent JJA means over the TP during 1979–2017 is 0.44, which is statistically significant above the 95 % confidence level. This significant positive correlation indicates that negative or positive wintertime TCO anomalies over the TP appear to persist through the summer period (as shown in Fig. 5). Table 4 lists the correlation coefficients of TCO variations in a given season of the year with those in subsequent seasons. The correlation decreases from the buildup in winter to the end of summer, and there exists a sharp drop between the summer (JJA) and autumn (SON) which may reflect that dynamical variability is nearly absent during summer months and ozone simply drops off photochemically in a predictable way (Fioletov and Shepherd, 2003). Detailed analysis of the correlation between subsequent months of the year is provided in Table S4.
Correlation coefficient between ozone values in a given season and the subsequent season.
One lag is 3 months; bolded numbers are statistically significant within
Fioletov and Shepherd (2003) highlighted the seasonal persistence of midlatitude total ozone anomalies and indicated that seasonal predictability is applicable for latitudinal belts or large regions only. The seasonal persistence of ozone anomalies over the TP also implies a causal link between the wintertime ozone buildup due to planetary-wave-induced transport and the subsequent chemical loss. The ozone buildup in wintertime when transport dominates is largely modulated by QBO (Holtan and Tan, 1980). However, GH150 represents large part of wintertime variability in the ozone transport. In summertime, as expected, photochemical processes become more important, while dynamical impact from QBO decreases and almost disappears for GH150. Seasonal persistence in TCO anomalies shows that if there is more transport in DJF as represented by GH150 changes, higher ozone values will persist for at least 6 months, even though there is little correlation between summertime ozone anomalies and GH150. This analysis clearly highlights dynamical influence of the wintertime GH150 on the summertime (JJA) ozone concentrations.
To investigate the role of wintertime GH150 on ozone transport, we use the SLIMCAT 3-D chemical transport model to understand its role under different conditions. The simulated TCO time series obtained from the control experiment R1 are shown in Fig. S5. Overall, modelled TCO is consistent with the C3S-based TCO data although they are biased low. By applying the PWLT regression model in Eq. (3) to the simulated TCO time series, the percentage ozone change from each explanatory proxy is shown in Fig. S6. The simulation results are similar to the C3S regression results, although contributions from most explanatory proxies are larger except for the GH150. This difference is probably due to the coarse model resolution and the inhomogeneities in ERA-Interim data, especially before 2000 (e.g. Dhomse et al., 2011, 2013; McLandress et al., 2014). The contribution of the GH150 proxy to the simulated TCO variations over the TP is statistically significant in DJF but not in JJA. To further elucidate the role GH150 plays in the total ozone variability over the TP, we performed two sensitivity experiments (R2 and R3) with repeating dynamics from the years 2004 and 2008, respectively. For the two years, the wintertime difference in QBO is modest but GH150 is significantly different. We then take a 5-year average based on a time-slice simulation during 2004–2008 for each sensitivity experiment to ensure that other chemical factors (EESC, solar cycle, etc.) are the same between the simulations. Thus, the model settles down with the GH150 as the main proxy that influences the ozone variations over the TP.
A caveat is that none of the dynamical processes are independent. The GH150 proxy represents the overall tropospheric dynamical influence somewhat realistically as it incorporates coupling between various tropospheric teleconnection patterns and the local TP circulation. To better understand the zonal and meridional pathways, the vertical DJF mean GH differences between the years 2004 and 2008 as well as the 5-year averaged ozone differences (2004–2008) based on the SLIMCAT sensitivity simulations are represented by the contours and colours in Fig. 6. The shaded area shows the TP region. In DJF (Fig. 6a and b), a positive anomaly centre of the GH difference occurs near the 150 hPa pressure level, co-located with a negative ozone anomaly. In JJA (Fig. 6c and d), there are no such clear anomaly centres for mean GH and ozone differences over the TP.
By comparing the GH variation with latitude in Fig. 6a and c, we find that the DJF mean GH differences over the TP are mainly influenced by those over the high latitudes, and in JJA they are mainly influenced by those from low latitudes (as shown by the arrows therein). This may be because the TP lies near the boundary between the tropics and midlatitudes in the troposphere. Due to the movement of the Intertropical Convergence Zone (ITCZ), the TP in wintertime is located in midlatitude band where ozone variability is determined by the tropopause height or folds in the lower stratosphere, while in summer, the TP lies in the tropical band where ozone variability is largely determined by QBO (and QBO-induced circulation) in the mid-stratosphere (Baldwin et al., 2001).
The GH variation with longitude in Fig. 6b and d suggests a tropospheric coupling between the local TP circulation and some tropospheric teleconnection patterns (e.g. ENSO or Walker circulation). As the TP is an elevated heat source, the differences in heat distribution between the plateau and ocean will cause air motions in the zonal and vertical direction. In the normal condition, the pressure gradient force that results from a high-pressure system over the eastern Pacific Ocean and a low-pressure system over the TP will cause the global general circulation (such as the Walker circulation) and therefore affect the ozone distribution. A correlation analysis shows that the GH150 proxy over the TP is in a strong, negative relation to ENSO in DJF, which means during an El Niño event GH150 near the TP also increases, thereby increasing tropopause height, leading to a decrease in TCO over the TP. The positive–negative vertical band-like features in DJF mean ozone differences shown in Fig. 6b seem to closely resemble Walker-circulation-type anomalies (Hu et al., 2016). They also explain why the ozone differences over the TP and the Pacific Ocean are opposite in sign, as indicated by the dashed blue and red boxes therein. Thus, we suggest that wintertime GH fluctuations associated with ENSO events or Walker circulation may play an important role in controlling the TCO variability over the TP. In JJA, however, there are no distinctive features of GH and ozone differences near the TP. As the summertime ozone is less controlled by the dynamical processes (especially GH150), there would not exist such a clear correlation as that in wintertime. Overall, the model results support the hypothesis that wintertime TP ozone variations are largely controlled by tropical to high-latitude transport processes, whereas summertime concentrations result from the combined effect of photochemical decay and tropical processes.
In this study, we have analysed the variations and trends of the total column ozone and the relative total ozone low over the Tibetan Plateau in different seasons during the period of 1979–2017. The most recent C3S datasets based on model assimilation of meteorological and ozone observations are used and compared with merged SBUV satellite observations. We use the PWLT- and EESC-based multivariate regression models to analyse the contributions and trends associated with the dynamical and chemical processes that modify the total ozone changes over the TP and zonal areas. In addition to conventional regression proxies (EESC, solar cycle, QBO, ENSO, etc.), we also use the local thermal–dynamical proxy (ST or GH150) to account for the dynamical influence on the wintertime and summertime ozone changes over the TP. Based on the SLIMCAT 3-D model, we have performed sensitivity experiments to explore the role 150 hPa GH plays in the DJF mean ozone variations over the TP.
Our main conclusions are as follows:
The comparison of the C3S ozone dataset with the merged SBUV satellite-based
observations has verified the feasibility of using assimilated C3S data to
study long-term variations over the relatively small TP region. With the C3S data extended up to early 2018, the long-term variations of TCO
and TOL averaged in different seasons are compared over 1979–2017. The TOL
over the TP compared to the zonal mean at the same latitude band exists
throughout the year, though the magnitude and the centre location change with
season. Both PWLT and EESC-based multivariate regression models show a
change in TCO trends from the pre-1997 decline to the post-1997 recovery,
although the positive trend based on PWLT is not statistically significant.
Compared to the zonal mean trend over the same latitude band, the TP ozone
trend shows a relatively smaller rate of increase after 1997, which
highlights the zonal asymmetry in ozone recovery. Overall, regression results based on three groups of independent explanatory
variables show that the GH150 proxy improves the regression especially for
the TP region and is more significant than the ST proxy. By comparison of
the contributions of different proxies in DJF and JJA, dynamical proxies
(QBO and GH150) dominate the wintertime TCO variations over the TP, with
statistical significance at 99 % confidence level, but in summertime
photochemical processes dominate and the dynamical process decays (QBO at 10 hPa
persists but GH150 disappears). The positive correlation between the DJF and
JJA TCO over the TP indicates the seasonal persistence of total ozone
variations from the ozone buildup in winter to the decreasing period in
summer. Our analysis clearly highlights the influence of wintertime GH150
variations on summertime TCO trends. Results from the SLIMCAT control experiment (R1) reproduce the TCO time series
and regression results for the TP region, and are consistent with the
C3S-based results. Sensitivity experiments (R2 and R3) are performed to
explore the significant contribution of the GH150 proxy to the wintertime
Tibetan ozone variations. The composite analysis shows that GH150
fluctuations play a key role in controlling the DJF mean TCO variability
over the TP, which may be associated with ITCZ, ENSO events or the Walker
circulation.
Overall, our results show that stratospheric ozone recovery due to the impact of the Montreal Protocol is not expected to behave similarly at all longitudes within a certain latitude region. In the specific case of the Tibetan Plateau, other local factors, which vary with season, will affect column ozone variations. Given the impact of dynamical proxies described above, column ozone over the TP will be subject to long-term changes beyond halogenated ozone-depleting substances and needs careful monitoring.
The satellite and climate data used in this study are available at the sources and references in the dataset section. The model data used are available upon request (w.feng@ncas.ac.uk).
The supplement related to this article is available online at:
YL performed the data analysis and prepared the manuscript. MPC, WF, SSD, RJP, GD and FL gave support for discussion, simulation and interpretation, and helped to write the paper. All authors edited and contributed to subsequent drafts of the manuscript.
The authors declare that they have no conflict of interest.
We are grateful to the Copernicus Climate Change Service (C3S) for providing the global ozone dataset. The modelling work is supported by National Centre for Atmospheric Science (NCAS). We thank all providers of the climate data used in this study. We thank Jiankai Zhang (Univ. Cambridge) and Dingzhu Hu (Univ. Reading) for helpful suggestions on the Tibetan ozone trends and regression analysis. We also acknowledge the support of National Natural Science Foundation of China, Jiangsu provincial government scholarship programme and the Natural Science Foundation for universities in Jiangsu province.
This research has been supported by the National Natural Science Foundation of China (grant nos. 41127901, 91837311 and 41675039) and the Natural Science Foundation for universities in Jiangsu province (grant nos. 17KJD170004 and 18KJB170009).
This paper was edited by Marc von Hobe and reviewed by three anonymous referees.