2.1 Measurement data

We used observational data to evaluate the meteorological parameters and air pollutant concentrations simulated by the Weather Research and Forecasting (WRF)-CMAQ model. The daily meteorological observations were obtained from the National Meteorological Information Center (http://data.cma.cn, last access: 1 December 2019), including the daily average temperature at a height of 2 m, relative humidity at a height of 2 m, wind speed at a height of 10 m, and surface pressure at ∼700 ground weather stations in China. The observed concentrations of air pollutants were obtained from the China National Environmental Monitoring Center (http://106.37.208.233:20035/, last access: 1 December 2019), including SO₂, NO₂, CO, O₃, and PM_2.5. In 2013, there were 493 environmental monitoring stations in 74 major cities, mostly in urban areas. As a result, only these stations have continuous 5-year observations of pollutants from 2013 to 2017. With the increasing recognition of the air pollution problem in China, more monitoring stations have been built since 2013, and the total number exceeded 1500 in 2017. We applied data quality control to the observed pollutant concentrations to remove unreliable outliers following the approach used in previous studies (Lu et al., 2018; Song et al., 2017). The locations of environmental monitoring stations are presented in Fig. S1 in the Supplement.

To evaluate the model performance, we calculated some statistical parameters, including the mean observation, mean simulation, mean bias, mean absolute gross error, root mean square error, index of agreement, and correlation coefficient. The equations for these statistical parameters can be found in Fan et al. (2013).

2.2 Model settings

The CMAQ modeling system (Byun and Schere, 2006) was developed by the United States Environmental Protection Agency (US EPA) to approach air quality as a whole by including state-of-the-art capabilities to model multiple air quality issues, including tropospheric ozone, fine particles, toxins, acid deposition, and visibility degradation. This study used the CMAQ model (version 5.2.1), an offline chemical transport model without considering the effects of air pollutants on meteorological fields. The meteorological inputs are driven by the WRF model. Table S1 in the Supplement shows the settings of the physical parameterization schemes for the WRF model. The meteorological initial and boundary conditions were provided by NCEP/NCAR FNL reanalysis data with a horizontal resolution of 1^∘. Figure S1 shows the modeling domains for the WRF and CMAQ model with a horizontal resolution of 36 km. The model has 23 vertical layers and reaches 50 hPa at the top. The CMAQ modeling domain, which is a few grids smaller than the WRF modeling domain to reduce the effect of the meteorological boundary from the WRF model, covers all the land areas of China and the surrounding regions. The boundary conditions of chemical species for CMAQ were derived from the modeling results of the global chemistry transport model, Model for Ozone and Related Chemical Tracers, version 4 (MOZART-4) (http://www.acom.ucar.edu/wrf-chem/mozart.shtml, last access: 1 December 2019) (Emmons et al., 2010). We used SAPRC07TIC (Carter, 2010; Hutzell et al., 2012; Xie et al., 2013; Lin et al., 2013) as the gas-phase chemical mechanism and AERO6i (Murphy et al., 2017; Pye et al., 2017) as the aerosol mechanism in the CMAQ model.

The original CMAQ model includes the heterogeneous reactions of only NO₂, NO₃, and N₂O₅ on aerosol surfaces. Recent studies (K. Li et al., 2019a, b) have suggested that the heterogeneous reactions on aerosol surfaces, mainly the uptake of HO₂, played a significant role in the increasing O₃ concentrations in China from 2013 to 2017. To better simulate the effects of aerosol on ozone via heterogeneous reactions, we updated the heterogeneous reaction rates of NO₂ and NO₃ on the aerosol surface and incorporated more heterogeneous reactions into the CMAQ model, including the uptake of HO₂, O₃, OH, and H₂O₂. The detailed heterogeneous reactions in the updated CMAQ model are listed in Table S2. We select the “best guess” uptake coefficients of these gases, which have been widely used in previous chemical transport model studies (Jacob, 2000; Zhu et al., 2010; Zhang and Carmichael, 1999; Fu et al., 2019; Liao et al., 2004). These improvements help the CMAQ model better simulate ozone and other pollutants, and their influence and that of aerosol on the ozone concentration via various heterogeneous reactions are evaluated in the companion paper (Part 2; Liu and Wang, 2020).

2.3 Emissions

For anthropogenic emissions, we used the Multi-resolution Emission Inventory for China (MEIC) for 2013 to 2017 (http://www.meicmodel.org/, last access: 1 December 2019), which was developed by Tsinghua University and has been evaluated by satellite data and ground observations (Zheng et al., 2018). International shipping emissions in 2010 were obtained from the Hemispheric Transport Atmospheric Pollution emissions version 2.0 dataset (Janssens-Maenhout et al., 2015). Biogenic emissions from 2013 to 2017 were calculated from the Model of Emissions of Gases and Aerosols from Nature (MEGAN) (Guenther et al., 2006) and driven by the interannual summer meteorological inputs from the WRF model.

2.4 Experiment settings

The model simulations were conducted for the summers (June, July, and August) from 2013 to 2017 and driven by interannual meteorology and anthropogenic emissions, namely the base simulations. The shipping emissions remained unchanged in the 5-year simulation due to a lack of data for recent years. To investigate the causes of the increasing surface ozone levels in China, we conducted four sets of modeling experiments based on the simulation of 2013. The first was designed to evaluate the effects of changes in meteorological conditions and anthropogenic emissions (Table S3). We derived the effects of meteorological variation by comparing the simulated ozone concentrations in different years but with the same anthropogenic emissions and chemical boundary conditions as those from 2013. The effects of changes in anthropogenic emissions were derived by comparing the simulated ozone values in 2013 but with anthropogenic emissions from different years. The second set was designed to evaluate the effects of variations in biogenic emissions driven by meteorological conditions (Table S4), which were derived by comparing the simulated ozone values in 2013 but with biogenic emissions from different years. The third set was designed to evaluate the contributions of the individual meteorological parameters to the ozone change from 2013 to 2017 (Table S5), including temperature, specific humidity, wind field, PBL height, clouds, and precipitation. Here we used specific humidity rather than relative humidity, because the specific humidity, which scaled with water vapor concentrations and was simulated by the model, was more useful for understanding the ozone formation chemistry. The fourth set (Table S6) was designed to evaluate the contribution of long-range transport from outside the CMAQ modeling domain (Fig. S1) by comparing the simulated ozone levels in 2013 with those with chemical boundary conditions from MOZART from different years.

Table 1Evaluation results for the meteorological factors (T2 is temperature at a height of 2 m; RH2 is relative humidity at a height of 2 m; WS10 is wind speed at a height of 10 m; PRS is surface pressure; Num is number of sites with available observation for statistics; OBS is mean observation; SIM is mean simulation; MB is mean bias; MAGE is mean absolute gross error; RMSE is root mean square error; IOA is index of agreement; r is correlation coefficient; OBS, SIM, MB, MAGE, and RMSE have the same units as given in the first column, while IOA and r have no unit).

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3.1 Model evaluation

Table 1 shows the evaluation results for temperature, relative humidity, wind speed, and surface pressure. The results for all weather stations in China were averaged. The simulated temperatures at the height of 2 m were slightly underestimated with biases of less than 0.6 ^∘C in 5 years. The high correlation coefficients (over 0.82) indicate that the WRF model can capture variations in temperature. Like temperature, the simulated relative humidity values were also slightly under-predicted and had a high correlation coefficient with the observations. The simulated wind speeds at the height of 10 m were slightly overestimated by about 0.5 m s⁻¹ due to the underestimation of the effects of urban topography in the WRF model and were often found in other WRF modeling studies (Fan et al., 2015; Hu et al., 2016). The WRF model faithfully reproduces surface pressures for 5 years with low biases and high correlation coefficients. The WRF model could also capture the temporal variations in meteorological parameters. For example, the simulated temperature at the height of 2 m decreased from 2013 to 2015 and then increased from 2015 to 2017, which is consistent with the observations. The good performance of the WRF model gives us the confidence to use the simulations to study the effects of variations in meteorological conditions on ozone levels.

Table 2Evaluation results for the air pollutants in China (Num is number of sites with available observation for statistics; OBS is mean observation; SIM is mean simulation; MB is mean bias; MAGE is mean absolute gross error; RMSE is root mean square error; IOA is index of agreement; r is correlation coefficient; OBS, SIM, MB, MAGE, and RMSE have the same units as given in the first column, while IOA and r have no unit).

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Table 2 presents the evaluation results for air pollutant concentrations in China. Generally, the CMAQ model has excellent performance on simulating pollutant concentrations with low biases, high index of agreement, and high correlation coefficients. The simulated NO₂ mixing ratio was slightly underestimated for these 5 years in general, which can be explained in part by the fact that the NO₂ concentrations in the national network were measured using the catalytic conversion method, which overestimates NO₂, especially during periods with active photochemistry and at locations away from primary emission sources (Xu et al., 2013; Zhang et al., 2017; Fu et al., 2019). The simulated CO mixing ratio is underestimated significantly by the CMAQ model, which might be due to the missing sources of CO such as biomass burning. The CMAQ model predicts a slightly higher MDA8 O₃ mixing ratio, which could be explained by the artificial mixing of ozone precursors in modeling grids leading to higher ozone production efficiency and positive ozone biases, especially for models with coarser resolutions (Young et al., 2018; Chen et al., 2018; Yu et al., 2016). However, the overall CMAQ model performance is acceptable and can support further investigation of the drivers of increasing ozone levels in China.

Figure 1Spatial distribution of the simulated surface maximum daily 8 h average (MDA8) O₃ mixing ratios in summer (June–August) of 2013 (a), 2014 (b), 2015 (c), 2016 (d), and 2017 (e). Circles with color are the available observed values at environmental monitoring stations in each year.

3.2 Rate of change in ozone due to meteorology and anthropogenic emission

Figure 1 shows the spatial distribution of the summer surface MDA8 O₃ level over land areas of China in summer from 2013 to 2017. The CMAQ model can faithfully capture the spatiotemporal variations in the observed MDA8 O₃ level. Both the simulations and the observations exhibit elevated concentration in midlatitude areas, including the North China Plain (NCP), Yangtze River Delta (YRD), Sichuan Basin (SCB), and large areas in central and western China. The O₃ levels in southern China are lower than those in northern China, but they are relatively high in the Pearl River Delta (PRD) region.

Figure 2Rates of changes in the simulated (a) and observed (b) surface MDA8 O₃ mixing ratios in summer from 2013 to 2017. In panel (b), only environmental monitoring sites (493) with data available in all 5 years are presented. (c) and (d) present the rates of changes in the simulated MDA8 O₃ mixing ratios due to variations in meteorological conditions and anthropogenic emissions in summer from 2013 to 2017 (see Methods section). The corresponding p values of regression are presented in Fig. S2.

We applied the linear regression method to obtain rates of change in the simulated and observed interannual MDA8 O₃, which are shown in Fig. 2. In general, the observed MDA8 O₃ mixing ratios present an increasing trend from 2013 to 2017 in many of the 493 monitoring stations (most located in urban areas of 74 major cities), while the trend result at some of these sites has relatively low confidence level as indicated by large p values (Fig. S2). The model captured 57 % of the observed rate of increase averaged for the 493 sites (Fig. S3). With the aid of the model simulations, the characteristics of the changes in MDA8 O₃ levels were revealed for all areas, including those with no monitoring stations. Both observations and model simulations show that NCP, YRD, SCB, northeastern China, and some areas in western China experienced increasing levels of ozone pollution. Interestingly, the model results revealed that MDA8 O₃ levels were decreasing in large parts of rural areas that could not be covered by the current monitoring stations, such as northwestern China and southern China.

We separated the changing rates of simulated MDA8 O₃ into those due to variations in meteorological conditions and changes in anthropogenic emissions (also see Fig. 2). Here, the impact of biogenic emission variation is included in the effects of meteorological variation, because it is affected by meteorology. The result shows that the changing rates of ozone over China were more affected by meteorological changes than by emission changes in terms of spatial distribution. The regions with an increasing or decreasing trend of ozone were generally consistent with the contributions from variations in meteorology except for some regions whose ozone trends were dominated by anthropogenic emission changes. The changes in anthropogenic emissions have resulted in ozone increases in NCP, YRD, PRD, SCB, and other scattered megacities but decreases in rural regions. This discrepancy can be explained by the different ozone formation regimes in urban (VOCs-limited) areas and rural (NO_x-limited) areas (K. Li et al., 2019a; N. Wang et al., 2019). A recent study reported the observations of surface ozone during 1994–2018 at a coastal site in southern China and revealed no significant changes in the ozone levels in the outflow of air masses from mainland China during recent years (T. Wang et al., 2019). These results suggest that nationwide NO_x emission reductions may have decreased ozone production over large regions despite causing an ozone increase in urban areas. The impact of anthropogenic emission changes on ozone levels in recent years remains a challenging and momentous topic and will be assessed in the companion paper (Part 2; Liu and Wang, 2020). The present paper focuses on the effects of meteorological conditions.

Figure 3Changes in the simulated summer surface MDA8 O₃ mixing ratios from the base simulation (All, a–d); those due to variations in meteorological conditions (Met, e–h); and anthropogenic emissions (Emis, i–l) in 2014, 2015, 2016, and 2017 relative to 2013.

3.3 Impact of meteorological conditions and anthropogenic emissions relative to 2013

We next quantified the impact of meteorological conditions and anthropogenic emissions on ozone changes from 2013 to 2017 relative to 2013 (Fig. 3). The changes in MDA8 O₃ from the base simulation varied spatially and yearly, mainly as a result of meteorological conditions. The variation in the MDA8 O₃ mixing ratio due to meteorological changes ranged from −12.7 to 15.3 ppbv over China from 2014 to 2017 relative to 2013. The emission-induced MDA8 O₃ changes in each year exhibited similar spatial patterns, which were consistent with those of the changing rates due to emission changes (Fig. 2d). The impact of emission changes on the MDA8 O₃ concentrations became increasingly significant as anthropogenic emissions were further reduced. Our results differ from those by P. F. Wang et al. (2019), who suggested a less important role of meteorology in the variation of ozone from 2013 to 2015. The discrepancy could be due to the difference in the chemical mechanisms and method used for quantifying the effects of emission changes. They calculated the changes in ozone levels due to emission variations by subtracting simulated changes due to meteorological conditions variations from total observed changes, and we calculated by comparing the simulated difference between the simulations in 2013 driven by anthropogenic emissions from different years.

Figure 4Interannual changes in the simulated (SIM) and observed (OBS) summer surface MDA8 O₃ mixing ratios and those due to variations in meteorological conditions (MET) and anthropogenic emissions (EMIS) in (a) Beijing, (b) Shanghai, (c) Guangzhou, and (d) Chengdu in 2013–2017 relative to 2013.

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We further found that in some specific regions and years, the changes in MDA8 O₃ due to meteorological variation could be comparable to or greater than those due to emission changes, which highlights the significant role of meteorological conditions in ozone variations. As a result, we selected four megacities in different regions of China to further examine the impact of changes in meteorological conditions and anthropogenic emissions on ozone levels (Fig. 4) – Beijing, Shanghai, Guangzhou, and Chengdu – which are principal cities in the Beijing-Tianjin-Hebei (BTH) region in the north, the YRD in the east, the PRD in the south, and the SCB in the southwestern part of China, respectively (see Fig. S1 for their locations). The numbers of monitoring sites used to obtain the average values for these four cities were 12, 9, 11, and 8, respectively. Most of these sites are situated inside the city, and thus the average results represent the conditions in urban areas. As shown in Fig. 4, the changes in observed MDA8 O₃ varied in cities and years, which were generally captured by the model (except for the changes of MDA8 O₃ in Beijing in 2014 and 2015, which were underestimated, likely due to underestimation of anthropogenic emissions in Beijing and its surrounding regions in those 2 years). The contributions of anthropogenic emissions to MDA8 O₃ exhibited an almost linear increasing trend in the four cities from 2013 to 2017, whereas the contribution of meteorology could be positive or negative, depending on the region and year.

In Beijing (the BTH region), the variations in meteorological conditions had little effect on the MDA8 O₃ changes from 2014 to 2017 relative to 2013. The increase in ozone was driven primarily by the changes in anthropogenic emissions. This characteristic can also be identified in the larger BTH region, as shown in Fig. 3. In Shanghai (the YRD region), the effects of meteorology were comparable with those of anthropogenic emissions in terms of the absolute values of the contribution to MDA8 O₃ changes. From 2014 to 2016, the meteorology was unfavorable for ozone formation, which masks the ozone increase due to emission changes. However, meteorological conditions became a positive driver in 2017, leading to a drastic increase in the total MDA8 O₃ mixing ratio (over 10 ppb). In Guangzhou (the PRD region), the role of meteorological conditions was opposite to that in Shanghai. The weather changes were conducive to ozone formation from 2014 to 2016 compared with 2013, contributing to a large increase in MDA8 O₃ by over 10 ppbv; in 2017, however, the impact of changes in meteorological conditions on ozone levels decreased substantially, leading to a moderate increase in MDA8 O₃ in that year compared with 2013. In Chengdu (the SCB region), the impact of meteorological conditions on ozone variation was limited in these years, and the ozone level was mainly affected by emission changes, similar to the situation in Beijing. Our result is similar to those by P. F. Wang et al. (2019) for Shanghai and Guangzhou from 2013 to 2015, both indicating meteorological variations unfavorable for ozone formation in Shanghai and favorable in Guangzhou. On the other hand, our simulations differ from theirs that showed a considerable negative contribution of the meteorology variations to ozone levels in Beijing and Chengdu. Our study and that of Chen et al. (2019) suggest a weak role of meteorology variation in the summer ozone trend in Beijing. In addition to these four regions, we found a significant impact of meteorology on the ozone change in western China, especially the Qinghai–Tibetan Plateau (Fig. 3). The meteorological variations contributed to considerable increases in MDA8 O₃ in 2014–2017 in this region relative to 2013.

Figure 5The simulated daytime averaged temperature at a height of 2 m (Temp., a–e) and total biogenic isoprene emissions (Bio. isop. emis., f–j) in summer of 2013 from the base simulations and their changes in 2014, 2015, 2016, and 2017 relative to 2013. Panels (k)–(o) (MDA8 O₃) show the simulated summer surface MDA8 O₃ mixing ratios in 2013 from the base simulation and their changes due to variations in biogenic emissions in 2014, 2015, 2016, and 2017 relative to 2013.

3.4 Impact of meteorology-driven biogenic emissions relative to 2013

Temperature is an important meteorological driver of biogenic emissions. Figure 5 displays the temperature changes in land areas of China from 2014 to 2017 compared with 2013. The changes in the spatial distribution were similar in these 4 years. A decrease in temperature was found in midlatitude areas (25 to 40^∘ N), and an increase was found in southern (south of 25^∘ N) and northern China (north of 40^∘ N). As shown in Fig. S4, in midlatitude areas such as the BTH, YRD, and SCB, the temperature decreased from 2013 to 2015 and then increased from 2015 to 2017. In contrast, in southern China, such as the PRD region, the temperature increased during 2013–2014 and then slightly decreased during 2014–2017. The variation in the observed temperature is well captured by the WRF model, which enables the MEGAN model to calculate the variation of biogenic emissions driven by the realistic temperature. We present the results of biogenic isoprene emissions, because isoprene is generally the most abundant biogenic VOC and has the highest ozone formation potential (Zheng et al., 2009). Large isoprene emissions were found in the southern parts of China and northeast China, which have high vegetation covers in summer (Fig. 5f). The spatial and interannual variations of biogenic isoprene emissions followed the changes in temperatures in China, leading to similar changes in MDA8 O₃ concentrations.

In midlatitude areas of China, the variations in biogenic emissions induced a decrease in the MDA8 O₃ level after 2013. The most significant decrease in the MDA8 O₃ level was found in the YRD and SCB regions, where there were high biogenic emissions and a drastic temperature decrease. In 2014 and 2015, the MDA8 O₃ mixing ratio decreased by ∼5 ppbv in these two regions compared with 2013. The changes in ozone were less affected by biogenic emissions due to the lower biogenic emissions and smaller variation of temperature in the BTH region. In southern and northern China, the increase in temperature and then biogenic emissions since 2013 led to an enhancement of the MDA8 O₃ mixing ratio by up to 1 to 2 ppbv (Fig. 5). In Guangzhou, for example, affected by temperature-dependent biogenic emissions, the MDA8 O₃ increased by 0.8 ppbv from 2013 to 2014 and then decreased slightly from 2014 to 2017 (Fig. S5).

The changes in MDA8 O₃ concentrations due to changes in biogenic emissions in Shanghai and Guangzhou (Fig. S5) generally matched the total changes in ozone levels due to variations in meteorological conditions and provided a considerable contribution to them (Fig. 4). The variations in biogenic emissions were mostly affected by temperature. In Sect. 3.5, we also found that the changes in ozone levels caused by temperature variations via altering chemical reaction rates had an even more significant impact than via changing biogenic emissions in 2017 relative to 2013. As a result, temperature can play an important role in the variations in ozone levels in recent years. Previous studies also demonstrated the significant role of temperature in the ozone trend in China and other regions (Hsu, 2007; Jing et al., 2014; Lee et al., 2014). However, the role of meteorology is complex and the changes in other meteorological factors can counteract this effect. In Beijing and Chengdu, for example, the changes in ozone levels due to variations in meteorology were insignificant and could not reflect those caused by variations in temperature-dependent biogenic emissions (Fig. 4).

Figure 6The simulated averaged temperature (Temp.) and specific humidity (Humidity) at a height of 2 m, wind speed at a height of 10 m (Wind), planetary boundary layer (PBL) height, total clouds fraction (Clouds), and accumulated precipitation (Precip.) in the daytime in summer of 2013 from the base simulation (the left column) and their changes in 2017 relative to 2013 (the central column). The right column shows the changes in simulated summer surface MDA8 O₃ mixing ratios due to variations in temperature, specific humidity, wind fields, PBL height, clouds, and precipitation in 2017 relative to 2013.

3.5 Impact of individual meteorological parameters in 2017 relative to 2013

Figure 6 shows the individual effects of changing temperature, humidity, wind field, PBL height, clouds, and precipitation between 2017 and 2013 on the ozone level. Of all the meteorological parameters, the change in wind fields had the most significant impact on MDA8 O₃. It led to an increase in MDA8 O₃ mixing ratio in nearly all of China, with a maximum of 9.1 ppbv (Fig. 6i). Notable increases in MDA8 O₃ in western and eastern China due to the change in wind fields were identified, which contributed significantly to the meteorology-induced increasing ozone (Fig. 3h). In the Qinghai–Tibetan Plateau of western China, where terrain heights are greater than 3 km, the significant increase in the MDA8 O₃ mixing ratio (3 to 9 ppbv) due to wind change from 2013 to 2017 can be attributed in part to the enhanced downward transport from the upper troposphere as indicated by the increase in the potential vorticity (PV) (Fig. S6). In eastern China, the increase in O₃ level can be explained by the decrease in the wind speeds (Fig. 6h), which helps the accumulation of O₃ and its precursors and then increases ozone concentrations. There is no strong evidence for the change in the vertical transport from the free troposphere to the surface in eastern China and the horizontal transport from other regions within the modeling domain between these 2 years, according to the wind data (Fig. S7).

In addition to wind fields, other meteorological parameters contribute to the ozone change. Because a high temperature facilitates the formation of ozone via the increase in chemical reaction rates, the changes in ozone due to temperature were consistent with the changes in temperature in terms of spatial distribution (Fig. 6b and c). The MDA8 O₃ decreased in central China and increased in other parts of China. This change in the spatial distribution was similar to that due to the changes in biogenic emissions, because they were both affected by temperature. However, comparing Fig. 5o to Fig. 6c, we found that the impact of temperature via the change in the chemical reaction rates was generally more significant than that via the change in biogenic emissions from 2013 to 2017 (also see Fig. S8 for the quantitative comparisons in different cities).

The specific humidity decreased in central China and northeast China but increased in other parts of China from 2013 to 2017 (Fig. 6e). A decrease in the specific humidity in central China led to an increase in the MDA8 O₃ concentration in localized areas, and an increase in other parts of China resulted in a decrease in the MDA8 O₃ concentration in a large area (Fig. 6f). A negative correlation between ozone concentration and humidity in various regions over China was also reported in many previous studies (Ma et al., 2019; R. Li et al., 2019).

From 2013 to 2017, the PBL height increased in most parts of China, including NCP, northeast China, and northwest China (Fig. 6k). Our modeling results show that the increase in the PBL height enhanced the MDA8 O₃ concentration in most parts of China (Fig. 6l). A positive correlation between the PBL height and the ozone level in China is also revealed in the statistical results of He et al. (2017). Possible reasons for the ozone increase with the increase in the PBL height include lower NO concentration at the urban surface due to the deep vertical mixing, which then limits ozone destruction and increases ozone concentrations (He et al., 2017), and more downward transport of ozone from the free troposphere where the ozone concentration is higher than the near-surface concentration (Sun et al., 2010).

The cloud fraction increased in southwestern China and the Qinghai–Tibetan Plateau but slightly decreased in other parts of China from 2013 to 2017 (Fig. 6n). Because clouds can decrease ozone concentrations via aqueous-phase chemistry and photochemistry to enhance scavenging of oxidants and reduce the oxidative capacity of the troposphere (Lelieveld and Crutzen, 1990), the MDA8 O₃ mixing ratio in most parts of China increased except for southwestern China and the Qinghai–Tibetan Plateau (Fig. 6o).

The change in precipitation was similar to that of the cloud fraction in terms of spatial distribution (Fig. 6q) but made an opposite contribution to ozone levels compared with clouds (Fig. 6r). A positive correlation (p=0.95) between ozone and precipitation was also reported by the statistical results of R. Li et al.(2019). Although precipitation can decrease ozone concentrations via the scavenging of ozone precursors (Seinfeld and Pandis, 2006; Shan et al., 2008), an increase in precipitation may decrease aerosol concentrations that could increase ozone levels by altering photolysis rates and heterogeneous reactions.

The meteorological parameters that dominate ozone changes can differ in the four megacities (Fig. S9). The decrease in cloud cover was the important meteorological cause that increased MDA8 O₃ in Beijing from 2013 to 2017, the wind field change was the dominant factor that increased and decreased the MDA8 O₃ level in Shanghai and Guangzhou, respectively, and the decrease in temperature contributed primarily to the decline in MDA8 O₃ in Chengdu. However, the effect of the dominant meteorological factor on variations in the ozone level could be counteracted by the influence of other meteorological factors. For example, in Shanghai, the significant positive effect of changes in wind fields on ozone formation was offset by the negative effects of changes in temperature and precipitation, leading to the smaller increase in the ozone level due to the overall meteorological changes in 2017 compared with 2013 (Fig. 6b). The synergistic or counteracting effects from individual meteorological factors can give rise to the complex impact of the overall meteorology on ozone variations.

Figure 7Changes in the simulated summer surface MDA8 O₃ mixing ratios due to variations in long-range transport in 2014, 2015, 2016, and 2017 relative to 2013.

3.6 Impact of long-range transport relative to 2013

The chemical boundary conditions for the CMAQ model were derived from the results of the MOZART global model, which can represent the long-range transport of ozone and its precursors from outside the CMAQ modeling domain to China and surrounding regions (Fig. S1). We changed the chemical boundary conditions in 2013 to different years to investigate the role of long-range transport in ozone variations in China, and the results are shown in Fig. 7. Changes in long-range transport after 2013 increased the MDA8 O₃ mixing ratio over China except for some areas in northwestern China. Compared with a small increase in MDA8 O₃ (<1 ppbv) in eastern China, the Qinghai–Tibetan Plateau encountered a significant increase in MDA8 O₃ by about 1 to 4 ppbv due to changes in long-range transport after 2013. Increases in ozone levels were also found if we compared the changes in MDA8 O₃ due to variations in chemical boundary conditions relative to 2014. Ozone and its precursors can be transported a long distance and then affect surface O₃ in remote regions (West et al., 2009; Wild et al., 2004). A previous MOZART study by Li et al. (2014) found that the transport from the emissions of all Eurasian regions except China contributed 10 to 15 ppbv to the surface O₃ mixing ratio over western China. An analysis of 10 d back trajectories at Mount Waliguan (a remote mountain site in western China) also showed that the air mass from central Asia contributed to the high O₃ levels observed at the site during summer via long-range transport in the free troposphere (Wang et al., 2006). Our study indicates a considerable increase (1 to 4 ppbv) in this long-range transport contribution since 2013.