A study of the effect of aerosols on surface ozone through meteorology feedbacks over China

. Interactions between aerosols and gases in the atmosphere have been the focus of an increasing number of studies in recent years. Here, we focus on aerosol effects on tropospheric ozone that involve meteorological feedbacks induced by aerosol-radiation interactions. Specifically, we study the effects that involve aerosol influences on the transport of gaseous pollutants and on atmospheric moisture, both of which can impact ozone chemistry. For this purpose, we use the UK Earth System Model (UKESM1) with which we performed sensitivity simulations including and excluding the aerosol direct 15 radiative effect (ADE) on atmospheric chemistry, and focused our analysis on an area with high aerosol presence, namely China. By comparing the simulations, we found that ADE reduced the shortwave radiation by 11% in China, and consequently led to lower turbulent kinetic energy, weaker horizontal winds and a shallower boundary layer (with a maximum of 102.28 m reduction in north China). On the one hand, the suppressed boundary layer limited the export and diffusion of pollutants, and increased the concentration of CO, SO 2 , NO, NO 2 , PM 2.5 and PM 10 in the aerosol rich regions. The NO/NO 2 ratio generally 20 increased and led to more ozone depletion. On the other hand, the boundary layer top acted as a barrier that trapped moisture at lower altitudes and reduced the moisture at higher altitudes (the specific humidity was reduced by 1.69% at 1493 m averaged in China). Due to reduced water vapor, fewer clouds were formed, and more sunlight reached the surface, so the photolytical production of ozone increased. Under the combined effect of the two meteorology feedback methods, the annual average ozone concentration in China declined by 2.01 ppb (6.2%), which was found to bring the model in closer agreement with surface 25 ozone measurements from different parts of China.

clouds along the TRACE-P flight paths. For global scale, Liu et al. (2006) found that clouds have smaller impact on photolysis rates (less than -5%). Using the Cambridge p-TOMCAT chemical transport model (CTM), Voulgarakis et al. (2009aVoulgarakis et al. ( , 2009b showed that clouds have modest effect on global average ozone, but have a larger impact in the areas with higher cloud cover. Apart from the radiative effect, aerosol can also influence ozone through the chemical effect, which is the heterogeneous reaction. By reacting with ozone, nitrogen oxides, OH, HO2, H2O2, etc., mineral and carbonaceous aerosols can affect ozone 70 concentration directly and indirectly (Bauer, 2004;Ramachandran, 2015;Tang et al., 2017).
Based on the above, Figure 1 summarises five possible influences of aerosols on ozone: 1) heterogeneous reactions, 2) directly changing photolysis rate (ADE-PHO), 3) influencing the distribution of atmospheric pollutants, including ozone and its precursors through meteorological feedbacks (ADE-POL), 4) changing the photolysis rates through influencing moisture transport (ADE-MOI), 5) modifying clouds and, consequently, chemistry via microphysics, i.e. the aerosol indirect effect 75 (AIE). ADE-POL and ADE-MOI can be thought as the meteorological mechanisms that are both dominated by atmospheric transport feedbacks. Regarding the chemical effect, the impact of heterogeneous reactions on ozone has been investigated through a lot of laboratory and model studies (Bauer, 2004;Griffiths and Anthony Cox, 2009;Stewart and Cox, 2004;Tang et al., 2017). Regarding radiative effects, though the aerosol radiative influence on climate has been widely studied, the less abundant studies of their influence on ozone mainly focus on the ADE-PHO (Li et al., 2011a;Qu et al., 2019) and AIE (Hall 80 et al., 2018;Voulgarakis et al., 2009a;Wild et al., 2000), while ADE-POL and ADE-MOI are much less discussed in the literature. Therefore, in this paper, we exclude the impact of heterogeneous reactions, direct photochemical or microphysical effects, and focus on the combined effect of ADE-POL and ADE-MOI, i.e. the meteorological feedback, on tropospheric ozone. This enables a better understanding of the interaction between aerosols and ozone in China, and provides a more comprehensive scientific background for the control of atmospheric particulate matter, ozone and photochemical pollution. 85 A set of sensitivity simulations has been performed, by using the first version of the UK Earth System Model UKESM1, to investigate the influence of meteorological feedbacks of aerosols on ozone in different parts of East Asia. Section 2 introduces the observational data and numerical model set-up that is used in this study. Section 3.1 evaluates the performance of the numerical model by comparing to observational data. Section 3.2 discusses the aerosol-PBL feedback. Section 3.3 demonstrates the impact of ADE on atmospheric pollutants (ADE-POL). Section 3.4 demonstrates the impact of ADE on 90 moisture, clouds and then photolysis rates (ADE-MOI). Section 3.5 discusses the combined effect of ADE-POL and ADE-MOI on ozone. The conclusions and perspective are presented in Section 4.

UKESM1-AMIP
The 1 st version of the United Kingdom Earth System Model (UKSEM1) is jointly developed by Natural Environment Research 105 Council (NERC) and the Met Office Hadley Centre and has been released in Feb 2019 (Sellar et al., 2019). UKESM1 is based on the physical climate model HadGEM3 (Hewitt et al., 2011;Kuhlbrodt et al., 2018;Williams et al., 2018) and couples additional components, including the land biogeochemistry model (JULES;Clark et al., 2011), the UK Chemistry and Aerosols model (UKCA; Archibald et al., 2020;Mulcahy et al., 2018), the dynamic vegetation model (TRIFFID;Cox, 2001), and the Interactive ocean biogeochemistry model (MEDUSA; Yool et al., 2013). In this study, we used its atmosphere-only (UKESM1-110 AMIP) version to study the radiative effect of aerosols on ozone. Unlike the fully coupled UKESM1, the atmosphere-only including the mass and number of sulfate, black carbon, organic carbon, and sea salt. Dust aerosols are not available yet in GLOMAP-mode and so a bin scheme for mineral dust (Woodward, 2001) is used. The photolysis scheme in UKCA is Fast-JX (Telford et al., 2013), which provides the full scattering calculation for 18 wavelength bins over 177-850 nm. Fast-JX 120 allows the calculation of the interactive photolysis rates in the troposphere (Wild et al., 2000) and improves the calculation of photolysis rates in stratosphere (Bian and Prather, 2002). In order to focus on ADE-POL and ADE-MOI effects (see Sect. 1), Fast-JX has not been coupled with the GLOMAP-mode aerosol scheme, which means that photolysis rates are independent of the aerosol loading (Sellar et al., 2019).

Model evaluation 130
The model performance was evaluated by comparing the simulation results at the surface layer with the ground-based observations. The simulation with radiation feedback (EXPradon) was carried out as the control test. Figure 3 shows annual average concentrations of O3, CO, NO2, SO2, PM2.5 and PM10 simulated in EXPradon along with the concentrations observed at monitoring sites. Pearson's correlation coefficient (R) and mean bias error (MB) are shown in Table 1, using daily average concentration data. In terms of the spatial distribution, the simulation results are found to be in fairly good agreement with the 135 observations. With the economic and industrial development in north and east China, anthropogenic emissions lead to increased air pollution in these areas (Li et al., 2017a). The model captures the high SO2, CO, and NO2 concentrations and the high aerosol loading in north and east China. However, the model produces much higher SO2 concentrations than the observations, most likely due to an overestimation of the emissions. Under the clean air policies, the SO2 emission has declined by 62% during 2010-2017 (Zheng et al., 2018), but the CMIP6 emissions do not capture this reduction, with 2014 SO2 140 emissions being higher by 48% when compared to the region-specific Multi-resolution Emission Inventory for China (MEIC) (Paulot et al., 2018). For the spatial distribution of ozone, the model is in good agreement with observations. The simulated ozone concentration is well correlated with the observed values, with R reaching a maximum of 0.8 in the JJJ area. The radiation effect improved the model performance in China. When including the meteorological feedback of radiation effect, the average MB of ozone dropped from 10.03 to 5.63, while the average R remained the same (Table 1). In most areas, the 145 correlation between observed and simulated CO, NO, SO2, and particulate matter were higher in EXPradon than in EXPradoff, indicating that including these effects improves the simulation of tropospheric pollutants. Subsequently, we examine these effects in more detail.

Aerosol effect on meteorology
The aerosol effect on meteorology was assessed by taking averages over the 1-year simulation and taking the difference 150 between EXPradon and EXPradoff. Figure 4 shows the changes in net downwelling surface shortwave radiation, turbulent kinetic energy, planetary boundary layer height (PBLH), and 10-m wind due to the direct effect of aerosols on radiation. Shortwave radiation is generally reduced due to aerosols over China and the largest reduction is found in more aerosol-rich parts of the country (Fig. 3l,m), i.e. north and east China. Shortwave radiation was reduced by 30.24 W m -2 (18.85%), 19.73 W m -2 (12.98%), 20.45 W m -2 (11.22%) and 16.27 W m -2 (13.53%) in JJJ, YRD, PRD and SCB, respectively (Figure 4a). The high-155 resolution regionally-focused WRF-Chem simulation performed by Wang et al. (2016) similarly showed that due to ADE, the solar radiation in China decreased by 20 W m -2 , and the percentage changes ranged from 11.7% to 14.3% in different areas.
A decreased downwelling solar radiation could cool the surface and cause weaker thermal turbulence in the boundary layer Quan et al., 2013). The temperature at 1.5 m is found to be reduced in the north China plain and southwest China (Fig. S1a) due to the radiation changes. TKE (Fig. 4b) showed the largest change in north China (JJJ), with a decline of 0.12 m 2 s -2 (-33.43%), which is consistent with China's largest SW radiation change area. This is in line with the findings of Wang et al., (2020), who found that during a haze episode in winter, the TKE in Beijing declined by 0.1-0.7 m 2 s -2 due to the aerosol-included effect. The reduction of TKE in YRD reaches 23.09% in our findings, which is the second-highest TKE reduction region in China.
The growth of boundary layer mainly depends on the atmospheric thermal structure and turbulent exchange intensity (Garratt, 165 1994;Serafin et al., 2018). Owing to the reduced solar radiation and TKE, the development of the PBL was supressed, and resulted in a shallower and more stable boundary layer (Fig. 4c,d). In north China (JJJ), the annual average planet boundary layer height (PBLH) was reduced by 102.28 m (22.01%) due to the ADE. Observations in this area also showed that the average PBLH was reduced by 334 -710 m during severe pollution periods compared to clean days (Tang et al., 2016;Zhang et al., 2015). The annual change of PBLH in YRD was -53.39 m (16.26%), and this reduction was consistant with the WRF-170 Chem simulation by Wang et al. (2016), who found that PBLH in East china decreased by 75. usually also accompanied by calm winds and higher relative humidity values (Yin et al., 2019). Here, the 10-m wind is found to be slower by -1% to -7.5% (Fig. 4d), and relative humidity at 1.5 m increased with a maximum of 5.7% (Fig. S1b). The 175 variations in wind and boundary layer stability would influence horizontal transport and pollutants and moisture accumulation, as well as the vertical dispersion and the exchange of clean air with the free troposphere.

Impact of meteorology feedback via atmospheric pollutants (ADE-POL)
The aerosols direct radiative feedback was found to reduce solar radiation which resulted in the suppression of PBL height and turbulent intensity, while the suppressed PBL in turn limits the export and diffusion of pollutants. Figure 5 shows the influence 180 of ADE on surface CO, SO2, NO, NO2, PM2.5 and PM10 concentrations. Overall, pollutant concentrations increased when including aerosols, due to the decreasing wind speeds and PBLH. The CO increase caused by ADE averaged over China was 11.04%, with the biggest changes appearing in north China (JJJ), east China (YRD), and central China (up to 12.25-16.17%).
The distribution of SO2, NO and NO2 changes is similar to that of CO, with increases of 5.66-38.99%, 7.71-55%, and 2.78-40.63%, respectively. For fine and coarse aerosols (PM2.5 and PM10), the increases are between 9.5% and 18.6% in the four 185 selected areas and the spatial distributions of changes are similar to those of gaseous pollutants. Changes in gas and aerosol pollutants were the result of the changes in meteorological conditions. The shallower PBLH reduced the vertical dispersion and compressed the pollutants in PBL, resulting in higher surface pollutant concentrations. The increased boundary layer stability and reduced wind speed also led to the accumulation of pollutants at their emission sources. The spatial distribution of the changes in pollutant concentration is similar to the spatial distribution of meteorological conditions changes and emission 190 sources. With a larger population and more developed industries, north and east China were considered to be the high-emission areas of the country (Wang, 2015;Zheng et al., 2018). These areas are more sensitive to the accumulation of pollutants and showed a stronger increase in the pollutants concentrations due to aerosol effects. Western China is less developed than the https://doi.org/10.5194/acp-2020-727 Preprint. Discussion started: 22 October 2020 c Author(s) 2020. CC BY 4.0 License. eastern parts, and its population and anthropogenic emissions are also lower (Saikawa et al., 2017;Shi et al., 2014). As a result of that, the ADE in west China caused a small increase and even a decrease in pollutant concentrations. In southwest China,195 SCB is more developed than the surrounding cities, and its bowl-shaped topography helps trap air pollutants (Ning et al., 2018).
More pronounced increases in pollutants' concentrations are also found in this area, but the magnitude is lower than that in JJJ and YRD. Changes in air pollutants (including NO and NO2) in different regions affect the ratio of NO/NO2, which is related to the loss and the production process of ozone. The change in NO/NO2 and ozone will be further discussed in section 3.5.

Impact of meteorology feedback via moisture (ADE-MOI) 200
The changes in boundary layer stability and PBLH would not only contribute to the pollutant accumulation, but also linked to the moisture accumulation. The change in horizontal water vapor flux over the land area is small (Fig. S2). However, a low PBLH could limit the vertical transport of water vapor from the boundary layer to the free troposphere. Figure 6 shows the vertical profile of changes of specific humidity in different parts of China. In most seasons, climatological specific humidity increases in the lower troposphere and drops in the higher layers. In JJJ, the area most affected by ADE, the surface moisture 205 content increases more when comparing EXPradon with EXPradoff, with a maximum change of 4.28% (6.55×10 -4 kg kg -1 ) in June. The annual mean specific humidity decreased by a maximum of 1.69% (1×10 -4 kg kg -1 ) at 1493 m in China.
When more water vapor was trapped in the lower troposphere, there would be less moisture to form cloud in the upper layers (Allen et al., 2019). The annual average cloud amount decreases by 4% due to aerosol effects on radiation over the whole country (Fig. 7). The area with the largest decline is YRD, and the percentage change is -5%. The cloud optical depth also 210 drops by 7%-15.6% in China, with the regional distribution of changes being similar to the cloud amount changes. Clouds attenuate solar radiation, leading to diminished photolysis rates beneath the cloud (Tang et al., 2003;Voulgarakis et al., 2009aVoulgarakis et al., , 2009bVoulgarakis et al., , 2010. We found that surface photolysis rates JNO2 and JO1D are both enhanced because of the aforementioned cloud reductions. The national average JNO2 and JO1D rose by 4.1% and 3.3%, respectively. SCB is the region with the largest increase in JNO2 and JO1D, with percentage increases of 8% and 7.9%, respectively. The increase in JNO2 and JO1D could lead to an 215 increase/decrease in ozone concentration.

O3 changes due to aerosol's meteorology feedback
The meteorological feedbacks that we study, ADE-POL and ADE-MOI, may have contrasting effects on ozone. For ADE-POL, the relationship between NO and NO2 concentrations could be used to predict the changes in ozone concentration, because NO and NO2 lead to the loss and production of ozone, respectively. Figure (Han et al., 2011) have demonstrated that an increasing NO/NO2 ratio could consume more ozone and https://doi.org/10.5194/acp-2020-727 Preprint. Discussion started: 22 October 2020 c Author(s) 2020. CC BY 4.0 License. reduce ozone concentration. In ADE-MOI, JNO2 and JO1D were both increased due to the cloud amount and optical depth 225 changes. Tang et al., (2003) found that the JNO2 was more sensitive to cloud than JO1D and most other photolysis rates, and the decrease of cloud cover could lead to higher net ozone production below the cloud layer. Therefore, changes in the atmospheric water content and subsequent cloud changes could lead to local increases in surface ozone concentration.
These two opposite effects compete against each other, resulting in different ozone changes in different regions and seasons. ozone percentage change appears to depend on the relative magnitude of the NO/NO2 ratio changes and the photolysis rates change. In northern cities, such as JJJ, the monthly variation in ozone changes showed a double-peak pattern, with the largest 235 decline in spring and autumn, while in south China, the change in ozone only reaches its largest reduction in winter. The latitudes of YRD and SCB are in between the latitudes of JJJ or PRD, and therefore the seasonal patterns are not as clear as for JJJ or PRD. In YRD, the combined effect leads to ozone changes ranging from -5 ppb to 0.07 ppb. Xing et al., (2017) found that the meteorology changes reduced the surface concentration of ozone in east China in January by 5-24 µg m −3 (2.33-11.19 ppb). The reason for the difference might be that they did not include the positive feedback of ADE-MOI when analyzing 240 meteorological effects. The reaction flux changes in Fig. S3 shows that, on annual average, the combined effect of ADE-POL and ADE-MOI led to more ozone consumption than ozone production, suggesting that ADE-POL dominates. Figure 10 shows the spatial distribution of annual average ozone changes. The region with the highest ozone reduction is consistent with the region of largest NO/NO2 ratio increase. Ozone concentration was found to decrease by 3.84 ppb (14.9%), 2.45 ppb (8.7%), 1.48 ppb (4.3%), and 1.78 ppb (7.1%) in JJJ, YRD, PRD and SCB on annual average, and it decreased by around 2.01 ppb 245 (6.2%) averaged over the whole country.

Conclusions
In this paper, we used a coupled global earth system model, UKESM1-AMIP, to evaluate the influence of aerosol's meteorology feedback on tropospheric ozone over China. Aerosols reduced surface net downward shortwave radiation by 11% through the scattering and absorbing effect, and reduced the surface turbulent kinetic energy by 16.7%. The boundary layer 250 was therefore less heated and developed, the height of which was found to decrease by 102.28 m in north China. The meteorology changes in the lower troposphere can influence the dispersion and mixing of pollutants (ADE-POLL effect) and moisture (ADE-MOI effect). Gaseous pollutants such as CO, SO2, NO, NO2 all increased in the aerosol rich regions, and particulate matter (PM2.5 and PM10) increased by 9.5%-18.6% in the four selected areas. Different changes in NO and NO2 affect the NO/NO2 ratio, which is related to the loss and the production process of ozone. Moisture was found to be more 255 trapped in the boundary layer, with specific humidity increasing in the PBL, and the strongest effects found in June in JJJ https://doi.org/10.5194/acp-2020-727 Preprint. Discussion started: 22 October 2020 c Author(s) 2020. CC BY 4.0 License.
(4.28%). With more moisture accumulated near the ground, less moisture was transported to higher layers to form clouds. The cloud amount reduced by 4% and clouds became more transparent. The photolysis rates for NO2 and O 1 D were thereby found to be increased by 4.1% and 3.3%, respectively.
Increased NO/NO2 ratio (ADE-POL) consumes more ozone, while increased photolysis rate (ADE-MOI) produces more ozone. 260 The net magnitude of ozone change due to aerosols is linked to the relative magnitude of the NO/NO2 ratio change and the photolysis rates change. In general, the NO/NO2 change dominated the ozone concentration change and led to reduced annual average ozone in China, with the value of -2.01 ppb (6.2%).
Overall, our study reveals that, except for the direct effect through photolysis rates changes, ADE can influence ozone concentration through two meteorological mechanisms: one is to affect the abundances of atmospheric pollutants, including 265 ozone consumers and producers (ADE-POL); and the other is to affect the vertical transmission of water vapour, thus affecting the optical characteristics of clouds, and therefore ozone photochemical production through photolysis (ADE-MOI). The combined effect and relative importance of meteorological feedbacks, direct photolysis influences, and microphysical influences needs to be assessed in a future study.

Data availability 270
The data used in this study are available upon request from Yawei Qu (yawei_qu531@163.com).

Author contributions
YQ designed the research study, ran model simulations and performed the data analysis under the close supervision of AV, with some additional supervisory support from TW. TW provided access to CNEMC data. MK, CW, SV and LM offered continued guidance and technical support on UKESM simulation. YQ wrote the original manuscript and AV, TW and CY 275 reviewed the manuscript.

Competing interests
The authors declare that they have no conflict of interest. were performed using the MONSooN (Met Office and NERC Supercomputing Nodes), a shared HPC platform within a collaborative computing environment, which is developed by Met Office and NERC. Also, we wish to thank Luke Abraham 285 from University of Cambridge and Mohit Dalvi from the UK Met Office for their support with using the UKESM model.