Opposite Effects of Aerosols on Daytime Urban Heat Island Intensity between Summer and Winter

The urban heat island intensity (UHII) is the temperature difference between urban areas and their rural 20 surroundings. It is commonly attributed to changes in the underlying surface structure caused by urbanization. Air pollution caused by aerosol particles can affect the UHII by changing the surface energy balance and atmospheric thermodynamic structure. By analyzing satellite data and ground-based observations collected from 2001 to 2010 at 35 cities in China and using the WRF-Chem model, we found that aerosols have very different effects on daytime UHII in different seasons: reducing the UHII in 25 summer, but increasing the UHII in winter. The seasonal contrast in the spatial distribution of aerosols between the urban centers and the suburbs lead to a spatial discrepancy in aerosol radiative effect (SDARE). Additionally, different stability of the planetary boundary layer induced by aerosol is closely associated with a dynamic effect (DE) on the UHII. SD-ARE reduces the amount of radiation reaching https://doi.org/10.5194/acp-2020-162 Preprint. Discussion started: 24 February 2020 c © Author(s) 2020. CC BY 4.0 License.


Introduction
The global population has been increasingly concentrated in cities (Heilig 2012). Urbanization in China has dramatically increased from 26% in 1990 to 60% in 2018, resulting in a marked change of landscape. 40 It has a significant impact on the urban and rural climate and will continue to make an impact as cities continue to develop (Han et al. 2014).
Urbanization leads to a dramatic change in the underlying surface structure, properties, and spatial distribution of a city, such as a reduction in green areas and a corresponding increase in impervious areas.
These changes increase the temperature difference between urban and rural areas, which is known as the Aerosols can also alter the radiation balance of the climate system. Their thermodynamic effect reduces the amount of radiation reaching the ground, and their microphysical effect can influence cloud properties and precipitation regimes through their impacts on cloud microphysical and dynamic processes (Rosenfeld et al. 2008, Li et al. 2011, Fan et al. 2013, Li et al. 2016, Liu et al. 2019. The 60 effect of urbanization on clouds and precipitation has been the focus of many studies (Changnon et al. 1977, Ackerman et al. 1978, Changnon et al. 1991, Shepherd et al. 2002, Shepherd and Burian 2003.
Aerosols can increase cloudiness and cloud thickness and thus change the stability of the planetary boundary layer. In humid regions, aerosols may reduce the frequency of light rain but increase heavy rainfall, while in dry areas, aerosols aggravate droughts. Aerosols can also intensify convection by 65 delaying the occurrence of convection and enhancing gust fronts (Khain et al. 2005, Carrió et al. 2010, Carrió and Cotton 2011, Han et al. 2012, Lee and Feingold 2013, Guo et al., 2016a. UHI, surface roughness and higher aerosol concentrations have been proposed to explain observed urban clouds and precipitation anomalies. Increased urban surface roughness likely does not play a major role 70 in urban-induced precipitation. Rather, the UHI and higher aerosol concentrations may play more important roles (Han et al. 2014). The UHI can alter the water vapor flux (accelerate evaporation), reduce horizontal wind speeds and enhance vertical turbulence, reduce the temperature difference between daytime and nighttime, increase the absorption rate of solar radiation by land, and change underlying surface characteristics (e.g., sensible heat dissipation, convection efficiency, evaporation and cooling, 75 sunlight reflection, and anthropogenic heat transfer) (Jauregui and Romales 1996, Taha 1997, Bornstein and Lin 2000, Givati and Rosenfeld 2004, Grimmond 2007, Carrióet al. 2010, Zhao et al. 2014, Kaspersen et al. 2015, Yang B et al. 2019.
The UHI and aerosols may interact over cities. Aerosols generally reflect and absorb solar radiation and reduce the amount of shortwave radiation reaching the ground, i.e., the cooling effect of aerosols on 80 ground temperature. Some numerical modelling studies have demonstrated that landscape change reduces near-surface concentrations of 2.5 and that the UHI effect can influence the dispersion of air pollutants (Liu et al. 2009, Liao et al. 2015, Tao et al. 2015, Zhong et al. 2017. Moreover, aerosols can enhance the UHI at night (by 0.7±0.3 K) for semi-arid cities, and the UHI alters the aerosol concentration https://doi.org/10.5194/acp-2020-162 Preprint. Discussion started: 24 February 2020 c Author(s) 2020. CC BY 4.0 License. (Cao et al. 2016, Fallmann et al. 2016, Lai 2016. Heavy pollution can reduce UHII in China, especially 85 during the day (Wu et al. 2017, Yang et al. 2020.
Weather Research and Forecasting/Chemistry (WRF-Chem) model are used extensively in the simulation and prediction of air quality, the aerosol radiation effect and aerosol-cloud interactions, and the change of meteorological fields and regional climate (Grell et al. 2005, Chapman et al. 2009). Coupled with the urban canopy model, WRF can account for the influences of aerosols and land surface changes on the 90 radiative processes if such parameters are fed to the model as aerosol loading and single scattering albedo, surface albedo and thermal emissivity, roughness, etc (Miao et al. 2009, Chen et al. 2011. Many previous pertinent studies are done to date just focused on the annual effects without investigating any seasonal differences and the underlying mechanism. This study aims to fill this gap by analyzing the annual and seasonal effects of aerosols on UHII and proposing mechanisms that may explain the seasonal differences.

Study areas and data
Thirty-five big cities evenly distributed across China were selected in our study. Table S1 lists these cities of different sizes. They represent the major and well-developed metropolitan regions in China. The Landsat data are used to identify and outline urban areas and urban contour. The spatial resolution is 30 m. Summertime (June, July, and August) images from 2000 and 2015 were examined to ensure the accuracy and consistency of results.
The MODIS LST product (MYD11A2) at a 1-km spatial resolution was used to calculate urban and rural 110 UHIIs. Since this study is mainly focused on the daytime UHI effect, only data (eight-day clear-sky LST observations with 1 km spatial resolution) at 13:30 BJT for the period 2001-2015 were used. The https://doi.org/10.5194/acp-2020-162 Preprint. Discussion started: 24 February 2020 c Author(s) 2020. CC BY 4.0 License.
MYD11A2 product uses the MODIS cloud mask product (MYD35) to filter out cloudy conditions. A generalized split-window algorithm is applied using MODIS data in two longwave bands in the atmospheric window to correct for atmospheric water vapor, haze effects, and the sensitivity to errors in 115 the surface emissivity. To obtain the LST from brightness temperatures, changes in surface emissivity have been accounted for (Wan and Dozier 1996, Snyder et al. 1998, Yu et al. 2011, Cao et al. 2016).
The MODIS Multi-Angle Implementation of Atmospheric Correction (MAIAC) AOD product is used that has a 1-km spatial resolution with daily global coverage. It was retrieved by virtue of a time series 120 analysis and a combination of pixel-and image-based processing to improve the accuracies of cloud detection, aerosol retrievals, and atmospheric correction (Lyapustin et al. 2011a(Lyapustin et al. , 2011b(Lyapustin et al. , 2012. A large volume of meteorological data are analyzed including visibility, surface wind speed, temperature, precipitation, and other parameters every three hours, together with hourly 2.5 data in urban and surrounding rural areas. To be consistent with the satellite imaging time (13:30 BJT), the meteorological 125 data and 2.5 data observed at 13:00 and 14:00 BJT are selected. Due to the lack of long-term records of aerosol concentration, visibility is frequently used as a proxy for aerosol loading , Wu et al. 2012, Yang et al. 2013).
The L-band sounding data are employed that were acquired at the five radiosonde stations in Beijing, Chengdu, Nanjing, Shenyang, and Xi'an operated by the China Meteorological Administration since 2006. 130 They contain the high-resolution profiles of temperature, pressure, relative humidity, and wind speed and direction at 08:00 Beijing time (BJT, UTC+8) and 20:00 BJT (Zhang et al., 2018;Lou et al., 2019). The data quality of radiosonde measurements has been well validated and is good enough to study the UHI effect (Guo et al., 2016b). that corrects for the influence of soil brightness when the vegetative cover is low (Huete 1988, Qi et al. 140 1994, Rondeaux et al. 1996). After some tests, the difference − were used to extract urban impervious surfaces because of its ability to differentiate urban impervious surfaces from other land-use types: 145 where L is the soil adjustment factor whose value is 0.5, and is the Landsat reflectance of band n. We then used different thresholds to extract urban impervious surfaces after calculating − .
Results are verified by the Google Earth and a land-use map with a 1:100,000 scale from the Resource and Environment Science Data Center of the Chinese Academy of Sciences. 150

Research windows
Many previous studies have extracted urban areas from nighttime stable-light data. However, the spatial resolution of such data is low, so the extraction accuracy would be significantly affected in urban areas with uneven zoning and in regions with irregular urban development as in most municipalities in China.
The TM/ETM+ data are used to accurately extract the physical boundaries of urban areas. The difference 155 in the underlying surfaces of urban and rural areas forms the basis of the urban physical boundary extraction. Urban surfaces are generally covered by impervious materials, and rural surfaces are mainly covered by natural surfaces. The influence of the UHI is not only felt within the physical boundaries of urban areas but also beyond it. In terms of area, this influence can extend to 2-4 times the extent of an urban area. In terms of distance, the influence of the UHI can be felt as far as 3-6 km away from an urban 160 physical boundary (Zhou et al. 2015).
For each city, nine research windows (6 km x 6 km each) were selected. The windows include one urban window, four suburban windows, and four rural windows. For the study period considered (2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015), the urban window represents an area that remained urban and developed during this time. The suburban windows represent areas that were vegetated before the study period. As cities expand, these areas were that remained vegetated during the study period. These windows were 10 km away from the urban physical boundary to ensure that these windows were not or weakly affected by the UHI. The elevations of the areas covered by each window are within 100 m of each other for a given city based on DEM (Digital Elevation Model) data. Water bodies are excluded. Figure S1 shows the spatial distribution of 170 the nine research windows for a given city. The UHII is the temperature difference between the average temperature of the urban core window and the average temperature of rural windows.

Aerosol parameters (AOD, PM2.5)
Validation using Aerosol Robotic Network AOD retrievals shows that the MAIAC and MODIS aerosol 175 retrieval algorithms have similar accuracies over dark and vegetated surfaces and that the MAIAC algorithm generally improves the accuracies of AOD retrievals over bright surfaces such as deserts and urban surfaces (Lyapustin, et al. 2011a, 2011b, 2012, Wei et al., 2019c. Sounding data and 2.5 measurements were available from 2013 to 2015. MAIAC AOD retrievals for each area were averaged to obtain the spatial distribution of AOD over each city, then the difference of AOD between 180 urban and rural areas was calculated.

WRF-Chem model simulation
The model used in this study is WRF-Chem 3.9.1 coupled with a single-layer urban canopy model. As shown in Figure (Table S2).

The Urban Heat Island (UHI) effect
We used the difference NDBI − SAVI to extract urban impervious surfaces, and then determined urban contours based on the identification of impervious surfaces. Figure 1 shows the urban contours of all  Figure S2 shows UHII and visibility trends. For most cities, no matter before or after 2008, there are similar trends between UHII and visibility; and there is an obvious difference between trend before 2008 and trend after 2008 of both UHII and visibility. Figure 2 shows the relationships between UHII and visibility based on their respective trends shown in Figure S2. UHII and visibility are positively correlated 220 grossly. Higher visibility means a lower aerosol concentration, leading to a higher UHII, and vice-versa.
On the other hand, the two may also change in opposite directions if the expansion of a city is more associated with the heavy industry of strong emissions. In such a case, the expansion can produce both https://doi.org/10.5194/acp-2020-162 Preprint. Discussion started: 24 February 2020 c Author(s) 2020. CC BY 4.0 License. more aerosol particles, especially secondary aerosols converted from precursor gases, and stronger UHI, but they have no causal relation. This is likely a reason for the diverse relationships between the trends of 225 the two variables. Of course, the complication originates from highly different pathways of city expansions among these cities. The overall positive relationships revealed in Figure 2 may thus serve as a testimony to the dominance of their causal relationship, implying that aerosol loading does influence the UHII to a varying degree. 230 Figure 2. (a) Visibility trend (unit: km yr -1 ) shown as a function of the UHII trend (K yr -1 ), and (b) visibility (unit: km) shown as a function of UHII (unit: K). The blue line is the linear best-fit line through the points. The least-squares regression equation is given in each panel. The coefficient correlation (R) and p-value are also given.    Compared with rural areas, urban impervious surfaces have low thermal capacity and their temperatures are thus more sensitive to heat changes. Note that the DE and SNA-RE are not independent and that there is an indirect effect between them due to potential urban-rural circulation.

Analyses of Influential factors
Urban-rural differences in air quality: The urban-rural differences in air quality were analyzed by calculating the spatial differences of 2.5 and AOD under cloudless conditions between urban and rural areas. Their spatial differences are then analyzed between summer and winter: The measurements of urban 2.5 concentrations were divided into four categories: 0-50, 50-100, 100-285 150, and > 150 µg m -3 based on urban pollution. Figure 5 shows the mean urban-rural differences in each PM2.5 concentration bin of all cities. On average, the spatial difference in summer is larger than in winter across all PM2.5 concentration bins. Figure 6 shows the variation trends of mean AOD as a function of distance from the urban geometrical center of each city in winter and summer. As the distance from the urban geometrical center increases, summertime AODs decrease more rapidly than wintertime AODs. 290 Both Figures 5 and 6 indicate that the spatial difference of air pollution between urban and rural areas in summer is larger than that in winter. Moreover, in summer, urban pollution is often more serious than rural pollution. In winter, both urban and rural pollution is serious, and rural may be more serious than urban.    In winter, airflow significantly weakens with the increasing pollution, the PBL becomes very stable, and 340 heat exchanges significantly decrease inside the PBL.
UHII response to variation of visibility: Figure S5 shows the relationship between UHII and visibility difference. For most cities, higher visibility difference causes smaller UHII in summer, while UHII barely changes as visibility difference change in winter. This result indicates that UHII is more sensitive for visibility difference in summer than winter, namely, the SD-ARE has an obvious effect in summer, but it 345 is very weak in winter.
The above analyses indicate that the two mechanisms behave differently roles in summer and winter. In summer, the SD-ARE plays a more important role than the DE to change the UHII, while the importance of two mechanisms is opposite in winter.   Figure 11 depicts the averaged diurnal variations of UHII differences (∆UHII) between UHII with aerosol radiation effect (ARE) and UHII without ARE, with negative values meaning the reduction of UHII by aerosols reduce UHII and positive values showing the opposite. In summer (Figure 11 a), aerosols reduce 365 UHII throughout all day; but in winter (Figure 11 b), aerosols enhance UHII in the afternoon. These results are consistent with the observational results shown in Figure 3. The averaged diurnal variation of downward shortwave radiation at the surface (SWDOWN) between urban and rural areas shows that the SWDOWN difference between urban and rural areas in summer is larger than that in winter ( Figure S8).
The results in Figure S6 and S8 indicate that the spatial difference of air pollution in summer is larger 370 than that in winter, and the wintertime pollution is more serious than summertime pollution, which is consistent with observational results shown in Figures 5-6. Figure S9 shows the model simulated temperature reductions below 1.5 km, suggesting the ARE is more significant on the temperature lapse rate in winter than that in summer in both urban and rural areas. Moreover, the temperature lapse rate in summer is far more than that in winter. They are also consistent with the observational results shown in 375 Figure 9.

Conclusion and discussions
Satellite, ground-based, sounding data and WRF-Chem mods were used to analyze the UHII under polluted and clean conditions at 35 cities in China. Seasonal differences in UHII between summer and winter were also compared. On an annual basis, aerosols reduce the UHII, which is consistent with previous work (Wu et al. 2017). In summer, aerosols reduce the UHII, but in winter, aerosols enhance the 385 UHII. Furthermore, we used the concepts of the Spatial Discrepancy in Aerosol Radiative Effect (SD-ARE) and the Dynamic Effect (DE) to explain how aerosols influence the UHII in different seasons. We then verified The mechanisms by means of observational analyses and model simulations.
In summer, airflow changes slightly within the PBL under polluted conditions. There is still a strong heat release and heat exchange between urban and rural areas, so the dynamic effect is weak. The spatial 390 discrepancy in aerosol radiative effect differs between urban and rural areas because of the inhomogeneous spatial distribution of air pollution between these two areas. Since urban pollution is often more severe than rural pollution, less solar radiation reaches urban areas than in rural areas. The urban temperature enhancement is thus weaker under polluted conditions than clean ones, weakening the UHII. Figure 12a shows a diagram of how aerosols influence the UHII in summer. 395 In winter, the spatial discrepancy in aerosol radiative effect is weak but the dynamic effect is significant under polluted conditions in winter. Concerning the spatial discrepancy in aerosol radiative effect, the spatial difference of air pollution between urban and rural areas is small, and urban and rural areas likely experience the same severe pollution, this heats the atmosphere and reduces the similar amount of solar radiation reaching the urban and rural ground. Concerning the dynamic effect, airflow intensity and 400 temperature gradients significantly decrease, stabilizing the PBL and weakening the heat release and heat exchange. Since pollution conditions in both urban and rural areas are similar, the spatial discrepancy in aerosol radiative effect is not a major factor causing higher UHIIs. the dynamic effect weakens airflow, reducing temperature gradients significantly, which in turn, reduces the heat exchange between urban and https://doi.org/10.5194/acp-2020-162 Preprint. Discussion started: 24 February 2020 c Author(s) 2020. CC BY 4.0 License. rural areas and the surface heat release. Increasing heat thus accumulates in urban areas, thereby 405 increasing the UHII. Figure 12b shows a diagram of how aerosols influence the UHII in winter.
Our analysis shows the seasonally different effects of aerosols on the UHII and explains the different mechanisms in different seasons, and the mechanism summary is shown in tables at the bottom of Figures   12a and 12b. Although this study comprehensively explains potential aerosol effects, other effects may be at play such as land surface and aerosol properties (e.g., absorbing versus scattering aerosols). More 410 work needs to be done to verify this. Additionally, this study analyzed observations made at 35 cities located in China and some results of a few cities that were at odds with generalized findings of 35 cities; the different results in some cities may be due to the unique characteristics of these cities regarding their location, terrain, climatic background, etc. This warrants further investigations.

Competing interests
The authors declare that they have no conflict of interest. Cao, C., Lee, X., Liu, S., Schultz, N., Xiao, W., Zhang, M., and Zhao, L