Synergy between air pollution and urban meteorological changes through aerosol-radiation-diffusion feedback―A case study of Beijing in January 2013

Atmospheric Environment 171 (2017) 98–110

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Synergy between air pollution and urban meteorological changes through aerosol-radiation-diffusion feedback―A case study of Beijing in January 2013

MARK

Mizuo Kajinoa,b,c,∗, Hiromasa Uedad,e, Zhiwei Hanf, Rei Kudoa, Yayoi Inomatag,a, Hidenori Kakue,h a

Meteorological Research Institute, Japan Meteorological Agency, Tsukuba, Ibaraki 305-0052, Japan Faculty of Life and Environmental Sciences, University of Tsukuba, Tsukuba, Ibaraki 305-8577, Japan RIKEN Advanced Institute for Computational Science, Kobe, Hyogo 650-0047, Japan d Disaster Prevention Research Institute, Kyoto University, Uji, Kyoto 611-0011, Japan e Suuri-Keikaku Co. Ltd., Chiyoda, Tokyo 101-0064, Japan f Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China g Institute of Nature and Environmental Technology, Kanazawa University, Kanazawa, Ishikawa 920-1192, Japan h Information Literacy Co. Ltd., Chiyoda, Tokyo 101-0064, Japan b c

A R T I C L E I N F O

A B S T R A C T

Keywords: Severe haze Numerical simulation Atmospheric stratification Fog formation Positive feedback of emission control

The interactions of aerosol-radiation-stratification-turbulence-cloud processes during a severe haze event in Beijing in January 2013 were studied using a numerical model. For the clear days, solar radiation flux was reduced by approximately 15% and surface temperature was slightly decreased from 0 to 0.5 K throughout the day and night, except for a 1.4 K decrease around sunrise when fog was presented. The longwave radiation cooling was intensified by the fog or drizzle droplets near the top of the fog layer. Thus, in Beijing, both in the daytime and at night, the surface air temperature was decreased by air pollutants. In the presence of the lowlevel stratus and light precipitation, the modification of meteorology by aerosols was amplified and changed the wind speed and direction much more significantly compared to clear days. The non-linear effect (or positive feedback) of pollutant emission control on the surface air concentration was newly assessed―severe air pollution leads to the intensification of stable stratification near the surface at night and delays the evolution of the mixing layer, which in turn causes more severe air pollution. The non-linear effect was not significant for the current emission levels in the current case, approximately 10%. In another word, the mixing ratio of aerosols became higher by 10% due to their radiation effects.

1. Introduction Because of rapid industrialization and growth of urban populations, regional SOx and NOx emissions in East Asia have become the highest in the world, about twice as much as emissions in Europe and North America (Crippa et al., 2016). Thus, air pollution is an important environmental issue in East Asia, and its detrimental impacts on human health, agriculture and ecosystems are a matter of great concern. It is also suspected to cause climate and meteorological changes through feedback among air pollution, radiation, diffusion, and cloud physical processes. In East Asia, other anthropogenic pollutant emissions, in addition to SOx and NOx, are also believed to be the largest in the world. Of these pollutants, aerosols tend to reduce downward shortwave (solar) ∗

radiation by scattering, and this results in a decrease in atmospheric and ground surface temperatures and evaporation (direct aerosol effect, or recently called Radiative Forcing from aerosol-radiation interactions; RFari) (Boucher et al., 2013). In addition, absorption of shortwave radiation by aerosols results in atmospheric heating and directly changes the atmospheric temperature and wind fields; this can also change atmospheric conditions indirectly through changing the atmospheric stability (semi-direct effect, or rapid adjustments to aerosol-radiation interactions). Black carbon aerosol is particularly important because of its strong absorbance of shortwave radiation and the resulting radiative heating; the absorptivity is more than three orders of magnitude larger than other aerosols (Jacobson, 2005). Moreover, aerosol chemical and microphysical properties affect cloud microphysical properties (first indirect effect, or Radiative Forcing from aerosol-cloud interactions;

Corresponding author. Nagamine 1-1, Tsukuba, Ibaraki 305-0052, Japan. E-mail address: [email protected] (M. Kajino).

http://dx.doi.org/10.1016/j.atmosenv.2017.10.018 Received 26 January 2017; Received in revised form 12 September 2017; Accepted 7 October 2017 Available online 10 October 2017 1352-2310/ © 2017 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/BY-NC-ND/4.0/).

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with different levels of pollutant emissions for Beijing in winter, as a case study, when a severe haze episode was reported and studied (Zhang et al., 2014; Zheng et al., 2015; Zhang et al., 2015; Li and Han, 2016). Special attention was paid to the interaction between shortwave and longwave radiation with air pollutants and the resulting change in the thermal structure and temperature stratification, which caused substantial changes in the turbulent diffusion of momentum, heat and air pollutants. In addition, the formation of radiation fog and haze in the early morning on clear days and the formation of low-level stratus and precipitation through interactions among radiation, aerosol dynamics and cloud physical process were examined. The synergy between air pollution and urban meteorology change was investigated. That is, severe air pollution that led to climate and meteorological changes was investigated; the latter in turn can cause more severe air pollution, and vice versa. A series of such processes is called a positive feedback, and this nonlinear effect of air pollution was newly assessed in the study. In Section 2, the importance of the turbulent diffusion process was emphasized, and the model setup used was explained. In Section 3 direct and semi-direct effects of aerosols under clear weather were discussed, relating them to atmospheric stability, and the influence of aerosols on fog, cloud formation and precipitation were examined. Finally, we summarized these results and concluded with some remarks for future work.

RFaci), cloud lifetime and precipitation (second indirect effect, or rapid adjustments to aerosol-cloud interactions), which in turn changes the temperature, wind and atmospheric stability. Whereas black carbon works for global warming through aerosol-radiation interactions, hygroscopic secondary aerosols, such as sulfate and nitrate, may contribute to global cooling through aerosol-cloud interactions. In addition to these aerosol effects, longwave (thermal) radiation cooling and heating by radiation participating gaseous and particulate pollutants as well as hydrometeors contribute to climate change (Boucher et al., 2013). On a global-scale, these aerosols are known to be a potential cause of climate changes (UNEP and WMO, 2011). Basing upon an estimation that reducing black carbon, methane and tropospheric ozone, the shortlived climate pollutants (SLCP) would cause a great reduction in global warming (0.6 °C by the year of 2050, which is approximately half of the CO2 global warming contribution), various emission control measures have been planned, and the efficiency of these emission controls have recently been investigated on a regional scale. The urban-scale effects of air pollutants on meteorology and climate were investigated in the early 1970s. Models of long- and short-wave radiation transfer were developed for gaseous and particulate pollutants (e.g., Bergstrom, 1972) and coupled with turbulent dispersion models, allowing the effects of pollutants on turbulent dispersion and thermal structure to be studied (Atwater, 1972; Zdunkowski and Mcquage, 1972; Bergstrom and Viskanta, 1973a, 1973b, 1973c). Atwater (1972) and Zdunkowski and Mcquage (1972) showed that the effects can be substantial within a period of a few days. The surface temperature was predicted to become warmer at night and colder during the day because of longwave radiation and shortwave radiation properties of pollutants, respectively. In Bergstrom and Viskanta (1973a,b,c), the influence of the radiative effects of air pollution and its diffusion were coupled, and the thermal structure and pollutant dispersion were predicted using a one-dimensional model of the lower troposphere. They showed that the particulate pollutants, mainly black carbon, reduced the shortwave radiant flux on the ground surface, which in turn lowered the surface temperature by 1 K during the daytime. Under the conditions investigated, the largest surface temperature reduction was 2 K, and the maximum rise of the atmospheric temperature due to shortwave radiative heating was 1 K. In contrast, gaseous and particulate pollutants increased the downward longwave radiative flux and the increase in the surface temperature at night. The maximum predicted surface temperature increase was approximately 3 K after a two-day simulation. In these studies, numerical models used were one-dimension and did not account for advective processes. In addition, the effects of atmospheric stratification on turbulent diffusion processes were too simply parameterized. Moreover, these models did not include atmospheric chemical reactions and cloud microphysical processes, and thus the numerical simulations performed were only for cloudless clear conditions without photochemical ozone and secondary aerosol formation. Thanks to the recent advancements of computer resources, numerical investigations of atmospheric chemistry, aerosol dynamics and meteorology feedback can be made by sophisticated models, long-term data analyses and field experiments (Qian et al., 2009; Zhang et al., 2015). Using an online coupled meteorology-chemistry models, Zhang et al. (2010) made numerical simulations for North America, Forkel et al. (2012) made simulations for Europe, and Zhang et al. (2015), Li and Han (2016), and Gao et al. (2016) made simulations for China. They showed strong regional-scale impacts on precipitation, wind and temperature fields. The purpose of the present work is to elucidate the extent of the contribution of each process associated with the feedback between air pollution and urban climate. Impacts of air pollution on the meteorological change on the urban scale were investigated quantitatively, accounting for the feedbacks between atmospheric chemistry, aerosol dynamics and meteorology. A numerical experiment was conducted

2. Methods 2.1. Model setup WRF/Chem 3.5.1 was applied for the case studies presented here. For the chemistry and aerosol dynamics, the RADM2 gas phase chemistry (Stockwell et al., 1990) and the MADE/SORGAM aerosol module (Schell et al., 2001) were applied. MADE/SORGAM is a modal model describing the Aitken mode (nucleation mode 0.1 μm diameter), the accumulation mode (0.1–2 μm), and the coarse mode (> 2 μm) by lognormal size distributions. These chemistry and aerosol modules were coupled to aqueous phase chemistry. For the direct and semi-direct effect simulations, the Maxwell approximation was used to calculate the aerosol optical properties. For the indirect effect simulation, the Morrison double-moment cloud microphysics scheme (Morrison et al., 2009) was used, which was interactively coupled with aerosol properties. Other physics modules used were the NOAH land surface model (Chen and Dudhia, 2001), the RRTM longwave and Goddard shortwave radiation schemes (Iacono et al., 2000), the Grell-Freitas ensemble cumulus parameterization with radiative feedback and shallow convection (Grell and Freitas, 2014), and the Mellor-Yamada-Janjic turbulent diffusion model (MYJ; Janjic, 2002). Selection of turbulent diffusion model is important for the study. Thermal stratification affects the turbulent diffusion of momentum, heat and mass, and as a result it substantially changes the wind and temperature fields and air pollutants dispersion. The stratification attenuates turbulent diffusion under stable conditions and intensifies it under unstable conditions (Ueda et al., 1981; Komori et al., 1982, 1983). The turbulent diffusivity for heat and mass transfer, KH, changes with the gradient Richardson number, Ri, by more than four orders of magnitude in the atmosphere (see Fig. 6 of Ueda et al., 1981). The effects of stratification on the diffusivity are different between momentum transfer, heat and mass transfer. Moreover, the stratification effect is different in the vertical, stream-wise and span-wise directions, and it is different between the atmospheric surface layer and the layer aloft (Ueda et al., 1981, 2012). To explain such a complex phenomenon, we presented an algebraic stress model for stratified turbulent flows (Uno et al., 1989), but not implemented to WRF. Similar results were obtained by the Mellor and Yamada model (Mellor and Yamada, 1974, 1982), and the modified version, the MYJ model (Janjic, 2002). Because it has been validated extensively in atmospheric and ocean 99

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Table 1 Comparative statistical analysis of observed and simulated data in Beijing.

Units Time resolution Average (Sim., Obs.) Sim./Obs. (Average) Rb FA2c a b c

a

PM2.5

Relative humidity

Temperature

Surface pressure

μg/m3

%

K

hPa

1d

1d

6h

6h

233.8 0.91 0.61 1.0

258.1

87.8 1.05 0.99 1.0

83.3

270.9 1.00 0.91 1.0

270.8

1019.0 1.00 0.98 1.0

1020.8

Simulation, observation. Correlation coefficient. Number fraction of simulated data within a factor of two of the observed data. Fig. 1. Time series of (a) BC (black, left axis) and SO2 + NOx (blue, right axis), (b) T2 (red, left axis) and SWdown (black, right axis), and (c) PBLH (green, left axis) and RMOL (orange, right axis) in Beijing. The solid lines and dashed lines in (b) and (c) indicate simulation results for the EMx1 and EM0 cases, respectively. Note that the right axis of (c) is linear-scale below 0.1 and log-scale above 0.1.

with the emission flux sensitivity was performed for a period from 25 to 31 January 2013, with the first two days with nudging toward the meteorological analysis. The horizontal grids of the finer domain were 30 × 30, and the grid spacing was 9 km; vertically, the same 35 levels were used. The results for the latter (Δx = 9 km) are illustrated and discussed in this paper. Emissions inputs for the simulations were the monthly REAS2 anthropogenic emission in Asia (Kurokawa et al., 2013) and the global fire

research and has been proven to safely execute in WRF model simulations, as a first step, we used MYJ as the planetary boundary layer physics scheme. The simulation was made for a domain centered in Beijing, with 40 × 40 horizontal grids and grid spacing of 20 km; vertically, there were 35 levels from the ground surface to 100 hPa, concentrated in the planetary boundary layer. The simulation was performed for the entire month of January 2013. Next, a finer resolution simulation together 100

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Fig. 2. Time series of (a) vertical profiles of the hydrometeor (water droplets) mixing ratio for the EMx1 (shade) and EM0 (contour) cases and (b) surface precipitation rate for the EMx1 (black) and EM0 (red) cases.

2.3. Observation and model validation

emissions, from GFEDv3.1 (Giglio et al., 2010). We selected a base year of 2008 for REAS2 because it is the latest year. Chinese emissions at the simulation year 2013 could be different from those of 2008 because of emission control measures, such as the installation of desulfurization equipment, relocation of factories towards the suburbs, and traffic control based on the license plate of cars. However, we did not consider the inter-annual changes of emissions because we focused on the feedback mechanisms in the meteorology-chemistry coupling system rather than the precise estimation of the emissions. There is uncertainty in the current simulation results because of the difference in the base years of the emissions. Biogenic emissions were calculated online based on the parameterization by Guenther et al. (1994) and the USGS (U.S. Geological Survey) 24 category land use data as supplied by the standard WRF configuration. Sea salt and dust emissions (Ginoux et al., 2001; Chin et al., 2002) were parameterized as functions of the wind speed at 10 m for water and non-urban land surfaces with sparse vegetation, respectively. WRF standard “mid latitude background” lateral boundary conditions were used with NOx concentrations of approximately 1 ppb, 30–50 ppb of ozone, and 80 ppb of CO in the boundary layer.

The evaluation of the WRF/Chem model used a similar set of physics, aerosol dynamics, and chemistry modules as have been used with North American, European, and Chinese observational data by Zhang et al. (2010), Forkel et al. (2012), and Gao et al. (2016), respectively. The model performance is reasonably good in terms of its overall capability of reproducing both observed meteorological variables and concentration of pollutants. To see the performance of the current model setup, the model prediction was compared to field observations. Surface observations of meteorological variables, including wind speed and direction at 10 m and air temperature at 2 m (T2) at 00:00, 06:00, 12:00, and 18:00 (UTC), as well as the daily mean observed relative humidity at 2 m (RH2; due to the lack of 6 hourly data) in Beijing (39.8 °N, 116.47 °E), were obtained from China's Meteorological Data Sharing Service System. Daily mean PM2.5 concentrations in Beijing during January 2013 were derived from the Air Quality Index (AQI) data collected from the Beijing Municipal Environmental Protection Bureau. When PM2.5 is the primary pollutant, the AQI data can be converted to PM2.5 concentrations according to the Technical Regulation on the Ambient Air Quality Index of China. The AQI data were the average of observations from 35 monitoring sites in Beijing (including 28 urban sites, 6 rural sites, and 1 background site), and the daily mean value was calculated using an hourly average over a day (00:00–23:00 LST). To compare the observed PM2.5 with the simulated, the areal mean value of the simulated PM2.5 was used, covering urban and suburban regions of Beijing city (39.7–40.2 °N, 115.9–116.8 °E). Table 1 shows the comparative statistical measures between the simulation and the observations. The simulated quantities, diurnal and daily variations of these variables agreed with those observed. Despite the difference in the horizontal allocation of the observed and simulated PM2.5, the ratio of the simulated average to the observed was 0.91. The lower correlation coefficient R = 0.61 would be caused by

2.2. Emission flux sensitivity test The control simulation, represented as EMx1, is the current state of pollutants’ emission levels, estimated by the REAS2 emission inventory for the base year of 2008 (Kurokawa et al., 2013), while EM0 is the zero emission condition, where EM0 is defined as the emissions of all anthropogenic species set to 0.001 times smaller than in EMx1, and the natural emission are kept as is. We also conducted several emission control cases, defined as EMx0.1, EMx0.4, EMx4, and EMx10, where the emissions were 0.1, 0.4, 4.0, and 10.0 times EMx1, respectively.

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Fig. 3. Time-height cross sections of (a) potential temperature for the EMx1 case (shade) and the difference of the two cases (EMx1 - EM0, contour) and (b) horizontal wind vectors for the EMx1 (black) and EM0 (red) cases in Beijing.

amount and water droplet content, simulated from 27 to 31 January 2013 are presented in Figs. 1–3. Time variations of the surface concentration of black carbon (BC), SO2 and NOx are also illustrated as representative primary air pollutants. In the diagrams SWdown is the surface shortwave radiation downward, T2 is the temperature at 2 m, PBLH is the planetary boundary layer height, and RMOL is the reciprocal of Monin-Obkhov length. As seen in Figs. 1 and 2, the three days from 27 to 29 January were assumed to be typical clear days, and the maximum shortwave radiation flux downward on the ground had almost the same value, 575 W m−2 in the case of EM0, although the shortwave radiation flux was reduced with increasing concentrations of air pollutants and cloud water contents in the EMx1 case. The mixing layer is more unstable for the EM0 case than EMx1 (RMOL of EM0 was lower) during the daytime. On 30 January, low level stratus clouds formed in the middle and upper part of the planetary boundary layer (EMx1). In the case of EM0, the clouds appeared earlier on late 29 to early 30 January (Fig. 2). On the last day, 31 January, it was cloudy and rainy during the day. Fig. 3 shows the time variations of potential temperature and wind vectors and their difference from EM0. When EMx1 and EM0 are compared, up to the evening of 29 January, the change in the potential temperature was small and the wind direction did not substantially change. The wind speed slightly increased. From the night of 29–31 January, the potential temperature and wind vectors significantly deviated from EM0. It coincided to the formation of low-level stratus clouds and precipitation (Fig. 2). Thus, in this section, feedback among radiation, aerosol, stratification, turbulent diffusion and meteorology will be analyzed with respect

the difference in the horizontal allocation. The statistics for wind speed and wind direction are not listed in Table 1 because of the low resolutions of the observations (1 m/s for wind speed and 10° for wind direction), but the simulation was successful in terms of the averaged wind speed (sim.:obs. = 1.70:1.55 m/s) and the prevailing wind direction (southerly for 27–30 and northerly for 30–31, January, both for the simulation and the observation). The simulated chemical compositions were also compared with those observed. Since the observation data was not available in our study, we obtained the values from Fig. 1 of Zheng et al. (2015) using an image analysis software. The mean surface concentrations from 27 to 31 January of Organic Carbon (OC), Elemental Carbon (EC), SO42−, and NO3− were 66 μgC/m3, 9.8 μgC/m3, 67 μg/m3, and 56 μg/m3, respectively. The simulated concentrations were 33.5, 15.2, 47.9, and 39.5 μg/m3 for OC, EC, SO42−, and NO3−, respectively. The simulated PM2.5 concentration agreed well with the observation, while the simulated EC was overestimated by approximately 50%: This indicates that the simulated light extinction was reasonable, while light absorption by BC was overestimated in the aerosol – meteorology feedback simulation.

3. Results and discussion The finer resolution runs with emission sensitivity tests were conducted from 25 to 31 January with a 2-d spin up period. In the section, the interaction between meteorology and air pollution for 27 to 31 January is explored and extensively discussed. The time series of surface meteorological parameters, precipitation 102

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Fig. 4. Time series of (a) SWdown of the EMx1 case (solid, left axis) and the difference of the two cases (EMx1 – EM0, dashed, right axis), (b) potential temperature of the EMx1 case (solid, left axis) and the difference of the two cases (EMx1 – EM0, dashed, right axis), (c) net radiation fluxes (positive for downward) of shortwave (sw, red), longwave (lw, orange), sensible heat (hfx, green), latent heat (lh, blue), and the budget (sw + lw + hfx + lh, black), and (d) downward (red) and upward (blue) longwave radiation fluxes in Beijing. The solid and dashed lines in (c) and (d) indicate simulations with the EMx1 and EM0 cases, respectively.

3.1. Direct and semi-direct effects of aerosols under clear weather conditions

to weather conditions that appeared in this simulation period. That is, in Section 3.1, the synergy between air pollution and meteorological change on 27–29 January under typical clear conditions will be discussed. The influence of aerosols on the radiation fog that occurred on the night of 28 and 29 January and the influence of aerosols on the precipitation occurring on 30 and 31 due to low-level stratus will be discussed in Sections 3.2 and 3.3, respectively. Finally, in Section 3.4 the positive feedback, nonlinear degradation of air pollution caused by meteorological changes, including coupling with microphysical processes, will be observed, and its quantification will be examined for the weather conditions of Sections 3.1.

Absorption and scattering of shortwave radiation by aerosols reduce shortwave radiation downward on the ground surface. This is called the direct effect. Absorption of radiation energy is represented by the imaginary part, κ, of the complex refractive index for each substance and a function of wave length, λ. At λ = 0.5 μm, the shortwave radiation κ value of black carbon is 0.74, while κ = 0.0005, 0.00015 and 1.0 × 10−9 for (NH4)2SO4(s), NaCl(s) and H2O(aq), respectively (Jacobson, 2005). That is, black carbon has an extremely large absorptivity for shortwave radiation. In this section, the aerosol-radiation-stratification-turbulence interaction, i.e., interaction between turbulent diffusion of momentum, heat, and mass (particularly pollutants) and long- and short-wave radiation, 103

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Fig. 5. Time-height cross section of potential temperature of the EMx1 case (shade) and the difference of the two cases (EMx1 – EM0, contour) in Beijing.

Fig. 6. Time-height cross section of gradient Richardson number of the EMx1 (positive/black, negative/red) and EM0 (positive blue, negative yellow) cases in Beijing.

Fig. 7. Time variations of vertical profiles of (a) potential temperature and (b) black carbon mixing ratio from 06:00 to 11:00 LST on 27 January in Beijing. Solid lines and dashed lines indicate simulations with the EMx1 and EM0 cases, respectively.

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Fig. 8. Same as Fig. 7 but from 15:00 to 20:00 LST on 28 January.

Fig. 9. Same as Figs. 7 and 8 but from 21:00 LST on 28 January to 07:00 LST on 29 January.

developed, with changes of RMOL from negative to positive. The magnitude of RMOL during the nighttime is approximately 0.05, except from the evening of 28 to the morning of 29 January, when the stratification was extremely stable (RMOL ∼ 5/m) and the worst air pollution in Beijing during January 2013 occurred. On 27–29 January 2013, the weather was clear. As shown in Fig. 4, shortwave radiation flux downward on the ground in the EMx1 case was reduced by approximately 15% compared to the zero emission case, EM0. It is almost the same level as in other polluted mega cities. The surface temperature difference remained at a lower level of 0 to −0.5 K throughout day- and night-time, except for around sunrise, where it was below −1 K. The budget of the heat flux on the ground was presented in Fig. 4c, where the sw and lw are the net flux of short and longwave radiation, respectively, i.e., downward minus upward flux of them. The hfx and lh are the downward fluxes of sensible heat and latent heat, respectively. In terms of lw, the downward and upward fluxes are separately shown in Fig. 4d. During the daytime, about half of the shortwave radiation reduction was compensated for by the reduction of upward sensible heat flux. Another half was the downward heat flux to the ground, which is the remainder of the sw, lw, hfx and lh, represented by black solid lines and was smaller in the case of EMx1. At night, hfx and lh fluxes were almost zero, as well as sw, and the

which occurs every day as a diurnal variation under clear weather, is discussed, with separately focusing on “before and after sunrise” (from midnight to the next morning) and “before and after sunset” (afternoon to late evening). As shown in Fig. 1c, before sunrise, the surface inversion develops and surface concentration of pollutants increases. After sunrise, the mixing layer develops and breaks the nocturnal inversion, with changes of RMOL from positive to negative (except before sunrise of 28 January, in the presence of fog1). The capping inversion is formed at the top of mixing layer and its height increases with time. The thickness of the mixing layer or the height of the capping inversion base is referred to as the PBLH (planetary boundary layer height) in this paper. PBLH in the daytime was approximately 500 m during the whole period, with RMOL of approximately −0.05 as minimum. Before and after sunset, the mixing layer dissipated and the surface inversion layer

1 During the period, the stratification would be very stable for EM0 (RMOL ∼ 1/m). For EMx1, the pollutants and the thicker fog enhanced the downward longwave radiation flux at the groud surface (Fig. 4d), which cooled the air and heated the surface. Consequently, upward sensible heat flux slightly increased (Fig. 4c) and RMOL became negative (Fig. 1c). Still, as shown in Fig. 9a, the vertical profiles of potential temperature during the period show entirely stable stratification for the lower atmosphere including the fog layer. In the fog layer, due to the longwave radiation cooling, the stratification of EMx1 became more stable than that of EM0.

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Fig. 10. Time series of (a)–(d) vertical profiles of the hydrometeor mixing ratio for the EMx0.1, x0.4, x4, and x10 cases, respectively and (e) the surface precipitation rate for the four cases.

Around sunrise (07:26 LST) on 28 Jaunary, the surface temperature decrease by 1.4 K more than in the case of EM0. This is linked to the strong longwave radiation cooling of air by particulate pollutants, which is concentrated in the lowest layers with strong stratification adjacent to the ground. Just after sunrise, the incoming angle of shortwave radiation is small from the horizon, and strong shortwave radiation absorption takes place: approximately a half of the incoming shortwave radiation was absorbed in the case of EMx1 for two hours, in contrast to a 16% reduction at noon. Around noon of 28 January, shortwave absorption heats the upper part of the aerosol layer at approximately 300 m and its temperature was more than 0.4 K higher than in the case of EM0, as shown in Fig. 5. It, in turn, intensified the stable stratification in that layer. The stratification level was represented by Ri, and its contour is presented in Fig. 6. The intensified stable stratification in the layer near the ground to the middle of the planetary boundary layer suppressed the evolution of a mixing layer, and the mixing layer began to develop at approximately 10:30 LST. It was one hour later than in the case of EM0 (Fig. 1c). The similar daytime structure was also found on 27 January (Figs. 5 and 6). Because of the strong stratification dependence of the turbulent diffusivity for mass, pollutant dispersion is depressed in the layers near the ground, and the stratification is intensified more by the heated upper part of the aerosol layer due to absorption of shortwave radiation. Moreover, the stronger inversion depressed the development of a mixing layer in the morning. Time variations of the potential temperature and BC concentration profiles on 27 January are presented in Fig. 7. As seen in the diagrams, the evolution of the mixing layer with

longwave radiation, lw, was dominant. It was upward to the atmosphere, but it was almost the same level as in the EM0 case. It is considered to cause the lower surface air temperature (T2) difference level, 0 to −0.5 K, in the case of EMx1 and thus the upward longwave flux was slightly decreased. The downward longwave flux of EMx1 was slightly larger due to aerosols, except during the sunrise of 28 January when the downward flux was larger by 20 W/m2 due to the presence of the radiation fog than that of the EM0 case, as discussed later in Sect. 3.2. Despite the slight increase in downward longwave radiation flux, T2 decreased due to aerosols, particularly before and just after sunrise. The cooling mechanism is briefly summarized as follows: Significant decrease in surface air temperature due to aerosols before and just after the sunrise was caused by changes in both longwave and shortwave radiations. Scattering and absorption of longwave radiation by aerosols work to heat the air by absorbing upward longwave radiation from the surface and cool the air by emitting downward radiation from the aerosol layer toward the surface. During the night, aerosols cool the air because the cooling rate (emission) is larger than the heating effect (absorption). This cooling effect is larger as the light absorbing aerosols such as black carbon is more. Aerosols heat the ground at night, whereas aerosols cool the air at the same time. T2 is affected by both ground temperature (heated) and air temperature of the aerosol layer (cooled). Actually, T2 was decreased by aerosols because the latter effect dominates the former effect. Just after the sunrise, aerosols significantly decrease downward shortwave radiation, suppressing rise in ground temperature as well as surface air temperature. 106

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boundary layer. The time variation of BC concentration profiles is presented in Fig. 7b. The depression of the turbulent dispersion and mixing layer thickness, together with that of convective motions, cause more severe air pollution than in the case of EM0 up to 11:00 LST. The worst air pollution in Beijing occurred in the period from the evening of 28 to the morning of 29 January 2013 (Fig. 1a), when the stratification was extremely stable (Fig. 1c). The BC and SO2 + NOx concentrations were the highest in the winter of year 2013. As seen in Fig. 3, a strong northerly wind of more than 10 m s−1 started to blow at the 550 m level and affected downward to the wind field in the lower atmosphere, which had been southerly, and finally the surface wind became calm or week-northerly at mid night to the next morning. Vertical profiles of the potential temperature and BC concentration during the sunset of 28 January and in the period from the night of 28 to the sunrise of the next day 29 January are presented in Figs. 8 and 9, respectively. From the sunset, the mixing layer dissipated and the surface inversion layer developed (Figs. 1c and 8a). The surface inversion was extremely strong (RMOL ∼5/m) and the surface mixing ratio of BC rapidly increased in the evening (4 times in 5 h only in the lowest model layer). Similar to Fig. 7, the surface inversion was created up to 100–150 m level (Figs. 8 and 9). However, because of the very week wind condition the surface concentration was much higher and caused the most severe air pollution in Beijing. There are also gradual increases in BC mixing ratios in the upper part of the boundary layer around the 550 m level in Fig. 8b. It may be corresponding to a “regional-scale” elevated inversion (for example, one caused by subsidence associated with anticyclones, and hereafter simply referred to as “elevated inversion” in this paper and does not include the capping inversion at the top of mixing layer evolved in the morning under fine weather), as represented by highly positive Ri regions in Fig. 6. From the late night of 28 January to the next morning (Fig. 9), the pollution mixing ratios increased in layers between the ground surface and the elevated inversion. When an elevated inversion covers in the middle or upper part of the planetary boundary layer and stays all day long or more, pollutants are accumulated in the layer below the elevated inversion and severe air pollution usually occur if the weather is clear. The elevated inversion which had begun to develop in the evening of 28 January, stayed over the Beijing area through the night into the morning of 30 January 2013 (Figs. 1a and 6). When the stable stratification was intensified during the night of 28 January, the elevated inversion started to develop in the layer above 500 m high, with the maximum of Ri greater than 300 in the morning of 29 January (Fig. 6). BC was trapped and accumulated at the base of the elevated inversion, which is clearly seen in Figs. 8b and 9b (a local maximum and a local minimum appear at levels approximately 550 m and just above 300 m, respectively). The development of the elevated inversion, together with the abrupt wind direction change from southerly to northerly and the resulted low wind speed in the lowest surface layer, a reduction of turbulent diffusivity was caused there and the surface inversion was intensified. In the surface inversion layer, aerosol concentration became high, and it in turn decreased the air temperature in the layer and intensified the surface inversion more than that in the EM0 case, particularly around sunrise below 150 m high (Fig. 9a). It is due mainly to the longwave radiation cooling of air due to the accumulated aerosols and enhanced fog water formation.

Fig. 11. Relationship between emission control scenarios EM0, EMx0.1, x0.4, x1, x4 and x10 on the x-axis and the clear days mean (i.e., 27–29 January) for the (a) reduction rate in daytime shortwave radiation, (b) decrease rate in absolute values of reciprocal MoninObhkov length and (c) BC-ratio, the nonlinearity in the BC mixing ratio, i.e., the ratios of the BC mixing ratio of the EM0, EMx0.1, x0.4, x1, x4 and x10 runs, multiplied by 1000, 10, 2.5, 1, 0.25 and 0.1, respectively, to the BC mixing ratio of EMx1, on the y-axis.

3.2. Influence of aerosols on fog formation

uniform temperature and BC concentration is depressed by the upper stable layers. From 6:00 to 8:00 LST, just after the sunrise (7:25), surface temperature continued to decrease. After the sunrise, mixing layer with uniform temperature and BC concentration started to evolve over the ground surface but it was depressed by the upper stable layers. At 9:00 LST, the mixing layer thickness was 50 m and after this point the mixing layer depression became more significant because the shortwave radiation heating of BC in the middle part of the planetary

Time series of vertical profiles of hydrometeor mixing ratio were presented in Fig. 2 for EMx1 and Fig. 10 for other emission levels. During the nights of 27–28 January and 28 to 29 January, a fog started to develop at around midnight near the ground and disappeared 2–3 h after sunrise. The thick-fog layer was up to 100 m high. It disappeared at 150 m high, being suppressed from above by stable atmosphere. The fog water mixing ratio decreased with height, and the 107

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maximum value was 0.16 and 0.15 g kg-air−1 on the two nights, respectively. Visibility was estimated to be approximately 130 or 230 m, respectively, when the diameter of the fog droplets was assumed to be 8 or 15 μm, respectively (Trabert, 1901). In contrast, in the zero emission case, EM0, the maximum of the fog water mixing ratio was 0.03 and 0.04 g kg-air−1 on the two nights, respectively, which corresponds to a visibility of 600 m or more. As presented in Fig. 10, the numerical experiments of fog formation for the changing emission levels, 0.1, 0.4, 4.0, and 10.0 times the present status, EMx1, revealed that in the lowest air layer, the maximum values of the fog water mixing ratio for these emission levels did not significantly change with the emission level. This is considered to indicate that even for the lowest emission level of anthropogenic pollutants, EMx0.1 at 1/10 of the present emission level, EMx1.0, secondary gaseous and particulate pollutants and other radiatively participating pollutants works for fog formation. In contrast, the fog layer could be extended aloft by 100 m in the cases of EMx4 and EMx10 because of the stronger radiative cooling caused by particles near the ground surface, as indicated by Fig. 5.

to decrease ground surface temperature and evaporation (direct aerosol effect). In addition, shortwave radiation absorption by mainly black carbon directly changes the atmospheric temperature and wind fields and indirectly changes via changes in atmospheric stability (semi-direct effects). Moreover, aerosol dynamics affects microphysical processes and cloud and fog formation, which in turn changes temperature, wind and atmospheric stability fields, together with longwave radiation cooling by radiation participating gaseous and particulate pollutants and hydrometers. Such a coupling among aerosol, cloud, radiation and climate causes a ‘positive’ feedback between air pollution and meteorological changes. That is, severe air pollution is caused by large amount of pollutant emission. It changes urban climate and meteorology, which in turn can cause more severe air pollution. Fig. 11 presents the reduction in shortwave radiation, changes in the Monin-Obkhov length and the BC-ratio, a measure of the non-linearity effect in the BC mixing ratio: Ratios of the BC mixing ratio of EMx0.1, x0.4, x1, x4 and x10 runs, multiplied by 10, 2.5, 1, 0.25 and 0.1, respectively, to the BC mixing ratio of EMx1, respectively. In the daytime (8:00 to 17:00 LST) on clear days, the reduction of the maximum downward shortwave radiation was 1.2% (range: 0.8–1.7%), 4.7% (range: 3.3–6.7%), 12% (range: 8.4–16%), 38% (range: 32–47%) and 64% (range: 58–71%) for the cases of the emission levels, EMx0.1, x0.4, x1.0, x4.0 and x10.0, respectively. It reduced the maximum upward heat flux by 2%, 9%, 21%, 56% and 87% and resulted in a decrease of the absolute values of the reciprocal of MoninObkhov length by 2%, 5%, 10%, 20, and 30% with increasing emission levels. For the lower levels and at present emission levels, EMx0.1, EMx0.4 and EMx1.0, the BC-ratio slightly changed, ranging from 0.93 to 1.15 times, but for larger emission levels, EMx4.0 and EMx10.0, the BC-ratio was 1.2 and 1.6 times, respectively. During the night (18:00 to the next 7:00 LST), the BC-ratio did not depend on emission level on clear days, changing only in the range of 0.91–1.07. However, even on a clear day, a thick radiation fog evolved on 28 January. It stabilized the surface air layer and depressed mixing layer development. The BC-ratio increased with the emission level, from 0.7 in the EMx0.4 case to 1.5 and 1.7 in the cases of EMx4.0 and x10.0, respectively. On cloudy days with low level stratus, the BC-ratio increased with the emission level from 0.7 in the EMx0.4 case to 1.4 and 1.6 in the cases of EMx4.0 and x10.0, respectively. Enhancement of the BC-ratio was similar to the case with thick radiation fog formation in the early morning on clear days. Counterbalancing the dependencies of the BC-ratio on meteorological conditions, including cloudy and rainy days, the diurnal mean BC-ratio was 0.93, 1.2 and 1.4 for EMx0.1, x4.0 and x10.0, respectively. Thus, it indicates that the surface concentration of air pollutants increases almost proportionally to the emissions, and the nonlinearity between the emissions and the concentration of pollutants is 40% when the emissions were increased to 10 times larger than the present emission level during winter in Beijing.

3.3. Influence of aerosols on cloud formation and precipitation From midnight on 29 January to the late evening on 31 January, low-level stratus developed in the middle to upper part of the planetary boundary layer. On 29 January, the stratus was extended in the layer from 150 m to 600 m high, with the maximum hydrometeor mixing ratio of 0.33 g kg-air−1, and on 30–31 January, it was in the layer from 150 m to 1000 m high, with the maximum mixing ratio of 0.44 g kgair−1; the stratus was at 550 m on 30 January with a mixing ratio of 0.26 g kg-air−1 and at a level of 700 m on 31 January. The mixing ratio significantly changes as the emission level changes, while the cloud heights do not significantly change. Aerosols affect precipitation in two opposing ways, either suppression or enhancement of precipitation, and have been detected from analyses of historic data and field experiments. Zhang et al. (2010) introduced enhanced rainfall that was found in and downwind of major urban areas, suggesting giant CCN can enhance precipitation, while suppression of precipitation was observed in tropical rain forest fires, agricultural vegetation burning and in orographic precipitation suppression by anthropogenic aerosols in hilly areas in California, Israel and near Xi'an in China under high concentrations of aerosols and small CCN. For the radiation fog events (27–29 January), aerosols enhanced precipitation, due to stronger radiative cooling. For the low-level stratus case (29–31 January), the precipitation was enhanced from EM0 to EMx1 (Fig. 2b). In EMx1, the cumulative precipitation was enhanced 2.8 times compared to EM0, but there is no significant difference between EMx1 and EMx0.1 (only 1.2 times). For EM0, the case of 0.001 times the current emission level, critical supersaturation increased significantly, which resulted in suppressing cloud formation and precipitation. From EMx1 to EMx10, aerosols suppressed precipitation and the initiation time of precipitation was slightly delayed (Fig. 10e). This is consistent with the so-called second indirect effect (more aerosols and smaller sizes of initial cloud droplets delay the evolution of cloud microphysical processes). For the range of emission levels from EMx0.1 to EMx1, the changes in the critical supersaturation or changes in the initial cloud droplet sizes were found to be less sensitive to cloud formation and precipitation. Even for the same season, location, and synoptic-scale patterns, the aerosols modified the duration and the amount of precipitation in both ways—the aerosol-cloud interaction is highly nonlinear.

4. Conclusions The interaction of aerosol-radiation-stratification-turbulence-cloud processes during the severe haze event in Beijing in January 2013 was studied using an on-line coupled meteorology/chemistry model WRFChem. Our findings are summarized as follows: ・ For the clear days, shortwave radiation was reduced by approximately 15%, and surface temperature decreased from 0 to 0.5 K throughout the day and night, except for around sunrise time, 1.4 K, when fog was present because the fog or drizzle droplets intensified the longwave radiation cooling of the air. ・ In the presence of the low-level stratus and light precipitation (indirect effect), the modification of meteorology by aerosols amplified and changed the wind speed and direction much more significantly

3.4. Nonlinearity between pollutant emission level and concentration As discussed in Section 3.1, the scattering of shortwave radiation by aerosols works to reduce downward shortwave radiation flux and tends 108

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compared to the clear days, in the presence of only the direct effects. ・ The current study newly assessed the non-linear effect (positive feedback) of pollutant emission control on the surface air concentration by introducing a measure of the non-linearity effect in the BC mixing ratio: BC-ratio. The non-linearity effect was not significant (less than 10%) up to the current emission levels. In another word, the mixing ratio of aerosols became higher by 10% due to their radiation effects. ・ The non-linearity effect was approximately 40% for the case when the emissions were 10 times the current level (the surface concentrations become 14 times when the emissions become 10 times). Aerosol mixing state is important in predicting cloud condensation nuclei (CCN) activity and optical properties, especially for black carbon (Oshima et al., 2009), when taken together with its morphology (Adachi et al., 2010). We incorporated the evolution processes of mixing state and morphology of black carbon into a 3-D chemical transport modeling framework (Kajino and Kondo, 2011), but the effort is still underway: The aerosol feedback processes to meteorology have not been incorporated yet. The effect of aerosol mixing state on the feedback process needs to be assessed in the future. Modeling such a highly nonlinear aerosol-cloud-radiation interaction system could contain huge uncertainty. Since the current study is conducted using a single model for a limited period, a multi-model assessment for a longer period will be needed, especially for the study of this non-linear system. To accurately simulate these interaction processes, further development in the modeling of each elemental process, such as turbulent diffusion and aerosol mixing state evolution, will be indispensable, together with observations of vertical profiles and detailed structures of turbulence, radiation, and aerosol and hydrometeor properties using remote sensing techniques and in-situ measurements using the soundings or a meteorological observation tower. Acknowledgments The current research was supported by the Fundamental Research Budget of MRI (C3) and the Integrated Research Program for Advancing Climate Models (TOUGOU Program) of the Ministry of Education, Culture, Sports, Science, and Technology Japan (MEXT), the Japanese Society for the Promotion of Sciences (JSPS) (KAKENHI grant nos. 15K16121 and 15H02811), the Environmental Research and Technology Development Fund of the Environmental Restoration and Conservation Agency (ERCA) (5-1605, 2-1403, and S-12), and the National Natural Science Foundation of China (nos. 91644217 and 41375151). References Adachi, K., Chung, S.H., Buseck, P.R., 2010. Shapes of soot aerosol particles and implications for their effects on climate. J. Geophys. Res. 115, D15206. http://dx.doi. org/10.1029/2009JD012868. Atwater, M.A., 1972. Thermal effects of urbanization and industrialization in the boundary layer: a numerical study. Boundary-Layer Meteorol. 3, 229–245. Bergstrom, R.M., 1972. Prediction of the spectral absorption and extinction coefficients of an urban air pollution aerosol model. Atmos. Environ. 6, 247–258. Bergstrom, R.M., Viskanta, R., 1973a. Modeling of the effects of gaseous and particulate pollutants in the urban atmosphere. Part I: thermal structure. J. Appl. Meteorol. 12, 901–912. Bergstrom, R.M., Viskanta, R., 1973b. Modeling of the effects of gaseous and particulate pollutants in the urban atmosphere. Part II: pollution disperssion. J. Appl. Meteorol. 12, 913–918. Bergstrom, R.M., Viskanta, R., 1973c. Prediction of solar radiant flux and heating rates in a polluted atmosphere. Tellus 25, 486–498. Boucher, O., et al., 2013. Clouds and aerosols. In: Stocker, T.F. (Ed.), Climate Change 2013: the Physical Science Basis, Working Group I Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge Univ. Press, New York, pp. 571–657. Chen, F., Dudhia, J., 2001. Coupling an advanced land-surface/hydrology model with the Penn State/NCAR MM5 modeling system. Part I: model description and implementation. Mon. Wea. Rev. 129, 569–585.

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5194/acp-15-2969-2015. Zhang, J.K., Sun, Y., Liu, Z.R., Ji, D.S., Hu, B., Liu, Q., Wang, Y.S., 2014. Characterization of submicron aerosols during a month of serious pollution in Beijing, 2013. Atmos. Chem. Phys. 14, 2887–2903. http://dx.doi.org/10.5194/acp-14-2887-2014. Zhang, L., Wang, T., Lv, M., Zhang, Q., 2015. On the severe haze in Beijing during January 2013: unraveling the effects of meteorological anomalies with WRF-Chem.

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