Assessing the Influence of Aerosol on Radiation and Its Roles in Planetary Boundary Layer Development

  • Corresponding author: Jiannong QUAN, jnquan@ium.cn
  • Funds:

    Supported by the National Key Research and Development Program of China (2017YFC0209604 and 2018YFF0300101) and Beijing Natural Science Foundation (8204062)

  • doi: 10.1007/s13351-021-0109-z

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  • A comprehensive measurement of planetary boundary layer (PBL) meteorology was conducted at 140 and 280 m on a meteorological tower in Beijing, China, to quantify the effect of aerosols on radiation and its role in PBL development. The measured variables included four-component radiation, temperature, sensible heat flux (SH), and turbulent kinetic energy (TKE) at 140 and 280 m, as well as PBL height (PBLH). In this work, a method was developed to quantitatively estimate the effect of aerosols on radiation based on the PBLH and radiation at the two heights (140 and 280 m). The results confirmed that the weakened downward shortwave radiation (DSR) on hazy days could be attributed predominantly to increased aerosols, while for longwave radiation, aerosols only accounted for around one-third of the enhanced downward longwave radiation. The DSR decreased by 55.2 W m−2 on hazy days during noontime (1100–1400 local time). The weakened solar radiation decreased SH and TKE by enhancing atmospheric stability, and hence suppressed PBL development. Compared with clean days, the decreasing rates of DSR, SH, TKE, and PBLH were 11.4%, 33.6%, 73.8%, and 53.4%, respectively. These observations collectively suggest that aerosol radiative forcing on the PBL is exaggerated by a complex chain of interactions among thermodynamic, dynamic, and radiative processes. These findings shed new light on our understanding of the complex relationship between aerosol and the PBL.
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  • Fig. 1.  Mean diurnal variations in radiation on hazy (red) and clean (green) days at 280 m, including (a) DSR, (b) USR, (c) albedo, (d) DLR, (e) ULR, and (f) Rn. The box-and-whisker plots represent 25%–75% and 10%–90% of the dataset; the dot and line in each box represent the mean and median of the data, respectively.

    Fig. 2.  As in Fig. 1, but at the height of 140 m.

    Fig. 3.  Radiation at 140 and 280 m on clean and hazy days. All data are shortwave radiation at noontime (1100–1400 LT).

    Fig. 8.  Diurnal variations of PBLH on hazy (red) and clean (green) days.

    Fig. 4.  Weather systems at 850 hPa and at the surface on (a, c) clean days and (b, d) hazy minus clean days. Data are from ERA-Interim. Colored contours show the geopotential height (m) at 850 hPa and sea level pressure (Pa) at the surface. The red triangle shows the location of the IAP tower.

    Fig. 5.  Mean profiles of temperature (T; blue), relative humidity (RH; red), and wind (black) in Beijing during clean (solid) and hazy (dotted) days at (a) 0800 and (b) 2000 LT. All data are from radiosonde observations.

    Fig. 6.  Diurnal temperature variations at 140 m (green) and 280 m (red) on hazy and clean days.

    Fig. 7.  Diurnal variations of (a) SH and (c) TKE on hazy (red) and clean (green) days at 140 m, and (b, d) their differences between hazy and clean days.

    Fig. 9.  Rate of decrease of DSR, SH, and TKE at 140 m, as well as PBLH, along with the rate of increase of PM2.5 at ground level, under high aerosol concentrations.

  • [1]

    Boers, R., and E. W. Eloranta, 1986: Lidar measurements of the atmospheric entrainment zone and the potential temperature jump across the top of the mixed layer. Bound.-Layer Meteor., 34, 357–375. doi: 10.1007/BF00120988.
    [2]

    Bond, T. C., S. J. Doherty, D. W. Fahey, et al., 2013: Bounding the role of black carbon in the climate system: A scientific assessment. J. Geophys. Res. Atmos., 118, 5380–5552. doi: 10.1002/jgrd.50171.
    [3]

    Brooks, I. M., 2003: Finding boundary layer top: Application of a wavelet covariance transform to lidar backscatter profiles. J. Atmos. Oceanic Technol., 20, 1092–1105. doi: 10.1175/1520-0426(2003)020<1092:FBLTAO>2.0.CO;2.
    [4]

    Burba, G., 2013: Eddy Covariance Method for Scientific, Industrial, Agricultural, and Regulatory Applications: A Field Book on Measuring Ecosystem Gas Exchange and Areal Emission Rates. LI-COR Biosciences, Lincoln, NE, USA, 110–112.
    [5]

    Cohn, S. A., and W. M. Angevine, 2000: Boundary layer height and entrainment zone thickness measured by lidars and wind-profiling radars. J. Appl. Meteor., 39, 1233–1247. doi: 10.1175/1520-0450(2000)039<1233:BLHAEZ>2.0.CO;2.
    [6]

    Cuesta, J., D. Edouart, M. Mimouni, et al., 2008: Multiplatform observations of the seasonal evolution of the Saharan atmospheric boundary layer in Tamanrasset, Algeria, in the framework of the African Monsoon Multidisciplinary Analysis field campaign conducted in 2006. J. Geophys. Res. Atmos., 113, D00C07. doi: 10.1029/2007JD009417.
    [7]

    Ding, A. J., X. Huang, W. Nie, et al., 2016: Enhanced haze pollution by black carbon in megacities in China. Geophys. Res. Lett., 43, 2873–2879. doi: 10.1002/2016GL067745.
    [8]

    Dou, J. X., S. Grimmond, Z. G. Cheng, et al., 2019: Summertime surface energy balance fluxes at two Beijing sites. Int. J. Climatol., 39, 2793–2810. doi: 10.1002/joc.5989.
    [9]

    Gao, Y., M. Zhang, Z. Liu, et al., 2015: Modeling the feedback between aerosol and meteorological variables in the atmospheric boundary layer during a severe fog–haze event over the North China Plain. Atmos. Chem. Phys., 15, 4279–4295. doi: 10.5194/acp-15-4279-2015.
    [10]

    Han, S. Q., H. Bian, X. X. Tie, et al., 2009: Impact of nocturnal planetary boundary layer on urban air pollutants: Measurements from a 250-m tower over Tianjin, China. J. Hazard. Mater., 162, 264–269. doi: 10.1016/j.jhazmat.2008.05.056.
    [11]

    Koll, D. D. B., and T. W. Cronin, 2018: Earth’s outgoing longwave radiation linear due to H2O greenhouse effect. Proc. Natl. Acad. Sci. USA, 115, 10,293–10,298. doi: 10.1073/pnas.1809868115.
    [12]

    Liu, Q., X. C. Jia, J. N. Quan, et al., 2018: New positive feedback mechanism between boundary layer meteorology and secondary aerosol formation during severe haze events. Sci. Rep., 8, 6095. doi: 10.1038/s41598-018-24366-3.
    [13]

    Liu, X. M., F. Hu, L. H. Quan, et al., 2009: Validation of the local similarity in urban boundary layer. Climatic Environ. Res., 14, 183–191. (in Chinese)
    [14]

    Ma, Y. J., H. J. Zhao, Y. S. Dong, et al., 2018: Comparison of two air pollution episodes over Northeast China in winter 2016/17 using ground-based lidar. J. Meteor. Res., 32, 313–323. doi: 10.1007/s13351-018-7047-4.
    [15]

    Marsham, J. H., D. J. Parker, C. M. Grams, et al., 2008: Observations of mesoscale and boundary-layer scale circulations affecting dust transport and uplift over the Sahara. Atmos. Chem. Phys., 8, 6979–6993. doi: 10.5194/acp-8-6979-2008.
    [16]

    Menon, S., J. Hansen, L. Nazarenko, et al., 2002: Climate effects of black carbon aerosols in China and India. Science, 297, 2250–2253. doi: 10.1126/science.1075159.
    [17]

    Messager, C., D. J. Parker, O. Reitebuch, et al., 2010: Structure and dynamics of the Saharan atmospheric boundary layer during the West African monsoon onset: Observations and analyses from the research flights of 14 and 17 July 2006. Quart. J. Roy. Meteor. Soc., 136, 107–124. doi: 10.1002/qj.469.
    [18]

    Nieuwstadt, F. T. M., and P. G. Duynkerke, 1996: Turbulence in the atmospheric boundary layer. Atmos. Res., 40, 111–142. doi: 10.1016/0169-8095(95)00034-8.
    [19]

    Peng, Z., and F. Hu, 2006: A study of the influence of urbanization of Beijing on the boundary wind structure. Chinese J. Geophys., 49, 1608–1615. doi: 10.3321/j.issn:0001-5733.2006.06.005. (in Chinese)
    [20]

    Petäjä, T., L. Järvi, V.-M. Kerminen, et al., 2016: Enhanced air pollution via aerosol-boundary layer feedback in China. Sci. Rep., 6, 18998. doi: 10.1038/srep18998.
    [21]

    Quan, J. N., Y. Gao, Q. Zhang, et al., 2013: Evolution of planetary boundary layer under different weather conditions, and its impact on aerosol concentrations. Particuology, 11, 34–40. doi: 10.1016/j.partic.2012.04.005.
    [22]

    Quan, J. N., X. X. Tie, Q. Zhang, et al., 2014: Characteristics of heavy aerosol pollution during the 2012–2013 winter in Beijing, China. Atmos. Environ., 88, 83–89. doi: 10.1016/j.atmosenv.2014.01.058.
    [23]

    Ren, Y., S. W. Zheng, W. Wei, et al., 2018: Characteristics of turbulent transfer during episodes of heavy haze pollution in Beijing in winter 2016/17. J. Meteor. Res., 32, 69–80. doi: 10.1007/s13351-018-7072-3.
    [24]

    Stewart, I. D., and T. R. Oke, 2012: Local climate zones for urban temperature studies. Bull. Amer. Meteor. Soc., 93, 1879–1900. doi: 10.1175/BAMS-D-11-00019.1.
    [25]

    Tang, G. Q., J. Q. Zhang, X. W. Zhu, et al., 2016: Mixing layer height and its implications for air pollution over Beijing, China. Atmos. Chem. Phys., 16, 2459–2475. doi: 10.5194/acp-16-2459-2016.
    [26]

    Wang, Y., C. F. Zhao, Z. P. Dong, et al., 2018: Improved retrieval of cloud base heights from ceilometer using a non-standard instrument method. Atmos. Res., 202, 148–155. doi: 10.1016/j.atmosres.2017.11.021.
    [27]

    Wyngaard, J. C., 1990: Scalar fluxes in the planetary boundary layer—Theory, modeling, and measurement. Bound.-Layer Meteor., 50, 49–75. doi: 10.1007/BF00120518.
    [28]

    Yang, X., C. F. Zhao, J. P. Guo, et al., 2016: Intensification of aerosol pollution associated with its feedback with surface solar radiation and winds in Beijing. J. Geophys. Res. Atmos., 121, 4093–4099. doi: 10.1002/2015JD024645.
    [29]

    Zhang, Q., X. C. Ma, X. X. Tie, et al., 2009: Vertical distributions of aerosols under different weather conditions: Analysis of in-situ aircraft measurements in Beijing, China. Atmos. Environ., 43, 5526–5535. doi: 10.1016/j.atmosenv.2009.05.037.
    [30]

    Zhang, Q., J. Zhang, J. Qiao, et al., 2011a: Relationship of atmospheric boundary layer depth with thermodynamic processes at the land surface in arid regions of China. Sci. China Earth Sci., 54, 1586–1594. doi: 10.1007/s11430-011-4207-0.
    [31]

    Zhang, Q., J. N. Quan, X. X. Tie, et al., 2011b: Impact of aerosol particles on cloud formation: Aircraft measurements in China. Atmos. Environ., 45, 665–672. doi: 10.1016/j.atmosenv.2010.10.025.
    [32]

    Zhao, C. S., X. X. Tie, and Y. P. Lin, 2006: A possible positive feedback of reduction of precipitation and increase in aerosols over eastern central China. Geophys. Res. Lett., 33, L11814. doi: 10.1029/2006GL025959.
    [33]

    Zhong, J. T., X. Y. Zhang, Y. Q. Wang, et al., 2018: Heavy aerosol pollution episodes in winter Beijing enhanced by radiative cooling effects of aerosols. Atmos. Res., 209, 59–64. doi: 10.1016/j.atmosres.2018.03.011.
  • Zhigang CHENG and Jiannong QUAN.pdf

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Assessing the Influence of Aerosol on Radiation and Its Roles in Planetary Boundary Layer Development

    Corresponding author: Jiannong QUAN, jnquan@ium.cn
  • Institute of Urban Meteorology, China Meteorological Administration, Beijing 100089
Funds: Supported by the National Key Research and Development Program of China (2017YFC0209604 and 2018YFF0300101) and Beijing Natural Science Foundation (8204062)

Abstract: A comprehensive measurement of planetary boundary layer (PBL) meteorology was conducted at 140 and 280 m on a meteorological tower in Beijing, China, to quantify the effect of aerosols on radiation and its role in PBL development. The measured variables included four-component radiation, temperature, sensible heat flux (SH), and turbulent kinetic energy (TKE) at 140 and 280 m, as well as PBL height (PBLH). In this work, a method was developed to quantitatively estimate the effect of aerosols on radiation based on the PBLH and radiation at the two heights (140 and 280 m). The results confirmed that the weakened downward shortwave radiation (DSR) on hazy days could be attributed predominantly to increased aerosols, while for longwave radiation, aerosols only accounted for around one-third of the enhanced downward longwave radiation. The DSR decreased by 55.2 W m−2 on hazy days during noontime (1100–1400 local time). The weakened solar radiation decreased SH and TKE by enhancing atmospheric stability, and hence suppressed PBL development. Compared with clean days, the decreasing rates of DSR, SH, TKE, and PBLH were 11.4%, 33.6%, 73.8%, and 53.4%, respectively. These observations collectively suggest that aerosol radiative forcing on the PBL is exaggerated by a complex chain of interactions among thermodynamic, dynamic, and radiative processes. These findings shed new light on our understanding of the complex relationship between aerosol and the PBL.

1.   Introduction
  • Aerosols change the energy balance by absorbing and scattering solar and/or longwave radiation (Menon et al., 2002; Zhao et al., 2006; Bond et al., 2013; Yang et al., 2016). During haze events, solar radiation can decrease by up to several hundred W m−2, and researchers attribute this decrease to aerosols when analyzing their relationships with the planetary boundary layer (PBL; Quan et al., 2014; Liu et al., 2018; Zhong et al., 2018). However, the simulated decrease in radiation caused by aerosols tends to be lower than the observed. For example, Gao et al. (2015) showed that, due to the effect of aerosols on radiation, shortwave radiation fluxes at the surface were reduced by 8–36 W m−2 (daily average) during haze events. This raises the possibility that the effects of aerosol on radiation are underestimated in models and/or overestimated in observations.

    The PBL is the lowest part of the troposphere, where the atmosphere is directly influenced by surface and human activities. An inversion layer at the top of the PBL (a very low turbulent mixing rate) prevents aerosols from being transported up to the free troposphere (Han et al., 2009; Zhang et al., 2009, 2011a; Petäjä et al., 2016; Liu et al., 2018), while inside the PBL pollutants are transported vertically due to strong turbulence (Wyngaard, 1990; Nieuwstadt and Duynkerke, 1996). As a result, aerosols are strongly constrained and vertically mixed within the PBL, since pollutants are generally emitted from the surface. During haze events, the PBL height (PBLH) decreases significantly, accelerating the accumulation of pollutants inside the PBL (Quan et al., 2013; Ding et al., 2016; Tang et al., 2016; Zhong et al., 2018). Observations have shown that aerosol concentrations are anticorrelated with the PBLH (Quan et al., 2013; Liu et al., 2018; Ma et al., 2018).

    The development of the PBL is driven predominantly by heat flux and momentum (Zhang et al., 2011b; Ren et al., 2018). In the morning, the PBLH starts to increase with rising solar radiation, and reaches its maximum in the afternoon; after sunset, heat flux and turbulence weaken drastically and the PBLH decreases correspondingly. The attenuated radiation in haze events may weaken heat flux and momentum, which in turn influence PBL development. Hence, there is a pressing need to quantify the effect of aerosols on radiation and its role in PBL development. To this end, a comprehensive measurement of PBL meteorology was conducted at the IAP (Institute of Atmospheric Physics, Chinese Academy of Sciences) meteorological tower, including temperature, sensible heat flux (SH), turbulent kinetic energy (TKE), and four-component radiation (downward/upward longwave/shortwave radiation) at the heights of 140 and 280 m. In addition, aerosols and PBL structure were also observed.

    The rest of the paper is organized as follows. Section 2 describes the measurements used in this study. The results and analyses are given in Section 3. The analyses focus on: (1) the difference in radiation in the two heights (140 and 280 m) during hazy and clean days; (2) quantitative estimation of the influence of aerosols on radiation based on observations; and (3) the relationships among aerosols, radiation, SH, turbulence, and the PBL. Section 4 provides concluding remarks.

2.   Observations
  • All observations were conducted at the IAP meteorological tower (39°58′N, 116°22′E), Chinese Academy of Sciences, Beijing. This is located between the North 3rd Ring Road and North 4th Ring Road and is surrounded by residential, commercial (i.e., hotels, restaurants, supermarkets, shopping malls, offices), and institutional (i.e., universities, hospitals) buildings. Most buildings in this area are 15–30 m tall (average: 25.5 m); the tallest is 104 m (~100 m to the southwest), several other tall buildings (65–75 m) are located within ~300 m south, and many buildings over 50 m tall are located within ~500 m to the north, northwest, and southeast (Dou et al., 2019). This mix of relatively lower buildings and clusters of much taller buildings is typical of Beijing, many urban areas in China, and cities worldwide. Overall, the area can be characterized as “compact high-rise” (LCZ1) and “compact midrise” (LCZ2) by using the Stewart and Oke (2012) local climate zone (LCZ) system. Peng and Hu (2006) showed that the urban canopy layer was approximately 47–63 m thick; thus, the 140- and 280-m observational layers were above the constant flux layer (Liu et al., 2009).

    In this study, two months (November 2016 and November 2017) of observations were used to analyze the role of aerosol in PBL development during haze events. Cloudy days were excluded from the data to remove the influence of clouds on radiation, which were defined as days when cloud-cover time exceeded 2 h day−1 in the mini-micro-pulse lidar (mini-MPL, Sigma Space Co., USA) observations (Wang et al., 2018). The radiation data were recorded by Kipp & Zonen (Netherlands) four-component unventilated radiometers (CNR1) installed at 140 and 280 m on the IAP tower, providing the downward and upward shortwave (DSR and USR) and longwave (DLR and ULR) radiation separately. All radiation data had an accuracy of ± 2.5% with a temporal resolution of 30 min; the net all-wave radiation (Rn) was calculated by using the four-component radiation. The thermodynamic parameters (sensible heat and turbulence) were observed by ultrasonic anemometers (CSAT3, Campbell Scientific, USA) installed on a 2.2-m boom from the tower’s center at the same height as the radiation instruments. The CSAT3 data were sampled at 10 Hz and calculated in 30-min segments by using EDDY-PRO software (Burba, 2013). The air temperatures at the heights of 140 and 280 m were measured by HMP45C temperature probes (Vaisala, Finland) with an accuracy of ± 0.2°C.

    A mini-MPL was used to study the evolution of the PBL. The pulse repetition frequency was 2500 Hz, with a laser wavelength of 532 nm and peak optical energy of 4 μJ. The pulse duration was set to 10 ns and the pulse interval to 200 ns, corresponding to a spatial resolution of 30 m. The PBLH was defined as the altitude where a sudden decrease in the scattering coefficient occurred (Boers and Eloranta, 1986; Cohn and Angevine, 2000; Brooks, 2003), based on the large gradient in aerosol concentration between the boundary layer and free troposphere. PM2.5 observations from Environmental Protection Bureau’s stations in Beijing were used (http://zx.bjmemc.com.cn/). ERA-Interim reanalysis data and radiosonde observations in Beijing at 0800 and 2000 local time (LT = UTC + 8 h) were used to understand the background weather.

3.   Results and discussion
  • To examine the effects of aerosol on radiation, observations were divided into clean and hazy days based on the daily averaged PM2.5 mass concentrations at ground level. In this work, the clean and hazy days were defined by a threshold value of 75 μg m−3 based on the National Ambient Air Quality Standards of China (GB3095-2012; Quan et al., 2014). As a result, there were 13 clean days and 6 hazy days in the two-month (November 2016 and November 2017) period. The average PM2.5 concentration on clean and hazy days was 30.6 and 164.4 μg m−3, respectively. The radiation at 280 m on clean and hazy days is shown in Fig. 1. The observations show that DSR decreased while USR increased under high aerosol conditions, indicating weakened solar radiation caused by aerosols on hazy days. The albedo at this height, calculated as the ratio of USR to DSR, was 0.121 and 0.143 on clean and hazy days, respectively. Compared with clean days, the DSR on hazy days decreased by 35.1 W m−2, with a decreasing rate of 7.2%, while USR on hazy days increased by 5.7 W m−2, with an increasing rate of 9.7%. As a result, the net shortwave radiation entering the lower layer (< 280 m), calculated as the difference between DSR and USR, decreased by 40.8 W m−2. Different to shortwave radiation, both DLR and ULR increased under high aerosol conditions. Compared with clean days, the DLR and ULR on hazy days increased by 31.8 and 21.0 W m−2, with increasing rates of 12.8% and 6.0%, respectively. The Rn was further analyzed to understand the variation of radiation between clean and hazy days. Rn was calculated as follows:

    Figure 1.  Mean diurnal variations in radiation on hazy (red) and clean (green) days at 280 m, including (a) DSR, (b) USR, (c) albedo, (d) DLR, (e) ULR, and (f) Rn. The box-and-whisker plots represent 25%–75% and 10%–90% of the dataset; the dot and line in each box represent the mean and median of the data, respectively.

    $${\rm{Rn}} = {\rm{DSR}} + {\rm{DLR}} - {\rm{USR}} - {\rm{ULR}}.$$ (1)

    The Rn results suggest that the 0–280-m layer received less energy during daytime and lost less energy at nighttime under high aerosol conditions. All the radiation data analyzed were for noontime (1100–1400 LT). These variations in radiation may influence temperature, SH, turbulence, diurnal temperature variations, atmospheric stability, and PBL development. Detailed analyses were carried out, the results of which are reported in the following sections.

    Observations at the height of 140 m showed similar trends to those at 280 m (Fig. 2). Compared with the height of 280 m, the DSR at 140-m layer further decreased by 20.7 W m−2 on hazy days—more significantly than on clean days (0.6 W m−2; Fig. 3). The USR decreased by 10.8 and 1.5 W m−2 on hazy and clean days, respectively. Notably, the albedo at 280 m was larger than that at 140 m, and the difference on hazy days was more significant than on clean days (Fig. 3). Aerosol can scatter and reduce the total downward solar radiation, but enhance the diffusive fraction (Quan et al., 2014; Liu et al., 2018). As a result, the DSR at 140 m was lower than that at 280 m on both clean and hazy days; whereas, for USR, its vertical variation was more complicated because it was influenced jointly by downward solar radiation, scattering radiation, and upward solar radiation from the lower layer. For example, the USR at 280 m increased from 58.9 W m−2 on clean days to 64.6 W m−2, but decreased from 57.4 W m−2 on clean days to 53.8 W m−2 at 140 m. Well-designed model simulations are needed in the future to better understand the influence of aerosol on atmospheric albedo.

    Figure 2.  As in Fig. 1, but at the height of 140 m.

    Figure 3.  Radiation at 140 and 280 m on clean and hazy days. All data are shortwave radiation at noontime (1100–1400 LT).

  • The above results show clearly the differences in radiation between clean and hazy days, but it remains unclear whether, or to what extent, these variations were related to aerosol effects. To quantitatively estimate the effect of aerosols on radiation, a method was developed in this work based on the PBLH and radiation at the two heights (140 and 280 m). First, the difference in radiation at 280 and 140 m was calculated [$\Delta {R}_{i280 - 140}$; Eq. (2)]. Then, the radiation on clean days was set as the background level to exclude the influence of other factors [$\delta {R}_{i280 - 140};$ Eq. (3)]. Finally, the effect of aerosol on radiation [$\delta {R}_{i{\rm{a}}};$ Eq. (4)] was quantitatively calculated based on the PBLH and radiation difference between the two heights (140 and 280 m). In this calculation, aerosols were assumed to be confined and uniformly distributed within the PBL (Zhang et al., 2009, 2011a; Quan et al., 2013; Liu et al., 2018).

    $$\Delta {R}_{i280 - 140} = {R}_{i280} - {R}_{i140},$$ (2)
    $$ \delta {R}{_{i280 - 140}} = \Delta {R}{_{i280 - 140{\rm{haze}}}} - \Delta {R}_{i280 - 140{\rm{clean}}}, $$ (3)
    $$ \delta {{R}_{i{\rm{a}}}} = \delta {{R}_{i280 - 140}} \times \left( {{\rm{PBLH}} - 140} \right)/140, $$ (4)

    where Ri denotes radiation of type i, including DSR, USR, DLR, ULR, and Rn, in units of W m−2. The unit for PBLH is m.

    Based on above method and observations (Figs. 1, 2), the differences in radiation between 280 and 140 m were calculated. The $\delta {\rm{DS}}{{\rm{R}}_{280 - 140}}$ was ~20.1 W m−2 during the noontime (1100–1400 LT), and the PBLH was about 515 m on hazy days (see Section 3.3). Therefore, the total weakened DSR reaching 140 m because of aerosol ($\delta {\rm{DS}}{{\rm{R}}_{{\rm{a}}}}$) was estimated as 53.8 W m−2 [calculated by Eq. (4)]. This value was consistent with the observed decrease in DSR at 140 m on hazy days (55.2 W m−2; Fig. 3). These results suggest that the weakened DSR on hazy days could be mainly attributed to aerosols.

    Figure 8.  Diurnal variations of PBLH on hazy (red) and clean (green) days.

    Similarly, the variation of DLR on hazy and clean days was also calculated, which was 0–11 W m−2. This value accounted for only one-third of the total enhanced DLR (20–35 W m−2), indicating that other processes exist besides aerosols that affect the DLR. The wind in the tropospheric layer usually changes from northwest to south on hazy days (Fig. 4), warming the atmosphere (Fig. 5). The warmer tropospheric layer thus emits more longwave radiation and enhances the DLR. To confirm this viewpoint, the influence of warm airmasses on longwave radiation was estimated by using the Stefan–Boltzmann law (Koll and Cronin, 2018):

    Figure 4.  Weather systems at 850 hPa and at the surface on (a, c) clean days and (b, d) hazy minus clean days. Data are from ERA-Interim. Colored contours show the geopotential height (m) at 850 hPa and sea level pressure (Pa) at the surface. The red triangle shows the location of the IAP tower.

    Figure 5.  Mean profiles of temperature (T; blue), relative humidity (RH; red), and wind (black) in Beijing during clean (solid) and hazy (dotted) days at (a) 0800 and (b) 2000 LT. All data are from radiosonde observations.

    $${\rm{LR}} = \sigma {T^4}, \hspace{150pt} $$ (5)

    where LR denotes longwave radiation, σ is the Stefan–Boltzmann constant (5.67 × 10−8 W m−2 K−4), and T is the airmass temperature in K. The warm layer ranged from the ground to nearly 10,000 m (Fig. 5), so the average temperatures in the layer from 140 to 10,000 m were used in the calculation. The airmass temperature from radiosonde observations on hazy and clean days was 255.9 and 250.0 K, respectively. The difference in LR influenced by the airmass temperature between hazy and clean days was 21.8 W m−2, which could explain the remaining two-thirds of the total enhanced DLR.

  • High concentrations of aerosols influence radiation processes, which in turn affect temperature variation. The diurnal temperature range (DTR), calculated as the difference between daily maximum and minimum temperature, decreased by 1.4 and 1.2°C at 280 and 140 m under high aerosol conditions (Fig. 6). As a result, atmospheric stability enhanced. This enhanced atmospheric stability, together with decreased solar radiation, will decrease SH and TKE (Fig. 7). The SH decreased from 127.6 W m−2 on clean days to 84.7 W m−2 on hazy days, with a decreasing rate of 33.6% during noontime (1100–1400 LT). The TKE decreased from 2.4 m−2 s−2 on clean days to 0.6 m−2 s−2 on hazy days, with a decreasing rate of 73.8%. Heat flux and momentum are predominant factors that drive PBL development (Zhang et al., 2011b; Ren et al., 2018). Maximum PBLH is highly correlated with the accumulation of surface SH (Cuesta et al., 2008; Marsham et al., 2008; Messager et al., 2010). Zhang et al. (2011a) showed that the depth of the PBL is closely correlated with thermodynamic features in both the development and maintenance stages and that more energy is consumed in the former; a higher Rn, DTR, and SH resulted in a greater PBLH. Hence, the decreased Rn, DTR, SH, and TKE on hazy days will suppress PBL development. The PBLH during noontime (1100–1400 LT) decreased from 1105 m on clean days to 515 m on hazy days, with a decreasing rate of 53.4% (Fig. 8).

    Figure 6.  Diurnal temperature variations at 140 m (green) and 280 m (red) on hazy and clean days.

    Figure 7.  Diurnal variations of (a) SH and (c) TKE on hazy (red) and clean (green) days at 140 m, and (b, d) their differences between hazy and clean days.

    It is noteworthy that the decreasing rates of SH (33.6%), TKE (73.8%), and PBLH (53.4%) were much higher than that of DSR (11.4%). Furthermore, the increasing rate of PM2.5 (436.8%) was higher than the decreasing rate of PBLH (Fig. 9). Compared with clean days, the PBLH on hazy days decreased only by 53.4%. Hence, PM2.5 should increase by almost 100% assuming that aerosols are uniformly distributed within the PBL. This discrepancy may be caused by the chemical feedback mechanism between boundary layer meteorology and secondary aerosol formation (Liu et al., 2018). The decreased PBL associated with weakened radiation increases the relative humidity (RH) by weakening the vertical transport of water vapor, which in turn enhances secondary particle formation through heterogeneous aqueous reactions, further enhancing PM2.5. The above results collectively suggest that aerosol radiative forcing on the PBL is exaggerated by a complex chain of interactions among dynamic, thermodynamic, and even chemical processes. These findings shed new light on our understanding of the complex relationship between aerosol and the PBL.

    Figure 9.  Rate of decrease of DSR, SH, and TKE at 140 m, as well as PBLH, along with the rate of increase of PM2.5 at ground level, under high aerosol concentrations.

4.   Conclusions
  • Comprehensive observations of radiation collected at the heights of 140 and 280 m at the IAP tower in November 2016 andNovember 2017, including SH, turbulence, temperature, PM2.5, and PBLH, were analyzed to quantify the effect of aerosols on radiation and its role in PBL development. The results can be summarized as follows:

    (1) The DSR at 280 and 140 m decreased by 35.1 and 55.2 W m−2 on hazy days, respectively. The albedo, calculated as the ratio of USR to DSR, on hazy days, was higher than on clean days at both 280 and 140 m; its value at 280 m was higher than at 140 m on both clean and hazy days, caused by a more significant decreasing rate of USR.

    (2) A method was developed to quantitatively estimate the effect of aerosols on radiation based on the PBLH and radiation at the two heights (140 and 280 m). The results confirmed that the weakened DSR on hazy days can be attributed predominantly to increased aerosols, while for longwave radiation, aerosols only accounted for around one-third of the enhanced DLR.

    (3) The decreasing rates of SH (33.6%), TKE (73.8%), and PBLH (53.4%) were much higher than that of DSR (11.4%). Furthermore, the increasing rate of PM2.5 (436.8%) was higher than the decreasing rate of PBLH.

    The above results collectively suggest that aerosol radiative forcing on the PBL is exaggerated by a complex chain of interactions among dynamic, thermodynamic, and even chemical processes.

    Acknowledgments. Thanks to Jia Jingjing and Li Aiguo, who work at the IAP, Chinese Academy of Sciences, for their help with the data analysis in this paper.

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