The remarkable development of China has resulted in rapid urbanization (urban heat island and dry island) and severe air pollution (aerosol pollution). Previous studies demonstrate that these two factors have either suppressing or promoting effects on fog, but what are the extents of their individual and combined effects? In this study, a dense radiation fog event in eastern China in January 2017 was reproduced by the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem), and the individual and combined effects of urbanization and aerosols on fog (indicated by liquid water content – LWC) are quantitatively revealed. Results show that urbanization inhibits low-level fog, delays its formation and advances its dissipation due to higher temperatures and lower saturations. In contrast, upper-level fog could be enhanced because of the updraught-induced vapour convergence. Aerosols promote fog by increasing LWC, increasing droplet concentration and decreasing droplet effective radius. Further experiments show that the current pollution level in China could still be below the critical aerosol concentration that suppresses fog. Urbanization influences fog to a larger extent than aerosols do. When urbanization and aerosol pollution are combined, the much weaker aerosol-promoting effect is counteracted by the stronger urbanization-suppressing effect on fog. Budget analysis of LWC reveals that urban development (urbanization and aerosols) alters the LWC profile and fog structure mainly by modulating condensation–evaporation process. Our results infer that urban fog will be further reduced if urbanization keeps developing and air quality keeps deteriorating in the future.
During the past 5 decades, China has achieved remarkable developments, accompanied by strong anthropogenic activities (rapid urbanization and severe air pollution). Urbanization and air pollution have significantly affected climate change, monsoons, air quality, fog, clouds and precipitation (e.g. Li et al., 2016, 2017). Previous studies have linked the changes in clouds and precipitation to urbanization and aerosols. Urbanization destabilizes the boundary layer, which triggers strong updraughts and invigorates convection (e.g. Rozoff et al., 2003; Shepherd, 2005). Aerosols modify the macroscopic, microphysics, thermodynamics and radiative properties of clouds through complicated pathways, which are called aerosol–radiation and aerosol–cloud interactions and have been systematically reviewed by Fan et al. (2016), Rosenfeld et al. (2014), Tao et al. (2012), etc. Fog can be viewed as a cloud (Leng et al., 2014) that occurs near the surface. Land use features and aerosol properties may instantly affect fog, so fog is more sensitive to anthropogenic activities than other types of clouds (Zhu and Guo, 2016). Previous studies have analysed the effects of urbanization and aerosols on fog, mostly in segregated manners.
Urbanization is featured with urban heat island (UHI) and dry island (UDI) effects. The urban surface has a lower albedo than rural surface, which reduces the reflected solar radiation and enhances heat storage. Urban expansion decreases the coverage of cropland, water bodies and forestland, which reduces the sources of water vapour. As a result, urban areas commonly experience higher temperatures and lower vapour contents. These conditions induce a lower relative humidity that is unfavourable for fog formation (Gu et al., 2019). At the long-term scale, urban fog days are reported to decrease significantly (e.g. Guo et al., 2016; LaDochy, 2005; Sachweh and Koepke, 1995; Shi et al., 2008; Yan et al., 2019). Although UHI and UDI inhibit near-surface fog, the upward motions can promote upper-level fog (Li et al., 2011; S. Niu et al., 2010). Surface roughness and thermal circulation cause strong updraughts (Rozoff et al., 2003), which transfer water vapour aloft and cause the wet-island phenomenon in the upper level (Kang et al., 2014). The fog at that altitude may be subsequently enhanced.
Aerosols exert sophisticated impacts on fog through direct (radiation) effects and indirect (microphysical) effects (Khain and Pinsky, 2018). Aerosols attenuate shortwave radiation, influencing PBL (planetary boundary layer) structure and the vertical profile of moisture and aerosols (Tie et al., 2017, 2019), which can alter the formation and dissipation condition of fog. Scattering aerosols block downwelling solar radiation in the daytime, thus delaying the dissipation and elongating the duration of fog (Shi et al., 2008; Maalick et al., 2016). Although they increase downwelling longwave radiation at night, scattering aerosols have negligible effects on the fog formation time (Stolaki et al., 2015; Maalick et al., 2016). The effect of absorbing aerosols like black carbon (BC) on fog depends on its residence height. If BC resides above the fog layer, BC causes a dome effect (Ding et al., 2016) which blocks solar radiation and prevents the dissipation of fog (Bott, 1991). If BC resides within the fog layer, BC heats fog droplets and accelerates the dissipation of fog (Maalick et al., 2016). The aerosol indirect effect on cloud is addressed as one of the most uncertain factors in the IPCC report (IPCC, 2013). This effect on fog is also complex and twofold, which is determined by aerosol concentration. Under saturation conditions, increasing aerosols commonly result in more cloud condensation nuclei (CCN). This promotes activation and condensation, yielding more but smaller droplets and increasing cloud water content (Fan et al., 2018; Rosenfeld et al., 2008). These changes have two kinds of positive feedback on fog (Maalick et al., 2016): more droplets cause stronger radiative cooling at fog top and enhance condensation (Jia et al., 2018), and smaller droplet size inhibits sedimentation and the depletion of cloud water (Zhang et al., 2014). However, if aerosol concentration exceeds a certain threshold, this promoting effect disappears (Quan et al., 2011) or even turns into a suppressing effect due to the strong vapour competition (Guo et al., 2017; Koren et al., 2008; Liu et al., 2019; Rangognio et al., 2009; Wang et al., 2015). Additionally, large-scale aerosol pollution can change weather patterns and affect large-scale fog formation conditions (F. Niu et al., 2010). Ding et al. (2019) found that the dome effects of BC induce a land–sea thermal contrast and generate a cyclonic anomaly over coastal areas. This anomaly results in more vapour transported inland and enhances advection–radiation fog.
Our recent observational work (Yan et al., 2019) indicated a decreasing trend in fog days, and the inhibiting effects of urbanization outweigh the promoting effects of aerosols on fog during the mature urbanization stage. This study aims to quantitatively confirm the roles of urbanization and aerosols in a dense fog event by an online-coupled synoptic and air quality model, the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem). This event is radiation fog event with weak synoptic forcing (detailed in Sect. 3.1), so the effects of urbanization and aerosols should be obvious. Determining the quantitative extents of the urbanization effect, aerosol effect and their combined effect is an interesting topic, which has rarely been studied previously to the best of our knowledge. This work is expected to facilitate the understanding of how anthropogenic activities affect the natural environment, fog (cloud) physics and aerosol–cloud interactions near the surface.
In this study, urbanization mainly refers to UHI and UDI induced by
anthropogenic heating and land use change with the corresponding surface
property changes (e.g. surface albedo, surface roughness, surface flux),
excluding the increasing aerosol pollution caused by urban expansion. Air
pollution refers to aerosols and is indicated by anthropogenic emissions
because aerosol concentration is highly proportional to emission intensity.
Liquid water content (LWC) and cloud–fog droplet number concentration
(
The first set of data are the hourly automatic weather station data from the
Shouxian National Climate Observatory (SX; 32.4
The model used in this study is the WRF-Chem (V3.9.1.1) model. It is an
online-coupled mesoscale synoptic and air quality model that considers the
sophisticated interactions among various dynamic, physical and chemical
processes (Chapman et al., 2009; Fast et al., 2006). WRF, or WRF-Chem, has
been successfully used in simulating fog events (Jia and Guo, 2012, 2015; Jia et al., 2018) and exploring aerosol–cloud interactions (Fan
et al., 2018). Two nest domains are set up (Fig. 1).
The d01 domain has a size of
The performance of the simulated fog zone at 08:00 on 3 January
2017.
Fog simulation is highly sensitive to vertical grids (Gultepe et al., 2007).
A fine vertical resolution with a proper lowest model level can better
resolve turbulence, thus yielding a reasonable fog structure (Yang et al.,
2019). Here, 42 vertical levels are established with the first five
Two sub-regions
Fog simulation is also sensitive to physical schemes (Gu et al., 2019). Through numerous experiments, radiation, microphysics and boundary schemes are found to significantly influence the model performance, and the boundary layer scheme plays a decisive role (Chaouch et al., 2017). The radiation schemes are the RRTM (rapid radiative transfer model) longwave scheme and the Goddard shortwave scheme. The microphysical scheme is the Morrison double-moment scheme (Morrison et al., 2005). The boundary layer scheme is the Yonsei University (YSU) 1.5-order closure non-local scheme, which yields better results than any other schemes. The major schemes are listed in Table 1.
Summary of major parameterization schemes.
The model is driven by the highest-resolution product
(0.125
The study site is SX because only its visibility is observed hourly and is a multiple of 1 m, which is suitable for evaluating the model performance. To investigate the effects of urbanization and aerosols on fog, we change the land use and emission intensity around SX. Four experiments, i.e. u0e0, u3e0, u0e3 and u3e3, are designed. u0e0 is the base experiment, with no urbanization and weak emission at SX. u3e0 is set as the urbanization condition. u0e3 is set as the polluted condition. u3e3 is set as the urban development condition (urbanization and pollution coexist). The experiment settings are listed in Table 2.
Settings of sensitive experiments. “N” represents no changes.
On the setting of the urbanized condition, we replace the land use of SX with that
of Hefei, the most urbanized city and the capital of Anhui Province. The
downtown of Hefei has a built area of approximately 570 km
The downtown of Hefei has much higher emissions than SX. For example, the
PM
The LWC is the proxy of fog as mentioned above. Since the LWC is not
observed, and visibility (VIS) is related to LWC, the VIS is used to assess
the model performance. VIS is not diagnosed by the model and can be
parameterized by the function of LWC,
The atmospheric extinction (
During the fog period (Fig. 4 shaded zone), the three methods nearly yield the same results (Fig. S1 in the Supplement), so the last method is used to calculate the simulated VIS.
From 1 to 6 January 2017, eastern China is dominated by zonal circulation, with a weak trough, ridge, pressure gradient and atmospheric diffusion (Zhang and Ma, 2017). Under this stable weather pattern, the accumulation of pollutants and water vapour promotes the occurrence of fog–haze events. From the evening of 2 January to noon of 3 January, a dense fog event occurs in wide regions of eastern China. The fog reaches its peak at 08:00 on 3 January, covering southern Hebei, eastern Henan, western Shandong, Anhui, Jiangsu and Shanghai (Fig. 2a). Figure 4a shows the temporal variation in visibility at SX. The fog forms at 18:00 on 2 January and dissipates at 12:40 on 3 January. This is radiation fog which is promoted by strong radiative cooling at night and weak easterly water vapour transport from the northwestern Pacific (Zhu et al., 2019).
The performance of the simulated meteorological parameters at the
SX site.
Lee (1987) and Sachweh and Koepke (1995) observed “fog holes” over urban areas on satellite images. Here, the fog hole means the low liquid water path (LWP) region within the fog region, which is visualized as pixels with weak fog (high visibility) or clear sky surrounded by dense fog. These holes demonstrate that urban development (urbanization and aerosols) has a clearing effect on fog. In this fog event, fog holes are also present over urban areas in the Himawari 8 image at 11:00 on 3 January (Fig. 3). We hypothesize that urbanization could have profound effects on fog by reducing the LWP or advancing the dissipation of fog, and the role of aerosols on fog is weaker than that of urbanization.
The model performance is evaluated by comparing the fog spatial coverage.
Satellite cloud image and modelled LWP (
The model performance is also evaluated by comparing the visibility and
other basic parameters at the SX site (Fig. 4). Seen
from the visibility, the simulated fog forms at 19:30, 1.5 h later than the
observation, and dissipates at 12:20, 30 min earlier than the observation.
During the fog period, the simulated visibility agrees well with the
observation. The other parameters such as temperature, wind speed and
relative humidity are also effectively reproduced by the model, with
relative small root-mean-square errors (RMSEs) of 0.8 K, 0.7 m s
From different sensitivity experiments (u3e0, u0e3 and u3e3), we can deduce the extents of the separate or combined effects of urbanization and aerosols on fog. Figure 5 compares the LWC between u0e0 and u3e0. The general results are as follows: (1) before 02:00, urbanization leads to a decreasing LWC in all layers. Fog forms on the surface at 22:30 in u3e0, 3 h later than in u0e0. (2) After 02:00, the LWC decreases in the low level while it increases in the upper level. Fog dissipates at 10:50 in u3e0, 1.5 h earlier than in u0e0. To better explain the LWC difference, its profiles are shown in Fig. 6. At 23:00, although fog formed in u3e0, the fog is rather weak compared with u0e0, which is caused by the higher temperature (Fig. 6f) and lower saturation associated with UHI and UDI. At 02:00, fog develops in u3e0, but its intensity (the value of LWC) cannot reach the same level as that in u0e0.
Time–height distribution of the LWC (g kg
Profiles of the LWC
An interesting phenomenon is the opposite change of LWC in the low level and
upper level after 02:00. This phenomenon can be explained by the role of
updraughts. The increasing roughness length and extra warming in urban
conditions could trigger horizontal wind convergence (Fig. S2) and the
enhanced updraughts (Fig. 5c). The stronger updraughts
in u3e0 affect condensation via two possible pathways: (1) the vertical
transport of vapour
Figure 7 compares the LWC between u0e0 and u0e3. The formation time, dissipation time of fog and fog top show almost no changes. The LWC increases at almost all layers in the polluted condition. Accordingly, the LWP also increases (Fig. 7c). It is probable that the current pollution level of China always promotes fog occurrence. To testify whether u0e3 is below the transition point that suppresses fog, eight additional experiments (D10, D7.5, D5, D2.5, M2.5, M5, M7.5 and M10) are performed. These experiments are the same as u0e3, except that the emissions around SX (the black box in Fig. 1b) are multiplied (the “M” prefix) or divided (the “D” prefix). For example, M2.5 means multiplying by 2.5; D10 means dividing by 10.
Similar to Fig. 5 but for the aerosol effect (u0e3 minus u0e0).
Relationships of the microphysical parameters (LWC,
Figure 8 compares the LWC,
Rosenfeld et al. (2008) revealed that the turning point in convective clouds
is CCN
Figure 9 compares the LWC between u0e0 and u3e3. The
u3e3-induced change is quite similar to but not the same as the u3e0-induced
change. The time–height averages of absolute change of LWC induced by u3e0,
u0e3 and u3e3 are 0.120, 0.019 and 0.124 g kg
Similar to Fig. 5 but for the combined effect of urbanization and aerosols (u3e3 minus u0e0).
In WRF, the budget of LWC is composed of the following items:
The microphysical tendency is further decomposed into the following items:
All the processes regarding precipitation and cold phase (the cumu, cold,
auto and accr subscripts) are not analysed because no precipitation occurs,
and the temperature is above 0
The combined effect of urbanization and aerosols (u3e3 minus
u0e0) on various items of the LWC budget. The three rows are the tendencies
(g kg
We can also infer that the extents to which the various physical processes affect fog through the sensitivity experiments (u3e0, u0e3 and u3e3). Additional aerosols weakly influence these processes (Fig. S3, right column) and subsequently result in weak LWC change (Fig. 7c). Compared with aerosols, the urbanization effect is much more considerable (Fig. S4, right column); it dominantly accounts for the variation in physical tendencies from u0e0 to u3e3 (Fig. 10, right column). In the u3e3 condition, urban development (urbanization and aerosols) induces different magnitudes of changes in different physical tendencies. The relative magnitudes are 52.1 %, 38.3 % and 9.6 % for the microphysical, boundary layer and advection processes, respectively, indicating that microphysics is most susceptible to urban development and contributes most to the LWC change. Among various microphysical processes, condensation–evaporation contributes most (72.7 %) to the change in microphysical tendency (Fig. 11, right column). The above results indicate that urban development affects the LWC mainly by modulating the condensation–evaporation process. Since the u3e3 condition still witnesses higher temperatures and stronger updraughts (figure not shown), the notable variation in condensation–evaporation tendency induced by u3e3 can also be attributed to the predominant role of UHI, UDI and updraughts. The mechanism has been analysed in Sect. 3.3.
The combined effect of urbanization and aerosols (u3e3 minus
u0e0) on various items of the microphysical tendency. The three rows are the
tendencies (g kg
As mentioned above, urbanization influences fog to a larger extent than aerosols do; the LWC in fog does not vary substantially with pollution level. This section discusses the rationality and reliability of our results through mechanism analysis and observational evidence.
The sensitivity of cloud properties to aerosols depends on aerosol
concentration and saturation environment. In convective clouds with intense
upward motions and high saturations, the response of cloud properties to
additional aerosols is significant (“aerosol-limited regime”) (Fan et al.,
2018). However, in fog with much weaker updraughts and lower saturations, this
response could be more sensitive to vapour content rather than aerosol
concentration (“vapour-limited regime”). It possibly implies that the LWC in
fog varies slightly with pollution level but considerably with the saturation
condition that related to urbanization. Our results reveal that the
time–height average LWC varies within the extent of 0.07 g kg
In terms of observational evidence, Yan et al. (2019) revealed that fog days in polluted regions of eastern China have decreased since the 1990s. Through quantitative analysis, the promoting effects of aerosols are weakening, while the suppressing effects of urbanization are enhancing and dominantly cause this decrease. Sachweh and Koepke (1995) also claimed that the hindering effects of urbanization outweigh the promoting effects of aerosols on fog in southern Germany. Additionally, satellite images present discernible fog holes above urban areas (Fig. 3) (Lee, 1987; Sachweh and Koepke, 1995). Therefore, the above observational evidence supports the model results that the promoting effect of aerosols is counteracted by the hindering effect of urbanization. We believe that the results can also be applied to other large cities in China because these cities commonly witness strong UHI, UDI and severe air pollution.
A dense radiation fog event occurred in eastern China from 2 to 3 January
2017. Satellite images show that fog holes occur over urban areas,
demonstrating the remarkable effects of urbanization and air pollution on
fog. Hence, the mechanism is investigated by WRF-Chem. The model
captures the spatial coverage and temporal evolution of the fog well.
Furthermore, the separate and combined effects of urbanization (refers to
UHI and UDI) and air pollution (refers to aerosols) on fog (indicated by the
LWC) are revealed, and the extents of these effects are quantitatively
determined. Results show the following.
Urbanization redistributes the LWC profile by the UHI, UDI effect and
updraughts. The updraughts may be caused by surface roughness and extra warming.
The UHI and UDI suppress low-level fog, delay its formation by 3 h and
advance its dissipation by 1.5 h. However, the upper-level fog could be
enhanced due to the updraught-induced adiabatic cooling and vapour flux
convergence. Urbanization reduces the LWP most of the time, and this
reduction in the daytime can explain why fog holes are present above urban
areas on satellite images. Aerosols promote fog mainly by changing microphysical properties. The
increasing emissions (aerosol concentration) produce more CCN and fog
droplets, which decrease The role of urbanization far overweighs that of aerosols. Therefore, when
they act together, the urbanization effect is dominant, and the aerosol
effect is indiscernible. Budget analysis of LWC shows that increasing
aerosols influence various physical processes to a lesser extent, while
urbanization influences these processes to a larger extent, eventually
leading to a substantial LWC change in the urban development condition
(urbanization and aerosols). In this condition, the comparisons among
various physical processes reveal that microphysics dominates the change in
LWC, and condensation–evaporation dominates the change in microphysical
tendency. This result highlights the importance of the condensation–evaporation
process in modulating the LWC profile and fog structure. Mechanism analysis and the observational evidence support our key finding
that urbanization influences fog to a much larger extent than aerosol
pollution does. Therefore, we believe our results are reasonable and robust in
radiation fog events without strong synoptic forcings, and the results can
also be applied to other large cities in China due to the similar urban
development patterns. This study is expected to facilitate a better
understanding of how anthropogenic activities affect the natural
environment, fog (cloud) physics and aerosol–cloud interactions near the
surface. We can also infer the future change of fog occurrence. Under the
traditional urban development pattern, i.e. that in which urbanization keeps developing
and air quality keeps deteriorating, urban fog occurrence will be further
reduced.
Some of the data repositories have been listed in Sect. 2. The other data, model outputs and codes can be accessed by contacting Bin Zhu via binzhu@nuist.edu.cn.
The supplement related to this article is available online at:
SY performed the model simulation, data analysis and paper writing. BZ proposed the idea, supervised this work and revised the paper. YH provided the observation data at the SX site. JZ processed the observation data. HK offered help with the model simulation. CL and TZ also contributed to the paper revision.
The authors declare that they have no conflict of interest.
We are grateful to the High Performance Computing Center of Nanjing University of Information Science and Technology for doing the numerical calculations in this work on its blade cluster system. We thank American Journal Experts (AJE) for the English language editing.
This research has been supported by the National Key Research and Development Program (grant no. 2016YFA0602003) and the National Natural Science Foundation of China (grant nos. 91544229, 41575148, 41605091).
This paper was edited by Xiaohong Liu and reviewed by three anonymous referees.