Most previous modeling studies about black carbon (BC) transport and its
impact over the Tibetan Plateau (TP) conducted simulations with horizontal
resolutions coarser than 20 km that may not be able to resolve the
complex topography of the Himalayas well. In this study, the two experiments
covering all of the Himalayas with the Weather Research and Forecasting model
coupled with Chemistry (WRF-Chem) at the horizontal resolution of 4 km but
with two different topography datasets (4 km complex topography and 20 km
smooth topography) are conducted for pre-monsoon season (April 2016) to
investigate the impacts of topography on modeling the transport and
distribution of BC over the TP. Both experiments show the evident accumulation
of aerosols near the southern Himalayas during the pre-monsoon season,
consistent with the satellite retrievals. The observed episode of high
surface BC concentration at the station near Mt. Everest due to heavy
biomass burning near the southern Himalayas is well captured by the
simulations. The simulations indicate that the prevailing upflow across the
Himalayas driven by the large-scale westerly and small-scale southerly
circulations during the daytime is the dominant transport mechanism of southern
Asian BC into the TP, and it is much stronger than that during the nighttime.
The simulation with the 4 km topography resolves more valleys and mountain
ridges and shows that the BC transport across the Himalayas can overcome the
majority of mountain ridges, but the valley transport is more efficient. The
complex topography results in stronger overall cross-Himalayan transport
during the simulation period primarily due to the strengthened efficiency of
near-surface meridional transport towards the TP, enhanced wind speed at
some valleys and deeper valley channels associated with larger transported
BC mass volume. This results in 50 % higher transport flux of BC across
the Himalayas and 30 %–50 % stronger BC radiative heating in the atmosphere
up to 10 km over the TP from the simulation with the 4 km complex topography
than that with the 20 km smoother topography. The different topography also
leads to different distributions of snow cover and BC forcing in snow. This
study implies that the relatively smooth topography used by the models with
resolutions coarser than 20 km may introduce significant negative biases in
estimating light-absorbing aerosol radiative forcing over the TP during the
pre-monsoon season.
Highlights.
The black carbon (BC) transport across the Himalayas can overcome the
majority of mountain ridges, but the valley transport is much more efficient
during the pre-monsoon season.
The complex topography results in stronger overall cross-Himalayan
transport during the study period primarily due to the strengthened
efficiency of near-surface meridional transport towards the TP, enhanced
wind speed at some valleys and deeper valley channels associated with
larger transported BC mass volume.
The complex topography generates 50 % higher transport flux of BC
across the Himalayas and 30 %–50 % stronger BC radiative heating in the
atmosphere up to 10 km over the Tibetan Plateau (TP) than the
smoother topography, which implies that the smooth topography used by the
models with relatively coarse resolution may introduce significant negative
biases in estimating BC radiative forcing over the TP during the pre-monsoon
season.
The different topography also leads to different distributions of snow
cover and BC forcing in snow over the TP.
Introduction
The Tibetan Plateau (TP) is the highest plateau in the world with an average
elevation over 4 km and an area of approximately 2.5×106 km2; it is known as the world's third pole (Qiu, 2008), and its enormous
dynamic and thermal effects have a huge impact on large-scale atmospheric
circulation through the energy exchange with the atmosphere, especially the
troposphere, such as Asian monsoons (e.g., Ye and Wu, 1998; Duan and Wu,
2005; Wu et al., 2007, 2012a; Boos and Kuang, 2013; Chen and Bordoni, 2014;
He et al., 2019; Zhao et al., 2019). In addition, the glacial melting water
of the TP is one of the important sources of water resources of the Indus River,
Ganges River, Yangtze River and Yellow River in Asia (e.g., Singh and
Bengtsson, 2004; Barnett et al., 2005; Immerzeel et al., 2010; Lutz et al.,
2014). Previous studies found aerosols in the atmosphere over or around the TP
could change the regional climate of Asia (e.g., Qian et al., 2011, 2015;
Lau et al., 2017; Lau and Kim, 2018). Model simulations showed that the absorptive
aerosols changed the surface radiative flux over the TP by 5–25 W m-2
during the pre-monsoon season in April and May and led to the changes in
summer monsoon circulations (Qian et al., 2011). Meanwhile, aerosol may
affect the atmosphere by modulating the vertical structure of clouds and
precipitation around the TP and thus change the distribution of atmospheric
latent heat around the TP; this is the main driving force of regional
atmosphere circulations (e.g., Li and Min, 2010; R. Li et al., 2017, 2019). Moreover, when
absorbing aerosols settle on the snow-covered areas, they will blacken the
surface of snow cover and glaciers to a large extent (e.g., Hansen and
Nazarenko, 2004; Ramanathan and Carmichael, 2008; Lau et al., 2010;
Lee et al., 2013; Y. Zhang et al., 2017, 2018; Lau and Kim, 2018), reduce the snow albedo so as to
absorb more solar radiation, and cause the consequences of accelerated
melting (e.g., Ramanathan et al., 2007; Ming et al., 2009; Yasunari et al.,
2010; Ji et al., 2015; Zhang et al., 2015). According to the
Intergovernmental Panel on Climate Change Fifth Assessment Report (IPCC
AR5), the radiative forcing caused by the important component of absorbing
aerosols and black carbon (BC) on the surface snow is 0.04 W m-2 (0.02–0.09 W m-2) on average globally, and the regional forcing (such
as over the Arctic and the Himalayas) can be considerably large.
The TP is surrounded by various sources of pollutants. Over the south of the TP,
previous studies have suggested that southern Asia was the main source of
pollutants transported to the plateau (e.g., Cong et al., 2009, 2015a, b;
Kopacz et al., 2011; Lu et al., 2012; Zhao et al., 2013; Wang et al., 2015;
Zhang et al., 2015; Kang et al., 2016, 2019; Li et al., 2016; Chen et al.,
2018). A huge blanket or layer of “haze” is composed of light-absorbing
carbonaceous aerosol particles that often erupts in the pre-monsoon season
over southern Asia and has a significant influence on the plateau (e.g., Prasad
and Singh, 2007; Engling and Gelencser, 2010). Among them, biomass burning
emissions reaching the maximum in the pre-monsoon season over southern Asia is one
of the dominant sources (e.g., Cong et al., 2015b). Many studies
investigated the transport mechanisms of pollutants from southern Asia to the TP
and found that the pollutants transported across the Himalayas were mainly
due to the combination of a large-scale circulation and regional wind (e.g.,
Hindman and Upadhyay, 2002; Cao et al., 2010; Dumka et al., 2010; Marinoni
et al., 2010; Cong et al., 2015a; Kang et al., 2016; Lüthi et al., 2015;
R. Zhang et al., 2017). Cong et al. (2015b) suggested that strong large-scale
westerly and local small-scale mountain-valley wind passed through western
Nepal, northwestern India and Pakistan (i.e., southern Himalayas) in the
pre-monsoon season. Dumka et al. (2010) and Kang et al. (2016) inferred from
the trajectory analysis that long-distance transport from Africa and Europe
may also affect the BC concentration of the Himalayas in addition to the
influence of regional pollution. The synoptic troughs and ridges were also
found favoring the transport of pollutants into the TP from southern Asia
(Lüthi et al., 2015).
Although previous studies have confirmed the transport of pollutants across
the Himalayas, the complex topography of the Himalayas complicates transport
mechanisms. On one hand, Cao et al. (2010) revealed that the Himalayas acted
as a huge barrier to the transport of a large amount of BC over the plateau
based on model simulations. On the other hand, some studies found that the
valleys across the Himalayas served as channels for the efficient transport of
pollutants (e.g., Hindman and Upadhyay, 2002; Marinoni et al., 2010).
Marinoni et al. (2010) analyzed the observation of wind at a station of the
southern Himalayas and found that a distinct valley wind system with the
prominent southerly continuously transported pollutants to the plateau. Most
of these studies used observations and back-trajectory models to demonstrate
the transport pathways of pollutants to the TP, which cannot explicitly
reveal the transport mechanisms underneath, in particular quantifying the
impacts of the complex topography.
A few of modeling studies investigated the pollutant transport mechanisms
using 3-D chemical transport models (e.g., Kopacz et al., 2011; Liu et al.,
2015; R. Zhang et al., 2017; Yang et al., 2018). However, most of them
simulated transport processes at relatively coarse horizontal resolutions
(e.g., 20–100 km), which cannot resolve the complex topography of the
Himalayas well. It is noteworthy that studies about the aerosol climatic impact
over the TP also used the models at relatively coarse horizontal resolutions
(e.g., Flanner and Zender, 2005; Menon et al., 2010; Kopacz et al., 2011;
Qian et al., 2011, 2015; He et al., 2014; Zhang et al., 2015; Ji,
2016). So far, there is only one study that used a chemical transport model
at a horizontal resolution of less than 10 km to investigate pollutant transport
mechanisms over the eastern Himalayas (Cao et al., 2010). Furthermore, none
of the studies quantitatively assessed the impacts of topography on modeling the
pollutant transport across the Himalayas and hence on estimating aerosol
distribution and radiative forcing over the TP.
In order to examine the potential impacts of the complex topography on pollutant
transport across the Himalayas over the TP, this study conducts multiple
experiments with the Weather Research and Forecasting model coupled with
Chemistry (WRF-Chem; Grell et al., 2005; Skamarock et al., 2008). The
WRF-Chem model is selected because it includes the interaction between
meteorology and aerosol and is widely used for the regional modeling of aerosol
and its climatic impact (e.g., Cao et al., 2010; Zhao et al., 2010, 2011,
2012, 2014; Wu et al., 2013; Gao et al., 2014; Huang et al., 2015; Fan et
al., 2015; Feng et al., 2016; Zhong et al., 2017; Sarangi et al., 2019; Liu
et al., 2020). The model has also been used to investigate the aerosol
transport and climatic impact over the Himalayan region (e.g., Feng et al.,
2016; Cao et al., 2010; Sarangi et al., 2019). The model is suitable for
simulations at hydrostatic and non-hydrostatic scales and thus can be used
for investigating the impacts of resolution-dependent features, such as
topography, on modeling results. In particular, the meteorological part of
the model (WRF) has been systematically evaluated and used to investigate
the impacts of resolutions on simulations of moisture transport and climate
over the Himalayan region (e.g., Shi et al., 2008; Karki et al., 2017; Lin
et al., 2018; Zhou et al., 2017, 2018; Wang et al., 2020). All of these
previous studies with the model lay the foundation for this modeling study.
Two experiments with different topography representations are conducted to
investigate the impacts of topography complexity on the pollutant transport
across the Himalayas and the resulting radiative forcing over the TP. The
simulations are conducted for April 2016 in the pre-monsoon season because
southern Asia is seriously polluted during this period and the pollutants
transported to the TP during the period may have significant impacts on
the Asian monsoon system (e.g., Lau and Kim, 2006; Lau et al., 2006; Ding et al., 2009;
Kuhlmann and Quaas, 2010; Qian et al., 2011, 2015). In addition, the
observed concentration of BC at the observation station near Mt. Everest
shows an evident pollution episode from 5 to 16 April 2016,
deserving the investigation of the transport mechanisms. The rest of the
paper is organized as follows. Section 2 briefly describes the WRF-Chem
model, the physics parameterizations and the model configuration for this
study, followed by a description of the data for evaluation. The series of
numerical experiments at different resolutions are analyzed in Sect. 3.
The findings are then summarized and discussed in Sects. 4 and 5.
MethodologyModel and experimentsWRF-Chem model
In this study, the version of WRF-Chem updated by the University of Science and
Technology of China (USTC version of WRF-Chem) is used. This USTC version of
WRF-Chem includes some additional capabilities such as the diagnosis of
radiative forcing of aerosol species, land-surface-coupled biogenic-volatile-organic-compound (VOC) emission and aerosol–snow interactions compared
with the publicly released version (Zhao et al., 2013a, b, 2014, 2016; Hu et
al., 2019; Du et al., 2020). The Model for Simulating Aerosol Interactions
and Chemistry (MOSAIC; Zaveri et al., 2008) and the Carbon Bond Mechanism Z
(CBM-Z) gas phase mechanisms (Zaveri and Peters, 1999) are selected. The
MOSAIC aerosol scheme uses an approach of segmentation to represent aerosol
size distribution with four or eight discrete size bins (Fast et al., 2006).
It consists of a range of physical and chemical processes such as
nucleation, condensation, coagulation, aqueous phase chemistry and water
uptake by aerosol. The parameterization of the dry deposition of aerosol mass
and number is according to the method of Binkowski and Shankar (1995),
including particle diffusion and gravitational effects. Aerosol–cloud
interactions were included in the model by Gustafson et al. (2007) for
calculating the activation and resuspension between dry aerosols and cloud
droplets. The wet removal of grid-resolved stratiform clouds/precipitation
includes two aspects, namely in-cloud removal (rainout) and below-cloud
removal (washout) by Easter et al. (2004) and Chapman et al. (2009),
respectively. Aerosol optical properties such as single-scattering albedo
(SSA) and scattering asymmetry and so on are calculated at each model grid
through the function of wavelength. The shortwave (SW) and longwave (LW)
refractive indices of aerosols use the Optical Properties of Aerosols and
Clouds (OPAC) dataset (Hess et al., 1998), with a detailed description of
the computation of aerosol optical properties can be found in Barnard et al. (2010) and Zhao et al. (2013a). For both short wave and long wave radiation,
aerosol radiation feedback combined with the Rapid Radiative Transfer Model for General Circulation Models
(RRTMG; Mlawer et al., 1997; Iacono et al., 2000) was implemented by Zhao
et al. (2011). For the diagnosis of the optical properties and direct
radiative forcing of various aerosol species in the atmosphere, the method
described by Zhao et al. (2013a) is adopted. The radiative forcing of light-absorbing aerosol in surface snow is estimated with the Snow, Ice, and
Aerosol Radiative model (SNICAR; Flanner and Zender, 2005) in the land
surface scheme as introduced by Zhao et al. (2014). Please note that the
SNICAR model was recently updated by He et al. (2018) to include the impact
of non-spherical snow grains on aerosol snow-albedo effect that is not
included in this study. More details about the coupling between the WRF-Chem
and SNICAR models can be found in Zhao et al. (2014).
Numerical experiments
In this study, the WRF-Chem simulations are performed with two nested
domains (one-way nesting), one outer domain at 20 km horizontal resolution
with 350×250 grid cells (62–112∘ E,
1–38∘ N) and one inner domain at 4 km horizontal
resolution with 400×300 grid cells (75–92∘ E, 23–35∘ N) (Fig. 1). The inner domain roughly
covers all of the Himalayas. The WRF-Chem simulations conducted in this
study use the terrain following coordinate (Skamarock et al., 2008). To
resolve the vertical structure of transport across the Himalayas, the
simulations are configured with 54 vertical layers and denser layers near
the surface. For example, averaged over a region (26–28∘ N, 76–80∘ E) near the southern
Himalayas, there are about 17 layers below 2 km above the ground (Fig. 2).
The goal of this study is to investigate the impacts of different
representations of topography on the transport of BC across the Himalayas.
Therefore, besides this control experiment, one sensitivity (idealized)
experiment is also conducted with the same configuration as the control one
except that the terrain heights of the inner domain at 4 km resolution are
bilinearly interpolated from the terrain heights at 20 km resolution similar
as previous studies (e.g., Shi et al., 2008; Wu et al., 2012b; Lin et al.,
2018). The two experiments are referred to the simulations with the complex and
smooth topographies, respectively, hereafter.
Anthropogenic and fire emissions over the simulated regions
with 20 and 4 km resolutions; the black dot represents the Qomolangma
station (QOMS; 86.95∘ E, 28.36∘ N).
The thickness of each vertical layer in the simulations (54 layers
in total).
Figure 3 shows the spatial distribution of terrain height over the inner
domain with the complex (4 km dataset) and smooth (20 km dataset) topographies. It
is evident that the terrain is much smoother from the 20 km dataset than
from the 4 km dataset. The mountain ridges and valleys can be resolved to
some extent in the 4 km dataset but mostly missed or underestimated at
20 km. The probability distributions of terrain height over the Himalayas from
the 20 and 4 km datasets (Fig. S1 in the Supplement) show that
the difference between the two datasets is small for the terrain height
lower than ∼4.5 km but is significant for the terrain height
above ∼4.5 km. In addition, the slopes between the
neighboring grids are significantly reduced in general with the smooth
topography compared to with the complex topography, particularly over the
Himalayan region (Fig. S2). The difference of
results from the two experiments over the inner domain is analyzed as the
impacts of topography representations. Therefore, all the results shown
below are from the simulations of the inner domain at 4 km resolution with the
different topography if not otherwise stated. It is noteworthy that this
study focuses on understanding the impact of the complex topography resolved by
4 km instead of the difference between 4 and 20 km simulations.
Prescribing the topography at 4 km following the 20 km resolution
distribution is just one way to smooth the topography. In fact, the
sensitivity experiment at 4 km resolution with the topography from the 1 ∘ resolution dataset is also conducted, and the result is
consistent. In addition, although the topography at 4 km resolution resolves
the topography of the Himalayas much better than at 20 km resolution, it still
cannot fully resolve the complexity of the topography of the Himalayas. The higher
resolution (e.g., 1 km or less than 1 km) may be needed. Previous studies have
found that the simulations at the resolutions between 1 and 4 km can
produce generally consistent features, but the simulation at 1 km with
a better representation of topography can produce a little better
meteorological field compared to the observations (e.g., Karki et al.,
2017). One sensitivity experiment at 1.5 km resolution is also conducted in
this study and found that the difference between the simulations at 1.5 and
4 km resolutions is relatively small. However, it should be noted that the
simulation at 1.5 km resolution is only conducted covering a much smaller
region for a shorter period due to the computational cost. The experiment at
4 km instead of 1.5 km resolution is conducted finally for the study region
and period due to the balance of resolving the complex topography to some
extent and affordable computational cost.
Spatial distributions of terrain height from the dataset at 4 km
resolution with the complex topography (complex topo) and smooth topography (smooth topo), which was bilinearly interpolated from the 20 km
resolution dataset. The dashed line and two solid lines
represent the cross sections for analysis in the following.
The simulations are conducted for 29 March–20 April 2016 for the reason discussed in the Introduction. The results of 1–20 April are analyzed for the observed pollution episode to allow for a few days of spin-up
for the chemical initial condition. The meteorological initial and lateral
boundary conditions are derived from the European Centre for Medium-Range
Weather Forecasts (ECMWF) reanalysis data at 0.5∘× 0.66∘ horizontal resolution and 6 h temporal intervals
(ERA-Interim dataset). The modeled u and v component wind, atmospheric
temperature and geopotential height over the outer domain are nudged
towards the reanalysis data with a nudging timescale of 6 h following
previous studies (e.g., Stauffer and Seaman, 1990; Seaman et al., 1995; Liu
et al., 2012; Zhao et al., 2014; Karki et al., 2017; Hu et al., 2016, 2020).
The spectral-nudging method is applied to balance the performance of the simulation
at large and small scales (Liu et al., 2012) and only to the layers
above the planetary boundary layer (PBL) with nudging coefficients of
3×10-4 s-1. A wave number of 3 is selected for both the
south–north and west–east directions. Please note that the choices of
nudging coefficients and wave numbers for spectral nudging in this study are
empirical. The purpose of nudging is to simulate reasonably large-scale
features so that small-scale impacts from the complex topography can be
focused. Therefore, the modeling sensitivity to these choices is not tested
in this study. The results show that the simulations with the nudging method can
reproduce the large-scale circulation at 700 hPa and higher over the outer
domain compared to the reanalysis dataset with the spatial correlation
coefficient of 0.96–0.98.
The Mellor–Yamada–Nakanishi–Niino (MYNN) planetary-boundary-layer scheme
(Nakanishi and Niino, 2006), Community Land Model (CLM) land surface scheme
(Oleson et al., 2010), Morrison two-moment microphysics scheme (Morrison et
al., 2009), Kain–Fritsch cumulus scheme (Kain, 2004), and Rapid Radiative
Transfer Model (RRTMG) longwave and shortwave radiation schemes (Iacono et
al., 2000) are used in this study. It is noteworthy that the cumulus scheme
is only used in the outer domain at 20 km resolution and is turned off in
the inner domain at 4 km resolution. The chemical initial and boundary
conditions are provided by a quasi-global WRF-Chem simulation for the same
time period to include long-range transported chemical species. The
quasi-global WRF-Chem simulation is performed at 1∘× 1∘ horizontal resolution using a quasi-global channel
configuration with 360×130 grid cells (180∘ W–180∘ E, 60∘ S–70∘ N). More details about the
general configuration of a quasi-global WRF-Chem simulation can be found in
Zhao et al. (2013b) and Hu et al. (2016). The detailed configuration of
WRF-Chem experiments is summarized in Table 1. Due to the lack of publicly
available in situ observations, this study does not tend to systematically evaluate the simulated meteorological fields over the Himalayan region. However, as shown in Table 1, the choice of physical
parameterizations in this study follows that of one previous study (Karki et
al., 2017) that evaluated systematically the WRF simulation for 1 entire
year over the Himalayan region. Their results showed that the WRF simulation
at a convection-permitting scale could generally capture the essential
features of meteorological fields such as precipitation, temperature and
wind over the Himalayan region. Therefore, the WRF-Chem simulations in this
study are reliable to investigate the impacts of topography over the
Himalayan region.
Summary of model configurations.
DescriptionSelectionReferencesHorizontal grid spacing20 km (D1), 4 km (D2)Grid dimensions250×350, 300×400Topography30 arcsec (USGS)Vertical layers54 (roughly 17 layers below 2 km)Model top pressure50 hPaNesting approachOne-wayAerosol schemeMOSAIC eight binZaveri et al. (2008)Gas phase chemistryCBM-ZZaveri and Peters (1999)Longwave radiationRRTMGIacono et al. (2000); Zhao et al. (2011, 2013a)Shortwave radiationRRTMGCloud microphysicsMorrison two-momentMorrison et al. (2009)Cumulus cloudKain–FritschKain (2004)Planetary boundary layerMYNN level 2.5Nakanishi and Niino (2006)Land surfaceCLMOleson et al. (2010)Meteorological forcingERA-Interim, 0.5∘× 0.66∘, 6 hourlyEmissions
Anthropogenic emissions for outer and inner simulation domains are obtained
from the Hemispheric Transport of Air Pollution version 2 (HTAPv2) inventory at
0.1∘× 0.1∘ horizontal resolution and a monthly
temporal resolution for the year 2010 (Janssens-Maenhout et al., 2015), with the exception
that emissions of eastern Asia are from the mosaic Asian anthropogenic emission
inventory (MIX) at 0.1∘× 0.1∘ horizontal resolution
for 2015 (M. Li et al., 2017). Biomass burning emissions are obtained from the
Fire Inventory (FINN) of the National Center for Atmospheric Research with
hourly temporal resolution and 1 km horizontal resolution (Wiedinmyer et
al., 2011) for the simulation period and are vertically distributed
following the injection heights suggested by Dentener et al. (2006) from the
Aerosol Comparison between Observations and Models (AeroCom) project.
Sea-salt emission values follow Zhao et al. (2013b), which includes the correction of
particles with a radius less than 0.2 µm (Gong, 2003) and the dependence of
sea-salt emissions on sea surface temperature (Jaeglé et al., 2011). The
vertical dust fluxes are calculated with the Georgia Institute of Technology–Goddard Global
Ozone Chemistry Aerosol Radiation and Transport (GOCART) dust emission
scheme (Ginoux et al., 2001), and the emitted dust particles are distributed
into the MOSAIC aerosol size bins following a theoretical expression based
on the physics of scale-invariant fragmentation of brittle materials derived
by Kok (2011). More details about the dust emission scheme coupled with
MOSAIC aerosol scheme in WRF-Chem can be found in Zhao et al. (2010, 2013b).
As shown in Fig. 1, anthropogenic fossil fuel emissions of BC are high over
northeastern India. The fossil fuel BC emissions over Nepal, the country nearby
the southern Himalayas, are relatively low. Instead, biomass burning
emissions of BC are extremely high in Nepal and northwestern India (southern
Himalayas at 26–29∘ N). Averaged over the southern
Himalayas of the inner domain that may significantly affect the pollutant
transport into the TP, the biomass burning emissions of BC are much higher
than its anthropogenic fossil fuel emissions, particularly for the pollution
episode (Fig. 4). The anthropogenic BC emissions are set constant through
April, while biomass burning emissions show a strong fire event in 5–16 April. During the event, the biomass burning BC emissions can be a factor of
2 of the anthropogenic fossil fuel BC emissions over the southern Himalayas.
Time series of area-averaged daily fire emissions between
26 and 29∘ N over the simulation domain at 4 km
resolution. (The dashed line in the figure represents the anthropogenic
emissions).
Dataset
Three datasets are used to compare with the modeling results to demonstrate
the pollutant episode and spatial distribution. One is from the Moderate
Resolution Imaging Spectroradiometer (MODIS) instruments on the Aqua and Terra
satellites. The MODIS Aerosol Product monitors the ambient aerosol optical
thickness over the oceans globally and over the continents. Daily level 2
products of aerosol optical depth (AOD) at 550 nm with a spatial resolution
of 10 km × 10 km (at nadir) from both Aqua and Terra are applied.
When compared with the modeling results, the simulations are sampled at the
satellite overpass time and location. The second one is from the Aerosol
Robotic Network (AERONET; Holben et al., 1998) that has ∼100
similar globally distributed sun- and sky-scanning ground-based automated
radiometers, which provide measurements of aerosol optical properties
throughout the world (Dubovik and King, 2000; Dubovik et al., 2002). In this
study, AERONET measured AOD at 675 and 440 nm from two sites over the TP,
the QOMS_CAS site (86.95∘ E, 28.36∘ N) and
the NAM_CO site (90.96∘ E, 30.77∘ N), which are used
to derive the AOD at 550 nm (using the Ångström exponent) for comparison
with modeling results at 550 nm. All of the retrievals of AOD are at quality
level 2, and the uncertainty of AOD measurements is about 0.01 (Holben et
al., 2001). In this study, the available data in April 2016 are used to
evaluate the modeling results during the same period.
The third one is the measurement of surface BC mass concentration collected
during the simulation period for 4–20 April 2016 at the Qomolangma
Station for Atmospheric and Environmental Observation and Research (QOMS;
86.95∘ E, 28.36∘ N), which is located on the northern
slope of Mt. Everest, about 4276 m above sea level. The BC mass
concentration is measured with the widely used Aethalometer
(AE-33) instrument, which can provide real-time BC mass concentration measurements. The
calibration of air flow is routinely conducted to maintain the data quality.
The instrument estimates the BC mass concentration based on the optical
method through measuring the reduction in light intensity induced by BC. The
method assumes that the relationship between attenuation and BC surface
loading is linear for low attenuation values. However, this relationship
becomes nonlinear when the attenuation values are high due to a filter
saturation effect, which may lead to an underestimation of the high BC
concentration. The detection limit of the AE-33 instrument is 5 ng m-3, and
the uncertainty is estimated to be within 10 % (e.g., Chen et al., 2018;
Bansal et al., 2019; Kant et al., 2019). The dataset of BC mass
concentration used in this study was reported by Chen et al. (2018), where
more details about the measurements can be found.
ResultsSpatial distribution of BC around the TP
Figure 5 shows the spatial distributions of column-integrated BC mass within
the inner domain from the simulations at 4 km resolution with the complex and
smooth topographies averaged for 1–20 April 2016, and the difference between
the two is also shown. For both experiments, the Himalayas are an apparent
boundary line for the distribution of BC, with a sharp gradient across the
Himalayas. The high BC mass loading exists near the southern Himalayas
reaching over 10 mg m-2, which is largely contributed by the biomass
burning emissions during the period (Fig. 4), while the value reduces
significantly to less than 0.4 mg m-2 over the TP. The BC mass loading
near the central and eastern Himalayas is higher than near the western
Himalayas. In general, the column BC mass loading from the simulation with
the complex topography is higher over the TP and lower over the region to the
south of the Himalayas compared with the smooth topography, reflecting the
stronger transport of BC from the source region to the Himalayas and TP due
to the complex topography (see the discussion in Sect. 3.2). Figure 6
displays the spatial distributions of AOD from the MODIS retrievals and the
simulations at 4 km with two different topographies averaged for 1–20 April 2016. In general, both simulations reproduce the overall spatial
distribution of AOD, with the large values near the southern Himalayas,
consistent with the BC mass loading. In addition, both the simulations and
satellite retrievals show higher AOD near the central and eastern Himalayas
than that near the western Himalayas during the study period. The difference
between the simulations and retrievals may be partly related to the
uncertainties in emissions, particularly for biomass burning emissions. Other
than intense emissions, the wind circulation around the TP may also play an
important role in accumulating BC near the southern Himalayas. Because of
the blocking effect of the Himalayas, the wind circulation at 500 hPa is divided into the two
branches of westerly and northwesterly. Both of them are relatively dry
airflows with little effect on pollutant removal, favor the accumulation of
pollutants near the southern Himalayas and carry the pollutants to the TP
(e.g., Dumka et al., 2010; Kang et al., 2016; Cong et al., 2015a).
Spatial distributions of column-integrated BC mass and the
horizontal wind field at 500 hPa from the simulations with the complex and
smooth topographies (complex topo and smooth topo) averaged for 1–20 April 2016. The difference between the two is also shown.
Spatial distributions of AOD from the MODIS retrievals and the
simulations with the complex and smooth topographies averaged for 1–20 April 2016. The two black dots represent the two AERONET sites over the TP
(QOMS_CAS at 86.95∘ E, 28.36∘ N;
NAM_CO at 90.96∘ E, 30.77∘ N).
The AOD retrieved at two AERONET sites over the TP is compared with the two
simulations for 1–20 April 2016 (Fig. 7). The AOD at the
QOMS_CAS site near the northern Himalayas is higher than that
at the NAM_CO site inside of the TP. Both simulations can
capture this gradient. The simulation with the complex topography produces
higher AOD than the one with the smooth topography at both sites. The
modeling biases (normalized mean bias; NMB) reduce from -46 % (smooth
topography) to 9 % (complex topography) at the QOMS_CAS
site and from -26 % (smooth topography) to -10 % (complex topography) at
the NAM_CO site. Although the correlation coefficient between
the simulations and observation increases from 0.37 (smooth topography) to
0.53 (complex topography) at the QOMS_CAS site, it is similar
(∼0.2) between the two simulations at the NAM_CO site. The correlation coefficient is higher at the QOMS_CAS site near the source region than the NAM_CO site farther
away, which may indicate that the model processes affecting the transport over
the TP still need examination with more observations. The NAM_CO site over the eastern TP may also be affected by other sources that are
not counted in this study. The modeling of temporal variations of pollutants
over the TP deserves further investigation with more observations.
Hourly AOD from the measurements of AERONET and simulations by
WRF-Chem at the two sites over the TP (QOMS_CAS at
86.95∘ E, 28.36∘ N; NAM_CO at
90.96∘ E, 30.77∘ N) for 1–20 April 2016.
There is one in situ observational station (QOMS) near Mt. Everest
(black dot shown in Fig. 1) to collect the surface BC concentration. The
observed surface BC concentration at this station is compared with the
corresponding simulations for this period as shown in Fig. 8. Without a
local emission source, the surface BC concentration at QOMS is primarily
contributed by the transport. The temporal variation of observed surface BC
concentration correlates highly with the biomass burning emissions as shown
in Fig. 4, with the peak value on 11 April reaching ∼3µg m-3. One sensitivity experiment without biomass burning emissions
shows that the simulated BC concentration at QOMS will be significantly
reduced without the peak (not shown), which further proves that the BC
concentration over the northern Himalayas can be largely influenced by the
pollution episode near the southern Himalayas. It is noteworthy that both
simulations can reproduce the episode in time and magnitude, and the
difference at this station is small. The spatial distribution of difference
in near-surface BC concentration between the two simulations (Fig. S3) is
more heterogeneous than that of column BC mass (Fig. 5), reflecting the
impact of topography on near-surface transport (see the discussion in
Sect. 3.2).
The simulated (colored) and observed (black) temporal variability
of surface BC mass concentration at the measurement station during 1–20 April 2016.
Transport flux into the TP
To further understand the difference in BC surface concentration and column
mass loading over the TP between the two simulations with the different
topographies, Fig. 9 shows the longitude–height cross section of BC
transport flux along the cross line (shown as the black dashed line in Fig. 3)
from the two simulations at 03:00 and 15:00 local time (LT) averaged for
1–20 April to represent nighttime and daytime transport, respectively. The
PBL height along the cross line is also shown as the black dashed line. The
transport flux is calculated by projecting the wind field perpendicularly to
the cross line and then multiplying the BC mass concentration along the
cross line. More specifically, the transport flux is calculated as
follows:
TF=C⋅(u⋅sinα+v⋅sinβ),
where α is the angle between the east–west wind component and the cross
line, β is the angle between the south–north wind component and the cross
line, and C is the BC mass concentration at the grid along the cross line.
The flux is estimated at each model level. Positive values represent the
transport towards the TP, while negative values represent the transport away
from the TP. It is evident that BC is imported into the TP during the day
and night to the west of ∼85∘ E, although the
transport flux is much larger during the daytime than nighttime. To the east
of ∼85∘ E, BC is imported into the TP during the
day but exported slightly from the TP during the night. The difference of
transport flux between the western and eastern Himalayas is primarily due to
the influence of a large-scale westerly that is weak over the eastern
Himalayas (Fig. 5). The transport across the western Himalayas is controlled
by the large-scale westerly, while a local southerly dominates the transport
across the eastern Himalayas and also influences the transport across the
central Himalayas (Fig. S4). The stronger diurnal
variation of local southerly (towards the TP in the daytime to away from the
TP in the nighttime) compared to that of a westerly near the surface (Fig. S4) leads
to the large difference in diurnal variation of the transport between the
western and eastern Himalayas. The strong transport is primarily within the
PBL during the daytime, and the deeper PBL during the daytime allows BC over
the source region to be mixed at a higher altitude, which also leads to stronger
import transport during the day than the night. The relatively small
difference in simulated PBL heights and structure between the two
experiments can be due to their different surface heating resulting from
different topography complexity (e.g., Wagner et al., 2014).
Longitude–height cross section of BC transport flux along the
cross line (shown as the black dashed line in Fig. 3) from the simulations
with the complex and smooth topographies at 03:00 and 15:00 local time (LT)
averaged for 1–20 April. The PBL height along the cross section is shown
here as the black dashed line.
The difference between the simulations with two different topographies is
evident. The mountain ridges are much higher, and valleys are much deeper
with the complex topography than with the smooth topography. The simulation
with the smooth topography produces overwhelming cross-Himalayan transport
towards the TP within the PBL, in particular during the daytime. Although,
in the simulation with the complex topography, the mountain ridges resolved
weaken the cross-Himalayan transport compared to the simulation with the
smooth topography, the overall positive values near the surface indicate
that the transport can overcome most mountain ridges along the Himalayas.
The transport fluxes near the surface from the simulation with the complex
topography become close to zero only at a few mountain ridges that are 6.5 km or higher. To better demonstrate the transport pathway across mountain
ridges, one cross section across the mountain ridge as shown as one black
solid line in Fig. 3 is taken as one example. Figure 10 shows the
latitude–height cross section of BC mass concentration and transport flux
across one mountain ridge from the simulations with the complex and smooth
topographies at 03:00 and 15:00 LT averaged for 1–20 April 2016.
Near the southern part of mountain, the elevated concentration of BC mass
accumulates and can mix up, reaching as high as 5 km with the much stronger
transport during the daytime. It is obvious that the mountain ridge in the
simulation with the smooth topography is quite low. With the high mountain ridge
resolved by the complex topography, the simulated BC transport flux can
still cross the mountain. Analysis of transport flux across a few more
mountain ridges indicates similar results (not shown). The results above
indicate that the transport of pollutants can cross the majority of mountain
ridges of the Himalayas, which is consistent with the observation-based estimate
by Gong et al. (2019) that also found pollutants could overcome the blocking
effect of the mountain ridges of the Himalayas as a transport pathway. On the other
hand, the resolved deeper valleys in the simulation with the complex topography
enhance the transport flux compared to the one with the smooth topography.
Similarly, Fig. 11 shows one example of a latitude–height cross section of
BC mass concentration and transport flux across one valley from the
simulations with the complex and smooth topographies at 03:00 and
15:00 LT averaged for 1–20 April 2016. The transport is much stronger and
deeper along the valley from the simulation with complex topographies than the
one with the smooth topography. Again, analysis of transport flux across a few
more valleys does not show different results (not shown).
Latitude–height cross section of BC flux (vector) across the
mountain (shown as the eastern black solid line in Fig. 3) from the simulations
with the complex and smooth topographies at 03:00 and 15:00 local time (LT)
averaged for 1–20 April 2016. Contour represents the BC concentration.
Latitude–height cross section of BC flux (vector) along the
valley (shown as the western black solid line in Fig. 3) from the simulations
with the complex and smooth topographies at 03:00 and 15:00 local time (LT)
averaged for 1–20 April 2016. Contour represents the BC concentration.
In order to further demonstrate the overall inflow flux across the
Himalayas, the vertically integrated BC mass flux along the longitudinal
cross section (as shown in Fig. 9) from the simulations with different
topographies is shown in Fig. 12. The terrain heights from the two
simulations along the cross section are also shown as black lines. The total
mass flux is calculated by integrating the right-hand term of Eq. (1)
as follows:
ITF=∫z=zsfcz=ztopδz⋅C⋅(u⋅sinα+v⋅sinβ),
where δz is the thickness of each vertical model level.
Similarly, positive values represent the transport towards the TP, while
negative values represent the transport away from the TP. More evidently,
the positive BC inflows towards the TP occur not only through the valleys
but also across the mountain ridges with both topographies. The negative
values only exist to the east of 88∘ E. With the complex topography,
higher mountain ridges can reduce the transport flux to some extent compared
to the smooth topography. The complex topography results in a significantly
larger BC inflow towards the TP compared to the smooth topography,
particularly corresponding to the deep valleys, such as the Karnali River
valley around 82∘ E and the Kali Gandaki valley around
84∘ E.
One reason for the enhanced transport across the Himalayas with the complex
topography is the resolved deeper valleys that lead to the increased valley
wind. The wind across some valleys can be significantly larger with the
complex topography than the smooth one (Fig. S4). The enhanced valley wind
across the Himalayas has also been found by previous studies with
observations and numerical simulations (e.g., Egger et al., 2000; Zängl
et al., 2001; Carrera et al., 2009; Karki et al., 2017; Lin et al., 2018).
However, it is noteworthy that previous studies have found that the
orographic drag (including gravity wave drag and turbulence orographic form
drag) over a region with complex topography, such as the Himalayas and
other mountainous areas, would weaken the overall near-surface wind speed
(e.g., Beljaars et al., 2004; Horvath et al., 2012; Jiménez and Dudhia,
2012; Zhou et al., 2017, 2018; Lin et al., 2018; Wang et al., 2020).
Therefore, the near-surface wind speed is also examined. The complex
topography does lead to the overall reduction of near-surface wind speed
over the Himalayan area (Fig. S5), which is
consistent with previous studies. However, it is interesting to note that
the near-surface southerly wind during the daytime of the simulation period
is overall increased over the Himalayan area with the complex topography
(Fig. 13), which indicates that the transport towards the TP is strengthened
with the complex topography in the daytime, particularly over the central
and eastern Himalayas where the BC mass loading is higher (Fig. 5). During
the night, the meridional wind is dominated by a northerly over the Himalayan
region in the simulation with the smooth topography. The complex topography
weakens the transport away from the TP or changes the wind direction from
northerly to southerly over some areas of the Himalayas. Both effects enhance
the overall transport efficiency across the Himalayas towards the TP.
Therefore, although the complex topography weakens the overall near-surface
wind speed around the Himalayas, it induces more realistic small-scale
mountain-valley circulation that favors the BC transport across the
Himalayas towards the TP during the study period. The wind in the free troposphere
(4 km above the ground and above) is also examined, and the change due to
the topography is relatively small (not shown). Another effect of resolving
valleys is that the volume of relatively high-concentration BC could be
higher with deeper valleys (Fig. S6), which can also
result in stronger transport towards the TP even if the wind condition is
similar. For example, the altitude (above the ground) below which the BC
mass concentration is larger than 0.3 µg m-3 is much higher along the
valleys with the complex topography than with the smooth topography (Fig. S7). The correlation coefficient between the difference
of terrain heights of valleys and of volumes of
relatively high-concentration BC can reach -0.76, indicating that the lower
the valleys are, the higher the volumes of BC mass transported across
the Himalayas can be. The combined influence of these factors results in
significantly enhanced BC transport towards the TP with the complex
topography (Fig. 12), which can also be demonstrated by the distributions of
wind and BC mass concentration along the longitudinal cross section (Fig. S8a, b).
Longitudinal distribution of integrated BC mass flux along the
cross section in Fig. 3 from the simulations with the complex and smooth
topographies. The black lines represent the terrain heights with different
topographies.
Spatial distributions of meridional wind speed averaged within
500 m above the ground for day and night during 1–20 April 2016 from the
simulations with the complex and smooth topographies. The difference between the
two is also shown. Nighttime is defined as 21:00–06:00 local time, and
daytime is defined as 09:00–18:00 local time. Positive value denotes a southerly, and
negative value denotes a northerly. The results averaged within 2 km above the
ground are consistent.
The enhanced transport across the Himalayas shows that the overall BC
inflow with the complex topography is much stronger than that with the
smooth topography. Figure 14 shows the accumulated integrated total
transport flux of BC across the Himalayas estimated from the simulations
with the complex and smooth topographies for 1–20 April 2016. The accumulated
import flux of BC increases during the period in both experiments, and the
difference between the two experiments gradually increases with time. At
the end of the period, the simulation with the complex topography estimates a total
import flux of BC of ∼1.5×104 t that is
∼50 % higher than ∼1.0×104 t estimated based on the simulation with the smooth topography. The analysis
of the transport fluxes at different altitudes indicates that the transport
fluxes below 2 km (above the ground) dominate (>80 %) the
total flux. The sensitivity analysis by moving the cross line (cross section
of the analysis in Figs. 9, 12 and 14) towards or away from the TP within a
certain distance and recalculating the flux indicates that the impacts of
topography on the simulated results do not change significantly.
Accumulated integrated total transport flux of BC across the
Himalayas estimated from the simulations with the complex and smooth topographies
during 1–20 April 2016.
The analysis above focuses on investigating the BC transport flux across
the Himalayas. Although the inflow can reflect the impact of transport on
the BC mass over the TP to some extent, the change of BC mass concentration
is eventually determined by the convergence of transport. Therefore, the
contribution of each model process (transport, dry deposition, emission, PBL
mixing and wet deposition) to the increase of BC column mass averaged over
the TP (with elevation >4 km) during this episode is analyzed
for both simulations following the methodology introduced by Du et al. (2020). The results show that the two main processes affecting the BC column
mass over the TP during the period are transport and dry deposition. The
transport is the dominant process that increases the BC column mass over the
TP, while the dry deposition reduces it. The contribution of transport to
the increase of BC column mass over the TP during the episode from the
simulation with the complex topography is significantly larger than that with
the smooth topography, which is consistent with the results shown by
analyzing the transport flux across the Himalayas. Although the impacts of
PBL mixing and wet deposition on the BC column mass over the TP are also
different between the simulations with different topographies, their impacts
are much smaller than those of transport and dry deposition during the study
period.
Radiative forcing of BC over the TP
The BC transported over the TP could significantly influence the regional
climate and water resources over Asia through heating the atmosphere and
accelerating the melting of snow and glaciers (e.g., Qian et al., 2011, 2015;
Lau et al., 2017). Therefore, the impact of the complex topography on
estimating the BC radiative-heating profile in the atmosphere and radiative
forcing in surface snow deserves investigation. Figure 15 shows the vertical
profiles of BC-induced radiative-heating rate in the atmosphere averaged
over the TP (with elevation >4 km) within the inner domain shown
in Fig. 1 for 1–20 April 2016 from the simulations with the complex and smooth
topographies. Both simulations generate higher BC heating rates near the
surface, and the rate gradually decreases with altitude, which is consistent
with the vertical profiles of BC mass concentration averaged over the TP
(Fig. S9). The BC heating rate over the TP from
the simulation with the complex topography is ∼0.17 K d-1 near
the surface and reduces to ∼0.08 K d-1 at 8 km, which is
∼50 % and ∼30 %, respectively, higher than
that from the simulation with the smooth topography at the corresponding
altitudes. The higher BC heating rate over the TP estimated by the
simulation with the complex topography is consistent with its higher BC column
mass (Fig. 5) and concentration profile (Fig. S9).
Vertical profiles of BC-induced radiative-heating rate in the
atmosphere averaged over the TP (with elevation >4 km) from the
simulations with the complex and smooth topographies during 1–20 April 2016.
The BC radiative forcing in surface snow is controlled by both the
distributions of BC mass concentration and snow coverage (e.g., Zhao et al.,
2014). Figure 16 shows the spatial distributions of snow water equivalent
(SWE) averaged for 1–20 April 2016 from the simulations with two
topographies. The difference between the two is also shown. It shows that the
simulation with the complex topography generates more areas with higher SWE
compared to that with the smooth topography over the TP. Along the
Himalayas, the simulated SWE is higher over the mountain ridges with the
complex topography, particularly for the eastern Himalayas, while the smooth
topography leads to broader snow coverage over the western Himalayas. The
difference in SWE between the two simulations is highly correlated with
their difference in precipitation (Fig. S10).
Along the Himalayas, the simulated precipitation with the complex topography
is larger than that with the smooth topography at the mountain ridges and
smaller at the valleys. Over the TP, the overall precipitation is larger
with the complex topography than with the smooth topography (Fig. S10).
Previous studies have found that the topography could significantly affect
the precipitation over the Himalayan region (e.g., Bookhagen and Burbank,
2010; Wulf et al., 2016; Cannon et al., 2017; Karki et al., 2017).
Spatial distributions of snow water equivalent averaged for 1–20 April 2016 from the simulations with the complex and smooth topographies. The
difference between the two is also shown.
Figure 17 shows the spatial distributions of BC radiative forcing in the
surface snow over the TP averaged for 1–20 April 2016 from the simulations
with the two topographies, and the difference between the two is also shown. The
BC radiative forcing in surface snow is largely coincident with the spatial
distributions of SWE as shown in Fig. 16, mainly due to the heterogeneous
distributions of snow cover over the TP. The BC radiative forcing in surface
snow over the TP from the simulation with the complex topography reaches 5 W m-2 where the snow exists, larger than that with the smooth
topography. Along the Himalayas, the simulation with the complex topography
produces higher BC snow forcing over the mountain ridges, particularly over
the eastern Himalayas, while the one with the smooth topography simulates
higher BC snow forcing over most areas of the western Himalayas due to its
broader snow coverage there. Overall, the complex topography leads to higher
BC forcing in snow over the TP and the eastern Himalayas and lower BC
forcing in snow over the western Himalayas, and this therefore results in the
different distribution of BC forcing in snow over the TP and Himalayas,
compared to the smooth topography.
Spatial distributions of BC radiative forcing in the surface snow
averaged for 1–20 April 2016 from the simulations with the complex and smooth
topographies. The difference between the two is also shown.
Summary
In this study, the model experiments with different topographies are conducted
to illustrate the impacts of the complexity of the topography of the Himalayas on BC
transport from southern Asia to the TP. The observed pollution episode at the
QOMS station near Mt. Everest during the pre-monsoon season is
simulated. The observed surface BC concentration shows a peak of
∼3µg m-3, which is much larger than the background value of
<0.4µg m-3 over the TP. The observed temporal variation of
surface BC concentrations correlates highly with that of biomass burning
emissions near the southern Himalayas, indicating the significant impacts of
biomass burning on the pollutants over the TP. The simulations can reproduce
the episode in time and magnitude and are used to investigate the BC
transport mechanisms and the impacts of topography.
The high BC mass loading during the simulation period accumulates near the
southern Himalayas, driven by the large-scale westerly and small-scale
southerly circulations, which is also observed by satellites. The modeling
results demonstrate that the circulations favor the accumulation of
pollutants near the Himalayas, particularly the central and eastern parts,
and can carry the pollutants to the TP during the study period, which is
consistent with previous modeling studies (e.g., Kopacz et al., 2011). It is
noteworthy that the BC which accumulated near the southern Himalayas can be
transported across the Himalayas, overcoming the majority of mountain ridges,
which is consistent with the observation-based estimate by Gong et al. (2019) that also found that pollutants could overcome the blocking effect of the
mountain ridges of the Himalayas. However, the transport through the valleys is
found to be much stronger and more efficient than across the mountain ridges, and
the enhancement effect cannot be ignored. The complex topography results in
50 % higher overall transport flux across the Himalayas during the
simulation period than the smooth topography, primarily due to the
strengthened efficiency of near-surface meridional transport towards the TP,
enhanced wind speed at some valleys and deeper valley channels associated
with larger BC mass volume that can be transported into the TP, although the
overall wind speed is weakened due to the orographic drags with the complex
topography. This shows that the simulation with the complex topography
produces a 30 %–50 % higher BC radiative-heating rate in the atmosphere up to
10 km averaged over the TP than the simulation with the smooth topography.
For the BC radiative forcing in surface snow, the simulation with the complex
topography produces stronger forcing over the TP than that with the smooth
one. The complex topography makes the distribution of BC forcing in surface
snow quite different from the smooth topography, partly due
to its different distribution of surface snow. The simulated BC radiative
forcing in snow is distributed more heterogeneously than those in previous
studies using global models at relatively coarse resolutions (e.g., Qian et
al., 2011). He et al. (2014) used a global-chemical-transport model to
simulate the BC forcing in snow at the horizontal resolution of
∼0.2∘ and obtained a distribution similar to the
simulation with the smooth topography in this study with the high values over
the western Himalayas. However, their simulated values near the Himalayas
are higher than the simulated results of this study, which may be due to
their estimation being averaged for November–April.
This study highlights the importance of resolving the complex topography of the
Himalayas in modeling the aerosol transport across the Himalayas and
radiative impact over the TP. Although this study focuses on the impacts of
topography on the simulated results, the additional analysis (Figs. S11–S13) of the outer domain simulation at 20 km resolution
and the inner domain simulation at 4 km with different topographies indicates
that the resolution-dependent difference between 20 and 4 km is largely
contributed by their different representations of topography over the
Himalayan region, consistent with previous studies (e.g., Karki et al.,
2017; Lin et al., 2018). Therefore, the relatively smooth topography used by
the models at horizontal resolutions coarser than 20 km may result in
negative biases of aerosol transport from southern Asia to the TP during the
pre-monsoon season and lead to biases in distributions of aerosol radiative
forcing in the atmosphere and surface snow over the TP.
Discussion
Previous studies also found the induced change of circulation and transport
due to the complex topography at convection-permitting scales with a focus
on the meteorological fields over the Himalayan and TP regions (e.g., Karki
et al., 2017; Zhou et al., 2017, 2018; Lin et al., 2018; Wang et al., 2020).
Most of them either conducted the sub-10 km simulations covering a
relatively smaller region (e.g., 101×96 grids at 5 km in Karki et
al., 2017; 181×121 grids at 2 km in Lin et al., 2018;
∼330×230 grids at 3 km in Wang et al., 2020)
compared to this study (400×300 grids at 4 km) or conducted the
simulations covering all of the Himalayas but at the resolutions above 10 km
and with the subgrid orographic-drag parameterization to consider the
impact of complex topography. Although some previous studies also showed
that the resolved complex topography yielded more realistic small-scale
mountain-valley circulations and enhanced valley winds over the Himalayan
region compared to the smoother topography, the overall moisture transport
across the Himalayas towards the TP was weaker with the complex topography
due to the orographic drags.
The difference between previous studies and this study can be due to several
factors. First, previous studies focused on moisture instead of air
pollutants. The spatial (horizontal and vertical) distributions between air
pollutants and moisture are different and may contribute to the different
impacts of topography on the overall transport flux across the Himalayas.
However, the analysis of the moisture from the simulations in this study
shows the increase of moisture transport (not shown) and hence the increase
of precipitation over the TP with the complex topography (Fig. S10). Second,
most of previous studies focused on the monsoon season instead of the pre-monsoon
season. Therefore, the meteorological simulations for the monsoon season
(June–July–August) at different resolutions are also conducted in this
study. The results show that the moisture transport and precipitation are
reduced at the higher resolution with the complex topography and that the meridional
wind is overall weakened particularly over the central and eastern Himalayas
and TP (not shown), which is consistent with previous studies. This may
indicate that the different large-scale circulations between the two seasons
(a much stronger southerly during the monsoon season) may also lead to
different impacts of the complex topography on meridional winds and hence
cross-Himalayan transport.
Since this study only demonstrates the potential impacts for a relatively
short period, a longer-term study should be conducted to examine the impacts
of topography on the aerosol climatic effect over the TP in both the pre-monsoon and
monsoon seasons. In addition, the active convection during the monsoon
season may also play an important role in pollutant transport across the
Himalayas, which deserves further investigation. Furthermore, aerosol impact
on clouds and precipitation, particularly during the monsoon season, and thus
on the latent heat in the atmosphere and the associated responses may also
depend on the complex topography. Previous studies based on observations
found that the rain frequency and intensity reached their highest values and the
cloud thickness reached their deepest values at the foothill of the Himalayas and
decreased as the elevation increased up to the TP (e.g., Chen et al., 2017;
Fu et al., 2018; A. Zhang et al., 2018), which was explained by Fu et al. (2018)
due to the blocking of the air flow by the steep slope of the southern
Himalayas. However, the large amount of transported aerosol along the slope
from the foothill up to the TP may also play a role. These potential impacts
of aerosols on the regional hydroclimate around the TP and over Asia using a
high-resolution model that can resolve the complex topography of the Himalayas
and TP deserve further investigation.
Data availability
The released version of WRF-Chem can be downloaded from
http://www2.mmm.ucar.edu/wrf/users/download/get_source.html (last access: 14 May 2020; new users must first submit the request form).
The updated USTC version of WRF-Chem can be obtained from http://aemol.ustc.edu.cn/product/list/ (last access: 14 May 2020; new users must first submit the request form) or by contacting chunzhao@ustc.edu.cn.
Also, the code modifications will be incorporated in the release version of
WRF-Chem in the future.
The supplement related to this article is available online at: https://doi.org/10.5194/acp-20-5923-2020-supplement.
Author contributions
MZ and CZ designed the experiments and
conducted and analyzed the simulations. ZC provided the BC observations and helped analyze the data. QD, MX, YC and MC helped design numerical experiments. All authors contributed to
the discussion and final version of the paper.
Competing interests
The authors declare that they have no conflict of interest.
Special issue statement
This article is part of the special issue “Study of ozone, aerosols and radiation over the Tibetan Plateau (SOAR-TP) (ACP/AMT inter-journal SI)”. It is not associated with a conference.
Acknowledgements
The study used computing
resources from the High Performance Computing Center of the University of
Science and Technology of China (USTC) and the TH-2 of the National
Supercomputer Center in Guangzhou (NSCC-GZ).
Financial support
This research was supported by the National Key Research and Development
Program of China (grant no. 2016YFA0602001), the National Natural Science Foundation
of China (NSFC; grant no. 91837310), the second Tibetan Plateau Scientific
Expedition and Research Program (STEP; grant no. 2019QZKK0605), and the Fundamental
Research Funds for the Central Universities.
Review statement
This paper was edited by Hang Su and reviewed by four anonymous referees.
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