Global and Planetary Change 52 (2006) 248 – 260
www.elsevier.com/locate/gloplacha
An intercomparison of four wet deposition schemes used in
dust transport modeling
Eunjoo Jung, Yaping Shao ⁎
Department of Physics and Materials Science, City University of Hong Kong, Hong Kong SAR, China
Accepted 9 February 2006
Available online 27 April 2006
Abstract
The characteristics of four wet deposition schemes widely used in dust modeling studies are examined within the framework of
a regional scale dust model. Since these schemes are based on different formulations, the scavenging coefficients of them deviate
by a factor of 103 depending on precipitation rate and particle size. The four schemes coupled with the dust model are applied to
simulate a 2002 Asian dust event. The corresponding wet deposition patterns and scavenging efficiencies are compared. It is found
that apart from the scheme derived from scavenging coefficient measurements, the other three schemes give similar wet deposition
patterns although their scavenging efficiencies are different depending on the particle-size range. The results suggest that the
performances of these schemes are affected by the particle size distribution of the dust emission, together with the model's
performance of precipitation prediction.
© 2006 Elsevier B.V. All rights reserved.
Keywords: below-cloud scavenging; dust model; East Asia; scavenging coefficient; scavenging ratio
1. Introduction
Wet deposition is a major removal process for airborne dust particles. The efficiency of the wet deposition depends on many parameters such as particle size
distribution, raindrop size distribution and the chemical
characteristics of the particles. Field measurements have
been carried out to directly measure scavenging coefficients (e.g. Volken and Schumann, 1993). However,
it is difficult to obtain these parameters for the dust
transport modeling.
A number of dust modeling studies have been carried
out for Asian dust storms either focusing on dust emis⁎ Corresponding author.
E-mail addresses: [email protected] (E. Jung),
[email protected] (Y. Shao).
0921-8181/$ - see front matter © 2006 Elsevier B.V. All rights reserved.
doi:10.1016/j.gloplacha.2006.02.008
sion (e.g. In and Park, 2002) or dust budget including
wet/dry deposition (e.g. Zhao et al., 2003). The wet
deposition parameterizations employed by these modeling studies are often based on different formulations
but the characteristics of the wet deposition schemes
have not been examined in detail.
Wet deposition parameterizations used for dust
modeling can be classified into four types based on their
formulations: the first type calculates scavenging cozefzficient as a function of raindrop size distribution and
particle–raindrop collection efficiency. However, there
are uncertainties involved with particle–raindrop collection efficiency. The second type estimates scavenging
coefficients as a function of a single variable such as
precipitation rate (e.g. Nickovic et al., 1966) or relative
humidity (RH) (Pudykiewicz, 1989). The scavenging
coefficients of these two types of schemes can be
E. Jung, Y. Shao / Global and Planetary Change 52 (2006) 248–260
compared with those determined experimentally. For
submicron particles, experimentally determined scavenging coefficients exceed theoretically determined scavenging coefficients by a factor of 10 (Volken and Schumann,
1993). In the third type, an empirical relationship derived
from direct measurements of scavenging coefficients (e.g.
Laakso et al., 2003) is used. Finally, for the estimation of
large-scale dust deposition, scavenging ratio, which is
different parameter for describing the efficiency of the
removal process, is often used (e.g. Tegen and Fung,
1994).
In this study, we apply the four schemes to the prediction of below-cloud scavenging for dust storms in
East Asia. The below-cloud scavenging process is efficient for the removal of particles in the coarse mode,
while the in-cloud scavenging process is important for
the removal of submicron particles. It has been reported
that Asian dust has a major peak in the coarse particle
mode (Chun et al., 2001; Mori et al., 2003). Mori et al.
(2003) observed that during the dust event of March
2001, there was a major peak at 4.7–7.0 μm for the
particle size distribution of the aerosol at Beijing and two
peaks at Yamaguchi, one at 0.43–0.65 μm and the other
at 3.3–4.7 μm. Hence, it can be inferred that below-cloud
scavenging is important in East Asia. Beyond East Asia,
however, as shown by Zhao et al. (2003), in-cloud
scavenging is more important than below-cloud scavenging during the trans-Pacific dust transport.
For the simulation of dust event, CEMSYS (Computational Environmental Modelling System) is used in
this study. The model has been used for dust events in
Australia (Lu, 1999) and East Asia (Shao et al., 2002).
In this study, our main concern is to examine the characteristics of the below-cloud scavenging schemes.
Therefore, in-cloud scavenging is not included.
249
mental studies have been made to determine E(r, R) (e.g.
Wang and Pruppacher, 1977; Grover and Pruppacher,
1985; Pinsky and Khan, 2001). Beard and Ochs (1984)
compiled several numerical results into one set of
collection efficiencies for particles with radius between
1.58 and 31.6 μm and raindrops with radius between 50
and 501 μm. However, observations show that raindrop
radius usually extends to 2500 μm for weak rain events
(Willis and Tattelman, 1989) and can be as large as
3000 μm for heavy rain events, with rainfall rates larger
than 100 mm h− 1 (Mueller and Sims, 1966). Mason
(1975) presented a set of collection efficiencies for
particles with radii ranging from 2 to 20 μm and
raindrops ranging from 10 to 3000 μm. The datasets of
Beard and Ochs (1984) and Mason (1975) are combined
to produce a set of collection efficiencies for particles
with radii between 2 and 20 μm and raindrops with radii
between 50 and 3000 μm. It required some modifications to fit smooth curves across the size range. The
contours of the resulting collection efficiencies are
shown in Fig. 1. It is indicated that E(r, R) increases with
R for R b 600 μm but decreases with R for R N 600 μm.
This behavior of E(r, R) is supported by the experimental results (Pruppacher and Klett, 1997). However, E(r,
R) is rather insensitive to R for 100 b R b 1000 μm, the
most populated range for raindrops.
Eq. (1) requires raindrop size distribution, n(R, t),
which is parameterized as a function of precipitation
rate. In this study, we use a gamma distribution function
2. Below-cloud dust scavenging parameterizations
2.1. Scheme I: scavenging coefficient, λ1
Scavenging coefficient, λ, is defined as
Z l
1 AC
¼
kðr; tÞu−
pðr þ RÞ2 E ðr; RÞðVs −vs Þnð R; tÞdR
C At
0
ð1Þ
where C is dust concentration and n(R, t)dR is the
number of raindrops per unit volume in the size range of
R to R + dR; E(r,R) is the collection efficiency of
particles with radius r by raindrops with radius R; Vs and
vs are the raindrop and particle settling velocities,
respectively. Many theoretical, numerical and experi-
Fig. 1. Contours of collection efficiency as a function of drop radius
(R) and particle radius (r).
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E. Jung, Y. Shao / Global and Planetary Change 52 (2006) 248–260
(Willis and Tattelman, 1989) which is a three-parameter
function defined as
nðRÞ ¼ n0 Ra expð−kRÞ
ð2Þ
where n0 is given by n0 = 512.85 × 10− 6M/D04(1/D0)α in
[cm− 3 cm− 1] with α = 2.16 and λ = 5.5880/D0 where
D0 = 0.1571M0.1681 in [cm] and M = 0.062P0.913 with P
being precipitation rate in [mm h− 1] and R in [cm]. This
below-cloud scavenging scheme was used by Jung
(2005).
2.2. Scheme II: scavenging coefficient, λ2
Below-cloud scavenging coefficient of this scheme,
λ2 is assumed to be a function of precipitation rate
depending on precipitation type. This method is advantageous compared with the Scheme I because there are
uncertainties in determining particle–raindrop collection efficiency. Therefore this scheme is widely used in
particulate matter modeling (e.g. Brandt et al., 2002).
The scavenging coefficient, λ2, is given by
k2 ¼ APB
ð3Þ
where λ2 is in [s− 1] and A and B are constants which
depend on precipitation types. The subscript 2 is
introduced to distinguish the scavenging coefficients
calculated by Eq. (3) from those calculated by Eq. (1)
(hereafter λ1). ApSimon et al. (1985) used A = 1 × 10− 4
and B=0.8. In this study, we use A = 8.4 × 10− 5 and
B = 0.79 for both convective and large-scale precipitation, following Brandt et al. (2002).
2.3. Scheme III: scavenging coefficient, λ3
λ1, λ2 and λ3. For λ1, the semi-empirical relationship for
the collection efficiency of Slinn (1984) is used for
particles with radius smaller than 2 μm. The three
scavenging coefficients increase with precipitation rate
as expected. λ2 is independent on particle size and λ3 is
less sensitive to precipitation rates than λ1 and λ2. For
submicron particles, λ1 is smaller than λ2 and λ3 by a
factor of up to 1000 depending on precipitation rate.
However for particles with radii larger than 3 μm, λ1
exceeds λ2.
2.4. Scheme IV: scavenging ratio
Scavenging ratio, Z, is defined as the concentration in
precipitation, Crain, divided by the concentration in the
air, Cair,
Z ¼ Crain =Cair
Field measurements have been carried out to estimate
scavenging coefficients for aerosols (e.g. Volken and
Schumann, 1993; Laakso et al., 2003). It was reported
that the scavenging coefficient not only depends on
precipitation rate but also on particle size. Based on
these measurements, Laakso et al. (2003) suggested the
following relationship for λ3
log10 k3 ¼ a1 þ a2 =½log10 d4 þ a3 =½log10 d3
þ a4 =½log10 d2 þ a5 =½log10 d þ a6 P0:5
Fig. 2. Comparison of simulated scavenging coefficients for three
different types of wet deposition schemes.
ð4Þ
where d is particle diameter in [μm], a1 = 274.4,
a2 = 3.328 × 105, a3 = 2.267 × 105, a4 = 5.801 × 104, a5 =
6.588 × 103 and a6 = 0.245. In Fig. 2 we have compared
ð5Þ
Scavenging ratio is often calculated based on surface
precipitation and aerosol data but can change with
heights. However, Davis et al. (1997) reported that
scavenging ratios obtained from surface concentration
data are in a comparable range with those obtained from
aircraft measurements. Scavenging ratio depends on the
particle size distribution, precipitation rates and the
chemical characteristics of the particle. For dust particles, Buat-Menard and Duce (1986) reported the scavenging ratios ranging from 500 to 1000 for submicron
particles and about 300 for larger particles. Duce et al.
(1991) used a scavenging ratio of 200 for the North
Atlantic Ocean and 1000 for the rest of the world ocean.
A value of Z = 750 was used by Tegen and Fung (1994)
E. Jung, Y. Shao / Global and Planetary Change 52 (2006) 248–260
251
Fig. 3. Mean sea-level pressure (hPa) (contour) and 1000-hPa wind speed (m s− 1) (shaded) over East Asia at 12:00 UTC on March 19, 2002.
for clay-sized mineral aerosol. In this study, we use a
scavenging ratio of Z = 500.
3. Model descriptions
CEMSYS consists of three major components: an atmospheric model, a dust emission/transport model, and a
GIS (Geographic Information System) database that
provides two models with surface parameters. The atmospheric model provides the wind data for the dust model,
initiating dust emission and driving dust transport at each
time step. Awide range of land surface parameters required
by the dust model are derived from the GIS database. The
GIS data used in this study have a higher horizontal
resolution (0.05°× 0.05°) than the numerical models which
enabled us to estimate dust emission on scales much finer
than the atmospheric model resolution (50 km × 50 km).
(2002). The essence of the dust emission model is that
the dust emission rate is proportional to intensity of
saltation which is considered to be a major mechanism
for dust emission. Both theoretical and experimental
studies show that the vertically integrated streamwise
3
sand flux, Q, is proportional to u⁎
where u⁎ is the friction
velocity (Owen, 1964; White, 1979). Following White
(1979), Q is predicted by
Q ¼ ðc0 qu3* Þ=gð1−u*t =u* Þð1 þ u*t =u* Þ2
ð6Þ
where c0 is the dimensionless coefficient, ρ the air
density and u⁎t the threshold friction velocity at which
soil particles are set in motion. The dimension of Q is
[M L− 1T− 1]. u⁎t is parameterized using the following
equation
u*t ðd; k; wÞ ¼ u*t0 ðdÞfk fw
ð7Þ
3.1. Dust emission parameterization
The dust emission model of CEMSYS has been
described in detail by Shao (2001) and Shao et al.
where u⁎t0 is the threshold friction velocity for a dry,
loose and smooth erodible soil and fλ and fw are
functions which describe the influences of surface
Table 1
Accumulated dust emission (Tg) and dry deposition (Tg) integrated over the model domain (183 × 141)
Dust size class
(μm)
db2
2–11
11–22
Total
March 19
March 20
March 21
Emi
Dry
Emi
Dry
Emi
Dry
3.5
199.5
75.1
278.2
2.3
149.1
57.8
209.3
1.2
29.3
17.0
47.4
1.3
38.7
16.8
56.9
1.1
33.7
16.7
51.5
1.1
32.3
14.6
47.9
252
E. Jung, Y. Shao / Global and Planetary Change 52 (2006) 248–260
roughness elements and soil wetness on u⁎t. Following
Shao and Lu (2000), u⁎t0 is parameterized by
u*t0 ¼ ½AN ðrp gd þ g=ðqdÞÞ1=2
ð8Þ
aggregates available for disintegration, Ω is the soil
volume excavated by a saltator, m is the dust mass and δ
is a parameter which determines the particle size distribution in the air.
where AN ∼ 0.0123, γ ∼ 3 × 10− 4 kg s−2, σp = ρp/ρ where
ρp is particle density, and g is gravitational acceleration.
Then, the vertical dust flux F is estimated using Q as
3.2. Dust transport and removal
F¼Q
1 Aps C 1
1 Að ps r CÞ
þ jd ð ps VC Þ þ
−
ps
At
ps
ps Ar
¼ jd ð KqjC=qÞ−D−W
CY ½ð1−dÞ þ dðpm =pf Þ
½ðqb gf X þ gc mÞg=½u* m
2
ð9Þ
where cY is a coefficient, pm and pf are the minimally and
fully disturbed parent soil particle size distributions, ηf
and ηc are the total fraction of dust which can be
released from unit soil mass and the mass fraction of soil
The conservation equation for dust concentration in
σ coordinates is given as
ð10Þ
where ps is surface pressure, V and σ˙ are the horizontal
and vertical velocities in σ coordinates, K is the
diffusion coefficient for dust particles, D represents a
Fig. 4. Daily averaged model dust concentration (μg m− 3) in the lowest model layer (σ = 0.999) for (a) March 20, 2002, (b) March 21, 2002, and (c)
March 22, 2002.
E. Jung, Y. Shao / Global and Planetary Change 52 (2006) 248–260
sink term due to dry deposition and W a sink term due to
wet deposition. The tendency due to horizontal advection is calculated using a flux-limiter method following
LeVeque (1993). This method is second-order accuracy
and is stable provided that the CFL condition is satisfied. The “MC” limiter (van Leer, 1977) is used. The
advection in the vertical direction is solved using the
Bott flux-form advection scheme (Bott, 1989). This
scheme is mass conservative, positive-definite, monotone, and characterized by comparatively low artificial
diffusion (Dabdub and Seinfeld, 1994). The diffusion
coefficients are obtained from the atmospheric model.
For the parameterization of dry deposition, CEMSYS
uses the following expression for the dry deposition
velocity, vd, which was suggested by Raupach et al.
(2001)
vd ðdÞ ¼ vs ðdÞ þ cam ½cf af EIM þ ð1−cf Þav Sc−2=3 ð11Þ
where vs is particle terminal velocity, cam is the bulk
aerodynamic conductance for momentum given by
2
cam = u⁎
/Ur, where Ur is the mean wind speed at
reference level zr, αf and αv are the empirical parameters,
f is the fraction of the total canopy drag exerted as form
253
drag, EIM is the particle impaction efficiency and Sc is
the particle Schmidt number given by Sc = νa/Dp where
νa is the kinematic viscosity of air and Dp is the
Brownian diffusivity for particles in the air. In this study,
we use αf = 2 and αv = 8. EIM is specified as a function of
the Stokes number, St, by
EIM ¼ ½St =ðSt þ pÞq
ð12Þ
where p and q are constants and St is the Stokes number
defined by
St ¼ ð2sUc Þ=Ic
ð13Þ
where τ is the Stokes relaxation time given by τ = ρpd2/
(18ρνa), Uc is the flow velocity above the canopy
elements and lc is the dimension of the canopy elements.
Bache (1981) and Peters and Eiden (1992) proposed
p = 0.8 and q = 2.
Dust particles entrained from the surface have a
wide range of size distribution. In CEMSYS, the dust
particles are handled in independent size classes with
logarithmically spaced intervals and Eq. (9) is solved
for each size class. Therefore, the total dust concentration is given as a sum
P of each dust class'
concentration, Ci, as C ¼ ni¼1Ci . For this study,
Fig. 5. Comparison of observed and simulated dust concentrations at Beijing, Seoul and Nagasaki.
254
E. Jung, Y. Shao / Global and Planetary Change 52 (2006) 248–260
three particle size classes, d b 2 μm, 2–11 μm and 11–
22 μm have been chosen.
25σ levels in the vertical and a horizontal grid spacing
of 50 km. A second-order differencing scheme is used
for the advection terms in HIRES.
3.3. Atmospheric model
4. Asian dust event in March 2002
The high-resolution limited area model (HIRES) is
an incompressible hydrostatic atmospheric model. The
model is designed for short-term forecasting with a
horizontal resolution ranging from 15 km to 75 km. It
was developed by Leslie et al. (1985) and had been
tested by Leslie and Skinner (1985) and Skinner and
Leslie (1999). HIRES employs a Mellor–Yamada
high-order closure scheme (modified v2.5) for the
boundary layer (Mellor and Yamada, 1974). The
prediction of land surface process is made through
the Atmosphere and Land Surface Interaction Scheme
(ALSIS) (Irannejad and Shao, 1998). For deep
cumulus parameterization, the Kain–Fritsch scheme
(Kain and Fritsch, 1993) is used. The integrations are
carried out on the staggered Arakawa C grid using a
centered semi-implicit time differencing scheme with
High winds for lifting dust from the desert areas in
East Asia were generated by a Mongolian cyclone. A
weak surface low-pressure formed in northern Mongolia
on March 19 and the system deepened as it moved
eastward. Accompanied by a high-pressure system to
the west, it produced strong winds reaching 20 m s− 1 in
southern Mongolia, well above the threshold for dust
emission [around 5 m s− 1 (Natsagdorj et al., 2003)]. The
analysis of mean sea-level pressure at 12:00 UTC on
March 19 is shown together with 1000-hPa wind speed
in Fig. 3. The eastward-moving cyclone further
intensified with the lowest surface pressure at 991 mb
by March 21 in northeast China. The dust event severely
affected northern China, reducing visibility to less than
50 m in some areas in Gansu and Ningxia provinces.
Fig. 6. Comparison between observed (a, b) and simulated (c, d) precipitations (mm) for March 20, 2002 (a, c) and March 21, 2002 (b, d).
E. Jung, Y. Shao / Global and Planetary Change 52 (2006) 248–260
255
Fig. 7. Daily accumulated wet deposition due to below-cloud scavenging (shaded, mg m− 2) and ratio of the wet deposition to total deposition
(contoured) calculated from (a) Scheme II and (b) Scheme III for the period of March 19–21, 2002.
The thick dust plume affected Beijing on March 20 and
reached the Korean Peninsula and Japan on March 21.
5. Model results and discussion
5.1. Reference simulation without wet deposition
The reference simulation is carried out without wet
deposition. The model simulated strong dust emission
from the source region in northern China and southern
Mongolia on March 19 (see Fig. 3). Table 1 shows daily
accumulated dust emission and dry deposition integrated over the model domain during the dust event. Most of
the total dust emission during the period occurred on
March 19. The averaged dust emission rate over the
source region in the Gobi Desert is 977 μg m− 2 s− 1 on
March 19. Fig. 4 shows the daily averaged model dust
concentration at the lowest model layer (σ = 0.999). The
dust cloud initiated over the source region in the Gobi
Desert moved eastward in the following days.
For comparison with observations, we used TSP
(Total Suspended Particle) measurements and TSP
concentrations estimated from visibility measurements
at synoptic weather stations. In the study of dust events in
Northeast Asia, Shao et al. (2003) found the following
empirical relationships between visibility and TSP
concentration by fitting the TSP observations to visibility
CTSP ¼ 3802:29D−0:84
v
CTSP ¼ expð−0:11Dv þ 7:62Þ
for Db3:5 km
for Dz3:5 km
ð14Þ
where CTSP is TSP concentrations in μg m− 3 and Dv is
visibility in km. A comparison between observed and
simulated dust concentrations at three selected locations
is shown in Fig. 5. The model dust concentration
(d b 30 μm) at the lowest model layer was used for the
comparison. For Beijing and Nagasaki, CTSP is used for
the comparison because there were no available dust
measurements at those locations. For Beijing the modeled dust concentrations are in a comparable range with
CTSP. The modeled dust concentration ranges 10 to
4163 μg m− 3, whereas CTSP ranges 75 to 3510 μg m− 3.
However, the model predicted dust concentration at
Seoul and Nagasaki higher than the measurements. For
Seoul, the model dust concentration ranges 11 to 5836 μg
m− 3 whereas the TSP measurements range 226 to
3262 μg m− 3. For Nagasaki, the model dust concentration ranges between 1 and 5209 μg m− 3 whereas CTSP
ranges 226 to 2124 μg m− 3. The arrival of the dust plume
at the locations was predicted well apart that the plume
was predicted a few hours earlier than observation at
Beijing.
5.2. Comparison of four wet deposition schemes
The model results of the four wet deposition schemes
described in Section 2 are examined in this section. First
we examine the model precipitation closely associated
with the performances of the four schemes. A comparison
of the observed and simulated precipitation is shown in
Fig. 6 for March 20–21, during which period the model
predicted considerable amount of wet deposition at some
locations. The observed precipitation was generated from
the measurements at a large number of synoptic weather
stations using an interpolation scheme with weights being
proportional to inverse squared distance. The marks in
Fig. 6 indicate the locations of the weather stations. The
comparison shows that the model predicted reasonably
well the amount of precipitation and its pattern in the
southeastern part of China. The precipitation over
northeast Asia is closely associated with an eastwardmoving cyclone which initiated the dust event.
Daily accumulated wet deposition due to below-cloud
scavenging and the ratio of the wet deposition to total
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E. Jung, Y. Shao / Global and Planetary Change 52 (2006) 248–260
Table 2
Area-averaged daily wet deposition (mg m− 2) and correlation coefficient between contribution of wet deposition to total deposition and precipitation
during March 19–21, 2002
Scheme
I
II
III
IV
Area-averaged daily precipitation (mm)
Area-averaged daily wet deposition
(mg m− 2)
(the ratio of wet deposition to total deposition)
Correlation coefficient
Region I
(95–115°E, 35–50°N)
Region II
(115–135°E, 35–50°N)
Region I
(95–115°E, 35–50°N)
Region II
(115–135°E, 35–50°N)
2653 (9.1%)
1791 (2.3%)
9792 (9.0%)
6043 (10.7%)
0.71
1626 (28.3%)
1340 (10.1%)
2298 (13.4%)
1948 (20.2%)
1.6
0.462
0.618
0.049
0.287
0.094
0.196
− 0.096
0.011
deposition (η) for the schemes are shown in Fig. 7. Since
the three schemes, the Schemes I, II and IV show a
similar wet deposition pattern, only a comparison of the
Scheme II and Scheme III is shown. For the Scheme II, η
tends to increase toward the downstream area where the
contribution of dry deposition is small. The result is
consistent with the modeling work of Zhao et al. (2003).
Table 2 presents area-averaged daily accumulated wet
deposition, η and the correlation between η and precipitation together with 24 h-accumulated precipitation for
two regions for March 19–21. As expected, the
contribution of wet deposition to total deposition is
higher in Region II enclosed by (115–135°E, 35–50°N)
in downstream than in Region I enclosed by (95–115°E,
35–50°N), which includes the source regions. The
contribution of wet deposition has positive correlation
with precipitation for both regions except for the Scheme
III in Region II. The correlation between two parameters
is higher in Region I except for the Scheme III. In both
regions, the Scheme III shows one order smaller
correlation with precipitation than the other schemes.
We examined the simulated dust concentrations of
three dust size classes for the four schemes in Fig. 8. The
dust concentrations were area-averaged for the area
enclosed by (37.5–47.5°N, 115–125°E) near the source
region. For the first size range (d b 2 μm), the Scheme IV
predicted the lowest dust concentration among the
schemes and the Scheme I the highest. In fact, the dust
concentrations of the Scheme I are not so different from
those of the reference simulation. For the second and
third size ranges (2–11 and 11–22 μm), the Scheme III
predicted the lowest dust concentration among the
Fig. 8. Comparisons of area-averaged (37.5–47.5°N, 115–125°E) dust concentration obtained by each scheme for three dust size ranges of (a)
d b 2 μm, (b) 2–11 μm and (c) 11–22 μm. In (d), the time series of the area-averaged 3-hourly precipitation is shown.
E. Jung, Y. Shao / Global and Planetary Change 52 (2006) 248–260
257
Fig. 9. Scavenging efficiencies of the four scavenging schemes for (a) d b 2 μm, (b) 2–11 μm and (c) 11–22 μm.
schemes and the Scheme II the highest. The results are
closely related to the relatively large scavenging
coefficients of the Scheme III for micron particles.
The results are similar for other areas (not shown).
Using the area-averaged dust concentration, we
compared scavenging efficiency, ζ [= (Cr − C) / Cr], of
the four schemes with the reference concentration as Cr
in Fig. 9. The Scheme I and II have similar ζ values, less
than 0.5 for the second and third size ranges. For the first
size range, the ζ values of the Scheme I are negligible as
expected. The Scheme II and III have similar ζ values,
less than 0.4 for the first size range while for the second
and third size ranges, the ζ values of the Scheme III are
three times larger than those of the Scheme II. The results
imply that the Scheme II overpredicts wet deposition by
the Scheme I when the portion of dust emission in the
submicron range increases, and the Scheme III overpredicts wet deposition by the Scheme II when the
Table 3
Total accumulated wet deposition due to below-cloud scavenging of four wet deposition schemes
Scheme
Dust size class
(μm)
I
db2
2–11
11–22
Total
db2
2–11
11–22
Total
db2
2–11
11–22
Total
db2
2–11
11–22
Total
II
III
IV
Accumulated wet deposition
(Tg)
March 19
March 20
March 21
Fraction
1.4 × 10− 3
18.3
4.6
23.0
0.3
12.8
2.6
15.7
0.4
58.0
24.5
82.9
0.9
38.3
10.0
49.2
3.3 × 10− 3
13.9
2.6
16.6
0.5
11.6
1.8
13.9
0.5
12.5
5.5
18.5
0.6
16.3
4.2
21.1
1.9 × 10− 3
3.5
0.6
4.1
0.2
3.0
0.5
3.6
0.2
5.3
2.0
7.5
0.3
4.7
1.0
6.0
0.015%
81.7%
17.8%
43.7 Tg
3.0%
82.5%
14.8%
33.2 Tg
1.0%
69.6%
29.4%
108.9 Tg
2.4%
77.7%
19.9%
76.3 Tg
258
E. Jung, Y. Shao / Global and Planetary Change 52 (2006) 248–260
Fig. 10. Comparison of observed and simulated dust concentrations with four different wet deposition schemes at Beijing, Seoul and Nagasaki.
portion of dust emission in the coarse mode increases.
The Scheme IV has ζ values two times larger than the
Scheme II. It appears that Z = 500 is too large for the
particle size ranges.
The total wet depositions due to below-cloud
scavenging for the four schemes are summarized in
Table 3. The Scheme III calculated the largest wet
deposition and the Scheme II the smallest. The belowscavenging occurred mainly in the second size range,
which accounted for 83% of the total scavenging for the
Scheme II and 70% for the Scheme III.
Lastly we compared the predicted dust concentrations of the four schemes at the selected locations in
Fig. 10. For Beijing, the simulated dust concentrations
for the Scheme III and IV largely underestimated the
observations. For Seoul, the dust concentrations for the
Schemes I and II are in relatively good agreements with
the measurements. For Nagasaki, it is difficult to
distinguish the dust concentrations of the Schemes II,
III and IV because of their close values. The Scheme I
predicted higher concentrations than the schemes. It is
associated with the small scavenging coefficients of the
Scheme I for the submicron range.
6. Summary and conclusion
Below-cloud scavenging is a major wet removal
process for coarse particles in East Asia. In this study we
examined the characteristics of the four below-cloud
scavenging schemes based on different formulations.
The scavenging coefficients of the Schemes I, II and III
diverge by a factor of 1000 depending on the precipitation rate and particle size.
CEMSYS was applied to the Asian dust event that
occurred in March 2002. The reference simulation without wet deposition parameterization illustrates the importance of wet deposition. The model predicted well
the arrival of the dust cloud and its duration at several
selected locations but it predicted higher concentrations
than observations.
The characteristics of the four wet deposition schemes
were examined. Apart from the Scheme III derived from
field measurements, the other schemes showed similar
wet deposition patterns although their scavenging efficiencies were quite different depending on particle size
range. The four schemes tested predicted the contribution of wet deposition due to below-cloud scavenging
to total deposition increasing toward the downstream
area. It was found that the contribution of wet deposition
has positive correlation with precipitation except for the
Scheme III. The wet deposition predicted by the Scheme
III showed positive correlation with precipitation in the
area close to the source regions but negative correlation
in downstream area. The Scheme IV showed the largest
scavenging efficiencies for submicron particles and
Scheme III showed the largest scavenging efficiencies
in micron particles. The scavenging efficiencies of the
Scheme I were negligible for submicron range.
The characteristics of the four schemes affected the
performances of the schemes to predict dust
E. Jung, Y. Shao / Global and Planetary Change 52 (2006) 248–260
concentrations at the selected locations. For Beijing and
Seoul, the simulated dust concentrations with the
Schemes III and IV largely overestimated the observations. For Seoul, the dust concentrations for the
Schemes I and II were in relatively good agreements
with observations. For Nagasaki, the dust concentrations for the Scheme I were in a comparable range with
the observed. The other schemes greatly underestimated
the observations.
In this study we did not consider in-cloud
scavenging. In the future, the below-scavenging
schemes tested in this study can be combined with
in-cloud scavenging for the removal of submicron
particles.
Acknowledgments
The work described in this paper was fully supported
by a grant from the Research Grants Council of the Hong
Kong Special Administrative Region China [Project No.
City U 101903].
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