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The impacts of moisture transport on drifting snow sublimation in the saltation layer

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Abstract

Drifting snow sublimation (DSS) is an important physical process related to moisture and heat transfer that happens in the atmospheric boundary layer, which is of glaciological and hydrological importance. It is also essential in order to understand the mass balance of the Antarctic ice sheets and the global climate system. Previous studies mainly focused on the DSS of suspended snow and ignored that in the saltation layer. Here, a drifting snow model combined with balance equations for heat and moisture is established to simulate the physical DSS process in the saltation layer. The simulated results show that DSS can strongly increase humidity and cooling effects, which in turn can significantly reduce DSS in the saltation layer. However, effective moisture transport can dramatically weaken the feedback effects. Due to moisture advection, DSS rate in the saltation layer can be several orders of magnitude greater than that of the suspended particles. Thus, DSS in the saltation layer has an important influence on the distribution and mass-energy balance of snow cover.
1
1 The impacts of moisture transport on drifting snow
2 sublimation in the saltation layer
3
4 N. Huang and X. Dai
5 Key Laboratory of Mechanics on Disaster and Environment in Western China,
6 Lanzhou University, Lanzhou 730000, China
7 Corresponding to: N. Huang (huangn@lzu.edu.cn)
Atmos. Chem. Phys. Discuss., doi:10.5194/acp-2015-795, 2016
Manuscript under review for journal Atmos. Chem. Phys.
Published: 11 February 2016
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2
1
2 Abstract
3 Drifting snow sublimation (DSS) is an important physical process related to moisture
4 and heat transfer that happens in the atmospheric boundary layer, which is of
5 glaciological and hydrological importance. It is also essential in order to understand
6 the mass balance of the Antarctic ice sheets and the global climate system. Previous
7 studies mainly focused on the DSS of suspended snow and ignored that in the
8 saltation layer. Here, a drifting snow model combined with balance equations for heat
9 and moisture is established to simulate the physical DSS process in the saltation
10 layer. The simulated results show that DSS can strongly increase humidity and
11 cooling effects, which in turn can significantly reduce DSS in the saltation layer.
12 However, effective moisture transport can dramatically weaken the feedback effects.
13 Due to moisture advection, DSS rate in the saltation layer can be several orders of
14 magnitude greater than that of the suspended particles. Thus, DSS in the saltation
15 layer has an important influence on the distribution and mass-energy balance of snow
16 cover.
Atmos. Chem. Phys. Discuss., doi:10.5194/acp-2015-795, 2016
Manuscript under review for journal Atmos. Chem. Phys.
Published: 11 February 2016
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3
1 1 Introduction
2 Drifting snow is a special process of mass-energy transport in the hydrological
3 cycle of snow. It not only changes the snow distribution but also results in phase
4 changes of ice crystals into water vapor, which is known as DSS. Snow sublimation
5 not only significantly influences the mass-energy balance of snow cover (e.g., Zhou
6 et al., 2014) by changing surface albedo (Allison, 1993) and the runoff of snowmelt
7 in cold regions (Marks and Winstral, 2001), but also has a pivotal status on moisture
8 and heat transfer in the atmospheric boundary layer(Pomeroy and Essery, 1999;
9 Anderson and Neff, 2008). Thus, it is of glaciological and hydrological importance
10 (Sugiura and Ohata, 2008). In high cold area, the reduction of snow cover may cause
11 the surface temperature to increase in the cold seasonHuang et al., 2008, 2012. The
12 thickness of seasonally frozen ground has decreased in response to winter warming
13 (Huang et al., 2012). On the other hand, both dust and biomass burning aerosols may
14 impact the surface albedo when deposited on snow; soot in particular has large
15 impacts on absorption of radiation (Huang et al., 2011). In addition, a large, but
16 unknown, fraction of the snow that falls on Antarctica is removed by the wind and
17 subsequently sublimates. Therefore, a detailed knowledge of DSS is also essential in
18 order to understand snow cover distribution in cold high area as well as the mass
19 balance of the Antarctic ice sheets, and further the global climate system (Yang et al.,
20 2010).
21 In drifting snow, snow particles can experience continuous sublimation, which
22 induces a heat flux from the surrounding air to the particle and a moisture flux in the
23 opposite direction (Bintanja, 2001a). Thus, DSS can cause increases in humidity and
24 cooling of the air (Schmidt, 1982; Pomeroy et al., 1993) and has an inherent self-
25 limiting nature due to the feedback associated with the heat and moisture budgets
26 (Déry and Yau, 1999; Groot Zwaaftink et al., 2011, 2013). On one hand, snow
27 sublimation absorbs heat and decreases the temperature of the ambient air, which in
28 turn reduces the saturation vapor pressure and hence the sublimation rate; on the
29 other hand, the increment in the moisture content of the ambient air decreases the
30 sublimation rate of drifting snow, as it is proportional to the under-saturation of the
31 air.
32 Saltation is one of the three modes of particle motion, along with suspension and
33 creep. Among the three modes, saltation is important and the DSS in the saltation
Atmos. Chem. Phys. Discuss., doi:10.5194/acp-2015-795, 2016
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4
1 layer may constitute a significant portion of the total snow sublimation (Dai and
2 Huang, 2014). Previous studies of DSS mostly focused on the sublimation of
3 suspended snow, which was mainly due to the consideration that sublimation will
4 soon vanish in the saltation layer because the feedback of DSS may lead to a
5 saturated layer near the surface (Bintanja, 2001b). However, the field observation
6 data of Schmidt (1982) showed that relative humidity only slightly increases during
7 snowdrift events and the maximum humidity was far below saturation. Further
8 studies (Groot Zwaaftink et al., 2011; Vionnet et al., 2013) also showed that the
9 relative humidity does not reach saturation even at the lowest atmosphere level after
10 DSS occurs. Some scientists argued that it was caused by moisture transport, such as
11 diffusion and advection of moisture, which inevitably accompany the drifting snow
12 process (Vionnet et al., 2013). Therefore, it is necessary to study the feedback
13 mechanism of DSS in the saltation layer and the effect of moisture transport on it.
14 In this study, a wind-blown snow model, balance equations for heat and moisture
15 of an atmospheric boundary layer, and an equation for the rate of mass loss of a
16 single ice sphere due to sublimation were combined to study the sublimation rate of
17 drifting snow by tracking each saltating particle in drifting snow. Then, the effects of
18 DSS on the humidity and temperature profiles, as well as the effects of diffusion and
19 advection of moisture on DSS in the saltation layer, were explored in detail.
20
21 2 Methods
22 2.1 Model Description
23 Saltation can be divided into four interactive sub-processes, i.e., aerodynamic
24 entrainment, particle trajectories, particle-bed collisions, and wind modification
25 (Huang et al., 2011).
26 The motion equations for snow particles are (Huang et al., 2011)
27 , (1)
p f p
p D
r
dU U U
m F
dt V
28 , (2)
p f p
p g B D
r
dV V V
m W F F
dt V
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1 , (3)
p
p
dx
U
dt
2 . (4)
p
p
dy
V
dt
3 where and are the mass and weight of the snow particle, respectively; , ,
p
m
g
W
f
U
f
V
4 and are the horizontal and vertical velocities of the airflow and snow particle,
p
U
5 respectively;
2 2
r f p f p
V U U V V
is the relative velocity between the
6 airflow and snow particle;
p
x
and
p
y
are the horizontal position and vertical height of
7 the snow particle, respectively;
3
1
6
B f
F D g
and
2 2
1
8
D D f r
F C D V
are the
8 buoyancy force and the drag force applied on the snow particle, respectively;
f
is the
9 air density;
D
is the diameter of the snow particle;
g
is the acceleration of gravity; and
10
D
C
is the drag coefficient.
11 Within the atmospheric boundary layer, the mean horizontal wind
12 velocity
u
satisfies the Navier-Stokes equation (Werner, 1990). According to
13 Prandtl’s mixing length theory for the steady flow fully developed over an infinite
14 planar bed,
u
is
15
2 2
( ) 0
f x
du du
y F
y dy dy
, (5)
16 where
x
is the coordinate aligned with the mean wind direction,
y
is the vertical
17 direction,
is the von Karman constant, and
x
F
is the force per unit volume that the
18 snow particles exert on the fluid in the stream-wise direction and can be expressed as
19
1
n
x p i
i
F m a
. (6)
20 where
n
is the number of particles per unit volume of fluid at height
y
, and
i
a
is the
21 horizontal acceleration of particle
i
.
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1 When the bed shear stress is greater than the threshold value, snow particles begin
2 lifting off the surface. The number of aerodynamically entrained snow particles
a
N
is
3 (Shao and Li, 1999)
4
2
3
*
*
2
*
1
t
a
u
N u D
u
. (7)
5 where
is a dimensionless coefficient (
3
1 10
in our simulations),
*
u
is the friction
6 velocity, and
*t
u
is the threshold friction velocity. Following the previous saltation
7 models (McEwan and Willetts, 1993), the vertical speed of all aerodynamically
8 entrained particles is
2gD
.
9 The following three splash functions for drifting snow proposed by Sugiura and
10 Maeno (2000) based on experiments are used to determine the number and motion
11 state of the splashed particles.
12
1
1
exp
v
v v v
e
S e e
, (8)
13
2
2
2
1
exp
2
2
h
h h
e
S e

, (9)
14
1
e
e
e
m n
n
e e m n
S n C p p
. (10)
15 In Eq. (8),
v
S
is the probability distribution of the vertical restitution coefficient
v
e
,
16
( )
is the gamma function, and
and
are the shape and scale parameters for the
17 gamma distribution function. In Eq.(9),
h
S
is the probability distribution of the
18 horizontal restitution coefficient
h
e
, and
and
are the mean and variance,
19 respectively. In Eq. (10),
e
S
is the probability distribution function of the number of
20 ejected particles
e
n
, a binomial distribution function with the mean
mp
and the
21 variance
(1 )mp p
.
22 The potential temperature
and specific humidity
q
of the ambient air satisfy the
23 following prognostic equations (Déry and Yau, 1999)
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1
s
f
L S
K
t y y C
, (11)
2
q
f
q q S
K Q
t y y
. (12)
3 where
* T
K u y K
and
*q V
K u y K
are the heat and moisture diffusivities (the
4 sum of eddy diffusivity and molecular diffusivity), respectively,
S
is the sublimation
5 rate summed over all particles at each height above the surface,
s
L
is the latent heat of
6 sublimation (2.835×10
6
J kg
-1
),
C
is the specific heat of air,
q
Q u
x
is the horizontal
7 advection of moisture at each height above the surface, and
q
x
represents the
8 horizontal gradient in specific humidity. When the external dry air with specific
9 humidity
out
q
enters into the study domain, we hypothesize that the specific humidity
10 in the study domain is linearly distributed along the horizontal direction and
11 possesses the value of
in
q
at the exit. Thus, the horizontal advection of moisture can be
12 simplified to
( ) /
in out
Q u q q l
, with
l
being the length of the domain.
13 The total DSS rate
S
Q
(kg s
-1
) of the saltation layer within the computational
14 domain is obtained by summing the mass loss of all saltating particles in the domain.
15
S
i
i
dm
Q
dt
, (13)
16 where
i
dm
dt
is the mass loss rate corresponding to the i-th particle. At the air
17 temperature
T
and undersaturation
(
1 RH
), the rate of mass change of a single
18 particle with radius
r
due to sublimation is (Thorpe and Mason, 1966)
19
2
( 1)
s s v
v v s
dm r
L L R T
dt
KTNu R T D She
 
, (14)
20 where
RH
is the relative humidity of air,
K
is the molecular thermal conductivity of
21 the atmosphere (0.024 J m
−1
s
−1
K
−1
),
v
D
is the molecular diffusivity of water vapor in
22 the atmosphere,
v
R
is the gas constant for water vapor (461.5J kg
-1
K
-1
),
s
e
is saturated
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1 vapor pressure with respect to an ice surface, and
Nu
and
Sh
are the Nusselt number
2 and the Sherwood number, respectively, both of which are dimensionless and depend
3 on the wind velocity and particle size (Thorpe and Mason, 1966; Lee, 1975).
4
0.5
0.5
1.79 0.606 Re 0.7 Re 10
1.88 0.580 Re 10 Re 200
Nu Sh
. (15)
5 where
Re /
r
DV
is the Reynolds number and
is the kinematic viscosity of air.
6 For the purpose of comparison with the sublimation of suspended particles, the
7 initial relative humidity profile in accordance with that of Xiao et al. (2000) is
8
0
1 ln( / )
S
RH R y y
, (16)
9 where
0
y
is roughness length and
0.039469
S
R
.
10 The conversion relation between relative humidity and specific humidity is
11
0.622
s
s
e
q RH
p e
, (17)
12 where
610.78exp 21.87 273.16 7.66
s
e T T
.
13 The constant initial potential temperature
0
is 263.15K (but is 253.16 K in the
14 comparison with Xiao et al. (2000)) and the initial absolute temperature is
15
0.286
0 0
0
p
T
p
, (18)
16 where
p
is the pressure and its initial distribution is based on the hypsometric
17 equation
18
0
0
exp
d
yg
p p
R
. (19)
19 where
0
p
is taken as 1000 hPa and
d
R
is the gas constant for dry air (287.0 J kg
-1
K
-1
).
20 2.2 Calculation Procedure
21 The procedure for the calculations is enumerated below.
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1 1. The length, width and height of the computational domain sampled from the
2 saltation layer above the surface are 1.0 m, 0.01 m, and 1.0 m, respectively. The
3 initial and boundary conditions of temperature and humidity are set from Eqs. (16)-
4 (19).
5 2. Snow particles are considered as spheres with diameter of 200
m
and density
6 of 910 kg m
-3
. The threshold friction velocity of snow is 0.21 m s
-1
and the snow bed
7 roughness is 3.0 × 10
−5
m (Nemoto and Nishimura, 2001).
8 3. The initial wind field is logarithmic. If the bed shear stress is greater than the
9 threshold value, particles are entrained from their random positions on the snow
10 surface at vertical speed
2gD
and the number of aerodynamically entrained snow
11 particles satisfies Eq. (7).
12 4. The snow particle trajectory is calculated using Eqs. (1)-(4)every 0.00001 s in
13 order to obtain the velocity used in the calculation of sublimation rate and the new
14 location of each drifting snow particle to determine whether the snow particle falls on
15 the snow bed.
16 5. As the snow particles fall on the snow bed, where they impart their energy to
17 other snow particles and splash or eject other snow particles, the velocity and angle of
18 the ejected particles satisfy the splash functions, i.e., Eqs. (8)-(10), according to the
19 motion state of the incident particles and the actual wind field at that time. The
20 number of snow particles is re-counted every 0.00001 s.
21 6. The reactive force
x
F
that the snow particles exert on the wind field induces
22 wind modification according to Eq. (5).
23 7. Based on the process above, the velocity and location of each drifting snow
24 particle are derived and then used in Eqs. (13)-(15) to calculate their sublimation rate
25 every 0.00001 s. Under the effect of DSS, potential temperature and specific
26 humidity at different heights under the diffusion or advection moisture transport are
27 calculated every 0.00001 s.
28 8. The new values of wind field calculated in step 6 are used in step 3, and then
29 steps 4 to 7 are recalculated. Such a cycle is repeated to finish the calculation of DSS
30 under thermodynamic effects. Each calculation takes 60 s.
31 3 Results and Discussion
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1 3.1 Relative Humidity and Temperature
2 The relative humidity at 1 cm height for different defined wind velocities generally
3 reaches saturation within 10 s when moisture transport is not included (Fig. 1a).
4 Snow sublimation will not occur, and the temperature will not change (Fig. 1b).
5 However, when moisture transport is included, the snow sublimation occurs
6 throughout the simulation period, and temperature decreases continually. Moreover,
7 under the same moisture transport mechanism, the greater the wind friction velocity,
8 the higher the relative humidity and temperature change (Fig. 1). The relative
9 humidity at 1 cm shows a trend of rapid decrease, then rapid increase, and finally a
10 slow increase (Fig. 1a), but does not reach saturation in the simulation period of 60 s.
11 Early in the wind-blown snow stage, the sublimation rate is smaller as only a few
12 saltating particles sublime and the moisture at the lower height largely moves
13 outwards due to the effect of moisture transport, resulting in relative humidity
14 decrease. With continuing wind-blown snow, more snow particles leave the surface,
15 which increases the sublimation rate and hence the relative humidity. When it reaches
16 a steady state, the amount of snow particles in the saltation layer will no longer
17 increase, but fluctuate within a certain range. Thereafter, because of the increase in
18 humidity and cooling, DSS weakens (Fig. 2). The results indicate that DSS in the
19 saltation layer has a self-limiting nature.
20 3.2 Sublimation Rate
21 Moisture transport could remove some moisture, attenuating the increase of relative
22 humidity and thus negative feedback, leading to higher sublimation rates with
23 moisture transport than without (Fig. 2). With moisture removal only by diffusion,
24 the sublimation rate at 60 s is roughly the same at 3 wind velocities, meaning that
25 sublimation still shows obvious negative feedback. However, with moisture transport
26 by diffusion and advection, the sublimation rate increases significantly as the
27 negative feedback effect is effectively reduced. Moreover, the sublimation rate
28 increases with the friction velocity and can be even greater than that at the highest
29 wind velocity without advection. For example, the sublimation rate at 60 s with
30 advection is 0.6110
-5
kg m
-2
s
-1
at a friction velocity of 0.3 m s
-1
, greater than that of
31 0.4410
-5
kg m
-2
s
-1
at a friction velocity of 0.5 m s
-1
without considering advection.
32 The sublimation rate even reaches 0.9610
-5
kg m
-2
s
-1
, equaling the 0.83 mm d
-1
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1 snow water equivalent (SWE) at a friction velocity of 0.5 m s
-1
with advection
2 included (Fig. 2). Furthermore, sublimation continues to occur. Thus, it can be seen
3 that effective moisture transport can weaken the negative feedback of sublimation,
4 hence significantly affecting DSS. Because the occurrence of wind-blown snow must
5 coincide with the airflow, DSS in the saltation layer is not negligible, and the
6 assumption that the saltation layer is a saturation boundary layer is inadvisable.
7 Air temperature decreases with decreasing height, along with air saturation degree
8 during wind-blown snow, which is adverse to sublimation in contrast to higher
9 heights above the surface. Nevertheless, the volume sublimation rate increases with
10 decreasing height (Fig. 3). This is in agreement with the vertical profiles of the
11 horizontal mass flux of snow particles (Huang et al., 2011). That is, there are more
12 snow particles that can participate in sublimation at lower heights, leading to higher
13 sublimation rates even in environments adverse to sublimation. The results indicate
14 that the particle density is an important controlling factor for sublimation rate, which
15 is consistent with a previous study (Wever et al., 2009). A comparison between our
16 simulated results and that of four models for suspended snow, i.e., PIEKTUK-T,
17 WINDBLAST, SNOWSTORM and PIEKTUK-B, shows that the local sublimation
18 rate of the suspended snow at 60 s can reach 10
-6
kg m
-3
s
-1
at most (Xiao et al.,2000)
19 (Fig. 3), smaller than that of our calculated results (10
-4
kg m
-3
s
-1
) by 2 orders of
20 magnitude at the same initial temperature and relative humidity. This result shows
21 that the assumption that sublimation in the saltation layer can be ignored by
22 considering it a saturation boundary layer is inadvisable. Therefore, DSS in the
23 saltation layer is of non-negligible importance and requires further detailed study.
24 4 Conclusions
25 In this study, we established a wind-blown snow model and balance equations for
26 heat and moisture to study the effect of different moisture transport mechanisms on
27 DSS in the saltation layer. As has been reported (e.g., Schmidt, 1982), DSS could
28 lead to strong increases in humidity and cooling, which in turn can significantly
29 reduce the DSS rate, i.e., DSS has an inherently self-limiting nature. Moreover, the
30 relative humidity in the saltation layer quickly reaches saturation when moisture
31 transport is not considered. However, effective moisture transport, such as advection,
32 can dramatically weaken the negative feedback of sublimation and prolong the
33 duration of the higher DSS rate and hence has a profound effect on DSS. Because of
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1 the presence of advection, DSS rate increases with the friction velocity and the
2 volume sublimation rate of saltating particles is several orders of magnitude greater
3 than that of the suspended particles due to the higher particle density in the saltation
4 layer. Thus, DSS in the saltation layer plays an important part in the energy and mass
5 balance of snow cover and needs to be further studied.
6 Acknowledgments
7 This work is supported by the State Key Program of National Natural Science
8 Foundation of China (91325203), the National Natural Science Foundation of China
9 (41371034), and Innovative Research Group of the National Natural Science
10 Foundation of China (11421062).
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29 2191–2245, doi:10.5194/tcd-7-2191-2013, 2013.
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Manuscript under review for journal Atmos. Chem. Phys.
Published: 11 February 2016
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13 2010, 2010.
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Atmos. Chem. Phys. Discuss., doi:10.5194/acp-2015-795, 2016
Manuscript under review for journal Atmos. Chem. Phys.
Published: 11 February 2016
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Author(s) 2016. CC-BY 3.0 License.
16
1
2
3 Figure 1.Temporal evolution of relative humidity (a) and temperature (b) at 1 cm
4 above the surface for three wind force levels neglecting the effects of moisture
5 transport, considering only moisture diffusion, and both moisture diffusion and
6 advection.
Atmos. Chem. Phys. Discuss., doi:10.5194/acp-2015-795, 2016
Manuscript under review for journal Atmos. Chem. Phys.
Published: 11 February 2016
c
Author(s) 2016. CC-BY 3.0 License.
17
1
2
3 Figure 2.Temporal evolution of drifting snow sublimation rate for three wind force
4 levels neglecting moisture transport, considering only moisture diffusion, and both
5 moisture diffusion and advection.
Atmos. Chem. Phys. Discuss., doi:10.5194/acp-2015-795, 2016
Manuscript under review for journal Atmos. Chem. Phys.
Published: 11 February 2016
c
Author(s) 2016. CC-BY 3.0 License.
18
1
2
3 Figure 3. Comparison of the sublimation rate for the saltation layer and suspension
4 layer (the inset figure) at 60 s as a function of height. The inset figure shows the
5 sublimation rate of four models for the suspension layer with initial friction velocity
6 of 0.87 m s
-1
reported in Xiao et al. (2000). Our results for the sublimation rate in the
7 saltation layer are obtained for three wind force levels (<0.87 m s
-1
) with moisture
8 diffusion and advection included with the same initial temperature (253.16 K) and
9 relative humidity as Xiao et al. (2000).
Atmos. Chem. Phys. Discuss., doi:10.5194/acp-2015-795, 2016
Manuscript under review for journal Atmos. Chem. Phys.
Published: 11 February 2016
c
Author(s) 2016. CC-BY 3.0 License.
... [2] As one of the basic elements of Earth's environment, snow is widely distributed in high and cold areas, and its temporal and spatial evolution is crucial for the hydrological cycle [Zhou et al., 2014]. The mass and energy balance of ice shelves, which plays a key role in climate change [Cess and Yagai, 1991;Solomon et al., 2007], is largely affected by 'drifting snow', describing the transport of snow by wind, a phenomenon common in high and cold regions [Huang et al., 2016;Schmidt, 1972]. ...
... In fact, drifting snow may slow down global warming due to heat-trapping during snow crystal sublimation [Solomon et al., 2007], which occurs when the humidity of air is unsaturated [Schmidt, 1972]. As the sublimation rate of drifting snow particles is generally larger than that of ground snow due to the larger area of exposure [Dai and Huang, 2014;Huang et al., 2016], and since the accumulated time of drifting snow accounts for up to 1/3 of the duration of winter in the polar region [Mahesh et al., 2003;Mann et al., 2000], understanding the drifting snow phenomenon can play an important role in modeling mass and energy variations of the ice sheet. ...
... [3] However, although numerous experimental [Bintanja, 2001b;Mann et al., 2000;Schmidt, 1982;Wever et al., 2009] and theoretical studies [Déry and Yau, 2002;Groot Zwaaftink et al., 2011;Vionnet et al., 2013;Xiao et al., 2000] on drifting snow sublimation have been conducted, sublimation of drifting snow within the saltation layer (the near-surface layer in which most particles move in characteristic ballistic hops) is only rarely investigated [Dai and Huang, 2014;Huang et al., 2016;Sharma et al., 2018] as the air humidity near the surface saturates rapidly when drifting snow occurs because of a high snow concentration [Bintanja, 2001a;Mann et al., 2000]. ...
Preprint
Sublimation of drifting snow, which is significant for the balances of mass and energy of the polar ice sheet, is a complex physical process with intercoupling between ice crystals, wind field, temperature, and moisture. Here a three-dimensional drifting snow sublimation model in a turbulent boundary layer is proposed. In contrast to most previous models, it takes into account turbulent diffusion of moisture from lower to higher elevations, allowing the air humidity near the surface to be undersaturated and thus sublimation to occur. From simulations with this model, we find that snow sublimation in the saltation layer near the surface dominates overall snow sublimation, despite an only marginal departure from humidity saturation (<1%<1\%), because of a large particle concentration.
... As one of the basic elements of Earth's environment, snow is widely distributed in high and cold areas, and its temporal and spatial evolution is crucial for the hydrological cycle (Zhou et al., 2014). The mass and energy balance of ice shelves, which plays a key role in climate change (Cess & Yagai, 1991;Solomon et al., 2007), is largely affected by "drifting snow," describing the transport of snow by wind, a phenomenon common in high and cold regions (Huang et al., 2016;Schmidt, 1972). In fact, drifting snow may slow down global warming due to heat-trapping during snow crystal sublimation (Solomon et al., 2007), which occurs when the humidity of air is unsaturated (Schmidt, 1972). ...
... In fact, drifting snow may slow down global warming due to heat-trapping during snow crystal sublimation (Solomon et al., 2007), which occurs when the humidity of air is unsaturated (Schmidt, 1972). As the sublimation rate of drifting snow particles is generally larger than that of ground snow due to the larger area of exposure (Dai & Huang, 2014;Huang et al., 2016), and since the accumulated time of drifting snow accounts for up to one third of the duration of winter in the polar region (Mahesh et al., 2003;Mann et al., 2000), understanding the drifting snow phenomenon can play an important role in modeling mass and energy variations of the ice sheet. ...
... However, although numerous experimental (Bintanja, 2001b;Mann et al., 2000;Schmidt, 1982;Wever et al., 2009) and theoretical studies (Déry & Yau, 2002;Groot Zwaaftink et al., 2011;Vionnet et al., 2013;Xiao et al., 2000) on drifting snow sublimation have been conducted, sublimation of drifting snow within the saltation layer (the near-surface layer in which most particles move in characteristic ballistic hops) is only rarely investigated (Dai & Huang, 2014;Huang et al., 2016;Sharma et al., 2018) as the air humidity near the surface saturates rapidly when drifting snow occurs because of a high snow concentration (Bintanja, 2001a(Bintanja, , 2001bMann et al., 2000). However, because of moisture transportation, there are always slight deviations from the saturated state, which give rise to snow sublimation, and the impact of such deviations has not yet been quantified. ...
Article
Full-text available
Sublimation of drifting snow, which is significant for the balances of mass and energy of the polar ice sheet, is a complex physical process with intercoupling between ice crystals, wind field, temperature, and moisture. Here a three-dimensional drifting snow sublimation model in a turbulent boundary layer is proposed. In contrast to most previous models, it takes into account turbulent diffusion of moisture from lower to higher elevations, allowing the air humidity near the surface to be undersaturated and thus sublimation to occur. From simulations with this model, we find that snow sublimation in the saltation layer near the surface dominates overall snow sublimation, despite an only marginal departure from humidity saturation (< 1%), because of a large particle concentration.
... With the rapid development of computer technology, more and more researchers employed the Computational Fluid Dynamic (CFD) method to study the snowdrift problems (e.g. Alhajraf, 2004;Tominaga et al., 2011;Wang and Huang, 2016;Huang et al., 2016;Wang et al., 2019). This approach is considered to be an effective tool to solve snowdrift problems in engineering. ...
Article
Snow redistribution caused by snowdrift may worsen the traffic condition of bridge decks in snowy regions, hence it needs to be considered in bridge designs. In this study, a quasi-steady numerical method is adopted to simulate snowdrift on a typical bridge deck with barriers. The whole process of snowdrift on bridge decks is divided into several phases to simulate the variation in profile of snowpack, and a two-dimensional (2D) steady Computational Fluid Dynamics (CFD) simulation method is employed in each phase. To investigate the effect of barriers on the snow redistribution on bridge decks, the simulation results of wind velocity field, wall friction velocity, snow concentration field and snow redistributions are analyzed in detail. Generally, as the barrier porosity decreases, the total amount of snow erosion decreases due to the blocking effect of the barrier, but the decrease is limited when the barrier porosity is below 37.5%. Heavy snow accumulation mainly occurs behind the windward barrier when the barrier porosity is below 62.5%. To avoid snow accumulation, barrier porosity above 75% is suggested as the optimal choice for improving the traffic condition of bridge decks.
... Therefore, sublimation associated with these layers is not accounted for. Other studies have shown that drifting snow sublimation within the salutation layer can be very significant (Huang et al., 2016). There is a further point to be made with respect to clouds that relates to (5). ...
Article
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Blowing snow processes commonly occur over the earth's ice sheets when the 10 m wind speed exceeds a threshold value. These processes play a key role in the sublimation and redistribution of snow thereby influencing the surface mass balance. Prior field studies and modeling results have shown the importance of blowing snow sublimation and transport on the surface mass budget and hydrological cycle of high-latitude regions. For the first time, we present continent-wide estimates of blowing snow sublimation and transport over Antarctica for the period 2006–2016 based on direct observation of blowing snow events. We use an improved version of the blowing snow detection algorithm developed for previous work that uses atmospheric backscatter measurements obtained from the CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) lidar aboard the CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) satellite. The blowing snow events identified by CALIPSO and meteorological fields from MERRA-2 are used to compute the blowing snow sublimation and transport rates. Our results show that maximum sublimation occurs along and slightly inland of the coastline. This is contrary to the observed maximum blowing snow frequency which occurs over the interior. The associated temperature and moisture reanalysis fields likely contribute to the spatial distribution of the maximum sublimation values. However, the spatial pattern of the sublimation rate over Antarctica is consistent with modeling studies and precipitation estimates. Overall, our results show that the 2006–2016 Antarctica average integrated blowing snow sublimation is about 393 ± 196 Gt yr-1, which is considerably larger than previous model-derived estimates. We find maximum blowing snow transport amount of 5 Mt km-1 yr-1 over parts of East Antarctica and estimate that the average snow transport from continent to ocean is about 3.7 Gt yr-1. These continent-wide estimates are the first of their kind and can be used to help model and constrain the surface mass budget over Antarctica.
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Owing to the controllability and reliability, wind tunnel experiment is considered as a powerful and effective approach for the investigation of snow drifting. Most of the previous experimental studies were mainly concerned with snow drifting on roofs without snowfall, in which a layer of particles was uniformly paved on the roof surface to model the initial snow cover. Hence, a cryogenic wind tunnel experiment using artificial snow particles was conducted to investigate snow drifting on flat roofs during snowfall. The similarity requirement related to snowfall intensity is derived according to the similarities of snow distribution pattern and the development of snow transport. Based on the results of snow distributions and transport rates for different roof spans and snowfall intensities, the features of snow drifting under snowfall conditions are analyzed in detail. The concurrence of snowfall is further revealed to greatly affect the development of snow drifting on flat roofs and thereby result in a reduction in the required fetch for reaching the saturated state. And the typical distribution patterns for snow drifting on flat roofs with and without snowfall are also summarized. Besides, the snow transport rate is found to increase along the roof surface before the achievement of the saturated state, whereas a slight decrease in the degree of snow erosion with roof span is observed. But the location of the crest formed near the windward edge is nearly unaffected by roof span as well as snowfall intensity. The results also indicate obvious linear characteristics in the relationship between the degree of erosion on the roof surface and snowfall intensity. And the normalized development process of snow transport before reaching the saturated state is nearly independent of snowfall intensity, although both the saturated transport rate and the required fetch for saturated drifting are strongly affected in the meantime.
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Snow cover in mountainous terrain plays an important role in regional and global water and energy balances, climate change, and ecosystems. Blowing snow is a frequent and important weather phenomenon over the Tibetan Plateau (TP); however, this process is neglected in most current land surface models, despite the consequential role it plays in the land surface and atmospheric water and energy budgets. In this paper, we present a blowing snow model PIEKTUK coupled with the Community Land Model (CLM4.5) to provide a better estimate of the snow dynamics for the consideration of snow redistribution induced by wind. Two simulations with a 0.065° spatial resolution were performed in 2010 over the TP, namely, a sensitivity experiment with the inclusion of blowing snow effects (CLM_BS) and a control run with the original model (CLM). A specific objective of this study was to evaluate the improvements in the simulations of snow dynamics and other key variables in surface energy partitioning provided by the coupled model, such as the surface albedo and land surface temperature (LST). Compared with a variety of remote‐sensing observations, the results show that the surface snow cover, snow depth, and surface albedo can be better reproduced in most of the TP region by CLM_BS than by the original CLM, particularly in the Kunlun Mountains, Hoh Xil area, and the southwestern TP. In areas with reduced bias, variations in the monthly mean snow cover fraction can be reflected particularly well by CLM_BS. For LST, however, a significant decrease in the nighttime LST bias was detected in CLM_BS, while the bias in the daytime LST increases. The results show considerable potential for the inclusion of the blowing snow process to promote the modeling of snow dynamics and land‐atmosphere interactions on the TP.
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Drifting snow storm is an important aeolian process that reshapes alpine glaciers and polar ice shelves, and it may also affect the climate system and hydrological cycle since flying snow particles exchange considerable mass and energy with air flow. Prior studies have rarely considered the full-scale drifting snow storm in the turbulent boundary layer, thus, the transportation feature of snow flow higher in the air and its contribution are largely unknown. In this study, a large eddy simulation is combined with a subgrid scale velocity model to simulate the atmospheric turbulent boundary layer, and a Lagrangian particle tracking method is adopted to track the trajectories of snow particles. A drifting snow storm that is hundreds of meters in depth and exhibits obvious spatial structures is produced. The snow transport flux profile at high altitude, previously not observed, is quite different from that near the surface, thus, the extrapolated transport flux profile may largely underestimate the total transport flux. At the same time, the development of a drifting snow storm involves three typical stages, the rapid growth, the gentle growth and the equilibrium stages, in which the large-scale updrafts and subgrid scale fluctuating velocities basically dominate the first and second stage, respectively. This research provides an effective way to get an insight into natural drifting snow storms.
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Snow distribution modeling is important to hydrological research of alpine catchments, for the temporal and spatial evolution of snow cover has significant influence on snowmelt runoff. However, few snow distribution models have considered the slope effect on drifting snow transport. In this work, a drifting snow parameterization scheme considering the slope effect is proposed, and a snow distribution model based on snowfall and snow drifting simulations is established for alpine terrain. The validation wind-tunnel experiments and field observations show high accuracy of our model in snow depth evaluation. Then we have analyzed the snow deposition patterns of complex terrain in detail. The results show different deposition patterns for snowfall and drifting snow, for deposition patterns for snowfall are controlled by both terrain and wind, while deposition patterns for drifting snow are mainly dominantly controlled by terrain, and the erosion or deposition rate is sensitive to the local wind speed. This work has considerable value in improving the accuracy of snow distribution prediction in alpine area, which we believe is essential for hydrological research of alpine catchments.
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Drifting snow sublimation is a physical process containing phase change and heat change of the drifting snow, which is not only an important parameter for the studying of polar ice sheets and glaciers, but a significant one for the ecology of arid and semi-arid lands, where snow cover is the main fresh water resource. However, in the previous studies drifting snow sublimation near surface was ignored. Herein, we built a drifting snow sublimation model containing vertical moisture diffusion equation and heat balance equation, to study drifting snow sublimation near surface. The results showed that though drifting snow sublimation near surface was strongly reduced by negative feedback effect, relative humidity near surface didn’t reach the saturation state caused by vertical moisture diffusion. Therefore, the sublimation near surface will not stop in drifting snow near surface. The sublimation rate near surface is 3–4 orders of magnitude higher than that at 10 m. And the mass of snow sublimation near surface accounts for even more than half of the total if the wind velocity is small. Therefore, drifting snow sublimation near surface can't be neglected.
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Snow sublimation is an important hydrological process and one of the main causes of the temporal and spatial variation of snow distribution. Compared with surface sublimation, drifting snow sublimation is more effective due to the greater surface exposure area of snow particles in the air. Previous studies of drifting snow sublimation have focused on suspended snow, and few have considered saltating snow, which is the main form of drifting snow. In this study, a numerical model is established to simulate the process of drifting snow sublimation in the saltation layer. The simulated results show 1) the average sublimation rate of drifting snow particles increases linearly with the friction velocity; 2) the sublimation rate gradient with the friction velocity increases with increases in the environmental temperature and the undersaturation of air; 3) when the friction velocity is less than 0.525 m/s, the snowdrift sublimation of saltating particles is greater than that of suspended particles; and 4) the snowdrift sublimation in the saltation layer is less than that of the suspended particles only when the friction velocity is greater than 0.625 m/s. Therefore, the drifting snow sublimation in the saltation layer constitutes a significant portion of the total snow sublimation.
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Snow particle shape is an important factor affecting the development of blowing snow. In this paper, we established a numerical model of blowing snow development and compared the changes in numbers of endurance spherical, ellipsoidal, star, hexagonal prism, and cylindrical snow particles in the air with time and their transport rates with time and height during the development. The following are the major conclusions. (1) The effects of snow particle shapes on the numbers of endurance snow particles in the air and the transport rates of snow vary so dramatically, even in a few orders of magnitude, that snow particles should not be simplified as spheres or ellipsoids in simulation. (2) In the logarithmic wind field, the potential energy of spherical snow particles obtained from wind at higher heights is much greater than that of star snow particles at lower heights. Thus, the snow particles with greater energy can eject more snow particles when precipitating to the snow bed. (3) The five snow particles differ in their duration to reach dynamic equilibrium but not in the variation of the numbers of endurance snow particles in the air and the snow transport rates with time. (4) At dynamic equilibrium, the number of endurance snow particles in spherical, ellipsoidal, and star shapes and their heights and transport rates with time are at least one order of magnitude larger than those of the endurance snow particles in hexagonal prism and cylindrical shapes.
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The computation of sand grain trajectories in the wind and of the wind velocity profile modified by saltating grain drag forces is combined with experimental data on grain-bed collisions in an iterative simulation scheme which resembles the time-development of natural saltation. -from Author