The Impact of Land Surface Processes on Dust Storm
Simulations in Northern China
Zhaohui Lin1, Hang Lei1,2, Jason K. Levy3, Jianhua Sun1 and Michelle L. Bell4
5
1 – Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
2 – Department of Atmospheric Sciences, University of Illinois at Urbana-Champaign,
Urbana, Illinois, USA
3 – Department of Environmental Studies, Huxley College of the Environment,
Western Washington University, Bellingham, Washington, USA 10
4 – School of Forestry and Environmental Studies, Yale University, New Haven,
Connecticut, USA
ABSTRACT
Dust storms in northern China result in elevated particulate matter levels, 15
detrimentally affecting human health, as well as socio-economic and ecological
systems. To investigate the impact of land surface processes on dust storm simulations
in northern China, a dust storm prediction system was developed at the Institute for
Atmospheric Physics (IAP). One version of the IAP System (IAPS 1.0) is based on
the Oregon State University (OSU) land surface model, while a second version (IAPS 20
2.0) integrates the OSU and NOAH land surface models. For spring 2002 dust storm
events in northern China, results show that IAPS 2.0 significantly improved soil
moisture simulations, leading to better threshold frictional velocity estimates, a key
parameter for estimating surface dust emissions. This study also discusses specific
mechanisms by which land surface processes impact modeling results and makes 25
recommendations to improve numerical dust storm simulations.
30
35
1. Introduction
Spring dust storms occur frequently in northern China, producing high
concentrations of airborne dust particles. These particles lead to adverse impacts
including increased risk of mortality and other health consequences, as well as
socio-economic losses [Chen et al., 2004; Cheng and Ma, 1996]. In recent years, dust 40
storm monitoring [Kim et al., 2004; Wu et al., 2004; Wehner et al., 2004; Ding et al.,
2005] and background analysis of dust activities [Qian et al., 2002; Lin et al., 2004;
Fan and Wang, 2004] have received more attention, as has the use of numerical dust
storm modeling for the simulation and prediction of dust emissions and transportation
processes [Shao, 2004; Shen et al., 2005; Song 2004; Zhang et al., 2003a and 2003b; 45
Gong et al., 2003]. Dust emission modeling involves simulating synoptic processes,
land surface conditions (e.g., soil moisture, soil temperature), dust emissions, and
transportation processes as well as parameterizing the wind erosion friction velocity
(u*) and the wind erosion threshold friction velocity (u*t). In general, u* is dependent
on the structure of the atmospheric boundary layer while u*t depends on land surface 50
properties, including the soil moisture over land surfaces, which can be predicted by a
land surface model (LSM).
To better understand the impact of land surface processes on dust storm
simulations, north China dust storm events occurring in the spring of 2002 are
simulated with two versions of the dust storm numerical modeling and prediction 55
system (hereafter referred to as IAPS 1.0 and IAPS 2.0). Specifically, IAPS 1.0 [Sun
et al., 2004], developed at the Institute for Atmospheric Physics (IAP), incorporates
the Oregon State University (OSU) LSM while IAPS 2.0 (also developed at IAP) uses
the NOAH LSM. Results derived from these two land surface models are compared,
and the specific mechanisms by which land surface processes impact the simulated 60
dust storms are investigated. Finally, recommendations are provided in order to
enhance the aforementioned dust storm modeling systems.
2. IAP Dust Storm Simulation and Prediction System (IAPS)
IAPS 1.0 consists of the Pennsylvania State University (PSU) / National Center 65
for Atmospheric Research (NCAR) Meso-scale Meteorological Model (MM5)
[Dudhia et al. 2005], the OSU/Eta LSM, a wind erosion model, a dust
transportation-deposition scheme, a pre-processor system, and a geographic
information system (GIS) database (Figure 1a). The pre-processor components
involve creating a GIS database with vegetation, soil, and landuse information. Data 70
generated through the pre-processor components are input into the tightly coupled
LSM, MM5, wind erosion and dust transportation models. At each time step u*, the
friction velocity from the planetary boundary layer (PBL) scheme, and the
surface-layer soil moisture (from the land surface scheme) are used by the wind
erosion model to calculate the dust emission rate for six particle-size groups. The dust 75
transport and deposition scheme considers advection, diffusion, and dry deposition
based on the domain and grid specifications of the MM5 model.
The wind erosion scheme comprises three key parameterizations including u*t
[Shao and Lu, 2000], the streamwise sand flux, Q [Owen, 1964] and the dust emission
rate, F [Shao, 2001] (Figure 1b). Six dust particle diameter (d) sizes are considered: 80
d≤2 µm, 2<d≤11 µm,11<d≤22 µm, 22<d≤52 µm, 52<d≤90 µm, and 90<d≤125
µm. For each of the n groups, dust emissions are estimated when u* exceeds u*t (for a
specific dust particle size in a given region). The u* is determined by the MM5
boundary layer scheme while u*t depends on land surface properties as calculated by
⎟⎟⎠
⎞⎜⎜⎝
⎛+=
dρα
gdσαRHMu pt2
1* (1) 85
where R (surface roughness), H (soil moisture), and M (soil aggregation) are
estimated from soil and vegetation data, g is gravitational acceleration, d is particle
diameter, �p is the ratio of particle density to air density, � is the density of air, and
�1 and �2 are coefficients [Shao and Lu 2000; Sun et al., 2006]. The sand flux (Q)
and dust emissions rate (F) are also calculated separately for each particle size group. 90
The main input data for the wind erosion scheme are soil texture, vegetation type,
vegetation cover and dust emissions, calculated for erodible lands. Calculating u*t
requires soil moisture data obtained from the LSM and frontal area surface roughness
which is assumed to be constant for a given location over a period of about two weeks.
Moreover, frontal area surface roughness is primarily a function of vegetation and 95
varies slowly with time. IAPS 1.0 uses the OSU LSM, which has a single canopy
layer, and involves the following prognostic variables: soil moisture and temperature;
water stored in or on the canopy; snowpack depth; and water equivalent. IAPS 1.0 has
been recently applied to the simulation of northern China dust storm events [Sun et al.,
2006]. 100
IAPS 2.0 couples the mesoscale atmospheric model MM5 (version 3.6) and the
Unified NOAH LSM with the wind erosion model, the dust transportation model, the
GIS database for land surface characteristics, and a pre-processor for the wind erosion
model. As well, the NOAH LSM integrates the diurnally dependent Penman potential
evaporation equation, the multilayer soil model, and the primitive canopy layer. This 105
modeling system predicts soil moisture and temperature in four layers (at 10, 30, 60
and 100 cm depth), as well as canopy moisture and water-equivalent snow depth. It
also outputs surface and underground run-off accumulations. Compared with the
original OSU LSM, many improvements have been achieved for the enhanced
representation of physical processes in order to better predict variables such as snow 110
depth, snow cover, and frozen soil effects. This research investigates the degree to
which IAPS 2.0 improves model simulations, compared to IAPS 1.0 results
In order to evaluate the impact of land surface processes on dust storm modeling,
two typical northern China dust storms, occurring on 24-25 March 2002 and 21-24
April 2002, were simulated with the IAPS 1.0 and IAPS 2.0 modeling systems, and 115
the results were compared. The simulated domain area contains Mongolia, China, the
Korean Peninsula and Japan, centered at 40°N and 115°E, with 150 grid cells in
longitude and 120 grid cells in latitude, a horizontal resolution of 45 km, and 16
vertical levels. Model initialization and boundary conditions were based on National
Centers for Environmental Prediction (NCEP) reanalysis atmospheric data with a 120
horizontal resolution of 2.5° and a one-day spin-up period. The NCEP data were
interpolated horizontally onto the model grid points and then interpolated from
pressure levels onto model’s σ-levels. The initial values of dust concentration of each
particle size group were set to zero. Numerical simulation results were compared and
verified with station observations. The impact of land surface processes on the dust 125
storm simulations was investigated.
3. Impact of land surface processes on dust storm simulations
Active dust storm activity occurred throughout northern China in the spring of
2002, with strong dust storm events occurring every 2 to 5 days. The two analyzed 130
dust storm episodes (24-25 March 2002 and 21-24 April 2002) involve markedly
different weather conditions, dust sources and dust distribution areas, although these
episodes are typical for northern China.
3.1 Dust Episode of 24-25 March 2002 135
For the 24-25 March 2002 dust storm event, 850 hPa NCEP reanalysis
geopotential height variations at 8:00 Beijing Standard Time (BST) were used,
controlled by a southeastward moving cyclone. Prior to this event, a cyclone from the
Lake Baikal area began moving in a southeastward direction, accompanied by strong
northwesterly wind behind a cold front. By 8:00 BST on March 25, the center of the 140
cyclone was located in the vicinity of 122°E, 52°N. Behind this cold front were very
strong NW and WNW winds, reaching speeds as high as 16 m/s. This dust episode
was mainly limited to northeastern China including the eastern regions of Liaoning,
Jilin, Hebei and Heilongjiang. Figure 2 shows dust deposition for this storm for
observational data (Figure 2a) and under the IAPS 1.0 and 2.0 modeling systems 145
(Figures 2b and 2c). Elevated dust levels were also observed in Xinjiang (Figure 2a),
and severe dust storms occurred in eastern Inner Mongolia.
IAPS 1.0 dust deposition results capture the affected regions noted in
observational data reasonably well, with high deposition estimated for regions
observed to have severe dust storms (Figures 2a and 2b). However, large areas not 150
affected by the dust storm are predicted to have high dust deposition, primarily in the
central northern regions (Figure 2b). Dust deposition estimates are improved with
IAPS 2.0, particularly in eastern China (Figure 2c). IAPS 2.0 simulations are, in
general, more accurate than the IAPS 1.0 results for northern China (Figure 2d).
Specifically, the wetter regions in Xinjiang and Gansu (Figure 2d) plausibly explain 155
discrepancies between the IAPS 1.0 and IAPS 2.0 results. The distribution of u* - u*t,
which directly represents erodibility, is shown for the IAPS 1.0 and IAPS 2.0
simulation results (Figures 3a and b).
3.2 Dust Episode of 21-24 April 2002 160
A complicated dust storm event occurred on 21-24 April 2002 in northern China,
which was affected by a southeastward moving cyclone, a cold front and an
anticyclone system. A cyclone, centered at approximately 122°E, 50°N, moved
eastward from northeastern China, and was accompanied by strong northwesterly
wind behind the cold front. By 8:00 BST on April 22, the center of the cyclone was 165
located in the vicinity of 130°E, 52°N and a high pressure system was present in
western Mongolia, with the ridge of high pressure reaching Liaoning. This ridge of
high pressure was preceded by very strong SE wind, reaching 17-20 m/s in some
areas. Next, an anticyclone system centered at approximately 102°E, 45°N formed
across the Mongolia-China border. By 8:00 BST on April 23 this eastward moving 170
anticyclone system had affected most of northern China. By 8:00 BST on April 24,
the anticyclone system weakened significantly as it moved into central China.
Accordingly, dust storms were observed throughout most of northwestern China and
in some parts of northeastern China, covering Xinjiang, Qinghai, Gansu, Inner
Mongolia, Liaoning, Beijing, Hebei and Shanxi, as shown in the observational data 175
(Figure 4a). Specifically, synoptic records show that from 21-24 April 2002, the
following regions were affected by severe and extensive dust storms: Xingjiang,
Qinghai, Gansu, Inner Mongolia, Ningxia, Shaanxi, Shanxi, Hebei, Beijing, Tianjin,
Liaoning, and Shandong (Figure 4a). Severe dust storms also occurred in Xinjiang,
Inner Mongolia and Ningxia. The spatial distribution of this dust covers a large area 180
and is asymmetric. The severe dust deposition is mainly concentrated in northwestern
China, while lighter dust deposition occurs in northeastern China.
The IAPS 1.0 21-24 April 2002 simulation (Figure 4a) shows the same basic
patterns as the observational data (Figure 4a). However, the model over-predicts dust
storm activity in some regions, particularly in the northeast. This may be due to heavy 185
pollution that affects large areas of northeastern China, Visual inspection suggests that
IAPS 2.0 results better match observational data than IAPS 1.0 results (Figure 4a and
4c). Dust deposition simulated by IAPS 2.0 exceeds observed values for some regions,
such as western Inner Mongolia (around 102°E, 40°N). In the East Liaoning
Peninsula (located at approximately 122°E, 40°N), a significant dust concentration 190
was simulated, while no dust activities were reported by observational data.
4. Conclusions and Discussion
Two dust storm numerical modeling and prediction systems (IAPS 1.0 and IAPS
2.0) were applied to the prediction of dust storm events over northern China for two 195
typical dust storm episodes representing different weather conditions, dust sources
and affected areas. Both modeling systems provided reasonable estimates as gauged
by comparison to observational data (Figures 2 and 4), but in both cases the IAPS 2.0
system (which uses the NOAH LSM) was better able to predict dust storm sources
and capture dust storm patterns than IAPS 1.0 (which uses the OSM LSM). 200
Specifically, IAPS 2.0 improves the modeling of physical processes (e.g., the physics
of frozen soil, fractional snow cover, time-varying snow density, and the roughness
length calculations over snow covered areas). In particular, IAPS 2.0 more accurately
simulates soil moisture, which likely improves simulated values for u*t, a key
parameter for the emission of surface dust. 205
While IAPS 2.0 results were often superior to IAPS 1.0, the IAPS 2.0 simulations
differed from observational data in certain regions of China including western Inner
Mongolia (around 102°E, 40°N) and the eastern Liaoning Peninsula (around 122°E,
40°N). Several possible reasons exist for these discrepancies. First, the geographic
information data sets are limited in several ways. For example, the historical data does 210
not capture land use and land cover changes due to anthropogenic or natural factors
over the past decade. The data also contains uncertainties in critical areas such as
vegetation type, soil particle size distribution, and the soil type. Second, the NCEP
reanalysis data, which was used for the initialization and boundary conditions of the
atmospheric model, have a coarser resolution (2.5o) than that of the atmospheric 215
model itself (45 km) and therefore may miss sub-scale heterogeneity. Third, initial
dust concentration estimates are currently unavailable, and wet deposition is not
thoroughly considered in the present IAP 2.0 system. Finally, the wind erosion
scheme and deposition scheme may be overly-simplistic and fail to capture important
factors (e.g. frozen soil effects) involved in the dust emission process. In summary, 220
this work indicates that the application of the IAPS 2.0 model provides significant
improvements over the IAPS 1.0 for the simulation of dust storm events in northern
China. Future work should address the aforementioned modeling challenges in order
to improve IAPS 2.0 dust estimates.
225
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Figure 1. The structure of the integrated dust storm numerical modeling systems, with
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Figure 2. Observed dust deposition from the 24-25 March 2002 sand-dust storm [Niu
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Figure 3. Overall distribution of the positive value of (u*-u*t) in the IAPS 1.0
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simulation (b).
Figure 4. Observed dust deposition from the 21-24 April 2002 sand-dust storm [Niu
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2.0 simulations of average dust deposition (mg m-2s-1) (c).
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Figure 1
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(c)
(d)
Figure 2 310
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(b)
Figure 3
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(c)
Figure 4