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

References

Chen, Y.-S., P.-C. Sheen, E.-R. Chen, Y.-K. Liu, T.-N. Wu, and C.-Y. Yang (2004),

Effects of Asian dust storm events on daily mortality in Taipei, Taiwan, Environ.

Res., 95(2), 151-5.

Cheng, L. S., and Y. Ma (1996), The developing structure of a black storm and its 230

numerical experiment of different model resolution, Q. J. Appl. Meteorol. 7(4),

386–395. (in Chinese, with English Abstr.)

Ding, R., J. Li, S. Wang, and F. Ren (2005), Decadal change of the spring dust storm

in northwest China and the associated atmospheric circulation, Geophys. Res. Lett.

32(2), 1-4. 235

Dudhia, J., D. Gill, K. Manning, W. Wang, and C. Bruyere (2005), PSU/NCAR

Mesoscale Modeling System Tutorial Class Notes and Users’ Guide (MM5

Modeling System Version 3). National Center for Atmospheric Research (NCAR),

Boulder, CO.

Fan, K. and H. J. Wang (2004), Antarctic oscillation and the dust weather frequency in 240

North China, Geophys. Res. Lett. 31(10), doi:10.1029/2004GL019465.

Gong, S. L., X. Y. Zhang, T. L. Zhao, I. G. McKendry, D. A. Jaffe, and N. M. Lu

(2003), Characterization of soil dust aerosol in China and its transport and

distribution during 2001 ACE-Asia: Model simulation and validation. J. Geophys.

Res. D: Atmospheres 108(9), ACH4-1, ACH4-19. 245

Kim, Y. S., Y. Iwasaka, G.-Y. Shi, T. Nagatani, T. Shibata, D. Trochkin, A. Matsuki, M.

Yamada, B. Chen, D. Zhang, M. Nagatani, and H. Nakata, H. (2004), Dust particles

in the free atmosphere over desert areas on the Asian continent: Measurements

from summer 2001 to summer 2002 with balloon-borne optical particle counter and

lidar, Dunhuang, China, J. Geophys. Res. D: Atmospheres 109(19), D19S26, 250

doi:10.1029/2002JD0032.

Lin Z., H. Chen, S. Zhang, and X. Xu (2004), Climatic and environmental

background for the anomalous spring sandstorms over the northern China during

2003, Clim. Environ. Res. 9, 191-202.

Niu, R., “Sand-dust Weather Almanac 2002”, Qixiang Press, 2004. 255

Owen, R. P. (1964), Saltation of uniform grains in air, J. Fluid Mech. 20, 225–242.

Qian, W. H., L. S. Quan, and S. Y. Shi (2002), Variations of the dust storm in China

and its climatic control, J. Clim. 15(10), 1216-1229.

Shao, Y. (2004), Simplification of a dust emission scheme and comparison with data,

J. Geophys. Res. D: Atmospheres, 109(10), doi:10.1029/2003JD004372. 260

Shao, Y. (2001), A model for mineral dust emission, J Geophys. Res. D: Atmospheres

106(D17), 20239-20254.

Shao, Y., and H. Lu (2000), A simple expression for wind erosion threshold friction

velocity, J. Geophys. Res. D: Atmospheres, 105(D17), 22437–22443.

Shen, Y., Z. Shen, M. Du, and W. Wang (2005), Dust emission over different land 265

surface in the arid region of Northwest China, J. Meteorol. Soc. Jpn. 83(6),

935-942.

Song, Z. (2004), A numerical simulation of dust storms in China, Environm. Model.

Softw. 19(2), 141-151.

Sun J., L. Zhao, S. Zhao, and R. Zhang (2006), An integrated dust storm prediction 270

system suitable for east Asia and its simulation results, Glob. Planet. Change

52(1-4), 71-87.

Sun, J., L. Zhao and S. Zhao (2004), A numerical simulation on severe dust storm

events in North China and their dust sources, Clim. Environ. Res. 9, 139-154.

Wehner, B., A. Wiedensohler,, T. M. Tuch, Z. J. Wu, M. Hu, J. Slanina, and C. S. 275

Kiang (2004). Variability of the aerosol number size distribution in Beijing, China:

New particle formation, dust storms, and high continental background, Geophys.

Res. Lett. 31(22), 1-4.

Wu X., X. Zheng, X. Li, J. Liu, L. Kang, X. Jiang (2004), Analyses on the

characteristics and weather pattern classifications of East-Asia spring dust storms 280

by using meteorological satellite images. Clim. Environ. Res. 9, 1-13.

Zhang, X. L., L. Cheng, and Y. S. Chung (2003a). Development of a severe sand-dust

storm model and its application to Northwest China, Water Air Soil Pollut. 3(2),

173-190.

Zhang, X.Y., S. L. Gong, T. L. Zhao, R. Arimoto, Y. O. Wang, and Z. J. Zhou (2003b). 285

Sources of Asian dust and role of climate change versus desertification in Asian

dust emission. Geophys. Res. Lett. 30(24), ASC 8-1 – ASC 8-4.

Figure 1. The structure of the integrated dust storm numerical modeling systems, with

the dashed lines representing the IAPS modeling structure (a); structure of the IAPS 290

wind-erosion scheme (b).

Figure 2. Observed dust deposition from the 24-25 March 2002 sand-dust storm [Niu

et al. 2004] (a); IAPS 1.0 simulations of average dust deposition (mg m-2s-1) (b);

IAPS 2.0 simulations of average dust deposition (mg m-2s-1) (c); difference in soil

moisture from IAPS 2.0 and IAPS 1.0 simulations (mg m-2s-1) (d). 295

Figure 3. Overall distribution of the positive value of (u*-u*t) in the IAPS 1.0

simulation (a); overall distribution of the positive value of (u*-u*t) in the IAPS 2.0

simulation (b).

Figure 4. Observed dust deposition from the 21-24 April 2002 sand-dust storm [Niu

et al. 2004] (a); IAPS 1.0 simulations of average dust deposition (mg m-2s-1) (b); IAPS 300

2.0 simulations of average dust deposition (mg m-2s-1) (c).

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(a) (b)

Figure 1

(a) (b)

(c)

(d)

Figure 2 310

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(a)

(b)

Figure 3

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(a)

(b)

(c)

Figure 4