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    Atmospheric Environment 46 (2012) 299e308

    Contents lists available

    Atmospheric Environment

    journal homepage: www.elsevier .com/locate/atmosenv

    Long-range transport of spring dust storms in Inner Mongolia and impacton the China seas

    Sai-Chun Tan a,b,*, Guang-Yu Shi a, Hong Wang c,d

    a State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences,Beijing 100029, ChinabKey Laboratory of Meteorological Disaster of Ministry of Education, Nanjing University of Information Science and Technology, Nanjing 210044, ChinacCentre for Atmosphere Watch and Services, Chinese Academy of Meteorological Sciences, Beijing 100081, Chinad State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, China

    a r t i c l e i n f o

    Article history:Received 29 March 2011Received in revised form18 August 2011Accepted 27 September 2011

    Keywords:Dust stormTransportDepositionProbabilityThe China seas

    * Corresponding author. State Key Laboratory of Nuspheric Sciences and Geophysical Fluid Dynamics, InstChinese Academy of Sciences, Beijing 100029, Chifax: 86 10 82995097.

    E-mail address: sctan@mail.iap.ac.cn (S.-C. Tan).

    1352-2310/$ e see front matter 2011 Elsevier Ltd.doi:10.1016/j.atmosenv.2011.09.058

    a b s t r a c t

    Analysis of daily observations from 43 meteorological stations in the Inner Mongolia Autonomous Region,China, showed the distribution of spring dust storm events during 2000e2007. Guaizihu and Sunitezuoqistations had the highest frequencies of dust storms. The interannual and seasonal variations of duststorms were closely related to weather conditions, especially the wind speed. A forward trajectory modeland satellite observations were used to investigate the transport paths and dust layers of dust storms fromGuaizihu and Sunitezuoqi stations to the China seas and their probability of influencing the seas duringspring 2000e2007. Forward trajectories showed that dust storms at Guaizihu and Sunitezuoqi stationshad the highest probability of affecting the Yellow Sea, followed by the Bohai Sea, the East China Sea, andthe northern South China Sea. The dust particles from Sunitezuoqi station affected these four seas directlythrough coastal areas, while those from Guaizihu station were transported via the Inner Mongoliandeserts and/or the Loess Plateau. The dust storms from Sunitezuoqi station impacting the four seas werecharacterized by a single dust source and a short transport distance, while those from Guaizihu stationwere characterized by multiple sources and relatively long transport distances. The dust particles fromthese two stations were mostly transported in a

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    Qian et al., 2006) and is responsible for approximately 31% of thetotal dust production (Zhang et al., 2003b).

    Transport of dust particles is closely related to synoptic condi-tions and the characteristics of the source regions (Tsai et al., 2008).Asian dust is usually associated with frontal systems and/orcyclones (Sun et al., 2001; Uno et al., 2004; Tsai et al., 2008). Dustparticles can be transported by surface-level northwesterly windsassociatedwith the Asianwintermonsoon over the Asian continentand by westerly winds in the free troposphere from the easternAsian continent to the Pacific Ocean (Uematsu et al., 1983; Unoet al., 2004; Zhao et al., 2006). Lidar observations and modelsimulations indicated that dust particles from the Gobi Desert areusually entrained in a lower layer (3 km) and can be deposited inproximal regions. Dust from the Taklimakan Desert uplifted to5 km has a better chance of being transported over long distances,because the surrounding high mountains exceed 5 km in heightand the stronger westerly jet dominates in this altitude range(Iwasaka et al., 1983, 2003; Sun et al., 2001; Matsuki et al., 2003;Uno et al., 2004).

    Many studies of the transport of Asian dust to downwind areas,including the China seas, have been reported. Zhang and Gao(2007) analyzed the source and movement route of dust stormsto downwind seas during 2000e2002 based on meteorology datafrom the Meteorological Information Comprehensive Analysis andProcess System (MICAPS). Gao et al. (2009) reviewed previousstudies of the transport pathways of Asian dust and its influenceareas. However, most studies concentrated on the transport of duststorms across the continent (Zhang et al., 2003a; Chen et al., 2006)or performed a case study of the influence of Asian dust storm ondownwind areas or seas (Iwasaka et al., 1983; Liu et al., 1998; Fanget al., 1999; Husar et al., 2001; Kim and Park, 2001; Lin et al., 2007).Studies of the long-term impacts of dust storms on the seas are stillrare. In addition, lidar and balloon observations and model simu-lations were used to monitor and understand the vertical distri-bution and transport processes of dust storms generally. Withadvances in remote sensing technology, satellites will increasinglybe able to monitor dust transport, including even the verticalstructure of transported dust (Winker et al., 2009). In this study, wetried to clarify the influence of dust storms on the China seas duringa long-term period.

    We investigated the impacts of spring (MarcheMay) duststorms on the China seas (the Bohai Sea, Yellow Sea, East China Sea,and South China Sea) through combinations of surface observa-tions, satellite observations, trajectories, and dust model simula-tions during the period 2000e2007. First, the transport processesof dust storms from the source regions to the seas were analyzedusing a forward trajectorymodel and satellite vertical observations.Second, the probability of spring dust storms affecting the Chinaseas was examined through the forward trajectory model. Finally,satellite-derived aerosol index and dust deposition from a numer-ical model were used to test the influence of dust storms on theChina seas derived from the forward trajectory model.

    Fig. 1. The distribution of averaged annual occurrence frequency of spring dust storms(days year1) for the period 2000e2007 in Inner Mongolia. Plus sign shows thelocations of 43 meteorological stations.

    2. Data and methods

    The daily occurrence time records of dust storms at 43 meteo-rological observation stations in Inner Mongolia, China during2000e2007 were obtained from the National MeteorologicalInformation Center of the China Meteorological Administration(NMIC/CMA). The records contained the starting and ending timesof dust storms each day at each station, allowing for calculation ofthe occurrence frequency of dust storms (days month1 ordays year1) at each station. At each station, dust storm weatherphenomenon were defined as storms with minimum visibility of

    1 km and instantaneous maximum wind speed of 10 m s1(Qian et al., 1997).

    Daily mean wind speed, pressure, temperature and relativehumidity and daily rainfall (mm) datasets at the same 43 meteo-rological stations were used to examine the relationships of thesefactors to dust storm occurrence frequency. These data were alsoprovided by the NMIC/CMA.

    The aerosol index is an effective measure of the dust loading inthe atmosphere (Herman et al., 1997). Many studies found that theaerosol index correlated closely with modeled dust loading (Unoet al., 2004; Zhao et al., 2006). Here, we used the aerosol index tomeasure the dust loading over the China seas. From 2000 to 2004,the aerosol index was retrieved from the Total Ozone MappingSpectrometer (TOMS, resolution 1.25 1), and from 2005 to 2007it was retrieved from the Ozone Monitoring Instrument (OMI,resolution 1 1). The TOMS data were interpolated into the samespatial resolution as the OMI data.

    Dust deposition (including dry and wet depositions) from theGlobal/Regional Assimilation and Prediction System/the ChineseUnified Atmospheric Chemistry Environment for Dust AtmosphericChemistry Module (GRAPES/CUACE-Dust) numerical model wasalso used to detect the impacts of spring dust storms in InnerMongolia on the China seas. The GRAPES/CUACE-Dust model wasdeveloped by the Numerical Prediction Research Center, CMA, andCenter for AtmosphereWatch and Services of the Chinese Academyof Meteorological Sciences (Wang et al., 2010). The model domaincovers 70e140E and 15e60N, including the source regions ofdust storms in East Asia. Comparisons of real-time predictionoutputs with surface observations and the TOMS aerosol indexhave indicated that the model can predict the outbreak, develop-ment, transport and depletion processes of dust storms accuratelyover China and the East Asian region (Wang et al., 2010). Details ofthe model have been described by Wang et al. (2010). Dust depo-sition was used in this study at 0.5 0.5 spatial resolution.

    The Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP)is a two-wavelength polarization lidar. It is the primary instrumenton the Cloud-Aerosol Lidar and Infrared Pathfinder SatelliteObservations (CALIPSO) satellite. CALIOP has provided a directmeasure of the vertical structure of aerosols in the troposphere andlower stratosphere since June 2006 (Winker et al., 2009). Theaerosol type data from CALIOP Level 2 products were used to detectthe vertical structure of dust aerosols in this paper. Six distinctaerosol types were labeled desert dust, smoke, clean continental,polluted continental, clean marine and polluted dust (Dubovik andKing, 2000; Omar et al., 2005; Winker et al., 2009). The verticalresolution was 30 m for altitudes from 0.5 to 8.2 km, 60 m foraltitudes from 8.2 to 20.2 km and 180 m for altitudes from 20.2 to30.1 km.

    To understand the impacts of dust storms from different sourceareas on the China seas, a forward trajectory analysis was

  • 1 2 3 4 5 6 7 8 9 10 11 12








    Dust storm Wind speed
















    Wind speed Dust storm a

    Year00 01 02 03 04 05 06 07

    Fig. 2. a. The interannual variation in spring dust storm occurrence frequency (days year1) and daily mean wind speed (m s1). b. Monthly climatological dust storm occurrencefrequency (days month1) and daily mean wind speed (m s1). Error bar of dust storms is the standard deviation.

    S.-C. Tan et al. / Atmospheric Environment 46 (2012) 299e308 301

    performed using the Hybrid Single Particle Lagrangian IntegratedTrajectory (HYSPLIT) model (Draxler and Rolph, 2010) with inputsfrom the National Centers for Environmental Prediction/theNational Center for Atmospheric Research (NCEP/NCAR) globalreanalysis meteorological data. The data were on a latitude-longitude grid with a spatial resolution of 2.5. The internalscaling height of HYSPLIT was 25 km. An air parcel appearing in themiddle of a dust storm event at the source stations was tracedforward.

    3. Results and analysis

    3.1. Characteristics of the occurrence frequency of spring duststorms in Inner Mongolia during 2000e2007

    Fig. 1 shows the distribution of the annual mean occurrencefrequency of spring dust storm events (days year1) averaged from2000 to 2007, as derived from the 43 meteorological observationstations. The standard deviation (SD) at the 43 stations is from 0.5

    0 3 6 9 12 150







    a WindSpeedWind speed = 0.21x + 2.93R = 0.68, P

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    frequency (days year1) appeared in 2001, followed by 2006 withthe second highest frequency (Fig. 2a). Dust storms in Inner Mon-golia occurred most frequently in spring (Fig. 2b). During2000e2007, spring dust storms accounted for 77.2% of the annualtotal in Inner Mongolia.

    The interannual and seasonal variations in dust storms wereclosely related to weather conditions. Significant correlation exis-ted between the dust storms and environmental factors, includingdaily mean wind speed (m s1), relative humidity (%), pressure(hPa), and rainfall (mm) (Fig. 3). Dust storms were positivelycorrelated to wind speed and negatively correlated to rainfall,relative humidity, and pressure. However, there was no significantcorrelation between dust storms and temperature. The interannualvariation in spring dust storms correlated to wind speed witha correlation coefficient of 0.75 (significance level 0.05), whereasthe monthly climatological variation correlated to wind speed witha correlation coefficient of 0.94 (significance level 0.01) and torelative humidity with a correlation coefficient of 0.82 (signifi-cance level 0.01).

    3.2. Characteristics of spring dust storms during transport

    Statistically, the stronger the intensity of the dust storm, thelonger its duration timewill be. This may induce a greater chance of

    Fig. 4. The transport paths of dust storms at Guaizihu station (pentacle in the figure) to theand South China Sea (d) using 96-h trajectory. The top and bottom panels show horizonShandong, Jiangsu, Zhejiang, Fujian and Guangdong Province. AGL means above ground lev

    long-range transport of dust particles. Therefore, dust storms withshort duration time were excluded from the analysis, and duststorms with duration times over 1 h at representative stations wereused for the forward trajectory analysis. Because Guaizihu andSunitezuoqi had the highest dust storm occurrence frequencies, thestorms at these two stations were used to analyze the influence ofdust on the China seas. During spring 2000e2007, 77 and 79 duststorms with durations over 1 h were recorded at Guaizihu andSunitezuoqi, respectively.

    3.2.1. Trajectory analysis of transport paths from dust sourceregions to the seas

    The transport paths of dust storms from Guaizihu station to theChina seas passed by some Inner Mongolia deserts and/or the LoessPlateau and the North China Plain (sometimes over Beijing/Tianjincity) and then (1) entered the Bohai Sea from Tianjin city, Hebei,Liaoning, or Shandong Province (Fig. 4a); (2) entered the Yellow Seathrough Tianjin city and Bohai Sea, Hebei, Liaoning, Shandong, orJiangsu Province (Fig. 4b); (3) entered the East China Sea throughShandong, Jiangsu, Zhejiang, or Fujian Province, Shanghai city and/or Yellow Sea (Fig. 4c); or (4) entered the South China Sea throughShandong, Jiangsu, Zhejiang, Fujian, or Guangdong Province and/orBohai Sea, Yellow Sea, and East China Sea (Fig. 4d). These pathswere characterized by long distances and multiple sources.

    Bohai Sea (a), Yellow Sea (b), East China Sea (c) using 72-h forward trajectory analysistal and vertical motion, respectively. LP: Loess Plateau area. A-G are Liaoning, Hebei,el.

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    The transport paths of dust storms from Sunitezuoqi station tothe China seas were as follows. The dust particles were depositedinto the China seas through Beijing/Tianjin city and/or Hebei,Liaoning, or Shandong Province (Fig. 5). As with the paths fromGuaizihu, sometimes the dust entered East China Sea via Bohai Seaand/or Yellow Sea and entered...


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