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Atmospheric Environment 45 (2011) 326e337

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Atmospheric Environment

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Influence of Arctic Oscillation on dust activity over northeast Asia

Rui Mao a, Chang-Hoi Ho a,*, Yaping Shao b, Dao-Yi Gong c, Jhoon Kimd

a School of Earth and Environmental Sciences, Seoul National University, Seoul, Republic of Koreab Institute of Geophysics and Meteorology, University of Cologne, Cologne, Germanyc State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing, ChinadDepartment of Atmospheric Sciences, Yonsei University, Seoul, Republic of Korea

a r t i c l e i n f o

Article history:Received 21 June 2010Received in revised form9 October 2010Accepted 12 October 2010

Keywords:Arctic oscillationAsian dustNortheast AsiaIntegrated Wind Erosion ModelingSystem (IWEMS)

* Corresponding author. Climate Physics Laboratorronmental Sciences, Seoul National University, Seoul

E-mail address: hoch@cpl.snu.ac.kr (C.-H. Ho).

1352-2310/$ e see front matter � 2010 Elsevier Ltd.doi:10.1016/j.atmosenv.2010.10.020

a b s t r a c t

The northeast Asian dust process during the spring seasons in the years 1982e2006 was simulated bythe Integrated Wind Erosion Modeling System (IWEMS). The influence of Arctic Oscillation (AO) on dustactivities was investigated by analyzing surface observations and model simulations. There is a signifi-cant relationship between AO and dust activity; a positive AO phase is associated with decreased(increased) dust storm frequency in Mongolia (Taklimakan Desert) and enhanced anticyclonic (south-eastward) dust transport over northwestern China (North China). The AO-dust relation is mainly due tochanges in the westerly jet and geopotential height in the middle troposphere; a positive AO phaseinduces a northward shift of the polar jet, an intensified westerly jet over northern Tibetan Plateau, anda positive geopotential height anomaly over Mongolia. The northern shift of the polar jet reduces thefrequency of intense cyclones in Mongolia, thereby causing a decrease in the dust storm frequency. Theintensified westerly jet stream over the northern Tibetan Plateau increases the dust storm frequency inthe Taklimakan Desert. The positive geopotential height anomaly over Mongolia initiates an anticyclonicdust transport anomaly in the middle troposphere over northwestern China. It also induces a south-eastward dust transport anomaly over North China. The reverse situations are true for a negative AOphase.

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1. Introduction

Many previous studies have demonstrated that dust producedby dust storms play an important role in climate change. Dustdirectly influences radiative balance by modifying reflection, scat-tering, and absorption of solar radiation (Tegen et al., 1996). Theindirect effect of dust is caused by its interaction with clouds andmarine biological activities (Chadwick et al.,1999). It also influencesmeteorological processes that could affect the occurrence andmagnitude of storms in the middle troposphere (Uno et al., 2009).Dust storms frequently occur in the spring (Zhou and Zhang, 2003;Natsagdorj et al., 2003). Recently, somemodeling and observationalstudies have indicated that the dust produced by dust storms innortheast Asia can be transportedmore than once around the globeby westerly winds, and hence, may have a potential impact onglobal climate change (Uno et al., 2009; Yumimoto et al., 2009). Inthis study, we discuss the variations in the northeast Asian dustactivities and its causes by using a climate model.

y, School of Earth and Envi-151-747, Republic of Korea.

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The variations in the northeast Asian dust activities (e.g., dustevent and dust transport) are believed to be related to changes inplanetary-scale atmospheric circulations. Planetary-scale circula-tions not only provide a favorable background for the formation ofsynoptic systems that directly influence the occurrence of duststorms, but also modulate the magnitude and direction of dusttransport in the middle troposphere (Sun et al., 2001; Qian et al.,2002; Gong et al., 2006a). Arctic Oscillation (AO), which is a domi-nant mode in the mid-high latitudes of the Northern Hemisphere,has significant influence on the Asian climate. When AO is ina positive phase, warmer surface temperatures, weaker weathervariances, and less frequent cold surges are observed over northernChina (Gong et al., 2001; Jeong and Ho, 2005). The reverse is truefor a negative AO phase. It is thus appropriate to assume that AO hasthe ability to influence the dust in this region. In fact, this is evidentfrom geological records, including Chinese loess (Han et al., 2008),deep-sea sediments in the Pacific and North Atlantic (Han et al.,2008), and ice cores from Mt.Everest (Kang et al., 2003; Xu et al.,2007). These geological evidences showed that AO variation isrelated to both dust event frequency and trans-Pacific dust trans-port on a millennial timescale. In addition, ground observationshave also verified that AO is closely correlated with the dust stormfrequency in North China in the spring (Gong et al., 2006b).

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Although AO appears to be related to dust activities, thefollowing subjects, namely, the relationship of AO with dust stormfrequency and dust transport over the region, and the relevantphysical processes, should be thoroughly examined. In previousstudies, mainly observational studies, the influence of AO on duststorm frequencywas analyzed (Gong et al., 2006b; Han et al., 2008).Some studies focused on achieving a thorough understanding ofthe impact of AO on dust storm frequency and dust transport.Beside observational studies, few modeling studies tried to finda linkage between AO and dust activities over northeast Asia.However, these studies mainly examined the behaviors of trans-Pacific dust transport induced by othermajor climatic factors (Gonget al., 2006a; Hara et al., 2006).

Aforementioned, AO plays an essential role in determining thevariability of zonal winds in the middle and upper troposphere;that is, the zonal winds carry dust particles from source regions tonortheast Asia. We thus hypothesize that AO variation inducechanges in dust transport over the target region. Here, we discussthe influence of AO on both dust storm frequency and dust trans-port in northeast Asia, and then speculate the possible mechanismsconnecting the AO with dust activities.

In this paper, (1) the relationshipbetweenAOandnortheastAsiandust is analyzed on interannual timescale and (2) the simulatedclimatology of northeast Asian dust is used to clarify the influence ofAO on dust. As indicated in the first point, a comparison of correla-tions established from two time series of AO and the dust variablesshow that the correlation coefficients of AO and the dust variablesderived on an interannual timescale is robust and convincing (Gonget al., 2007). This is because the two time series may include theinterdecadal trend that could produce high correlation coefficientseven when there is no physical linkage between them. We thusanalyze the impact of AO on an interannual timescale. In addition,motivatedby the studies of Zhao et al. (2006) andGonget al. (2006a),weused thesimulatedclimatology fromthe IntegratedWindErosionModeling System (IWEMS) to represent the relationship betweenAOand dust. Zhao et al. (2006) implemented a size-distributed activeaerosol algorithm into a regional climate model, the NorthernAerosol Regional Climate Model (NARCM), to obtain the climatologyof dust in both northeast Asia and northern Pacific for the period1960e2003. On the basis of these simulations, Gong et al. (2006a)analyzed the relationships of major climate indices with dust emis-sion, dust transport, and other dust variables. Therefore, we areconfident that studies based on simulations from IWEMS canobjectively reflect the influence of AO on northeast Asian dust.

The remainder of this paper is structured as follows. Section 2briefly describes both IWEMS and the data used. In Section 3, themodel simulations are validated and the climatology of both duststorm frequency and dust transport in the middle troposphere isdescribed. In Section 4, an outline of the variations in the dustinduced by AO changes is given; in particular, the variations in duststorm frequency, dust concentration, and dust transport. Andfinally, the possiblemechanisms and summary are given in Sections5 and 6, respectively.

2. Model, analysis method, and data

2.1. Model

In this study, we use IWEMS to simulate the climatology ofnortheast Asian dust. This model has been successfully applied tosimulate Asian dust events (Shao et al., 2003). IWEMS was runcontinuously from 1982 through 2006, using National Centers forEnvironmental Prediction and National Center for AtmosphericResearch (NCEP/NCAR) reanalysis data as the initial and boundaryconditions. The boundary conditions were updated every 6 h.

During the simulation period, IWEMS took account of monthlyvegetation variation, and updated leaf area index (LAI) and vege-tation cover (VC) every month. In addition, because the changes insoil texture, vegetation classification, and desertification wereinsignificant during the simulation period, we used constant dustsource, soil, and vegetation types to clarify pure meteorologicalimpact and the effect of vegetation change on the dust phenom-enon. For each year, a four-month run was made, ranging from 1February to 31 May. This allowed us to follow the northeast Asiandust in spring (i.e., March, April, and May).

2.2. Analysis method

To delineate the relationship between AO and northeast Asiandust, we derived a composite difference in the dust between typicalpositive AO years and typical negative AO years (i.e., the positive AOyears minus the negative AO years). Because wewanted to examinethe AO-dust relation on an interannual timescale, the typical AOyears for the composite difference were determined according tothe interannual component of the AO time series. The AO timeseries was first filtered by the Butterworth filter with a cutofffrequency at 1/10 of a year, and then, only the interannualcomponents of the AO time series were retained from 1982 through2006. We selected five maximumyears and five minimumyears forthe period; 1982, 1986, 1990, 1994, and 1997 were classified astypical positive AO years, and 1987,1988,1991,1993, and 2005wereclassified as typical negative AO years. We then used hypothesistesting to analyze the composite difference. Because it was uncer-tain whether the source population of dust variables would havea normal distribution, we used non-parametric testing (i.e., theWilcoxon rank sum test) to test the mean values of two samplesfrom the positive and negative AO phases. Any measured test valuethat fell outside the range, bounded by 4 and 21, was consideredstatistically significant beyond the 90% confidence level for a non-directional test (Dowdy et al., 2004). Because the main feature forboth the positive and negative AO phases were similar, except forthe change in polarities, only the patterns for the positive AOcomposite differences are shown in the following analysis.

2.3. Data

The data used in this study mainly uses NCEP/NCAR reanalysisdata, observational dust records, and vegetation information andaerosol index (AI) derived by remote sensing. The primary data arethe NCEP/NCAR reanalysis data involving winds, temperature,geopotential height, and sea level pressure (SLP), which hada spatial resolution of 2.5� � 2.5�. There are three advantages whenusing the NCEP/NCAR reanalysis dataset: to nudge the IWEMSatmospheric model, to construct the original AO time series, and toproduce westerly jet core frequency and cyclone frequency data.The process of constructing the AO index is shown in the works byGong et al. (2006b). The westerly jet core frequency was developedby following the procedure of Schiemann et al. (2009). This iden-tification successfully captured the major jet axis and has beenverified by other studies (Koch et al., 2006; Schiemann et al., 2009).

In addition, the cyclone frequency was derived from a cyclonedataset that was produced by the NCEP/NCAR reanalysis data usingthe algorithm developed by Serreze et al. (1997). The cyclonedataset is available at <ftp://sidads.colorado.edu/pub/DATASETS/atmosphere/nsidc0423_cyclone_ncep_ncar_reanalysis>. We usedthis dataset to calculate the frequency of intense cyclones. Accord-ing to Wang et al. (2006), an intense cyclone center was defined asthe center with local Laplacian intensity above 10 � 10�5 hPa km�2

and central pressure below 1010 hPa. The frequency of intensecyclones in spring is defined as the sum of intense cyclone centers.

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The secondary dataset we analyzed was the dust stormfrequency from observations in China and simulations fromnortheast Asia. Because the observational dust storm frequency isequal to the sum of the daily dust record in spring for each station,the unit of dust storm frequency is days (the same as for simulateddust storm frequency). The observational dust record was reportedthrough the meteorological network of the China MeteorologicalAdministration (Zhou and Zhang, 2003). We also retrieved simu-lated dust storm frequency data from IWEMS simulations. Becausethe dust model could not produce horizontal visibility as is used fordust weather classification in observations, we referenced thetechnical guidelines for sand and dust storm monitoring (GB/T,2006), published by the Standardization Administration of China,and obtained a surface dust concentration of 2 mg m�3 as thethreshold value for a dust storm to occur. For a given grid point,when the simulated daily dust concentration near the surfaceexceeded 2 mg m�3, a dust stormwas considered to have occurredat this grid point. Accordingly, the simulated dust storm frequencyis equal to the sum of dust storms occurring in spring for each gridpoint, and its unit is days. It should be noted that the 2 mg m�3

benchmark value is slightly higher for testing dust storms in theperiphery and downwind of dust source areas. We thereforeconsidered the changes in surface dust concentration to indirectlyindicate the variations in dust storm frequency in the peripheraland downwind areas.

The tertiary data we used included vegetation and AI. IWEMStakes into account the monthly vegetation variation; the vegeta-tion data used in IWEMS consists of monthly VC and LAI. Themonthly LAI data were provided by Climate and VegetationResearch Group, Boston University (ftp://primavera.bu.edu/pub/Datasets/new_dataset/AVHRR_LAI/8km_monthly/global; Gangulyet al., 2008). The monthly VC was transferred from the normal-ized difference vegetation index (NDVI) by referencing the formulaof Gutman and Ignatov (1998). These NDVI datasets were obtainedfrom the Global Land Cover Facility, Global Inventory Modelingand Mapping Studies, at a spatial resolution of 8 km � 8 km(Tucker et al., 2005).

We also used AI from Nimbus 7 and the Earth Probe Total OzoneMapping Spectrometer (TOMS) to examine the horizontal distri-bution of dust loading. The TOMS AI dataset has been highlysuccessful in detecting UV-absorbing aerosols, particularly fordesert dust. A positive AI value indicates the presence of UV-absorbing aerosols. It should be noted that there is a gap in theTOMS data between May 1993 and July 1996; however, this gap didnot significantly influence our conclusions because these two yearswere not used in both the composite difference and the climatologyof AI. In addition, there are large uncertainties in the AI data overthe Gangetic Plains because the transported dusts are mixed withlocal anthropogenic aerosols including black carbon (Gautam et al.,2009b). However, AI data is the only satellite-based data thatprovide horizontal aerosol measurements in southern Asia, wherein-situ observations are sparse. Furthermore, we focus on the pre-monsoon season (MarcheMay) when aerosol loading is stronglyinfluenced by the transport of dust outbreaks originating in theThar Desert in northwestern India and the anthropogenic pollutionturns out to be of minor importance in southern Asia (Gautam et al.,2009b; Kuhlmann and Quaas, 2010). Thus, this uncertainty may notchange our conclusions. All the datasets described above wereconfined to the period of 1982 through 2006 because this periodoverlapped with the vegetation data.

3. Climatology of dust variables

In this section, we validate IWEMS simulation results onspatial and temporal scales. We present a modeled climatology of

dust storm frequency on the basis of the 25-year IWEMS simu-lations and compare the simulated dust storm frequencies withthose from actual observations. This comparison confirms theability of IWEMS to simulate dust events quantitatively. In addi-tion, we construct a modeled climatology of dust transport in themiddle troposphere, which is used as the reference for compar-ison with the dust transport variations induced by AO change,discussed in Section 4.

3.1. Dust storm frequency

We first compared the spatial distribution of dust stormfrequency derived from both observations and simulations. Fig. 2ashows the climatology of observational dust storm frequencies inspring between 1982 and 2006. As seen in the figure, dust stormsinfluence most of northern China and the Tibetan Plateau. Everyspring, there are two regions with the maximum mean dust stormfrequency that exceeds 10 days: the Taklimakan Desert and the aridregions in northern China (mainly the Badain Jaran Desert, theTengger Desert, the Ulan Buh Desert, the Hobq Desert, and the MuUs sandy land). All regions are marked in Fig. 1. In addition to theseregions, dust storms are also relatively frequent in both north-eastern China and the southern Tibetan Plateau, with a frequency of2e5 days.

Fig. 2b shows the spatial features of the simulated dust stormfrequency. These features are consistent with the observations thatregions with the maximum dust storm frequency occur in theTaklimakan Desert and arid areas in northern China. The simulateddust storm frequency is 1e5 days in northeastern China. In general,the model reasonably reproduces the spatial distribution ofobserved dust activities.

Although there was general consistency in major spatial char-acteristics of the observed and simulated dust storm frequencies,there were two serious discrepancies: the simulated values aresomewhat lower than the observed values, particularly in theTaklimakan Desert; the simulation showed no dust storms occur-ring in the Tibetan Plateau. These disagreements are caused byinconsistent definitions for “dust storm” used in the observationand simulation, inaccurate land surface data in the TaklimakanDesert, and an incomplete dust source domain used in IWEMS. Infact, in the meteorological records of China, a “dust storm” isdefined by a reduction in horizontal visibility to less than 1 km, andthe dust concentration is not specified. By contrast, in the simula-tions, a “dust storm” record was determined by whether or not thesurface dust concentration exceeded 2 mg m�3. Therefore, theobservational dust storm records with surface dust concentrationhigher than 2 mg m�3 would be presented by the simulations.However, the observational dust storm records near the peripheryand downwind of the dust source regions, may not be completelyvisible in the simulated dust storm frequency due to their lowersurface dust concentration. This is the primary reason why thesimulations do not precisely coincide with the observations, andalso explain why the simulated dust storm frequency in the Takli-makan Desert tends to be lower than the observed. In addition, it islikely that the simulated dust emission in the Taklimakan Desertwas underestimated due to the inaccuracies of land surface data. Inthe end, due to IWEMS’s incomplete dust source domain for theTibetan Plateau, near-surface dust concentration at the TibetanPlateau was not simulated, and consequently, no simulated duststorm records were counted. However, the lack of a simulated dustsource in the Tibetan Plateau did not significantly alter ourconclusions because other researchers (Zhang et al., 2003) founda minor contribution of dust source in the Tibetan Plateau for bothdust storm frequency and dust transport.

Fig. 1. Simulation domain. The shaded yellow area is the dust source used in the modeling. Regions S1, S2, and S3, enclosed by dashed lines, are specified as northeast China, NorthChina, and the Tibetan Plateau, respectively. The numbers indicate the deserts and sandy land in China: (1) Taklimakan Desert, (2) Gurbantunggut Desert, (3) Kumtag Desert, (4)Badain Jaran Desert, (5) Tengger Desert, (6) Ulan Buh Desert, (7) Hobq Desert, (8) Mu Us sandy land, (9) Onqin Daga sandy land, and (10) Horqin sandy land. The red circles presentthe location of dust storm station used for constructing the observational dust storm frequency in North China. (For interpretation of the references to colour in this figure legend,the reader is referred to the web version of this article).

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Finally, we constructed two time series (i.e., observation andsimulation) of the domain-averaged dust storm frequency overNorth China (region S1 in Fig.1), and verified themodeling ability ofIWEMS on an interannual timescale. During the constructingprocess, two conditions were set for both highlighting the vari-ability of the dust storm frequencies and removing the influence ofstations or grids that had minor dust storm frequencies. These twoconditions were (1) the selected grid or station had a climatologicaldust storm frequency in spring exceeding 1 day during the analysisperiod, and (2) the selected grid or station had fewer than 5 yearswhen no dust storms occurred (Zhou et al., 2006). Based on theseconditions, the domain-averaged dust storm frequency in NorthChina for the observations and simulations, were determined byaveraging the dust storm frequency of the selected stations andgrids. Our observations showed that 24 dust stations were used toconstruct the dust storm frequency in North China (marked ascircles in Fig. 1).

Fig. 3 shows the temporal variations in domain-averaged duststorm frequency in North China derived by simulations andobservations. There are evident interdecadal changes, witha decreasing trend from 1982 through 1999 and a slightlyincreasing trend thereafter. The simulation time series show goodagreement with the observational time series; these two timeseries have a correlation coefficient of 0.70, significant at the 95%confidence level. However, it is true that the in few years thesimulated events are not identical with the observed events. It maybe caused by the threshold value (2 mgm�3) used to identify a duststorm event in the model. Some blowing dust events may havehigher dust concentrations above 2 mg m�3, but they are notrecorded as dust storm events. Hence, it is hard to make thesimulated frequency agree well with observational frequency everyyear. Considering that for most years, the predictions and theobservations are in good agreement, including the maxima andminima values, we are confident that the model is able to simulatethe annual evolution of spring dust episodes and is able to accu-rately capture major interannual variations in dust characteristicsshown in the observations.

3.2. Dust transport

Because AO effectively controls zonal winds in the middletroposphere over northeast Asia, we discuss the dust transport inthe middle troposphere. Motivated by certain studies (e.g., Zhaoet al., 2006; Hara et al., 2006), we calculated dust transport flux(dust concentrations multiplied by wind speeds) to estimate theamount and direction of dust transport. The dust transport fluxes,between 0.6 and 0.7 sigma levels, are arithmetically averaged torepresent the mid tropospheric dust transport. These levels corre-spond to the middle troposphere (2550e4000 m above the groundlevel) over the dust source regions.

Fig. 4a shows the climatological dust transport flux in themiddle troposphere. As seen in the figure, the dust transport ischaracterized by two eastward dust flows. A northern dust floworiginates in northwest China and passes through both Mongoliaand northern China, and finally reaches Korea. A southern dustflow occurs across the southern Tibetan Plateau and then propa-gates eastward to the East China Sea. The northern dust transportroute depicted in this figure was reported by Sun et al. (2001) andUno et al. (2009), and is based on observations and simulations.Similarly, the southern dust transport has been observed byauthors (e.g., Gautam et al., 2009a; Kuhlmann and Quaas, 2010)using the Cloud-Aerosol Lidar and Infrared Pathfinder SatelliteObservation (CALIPSO) data. Each year, the dusts generated innorthern India, Pakistan and/or Afghanistan are mobilized, andcan be transported southeastward to and over the Tibetan Plateau.In addition to these two dust transport routes, there is also ananticyclonic dust transport flux over southern Asia, which isrelated with springtime dust activities in northwest India (Gautamet al., 2009a).

In order to verify the simulated climatology of dust transportflux from IWEMS modeling, we constructed a climatology ofa remote-sensing-based AI in spring for comparison. In Fig. 4b,there are two bands of high AI in Asia, including a northern bandfrom the Taklimakan Desert through northern China to north-eastern China, and a southern band from northern India to

Fig. 2. Climatology of spring dust storm frequency for observations (a) and simulations (b) during the period 1982 through 2006. Unit: day. The circles indicate stations in (a) andgrid points in (b).

Fig. 3. Spring dust storm frequency in North China. Unit: day. The solid line and thedashed line represent the observation results and simulation results, respectively. TheNorth China is denoted as S1 in Fig. 1 and 24 stations (marked as circles in Fig. 1) areused for constructing the observational dust storm frequency.

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southern China. These two high AI bands may confirm major dusttransport routes simulated by IWEMS. In addition, there are twomaxima of AI located in the Taklimakan Desert and northern India,respectively. These two AI maxima show the importance of dustproduction in the middle troposphere.

4. Impact of Arctic Oscillation on dust activities

We then investigated the influence of AO on northeast Asiandust by applying composite difference analysis. The analyzed dustvariables included dust storm frequency, dust concentration in thelower and middle troposphere, and dust transport in the middletroposphere.

Fig. 4. (a) Climatology of both spring dust transport flux in the middle troposphere and (b) the spring aerosol index during the period 1982e2006. The dust transport flux (unit:mg m�2 s�1) is expressed as a vector with its length equal to the natural logarithm of the magnitude of dust transport flux; its direction indicates the dust transport direction, and thelargest vector length is indicative of 39 mg m�2 s�1. In (a), these contour lines represent climatological geopotential height at the 500 hPa level in spring (unit: 10 gpm). In (b) thelight and heavy shadings indicate the climatological AI exceeding 0.5, 1.0, and 1.5, respectively.

R. Mao et al. / Atmospheric Environment 45 (2011) 326e337 331

4.1. Dust storm frequency

Fig. 5a shows the composite difference of observational duststorm frequency in spring between the positive and negative AOphases. It indicates that the positive AO phase is related toa decreased dust storm frequency in North China, which wasobtained by statistical correlation from observations (Gong et al.,2006b). The positive AO phase is also accompanied by anincreased dust storm frequency in both northwest China and centralnorthern China (mainly the Taklimakan Desert, the Badain JaranDesert, and the Tengger Desert). The composite difference ofsimulated dust storm frequency shows similar results from obser-vations (Fig. 5b). There are negative anomalies of dust storm

frequencies in both southern Mongolia and North China, andsimultaneously, there are positive anomalies of dust stormfrequencies in both northwestern China and central northern China.The consistency between the observed and simulated dust stormfrequency further validates the simulation results from IWEMS.

In order to examine the influence of AO on dust stormfrequencies at the periphery and downwind of dust source areas, inFig. 5c we show the composite difference of surface dust concen-tration. During a positive AO phase, a large area of negative anomalyserves as themain signal in eastern Kazakhstan, southernMongolia,North China, northeastern China, and the Yellow Sea. Some positiveanomalies also occur in northwest China, central northern China,and the East Sea (located between Korea and Japan).

Fig. 5. Composite difference of (a) observational dust storm frequency, (b) simulated dust storm frequency, and (c) simulated surface dust concentration in spring between thepositive AO phase and the negative AO phase (value of the variable from the positive AO years minus that from the negative AO years). The difference in excess of the 90% confidencelevel is shaded. Triangles represent negative values and circles represent positive values. Unit: day for both (a) and (b), and mg m�3 is the unit in (c).

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4.2. Dust concentration and transport in the middle troposphere

Fig. 6a shows the composite difference of dust concentration inthe middle troposphere between the positive and negative AOphases. During a positive AO phase, positive anomalies of dustconcentration occur over eastern Kazakhstan, the GurbantunggutDesert (in the northern part of northwest China; see Fig. 1), Korea,and the East China Sea. At the same time, the positive AO phase isrelated to a large anomalous area of negative dust concentrationsover India.

In order to confirm these anomalies of dust concentration in themiddle troposphere derived from the simulations, we constructeda composite difference of remote-sensing-based AI (Fig. 6b). A largeanomalous area of negative AI occurs over India, and positive AIanomalies occur over regions from the western parts of north-western China through Mongolia (Fig. 6b). It is evident that the AIvariation supports the dust concentration anomalies in the middletroposphere derived from the simulations; positive and negative AIanomalies are indicative of positive and negative dust concentra-tion anomalies, respectively.

Furthermore, we analyzed the variations in the dust transportflux induced by the changes in AO. We found that a positive AOphase results in anomalies in the dust transport flux, as evidencedby an anticyclonic anomaly over northwest China, a cyclonicanomaly over India, and a northwesterly anomaly fromNorth Chinato the East China Sea (Fig. 6c). This means that a positive AO phaseis connected with both an increased westward dust transport overthe Taklimakan Desert and an increased eastward dust transportover Kazakhstan. In addition, compared with the climatology ofdust transport flux indicated in Fig. 4b, the cyclonic dust transportflux anomaly over India implies a weakened anticyclonic dusttransport flux. At the same time, the northwesterly dust transportflux anomaly from North China to the East China Sea may indicatean enhanced southward dust transport flux over that area.

5. Possible mechanism

In this section, we analyze observations to determine thepossible mechanisms by which AO influences northeast Asian dust.The observational data we analyzed consisted of three types ofdata: the frequency of intense cyclones, westerly jet core frequency,and geopotential height at the 500 hPa level. In this study, AOtriggers variations in both the westerly jet and the geopotentialheight. These circulation anomalies result in changes in thefrequency of intense cyclones and produce variability in both duststorm frequency and dust transport.

5.1. Variations in dust storm frequency

The most favorable atmospheric pattern to generate a duststorm is characterized by cold air outbreaks, which causeMongolian cyclonic depression and frontal systems sweepingacross the deserts in Mongolia and China (Sun et al., 2001). Theanalysis of the frequency of intense cyclones elucidates the realcause of variations in dust storm frequency. Fig. 7a presents the

Fig. 6. Composite difference of the springtime dust concentration in the middle tropospheremiddle troposphere and the springtime geopotential height at the 500 hPa level (c) betwepositive AO years minus that from the negative AO years). In (a), triangles represent negatshadings represent positive and negative values, respectively, and the light and heavy shadcontour lines represent the positive geopotential height anomaly (unit: 10 gpm) and bluecontour lines indicate the climatological geopotential height (unit: 10 gpm) for the positive A(unit: mg m�2 s�1) is expressed as a vector with its length equal to the natural logarithm of tand the largest vector length is indicative of 13 mg m�2 s�1. Values in excess of the 90% confidfigure legend, the reader is referred to the web version of this article).

composite difference of the frequencies of intense cyclonesbetween the positive and negative AO phases. It indicates thatduring a positive AO phase, there is a negative anomaly of thefrequency of intense cyclones located in both Mongolia and theTaklimakan Desert, and a positive anomaly of the frequency ofintense cyclones in central northern China. The negative anomalyof the frequency of intense cyclones in Mongolia is consistent withthe decreased dust storm frequency and similarly, the positiveanomaly of the frequency of intense cyclones in central northernChina results in an increased dust storm frequency. Because thedust storms in the Taklimakan Desert are generated by synoptic-scale cold air masses (Aoki et al., 2005), the decrease of thefrequency of intense cyclones in the Taklimakan Desert may implymore cold air activities in the Taklimakan Desert, which couldthen produce active dust storms. In general, the variations of thefrequency of intense cyclones reasonably explain the causes ofchanges in dust storm frequency in northeast Asia.

What factor causes the anomalous distribution of the frequen-cies of intense cyclones? It is known that the cyclones are deter-mined by westerly jets that act as baroclinic waveguides. Thewesterly jets can reinforce the amplitude of the disturbances in thetroposphere and consequently result in cyclone generation(Wallace et al., 1988). Thus, the variations of westerly jets may beresponsible for the anomalous distribution of the frequency ofintense cyclones. Fig. 7b shows the composite difference of west-erly jet core frequency between the positive and negative AOphases. In a positive AO phase, there are three bands of positiveanomaly: over Siberia, the northern part of the Tibetan Plateau, andSouth Asia. It also exhibits two bands of negative anomalies, locatedin the northern part of northwestern China and the southern part ofthe Tibetan Plateau. All these anomalies are significant at the 95%confidence level. A positive anomaly of the westerly jet corefrequency indicates an increased westerly jet activity, whilea negative anomaly of the westerly jet core frequency indicatesa decrease in westerly jet activity. Thus, both the negative anomalyin the northern part of northwestern China and the positiveanomaly in Siberia reflect a northern shift of the polar jet. Both thenegative anomaly in the southern Tibetan Plateau and the positiveanomaly in southern Asia imply a southern shift of the southbranch of the subtropical jet. The positive anomaly over thenorthern part of the Tibetan Plateaumeans that the north branch ofthe subtropical jet frequently occurs over the northern TibetanPlateau.

Fig. 8 further shows a schematic figure summarizing the processof how a positive AO phase affects dust storm frequencies bywesterly jet variations. As seen in the figure, the northern shift ofthe polar jet initiates a decreased frequency of intense cyclones inboth North China and Mongolia, which results in a decreased duststorm frequency in these regions. The southern shift of the southbranch of the subtropical jet may generate frequent, low-pressureactivities over India and then produce an increased dust stormfrequency in Northwest India. The frequent westerly jet activityover the northern Tibetan Plateau may induce an increasedfrequency of intense cyclones in the east of Northwest China, whichis responsible for an increased dust storm frequency in the central

(a), the springtime aerosol index (b), and both the springtime dust transport flux in theen the positive AO phases and the negative AO phases (value of the variable from theive values, and circles represent positive values; unit: mg m�3. In (b), the red and blueings indicate absolute values exceeding 0.1, 0.3, and 0.5, respectively. In (c), red soliddashed contour lines represent the negative geopotential height anomaly; the darkO phase (solid lines) and the negative AO phase (dashed lines); the dust transport flux

he magnitude of dust transport flux; its direction indicates the dust transport direction,ence level are shaded in (a) and (c). (For interpretation of the references to colour in this

Fig. 7. Composite difference of springtime frequency of intense cyclones (a) and springtime westerly jet core frequency (b) between the positive and negative AO phases (value ofthe variable from the positive AO years minus that from the negative AO years). The difference in excess of the 90% confidence level is shaded. In (a), the triangles represent negativevalues and the circles represent positive values. In (b), the dashed lines represent negative values and the solid lines represent positive values.

R. Mao et al. / Atmospheric Environment 45 (2011) 326e337 335

regions of northern China. At the same time, the increasedfrequency of intense cyclones in the east of Northwest China couldgenerate more dust storm frequency in the Taklimakan Desert. Thisis because when a low-pressure system passes through the east ofthe Taklimakan Desert, a part of the cold air behind the troughflows into the Taklimakan Desert from the eastern open area andblows up massive amounts of dust from the ground and into theatmosphere (Sun et al., 2001; Aoki et al., 2005). Hence, besides theimportance of complicated topography around the TaklimakanDesert, the frequency and intensity of cyclone across the easternpart of Taklimakan Desert are also important for the variability ofdust storm frequency in the Taklimakan Desert.

5.2. Variations in dust transport

Studies have indicated that dust transport in the middletroposphere over northeast Asia is controlled by the zonal circu-lation in the middle and upper tropospheres (Sun et al., 2001; Fanget al., 2004; Uno et al., 2009). As seen in Fig. 4a, a ridge occurs overnorthwest China and a trough occurs south of the Tibetan Plateau.The ridge produces the northwesterly winds prevailing in bothNorth China and Mongolia, and the trough creates vigorous west-erly winds across southern Asia. It can be concluded that both thenorthwesterly winds and the westerly winds depicted above,dominate the northern and southern dust flow (described in

A positive AO phase

Increased westerly jet

core frequency in

Siberia

Decreased westerly

jet core frequency in

the north part of

Northwest China

Increased

westerly jet

core frequency

in northern

Tibetan Plateau

Northern shift of the

polar jet

Southern shift of the

south branch of

subtropical jet

Increased

cyclone in the

central of

northern China

Increased

low-pressure activity

over India

Increased westerly jet

core frequency in

South Asia

Decreased westerly

jet core frequency in

the southern Tibetan

Plateau

Decreased cyclone in

the Mongolia and

North China

Decreased dust storm

in the Mongolia and

North China

Increased dust storm

in Northwest India

Increased dust

storm in the

Taklimakan

Desert and

central of

northern China

Increased

easterly winds

in Taklimakan

Desert

Fig. 8. Schematic of the processes of how AO impacts on dust storm frequency innortheast Asia.

R. Mao et al. / Atmospheric Environment 45 (2011) 326e337336

Section 3.2) in the middle troposphere, respectively. Thus, the dusttransport variations in the middle troposphere may be caused bycirculation change in the middle troposphere.

As discussed above, we analyzed the composite difference ofgeopotential height at the 500 hPa level to speculate the possiblecauses of dust transport variations in the middle troposphere.Fig. 6c shows that when AO is in a high phase, a positive geo-potential height anomaly occurs over Mongolia, and a negativegeopotential height anomaly occurs over northwest India. Thesegeopotential height anomalies are also evident in the lowertroposphere, where this type of structure is a nearly equivalentbarotropic feature. The positive and negative geopotential heightanomalies produce an anomalous convergence over northwestChina and then initiate a strong easterly wind anomaly over theTaklimakan Desert. It has been observed and modeled in somestudies that the easterly wind anomaly depicted above could dragmore dust to high elevations and then transport it westward (Sunet al., 2001; Yumimoto et al., 2009). Subsequently, the enhancedwestward dust transport over the Taklimakan Desert is inducedby the positive geopotential height anomaly over Mongolia;consequently, the dust transport first moves northward and theneastward. At the same time, the positive geopotential heightanomaly over Mongolia result in the buildup of northerly windsover North China, which transport more dust southward acrossNorth China and the Yellow Sea. In addition, the significantnegative geopotential anomaly over northwestern India may bethe reason for a cyclonic anomaly of dust transport flux in themiddle troposphere over India. Compared to the climatology ofdust transport over southern Asia (Fig. 4b), a cyclonic dusttransport anomaly implies a weakened anticyclonic dust trans-port. This results in decreased dust concentrations in the middletroposphere in southern Asia.

6. Summary

In the present study, we have documented the relationshipbetween AO and northeast Asian dust (mainly including dust stormfrequency and dust transport) on an interannual timescale duringthe spring season by using of composite difference maps ofboth dust simulations and observations for a 25-year period(1982e2006). The analyzed dust variables included dust stormfrequency, dust concentration, and dust transport. Our majorconclusions are summarized as follows:

1) We used IWEMS to simulate a climatology of northeast Asiandust in spring. The IWEMS atmospheric model was supple-mented by NCEP/NCAR reanalysis data for the analysis period,and IWEMS updated the vegetation data every month. Bycomparing the simulated dust storm frequency with corre-sponding observations on temporal and spatial scales, wefound that the simulations were in good agreement with theobservations. We are therefore confident that IWEMS is able tosimulate the annual evolution of spring dust episodes andcapture the observed major interannual variations in dustcharacteristics.

2) A positive AO phase may result in both a decreased dust stormfrequency in Mongolia and an increased dust storm frequencyin the Taklimakan Desert. A positive AO phase is also accom-panied by changes in dust transport in the middle troposphere,including an anticyclonic dust transport anomaly over north-western China, an enhanced southeastward dust transportacross North China and the East China Sea, and a cyclonic dusttransport anomaly over southern Asia.

3) We found that in a positive AO phase, there is a northern shiftof the polar jet and an increased westerly jet activity over thenorthern Tibetan Plateau. This shift initiates a decreased duststorm frequency in Mongolia by reducing the frequency ofintense cyclones. The increased westerly jet activities over theTibetan Plateau increase the dust storm frequency in theTaklimakan Desert. In addition, the positive AO phase is alsorelated to significant geopotential height anomalies in themiddle troposphere, including a positive anomaly overMongolia and a negative anomaly over northwest India. Thesepositive and negative geopotential height anomalies producean anomalous convergence over northwest China, and theninitiate a strong easterly wind anomaly over the TaklimakanDesert. The intensified easterly wind anomaly produce anenhanced westward dust transport. Subsequently, the positivegeopotential height anomaly over Mongolia inducesa strengthened eastward dust transport across Kazakhstan andMongolia and an anomalous southeastward dust transportacross North China and the Yellow Sea.

Acknowledgements

This research was funded by the Korea Meteorological Admin-istration Research and Development Program under grant CATER2006-4204. Dr. Rui Mao was supported by Project 20090460222 ofthe China Postdoctoral Science Foundation. Dr. Dao-Yi Gong wassupported by Project 2008AA121704 of the National High Tech-nology Research and Development Program of China.

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