ADVANCES IN ATMOSPHERIC SCIENCES, VOL. 26, NO. 3, 2009, 564–576

Aerosol Optical Properties and Its Radiative Forcing over Yulin, China in 2001 and 2002



CHE Huizheng∗1 ( 1

), ZHANG Xiaoye1 ( ), Stephane ALFRARO2 , Bernadette CHATENET2 , 3 ) Laurent GOMES , and ZHAO Jianqi4 (




Key Laboratory of Atmospheric Chemistry, Centre for Atmosphere Watch and Services,

Chinese Academy of Meteorological Sciences, China Meteorological Administration, Beijing 100081 2

Laboratoire Inter-Universitaire des Syst´ emes Atmosph´ eriques, Universit´ e Paris VII/Paris XII, 94010 Cr´ eteil Cedex, France 3

Centre National de la Recherche Scientifique,

Met´ eo-France/CNRM/GMEI/MNPCA, 31057, Toulouse, France 4

State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029 (Received 31 March 2008; revised 6 November 2008) ABSTRACT

The aerosol optical properties and direct radiative forcing over the Mu Us desert of northern China, acquired through a CE318 sunphotometer of the ground-based Aerosol Robotic Network (AERONET), are analyzed. The seasonal variations in the aerosol optical properties are examined. The effect of meteorological elements (pressure, temperature, water vapor pressure, relative humidity and wind speed) on the aerosol optical properties is also studied. Then, the sources and optical properties under two different cases, a dust event and a pollution event, are compared. The results show that the high aerosol optical depth (AOD) found in Yulin was mostly attributed to the occurrence of dust events in spring from the Mu Us desert and deserts of West China and Mongolia, as well as the impacts of anthropogenic pollutant particles from the middle part of China in the other seasons. The seasonal variation and the probability distribution of the radiative forcing and the radiative forcing efficiency at the surface and the top of the atmosphere are analyzed and regressed using the linear and Gaussian regression methods. Key words: aerosol optical properties, aerosol radiative forcing, Mu Us desert, China Citation: Che, H. Z., X. Y. Zhang, S. Alfraro, B. Chatenet, L. Gomns, and J. Q. Zhao, 2009: Aerosol optical properties and its radiative forcing over Yulin, China in 2001 and 2002. Adv. Atmos. Sci., 26(3), 564–576, doi: 10.1007/s00376-009-0564-4.

1.

Introduction

Aerosol particles play a very important role in global and regional climate change by backscattering and absorbing solar radiation (Ackerman and Toon, 1981; Charlson et al., 1992). It has been speculated that aerosol particles could contribute to global and regional dimming (Stanhill and Cohen, 2001; Che et al., 2005) and the change in regional precipitation (Menon ∗ Corresponding

et al., 2002). Dust aerosols from arid and semi-arid regions are a major component of natural aerosols in the atmosphere (Stanhill and Cohen, 2001; Che et al., 2005). They can be transported thousands of kilometers away from the original source regions (Uematsu et al., 1983; Gong et al., 2003; Prospero and Lamb, 2003). An estimation of the emission flux of desert aerosols subject to long-range transport is on the order of 1500 Tg yr−1 (Tegen and Fung, 1995). In addi-

author: CHE Huizheng, [email protected]

NO. 3

565

CHE ET AL.

tion, the emission from deserts in East Asia is nearly 800 Tg yr−1 , half of which might be deposited back to the source and adjacent regions (Zhang et al., 2001; Zhang, 2001). Although with considerable uncertainties (IPCC, 2001), mineral dust particles also have a great effect on climate change and the global environment, as well as the biogeochemical cycle (Li et al., 1996; Mikami et al., 2002; Jickells et al., 2005). Despite the long history of dust aerosol studies, knowledge on the dust optical properties is still far from being sufficient (Sokolik and Toon, 1999). In the past few years, there have been a number of studies on the optical properties of mineral aerosols in different regions of the Sahara desert (Zakey et al., 2004), West Asia (Nakajima et al., 1996a; Smirnov et al., 2002), India (Dey et al., 2004), Australia (Kalashnikova et al., 2007) and East Asia (Nakajima et al., 2003; Kim et al., 2004; Eck et al., 2005). Arid and semi-arid areas in northern China are one of the major source regions of mineral dust aerosols in East Asia (Zhang et al., 2003). In recent years, some scientists (e.g., Alfaro et al., 2003; Xia et al., 2005; Xin et al., 2005; Cheng et al., 2006; Gai et al., 2006) have begun to investigate the optical properties of dust aerosols in this area. These studies are vital to the understanding of the essential properties and variations in dust aerosols in northern China so that we can estimate more accurately the effect of mineral dust aerosols on global and regional climate change in the future. The aim of this work is to study mineral dust aerosol optical characteristics and their radiative forcing from the Mu Us desert in North China through observations of the aerosol optical properties in the Mu Us desert region from 2001 to 2002. This will be essential for further investigations on the influence of dust aerosols on regional climate change in the desert areas of northern China.

used to obtain the total precipitable water content in centimeters (Dubovik et al., 2000). Aerosol size distribution, refractive index, and single-scattering albedo are retrieved by using sky radiance almucantar measurements and direct sun measurements (Nakajima et al., 1996b; Dubovik et al., 2000). The total uncertainty in the optical depth is about 0.01–0.02 (Eck et al., 1999). The detailed, retrieved aerosol properties are used for calculating broad-band fluxes in a spectral range from 0.2 to 4.0 µm. The flux simulation relies on the retrieved real and imaginary parts of the complex refractive index. The spectral integration uses the real and imaginary parts of the complex refractive index that are interpolated/extrapolated from the real values and the imaginary parts of the complex refractive index retrieved from the AERONET wavelengths. Similarly, the spectral dependence of the surface reflectance is interpolated or extrapolated from the surface albedo values, assumed in the retrieval of the sun/sky-radiometer wavelengths. The gaseous absorption is accounted for using the radiative transfer model GAME (Global Atmospheric ModEl) (Dubuisson et al., 1996). This model performs spectral integration using a correlated-k distribution based on line-by-line simulations (Scott, 1974). The data in this article is from Level 2.0 quality-assured data of the AERONET data set (http://aeronet.gsfc.nasa.gov), which are automatically cloud screened (Smirnov et al., 2000) and manually inspected. The observation period is from April 2001 to October 2002. Due to the calibration of the instrument, there are only 155 days of data obtained, and there are insufficient observations in the winter80E

90E

100E

120E

110E

130E

50N

2.

Measurement and data

An automatic Cimel sun and sky scanning radiometer (CIMEL Electronique CE-318) was set up at Yulin (38.283◦N, 109.717◦E) from 2001 to 2002. It is one of the AERONET (AErosol RObotic NETwork) sites in North China. The site is located at the south edge of the Mu Us desert, which is a dust activity center in the middle of northern China (Fig. 1). The instrument takes direct spectral solar radiation measurements within a 1.2◦ full field-of-view every 15 minutes at the five normal bands of 440, 675, 870, 940, and 1020 nm, and three polarization bands at 870 nm (Holben et al., 1998). Measurements at 440, 675, 870, and 1020 nm are used to retrieve aerosol optical depths (AOD), and measurements at 940 nm are

Mu Us Desert

40N

Yulin

La nzho u

Ta iyua n

Yan'a n Xi'an

Zhe ngzhou

30N

20N

0

Fig. 1. Map of China showing the location of the observation site and of the main sand- and stone-paved (Gobi) deserts.

566

AOP AND ITS RADIATIVE FORCING OVER YULIN, CHINA

tn 1.20 en 1.05 op xe 0.90 0.75 m rot 0.60 sg nA0.45 dn a 0.30 D0.15 O A0.00

time. The limitation in the data set size precludes the establishment of a definitive aerosol climatology for this region. 3.

Results

3.1 3.1.1

Aerosol optical properties Interannual variability of optical parameters

Figure 2a illustrates the monthly averaged aerosol optical depth (AOD) at 440, 675, 870 and 1020 nm and the ˚ Angstr¨ om parameter in Yulin. A total of 155 daily averages contributed to the statistics of this figure. The monthly mean AOD at 675, 870, and 1020 nm increased from the March value to a peak in April, and decreased again by October. The mean values of AOD at 675, 870, and 1020 nm in April are 0.49, 0.48, and 0.46, respectively. In contrast, the mean AOD decreased to minimum values of about 0.12, 0.10, and 0.08, respectively in November. However, the variation in AOD at 440 nm is different from the above three wavelengths. The maximum occurs in July with a value of 0.66, while the minimum occurs in November with an AOD value of 0.18. According to Eck et al. (2005), we have selected a threshold ˚ Angstr¨ om parameter between 440 nm and 870 nm (α440−870 ) = 0.75 to separate fine versus coarse mode dominated aerosol cases in this article. The small ˚ Angstr¨ om exponent that ranged from 0.30 to 0.75 suggests that the coarse mode aerosol strongly dominated the optical depth in the spring over the Mu Us desert. Meanwhile, a large ˚ Angstr¨ om parameter ranging from 0.75 and 1.10 suggests that the fine mode aerosol dominates in the summer and autumn seasons over this region. The monthly averages of the single scattering albedo (SSA) for the whole period of observation are shown in Fig. 2b. A notable decrease in SSAs associated with the increase in wavelengths is found in July, August, September and November. Meanwhile, the SSAs increased from 440 nm to 675 nm, then decreased from 675 nm to 1020 nm in May, June and October. In April, the SSAs increased with wavelengths. The SSA at 440 nm is about 0.86 in the spring (March to May), which is lower than that in the summer (June to August) and the fall (September to November) on the average. The lowest SSA at 440 nm occurred in June, which means that the aerosol has greater absorption ability at this band. For the wavelength of 675, 870 and 1020 nm, the variations in SSA values are different. The mean monthly values show a general trend characterized by a March–April maximum and an August–September minimum. Figure 2c illustrates the monthly averaged volume

VOL. 26

440 nm 670 nm 870 nm 1020 nm

a

α

Mar Apr May Jun Jul Aug Sep Oct Nov

Month

1.00

440 nm 670 nm 870 nm 1020 nm

od eb 0.95 lA gn ir et 0.90 ac S leg 0.85 ni S 0.80

b

Mar Apr May Jun Jul Aug Sep Oct Nov

Month

0.25

c

Mar Apr May Jun Jul Aug Sep Oct Nov

0.20

) m µ / 3 m µ (r nl d/ V d 2

0.15

0.10

0.05

0.00 0.1

1

10

Radius (µm) Fig. 2. Mean monthly values of (a) the ˚ Angstr¨ om parameter and the aerosol optical depth at 440, 670, 870, and 1020 nm, (b) single scattering albedo at 440, 670, 870, and 1020 nm, and (c) water vapor content.

size distributions (dV /dlnr). The fine mode shows relative stability, while the coarse fraction changes significantly. There are 2–4 µm peaks of the volume concentration in the coarse mode from March to November. It can be seen that the coarse part is dominant in March, April, and May, indicating the effect of sand and dust. Yulin is located in the southern edge of the

NO. 3

a

1.6

)α (t ne no px E m ort sg nA

1.4 1.2 1.0 0.8 0.6 0.4 0.2 0.0

-0.2 0.0

0.4

0.8

1.2

1.6

2.0

AOD at 440 nm 2.0

b

1.6

m n 04 4t a D O A

jor source in Yulin. Table 1 presents the parameters of the bimodal lognormal aerosol volume size distributions. The parameters include the volume concentration (Cv , units: µm−3 µm−2 ), the volume geometric mean radius (Rv ), the geometric standard deviation (σ), and the effective radius (Reff ) to the fine and coarse modes. The fine mode geometric mean radii in March to June were smaller. Variations in the aerosol volume size distributions over Yulin were due to the changes in concentration of both the coarse aerosol fraction (variation coefficient, defined as the standard deviation divided by the average, of 76%) and the fine one (variation coefficient of 52%). The annual averaged particle fine and coarse mode geometric mean radii were 0.18±0.02 and 2.53±0.23 mm, respectively. Variation coefficients yielded values of 13% for the fine mode Rv and 8% for the fine mode σ, and 9% and 4% for the coarse mode parameters. Relationship between AOD vs. ˚ Angstr¨ om exponent and water vapor (WVC) The scattergram of the aerosol optical depth versus the ˚ Angstr¨ om parameter is shown in Fig. 3a. This representation often allows the physical definition of interpretable cluster regions for different types of aerosols (Smirnov et al., 2002). Two different distributions can be seen. One category is an increasing AOD with a decreasing ˚ Angstr¨ om exponent (α), which stands for large particles. Most likely, the origin of this aerosol is local or from regional dust events; the other is an increasing AOD with an increasing α, which reflects the presence of a significant fraction of fine particles in the aerosol size distribution, such as sulfate and black carbon. The relationship between WVC and AOD at 440 nm (Fig. 3b) shows a notable correlation. The correlation coefficient is about 0.51 with a significant confidence level larger than 99%. For the whole data set, a linear fit explained 39% of the variance: 3.1.2

1.2

0.8

Y =0.20026+0.2297 X

0.4

R =0.51 p<0.0001

0.0 0

1

2

3

4

Water Vapor Content (cm) c

1.6

)α (t ne no px E m ort sg nA

567

CHE ET AL.

1.4 1.2 1.0 0.8 0.6 0.4 0.2

AOD440 nm = 0.23WVC + 0.20 ,

Y =0.50813+0.25362 X R = 0.46

0.0

P <0.0001

- 0.2 0

1

2

3

4

Water Vapor Content (cm) Fig. 3. Scattergrams of (a) the ˚ Angstr¨ om parameter vs. the aerosol optical depth, (b) the aerosol optical depth vs. the water vapor content, and (c) the ˚ Angstr¨ om parameter vs. the water vapor content.

Mu Us Desert where dust events occur very frequently during the spring season. From June, the dust events become weaker so that the fine mode parts are very obvious and dominant, indicating that aside from aerosols, anthropogenic aerosols are also a ma-

where WVC is the water vapor content in the total atmospheric column (in cm of precipitable water). The scatterplot of the ˚ Angstr¨ om parameter versus WVC (Fig. 3c) also shows a notable positive correlation with the correlation coefficient of 0.46 (confidence level at 99%). The ˚ Angstr¨ om exponent increases with the increase in water vapor, which means the aerosol particles become smaller when the water vapor increases. 3.1.3

Frequency distribution of AOD, ˚ Angstr¨ om exponent, and WVC

The frequency histograms of AOD at 440 nm, the ˚ Angstr¨ om exponent and WVC are shown in Figs. 4a–c for all the instantaneous data. There is one peak dis-

568

AOP AND ITS RADIATIVE FORCING OVER YULIN, CHINA

VOL. 26

Table 1. Monthly mean parameters of the aerosol volume size distributions. Fine mode Cv

Rv (µm)

Reff (µm)

σ

Cv

Rv (µm)

Reff (µm)

σ

0.016 0.027 0.039 0.053 0.082 0.079 0.059 0.051 0.018 0.047 0.024 0.517

0.15 0.16 0.17 0.16 0.22 0.19 0.18 0.19 0.17 0.18 0.02 0.13

0.13 0.13 0.14 0.14 0.19 0.17 0.16 0.17 0.15 0.15 0.02 0.13

0.57 0.62 0.52 0.48 0.53 0.51 0.50 0.55 0.51 0.53 0.04 0.08

0.117 0.358 0.237 0.164 0.058 0.089 0.063 0.097 0.043 0.136 0.103 0.755

2.30 2.25 2.46 2.43 2.96 2.79 2.52 2.47 2.62 2.53 0.23 0.09

1.84 1.83 1.96 1.94 2.42 2.28 1.98 2.02 2.06 2.04 0.20 0.10

0.65 0.63 0.65 0.66 0.64 0.63 0.69 0.63 0.69 0.65 0.02 0.04

Month Mar Apr May Jun Jul Aug Sep Oct Nov Mean Standard deviation Variation coefficient 18

)

a

16

% ( s 14 e c n 12 e r r u 10 c c o 8 f o 6 y c n 4 e u q 2 e r F 0

0 .0

Coarse mode

3.1.4 0 .2

0 .4

0.6

0 .8

1 .0

1 .2

1 .4

1.6

1 .8

2 .0

AOD at 440 nm 14

b

) %12 ( s e c 10 n e r r 8 u c c o 6 f o y 4 c n e u 2 q e r F 0

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

Angstrom Exponent 18

) 16 % ( s 14 e c n 12 e r r u 10 c c o 8 f o 6 y c n 4 e u q 2 e r F 0

modal value of about 0.40. The accumulated frequency in the range of 0.20 to 0.50 is about 60%. The probability distribution of α is different. There is a two-peak distribution for α with two modal values of about 0.50 and 1.10. The frequency histogram of WVC also shows a one-peak distribution with a maximum at 0.60 cm. The frequency value is about 16%.

c

0 .4

0 .8

1 .2

1 .6

2.0

2 .4

2 .8

3 .2

3 .6

4 .0

Water vapor (cm) Fig. 4. Frequency of occurrences of (a) the aerosol optical depth, (b) the ˚ Angstr¨ om parameter, and (c) the water vapor content.

tribution character for the AOD distribution. The AOD probability distribution is rather narrow with a

Aerosol type classification

According to the method of Gobbi et al. (2007), the graphical method is used in this article to analyze the aerosol type in Yulin by using all data of an instantaneous AOD at 670 nm, the ˚ Angstr¨ om exponent between 440 nm and 870 nm (α440−870 ), and the difference between the ˚ Angstr¨ om exponent between 440 nm and 675 nm (α440−675 ) and that between 675 nm and 870 nm (α675−870 ). Figure 5 shows the simulations of the classification of the aerosol properties as a function of α440−870 and the difference in δα (α440−675 − α675−870 ) for bimodal, lognormal size distributions with the refractive index m=1.40.001i. The black solid lines are for a fixed size of the fine mode (Rf ), while the dashed blue lines are for a fixed fraction contribution (η) of the fine mode to the AOD at 675 nm. The Yulin data show high AODs (τ > 0.70), both clustering in the fine mode growth wing (τ > 0.70, δα < 0, η > 70%) and the coarse mode (τ > 0.70, δα > 0, η < 30%). The high extinction at Yulin is linked in many cases to dust storm events and a hygroscopic and/or coagulation growth from the aging of fine mode aerosols. Furthermore, the fine mode aerosols have hydrate and coagulated characters that become large particles, causing the AOD to increase. For Yulin, there is “typical pollution” with AOD>0.70 and α ∼ 1.40 (concentration) that corresponds to a fine fraction of η ∼ 80% and Rf ∼ 0.18 µm. The extension of the Yulin pollution to higher AODs takes place perpendicularly to

NO. 3

569

CHE ET AL.

lation between temperature and Cv − T, Cv − C, but there was an obvious Cv −F relationship. For the total mode, there is a negative correlation between temperature and Reff and Rv . However, this is contrary to the fine and coarse modes. Temperature has a significant positive correlation with Ri , but it has no significant negative correlation with Rr at all wavelengths in Yulin. A high temperature usually occurs in the summer season. In the summer, dust events seldom occur, but anthropogenic aerosol particles are dominant. These particles are smaller than the dust particles. Also, the water vapor is higher than in other seasons. The high temperature could obviously cause the Gas-to-Particle conversion process. 3.2.3 Fig. 5. The ˚ Angstr¨ om exponent difference, δα = Angstr¨ om exα440−670 − α675−870 , as a function of the ˚ ponent between 440 and 870 nm, and AOD at 675 nm. Only cloud-screened data with AOD>0.15 were used.

the black line, into a larger size of the fine mode and a fine fraction mainly between 70% and 90%; meanwhile, AOD growth in many cases remains associated with the fine mode growth. 3.2

Relationships between meteorological elements and aerosol optical properties

The meteorological condition is an important factor that could affect aerosol properties. In this article, the correlation characteristics between meteorological elements (including daily pressure, temperature, vapor press, relative humidity and wind speed) and daily aerosol optical properties have been calculated (Table 2). 3.2.1 Pressure Atmospheric pressure has an obvious negative correlation with τ , WVC, Cv − T, Cv − F, Cv − C, and Rr . No significant correlations were found between pressure and the ˚ Angstr¨ om parameter, SSA, the effective radius, the volume geometric mean radius, and the imaginary part of the refractive exponent. When the atmospheric pressure is higher, Yulin is affected mainly by the air mass from the northern desert region, without many industrial factories. The higher pressure can also make the aerosol particles diffuse easily, causing the AOD to become lower than the low pressure. 3.2.2 Temperature The temperature has an obvious positive relation with τ , WVC and α, but has a negative relation with ω (except 440 nm). There was no significant corre-

Vapor pressure

Vapor pressure is positively related to τ , WVC and α with a significance level of 0.01. It has an obvious negative correlation with ω from 670 to 1020 nm, but it has a positive correlation with ω at 440 nm. It has a positive relation with the fine mode Cv but a negative relation with the coarse mode Cv . It has a negative relationship with the total mode effective radius and the volume mean radius, but it has a positive relationship with the fine mode and coarse mode radii. For Reff and Rv , the correlation with vapor pressure is similar to the temperature for the total, fine and coarse modes. It was found that vapor pressure is negative with Rr , but is significantly positive with Ri at all wavelengths. 3.2.4 Relative humidity Relative humidity (RH) has significant correlations with all parameters in Table 2, indicating that it is a key factor affecting the aerosol optical characteristics in Yulin. Similar with vapor, RH is positively relative to τ , WVC and α. However, it is negatively relative to ω from 670 to 1020 nm, but positively related to ω at 440 nm. The correlations between RH and Cv , Reff and Rv in the total, fine and coarse modes, respectively are quite similar to those with the vapor pressure. The same is the case for the real and imaginary parts of the refractive indexes at all wavelengths. A higher RH could obviously cause the particles’ hygroscopic increase, which could result in greater extinction and a larger volume of fine particles. 3.2.5 Wind speed The wind speed is obviously negative to WVC and α, but positive to SSA at 670, 870 and 1020 nm. As compared to relative humidity, the correlation of wind speed to Cv , Reff and Rv in the total, fine and coarse modes, respectively is entirely the opposite. Different to the other four elements, wind speed has a positive correlation with Rr but a negative correlation with Ri

570

AOP AND ITS RADIATIVE FORCING OVER YULIN, CHINA

VOL. 26

Table 2. Correlation between meteorological elements and aerosol optical properties. Pressure τ (440 nm) WVC (cm) α (440–870 nm) ω (440 nm) ω (670 nm) ω (870 nm) ω (1022 nm) Cv − T Reff − T Rv − T Cv − F Reff − F Rv − F Cv − C Reff − C Rv − C Rr (440 nm) Rr (670 nm) Rr (870 nm) Rr (1022 nm) Ri (440 nm) Ri (670 nm) Ri (870 nm) Ri (1022 nm)

∗∗

−0.343 −0.539∗∗ 0.068 0.127 0.094 0.078 0.060 −0.245∗∗ −0.027 0.016 −0.235∗∗ −0.047 −0.049 −0.188∗ 0.001 0.054 −0.178# −0.167 −0.199# −0.204# −0.151 −0.051 −0.039 −0.023

Temperature ∗∗

0.369 0.758∗∗ 0.211∗∗ 0.050 −0.289∗∗ −0.297∗∗ −0.306∗∗ 0.045 −0.229∗∗ −0.270∗∗ 0.403∗∗ 0.191∗ 0.158∗ −0.052 0.184∗ 0.157# −0.141 −0.144 −0.078 −0.051 0.317∗∗ 0.333∗∗ 0.299∗∗ 0.288∗∗

Vapor Pressure ∗∗

0.411 0.927∗∗ 0.428∗∗ 0.335∗∗ −0.238∗ −0.284∗∗ −0.339∗∗ −0.121 −0.444∗∗ −0.574∗∗ 0.584∗∗ 0.538∗∗ 0.493∗∗ −0.262∗∗ 0.411∗∗ 0.357∗∗ −0.461∗∗ −0.523∗∗ −0.511∗∗ −0.495∗∗ 0.230∗∗ 0.431∗∗ 0.410∗∗ 0.414∗∗

RH

Wind Speed ∗

0.170 0.490∗∗ 0.464∗∗ 0.333∗∗ −0.245∗ −0.304∗∗ −0.361∗∗ −0.331∗∗ −0.515∗∗ −0.640∗∗ 0.446∗∗ 0.575∗∗ 0.536∗∗ −0.439∗∗ 0.309∗∗ 0.270∗∗ −0.566∗∗ −0.612∗∗ −0.624∗∗ −0.600∗∗ 0.195# 0.432∗∗ 0.436∗∗ 0.454∗∗

0.089 −0.176# −0.340∗∗ 0.011 0.386∗∗ 0.397∗∗ 0.403∗∗ 0.404∗∗ 0.479∗∗ 0.427∗∗ −0.138# −0.189∗ −0.151# 0.438∗∗ −0.028 −0.068 0.338∗∗ 0.307∗∗ 0.263∗ 0.240∗ −0.348∗∗ −0.410∗∗ −0.400∗∗ −0.404∗∗

τ : AOD; WVC: water vapor content in cm; ω: single scattering albedo; Cv : volume concentration in µm3 µm−2 ; Reff : effective radius; Rv : the volume geometric mean radius; “−T ”, “−F ”, “−C” stand for “total mode”, “fine mode” and “coarse mode”, respectively; Rr : real part of refractive exponent; Ri : imaginary part of refractive exponent; ∗∗ : significant level P < 0.01; ∗ : significant level P < 0.05; #: significant level P < 0.10.

at all wavelengths. A faster wind speed mainly occurs during the winter and spring seasons. During the winter and spring, the water vapor over Yulin is lower. Furthermore, coarse dust particles are much more distributed in the atmosphere, which could contribute to the smaller α and higher SSA. From the above analysis, it is found that meteorological elements have great effects on the aerosol optical properties in Yulin. Vapor pressure, relative humidity and wind speed have significant correlations with almost all of the aerosol optical properties in Yulin. The effect of pressure on the aerosol optical properties is smaller than the other four meteorological elements. 3.3

Sand dust and air pollution case studies

In this article, six days of data (with AOD>1.00) were selected to study the effect of the aerosol source on the aerosol optical properties over Yulin. Figure 6 shows seven-day back trajectories ending over Yulin (Draxler and Hess, 1998; Draxler and Rolph, 2003). Figure 6a illustrates that the air masses are from middle Asia and the northern Xinjiang re-

gions, which pass through the deserts in northern China and the Gobi deserts in Mongolia. The aerosol size distributions also show the characteristics of mineral dust particles (Fig. 7a). The coarse mode particles are dominant about 97%, 95%, and 96% (Table 3) of the total volume concentrations on 1, 2 May 2001 and 6 April 2002, respectively. It can be concluded then that because of these frequent dust events, mineral dust aerosol particles are a major component that contribute to Yulin during the spring. The deserts in middle Asia, western China, Mongolia and the Mu Us desert near Yulin are the aerosol sources. Different to Fig. 6a, Fig. 6b illustrates that the air masses over Yulin are from the eastern and middle regions of China where there are industrial activities. Large quantities of fossil fuels have been burned, causing an increase in anthropogenic aerosol loadings. From Fig. 7b, it is very clear that the aerosol particles show a typical distribution of anthropogenic aerosols. The fine mode aerosols are dominant at about 82%, 70% and 89% (Table 3) of the total volume of concentrations on 25 October 2001, 19 June 2001 and 5 July 2002, respectively.

NO. 3

571

CHE ET AL.

Fig. 6. Back-trajectories ending over Yulin at a pressure level of 850 hPa showing the transport paths of air masses under (a) dust events and (b) haze pollution events. 1.0

0.8

) m µ 3/ m µ (r nl d/ V d

0 .2 5

a

20010501 20010502

20011025 b 20020619 20020705

0 .2 0

) m µ 3/ m µ (r nl d/ V d

20020406

2

2

0.6

0 .1 5

0.4

0 .1 0

0.2

0 .0 5

0.0

0 .0 0 0 .1

1

10

0 .1

Radius (µm)

1

10

Radius (µm)

Fig. 7. Aerosol volume size distributions in the total atmospheric column under (a) dust events and (b) haze events conditions.

Table 3 presents more details about the aerosol optical properties over Yulin under two different atmospheric conditions. WVCs under pollution conditions are a factor of 1.8–3.6 to those under dust events. The ˚ Angstr¨ om exponent values under dust events are near zero and are even negative, which is considered typical characteristics of super-large aerosols. Meanwhile, for pollution events, the ˚ Angstr¨ om exponent values are larger than 1.00, which is considered typical characteristics of small aerosols. By comparing the SSA, the aerosol particles under the pollution case may have greater scattering ability than mineral dust particles. Refractive indexes show that mineral dust aerosols have higher real part but lower imaginary part values than anthropogenic aerosols in Yulin. During the dust event, the real (Rr ) and imaginary (Ri ) parts of the refractive index (Ri ) are about 1.56 to 1.60 and 0.003 to 0.004, respectively; meanwhile, during the haze event, Rr and Ri are about 1.42 to 1.47 and 0.005 to 0.009, respectively. Both for the fine and coarse modes, the asymmetry factor of aerosols under dust event days is smaller than under pollution event days. The Reff of the total mode under dust events to that under pollution events is a factor of 3.2–4.4. Meanwhile, for

the fine mode, the Reff under pollution events to that under dust events is a factor of 1.5–3.1. There is no obvious difference for Reff and Rv under two different atmospheric conditions. 3.4 3.4.1

Aerosol radiative forcing (ARF) Interannual variability of ARF

Figures 8a and b illustrate the monthly average of the aerosol radiative forcing at the surface and top of the atmosphere (TOA). From Fig. 8a, it can be seen that there are large radiative forcings at the surface (> 80 W m−2 ) from April to October. It fluctuates from March to June and decreases from July to November. Furthermore, the ARFs in July and August are also more than −100 W m−2 . The forcing varies in the range of −38 to −108 W m−2 (average −86 W m−2 ) at the surface. The maximum occurs in April with a mean value of −108±60 W m−2 . The minimum occurs in November with a mean value of −38±20 W m−2 . The ARF at TOA varies irregularly from March to November (Fig. 8b). The ARF at TOA is higher in April and from July to October. The mean values

572

AOP AND ITS RADIATIVE FORCING OVER YULIN, CHINA

VOL. 26

Table 3. Aerosol optical properties under dust activities and haze conditions. Dust events

τ (440 nm) WVC (cm) α (440–870 nm) ω (440 nm) Rr (440 nm) Ri (440 nm) g (440 nm)−F g (440 nm)−C Cv − T Reff − T Rv − T σ−T Cv − F Reff − F Rv − F σ−F Cv − C Reff − C Rv − C σ−C

20010501

20010502

1.21 0.69 0.04 0.91 1.56 0.003 0.68 0.79 0.99 1.49 2.08 0.66 0.03 0.19 0.23 0.60 0.96 1.94 2.25 0.52

1.42 0.85 0.05 0.89 1.60 0.004 0.67 0.79 1.08 1.40 2.04 0.71 0.04 0.19 0.24 0.58 1.03 1.88 2.23 0.56

Haze events 20020406

20011025

20020619

20020705

1.59 0.69 −0.02 0.89 1.60 0.003 0.58 0.79 1.46 1.16 2.26 0.83 0.06 0.10 0.14 0.90 1.40 2.08 2.53 0.60

1.92 1.51 0.90 0.94 1.47 0.007 0.73 0.85 0.33 0.34 0.49 0.96 0.27 0.29 0.34 0.55 0.06 1.99 2.42 0.67

1.15 2.37 0.95 0.93 1.42 0.009 0.76 0.88 0.23 0.36 0.57 1.06 0.16 0.28 0.32 0.55 0.06 2.12 2.43 0.55

1.62 2.45 0.76 0.96 1.47 0.005 0.75 0.85 0.27 0.35 0.46 0.85 0.24 0.31 0.36 0.50 0.03 2.31 2.90 0.70

g: asymmetry factor; σ: the geometric standard deviation; the others are the same as those in Table 2.

2 -

) m 0 W ( -20 ec -40 arf uS -60 ta -80 gn -100 ic ro -120 F -140 evi ta -160 id aR-180

M onth

Mar Apr May Jun Jul Aug Sep Oct Nov

2 -

a

) 0 m (W -5 A-10 O Tt a -15 gn ic -20 ro F-25 ev it iad -30 a -35 R

Month

Mar Apr May Jun Jul Aug Sep Oct Nov

b

Fig. 8. Mean monthly values of the aerosol radiative forcing (a) at the surface and (b) at the top of the atmosphere, with the bars indicating standard deviation.

are larger than −10 W m−2 . The forcing varies in the range of −5 to −19 W m−2 (average −10 W m−2 ) at the TOA. The ARF at the TOA in April is the maximum with a value of −19±15 W m−2 , while the minimum occurs in November, with a mean value of about −5±4 W m−2 . 3.4.2 Radiative forcing efficiency The correlation between all instantaneous AODs and ARFs at the surface and TOA and the linear regression parameters are shown in Figs. 9a and 9b. There are negatively linear correlations between AOD and ARF both at the surface and at the TOA, with very high confidence levels of more than 99.9%. The

relative coefficients are about −0.93 between AOD and ARF at the surface, and −0.75 between AOD and ARF at the TOA. The regression relationships are found to be as follows: ARFsurface = −26.02 − 130.80τ440 , ARFTOA = 3.57 − 29.92τ440 , where τ440 means AOD at 440 nm. The ratio of ARF to AOD is defined as the radiative forcing efficiency. From Figs. 10a and 10b, it can be seen that there are obvious seasonal variations in ARF efficiencies at the surface and at TOA. For the efficiency at the surface, it is lower in the summer

NO. 3

573

CHE ET AL.

2 -

AOD at 440 nm ) 0 .0 0 .2 0 .4 0.6 0 .8 1 .0 1.2 1 .4 m 0 W ( -5 0 ec far -1 0 0 uS ta -1 5 0 Y = A + B1 * X gn -2 0 0 ic Parameter Value Error ro -26.02 1.27 F-2 5 0 AB1 -130.80 2.24 ev R =-0.93 tia -3 0 0 P<0.001 id a -3 5 0 R

AOD at 440 nm 1 .6

1.8

2 .0

a

2 -

) m W ( A O Tt a gn ic ro F ev tia id a R

0.0 20

0.2

0 .4

0.6

0.8

1.0

1.2

1.4

Y = A + B1 * X

1 .6

1.8

2.0

b

Parameter Value Error A 3.57 1.14 B1 -29.92 2.24 R =-0.75 P<0.001

0

-20

-40

-60

Fig. 9. Strong correlation between the aerosol optical depth at 440 nm and the aerosol radiative forcing (a) at the surface and (b) at the top of the atmosphere. 2 -

) m 0 W ( ec arf -50 uS at -100 yc ne ic fi -150 E ngi -200 cr Fo veit -250 iad a -300 R lo osr -350 eA

Month

Mar Apr May Jun Jul Aug Sep Oct Nov

2 -

a

) m 0 (W A O Tt -15 a yc ne ciif -30 fE gn ic ro-45 F ev it ai da-60 R lo so re-75 A

Month

Mar Apr May Jun Jul Aug Sep Oct Nov

b

Fig. 10. Mean monthly values of the aerosol radiative forcing efficiency (a) at the surface and (b) at the top of the atmosphere, with the bars indicating standard deviation.

while higher in the spring and autumn. It decreases from March to July and then increases from July to November. The minimum occurs in July with a mean value of −215±61 W m−2 . The maximum occurs in November with a mean value of −316±27 W m−2 . The variation in the ARF efficiency at TOA is similar to that at the surface. It decreases from March to June and then increases until November. The efficiency in spring is larger than in autumn, and it is lowest in the summer. The maximum occurs in March with a mean value of 44±27 W m−2 , while the minimum occurs in June with a mean value of −10±30 W m−2 .

tions of the ARF efficiency are rather narrow with a modal value of about −240 W m−2 at the surface and about −30 W m−2 at TOA. Gaussian regression needs to be used to fit the probability distribution. The regression equations are given in Figs. 11a–d. The correlations of Gaussian regression are very obvious. The coefficients of determination (R2 ) are 0.96, 0.93, 0.97 and 0.94 for ARF at the surface, the ARF efficiency at the surface, ARF at TOA and the ARF efficiency at TOA.

3.4.3 Frequency distribution The frequency histograms of all instantaneous ARFs and ARF efficiencies at the surface and at TOA are shown in Figs. 11a–d for the calculated data. There are obvious single peak distributions for ARF and the RF efficiency both at the surface and at TOA. The probability distribution of ARF at the surface is rather narrow with a modal value of about −75 W m−2 . The frequency histogram of WVC also shows a one-peak distribution with a maximum at 0 W m−2 . The frequency value is about 22%. The probability distribu-

The principal conclusions drawn from this work can be summarized as follows. (1) The interannual variability of the optical properties (aerosol optical depth, single scattering albedo, volume size distribution and ˚ Angstr¨ om parameter) over Yulin was significant for the time period of this study. Seasonal variations in the optical parameters showed that the dust aerosols in the spring and the anthropogenic aerosols in the other seasons are the major contributors to the optical depth, single scattering albedo, volume size distribution and the ˚ Angstr¨ om

4.

Conclusion

574

AOP AND ITS RADIATIVE FORCING OVER YULIN, CHINA ) 20 % ( s e c 15 n e r r u c c 10 o f o y c n 5 e u q e r F 0

a

y=0.306+14.104e R =0.96

-2*((x+71.709)/81.456)**2

2

)25 % ( s e20 c n e r r u15 c c o f o10 y c n e 5 u q e r F0

-300 -270 -240 -210 -180 -150 -120 -90 -60 -30

Radiative Forcing at Surface (W m ) e c y=-0.059+10.504 R =0.97

0

-60

-2

)

15

b

y=0.510+17.905e R =0.93

-50

-40

VOL. 26

-2*((x+5.249)/20.412)**2

2

-30

-20

-10

0

10

Radiative Forcing at TOA (W m ) e d y=1.024+18.246 R =0.94

20

-2

)25

-2*((x+247.277)/115.850)**2 % ( s 2 e c n e 10 r r u c c o f o 5 y c n e u q e r F 0 -420-390-360-330-300-270-240 -210 -180-150-120 -90

Radiative Forcing Efficiency at Surface (W m ) -2

-2*((x+27.291)/35.240)**2

% ( s 20 e c n e r r 15 u c c o f 10 o y c n e5 u q e r F0

2

-80

-60

-40

-20

0

20

40

60

80

100

Radiative Forcing Efficiency at TOA (W m ) -2

Fig. 11. Frequency of occurrences and the Gaussian regression lines of the aerosol radiative forcing [(a) at the surface and (b) at the top of the atmosphere] and the aerosol radiative forcing efficiency [(c) at the surface and (d) at the top of the atmosphere].

parameter variability. The regression relationships between the aerosol optical depth and WVC in the total atmospheric column have been derived. There are significant positive correlations between AOD and WVC, and the ˚ Angstr¨ om parameter and WVC. The spectral variability of the single scattering albedo was different. When dust was not a major component, the SSA decreased with wavelength. In the presence of dust, the spectral dependence of SSA was almost neutral. The variations in the aerosol volume size distributions over Yulin were largely due to changes in the concentration of the coarse aerosol fraction (variation coefficient of 76%). There is one peak probability distribution for the AOD distribution with a modal value of about 0.40, but there is a two-peak distribution for the ˚ Angstr¨ om exponent with two modal values of about 0.5 and 1.1. (2) Using a graphical method, it can be confirmed that Yulin is affected both by the superposition of dust and pollution conditions. Dust events and pollution haze (fine mode aerosols) generate the largest aerosol loads, reaching AOD levels >1.00 over Yulin. (3) Using the back trajectory method, Yulin is found to not only be affected by the dust aerosols from the arid regions located in middle Asia, western China and Mongolia in the spring, but also by anthropogenic aerosols from central China during the summer season. (4) The correlation between meteorological elements and the daily aerosol optical properties showed

that vapor pressure, relative humidity and wind speed have significant correlations with almost all of the aerosol optical properties in Yulin. (5) A large value has been observed in ARF during the dusty spring season of April as compared to the clear season of November. The average surface forcing change was −70 W m−2 , while the TOA forcing change was −14 W m−2 . There are obvious seasonal variations in the ARF efficiencies at the surface and at TOA. A high degree of correlation has been found between AOD at the wavelength of 440 nm and ARF (negative or cooling) at the surface (R=0.93), and a moderate correlation (R=0.75) at the TOA. The aerosol forcing efficiency at the wavelength of 440 nm is found to be −253±32 W m−2 and −24±12 W m−2 at the surface and at TOA, respectively, over the edge of the Mu Us desert. There are obvious single peak distributions for ARF and the RF efficiency both at the surface and at TOA. The high degree of correlations using Gaussian regression are found with the coefficients of determination (R2 ) of 0.96, 0.93, 0.97 and 0.94 for ARF at the surface, the ARF efficiency at the surface, ARF at TOA, and the ARF efficiency at TOA. Acknowledgements. This work is financially supported by grants from the National Key Project of Basic Research (2006CB403702 and 2006CB403701), the CAMS Basis Research Project and National Natural Science Foun-

NO. 3

CHE ET AL.

dation of China under Grant No. 40405001. We are thankful to the Meteorological Information Center, CMA for providing the meteorological data. We would also like to thank Mr. Jiping Zhang for the instrument establishing and maintaining. REFERENCES Ackerman, P., and O. B. Toon, 1981: Absorption of visible radiation in atmosphere containing mixtures of absorbing and nonabsorbing particles. Appl. Opt., 20, 3661–3668. Alfaro, S. C., and Coauthors, 2003: Chemical and optical characterization of aerosols measured in spring 2002 at the ACE-Asia supersite, Zhenbeitai, China. J. Geophys. Res., 108(D23), 8641, doi: 10.1029/2002JD003214. Andreae, M. O., 1995: Climate effects of changing atmospheric aerosol levels. World Survey of Climatology, Future Climates of the World, A. Henderson-Sellers, Ed., Elsevier Amsterdam, 341–392. Charlson, R. J., S. E. Schwartz, J. M. Hales, D. Cess, J. A. Coakley, and J. E. Hansen, 1992: Climate forcing by anthropogenic aerosols. Science, 255, 423–430. Che, H. Z., G. Y. Shi, X. Y. Zhang, R. Arimoto, J. Q. Zhao, L. Xu, B. Wang, and Z. H. Chen, 2005: Analysis of 40 years of solar radiation data from China, 1961–2000. Geophys. Res. Lett., 32, L06803, doi: 10.1029/2004GL022322. Cheng, T. T., Y. Liu, D. R. Lu, Y. F. Xu, and H. Y. Li, 2006: Aerosol properties and radiative forcing in Hunshan Dake desert, northern China. Atmos. Environ., 40, 2169–2179. Dey, S., S. N. Tripathi, R. P. Singh, and B. N. Holben, 2004: Influence of dust storms on the aerosol optical properties over the Indo-Gangetic basin. J. Geophys. Res., 109, D20211, doi: 10.1029/2004JD004924. Draxler, R. R., and G. D. Hess, 1998: An overview of the HYSPLIT-4 Modelling system for trajectories, dispersion and deposition. Australian Meteorological Magazine, 47, 295–308. Draxler, R. R., and G. D. Rolph, 2003: HYSPLIT (HYbrid Single-Particle Lagrangian Integrated Trajectory) Model access via NOAA ARL READY website (http://www.arl.noaa.gov/ready/open/hysplit4.html). NOAA Air Resources Laboratory, Silver Spring, MD. Dubuisson, P., J. C. Buriez, and Y. Fouquart, 1996: High spectral resolution solar radiative transfer in absorbing and scattering media, application to the satellite simulation. Journal of Quantitative Spectroscopy and Radiative Transfer, 55(1), 103–126. Dubovik, O., and M. D. King, 2000: A flexible inversion algorithm for the retrieval of aerosol optical properties from Sun and sky radiance measurements. J. Geophys. Res., 105, 20673–20696. Dubovik, O., A. Smirnov, B. N. Holben, M. D. King, Y. J. Kaufman, T. F. Eck, and I. Slutsker, 2000: Accuracy assessments of aerosol optical properties re-

575

trieved from AERONET sun and sky radiance measurements. J. Geophys. Res., 105, 9791–9806. Eck, T. F., B. N. Holben, J. S. Reid, O. Dubovik, A. Smirnov, N. T. O’Neill, I. Slutsker, and S. Kinne, 1999: Wavelength dependence of the optical depth of biomass burning, urban and desert dust aerosols. J. Geophys. Res., 104, 31333–31350. Eck, T. F., and Coauthors, 2005: Columnar aerosol optical properties at AERONET sites in central eastern Asia and aerosol transport to the tropical mid-Pacific. J. Geophys. Res., 110, D06202, doi: 10.1029/2004JD005274. Gobbi, G. P., Y. J. Kaufman, I. Koren, and T. F. Eck, 2007: Classification of aerosol properties derived from AERONET direct sun data. Atmospheric Chemistry and Physics, 7, 453–458. Gai, C. S., X. Q. Li, and F. S. Zhao, 2006: Mineral aerosol properties observed in the northwest region of China. Global and Planetary Change, 52, 173–181. 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/distribution during 2001 ACE-Asia, 2. Model Simulation and Validation. J. Geophys. Res., 108, 4262, doi: 10.1029/2002JD002633. Holben, B. N., and Coauthors, 1998: AERONET-A federated instrument network and data archive for aerosol characterization. Remote Sens. Environ., 66, 1–16. IPCC, 2001: Climate Change 2001: The Scientific Basis. Houghton et al., Eds., Cambridge University Press, New York, 896pp. Jickells, T. D., and Coauthors, 2005: Global iron connections between desert dust, ocean biogeochemistry, and climate. Science, 308, 67–71. doi: 10.1126/science.1105959. Kalashnikova, O. V., P. M. Franklin, A. Eldering, and D. Anderson, 2007: Application of satellite and groundbased data to investigate the UV radiative effects of Australian aerosols. Remote Sens. Environ., 107, 65–80. Kim, D. H., B. J. Sohn, T. Nakajima, T. Takamura, T. Takemura, B. C. Choi, and S.-C. Yoon, 2004: Aerosol optical properties over East Asia determined from ground-based sky radiation measurements. J. Geophys. Res., 109, D02209, doi: 10.1029/2003JD003387. Li, X., H. Maring, D. Savoie, K. Voss, and J. M. Prospero, 1996: Dominance of mineral dust in aerosol light-scattering in the North Atlantic trade winds. Nature, 380, 416–419. Menon, S., J. E. Hansen, L. Nazarenko, and Y. F. Luo, 2002: Climate effects of black carbon aerosols in China and India. Science, 297, 2249–2252. Mikami, M., and Coauthors, 2002: The impact of aeolian dust on climate: Sino-Japanese cooperative project ADEC. Journal of Arid Land Studies, 11(4), 211– 222. Nakajima, T., T. Hayasaka, A. Higurashi, G. Hashida, N. Moharram-Nejad, Y. Najafi, and H. Valavi, 1996a:

576

AOP AND ITS RADIATIVE FORCING OVER YULIN, CHINA

Aerosol optical properties in the Iranian region obtained by ground-based solar measurements in the summer of 1991. J. Appl. Meteor., 35, 1265–1278. Nakajima, T., G. Tonna, R. Rao, P. Boi, Y. Kaufman, and B. Holben, 1996b: Use of sky brightness measurements from ground for remote sensing of particulate polydispersions. Appl. Opt., 35, 2672–2686. Nakajima, T., and Coauthors, 2003: Significance of direct and indirect radiative forcings of aerosols in the East China Sea region. J. Geophys. Res., 108(D23), 8658, doi: 10.1029/2002JD003261. Prospero, J. M., and P. J. Lamb, 2003: African droughts and dust transport to the Caribbean: Climate change implications. Science, 302, 1024–1027. Scott, N. A., 1974: A direct method of computation of the transmission function of an inhomogeneous gaseous medium—I: Description of the method. Journal of Quantitative Spectroscopy and Radiative Transfer, 14, 691–704. Smirnov, A., B. N. Holben, T. F. Eck, O. Dubovik, and I. Slutsker, 2000: Cloud screening and quality control algorithms for the AERONET database. Remote Sens. Environ., 73, 337–349. Smirnov, A., and Coauthors, 2002: Atmospheric aerosol optical properties in the Persian Gulf. J. Atmos. Sci., 59(3), 620–634. Sokolik, I. N., and O. B. Toon, 1999: Incorporation of mineralogical composition into models of the radiative properties of mineral aerosol from UV to IR wavelengths. J. Geophys. Res., 104, 9423–9444. Stanhill, G., and S. Cohen, 2001: Global dimming: A review of the evidence for a widespread and significant reduction in global radiation with discussion of its probable causes and possible agricultural conse-

VOL. 26

quences. Agricultural and Forest Meteorology, 107, 255–278. Tegen, I., and I. Fung, 1995: Contribution to the atmospheric mineral aerosol load from land source modification. J. Geophys. Res., 100, 18707–18726. Uematsu, M., R. A. Duce, J. M. Prospero, L. Chen, J. T. Merrill, and R. L. McDonald, 1983: Transport of mineral aerosol from Asia over the North Pacific Ocean. J. Geophys. Res., 88, 5343–5352. Xia, X. A., H. B. Chen, P. C. Wang, X. M. Zong, and P. Gouloub, 2005: Aerosol properties and their spatial and temporal variations over north China in spring 2001. Tellus(B), 57, 28–39. Xin, J., S. Wang, Y. Wang, J. Yuan, W. Zhang, and Y. Sun, 2005: Optical properties and size distribution of dust aerosols over the Tengger Desert in Northern China. Atmos. Environ., 39, 5971–5978. Zakey, A. S., M. M. Abdelwahab, and P. A. Makar, 2004: Atmospheric turbidity over Egypt. Atmos. Environ., 38, 1579–1591. Zhang, X. Y., 2001: Source distributions, emission, transportation, deposition of Asia dust and loess accumulation. Quaternary Science, 21, 29–40. (in Chinese) Zhang, X. Y., S. L. Gong, R. Arimoto, Z. X. Shen, F. M. Mei, D. Wang and Y. Cheng, 2001: Characterization and temporal variation of Asian dust aerosol from a site in the northern Chinese deserts. Journal of Atmospheric Chemistry, 44, 241–257. Zhang, X. Y., S. L. Gong, T. L. Zhao, R. Arimoto, Y. Q. Wang, and Z. J. Zhou, 2003: Sources of Asian dust and role of climate change versus desertification in Asian dust emission. Geophys. Res. Lett., 30, 2272, doi: 10.1029/2003GL018206.