Characteristics of aerosol transport and distribution in East Asia

Atmospheric Research 132–133 (2013) 185–198

Contents lists available at ScienceDirect

Atmospheric Research journal homepage: www.elsevier.com/locate/atmos

Characteristics of aerosol transport and distribution in East Asia Jian Wu a,b,⁎, Jun Guo a,c, Deming Zhao d a b c d

Department of Atmospheric Science, Yunnan University, Kunming 650091, China Yunnan Institute of Geography, Kunming 650091, China School of Atmospheric Physics, Nanjing University of Information Science & Technology, Nanjing 210044, China Key Laboratory of Regional Climate-Environment for Temperate East Asia, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China

a r t i c l e

i n f o

Article history: Received 23 October 2012 Received in revised form 8 April 2013 Accepted 27 May 2013 Keywords: Aerosol AOD Transport East Asia GOCART model

a b s t r a c t We used daily aerosol simulations for the period from 2001 to 2003 that were generated by the Goddard Chemistry Aerosol Radiation and Transport (GOCART) model to characterize aerosol transport and distributions in East Asia. In comparison with the AERONET, MODIS, and visibility observations, the model can capture the main distribution features of the aerosol optical depth (AOD) and its temporal changes with a correlation coefficient of 0.75 and 0.85, respectively. It was found that high AODs occur in Central China, the Sichuan basin, the Indo-China peninsula, the Indian subcontinent, and the Bay of Bengal because of black carbon, organic matter, and sulfate, whereas in the Taklimakan desert and its adjacent regions, high AODs occur because of dust. The potential effects of the hygroscopicity of aerosol particles on the AOD were mainly observed in the Sichuan basin, the Bay of Bengal, the Indo-China peninsula, and Central and Southern China. The East Asian aerosol transport was distinctly affected by the flux divergence induced by aerosol advection (AFD) and by the flux divergence induced by wind divergence/convergence (WFD). For black carbon, organic matter, and sulfate, the effect of AFD was a factor of 2 or 3 larger than that of WFD in the divergence region, whereas AFD dropped to 70% of WFD in the convergence region. The high AOD of black carbon, organic matter, and sulfate over the Sichuan basin related to the circulation characteristics of convergence in low altitudes and divergence in high altitudes, which can collect aerosol from adjacent regions at altitudes below 300 hPa and cause them to diverge easterly at higher altitudes. © 2013 Elsevier B.V. All rights reserved.

1. Introduction East Asia is one of the most significant sources of aerosol, and the AOD is higher than in most regions of the globe. East Asia is also a monsoon region with more complex topography than any other region, which adds further complexity to aerosol transport and distribution. The distribution characteristics of East Asian aerosols have been reported. The measured annual mean AOD was reported as a pattern related to the geographical features, with the maxima located over some basins (Luo et al., 2001). Luo et al.

⁎ Corresponding author at: Department of Atmospheric Science, Yunnan University, Kunming 650091, China. Tel.: + 86 87165033734. E-mail address: [email protected] (J. Wu). 0169-8095/$ – see front matter © 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.atmosres.2013.05.018

(2001) also reported that the AOD reaches its maximum in spring for most Chinese regions, but the season in which the minimum is reached differs from region to region. It was also found from analyzing MODIS AOD data that anthropogenic aerosol is the main component of aerosol in East China (Li et al., 2003). The aerosol component analysis found that the main components of aerosol in the North and South part of China are sulfate and organic matter, whereas black carbon and sea salt are scarce. Dust was mainly located in Northwestern China, and the maximum value was in April (Cui et al., 2009). A Total Ozone Mapping Spectrometer (TOMS) sensor on the Nimbus 7 satellite was used to map the global distribution of major atmospheric dust sources and found that the largest and most persistent sources in China are located in the Taklimakan desert and in Mongolia (Prospero et al., 2002), where most East Asian dust storms originate (Sun et al., 2001).

186

J. Wu et al. / Atmospheric Research 132–133 (2013) 185–198

Apart from the observations mentioned above, some numerical simulations have also been carried out. The simulation results revealed high levels of organic matter aerosols (larger than 16 mg·m−2) concentrated in the middle regions of the Yangtze River and the South part of China (Zhang et al., 2004). Another simulation using a coupled regional climate and atmospheric chemistry model reported that the AOD is high in Eastern China and in the deserts of Northwestern China and that the AOD in winter is apparently lower than that in other seasons (Han, 2010). Though there have been many similarities among the aforementioned works that reported the main spatial and temporal characteristics of different aerosol species in East Asia, some discrepancies can be found between the simulations and observations. The transport of East Asian aerosols has also been extensively studied. Black carbon from the Indo-China peninsula and the Chinese mainland could be transported to Japan and its adjacent seacoast areas (Uno et al., 2003). It was estimated that 800 Tg/a dust could enter the atmosphere, of which 20% could be transported on a regional scale and 50% was subject to long-range transport over the Pacific Ocean and beyond (Zhang et al., 1997; Duce, 1995; Perry et al., 2004). Based on satellite measurements, Yu et al. (2008) estimated that approximately 18 Tg/a of pollution aerosol was exported from East Asia to the Northwestern Pacific Ocean, of which approximately 25% reached the west coast of North America. The imported flux of 4.4 Tg/a to North America was equivalent to approximately 15% of the local emissions from the United States and Canada. Yu et al. (2012) further estimate that the imported dust and pollution mass from both shores of North America are comparable with domestic emissions in North America. The analyses of aerosol horizontal fluxes during the ACE-Asia observation summarized the main aerosol transport routes (Satake et al., 2004). Chang et al. (2010) demonstrate that anthropogenic pollutants from the Asian mainland including nitrates, sulfates, black carbon, gaseous pollutants (CO, SO2, and O3), and other fractions of fine particles, would influence the downwind regions as the Asian dust is transported. The aerosol distribution characteristics in East Asia and the aerosol outflow to the Pacific Ocean have been well documented, whereas the aerosol transport to inner East Asia and its effects on the optical extinction from different aerosol types, aerosol concentration, hygroscopicity, absorption, or scattering properties in East Asia still have not been well reported. The paper focuses on aerosol transport and different extinction features in East Asia. The paper is organized as follows: The data and main methods are depicted in Section 2. The reliability of the GOCART aerosol results is described in Section 3. Section 4 presents the East Asian aerosol distribution characteristics and their effects on optical extinction. Conclusions and discussions are given in Section 5, in which the aerosol transport features in East Asia are discussed.

meteorological fields of the Goddard Earth Observing System Data Assimilation System (GEOS-4) (Bloom et al., 2005) and has a horizontal resolution of 2 deg latitude by 2.5 deg longitude. The model has 30 vertical sigma layers with the top of the model at 0.01 hPa (Chin et al., 2002, 2003). This model has been well documented (Chin et al., 2000, 2002, 2003, 2004, 2007, 2009; Ginoux et al., 2001). The physical and chemical processes in the GOCART model of aerosol simulation include emissions, gas and aqueous chemical reactions, advection, boundary layer mixing, convection, and dry and wet deposition. Emission species include sulfur, dust, black carbon, organic matter, and sea-salt. The gaseous and aqueous chemicals included OH, H2O2, and NO3 distributions for sulfur oxidation. Advection is a flux-form semi-Lagrangian method. Boundary layer mixing is second-order closure scheme. The moist convection calculation uses archived cloud mass flux fields. Dry deposition has two parts, gravitational settling, which is a function of aerosol particle size and air viscosity, and surface deposition, which is a function of surface type and meteorological conditions. Wet deposition represents the process of rainout or washout in large-scale precipitation. This new version of the GOCART model includes updated emission sources. The annual anthropogenic emissions of SO2, black carbon, and organic matter for the period between 2000 and 2006 were taken from Streets et al. (2009). Other anthropogenic emissions were obtained from available database, such as Eyring et al. (2005) for ship emissions and Mortlock et al. (1998) for aircraft. Biomass burning emissions of SO2, black carbon, and organic matter use the Global Fire Emission Dataset ver. 2 (GFED v2) (van der Werf et al., 2003; Randerson et al., 2007), and the emission factors from Chin et al. (2004). Volcanic emissions of SO2 were obtained from the Global Volcanism Program database (Siebert et al., 2002), and satellite SO2 data from the Total Ozone Mapping Spectrometer (TOMS) (Carn et al., 2003) and the Ozone Monitoring Instrument (OMI) (Krotkov et al., 2006) are also included. Emissions of SO2 from continuously degassing volcanoes are assumed to be constant (Andres et al., 1998). Dust and sea salt emissions with particle radii from 0.01 to 10 μm are calculated as a function of surface winds and other conditions in the model (Ginoux et al., 2001; Gong, 2003). Fig. 1 shows the aerosol emissions, including carbonaceous aerosols (black carbon and organic matter), sulfur (SO2 and DMS, without volcanic), sea salt, and dust. The sources of carbonaceous aerosol are the Bay of Bengal and Central and Eastern China; the sulfur source lies in the region east of 110°E in the Chinese mainland; sea salt comes from the Northwestern Pacific Ocean; and dust sources are mainly located in the Taklimakan desert and the Northwest part of China (NWC) (Table 1). AOD (τ) in this model is calculated by formula (1):

2. Model description, data, and methodology

τ¼

3QM 4ρRe

ð1Þ

2.1. Model The Goddard Chemistry Aerosol Radiation and Transport (GOCART) model simulates the major tropospheric aerosol components, including sulfate, organic matter, black carbon, sea salt, and dust aerosols. The model is driven by the assimilated

where ρ is the aerosol particle density, Re is the effective radius, Q is the extinction coefficient from the Mie scattering theory using the aerosol size distribution and refractive index, M is the aerosol mass combined with both the mass of dry aerosol particles (Md) and the mass of water taken up by

J. Wu et al. / Atmospheric Research 132–133 (2013) 185–198

187

Fig. 1. Aerosol emission rate (unit: mg/m2/day). (a). Carbonaceous emission from black carbon and organic matter, (b) sulfur emission from SO2 and DMS, (c) sea salt, and (d) dust. Overlaid in (d) are the locations of 16 selected AERONET stations.

aerosols. Therefore, formula (1) can be further converted to formula (2):

τ¼

3QM M ¼ MEE•Md 4ρRe Md d

ð2Þ

where MEE is the mean mass extinction coefficient for the model atmosphere and contains all the humidification information. 2.2. Methodology The model results used in this study include the daily AOD at 0.55 μm and the mass concentration for black carbon, organic matter, sulfate, sea salt, and dust during 2001–2003. The results of the GOCART model can be obtained through the Goddard Earth Sciences Data and Information Services Center (http://disc.sci.gsfc.nasa.gov/giovanni). The AERONET AODs at 0.55 μm of 16 East Asian stations (Holben et al., 1998), the

MODIS AOD at 0.55 μm (MOD08 M3) for 1° × 1°, and the visibility from 730 observation stations from the China Meteorological Administration during the same period are used to evaluate the model simulations. The daily grid wind data and aerosol concentration data were used to analyze the aerosol transport. Refer to Sun et al. (2011) has computed the moisture flux and the moisture flux divergence components in the latitudinal and longitudinal direction. Using similar methods, we use the aerosol mass mixing ratio instead of the specific humidity and convert the vertically integrated results to a layer average using the factor of 1/(p1 − p2) (formula (3)). In this way, the latitudinal and longitudinal aerosol transport fluxes in each model layer (Fλ,Fϕ, μg·s/kg) were calculated as follows: 1 p ⇀ ⇀ ⇀ ∫ 2 C Vdp ¼ F λ i þ F ϕ j g0 ðp1 −p2 Þ p1 1 p2 1 p2 6 6 ∫ u•Cdp; F ϕ ¼ 10 • ∫ v•Cdp F λ ¼ 10 • g0 ðp1 −p2 Þ p1 g0 ðp1 −p2 Þ p1

⇀ F ¼ 106 •

ð3Þ

J. Wu et al. / Atmospheric Research 132–133 (2013) 185–198

188

Table 1 Region names mentioned in this paper (which are shown in Fig. 1c and d). Serial number

Full name

Lat. (°N)

Lon. (°E)

A B C D E F G H I J K L M NWC NC SWC SC East Asia

Northwestern China Northern China Northeastern China Western China Central China Eastern China Southwestern China Southern China The East China Sea The South China Sea The Indian subcontinent The Bay of Bengal The Indo-China peninsula The Northwest part of China The North part of China The Southwest part of China The South part of China East Asia

34–42 36–42 36–52 28–36 28–36 28–36 20–28 20–28 20–36 10–20 10–28 18–28 10–20 34–42 34–46 20–34 20–34 10–55

80–107.5 (approximate) 107.5–122.5 122.5–132 100–107.5 107.5–115 115–122.5 95–107.5 107.5–122.5 122.5–132 107.5–122.5 72.5–85 85–95 95–107.5 80–107.5 107.5–122.5 95–107.5 107.5–125 70–150

where g0 is acceleration due to gravity (m/s2), p1 and p2 are the pressures on the two interfaces of each layer (Pa), and u and v are the horizontal wind speed (m/s). C is the mass mixing ratio (g/kg). To get aerosol divergence, we compute the divergence of ⇀ ⇀ F(∇• F, ng·s/(kg·m)): 1 p ⇀ ∫ 2 ∇•C Vdp g0 ðp1 −p2 Þ p1  p  1 p ⇀ ⇀ 9 • ∫p2 V•∇Cdp þ ∫p2 C∇• Vdp : ¼ 10 • 1 1 g0 ðp1 −p2 Þ

⇀ 9 ∇• F ¼ 10 •

Then, convert the above formula to spherical coordinates: ⇀ 9 ∇• F ¼ 10 •

  1 1 ∂uC ∂ðvC cosφÞ p dp: •∫p2 þ 1 r cosφ g0 ðp1 −p2 Þ ∂λ ∂φ

The aerosol divergence is finally defined by formula (4) (in units of ng·s/(kg·m)):   1 1 ∂C ∂C cosφ p dp ∫p2 u þv g0 ðp1 −p2 Þ 1 r cosφ ∂λ ∂φ   1 C ∂u ∂v cosφ p2 9 dp þ 10 • ∫ þ g0 ðp1 −p2 Þ p1 r cosφ ∂λ ∂φ

⇀ 9 ∇• F ¼ 10 •

ð4Þ

where r is the earth radius (m), φ is latitude, and λ is longitude. The first item in the right hand side of formula (4) refers to the flux divergence induced by aerosol advection (AFD), which represents the aerosol transport due to an asymmetric concentration field. The second item refers to the flux divergence induced by wind divergence/convergence (WFD), which mainly shows the aerosol transport due to an asymmetric wind field. 3. Evaluations of model results with climatological monthly observations Comparisons among the GOCART, TOMS, AVHRR, AERONET, MODIS, and LIDAR data on a global scale have been carried out

in some studies, and the high correlation and the corresponding seasonal changes have been reported (Chin et al., 2002, 2004; Yu et al., 2003, 2010; Grosso et al., 2012). Here, we focus on model evaluation in East Asia. The AERONET AOD was regarded as the benchmark for comparison with the model and satellite data (Holben et al., 1996, 1998; Dubovik et al., 2000). The climatological monthly data from sixteen AERONET stations in the model domain were used to compare with simulation results; the locations of 16 stations and the four selected regions to be analyzed further are shown in Fig. 1d. The comparison between the AODs from AERONET and the model simulation is shown in Fig. 2. Good agreement can be found for stations 1–11 (Fig. 2), for which the maximum and the minimum correlation coefficient was 0.97 at station 3 and 0.51 at station 1, respectively. All of these correlation coefficients are computed by 36 monthly samples in each station throughout the research period and pass the significance t-test at a 0.1 confidence level, except for stations 15. Similar seasonal variations at stations 1–11 were reproduced by the model with relative errors less than 20% of the annual mean; however, the simulated AOD for station 3 was approximately 2 times larger and for station 5 was approximately 2 times lower than the observation, although a high correlation was also reported. The discrepancy in station 3 may mainly be attributed to overestimating the amount of dust in the dust source of Mongolia while using the dust parameterization. The discrepancy for station 5 may be the result of underestimating the source of sulfate in Beijing city, and the limited transportation of dust from its source region to station 5 may also account for the discrepancy between the model and observations. The simulated AOD for station 12 was lower than the observation because of the underpredicted aerosol input from the Chinese mainland and the Indo-China Peninsula, whereas a high correlation coefficient of 0.75 was still reached. At the other stations (13–16) in the southern part of Asia, the simulated AOD was nearly 50% lower than the observations, and the good correlation coefficients disappeared. Underestimated source emissions of black carbon and organic matter may be the main reason for the lower simulation results at station 14–16, whereas the lower input of aerosol from the Indian subcontinent could account for the low AOD of the GOCART model at station 13. Stations 1–11 were located in the dust transport routes, and they may also be affected by sulfate originating from Central and Eastern China. Therefore, dust and sulfate may be the main components at these stations and could account for the maximum optical depth appearing in April and June, respectively. In addition, the relative contributions of dust and sulfate to the total AOD at 16 stations were produced by model results. Dust was the dominant aerosol for stations 1–4 during most seasons because they were near the main dust source, i.e., the Taklimakan desert. Sulfate was the most important component in summer and autumn for stations 5–11. A decrease in sulfate during June to August was found at station 12, which was related to the onset of the East Asia summer monsoon, which may suppress the southward transportation of sulfate from the Chinese mainland. Station 13 was far away from the main aerosol sources, which led to the low AOD. Stations 14–16 were located on the Indo-China peninsula, which was one of the main sources of black carbon, organic matter, and sulfate; therefore, the proportions of these particles in the total aerosol

J. Wu et al. / Atmospheric Research 132–133 (2013) 185–198

189

Fig. 2. Comparisons of AOD at 0.55 μm from simulations (stacked bars) and AERONET observations (line) at 16 stations (the correlation coefficients and annual means for observations and simulations are shown in each figure, AE. for AERONET and GO. for GOCART).

190

J. Wu et al. / Atmospheric Research 132–133 (2013) 185–198

Fig. 3. Comparison of the AOD at 0.55 μm between the GOCART simulations (left column) and MODIS retrievals (middle column) in January, April, July, and October, where the visibility from station observations (right column, unit: km) is also shown for the four months.

values were greater. The annual mean AODs of simulation and observation are also listed in Fig. 2; in a word, the simulations agreed well with the observations at stations 1–11, except for stations 3 and 5, and the model underestimated the AOD in the lower latitude region. The simulated AOD was also compared with the AOD of MODIS Collection 5 (C5) L3 at 0.55 μm, in which the systemic warp of high and low AOD areas in the land surface was calibrated (Levy et al., 2007; Li et al., 2007). However, the MODIS data were usually valid in Chinese regions, with an error under 20% in the south part of China (Li et al., 2003). Both the MODIS Terra and Aqua products are accurate enough over China and can capture the aerosol distribution in this region; there are no significant differences between them (Wang, 2010). The 2001–2003 averaged GOCART monthly AOD was compared with MODIS in January, April, July, and October (Fig. 3). The model AOD agrees well with the MODIS data in January, despite a lack of effective MODIS observations in some regions of Northwestern China during the month. The major high AOD regions include the middle and low regions of the Yangtze River and Northern, Central, and Eastern China. In April, the main high AOD regions were similar to those in January, but there were some new areas with high AODs, including the Bay of Bengal and the Indian peninsula. At the same time, both the MODIS and model AOD increased from January's levels, with the highest

value located in the Sichuan basin. In July, a distinct decrease in the AOD could be found in Central and Eastern China, and a high AOD region moved northward compared with April. At the same time, the greater AOD over the Bay of Bengal and the Indo-China peninsula in April disappeared. The model results agreed well with MODIS data, especially in the Sichuan basin, Central, Eastern and Northern China in July. The AOD in October seemed similar to January, and the simulation also agreed well with the observation. To obtain additional local in situ measurements, particularly in highly polluted and populationdense regions, the visibility from 730 stations from the Chinese mainland is introduced into the comparison (Fig. 3). Before the comparison, some steps were taken to screen the visibility dataset reported by Wu et al. (2012). In this paper, we chose the daily visibility data, relative humidity data, and total cloud cover data, and only the visibility data at 14:00 fitting the conditions of low relative humidity (RH b 70%) with sunny skies (daily mean total cloud cover less than 0.4) will be analyzed. High aerosol pollution usually results in lower visibility near the surface. At the same time, the aerosol mainly comes from the surface, so the visibility is more relevant to the local air quality. In the four months studied, the visibility has a distribution pattern that is similar to the simulated AODs. The lower visibility can be found in Northeastern, Northern, Western, Central, Eastern, and Southern China, where high AODs exist. Northwestern and

J. Wu et al. / Atmospheric Research 132–133 (2013) 185–198

Southwestern China are relatively clean regions, so higher levels of visibility are found in these regions, except for the high AOD appearing in the Taklimakan desert in Spring (Fig. 3f, j) and in July (Fig. 3g, k). Visibility in the Sichuan basin is low in the four months, and the AODs are high. The AODs for the four selected regions (shown in Fig. 1d) and all of East Asia are listed in Table 2. The model results were higher than MODIS for NWC in the four months and lower than MODIS in SC, SWC, and East Asia in January, April, and July. The model successfully captured the large-scale AOD features and also reproduced the seasonal AOD change. Scatter plots of monthly averaged GOCART vs. AERONET and MODIS AOD are shown in Fig. 4, in which most points lie between the borderlines of 1:2 and 2:1. The correlation coefficient between the GOCART and AERONET models can reach 0.75; the previous global mean was 0.7 (Chin et al., 2002). The correlation coefficient of GOCART and MODIS reached 0.85, which showed good agreement between the two fields. Fig. 4 also shows the linear tendency of GOCART vs. AERONET/MODIS; the slopes were 0.47 for AERONET and 0.85 for MODIS, respectively. The comparison also revealed an underestimation of the AOD by the model compared with observations, but the main large-scale AOD features in East Asia were well captured by the model. 4. Aerosol distribution and transport 4.1. Spatial features of the aerosol distribution The distribution of the three-year mean AOD for each aerosol type is shown in Fig. 5 (shade). The regions with a high AOD of black carbon, organic matter, and sulfate included Central, Eastern and Southern China, the Sichuan basin, the Indo-China peninsula, the Bay of Bengal, and the Indian peninsula. The AOD of sea salt was high in the Northwestern Pacific Ocean and the inshore regions of the East Asia continent, whereas the dust AOD was high in the Taklimakan desert and the regions close to 40°N; the total AOD feature was similar to that of dust in Northwestern China. The total aerosol AOD could be influenced by both MEE and Md (formula (2)); MEE was determined by the optical parameters and the hygroscopicity of the aerosol particles. The MEE of each aerosol type at 0.55 μm is shown in Fig. 5

191

(contour). The MEE of black carbon, organic matter, sulfate, and sea salt showed similar patterns, where regions with high MEE values included the Southwest part of China (SWC), the Indo-China peninsula, the Northwestern Pacific Ocean, and both the north and the south edges of the domain. In the belt between 30°N and 50°N, MEE was small. In general, the high MEE regions for black carbon, organic matter, sulfate, and sea salt were located in regions with high water vapor concentrations. Dust is generally not a hygroscopic particle; thus, its MEE at 0.55 μm was constant (0.52 m2/g) and was only affected by its optical properties in the model domain (not shown). The different proportions of Md and AOD for each aerosol type in the total aerosol mass column loading (TAMCL) and total AOD are shown in Fig. 6. The proportions of black carbon in TAMCL and the total AOD were the smallest of the aerosols, and the larger ratio of black carbon was found in the Southwest part of China and on the Indo-China peninsula. Organic matter had a much larger concentration in TAMCL and in the total AOD than black carbon, which exceeded 10% in TAMCL and 20% in the total Indo-China peninsula AOD. Sulfate had a ratio exceeding 20% in TAMCL in Southern China and a ratio exceeding 65% in the total AOD was also found in the same region. The sea salt particle showed a different feature compared with black carbon, organic matter, and sulfate. The Md of sea salt held a larger proportion in TAMCL, whereas the ratio of its AOD to the total AOD was smaller, which could be deduced from its smaller MEE. For the region north of 30°N, dust was the most important aerosol in the total AOD. It appears that dust accounts for approximately 30%– 40% of the total AOD in Chinese non-dust source regions. This phenomenon may occur because Fig. 6e gives the annual mean results, so some heavy dust storms are included. In contrast, the model could also overpredict dust deflation in Western China and transportation to Eastern and Southern China. The different ratios between TAMCL and the total AOD for a same aerosol type are induced by different MEE value. The aerosol extinction parameter change induced by hygroscopic particles could account for the spatial changes of the ratio difference. For the hygroscopic black carbon and organic matter, the discrepancy was distinct in the Sichuan basin, Bay of Bengal, and Indo-China peninsula; the difference was obvious in the South part of China for sulfate and in the sea at approximately 10°N for sea salt. The hygroscopic effects of aerosol particles

Table 2 GOCART and MODIS AOD at 0.55 μm in the regions of Northwest (NWC), North (NC), Southwest (SWC) and South (SC) part of China, where East Asia is also listed, the standard deviation is around these mean values. GOCART

Jan Apr Jul Oct Annual

MODIS

NWC

NC

SWC

SC

East Asia

NWC

NC

SWC

SC

East Asia

0.18 (0.06) 0.58 (0.14) 0.49 (0.11) 0.28 (0.09) 0.39 (0.18)

0.25 (0.09) 0.62 (0.22) 0.49 (0.12) 0.34 (0.12) 0.42 (0.18)

0.16 (0.04) 0.30 (0.10) 0.23 (0.07) 0.27 (0.07) 0.24 (0.09)

0.34 (0.07) 0.56 (0.12) 0.32 (0.11) 0.47 (0.13) 0.45 (0.14)

0.16 (0.02) 0.33 (0.04) 0.27 (0.02) 0.21 (0.02) 0.24 (0.07)

0.16 (0.05) 0.43 (0.08) 0.43 (0.02) 0.22 (0.02) 0.31 (0.12)

0.35 (0.10) 0.55 (0.01) 0.51 (0.09) 0.29 (0.06) 0.43 (0.15)

0.17 (0.01) 0.41 (0.07) 0.29 (0.07) 0.18 (0.02) 0.26 (0.09)

0.41 (0.02) 0.58 (0.07) 0.40 (0.10) 0.38 (0.01) 0.45 (0.10)

0.24 (0.01) 0.35 (0.01) 0.31 (0.04) 0.20 (0.01) 0.27 (0.07)

192

J. Wu et al. / Atmospheric Research 132–133 (2013) 185–198

Fig. 4. Scatter plots of the GOCART vs. AERONET (left) and MODIS (right) AOD. The solid line is the 1:1 ratio, and the dotted lines are a factor of 2 departure. The correlation coefficient is denoted by R. Straight dashes represent the linear trends for GOCART–AERONET and GOCART–MODIS. The fitted equations lie in the same region.

were intensified in the mentioned regions. Due to the weaker hygroscopicity of sea salt compared with the above-mentioned aerosol types, the main contribution to the AOD was from its Md. The profiles of aerosol extinction (EXT), absorption (ABP), and back scattering (BKS) for the East Asian aerosol types are given in Fig. 7. The extinction profile of the total aerosol increased from the surface to the top of the atmosphere, with the maximum of 0.1 km−1 occurring below 900 hPa (Fig. 7a). The aerosol at lower altitudes formed a much larger contribution to the column burden. The simulated extinction profile of the total aerosol was in good agreement with some other observations

and simulations (Hara et al., 2011), where a higher extinction could be found by lidar observation and different chemical model simulations at layers lower than 5 km, with a maximum peak at approximately 0–1.5 km. The contributions to the total aerosol from different aerosol types vary with altitude. In the atmosphere below 750 hPa, the most distinct contribution was from sulfate, whereas most of the EXT was from dust in the upper atmosphere. Organic matter, black carbon, and sea salt constituted smaller contributions to the total EXT, and their EXT profiles were similar. Yu et al. (2010) reported extinction profiles for dust and non-dust aerosol

Fig. 5. Three-year mean AODs of black carbon, organic matter, sulfate, sea salt, dust, and total aerosol (shade) and MEE for each aerosol types except dust (contour, m2/g). Mean values in the model domain are shown above each figure and the standard deviation is around these mean values.

J. Wu et al. / Atmospheric Research 132–133 (2013) 185–198

193

Fig. 6. Percentage of each aerosol type in the total aerosol column mass loading (contour) and total AOD (shade) (unit: %), dust is only a percentage of the AOD because of its hydrophobicity.

obtained by model simulations and lidar observations; a similar characteristic was obtained in downwind regions of dust sources. The total aerosol ABP was influenced mainly by black carbon and dust, and the absorption of organic matter, sulfate, and sea salt was fairly small (Fig. 7b). At the lower atmosphere below approximately 950 hPa, both black carbon and dust may have similar absorption values with a maximum ABP extinction peak of 0.004 km−1, but dust played a more important role in the total ABP above 950 hPa because it decreased more slowly.

Fig. 7. Regional mean profiles of EXT (a, unit: km-1), ABP (b, unit: km-1), BKS (c, unit: km-1), and the percentage of ABP in EXT (d) (black carbon: red, organic matter: green, sulfate: yellow, sea salt: gray, dust: blue, mixed aerosol: orange, scatter is for mean value and line is for range of variation).

For most regions in the model domain, the dust particles could be transported into the free troposphere mainly through wind effects over dust source. The uplifting of low-level dust by lowpressure synoptic systems is another important source. These dust particles in the free troposphere could be transported to remote areas far from their sources; therefore, their maximum concentrations should occur across a boundary layer. Whereas the black carbon particles were emitted from vicinal sources, the maximum black carbon concentration appeared at lower altitudes (Chin et al., 2002). Black carbon is a typical absorptive aerosol. The total aerosol BKS was less than ABP and the total EXT by one and two magnitudes, respectively, and the BKS profile was similar to the ABP and EXT in shape (Fig. 7c). The most distinct BKS belonged to dust, which was close to half of its ABP, and the BKS of black carbon was the smallest of these aerosol types. The difference between BKS and EXT-ABP implied that the scattering in other directions was very distinct for the aerosol types. The ratio of ABP/EXT for each aerosol type depended on the single scattering albedo of each aerosol type, whereas the single scattering albedo of each aerosol type is further influenced by its unique hygroscopicity, refractive index, and size of dry particles. This ratio changes little in the vertical direction (Fig. 7d). Black carbon had the most distinct absorption with the ABP/EXT concentration close to 75%; the concentrations of dust and organic matter were 12% and 2%, respectively, and the ratio for sea salt or sulfate was close to zero. The total aerosol ABP contribution to the total aerosol extinction was between 5% and 10% for the layers below 600 hPa, with a minimum of 5% at 975 hPa, and it remained at 10% from 600 hPa to 250 hPa with a maximum of 18% at 150 hPa. The absorption ability of the total aerosol was weak in the lower troposphere and strong in the upper troposphere and lower stratosphere because the strong absorption of black carbon contributed more to the ratio of the total aerosol absorption in the upper atmospheric layers.

194

J. Wu et al. / Atmospheric Research 132–133 (2013) 185–198

4.2. Characteristics of aerosol transport The total flux divergences of each aerosol are shown in Fig. 8. Black carbon, organic matter, and sulfate have similar patterns, and the distinct convergence region was in the Sichuan basin, although the black carbon and organic matter emission in the Sichuan basin was also distinct (Fig. 1). The main divergent regions of black carbon, organic matter, and sulfate included Central, Eastern, and southern China, the Indo-China Peninsula, and the northern and eastern part of the Indian Subcontinent. If the aerosol transport for each layer was checked, it could be found that the main aerosol input channels to the Sichuan basin included the northward transport from the Guizhou province and the northeastern part of the Yunnan Province at approximately 850 hPa (figure not shown). The emissions of black carbon, organic matter, and sulfate in Guizhou and the northeastern part of Yunnan provinces were distinct (Fig. 1) because there were some coal mines in the region. The high AOD in the Sichuan basin should be attributed to large local emissions, surrounding injections, and complex terrain. Sulfate was convergent in the east part of Northern China (Fig. 8c) and mainly came from the Shanxi province in the west of Northern China. The sulfate divergence in Northeastern China was smaller than that of black carbon and organic matter, but the divergence of sulfate in Central, Eastern and Southern China was greater than that of black carbon and organic matter, and sulfate's distinct divergence could also be found in the Western Pacific Ocean; this divergence should be induced by the easterly transport of sulfate and SO2. Sea salt diverged in the Japanese sea and the Northwestern Pacific Ocean and converged in the southeastern seashore region of China, the Bay of Bengal, and the Indian subcontinent; the Indian subcontinent convergence came from the low-latitude regions of the Indian Ocean. The divergence of dust was revealed in the belt initiated from Xinjiang, and it extended easterly to Mongolia and Inner Mongolia. The dust's main convergent region was in the North and South part of China (NC and SC). The aerosol transport features were similar for each aerosol type, and the

main direction was from west to east in the region north of 30°N. The main transportation characteristics induced by the AFD (defined in formula (4)) are shown in Fig. 9. The regions with distinct AFDs were located in Central, Eastern, and Southern China for black carbon, organic matter, and sulfate. The AFD fields of black carbon and organic matter were similar, and they were positive in Northeastern, Northern, Central, Eastern, and Southern China. There were distinct divergence and convergence areas over the east part and west part of the Sichuan basin, respectively. The definition of AFD referred to the divergence induced by the asymmetrical concentration field, which should be divergent in the source region and convergent in other regions. At the eastern part of the Sichuan basin is the megacity of Chongqin, where a heavy industrial region is located. The western part of the Sichuan basin is the boundary area between the basin and the Tibet Plateau, and there was little emission of aerosols or their precursors. For the above-mentioned reasons, the AFD was positive in the eastern part and negative in the western part of the Sichuan basin for black carbon, organic matter, and sulfate. The AFD of sulfate was also positive in Northern, Central, Eastern, and Southern China, but it was greater in Eastern China. The transport of sea salt appeared to be a different pattern compared with other aerosol types, and the main divergence was located over the East China Sea and the Pacific Ocean in the analyzed domain. The sea salt divergence over the Chinese mainland was very small. The AFD of dust was large in the Taklimakan desert and the drought or semi-drought regions of Northwestern China, and it was fairly small in most regions of the Chinese mainland. The effects of WFD (defined in formula (4)) are also illustrated in Fig. 9. The Sichuan basin was a distinct convergent region, whereas Central China was the divergent area for black carbon and organic matter, and the divergence was very weak in Eastern China. The black carbon was injected into the Sichuan basin via two main channels: transported westerly from Central China at lower altitudes near the surface and transported easterly from Northwestern China at higher

Fig. 8. Total aerosol flux divergence (shaded, ng·s/(kg·m)) overlaid by a column mean transport streamline for each aerosol.

J. Wu et al. / Atmospheric Research 132–133 (2013) 185–198

195

Fig. 9. Aerosol flux divergence (shade: AFD; contour: WFD; unit: ng·s/(kg·m)). The arrows in a, d, e, and f approximate the main transport pathways at different altitudes (gray: surface to 700 hPa; green: 500–700 hPa; dark: 200–300 hPa), the width of arrows represents the proportional strengths of the fluxes. The streamline in f is the summed flux of black carbon, organic matter, and sulfate.

altitudes near 200 hPa. The easterly output of black carbon and organic matter from the Sichuan basin could be found in the 300 hPa layer, which was weaker than the input amount from Central China. The WFD feature was different from that of AFD in the Sichuan basin because WFD referred to the aerosol divergence induced by an asymmetric wind field. The WFD effects dominated the Sichuan basin total divergence (Fig. 8) for black carbon, organic matter, and sulfate. The regions with large WFD sulfate values included Central and Eastern China, and there was a wind divergent belt of sulfate in the eastern and northern parts of the Indian Subcontinent. The Sichuan basin, including the Southern Shanxi Province, Northern and Northeastern China, and Southwestern China had the most prominent wind convergence of sulfate. The sulfate input to the Sichuan basin came from the low-layer transport initiated from Central China, and its outflow included the easterly transfers to Central China between 600 and 350 hPa and the westerly transfers to Northwestern China at 350 hPa. The main regions with WFD sea salt convergence were the Bay of Bengal, the South China Sea, and the Northwestern Pacific Ocean, whereas the divergence in the Chinese mainland was fairly small except for the seashore regions in Southern China. The WFD of dust showed a different pattern in which the main convergence was found in the Taklimakan desert and the western part of Inner Mongolia, whereas the weak divergence was found in most regions of the Chinese mainland. The reason that dust convergence appeared in the regions should be attributed to the bare surface and the strong surface winds related to low-pressure synoptic systems that uplifted dust and formed dust storms easily in deserts and surrounding regions. The arrows in Fig. 9a, d, and e show the main transport pathways at different levels for these aerosol types. The arrows in Fig. 9a depict the total transport features of black carbon,

organic matter, and sulfate; Fig. 9d displays the transport features of sea salt, as does Fig. 9e for dust. All of these arrows clearly summarize the transport features mentioned above. Fig. 9f further gives the transport mechanism of black carbon, organic matter, and sulfate. This figure across the latitude of the 30°N line ‘mn’ in Fig. 9a, b, and c and the streamline represents the summed black carbon, organic matter, and sulfate flux. There are three transport channels that accumulate and uplift in the Sichuan basin, which is consistent with the description above. The net aerosol fluxes of each investigated region are summarized in Table 3. The main dust output area was the Northwest part of China, and the output amount was the most massive. Black carbon, organic matter, and sulfate had net input fluxes in the Northwest part of China. The characteristics of the net aerosol flux in the North part of China were different from those of the Northwest part of China. The North part of China was an important black carbon, organic matter, and sulfate emission source area and the main destination of dust originating in the Northwest part of China. The aerosol inflow to the South part of China was similar to that of the North part

Table 3 Annual aerosol net input in different regions (106μg•s•m/kg). Black carbon

Organic matter

Sulfate

Sea salt

Dust

Total

NWC 0.326 0.931 3.050 0.359 −810.043 805.377 NC −1.052 −0.671 −3.128 −0.523 162.575 157.201 SWC 0.723 3.234 5.802 6.009 105.562 121.330 SC −1.409 −3.919 −23.517 0.820 64.027 36.002 East Asia −2.321 −7.668 −10.590 34.303 14.192 27.916

196

J. Wu et al. / Atmospheric Research 132–133 (2013) 185–198

of China, whereas it had a much more massive output of black carbon, organic matter, and sulfate accompanied by less inflow of dust. It is also shown in Table 3 that all types of aerosol could be transported into the Southwest part of China; therefore, the net fluxes were positive in the region. For the large-scale view, the main sources of black carbon, organic matter, and sulfate in the Chinese mainland were the North and South China, and the most massive amount of dust originated in the Northwest part of China. The Southwest part of China was a relatively clean area despite the large emission amount in the Sichuan basin. An important source of sea salt was located in the Northwestern Pacific Ocean. The aerosol total divergence profiles for the four selected regions and East Asia are summarized in Fig. 10. The main aerosol transport layers were below 500 hPa for black carbon, organic matter, and sulfate, and dust aerosol could be lifted into higher altitudes by the strong ascending motion induced by the sand blasting synoptic systems. Therefore, the total divergence of dust displayed another maximum at 300 hPa. The aerosol injection could be found in the Southwest part of China in the layers below 800 hPa, and the black carbon, organic matter, and sulfate outputs also occurred between 750 and 500 hPa in the region, which was consistent with the above-mentioned characteristics of convergence at low altitudes and divergence at high altitudes in the region. The Northwest part of China was the main source of dust, and the amount of dust output was significant from the surface to 200 hPa. Meanwhile, the Northwest part of China was a destination for black-carbon, organic matter, and sulfate, which were injected into the region in the layers from the surface to 700 hPa. The North and South parts of China were the main sources of black

carbon, organic matter, and sulfate, and their output occurred on the layers between the surface and 700 hPa, in which the output amount of organic matter and sulfate was much greater in the South part of China. The destinations of sea salt included the Southwest and South part of China. The main characteristics revealed by the divergence profiles in each area could account for the results in Table 3. 5. Conclusion and discussion The features of the East Asian aerosol distribution and transport have been analyzed, and the main conclusions can be summarized as follows: 1. Model simulations can capture the large-scale features of the AOD distribution, and the simulations partially agree with the observation data in the analyzed domain; however, the simulation systematically underestimates the AOD in the Indo-China peninsula, the Indian subcontinent, the Bay of Bengal, and the East China Sea and overestimates the AOD in the Northwest part of China (NWC). 2. The distinct higher concentrations of black carbon, organic matter, and sulfate lie in Central and Eastern China, the Sichuan basin, the Indo-China peninsula, and the Indian subcontinent, whereas sea salt has high AODs for the Northwestern Pacific Ocean and inshore regions of the East Asian continent. The hygroscopicity of the black carbon and organic matter particles can increase the AOD distinctly in the Sichuan basin, the Bay of Bengal, and the Indo-China peninsula, and the AOD of sulfate in Central and Southern China can also be intensified by sulfate's hygroscopicity.

Fig. 10. Profiles of total aerosol flux divergence averaged in NWC (red), NC (green), SWC (blue), SC (yellow), and East Asia (gray), scatter is for total divergence and line is for range of variation.

J. Wu et al. / Atmospheric Research 132–133 (2013) 185–198

3. The AFD is more than a factor of 2 or 3 larger than the WFD in the divergent regions for black carbon, organic matter, and sulfate, whereas the AFD only accounts for 70% of the WFD in the convergent regions. Aerosol transport is another important contributor to the high AOD in the Sichuan Basin in addition to the strong local emissions in the region. The basic aerosol transport features in the Sichuan basin and its adjacent regions include the fact that aerosol was injected into the Sichuan basin at low altitudes from central China and the Yunnan and Guizhou provinces and the fact that it can be transported easterly out of the basin at 300 hPa. The aerosol transport features and the local emission can account for the prominent high concentrations of black carbon, organic matter, and sulfate AODs in the Sichuan basin (Fig. 9f). Several limitations should be taken into account while evaluating our results. First, some of the limitations come from the model itself. The GOCART model only includes five common aerosols and does not consider other important aerosol species such as nitrate and ammonium. The global model uses a relatively coarse model grid resolution, which also makes it difficult to represent regional or local information on topography, land use, and emission well. Second, some of the limitations come from our work. The calibration of the GOCART aerosol concentrations with the observed 3-dimensional aerosol data at a high precision level was not accounted for due to a lack of dense observation data. Few discussions on the seasonality of aerosol transport characteristics in this monsoon region were carried out. The cross-continent transport of East Asian aerosols was also not included in the current study. However, it is found that East Asian aerosols can be transported to North America. Therefore, an elaborate analysis of aerosol cross-continent transport should be considered in the future. Acknowledgments The study was sponsored by the National Key Program for Developing Basic Sciences of China (No. 2011CB952003), the Chinese Natural Science Foundation (40975092, 41275162), and the Program for Postgraduates Research and Innovation in the Universities of the Jiangsu Province, China (CXLX12_0498). References Andres, R.J., et al., 1998. A time-averaged inventory of subaerial volcanic sulfur emissions. J. Geophys. Res. 103 (D19), 25251–25261. Bloom, S., et al., 2005. Documentation and validation of the Goddard Earth Observing System (GEOS) data assimilation system—version 4. Technical Report Series on Global Modeling and Data Assimilation, NASA/TM-2005104606, vol. 26. Carn, S.A., et al., 2003. Volcanic eruption detection by the Total Ozone Mapping Spectrometer (TOMS) instruments: a 22-year record of sulfur dioxide and ash emissions. In: Oppenheimer, C., Pyle, D.M., Barclay, J. (Eds.), Volcanic Degassing, Special Publication of the Geological Society of London, No. 213. Geological Society, London, UK, pp. 177–202. Chang, S.C., et al., 2010. Asian dust and pollution transport—a comprehensive observation in the downwind Taiwan in 2006. Atmos. Res. 95, 19–31. http://dx.doi.org/10.1016/j.atmosres.2009.07.012. Chin, M., et al., 2000. Atmospheric sulfur cycle in the global model GOCART: model description and global properties. J. Geophys. Res. 105 (24), 24661–24687. Chin, M., Ginoux, P., et al., 2002. Tropospheric aerosol optical thickness from the GOCART model and comparisons with satellite and sun photometer measurements. J. Atmos. Sci. 59, 461–483.

197

Chin, M., Ginoux, P., et al., 2003. A global aerosol model forecast for the ACE-ASIA field experiment. J. Geophys. Res. 108 (D23). http://dx.doi.org/10.1029/ 2003JD003642. Chin, M., Chu, D.A., et al., 2004. Aerosol distribution in the northern hemisphere during ACE-Asia: results from global model, satellite observations, and sun photometer measurements. J. Geophys. Res. 109 (D23S90). http://dx.doi.org/ 10.1029/2004JD004829. Chin, M., Diehl, T., et al., 2007. Intercontinental transport of pollution and dust aerosols: implications for regional air quality. Atmos. Chem. Phys. 7, 5501–5517. Chin, M., Diehl, T., et al., 2009. Light absorption by pollution, dust and biomass burning aerosols: a global model study and evaluation with AERONET measurements. Ann. Geophys. 27 (9), 3439–3464. http://dx.doi.org/ 10.5194/angeo-27-3439-2009. Cui, Z.L., Zhang, H., et al., 2009. A modeling study with MATCH on the aerosol optical depth distribution over China in 2006. Remote Sens. Technol. Appl. 24 (2), 197–204 (in Chinese). Dubovik, O., et al., 2000. Accuracy assessments of aerosol optical properties retrieved from Aerosol Robotic Network (AERONET) sun and sky radiance measurements. J. Geophys. Res. 105 (D8). http://dx.doi.org/ 10.1029/2000JD900040. Duce, R.A., 1995. Sources, distributions and fluxes of mineral aerosols and their relationship to climate. In: Charlson, R.J., Heintzenberg, J. (Eds.), Aerosol Forcing of Climate. John Wiley & Sons Ltd., Chichester, New York (43–72 pp.). Eyring, V., et al., 2005. Emissions from international shipping: 1. The last 50 years. J. Geophys. Res. 110, D17305. http://dx.doi.org/10.1029/ 2004JD005619. Ginoux, P., et al., 2001. Source and distributions of dust aerosols simulated with the GOCART model. J. Geophys. Res. 106 (D17), 20255–20273. Gong, S., 2003. A parameterization of sea-salt aerosol source function for sub- and super-micron particles. Global Biogeochem. Cycles 17, 1097. http://dx.doi.org/10.1029/2003GB002079. Grosso, N., et al., 2012. Comparison of contrast reduction based MODIS AOT estimates with AERONET measurements. Atmos. Res. 116, 33–45. http:// dx.doi.org/10.1016/j.atmosres.2011.09.008. Han, Z.W., 2010. Direct radiative effect of aerosols over East Asia with a regional coupled climate/chemistry model. Meteorol. Z. 19 (3), 287–298. Hara, Y., et al., 2011. Seasonal characteristics of spherical aerosol distribution in Eastern Asia: integrated analysis using ground/space-based lidars and a chemical transport model. SOLA 7, 121–124. http://dx.doi.org/10.2151/sola, 2011- 031. Holben, B.N., Setzer, A., et al., 1996. Effect of dry-season biomass burning on Amazon basin aerosol aerosol concentrations and optical properties, 1992–1994. J. Geophys. Res. 101 (D14). http://dx.doi.org/10.1029/ 96JD01114. Holben, B.N., et al., 1998. AERONET—a federated instrument network and data archive for aerosol characterization. Remote. Sens. Environ. 66, 1–16. Krotkov, N.A., et al., 2006. Band residual difference algorithm for retrieval of SO2 from the AURA Ozone Monitoring Instrument (OMI). IEEE Trans. Geosci. Remote Sens. 44 (5), 1259–1266. http://dx.doi.org/10.1109/ TGRS.2005.861932 (AURA special issue). Levy, R.C., et al., 2007. Global aerosol optical properties and application to MODIS aerosol retrieval over land. J. Geophys. Res. 112 (D13210). http:// dx.doi.org/10.1029/2006JD007815. Li, C.C., Mao, Jie tai, et al., 2003. Characteristics of aerosol optical depth distributions over East China derived from MODIS data. Chin. Sci. Bull. 48 (19), 209422100 (in Chinese). Li, Z.Q., Niu, F., et al., 2007. Validation and understanding of moderate resolution imaging spectroradiometer aerosol products (C5) using ground-based measurements from the handheld sun photometer network in China. J. Geophys. Res. 112 (D22S07). http://dx.doi.org/ 10.1029/2007JD008479. Luo, Y., Lu, D., Zhou, X., Li, W., He, Q., 2001. Characteristics of the spatial distribution and yearly variation of aerosol optical depth over China in last 30 years. J. Geophys. Res. 106, 14,501–14,513. http://dx.doi.org/ 10.1029/2001JD900030. Mortlock, A.M., et al., 1998. Military, Charter, Unreported Domestic Traffic and General Aviation 1976, 1984, 1992, and 2015 Emissions Scenarios, NASA/CR-1998-207639. Perry, K.D., et al., 2004. Evidence for hygroscopic mineral dust particles from the intercontinental transport and chemical transformation experiment. J. Geophys. Res. 109 (D23). http://dx.doi.org/10.1029/2004JD004979. Prospero, J.M., et al., 2002. Environmental characterization of global sources of atmospheric soil dust identified with the NIMBUS 7 Total Ozone Mapping Spectrometer (TOMS) absorbing aerosol product. Rev. Geophys. 40 (1), 1002. http://dx.doi.org/10.1029/2000RG000095. Randerson, J.T., et al., 2007. Global Fire Emissions Database, Version 2 (GFEDv2.1). http://dx.doi.org/10.3334/ORNLDAAC/849 (Data set, available on-line at http://daac.ornl.gov/ from Oak Ridge National Laboratory Distributed Active Archive Center, Oak Ridge, Tennessee, USA).

198

J. Wu et al. / Atmospheric Research 132–133 (2013) 185–198

Satake, S., et al., 2004. Characteristics of Asian aerosol transport simulated with a regional-scale chemical transport model during the ACE-Asia observation. J. Geophys. Res. 109 (D19S22). http://dx.doi.org/10.1029/ 2003JD003997. Siebert, L., et al., 2002. Volcanoes of the World: an illustrated catalog of holocene volcanoes and their eruptions. Smithsonian Institution, Global Volcanism Program Digital Information Series, GVP-3. Streets, D.G., et al., 2009. Discerning human and natural signatures in regional aerosol trends, 1980–2006. J. Geophys. Res. 114, D00D18. http://dx.doi.org/ 10.1029/2009GL038984. Sun, J., Zhang, M., et al., 2001. Spatial and temporal characteristics of dust storms in China and its surrounding regions, 1960–1999: relations to source area and climate. J. Geophys. Res. 106 (D10), 10,325–10,334. http://dx.doi.org/10.1029/2000JD900665. Sun, B., Zhu, Y.L., Wang, H.J., 2011. The recent interdecadal and interannual variation of water vapor transport over Eastern China. Adv. Atmos. Sci. 28 (5), 1039–1048. http://dx.doi.org/10.1007/s00376-010-0093-1. Uno, I., Carmichael, G.R., et al., 2003. Analysis of surface black carbon distributions during ACE-Asia using a regional -scale aerosol model. J. Geophys. Res. 108 (D23), 8636. http://dx.doi.org/10.1029/2002JD003252. van der Werf, G.R., et al., 2003. Carbon emissions from fires in tropical and subtropical ecosystems. Global Change Biol. 9, 547–562. Wang, L.L., 2010. Assessment and comparison of three years of Terra and Aqua MODIS Aerosol Optical Depth Retrieval (C005) in Chinese terrestrial regions. Atmos. Res. 97, 229–240. http://dx.doi.org/10.1016/j.atmosres.2010.04.004.

Wu, J., Fu, C.B., et al., 2012. Trends of visibility on sunny days in China in the recent 50 years. Atmos. Environ. 55, 339–346. http://dx.doi.org/10.1016/ j.atmosenv.2012.03.037. Yu, H.B., et al., 2003. Annual cycle of global distributions of aerosol optical depth from integration of MODIS retrievals and GOCART model simulations. J. Geophys. Res. 108 (D3), 4128. http://dx.doi.org/10.1029/2002JD002717. Yu, H.B., et al., 2008. A satellite-based assessment of transpacific transport of pollution aerosol. J. Geophys. Res. 113 (D14S12). http://dx.doi.org/ 10.1029/2007JD009349. Yu, H.B., et al., 2010. Global view of aerosol vertical distributions from CALIPSO lidar measurements and GOCART simulations: regional and seasonal variations. J. Geophys. Res. 115, D00H30. http://dx.doi.org/ 10.1029/2009JD013364. Yu, H.B., et al., 2012. Aerosols from overseas rival domestic emissions over North America. Science 337, 566–569. http://dx.doi.org/10.1126/science.1217576. Zhang, X.Y., et al., 1997. Dust emission from Chinese desert sources linked to variations in atmospheric circulation. J. Geophys. Res. 102 (D23), 28, 041–28, 047. Zhang, M.G., et al., 2004. Modeling of organic carbon aerosol distributions over East Asia in the spring. China Part. 2 (5), 192–195.