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Keywords:

  • Asian dust;
  • air pollutants;
  • lidar;
  • CALIPSO;
  • chemical transport model

Abstract

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Observation Data and Model Configuration
  5. 3. Results and Discussion
  6. 4. Concluding Remarks
  7. Acknowledgments
  8. References
  9. Supporting Information

[1] Springtime outflow of Asian dust and air pollutants was investigated by a synergetic analysis of ground-based/space-borne Lidar observations and numerical models. We identified two prominent outflow patterns, and its occurrence frequency. Pattern I was induced within a typical warm-sector which lifted up dust particles into the free troposphere, and the existence of two sequential low-pressure systems played an important role. Pattern II was a ‘behind cold front’ outbreak. Atmospheric stratification was significantly different; Pattern I had weak stratification within the troposphere (potential temperature gradient of ∼2–3.4 K/km), and most of elevated dust layer (typically horizontally 1500–2000 km, vertically 2.5–4 km AGL) remained unmixed with pollutants. Pattern II was characterized by a strong stratification of ∼5 K/km; dust and pollutants were trapped and well mixed within the PBL, forming ‘polluted’ dust. Among the six cases of large-scale dust/pollutants outbreaks, only two cases are belonged to Pattern I.

1. Introduction

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Observation Data and Model Configuration
  5. 3. Results and Discussion
  6. 4. Concluding Remarks
  7. Acknowledgments
  8. References
  9. Supporting Information

[2] Mineral dust and anthropogenic pollutants and their mixing status play an important role in air quality, human health and global warming [e.g., Akimoto, 2003; Yu et al., 2003]. In Eastern Asia, anthropogenic air pollutant emissions have been increasing very rapidly in parallel with the fast economic growth since 1990 [e.g., Ohara et al., 2007; Irie et al., 2009], and are responsible for the degradation of regional air quality and trans-boundary pollutant transport. Airborne mineral dust generated in the deserts of northwestern China and Mongolia during the late winter and spring [Kurosaki and Mikami, 2003] is another serious environmental issue with a large impact, and this dust is frequently transported to the east coast of China and to Korea and Japan [Wang et al., 2000]. Transported dust can sometimes reach North America [Uno et al., 2001; Eguchi et al., 2009] and occasionally across the Atlantic and Europe and back to Asia, making a full circuit around the globe [Uno et al., 2009].

[3] The long-range transport (LRT) of mineral dust and air pollutants to the northwestern Pacific region occurs frequently in the spring; thus, a detailed study of springtime dust and pollutant outflow is important. Asian dust is transported either as isolated or mixed with air pollutants as a ‘polluted’ dust. Uno et al. [2008] reported the three-dimensional (3-D) elevated dust transport observed in early May 2007. A large-scale trans-boundary air pollution analysis, including the same period, by Itahashi et al. [2009] indicated a significant role of Chinese anthropogenic emissions in the increased level of boundary-layer photochemical O3 level over Japan. However, the detailed mixing process of dust and pollutants, their horizontal and vertical transport, and the associated controlling meteorological conditions remain largely unclear.

[4] In this paper, we studied the transport of Asian dust and air pollutants using an integrated analysis technique of lidar measurements and chemical transport models. We present two transport patterns identified as the springtime outflow of Asian dust and air pollutants and the associated meteorological conditions that play a major role in their generation and subsequent transport.

2. Observation Data and Model Configuration

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Observation Data and Model Configuration
  5. 3. Results and Discussion
  6. 4. Concluding Remarks
  7. Acknowledgments
  8. References
  9. Supporting Information

2.1. Ground-Based and Space-Borne Mie Lidar Observations

[5] Mie backscatter lidar is a powerful tool for measuring the vertical structure of dust and pollutant distributions [e.g., Shimizu et al., 2004]. A ground-based lidar network, established and supported by the National Institute for Environmental Study (NIES), Japan, is operated continuously at selected locations in Eastern Asia (see Figure S1 of the auxiliary material). This lidar network provides vertical profiles of aerosols and clouds backscatter with high spatial (30 m) and temporal (15 min) resolution. The aerosol extinction coefficient was derived from the observed backscatter coefficient, based on the backward Fernald's inversion method, with the boundary condition set at 6 km and S1 = 50sr.

[6] The space-based “Cloud-Aerosol Lidar with Orthogonal Polarization” (CALIOP), which is onboard the “Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations” (CALIPSO) has, since it launched in April 2006, provided continuous global measurements of aerosol and cloud vertical distribution with very high spatial resolution [Winker et al., 2007]. CALIOP is as two-wavelength (532 and 1064 nm) backscatter lidar that measures at two orthogonal polarizations, with sampling at 333 nm along the track. In this paper, we analyzed the Level 1B CALIOP data (version 2.01) of total attenuated backscatter at 532 and 1064 nm and the volume depolarization ratio (δ) at 532 nm. We derived the aerosol extinction coefficient using the Fernald inversion technique by setting the lidar ratio, S1, to 35sr [Shimizu et al., 2004]. Inversion started from 14 km down to the ground surface. Then, the retrieved vertical profiles were averaged to the resolution of CALIOP Level 2 data (5 km). The cloud-aerosol discriminator (CAD) index in the Level 2 CALIOP data was used for cloud layer detections. Compared to the NIES lidar measurement, a smaller value of S1 = 35 sr was used for extinction retrieval from the CALIOP data; thus, the extinction coefficient retrieved from CALIOP measurements would be smaller than that from the NIES lidar measurement. However, this point is not critical because we targeted a semi-quantitative analysis of dust/pollutant transport patterns. Details of these two lidar data analyses can be found in the work of Uno et al. [2008].

2.2. Dust and Chemical Transport Models

[7] The model used to simulate Asian dust transport was the RAMS/CFORS-4DVAR dust transport model (RC4) [Yumimoto et al., 2008]. RC4 was build on a meso-scale meteorological model RAMS (ver. 4.3) using its optional scalar transport options. The horizontal grids comprise 180 × 100 grids, with a resolution of 40 km. The vertical grids extend from the surface to 23 km with 40 stretching grid layers. In RC4, a four-dimensional variational (4DVAR) data assimilation system was used, based on the ground-based NIES Lidar Network observations. The observation data acquired at seven NIES lidar sites (see Figure S1 for locations) was assimilated in the 4DVAR calculation.

[8] The transport of air pollutants was simulated using the Community Multi-scale Air Quality (CMAQ; ver. 4.4) modeling system released by the US EPA [Byun and Schere, 2006]. Details of CMAQ simulation (with 80 km grid resolution) were reported by Uno et al. [2008]. In this study, the Statewide Air Pollution Research Center version 99 (SAPRC-99) chemical mechanism was used for producing gas phase chemistry, and the AERO3 module was employed for aerosol calculations. Anthropogenic emissions data were obtained from the Regional Emission Inventory in Asia (REAS) [Ohara et al., 2007].

[9] It is important to point out that the lidar observations are limited to either a 1-D vertical profile (ground-based NIES lidars) or a 2-D cross-section (satellite-borne CALIOP), while the transport models are capable of providing a detailed 3-D structure of dust and pollutant transport. Thus, in this paper, we performed a the synergetic analysis of the NIES Lidar Network, CALIOP measurements, and RC4/CMAQ models to determine the detailed 3-D outflow structure of dust and pollutants.

3. Results and Discussion

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Observation Data and Model Configuration
  5. 3. Results and Discussion
  6. 4. Concluding Remarks
  7. Acknowledgments
  8. References
  9. Supporting Information

[10] We studied the large-scale outflows of dust and pollutants occurring in May 2007. Figure 1a depicts the time series of a modeled dust emission flux over the Gobi Desert region (see Figure S1) and Figures 1b1e show the aerosol optical thickness (AOT) at Beijing, Seoul, Niigata, and Nagasaki during May 2007. AOT is calculated for altitudes lower than 6 km from the NIES ground-based lidar data. The fractions of spherical particles (mostly pollutants) and non-spherical particles (dust) were estimated using the depolarization ratio measurement [Shimizu et al., 2004]. Dust aerosol has a high depolarization ratio, because of the irregular shape of the dust particles, while other types of aerosols have a small depolarization ratio, close to zero, as demonstrated by the CALIOP measurements [Liu et al., 2008]. The extinction coefficients of pollutants were calculated by CMAQ for black carbon, organic carbon, and sulfate. The averaged vertical extinction profiles of dust and pollutants retrieved from the NIES Lidar measurement for each episode (A and B) at each site, respectively, are shown in the middle and right panels of Figures 1b1e.

image

Figure 1. (a) Modeled dust emission flux over the Gobi Desert region. (b–e) Comparisons of NIES Lidar observations (vertical bar; yellow shows dust, and pink shows pollutant fractions) and simulated AOT (lines; orange shows dust, and black shows total AOT) at Beijing (Figure 1b), Seoul (Figure 1c), Niigata (Figure 1d), and Nagasaki (Figure 1e). Horizontal blue bars show the meteorological conditions (rain or low clouds) for missing lidar observations. Selected episodes (A and B) are indicated by symbols and a light black dashed line. Vertical profiles of lidar observed extinction coefficients for dust (yellow) and pollutants (pink) during Episodes A and B are shown on the right.

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[11] Figure 1 shows several large dust episodes that were simulated. The simulated dust emission peaks correlated well with the dust peaks observed in Beijing (1–2 days delay from dust source to Beijing). The largest dust emission occurred on May 22, and other medium ones occurred on May 3, 5, 9, 14 and 27. The comparison shown in Figures 1b1e confirms that the observed temporal variations of AOT were captured well by the model simulations. It is evident that large-scale simultaneous outbreaks of Asian dust and air pollution occurred on May 7–9 (Episode A) and May 24–26 (Episode B) as indicated by the symbols and dashed lines, respectively, and were transported to Japanese sites. We focused on these two episodes for detailed analyses, and identified the differences in their transport patterns and structures and the associated meteorological conditions.

[12] Figures 2a2c portray the simulated spatial distributions of AOT for Asian dust (color) and air pollutants (green colored contours) of the two episodes, A and B, for the times indicated at the top of each panel. Purple lines are the CALIPSO orbit paths. In Figure 2a, the HYSPLIT forward air trajectories [Draxler and Hess, 1998] (T1 and T2) starting at a height of 2.5 km in the Gobi Desert are also shown (gray line). The vertical-longitude cross-section along the trajectories (T1 and T2) is presented in Figure 2d.

image

Figure 2. (a–c) CALIPSO orbit paths (purple line) and modeled AOT (color for dust, modeled by RC4; green for pollutants, modeled by CMAQ). Vectors indicate the wind field at 100 m above ground level. The thick gray line in Figure 2a is the HYSPLIT forward trajectory. (d) Time-height variations in the extinction coefficient simulated by RC4 for dust (color, normalized to its maximum value) and CMAQ for pollutants (green contour) along the trajectory (left) T1 and (right) T2 shown in Figure 2a. Also shown are the simulated potential temperature (black dashed contour) and the topography (black shaded region).

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[13] Figure 3 presents a vertical-latitudinal curtain plot of Asian dust and pollutants along the CALIPSO orbit path on May 7 and 25 (as indicated by the purple lines in Figure 2b). In Figure 3a, CALIOP total extinction coefficients (color) and CAD scores larger than 90 were plotted (pink shading areas). To discriminate between dust and air pollutants, the retrieved aerosol depolarization ratio, as shown in the middle panels in Figure 3, was examined. As shown in Figure 3c, results from the CALIOP measurements and RC4/CMAQ model simulations were in close agreement in cloud-free regions.

image

Figure 3. (a) Vertical cross-section of the CALIOP total extinction coefficient (color) and CAD (light pink area); (b) CALIOP retrieved aerosol depolarization ratio; (c) simulated RC4 dust extinction coefficient (color, normalized to its maximum value), CMAQ pollutants extinction coefficient (green contour), and potential temperature (black dashed contour).

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[14] Table 1 summarizes the AOT measured by the NIES Lidar and the simulated meteorological parameters averaged over the time periods of Episodes A and B and over the entire May 2007 period. At all sites, the measured dust AOT during Episode B was approximately twice as large as that during Episode A and the dust-to-total ratio was high. The primary reason for this was because the simulated dust emission flux in Episode B was twice as large as that of Episode A. From Beijing, Seoul, and Niigata, the measured (simulated) dust AOT decreased to 0.63, 0.22, and 0.24 (0.45, 0.38, and 0.30) for Episode A, and to 1.05, 0.47, and 0.49 (1.24, 0.51, and 0.35) for Episode B, respectively. Episode B showed a rapid decrease in AOT during its transport. The ratio of dust to total AOT decreased in Korea and Japan, because of the supply of additional anthropogenic pollutants and the deposition of dust particles during the LRT course. A comparison of the meteorological conditions indicated dramatic differences in the potential temperature gradient during Episodes A and B. The gradient of potential temperature during Episode B was greater than that during Episode A. In Seoul and Japan, the gradient value was approximately 1.5 times greater, while in Beijing, which was closer to the dust emission sources, the gradient value was approximately 2.4 times greater. In Episode A (see Figure 2d), the dust layer was elevated when it left the dust source region, whereas in Episode B, because of strong stratification, the dust layer was suppressed within the planetary boundary layer (PBL). These results indicate that the air mass in Episode B was suppressed from vertical diffusion; consequently, the dust was confined to the lower troposphere (mostly in the PBL) and was mixed with pollutants.

Table 1. AOT and Meteorological Parameters Averaged Over Time Periods of Episodes A, B, and May 2007a
 Episode A (Pattern I)Episode B (Pattern II)May 2007
  • a

    Meteorological parameters units: wind speed (WS) [m/s], temperature (Temp) [K], specific humidity (q) [g/kg], relative humidity (RH) [%], gradient of potential temperature (dθ/dz)[K/km]. WS and Temp were averaged over the PBL, and others parameters were averaged over the troposphere.

  • b

    Lidar observations; all other numbers are based on the model simulation.

Beijing, China
Dust AOT0.450.63b1.241.05b0.410.34b
Spherical AOT0.120.40b0.110.12b0.190.28b
Dust/Total ratio0.780.57b0.910.88b0.650.53b
WS, Temp9.12293.510.49291.88.28290.3
q, RH2.0528.62.3022.62.4533.2
dθ/dz2.03 4.91 3.62 
Seoul, Korea
Dust AOT0.380.22b0.510.47b0.290.18b
Spherical AOT0.270.38b0.210.33b0.360.33b
Dust/Total ratio0.590.37b0.720.59b0.450.34b
WS, Temp14.95287.113.21286.49.51284.9
q, RH2.7743.43.3332.83.6552.4
dθ/dz3.26 5.37 4.22 
Niigata, Japan
Dust AOT0.300.24b0.350.49b0.200.15b
Spherical AOT0.430.35b0.250.13b0.310.25b
Dust/Total ratio0.410.41b0.580.77b0.450.34b
Nagasaki, Japan
Dust AOT0.190.27b0.340.51b0.180.18b
Spherical AOT0.220.19b0.280.09b0.320.20b
Dust/Total ratio0.470.60b0.550.84b0.380.47b
Japan Region
WS, Temp18.28283.917.87282.711.78281.1
q, RH3.5851.63.4842.23.5459.1
dθ/dz3.40 5.06 4.33 
Gobi emission periodMay 3–7May 22–26May 1–31
(Tg/period/Gobi region)7.8514.1847.06

[15] From these analyses, we confirm that the horizontal/vertical scales of two important outflow patterns of Asian dust and air pollutants in the spring, were induced by different mechanisms and meteorological conditions. The first pattern, represented by Episode A, occurring during May 7–9, was characterized by an elevated dust layer in the free troposphere. The dust layer was lofted to altitudes of 2.5–4.0 km and had a south-to-north dimension of 1500–2000 km. Over Japan, air pollutants, slightly mixed with dust particles, were transported below the dust layer. The transport of the dust layer occurred primarily between the two continuous low-pressure systems, indicated by L1 and L2 in Figure 2 (L1 was slow-moving and finally became stagnant over Hokkaido and the Sea of Okhotsk). An elevated dust layer was formed within the warm sector of the low-pressure system L2. We refer to this transport pattern as Pattern I. The elevated dust layer originated from the Gobi Desert and ultimately reached the west coast of North America [Eguchi et al., 2009]. Similar meteorological conditions were also found in the large-scale elevated dust episodes observed during the ACE-Asia field campaign [Uno et al., 2004].

[16] The second pattern, referred to as Pattern II, is represented by the Episode B outbreak, occurring during May 24–26, in which the dust center was located in a dry slot behind the cold front of a well developed low-pressure system (indicated by L3). Because of the strong gradient of potential temperature, the vertical diffusion of dust was suppressed for this pattern, and the dust and pollutants were well mixed within the PBL (∼2 km and below). This outbreak quickly swept over Japan. The speed of the cold front driving this Pattern II outbreak was generally high.

[17] Finally, we summarize briefly the other cases of Asian dust outbreaks that occurred during May 2007. Large-scale dust emissions from the Gobi Desert region was occurred on May 3, 5, 9, 14, 22 and 27 (see Figure 1a). Among these large-scale dust event, two events corresponds to Pattern I (these event has occurred following by dust emissions on May 3 and 5), and the four other events were similar to Pattern II. We found that the existence of two sequential low-pressure systems was one of the necessary conditions to lift dust up over the warm-sector (Pattern I). Such a meteorological condition is relatively infrequent, compared with a single low-pressure system, such as that which generated Pattern II. Our current analysis reveal that the remote sensing lidar observations clearly captured such a difference in dust/pollutant structure, and that the chemical transport models used were able to accurately simulate the large-scale dust/pollutants transport processes.

4. Concluding Remarks

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Observation Data and Model Configuration
  5. 3. Results and Discussion
  6. 4. Concluding Remarks
  7. Acknowledgments
  8. References
  9. Supporting Information

[18] We analyzed two important layered outflows of dust and air pollutants over Eastern Asia during the spring by a synergetic analysis of both ground-based and space-borne lidar observations and dust/pollutant transport models. We identified two large-scale dust and pollutants transport patterns (Patterns I and II). The existence of two sequential low-pressure systems played an important role in the formation of Pattern I. The air flow within the warm sector lifted dust particles into the free troposphere. The Pattern I dust layer (typically of dimensions 1500–2000 km horizontally and 2.5–4 km AGL vertically) generally remained unmixed with pollutants and tended to be transported over a long distance. On the other hand, the Pattern II outflow typically occurred within the dry slot behind a well-developed cold front. In this pattern, both dust and pollutants tended to be trapped and mixed within the PBL (1–2 km thick) because of strong stratification. The potential temperature gradient of Pattern II was ∼5 K/km, which was greater than that of Pattern I (∼2–3.4 K/km). Among the six cases of large-scale dust transport events occurring in May 2007, four events corresponded to Pattern II and the other two cases (frequency of 30%) were classified as Pattern I. The Pattern II transport occurred with a single low-pressure system, whereas two continuous low-pressure systems were necessary for the Pattern I transport. Our study period of May 2007 consisted of a single month; thus, additional case studies are needed to clarify the frequencies with which Patten I transport of large-scale dust transport and Pattern II transport of dust/pollution within the boundary layer, forming ‘polluted dust,’ typically occur.

Acknowledgments

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Observation Data and Model Configuration
  5. 3. Results and Discussion
  6. 4. Concluding Remarks
  7. Acknowledgments
  8. References
  9. Supporting Information

[19] This work was partly supported by the Global Environment Research Fund, Ministry of Environment, Japan (C-091), and by the Joint Research Fund of the Research Institute for Applied Mechanics, Kyushu University. We thank T. Ohara and J. Kurokawa of NIES, Japan, for their valuable continuous support of CMAQ calculations. The CALIPSO data were acquired from the Atmospheric Science Data Center at the NASA Langley Research Center.

References

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Observation Data and Model Configuration
  5. 3. Results and Discussion
  6. 4. Concluding Remarks
  7. Acknowledgments
  8. References
  9. Supporting Information

Supporting Information

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Observation Data and Model Configuration
  5. 3. Results and Discussion
  6. 4. Concluding Remarks
  7. Acknowledgments
  8. References
  9. Supporting Information

Auxiliary material for this article contains a figure showing topography of the RC4 model domain.

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grl27434-sup-0001-readme.txtplain text document1Kreadme.txt
grl27434-sup-0002-fs01.tifTIFF image12306KFigure S1. Topography of the RC4 model domain and the NIES lidar observation sites.
grl27434-sup-0003-t01.txtplain text document2KTab-delimited Table 1.

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