Abstract

The regional coupled climate–chemistry/aerosol model (RegCM3) is used to investigate the difference in the spatial distribution of aerosol optical depth (AOD) between a strong summer monsoon year (SSMY; July 2003) and a weak summer monsoon year (WSMY; July 2002) under the actual- and same-emission scenarios. It is shown that the intensity of the Asian summer monsoon is primarily responsible for the AOD spatial distribution anomaly in midsummer over East Asia. Specifically, the AOD over southern China, upwind of the Asian summer monsoon, is greater in WSMY than in SSMY, but the opposite is observed for the AOD downwind over northern China and the Korean Peninsula. The AOD spatial distribution patterns simulated on the basis of the actual emission inventories for the SSMY and WSMY do not substantially differ from their counterparts that are based on the same emission inventory, confirming that the monsoon circulation, rather than local emissions or dry and wet deposition processes, is the predominant factor determining the regional AOD distribution. These modeling results are consistent with the analyses based on the Moderate Resolution Imaging Spectroradiometer (MODIS) products, NCAR–Department of Energy wind fields, and air parcel movements according to the 7-day trajectories of air parcels determined by the Hybrid Single-Particle Lagrangian Integrated Trajectory model.

1. Introduction

Aerosols—that is, the liquid and solid particulates suspended in the atmosphere—constitute an important atmospheric component not only by directly absorbing and scattering solar radiation and terrestrial thermal infrared emission, but also by affecting the water cycle through their indirect effect [i.e., acting as efficient cloud condensation nuclei (CCN) or ice nuclei (IN) (Ramanathan et al. 2001)]. Substantial uncertainties in our current knowledge of the aerosol impact on climate change have not been resolved primarily because of the substantial variations of the loading and microphysical properties of aerosols in both space and time (Solomon et al. 2007) and the complicated physical and chemical processes related to aerosols’ direct and indirect climatic effects. For this reason, the interactions between aerosols and the terrestrial climate system have been an active research subject and have received a great deal of attention from the atmospheric research community.

High aerosol concentrations in East Asia (Zhao et al. 2006; Ohara et al. 2007) give rise to significant regional aerosol radiative forcing (Giorgi et al. 2003; Chung et al. 2005) and consequently influence the regional and global climate systems (Ramanathan et al. 2001). Previous studies have shown significant temporal and spatial variations of aerosols in East Asia. Using the aerosol optical depth (AOD) derived from a global chemical transport model based on aerosol emission data in East Asia from 1980 to 2000, Streets et al. (2008) found a steady increase in aerosol concentration. Li et al. (2003) reported that increasing human activities in China are largely responsible for the increase in aerosol loading, particularly in regions where the population is dense and rapid industrialization is ongoing. In recent decades, the increasing aerosol loading has resulted in a reduction of the surface solar insolation (Che et al. 2005; Xia et al. 2006), a decrease in the horizontal visibility (Che et al. 2007), and an increase in the number of extreme climatic events (Menon et al. 2002) over China.

A close relationship exists between the airborne aerosols and the Asian summer monsoon system that prevails over South and East Asia in summer (Wang 2006). Zhao et al. (2010) reported that the strength and temporal–spatial extension of the summer monsoon play an important role in modulating the pollution over southeastern China and central East China from June to July. The Asian summer monsoon circulation can bring anthropogenic aerosols from East China to the downwind regions including the Yellow Sea, the Korean Peninsula, and the East China Sea (Yoon et al. 2010). Zhang et al. (2010) demonstrated that the concentration of summer particulate matter that is smaller than 2.5 μm in diameter (PM2.5) in the boundary layer over eastern China is associated with variations in the Asian summer monsoon. Using the Moderate Resolution Imaging Spectroradiometer (MODIS) retrieved AOD product, observational surface visibility, and tropospheric wind fields, Liu et al. (2011) showed that the intensity of the Indian summer monsoon in midsummer has an important impact on the AOD spatial distribution over East Asia. During a weak monsoon year, the AOD values in southern China tend to be higher than those in the region including north China and the Korean Peninsula, but a reversed pattern is observed in a strong monsoon year.

The present study was motivated by the observational data analysis by Liu et al. (2011), who reported the pronounced differences in the midsummer AOD spatial distribution patterns between a strong summer monsoon year (SSMY) and a weak summer monsoon year (WSMY). However, Liu et al. (2011) did not separate the relative contributions of the transport and deposition of aerosols from those of the monsoon circulation. The changes in the local aerosol emissions between the two years were also not considered in Liu et al. (2011). Using a modeling approach, this study intends to examine the influence of the summer monsoon on the aerosol spatial distribution patterns under actual emission conditions in East Asia. Differences in the aerosol distribution patterns with the same aerosol emission inventory are analyzed to isolate the impact of the Asian summer monsoon circulation. Specifically, this study analyzes the roles of various physical processes including the emission, transport, and wet–dry deposition in regulating the regional aerosol pattern.

2. Models and data

Following Liu et al. (2011), we chose for the present study the year 2003 as a strong summer monsoon year and year 2002 as a weak summer monsoon year, based on the Indian summer monsoon strength in midsummer (July).

a. RegCM3 and experimental design

A regional coupled climate–chemistry/aerosol model (RegCM3) is used to study and evaluate the relative significance of the physical processes involved in determining the spatial pattern of atmospheric aerosols in East Asia with various monsoon circulation intensities. RegCM3, developed at the Abdus Salam International Center for Theoretical Physics (ICTP), is a three-dimensional, sigma-coordinate, primitive equation, regional climate model and includes parameterizations of various physical processes in the atmosphere (Pal et al. 2007). The aerosol processes are described in Solmon et al. (2006) based on the schemes of Qian and Giorgi (1999) and Qian et al. (2001). Six chemical species are considered in the RegCM3 aerosol module: gaseous SO2, particulate SO42-, hydrophobic and hydrophilic black carbon (BCphobic and BCphilic), and hydrophobic and hydrophilic organic carbon (OCphobic and OCphilic). The aerosol processes include horizontal and vertical advection, horizontal and vertical turbulent diffusion, surface emission, gas phase chemical conversion, heterogeneous reactions, large-scale and convective cloud processes, under-cloud scavenging by precipitation (wet deposition), and dry deposition. The horizontal resolution of the model is 50 km with 18 vertical levels, the pressure at the top of the model atmosphere is 5 hPa, and the time interval of model integration is 90 s. Studies have demonstrated that RegCM3 performs well to capture the main climatological features of mean circulation, surface temperature, and precipitation at the annual and seasonal scales over East Asia (Zhang et al. 2008; Gao et al. 2008).

Two groups of simulations are conducted in this study: the first group for a strong summer monsoon year (2003) under the emission scenarios of 2003 (Exp03) and 2002 (Exp03_2), and the second group for a weak summer monsoon year (2002) under the emission scenarios of 2002 (Exp02) and 2003 (Exp02_3). The definition of monsoon intensity is based on the midsummer (July) Indian monsoon index (Wang 2006) as described in Liu et al. (2011). Because the actual emission scenarios are used in Exp03 and Exp02, the difference in AOD spatial distribution between Exp03 and Exp02 can be compared with the analysis of the observations by Liu et al. (2011). Moreover, since the same emission scenario (2002) is used in Exp03_2 and Exp02, the difference between Exp03_2 and Exp02 (i.e., Exp03_2 − Exp02) can be used to isolate the effects of the monsoon circulation on the regional aerosol spatial distribution. The same is true for Exp03 and Exp02_3 (Exp03 − Exp02_3). The present simulation domain is centered at 30°N, 100°E, and the number of grids is 160 × 128 (east–west by south–north). The initial conditions and the boundary conditions for every 6 h are provided by the National Center for Atmospheric Research–Department of Energy (NCAR–DOE) reanalysis data (Kanamitsu et al. 2002), and the sea surface temperature (SST) data are retrieved from the National Oceanic and Atmospheric Administration (NOAA) optimum interpolation SST dataset (Reynolds et al. 2002). Although all simulations are conducted for a period from 1 May to 1 September for both the 2002 and 2003 cases, only the simulation results for the month of July are reported here. In the present study, the modeled AOD at the spectral band of 350–640 nm is used to represent the spatial distribution of aerosols.

Accurate aerosol emission inventories are crucial for aerosol modeling studies. The emission inventory data used in RegCM3 simulations for the years 2002 and 2003 include both anthropogenic and natural emissions. The anthropogenic sources emission data (fuel combustion, industrial sources, and agriculture) are supplied by the Regional Emission inventory in Asia (REAS 1.11; Ohara et al. 2007), and the natural sources emission data (burned area and fire emissions) are obtained from the Global Fire Emissions Database, version 3 (GFED3; van der Werf et al. 2010). Both anthropogenic and natural emission inventories have a high resolution of 0.5° × 0.5°.

Figure 1 shows that the spatial distribution patterns of SO2, BC, and OC in July 2002 are similar to those in July 2003 over East Asia. The sources of SO2, BC, and OC are mainly from areas where large cities with dense populations are located, such as the Sichuan basin and north China. Note that the focal subregions in this study are the same as those defined in Liu et al. (2011). Specifically, one subregion is upwind of the summer monsoon in southern China [23°–32°N, 105°–120°E; hereinafter the South Region (SR)] and the other subregion is downwind of the summer monsoon including northern China and the Korean Peninsula [35°–44°N, 115°–130°E; hereinafter the North Region (NR)]. The two subregions are explicitly indicated by the two boxes in Fig. 2a.

Fig. 1.

Emissions inventories in East Asia of (a),(b) SO2 (10−9 kg m−2 s−1), (c),(d) BC (10−10 kg m−2 s−1), and (e),(f) OC (10−10 kg m−2 s−1) in July of (left) 2002 and (right) 2003.

Fig. 1.

Emissions inventories in East Asia of (a),(b) SO2 (10−9 kg m−2 s−1), (c),(d) BC (10−10 kg m−2 s−1), and (e),(f) OC (10−10 kg m−2 s−1) in July of (left) 2002 and (right) 2003.

Fig. 2.

(a),(b) RegCM3 simulated and (c),(d) NCAR–DOE observational 850-hPa wind fields in July of (left) 2002 and (right) 2003. The two boxes in this and subsequent figures indicate the north and south subregions focused on in this study. The yellow areas indicate elevations >2000 m.

Fig. 2.

(a),(b) RegCM3 simulated and (c),(d) NCAR–DOE observational 850-hPa wind fields in July of (left) 2002 and (right) 2003. The two boxes in this and subsequent figures indicate the north and south subregions focused on in this study. The yellow areas indicate elevations >2000 m.

Furthermore, we calculate the year-on-year variations of SO2, BC, and OC emissions in SR, NR, and East Asia, as shown in Table 1. The values of SO2 are much larger than the values of BC and OC in the region. When compared with the values in July 2002, the emissions of BC and OC in July 2003 changed slightly in both SR and NR, whereas the emission of SO2 increased by 17.16%, 10.69%, and 13.86% in SR, NR, and East Asia, respectively. It is noted that the emissions of SO2, BC, and OC in SR increased in July 2003, while the aerosol loading decreased in SR as shown in Liu et al. (2011).

Table 1.

Regionally averaged emission inventories (mg m−2 day−1) of SO2, BC, and OC for SR and NR and East Asia (20°–50°N, 100°–130°E) in July 2002 and 2003.

Regionally averaged emission inventories (mg m−2 day−1) of SO2, BC, and OC for SR and NR and East Asia (20°–50°N, 100°–130°E) in July 2002 and 2003.
Regionally averaged emission inventories (mg m−2 day−1) of SO2, BC, and OC for SR and NR and East Asia (20°–50°N, 100°–130°E) in July 2002 and 2003.

b. HYSPLIT model and air parcel movements

Air parcel trajectory analysis is an effective method to determine the incoming and outgoing directions of airflows carrying aerosols. The Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model, developed by the Air Resources Laboratory of NOAA, is used to compute the three-dimensional trajectories of forward or backward airflows (Draxler and Hess 1997). In this study, HYSPLIT V4.8, driven by the National Centers for Environmental Prediction–NCAR reanalysis data (Kalnay et al. 1996) at 4 times each day (0000, 0600, 1200, and 1800 UTC) for July in 2002 and 2003, is used to simulate the 7-day forward trajectories from the center of SR (27.5°N, 112.5°E) at 500 m above the ground surface. Cluster analysis (Stunder 1996; Draxler and Hess 1997) is applied to combine similar trajectories to statistically classify the spatial and temporal variations of the 7-day forward trajectories to illustrate the air movements.

c. Other observational datasets

In this study, several observational datasets are compared with the modeled results. For example, we use the July 850-hPa wind fields during 2000–09 with a resolution of 2.5° × 2.5° from the NCAR–DOE reanalysis (Kanamitsu et al. 2002) to represent the midsummer monsoon circulation features. A gauge-based precipitation analysis dataset (Xie et al. 2007) is utilized to evaluate the modeled precipitation. We use satellite-retrieved AOD data in July of 2002 and 2003 from MODIS to represent the observed aerosol spatial distribution patterns. The MODIS collection-5, level-3 monthly mean global AOD data at the 550-nm wavelength (Kaufman et al. 1997a,b) are derived from the Terra MODIS observations with a spatial resolution of 1° × 1°.

3. Results and discussion

a. Difference in wind fields between SSMY and WSMY

To validate the RegCM3 simulations, we compared the results from Exp03 and Exp02 with the NCAR–DOE wind field data (Fig. 2). As shown in the 850-hPa wind fields simulated by RegCM3, the July monsoon circulation in the WSMY simulation (Fig. 2a) was weak across the study region, because the Indian summer monsoon originating from the Bay of Bengal and passing through the Indo-China Peninsula did not turn north toward SR, but continued eastward and turned north at approximately 130°E. Therefore, the northwest Pacific region experienced a strong monsoon flow, which was not observed in the two focal subregions (i.e., SR and NR) where weak monsoon winds occurred. In the SSMY experiment (Fig. 2b), SR was completely under the influence of the southerly and southwesterly monsoon winds, because the Indian summer monsoon turned north at approximately 110°E over the South China Sea after passing through the Indo-China Peninsula. Meanwhile, the monsoon winds proceeded farther north and had a profound impact on NR. When compared with the wind fields of the NCAR–DOE reanalysis in 2002 (Fig. 2c) and 2003 (Fig. 2d), it is clear that RegCM3 captured the main features of the monsoon circulation in East Asia during the two study periods.

To better illustrate the differences in the monsoon circulations between the SSMY and WSMY cases, Fig. 3 shows the RegCM3-based 850-hPa wind field difference between Exp03 and Exp02 (Fig. 3a; Exp03 − Exp02) in conjunction with the corresponding wind field difference from the NCAR–DOE data (Fig. 3b; July 2003 − July 2002). In SSMY, there are stronger southwesterly wind anomalies in SR, driving the monsoon winds and aerosols to reach the NR subregion relative to the wind field of WSMY. Additionally, convergent flows and wind shear occur to the south of NR. The simulated results are in good agreement with the observations (Fig. 3b), confirming the capability of RegCM3 to simulate regional circulation patterns over East Asia.

Fig. 3.

(a) RegCM3 simulated and (b) NCAR–DOE observational 850-hPa wind field difference between the strong and weak monsoon circulation. Cluster means of 7-day forward trajectories [4 times each day (0000, 0600, 1200, and 1800 UTC) at 500 m above ground surface] for July originated from a single point (27.5°N, 112.5°E; center of SR) in (c) July 2002 and (d) July 2003. The yellow areas indicate elevations > 2000 m.

Fig. 3.

(a) RegCM3 simulated and (b) NCAR–DOE observational 850-hPa wind field difference between the strong and weak monsoon circulation. Cluster means of 7-day forward trajectories [4 times each day (0000, 0600, 1200, and 1800 UTC) at 500 m above ground surface] for July originated from a single point (27.5°N, 112.5°E; center of SR) in (c) July 2002 and (d) July 2003. The yellow areas indicate elevations > 2000 m.

Aerosol transport is associated with the wind field. To illustrate the potential aerosol transport in the study region, we studied the 7-day air parcel trajectories originating from the central location of SR for SSMY and WSMY using the HYSPLIT model. Cluster analysis of the 7-day-mean forward trajectories indicates that monsoon circulation in SSMY favors the northward transport of aerosols as compared with the monsoon circulation in WSMY. In July 2002 corresponding to WSMY (Fig. 3c), the proportion of the northward airflow cluster is 40% accompanied by a westward airflow cluster of 41% and a southward airflow cluster of 19%. Under the influence of the overall monsoon circulation, the southward airflow eventually turns to the east at 22°N. In July 2003 corresponding to SSMY (Fig. 3d), in contrast, the northward flows dominate the air parcel trajectories with a fast-moving northward airflow cluster of 53% and a slow-moving northward airflow cluster of 47%. This flow pattern enhances the transport of aerosols from the upwind of SR to the downwind of NR. The monsoon circulation differences between the two years and the mean forward-trajectory cluster analysis both indicate that the strong summer monsoon winds in East Asia facilitate the transport of aerosols from SR to NR.

b. Spatial distributions of AOD

To validate the RegCM3 simulated AOD in Exp03 and Exp02, we compared the spatial distributions of modeled AOD in July 2002 and July 2003 with MODIS observed AOD (Fig. 4). The modeled AOD of most areas in SR were higher in July 2002 (Fig. 4a) than in July 2003 (Fig. 4b), while the modeled AOD of most areas of NR were higher in July 2003 than in July 2002. When compared with the observed AOD by MODIS in July 2002 (Fig. 4d) and July 2003 (Fig. 4e), the spatial patterns of modeled AOD during two study periods were in general consistent with those of observed AOD. Because of deficiencies in the aerosols emission inventories and RegCM3 itself, the modeled AOD could not accurately capture all the details of the spatial distribution patterns over some areas. For example, the modeled results overestimated the AOD in the western part of SR in July 2003. Nevertheless, the model is capable of showing the responses of the aerosol distribution pattern to the changes in the monsoon circulation in East Asia.

Fig. 4.

(a),(b) RegCM3 simulated and (d),(e) MODIS observed AOD in July of (top) 2002 and (middle) 2003. The (c) simulated and (f) observed AOD differences between the strong monsoon circulation and the weak monsoon circulation.

Fig. 4.

(a),(b) RegCM3 simulated and (d),(e) MODIS observed AOD in July of (top) 2002 and (middle) 2003. The (c) simulated and (f) observed AOD differences between the strong monsoon circulation and the weak monsoon circulation.

With the actual aerosol emission inventories in Exp03 and Exp02, the differences in the AOD spatial distribution patterns simulated from RegCM3 show higher AOD values in SR during the WSMY than the SSMY, whereas the variation of AOD in NR shows a reversed pattern (Fig. 4c). This result is consistent with the observed patterns identified (Fig. 4f) in Liu et al. (2011). As shown by the simulated and observed differences in the AOD spatial distributions between SSMY and WSMY (Figs. 4c,f), the AOD of SR is higher during WSMY, but the AOD of NR is higher during SSMY. However, because of overestimation of AOD in the western part of SR in July 2003, the simulated differences in this area are positive (Fig. 4c) as compared with the observed values in Fig. 4f. In general, RegCM3 has the capability of reproducing the spatial distribution of aerosol in East Asia and capturing the major features in the AOD spatial distribution patterns. Both the observational data analysis in Liu et al. (2011) and present model-based study elucidate that the AOD in SR is greater during WSMY than SSMY, but the reverse is observed for the AOD in NR.

c. Influence of emission inventories

Although the RegCM3 simulations have reproduced the aerosol distribution patterns revealed by Liu et al. (2011) under the actual-emission scenario, the emission inventories in the two years were indeed different as analyzed in section 2a. To isolate the effect of the monsoon circulation from the variation in emission inventory on aerosol spatial distribution, we organized two groups of simulations to investigate the AOD spatial distributions in the WSMY and SSMY cases under the same-emission scenarios: one group (Exp02 and Exp03_2) with the 2002 emission inventory and the other group (Exp02_3 and Exp03) with the 2003 emission inventory. The differences in the AOD spatial distributions between SSMY and WSMY for the 2002 emission inventory and the 2003 emission inventory both show that, even with the same emission inventory, the AOD of SR is still higher in WSMY than in SSMY, whereas the AOD of NR shows the opposite pattern. As shown in Figs. 5a,b, the values of most model grids in SR are negative, whereas the values of most grids in NR are positive, suggesting reversed patterns between these subregions. This analysis demonstrates that the impact of the summer monsoon circulation on the AOD spatial distribution is evident regardless of the emission inventories. Because the differences in the emission inventories between 2002 and 2003 do not change the overall patterns of AOD distributions, we can conclude that the monsoon circulation pattern is the controlling factor and is more important than variations in the local aerosol emissions in determining the spatial distribution pattern of AOD in East Asia.

Fig. 5.

RegCM3 simulated AOD differences between the strong monsoon circulation and the weak monsoon circulation with the (a) 2002 and (b) 2003 emission inventories, respectively.

Fig. 5.

RegCM3 simulated AOD differences between the strong monsoon circulation and the weak monsoon circulation with the (a) 2002 and (b) 2003 emission inventories, respectively.

d. Effect of wet deposition

Wet deposition and dry deposition processes also exert influences on the AOD spatial distribution pattern (Chate et al. 2003; Laakso et al. 2003). It is well known that wet deposition has a close relationship with precipitation. Thus, the precipitation pattern simulated by RegCM3 should be evaluated before wet deposition is analyzed. To evaluate the performance of RegCM3 in simulating precipitation in East Asia during the study periods, we compare the RegCM3 simulated and gauge-based observational precipitation fields in July 2002 and 2003 (Fig. 6). In July 2002, the modeled precipitation pattern (Fig. 6a) is quite consistent with the observational precipitation pattern (Fig. 6d), although it is underestimated in the central part of SR and overestimated in the central northern part of NR. In July 2003, the modeled precipitation pattern (Fig. 6b) is also fairly consistent with the observational precipitation pattern (Fig. 6e), although it is overestimated in the western part of SR. In both modeled and observational precipitation fields, there is a south-flood and north-drought pattern in SSMY and a south-drought and north-flood pattern in WSMY (Figs. 6c,f), suggesting reversed precipitation patterns in the two subregions associated with different monsoon intensities. The above results show that RegCM3 can capture the main spatial–temporal distribution patterns of precipitation and further produce reasonable results for the wet deposition of aerosols.

Fig. 6.

(a),(b) RegCM3 simulated and (d),(e) gauge-based precipitation (mm day−1) in July of (top) 2002 and (middle) 2003, and the (c) simulated and (f) observed precipitation differences (mm day−1) between the strong monsoon circulation and weak monsoon circulation.

Fig. 6.

(a),(b) RegCM3 simulated and (d),(e) gauge-based precipitation (mm day−1) in July of (top) 2002 and (middle) 2003, and the (c) simulated and (f) observed precipitation differences (mm day−1) between the strong monsoon circulation and weak monsoon circulation.

To examine the effect of wet deposition on spatial distribution of aerosol loading, Fig. 7 shows the simulated wet deposition difference fields of SO2 (Fig. 7a), BC (Fig. 7b), and OC (Fig. 7c) between SSMY and WSMY. The simulated wet deposition fields can, at least, provide a qualitative assessment of the effect of the wet deposition process, although we are unable to validate the simulated wet deposition fields because of a lack of observational data. In Figs. 7a–c, the values of most model grids in SR are negative and the values of most model grids in NR are positive for wet depositions of SO2 (Fig. 7a), BC (Fig. 7b), and OC (Fig. 7c), suggesting the same spatial patterns as those of precipitation and AOD. The values of some model grids in central northern NR are negative because of overestimation of precipitation in July 2002 (Fig. 7a), which leads to more wet depositions in July 2002. The values of some model grids in western SR are positive because of overestimation of precipitation in July 2003 (Fig. 7b), which results in more wet depositions in July 2003. These results demonstrate that precipitation-related wet deposition plays an important role in balancing the spatial distribution of aerosols. During WSMY, the heavy aerosol loading in SR can be reduced by more precipitation, while the same is true for NR during SSMY. Although wet deposition processes are important, the aerosol loading patterns have not substantially changed because of increased precipitation.

Fig. 7.

(left) RegCM3 simulated wet deposition (mg m−2 day−1) and (right) column burden (mg m−2) difference fields of (a),(d) SO2, (b),(e) BC, and (c),(f) OC between July 2003 and July 2002.

Fig. 7.

(left) RegCM3 simulated wet deposition (mg m−2 day−1) and (right) column burden (mg m−2) difference fields of (a),(d) SO2, (b),(e) BC, and (c),(f) OC between July 2003 and July 2002.

To assess the importance and variation of wet deposition between an SSMY and a WSMY, Table 2 lists the RegCM3 simulated monthly regional averaged precipitation, and column burden, wet deposition, and dry deposition of SO2, BC, and OC for SR and NR in July 2002 and July 2003. In SR during July 2003, the strong summer monsoon is associated with less precipitation, which results in decreases in wet depositions of SO2, BC, and OC as compared with those during July 2002, and the decreases in column burdens of SO2, BC, and OC lead to less dry depositions. In NR during July 2003, the strong summer monsoon enhances precipitation, resulting in increases in wet depositions of SO2, BC, and OC as compared with the case for July 2002. Meanwhile, the ratios of wet deposition to column burden suggest that wet deposition processes can remove aerosols from air by rates ranging from 26.87% to 48.64% per day indicating the relative importance of wet deposition. However, the increases in wet depositions and dry depositions could not have resulted in less column burdens in NR during July 2003, suggesting the importance of aerosols transport by the southwesterly summer monsoon circulation.

Table 2.

RegCM3 simulated regionally averaged precipitation, AOD, column burden, and wet and dry deposition of SO2, BC, and OC for SR and NR in July 2002 and July 2003.

RegCM3 simulated regionally averaged precipitation, AOD, column burden, and wet and dry deposition of SO2, BC, and OC for SR and NR in July 2002 and July 2003.
RegCM3 simulated regionally averaged precipitation, AOD, column burden, and wet and dry deposition of SO2, BC, and OC for SR and NR in July 2002 and July 2003.

With the consideration of wet and dry depositions, the previous analysis confirms that it is the summer monsoon that plays an important role in regulating the spatial–temporal distributions of aerosols in East Asia. The column burdens of SO2, BC, and OC in SR are greater in WSMY than in SSMY, while the reversed patterns for the column burdens in NR are shown in Figs. 7d–f. More aerosols in the atmosphere column will eventually enhance the extinction of solar radiation through aerosol scattering and absorption, leading to higher AOD measurements (Table 2).

Note that our modeling study is based only on data for two months, although the modeling results render a confirmation and a physical explanation of the findings in Liu et al. (2011). However, given the consideration of aerosol processes including emissions, transport, and wet and dry depositions, the present results should be applicable to other periods of strong or weak summer monsoon circulations regarding the relationship between the monsoon intensity and spatial distribution of aerosols in East Asia. In other words, the aerosol loading during a weak monsoon period in southern China tends to be higher than that in a strong monsoon period, but such a relation is reversed for northern China.

4. Conclusions

In this study, we used RegCM3 to simulate the midsummer (July) aerosol distributions with weak (2002) and strong (2003) monsoon intensities in East Asia for the actual- and same-emission scenarios. We focused on the aerosol distributions in two subregions, one over southern China (SR) and one over northern China and the Korean Peninsula (NR). The RegCM3 simulations were first validated in comparison with the NCAR–DOE reanalysis for the wind fields. The simulated and observed monsoon circulations and the mean air parcel trajectories showed similar results. A strong monsoon circulation favors the transport of aerosols from the upwind SR to the downwind NR, whereas a weak monsoon circulation severely suppresses the aerosol transport, creating high aerosol levels in SR.

With the actual emission inventory data corresponding to the weak (2002) and strong (2003) monsoon circulations, the AOD spatial patterns from the RegCM3 simulations show that the AOD in NR was higher for the strong monsoon circulation and the AOD in SR was higher for the weak monsoon circulation, confirming the conclusion based on observational data analyzed in Liu et al. (2011).

Under the same-emission scenarios, the AOD spatial patterns from the RegCM3 simulations show that the AOD in NR tends to be higher in the strong monsoon experiment and the AOD in SR tends to be higher in the weak monsoon experiment, regardless of which emission inventory (2002 or 2003) is used. The differences in the AOD spatial distributions between the two experiments illustrate the tendency of the two subregions to have reversed patterns primarily because of different summer monsoon intensities. These results indicate that the intensity of the monsoon circulation is the dominant factor determining the spatial pattern of aerosols in East Asia, rather than the changes in the local emissions between 2002 and 2003.

Both the numerical simulations and observed AOD distribution patterns showed that SR had higher aerosol concentrations under weak monsoon circulation conditions than under strong monsoon circulation, while the aerosol concentrations in NR were higher under strong monsoon circulation conditions as compared to weak monsoon circulation.

In addition to the intensity of monsoon circulation and aerosol emissions, we considered other factors that may influence the AOD distribution patterns, such as wet and dry depositions for specific types of aerosols, such as SO2, black carbon (BC), and organic carbon (OC). As an important process in determining the spatial distribution of AOD, wet deposition can remove aerosols from air by rates up to 48.64% per day in our subregions. However, the increases in wet depositions and dry depositions associated with strong monsoon circulation have not resulted in lower column burdens of aerosols in NR during July 2003, suggesting the important roles played by the aerosol transport mechanisms.

Coupled climate–chemistry/aerosol models are powerful tools for improving our understanding of the relationships between the monsoon circulation and aerosols. We recognize that errors in the numerical simulations of AOD distribution patterns could have originated from the deficiencies of the models and also from the uncertainties in the emission inventories. Future work might focus on enhancing the model capabilities to accurately portray atmospheric processes and improving the aerosol modules, including the coupling of the indirect effects of aerosols and obtaining more accurate emission inventories. Furthermore, more detailed descriptions of the physical processes (e.g., physical transport, chemical reactions, under-cloud scavenging, and local emissions) should be implemented in the models to investigate the contributions of the monsoon circulation to the spatial patterns of aerosols over East Asia.

Acknowledgments

Xiaodong Liu acknowledges support from the 973 Program (2011CB403406), CAS (XDA05110101), and NSFC (40825008, 41075067). Ping Yang acknowledges support from the U.S. National Science Foundation (ATM-0803779) and the endowment funds associated with the David Bullock Harris Chair in Geosciences, College of Geosciences, Texas A&M University.

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