Next Article in Journal
Brightness Temperature and Wet Tropospheric Correction of HY-2C Calibration Microwave Radiometer Using Model-Derived Wet Troposphere Path Delay from ECMWF
Next Article in Special Issue
Impact of Aerosols on NPP in Basins: Case Study of WRF−Solar in the Jinghe River Basin
Previous Article in Journal
Identification and Analysis of Unstable Slope and Seasonal Frozen Soil Area along the Litang Section of the Sichuan–Tibet Railway, China
Previous Article in Special Issue
Enhancement of Photovoltaic Power Potential in China from 2010 to 2020: The Contribution of Air Pollution Control Policies
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Spatiotemporal and Vertical Distribution of Asian Tropopause Aerosol Layer Using Long-Term Multi-Source Data

1
College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China
2
Cryosphere Research Station on the Qinghai-Tibet Plateau, State Key Laboratory of Cryospheric Science, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
3
University of Chinese Academy of Sciences, Beijing 100049, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2023, 15(5), 1315; https://doi.org/10.3390/rs15051315
Submission received: 1 February 2023 / Revised: 21 February 2023 / Accepted: 25 February 2023 / Published: 27 February 2023

Abstract

:
The Asian tropopause aerosol layer (ATAL) is an enhanced aerosol concentration layer in the upper troposphere and lower stratosphere over Asia, and it has important effects on radiation balance, atmospheric circulation, regional climate, and atmospheric chemical processes. However, despite its importance, the specific structure and long-term variation trend of the ATAL have been rarely analyzed, which is critical for assessing the impact of ATAL on climate change and evaluating the performance of climate models. This study compared and analyzed the three-dimensional spatial distribution characteristics and temporal variability using CALIPSO, SAGEII, and MERRA-2 data and discussed the possible causes of the variation. The results showed that the ATAL began to appear in the mid-to-late 1990s and then strengthened rapidly until 2010, after which this trend was no longer observed. Moreover, significant heterogeneity existed in the distribution of aerosol concentration in the ATAL, showing north–south differences (NSDs) in both time and space. In addition, it was found that besides surface emissions, atmospheric circulation, the strength of convective transport, and stratosphere–troposphere exchange processes also contribute to this pattern. This study has important implications for quantifying the climate consequences of the ATAL.

1. Introduction

Since the industrial revolution, the human economy and society have developed rapidly. With the explosion of industry and the rapid development of the transportation and energy industries, the pollution problem has become increasingly serious [1,2]. Nitrogen and sulfide emissions form acid rain, damage forests, and destroy the ecological environment [3]. The emission of inhalable particulate matter deteriorates air quality and damages human health [4]. Urban pollution also leads to photochemical reactions under the irradiation of the sun, resulting in the formation of photochemical smog, which seriously harms both human health and the environment [5]. Furthermore, the impact of pollutant emissions on the climate should not be underestimated. Emissions of carbon dioxide and other greenhouse gases are significant contributors to global warming. In addition, climate problems related to anthropogenic emissions, such as the ozone hole, rising sea levels, and increased extreme weather events, are receiving increasing attention [6].
In recent years, as an important part of the global economy, South Asia has experienced rapid economic development among emerging economies in the region [7]. However, along with this development, rapid industrialization and urbanization have also caused many pollution emissions [8]. These emissions have caused serious, atmospheric pollution and air quality problems extending from the planetary boundary layer to the middle and upper troposphere and even to the stratosphere through upward motion, ultimately affecting the entire world [9,10,11,12]. In 2011, Vernier et al. found an aerosol layer that exists in the atmosphere with a vertical scale of 12–18 km and extends horizontally from the Eastern Mediterranean across India to Western China (5–105°E; 15–45°N) using CALIPSO data and named it the Asian tropopause aerosol layer (ATAL) [13,14]. It has been found that anthropogenic emissions from the ground can be transported upward to the upper troposphere lower stratosphere (UTLS) [15,16,17] and then accumulate to form the ATAL.
As the transition region between the troposphere and stratosphere, the UTLS is the key region of mass and energy exchange between the two [18]. The widespread existence of water vapor, ozone, and other chemicals in the UTLS region plays an important role in the energy and radiation balance of the earth–atmosphere system [19,20,21]. Thus, the ATAL has important implications for atmospheric radiation, circulation, and chemical processes in the UTLS region [22,23].
The discovery of the ATAL has drawn significant attention from researchers, who have conducted numerous studies on its formation mechanism and components. Many studies have shown that during the South Asian Summer monsoon (SASM), the near-surface air can be rapidly lifted and transported upward and controlled by the anticyclonic circulation under the combined action of the anticyclonic circulation and persistent deep convective activity [24,25,26]. The research on the ATAL’s components mainly includes observational and model simulation studies. Recent aircraft observations in 2017 and balloon measurements in 2015 show that the ATAL may be composed of sulfate, organics, nitrates, black carbon, and dust [14,27,28]. However, the results from model simulations are quite different. Using the ECHAM5-HAMMOZ model, Fadnavis et al. [29] found that mineral dust aerosols are also an important component. Yu et al. [30] pointed out that the ATAL mainly comprises secondary organic, sulfate, and primary organic aerosols. The simulation results by Fadnavis et al. [31] indicated a continuous maximum value of carbon aerosol in the ATAL. Both Fairlie et al. [32] and Gu et al. [33] confirmed the main contribution of nitrate.
Although there have been numerous studies focused on the ATAL’s formation mechanism and components, its specific three-dimensional structure and long-term change trend have rarely been analyzed. An accurate understanding of the temporal and spatial variation characteristics of the ATAL is important for studying its climate effects [34,35]. However, the reliability of the reanalysis data on the ATAL needs to be verified. In addition, the low spatial and temporal resolution of satellite observations also causes difficulties in the analysis of the ATAL. To solve these problems and clarify the spatiotemporal and vertical distribution characteristics of the ATAL, we employ multi-source data to compare and analyze the three-dimensional spatial distribution characteristics and temporal variability of the ATAL and discuss the possible causes for the variation trend. This study is organized as follows: The materials and methods are described in Section 2. Next, Section 3 shows the spatiotemporal variation characteristics of the ATAL. Section 4 then discusses the possible reasons for the regional differences. Finally, Section 5 provides a conclusion for the study.

2. Materials and Methods

2.1. CALIPSO

The Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite provides a new tool to investigate atmospheric aerosols. The CALIPSO dataset used here is level 1B version 4.10 orbital data in the June, July, and August (JJA) from 2006 to 2019, which can be obtained from https://asdc.larc.nasa.gov/project/CALIPSO/CAL_LID_L1-Standard-V4-10_V4-10 (accessed on 11 March 2021), and daytime data were discarded. CALIPSO Level 1B products offer several advantages for studying atmospheric aerosols. For example, the CALIPSO Level 1B products include 199 layers of data from bin 89 to bin 288, covering an altitude range from 8.3 to 20.2 km. The data has a horizontal resolution of 1 km and a vertical resolution of 60 m in the upper troposphere at this altitude. The high-resolution lidar measurements provide detailed vertical profiles of aerosol backscatter and extinction coefficients, enabling researchers to examine the fine structure and layering of aerosols in the atmosphere. Level 1B products also provide direct measurements of these aerosol properties, whereas Level 2 and Level 3 products are derived from these measurements. The Level 1B data undergoes a more rigorous calibration process, ensuring that it accurately reflects the true aerosol properties in the atmosphere. The detailed information available in Level 1B data allows for more in-depth analysis and investigation of aerosol properties, such as particle size distribution, shape, and composition, making it particularly useful for studying atmospheric aerosols. Quality control was implemented according to Equation (1), and data with a depolarization ratio of less than 0.05 were excluded and then interpolated into a regular grid of 1° latitude × 1° longitude [13].
DR = TAB PAB PAB ,
where DR is the depolarization ratio, TAB refers to total attenuated backscatter at 532 nm, and PAB represents perpendicular attenuated backscatter at 532 nm.

2.2. SAGEII

The Stratospheric Aerosol and Gas Experiment (SAGE) II aims to determine the spatial distributions of stratospheric aerosols, ozone, water vapor, and other substances [36,37,38]. It was launched in October 1984 and operated until September 2005, providing more than two decades of atmospheric data [39]. The instrument used solar occultation, a technique in which the satellite observes the sun as it sets or rises over the Earth’s limb, to measure the concentration and distribution of atmospheric gases and aerosols. By measuring the amount of solar radiation absorbed by different atmospheric components, SAGEII provided data on the vertical distribution of aerosols and gases, as well as their seasonal and latitudinal variations. This long-term stable dataset has proved invaluable in determining ozone trends [40] and identifying responses to episodic events such as volcanic eruptions [41]. SAGEII was a crucial tool for studying the composition and chemical processes of the atmosphere. SAGEII data has been used to investigate climate change, air quality, and the transport of pollutants in the atmosphere [42]. The satellite worked well between 1984 and 2005, and the SAGEII data has been used to confirm the enhancement of the aerosol concentration in the ATAL [43]. The summer-to-winter ratio (SWR) of Ext525 was calculated using SAGEII data, which can be obtained from https://sage.nasa.gov/missions/about-sage-ii/ (accessed on 1 September 2021), from 1994 to 2005 in the entire ATAL region according to the method of Thomason and Vernier [43] and Vernier, et al. [14].

2.3. MERRA-2

The Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), which is a long-term global reanalysis dataset with a high spatial resolution to assimilate space-based observations variables, such as temperature, wind, humidity, and precipitation, as well as aerosols, provides a global view of weather and climate beginning in 1980. It is produced by NASA’s Global Modeling and Assimilation Office and provides a comprehensive record of the Earth’s weather and climate over the past few decades. It is widely used in weather forecasting, climate modeling [44], air quality studies, and researching aerosols [45], precipitation [46], and other subjects [47]. Data used in this paper include aerosol mass mixing ratios, geopotential altitude, wind, tropopause altitude, and vertical pressure velocity from 1994 to 2019. The MERRA-2 data is open access, and the data link is https://gmao.gsfc.nasa.gov/reanalysis/MERRA-2/data_access/ (accessed on 1 September 2021).
This study defines the ATAL region as 5–105°E, 15–45°N, and 12–18 km vertically, according to Vernier et al. [13,14]. The TAB obtained from the CALIPSO level 1B version 4.10 orbital data is used to represent aerosol load in ATAL, and SWR is used for SAGEII. As to MERRA-2, the total aerosol mass mixing ratio is adopted, which is computed from aerosol mass mixing ratios according to Buchard et al. [45] and Provençal et al. [48].
To facilitate comparison among the various variables obtained from CALIPSO, SAGEII, and MERRA-2, we standardized the different variables in each dataset to make them dimensionless. Specifically, we used the original values in Figure 1, Figure 2, Figure 3, Figure 4, Figure 7 and Figure 9, while standardized data was used in Figure 5, Figure 6 and Figure 8 to allow for more effective comparison and analysis.

3. Results

3.1. Spatial Distribution Characteristics of ATAL

Figure 1 shows the data from CALIPSO level 1B at 20:22 on 4 July 2016. The line in Figure 1a represents the ground projection of the satellite orbit over the Qinghai-Tibet Plateau (QTP) and across the ATAL region, where the vertical distribution of the aerosol layer can be observed. The red part of the line is 0–50 degrees north latitude, representing the range of vertical distribution in Figure 1b. It can be seen from Figure 1b that at this moment, the aerosol layer is mainly distributed between 10 and 30 degrees north latitude and 11 and 19 km in height. At the same height, the aerosol concentration in this area is significantly higher than that in other areas, which confirms the existence of the ATAL.
Figure 2 shows the average pattern of the ATAL in the summer months (colors) with the 100-hPa geopotential height (white contours) and wind field (arrows) from 2006 to 2019. As can be seen from the figure, the powerful Asian summer monsoon anticyclone (ASMA) controls the entire south and east Asia region during the Northern Hemisphere summer [49,50]. The north side of the ASMA center is the westerly jet, while the south side is the easterly jet. In June, the 16,800-m isopotential height line of the ASMA extends to the Arabian Peninsula in the west and the eastern side of the QTP in the east. The central geopotential height of the ASMA is 16,861 m. The high-value region of the ATAL aerosol concentration over the Indian Peninsula is not yet evident. Even under the action of dry deposition and wet clearance, aerosol particles still have a considerable retention time [51,52,53]. Thus, the peak time of aerosol concentration is later than that of the UTLS. Southwesterly winds prevail on the west side of the ASMA, and relatively clean air is transported from south to north, forming a low-aerosol-concentration area over the Eastern Mediterranean.
In July, compared with June, the area surrounded by the 16,800-m isopotential height line of the ASMA decreases significantly, and the east–west range is about 45–90 degrees east longitude. The geopotential height of the center of the ASMA is 16,859 m, similar to that from June. The aerosol concentration in South Asia increases significantly, and the aerosol concentrations in the Indian Peninsula and Indochina Peninsula show obvious extreme points. In August, the area surrounded by the 16,800-m isopotential height line of the ASMA decreases further, and the east–west range is from 55 to 80 degrees east longitude. The geopotential height of the center of the ASMA is 16,850 m, with a significant decrease. The extreme regions of aerosol concentration over the Indian Peninsula and Indochina Peninsula are denser, intensifying the westward aerosol transport.
In conclusion, the ASMA located in the middle and upper troposphere dominates the atmospheric circulation over South Asia during the Northern Hemisphere summer. During the JJA, the easterly jet prevails on the south side of the ASMA, while the westerly jet prevails on the north side. Surface pollutants from South Asia can be transported upward to the UTLS region and accumulate there, and then mixed and dispersed in the ASMA along with the atmospheric circulation [54]. It is worth noting that in Figure 2, although the regional mean TAB in the ATAL is higher than that in the surrounding regions, the TAB in the northern region of the QTP (NQTP, red rectangle in Figure 2, which is also located on the north side of the ASMA, between 70 and 90 degrees east longitude and 32 and 42 degrees north latitude) is lower than the regional average of the entire ATAL, forming a clear north–south differences (NSDs), and this region persists during the JJA.

3.2. Temporal Trend of ATAL

Figure 3 shows the horizontal distribution of the temporal trend of TAB in the ATAL region during the JJA from 2006 to 2019. The dotted area indicates that the linear trend passes the 99% significance test. In June, TAB in the ATAL region, which ranges from the Mediterranean region to the eastern side of the QTP, showed an overall positive trend. In the Indian Peninsula and Indochina Peninsula, which are controlled by the SASM, the southerly air from the sea brings abundant water vapor, and the strong upward motion brings water vapor, aerosol, and other substances to the middle and upper troposphere [55,56], forming clouds at high altitudes [57]. In the process of quality control, by eliminating the data under the cloud or with a depolarization ratio less than 0.05, some data in this area are eliminated, thus forming blank areas. However, the high-value area of the positive trend of TAB can still be seen in the southern foot of the QTP from the Indian Peninsula to the Indochina Peninsula, which indicates that the aerosol concentration in the key area affected by the SASM gradually increased between 2006 and 2019. At the same time, the Mediterranean Sea and its coastal regions also show a positive trend in TAB. In contrast, the NQTP shows a weak negative trend, which means a trend discrepancy between this region and neighboring regions under an overall increase in ATAL aerosol concentration.
In July, the TAB in the area south of 30 degrees north latitude shows a positive trend of varying rates. The positive trend of the TAB is more significant from the Indian Peninsula to the Indochina Peninsula. Specifically, the northern Indian Ocean between the Persian Gulf and the Indian Peninsula, along the coast of the Bay of Bengal, and along the southern foothills of QTP show a clear positive trend in the high-value area, and it is higher than that in June. At the same time, compared with June, the area of the negative TAB trend in the NQTP significantly extends from the west side of the Iranian plateau to the east side of the QTP and shows a two-center mode. The western negative trend center is located in the Iranian plateau on the south side of the Caspian Sea. In contrast, the eastern negative trend center is located on the north side of the QTP, which is also the north side of the ASMA center. The negative trend on the north side of the QTP is significantly larger than that on the Iranian Plateau in area, and the maximum negative trend is also the same.
In August, the TAB in the area south of 30 degrees north latitude still shows a positive trend of unequal size, especially in South Asia. The high values in the northern Indian Ocean between the Persian Gulf and the Indian Peninsula, along the coast of the Bay of Bengal, and along the southern foot of the QTP are similar to those in July. On the north side of the QTP, the range of the negative trend area of TAB increases slightly compared to that in July, the two low-value areas in the east and west gradually extend to connect with each other, and the value decreases significantly. This indicates that the aerosol concentration in the negative trend area in the NQTP significantly reduced from 2006 to 2019. The decrease rate is higher when the range is larger in August.
From Figure 3a to Figure 3c, it can be seen that the aerosol concentration in key regions of the ATAL shows a significant increasing trend in the JJA between 2006 and 2019. The rate increases gradually from June to August. At the same time, there is also an area with a low linear trend of TAB in the NQTP, which means that the aerosol concentration in the NQTP presents a significant decreasing trend from 2006 to 2019. In terms of the decrease rate, whether in terms of area or intensity, the order from highest to lowest is August, July, and June.
To analyze the long-term change trend of the ATAL, we refer to the method described by Vernier, Thomason, and Kar [13] and analyze the time series of the ATAL vertical profile using the data of SAGEII from 1985 to 2005. In Figure 4, the horizontal axis represents the year, the vertical axis represents the height, and the fill color represents the SWR calculated using occultation observation data. Figure 4 shows that since the mid-to-late 1990s, there has been a significant region of high SWR between 12 and 17 km in altitude, and the central height is about 15 km, indicating that the ATAL has been gradually emerging since the mid-to-late 1990s. The high SWR from 13 km to 18 km in the early 1990s may be due to major volcanic eruptions during this period [58,59].
Figure 5 shows the ATAL time series of multi-source data from 1994 to 2019. Green lines and ordinates represent SAGEII, red lines, and ordinates represent CALIPSO, and blue lines and ordinates represent MERRA-2. The standardized SWR is used for SAGEII, the standardized TAB is used for CALIPSO, and the standardized total aerosol mass mixing ratio is used for MERRA-2. From satellite data (SAGEII and CALIPSO), the ATAL shows a trend of gradual enhancement since the mid-to-late 1990s. Between 2006 and 2010, the ATAL showed a period of rapid rise and then stabilized. In the early 1990s, MERRA-2 reanalysis data show a stronger response to large volcanoes. From the mid-to-late 1990s to 2010, the ATAL shows a steady upward trend in MERRA-2 reanalysis data, and the rate of increase accelerates from 2006 to 2010, consistent with satellite data. After 2010, both CALIPSO and MERRA-2 data show that the ATAL does not rise. The difference is that in CALIPSO data, the ATAL changes little and tends to be stable in the oscillation, while in MERRA-2 data, the ATAL shows a downward trend. Overall, the data indicate that the ATAL gradually emerged in the mid-to-late 1990s and thereafter showed a general trend of rapid enhancement until 2010, confirmed across different datasets. This behavior has also been confirmed in model simulation results by Bossolasco et al. [60]. Their results show a positive trend for all aerosols simulated by CESM-MAM7 over a long-term period (2000–2015). However, after 2010, the ATAL no longer showed an upward trend in either dataset.

4. Discussion

Many researchers have analyzed and discussed the causes of ATAL formation [14,54,61]. Since the late 20th century, the economy and society in South Asia have developed rapidly, and the regional pollution caused by burning fossil fuels and industrial development has become increasingly serious [7,8]. According to the “heat pump” theory [62,63], the special topography of the QTP makes it an important channel for the upward transport of pollutants, and the SASM also plays an important role in the upward transport of pollutants [26,62]. The QTP, as the highest plateau in the world, extends to the middle troposphere at approximately 500 hPa [64,65] and exerts significant thermal and dynamic effects on atmospheric circulation. During the Northern Hemisphere summer, the QTP acts as a heat source, heating the atmosphere [66,67]. The temperature difference between the sea and the land generates land-sea thermal circulation, which implies an upward branch of the circulation located on the south side of QTP. Additionally, the topographic barrier forces the south-to-north airflow to ascend, facilitating the upward transport of pollutants. During the SASM, strong convection and deep convection events frequently occurred over the southern foothills of the QTP. These deep convective events can transport pollutants from the boundary layer directly into the upper troposphere, and some penetrating convective events even into the lower stratosphere [54,68], creating favorable conditions for the upward transport of pollutants. In addition, large-scale uplifting movement in South Asia also promotes the transport of pollutants into the upper atmosphere [69,70]. In the upper troposphere, the trapping effect of the ASMA allows pollutants to accumulate [71]. However, according to a previous analysis, the distribution of pollutants in the ASMA is also uneven, showing significant NSDs.

4.1. The North–South Differences (NSDs)

In Figure 2, the analysis of the multiyear mean TAB in the ATAL region shows a low TAB in the NQTP throughout the summer, located on the northern side of the ASMA center, showing significant NSDs in the JJA. From June to August, the value of the northern low-value area gradually decreases, but the area gradually increases. In the linear trend diagram of TAB in Figure 3, the low-value area in the north also shows a different linear trend from the surrounding area. During the JJA, the regional mean TAB shows an obvious negative trend, which means that the aerosol concentration in this region decreases year by year. From June to August, the rate of decrease was more rapid, and the area expanded significantly. To compare and analyze the differences in aerosol concentration between the NQTP and the surrounding region, Figure 6 shows the time series of average TAB for the NQTP and the entire ATAL. As can be seen, except for a few years, the average TAB in the NQTP is smaller than that in the entire ATAL region. Combined with Figure 2, it can be confirmed that the NQTP is a low-value region of TAB in both the spatial distribution and time series. In terms of the changing trend, the average TAB in the entire ATAL region shows a gradual increase in the first 5 years and stabilizes at a high level in the last 9 years, while the average TAB in the NQTP shows a decreasing trend.
Figure 7 shows the vertical difference between the NQTP and ATAL, with the x-axis representing the SWR and the y-axis representing the height. As can be seen in Figure 7, the NQTP to ATAL ratio (NAR) is less than 1 when the altitude is above 8 km, which means that compared with the surrounding areas, the NQTP is a low-value area of aerosol content within an altitude of 8–20 km, and the difference increases with the altitude. At altitudes between 5 and 8 km, NAR fluctuates around 1. This indicates little difference in aerosol concentration between the NQTP and surrounding areas in the middle and lower troposphere. Considering that the average altitude of the QTP is above 4 km [72], the altitude interval of 5–8 km includes the boundary layer and the middle and lower troposphere in some areas of the plateau. Strong turbulent mixing and vigorous convective motion are conducive to increasing aerosol concentration at this altitude.
The discussion of TAB in the three-dimensional direction of space confirms that the aerosol distribution in the NQTP is significantly different from that in the other regions of the ATAL in the horizontal direction, vertical direction, and time series. This difference is shown as the lower aerosol concentration for the NQTP in three-dimensional space and the decreasing trend in the time series.

4.2. Causes of NSDs

It is well known that the ASMA plays an important role in forming and maintaining the ATAL. However, there are significant differences in the distribution of aerosols on the north and south sides of the ASMA center. There are also significant differences in the variation trend of aerosols on both sides. Except for the difference in surface emissions, atmospheric circulation also significantly influences aerosol NSDs. The “trapping effect” of the ASMA makes it difficult for the upward transport of pollutants to disperse in the ASMA, which makes the aerosol concentration on the south side of the ASMA center maintain a high level in summer. The north side of the ASMA is located in the westerly jet zone, which is conducive to the diffusion of pollutants under the background of the westerly belt.
Figure 8 shows the mean TAB from 70 to 90 degrees east longitude in August 2014. The horizontal coordinate represents latitude; the vertical coordinates represent altitude and pressure, respectively; and the black solid line represents the mean tropopause height over the years. As can be seen from Figure 8, in the vertical direction of the same latitude, the aerosol concentration is higher in the middle and upper troposphere, and the higher the altitude, the lower the aerosol concentration. In the easterly jet stream region south of about 30 degrees north latitude, the aerosol concentration in the UTLS region between 12 and 18 km is not much different from that in the upper troposphere region between 9 and 12 km. The blank filling in the UTLS region indicates that aerosol particles with a depolarization ratio less than 0.05 exist in this region, which may be cloud droplets with larger particle sizes. This means there is a strong deep convection in the area, and this deep convection activity plays a crucial role in the upward transport of aerosols [73,74]. Aerosol, in turn, affects the microphysical process in the cloud through the aerosol–cloud interactions, thus affecting the growth and development of clouds [75,76]. The high TAB in and under the cloud is evidence of the close interaction between clouds and aerosols. In the westerly jet stream region on the north side of the ASMA center, about 30 to 45 degrees north latitude, the aerosol concentration decreases rapidly from the upper troposphere upward. In the UTLS between 12 and 20 km, the aerosol concentration in the NQTP region is at a low level at the same height. In addition, no cloud droplets with large depolarization ratios are observed in this region. This indicates that the deep convection activity in the northern region is weaker than that in the southern region, and the weaker convection causes the weak upward transport of aerosols, which may be one of the reasons for the low aerosol concentration in the NQTP.
In terms of tropopause height, the tropopause drops between 30 and 45 degrees north latitude. At the same height, the area contains more relatively clean lower stratospheric air than areas south of 30 degrees north latitude, which also favors a northern area with lower aerosol concentrations. Meanwhile, according to the annual average zonal wind isoline, the high-value area of aerosol concentration in areas south of 30 degrees north latitude is mostly located in the easterly jet region, while the low-value area in the north latitude is mainly affected by the westerly jet. The tropopause rolling near the jet axis of the westerly jet forms tropopause folding events, which bring cleaner air from the lower stratosphere to the upper troposphere [77,78], thus forming a low-concentration area of aerosol in the UTLS region.
Figure 9 shows the spatial distribution of the average multiyear ω in the ATAL region in August, with the bottom layer representing the terrain and the filling layers representing the ω on the 750-, 500-, and 200-hPa pressure layers, respectively. During the Northern Hemisphere summer, the QTP becomes a heat source that heats the atmosphere. Thus, a thermal low-pressure system is formed in the QTP region, and the atmosphere converges and rises in the QTP region to form the ASMA in the upper troposphere [66,67]. Moreover, due to the mass continuity equation of the atmosphere, the sinking region of the atmosphere mass will be formed. As shown in Figure 9, at 750 hPa, the southern foot of the plateau in the lower troposphere and the South Asia region is dominated by updrafts, corresponding to the updrafts of the SASM circulation, and the northwest side of the QTP has downdrafts. The high topography of the QTP can block the northward movement of the SASM, resulting in the forced lifting of air. In the middle troposphere at 500 hPa, an obvious sinking motion can be seen on the north and west sides of the QTP, while an ascending movement can be seen on the south side from the lower troposphere to the upper troposphere at 200 hPa. This consistent upward motion can transport the middle and lower troposphere aerosols upward to the UTLS region, which is crucial for the formation of the ATAL.

5. Conclusions

The analysis of multi-source data showed that the ATAL was mainly distributed in the UTLS region of South Asia in summer and that its distribution differed from north to south, showing a low aerosol concentration in the NQTP. The ATAL emerged in the mid-to-late 1990s and then showed a rapid increase until 2010, after which the ATAL did not increase according to satellite and reanalysis data.
Moreover, there were clear differences in aerosol concentration between the NQTP region and the rest of the ATAL region. This difference was shown as the lower aerosol content in three-dimensional space and the aerosol content in the NQTP showed a decreasing trend from 2006 to 2019. At altitudes from 12 to 20 km, the difference gradually increased with height. From June to August, the area of the low-value area gradually extended outward, and the difference was more significant.
In addition, by analyzing the difference between NQTP and ATAL, it was found that, except for surface emissions, the formation of the low-value area in the north was mainly affected by the circulation situation, convective transport, and stratosphere–troposphere exchange processes. The easterly jet prevailed on the south side of the ASMA, and the pollutants transported from the lower troposphere were concentrated under the trapping effect of the ASMA. On the north side of the ASMA, the westerly jet prevailed. The westerly base flow was conducive to the diffusion of pollutants, thus forming the low-value area of aerosol content.
The southern side of the QTP was dominated by upward movement. Large-scale upward movement and intense deep convection provided conditions for the upward transport of pollutants. At the same time, the northern side of the plateau corresponded to downward movement, which was not conducive to the upward transport of pollutants. In addition, the NQTP was located in the region where the tropopause height dropped sharply, and there were frequent stratosphere–troposphere exchange processes in this region. A large amount of dry and clean air in the lower stratosphere was transported to the upper troposphere, which contributed to the formation of the low-value region in the NQTP to some extent.
Due to the low temporal resolution of satellite observation data and the differences between reanalysis data and satellite observation data, quantitative analysis of the ATAL is still difficult. Moreover, the quantitative analysis of the ATAL plays an important role in assessing the ATAL climate effects and evaluating and improving numerical models, which require further research.

Author Contributions

Conceptualization, H.L. and R.L.; Data curation, H.L. and J.M.; Formal analysis, H.L.; Funding acquisition, R.L.; Investigation, H.L.; Methodology, H.L.; Project administration, R.L.; Resources, J.M.; Software, H.L.; Supervision, R.L.; Validation, H.L., R.L. and J.M.; Visualization, H.L.; Writing—original draft, H.L.; Writing—review and editing, R.L. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the National Natural Science Foundation of China (42071093), the State Key Laboratory of Cryospheric Science (SKLCS-ZZ-2023), the Natural Science Foundation of Gansu Province (22JR5RA061, 22JR5RA054).

Data Availability Statement

The CALIPSO data presented in this study are NASA/LARC/SD/ASDC. CALIPSO Lidar Level 1B profile data, V4-10. NASA Langley Atmospheric Science Data Center DAAC; 2016. Available from: https://asdc.larc.nasa.gov/project/CALIPSO/CAL_LID_L1-Standard-V4-10_V4-10 (accessed on 11 March 2021); SAGEII data can be found at https://sage.nasa.gov/missions/about-sage-ii/ (accessed on 1 September 2021); and MERRA-2 data is Global Modeling and Assimilation Office (GMAO) (2015), MERRA-2 inst3_3d_aer_Nv: 3d,3-Hourly, Instantaneous, Model-Level, Assimilation, Aerosol Mixing Ratio V5.12.4, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC), at https://disc.gsfc.nasa.gov/datasets/M2I3NVAER_5.12.4/summary (accessed on 1 September 2021).

Acknowledgments

This work is based on the scientific computing platform of the Supercomputer Center of Lanzhou University.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Bucci, S.; Legras, B.; Sellitto, P.; d’Amato, F.; Viciani, S.; Montori, A.; Chiarugi, A.; Ravegnani, F.; Ulanovsky, A.; Cairo, F. Deep-convective influence on the upper troposphere–lower stratosphere composition in the Asian monsoon anticyclone region: 2017 StratoClim campaign results. Atmos. Chem. Phys. 2020, 20, 12193–12210. [Google Scholar] [CrossRef]
  2. Randel, W.J.; Park, M.; Emmons, L.; Kinnison, D.; Bernath, P.; Walker, K.A.; Boone, C.; Pumphrey, H. Asian monsoon transport of pollution to the stratosphere. Science 2010, 328, 611–613. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  3. Singh, A.; Agrawal, M. Acid rain and its ecological consequences. J. Environ. Biol. 2007, 29, 15. [Google Scholar]
  4. Morakinyo, O.M.; Mokgobu, M.I.; Mukhola, M.S.; Hunter, R.P. Health Outcomes of Exposure to Biological and Chemical Components of Inhalable and Respirable Particulate Matter. Int. J. Environ. Res. Public Health 2016, 13, 592. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  5. Chan, C.K.; Yao, X. Air pollution in mega cities in China. Atmos. Environ. 2008, 42, 1–42. [Google Scholar] [CrossRef]
  6. Niu, H.; Kang, S.; Gao, W.; Wang, Y.; Paudyal, R. Vertical distribution of the Asian tropopause aerosols detected by CALIPSO. Environ. Pollut. 2019, 253, 207–220. [Google Scholar] [CrossRef]
  7. Song, S. Demographic Changes and Economic Growth: Empirical Evidence from Asia. 2013. Available online: https://digitalcommons.iwu.edu/econ_honproj/121 (accessed on 10 December 2022).
  8. Chen, S. Environmental pollution emissions, regional productivity growth and ecological economic development in China. China Econ. Rev. 2015, 35, 171–182. [Google Scholar] [CrossRef]
  9. Yan, J.; Wang, G.; Yang, P. Study on the Sensitivity of Summer Ozone Density to the Enhanced Aerosol Loading over the Tibetan Plateau. Atmosphere 2020, 11, 138. [Google Scholar] [CrossRef] [Green Version]
  10. Gettelman, A.; Kinnison, D.E.; Dunkerton, T.J.; Brasseur, G.P. Impact of monsoon circulations on the upper troposphere and lower stratosphere. J. Geophys. Res. Atmos. 2004, 109. [Google Scholar] [CrossRef]
  11. Holton, J.R.; Haynes, P.H.; McIntyre, M.E.; Douglass, A.R.; Rood, R.B.; Pfister, L. Stratosphere-troposphere exchange. Rev. Geophys. 1995, 33, 403–439. [Google Scholar] [CrossRef]
  12. Solomon, S. Stratospheric ozone depletion: A review of concepts and history. Rev. Geophys. 1999, 37, 275–316. [Google Scholar] [CrossRef]
  13. Vernier, J.P.; Thomason, L.; Kar, J. CALIPSO detection of an Asian tropopause aerosol layer. Geophys. Res. Lett. 2011, 38. [Google Scholar] [CrossRef] [Green Version]
  14. Vernier, J.P.; Fairlie, T.D.; Natarajan, M.; Wienhold, F.G.; Bian, J.; Martinsson, B.G.; Crumeyrolle, S.; Thomason, L.W.; Bedka, K.M. Increase in upper tropospheric and lower stratospheric aerosol levels and its potential connection with Asian pollution. J. Geophys. Res. Atmos. 2015, 120, 1608–1619. [Google Scholar] [CrossRef] [Green Version]
  15. Babu, S.S.; Moorthy, K.K.; Manchanda, R.K.; Sinha, P.R.; Satheesh, S.; Vajja, D.P.; Srinivasan, S.; Kumar, V.A. Free tropospheric black carbon aerosol measurements using high altitude balloon: Do BC layers build “their own homes” up in the atmosphere? Geophys. Res. Lett. 2011, 38. [Google Scholar] [CrossRef]
  16. Kulkarni, P.; Ramachandran, S.; Bhavani Kumar, Y.; Narayana Rao, D.; Krishnaiah, M. Features of upper troposphere and lower stratosphere aerosols observed by lidar over Gadanki, a tropical Indian station. J. Geophys. Res. Atmos. 2008, 113. [Google Scholar] [CrossRef] [Green Version]
  17. Tobo, Y.; Iwasaka, Y.; Shi, G.-Y.; Kim, Y.-S.; Ohashi, T.; Tamura, K.; Zhang, D. Balloon-borne observations of high aerosol concentrations near the summertime tropopause over the Tibetan Plateau. Atmos. Res. 2007, 84, 233–241. [Google Scholar] [CrossRef]
  18. Gettelman, A.; Hoor, P.; Pan, L.; Randel, W.; Hegglin, M.I.; Birner, T. The extratropical upper troposphere and lower stratosphere. Rev. Geophys. 2011, 49. [Google Scholar] [CrossRef] [Green Version]
  19. Solomon, S.; Rosenlof, K.H.; Portmann, R.W.; Daniel, J.S.; Davis, S.M.; Sanford, T.J.; Plattner, G.K. Contributions of stratospheric water vapor to decadal changes in the rate of global warming. Science 2010, 327, 1219–1223. [Google Scholar] [CrossRef] [Green Version]
  20. Riese, M.; Ploeger, F.; Rap, A.; Vogel, B.; Konopka, P.; Dameris, M.; Forster, P. Impact of uncertainties in atmospheric mixing on simulated UTLS composition and related radiative effects. J. Geophys. Res. Atmos. 2012, 117. [Google Scholar] [CrossRef] [Green Version]
  21. Hossaini, R.; Chipperfield, M.; Montzka, S.; Rap, A.; Dhomse, S.; Feng, W. Efficiency of short-lived halogens at influencing climate through depletion of stratospheric ozone. Nat. Geosci. 2015, 8, 186–190. [Google Scholar] [CrossRef] [Green Version]
  22. Heckendorn, P.; Weisenstein, D.; Fueglistaler, S.; Luo, B.P.; Rozanov, E.; Schraner, M.; Thomason, L.W.; Peter, T. The impact of geoengineering aerosols on stratospheric temperature and ozone. Environ. Res. Lett. 2009, 4, 045108. [Google Scholar] [CrossRef]
  23. Kravitz, B.; Robock, A.; Shindell, D.T.; Miller, M.A. Sensitivity of stratospheric geoengineering with black carbon to aerosol size and altitude of injection. J. Geophys. Res. Atmos. 2012, 117. [Google Scholar] [CrossRef] [Green Version]
  24. Li, Q.; Jiang, J.H.; Wu, D.L.; Read, W.G.; Livesey, N.J.; Waters, J.W.; Zhang, Y.; Wang, B.; Filipiak, M.J.; Davis, C.P. Trapping of Asian pollution by the Tibetan anticyclone: A global CTM simulation compared with EOS MLS observations. Geophys. Res. Lett 2005, 32, L14826. [Google Scholar] [CrossRef]
  25. Randel, W.J.; Park, M. Deep convective influence on the Asian summer monsoon anticyclone and associated tracer variability observed with Atmospheric Infrared Sounder (AIRS). J. Geophys. Res. Atmos. 2006, 111. [Google Scholar] [CrossRef]
  26. Park, M.; Randel, W.J.; Emmons, L.K.; Bernath, P.F.; Walker, K.A.; Boone, C.D. Chemical isolation in the Asian monsoon anticyclone observed in Atmospheric Chemistry Experiment (ACE-FTS) data. Atmos. Chem. Phys. 2008, 8, 757–764. [Google Scholar] [CrossRef] [Green Version]
  27. Vernier, J.-P.; Fairlie, T.; Deshler, T.; Ratnam, M.V.; Gadhavi, H.; Kumar, B.; Natarajan, M.; Pandit, A.; Raj, S.A.; Kumar, A.H. BATAL: The balloon measurement campaigns of the Asian tropopause aerosol layer. Bull. Am. Meteorol. Soc. 2018, 99, 955–973. [Google Scholar] [CrossRef]
  28. Höpfner, M.; Ungermann, J.; Borrmann, S.; Wagner, R.; Spang, R.; Riese, M.; Stiller, G.; Appel, O.; Batenburg, A.M.; Bucci, S. Ammonium nitrate particles formed in upper troposphere from ground ammonia sources during Asian monsoons. Nat. Geosci. 2019, 12, 608–612. [Google Scholar] [CrossRef]
  29. Fadnavis, S.; Semeniuk, K.; Pozzoli, L.; Schultz, M.; Ghude, S.; Das, S.; Kakatkar, R. Transport of aerosols into the UTLS and their impact on the Asian monsoon region as seen in a global model simulation. Atmos. Chem. Phys. 2013, 13, 8771–8786. [Google Scholar] [CrossRef] [Green Version]
  30. Yu, P.; Toon, O.B.; Neely, R.R.; Martinsson, B.G.; Brenninkmeijer, C.A. Composition and physical properties of the Asian Tropopause Aerosol Layer and the North American Tropospheric Aerosol Layer. Geophys. Res. Lett. 2015, 42, 2540–2546. [Google Scholar] [CrossRef]
  31. Fadnavis, S.; Kalita, G.; Kumar, K.R.; Gasparini, B.; Li, J.-L.F. Potential impact of carbonaceous aerosol on the upper troposphere and lower stratosphere (UTLS) and precipitation during Asian summer monsoon in a global model simulation. Atmos. Chem. Phys. 2017, 17, 11637–11654. [Google Scholar] [CrossRef] [Green Version]
  32. Fairlie, T.D.; Liu, H.; Vernier, J.P.; Campuzano-Jost, P.; Jimenez, J.L.; Jo, D.S.; Zhang, B.; Natarajan, M.; Avery, M.A.; Huey, G. Estimates of regional source contributions to the Asian Tropopause Aerosol Layer using a chemical transport model. J. Geophys. Res. Atmos. 2020, 125, e2019JD031506. [Google Scholar] [CrossRef] [Green Version]
  33. Gu, Y.; Liao, H.; Bian, J. Summertime nitrate aerosol in the upper troposphere and lower stratosphere over the Tibetan Plateau and the South Asian summer monsoon region. Atmos. Chem. Phys. 2016, 16, 6641–6663. [Google Scholar] [CrossRef] [Green Version]
  34. Gupta, G.; Ratnam, M.V.; Madhavan, B.; Prasad, P.; Narayanamurthy, C. Vertical and spatial distribution of elevated aerosol layers obtained using long-term ground-based and space-borne lidar observations. Atmos. Environ. 2021, 246, 118172. [Google Scholar] [CrossRef]
  35. Zhu, J.; Xia, X.; Che, H.; Wang, J.; Cong, Z.; Zhao, T.; Kang, S.; Zhang, X.; Yu, X.; Zhang, Y. Spatiotemporal variation of aerosol and potential long-range transport impact over the Tibetan Plateau, China. Atmos. Chem. Phys. 2019, 19, 14637–14656. [Google Scholar] [CrossRef] [Green Version]
  36. Michelsen, H.A. ATMOS version 3 water vapor measurements: Comparisons with observations from two ER-2 Lyman-α hygrometers, MkIV, HALOE, SAGE II, MAS, and MLS. J. Geophys. Res. 2002, 107, ACH-2. [Google Scholar] [CrossRef] [Green Version]
  37. Taha, G.; Thomason, L.W.; Burton, S.P. Comparison of Stratospheric Aerosol and Gas Experiment (SAGE) II version 6.2 water vapor with balloon-borne and space-based instruments. J. Geophys. Res. Atmos. 2004, 109. [Google Scholar] [CrossRef] [Green Version]
  38. Pitts, M.; Poole, L.; McCormick, M. SAGE II observations of polar stratospheric clouds near 50°N January 31–February 2, 1989. Geophys. Res. Lett. 1990, 17, 405–408. [Google Scholar] [CrossRef]
  39. Lu, J.; Mohnen, V.A.; Yue, G.K.; Jäger, H. Intercomparison of multiplatform stratospheric aerosol and ozone observations. J. Geophys. Res. Atmos. 1997, 102, 16127–16136. [Google Scholar] [CrossRef]
  40. Cunnold, D.; Wang, H.; Thomason, L.; Zawodny, J.; Logan, J.; Megretskaia, I. SAGE (version 5.96) ozone trends in the lower stratosphere. J. Geophys. Res. Atmos. 2000, 105, 4445–4457. [Google Scholar] [CrossRef]
  41. Labitzke, K.; McCormick, M. Stratospheric temperature increases due to Pinatubo aerosols. Geophys. Res. Lett. 1992, 19, 207–210. [Google Scholar] [CrossRef]
  42. Del Genio, A.D.; Kovari, W., Jr.; Yao, M.S. Climatic implications of the seasonal variation of upper troposphere water vapor. Geophys. Res. Lett. 1994, 21, 2701–2704. [Google Scholar] [CrossRef] [Green Version]
  43. Thomason, L.; Vernier, J.-P. Improved SAGE II cloud/aerosol categorization and observations of the Asian tropopause aerosol layer: 1989–2005. Atmos. Chem. Phys. 2013, 13, 4605–4616. [Google Scholar] [CrossRef] [Green Version]
  44. Adhikari, P.; Mejia, J.F. Impact of transported dust aerosols on precipitation over the Nepal Himalayas using convection-permitting WRF-Chem simulation. Atmos. Environ. X 2022, 15, 100179. [Google Scholar] [CrossRef]
  45. Buchard, V.; Randles, C.; Da Silva, A.; Darmenov, A.; Colarco, P.; Govindaraju, R.; Ferrare, R.; Hair, J.; Beyersdorf, A.; Ziemba, L. The MERRA-2 aerosol reanalysis, 1980 onward. Part II: Evaluation and case studies. J. Clim. 2017, 30, 6851–6872. [Google Scholar] [CrossRef] [PubMed]
  46. Reichle, R.H.; Liu, Q.; Koster, R.D.; Draper, C.S.; Mahanama, S.P.; Partyka, G.S. Land surface precipitation in MERRA-2. J. Clim. 2017, 30, 1643–1664. [Google Scholar] [CrossRef]
  47. Gelaro, R.; McCarty, W.; Suárez, M.J.; Todling, R.; Molod, A.; Takacs, L.; Randles, C.A.; Darmenov, A.; Bosilovich, M.G.; Reichle, R. The modern-era retrospective analysis for research and applications, version 2 (MERRA-2). J. Clim. 2017, 30, 5419–5454. [Google Scholar] [CrossRef]
  48. Provençal, S.; Buchard, V.; da Silva, A.M.; Leduc, R.; Barrette, N. Evaluation of PM surface concentrations simulated by Version 1 of NASA’s MERRA Aerosol Reanalysis over Europe. Atmos. Pollut. Res. 2017, 8, 374–382. [Google Scholar] [CrossRef] [Green Version]
  49. Basha, G.; Ratnam, M.V.; Kishore, P. Asian summer monsoon anticyclone: Trends and variability. Atmos. Chem. Phys. 2020, 20, 6789–6801. [Google Scholar] [CrossRef]
  50. Manney, G.L.; Santee, M.L.; Lawrence, Z.D.; Wargan, K.; Schwartz, M.J. A moments view of climatology and variability of the Asian summer monsoon anticyclone. J. Clim. 2021, 34, 7821–7841. [Google Scholar] [CrossRef]
  51. Hussein, T.; Smolik, J.; Kerminen, V.-M.; Kulmala, M. Modeling dry deposition of aerosol particles onto rough surfaces. Aerosol Sci. Technol. 2012, 46, 44–59. [Google Scholar] [CrossRef] [Green Version]
  52. Knote, C.; Hodzic, A.; Jimenez, J. The effect of dry and wet deposition of condensable vapors on secondary organic aerosols concentrations over the continental US. Atmos. Chem. Phys. 2015, 15, 1–18. [Google Scholar] [CrossRef] [Green Version]
  53. Croft, B.; Lohmann, U.; Martin, R.V.; Stier, P.; Wurzler, S.; Feichter, J.; Hoose, C.; Heikkilä, U.; Van Donkelaar, A.; Ferrachat, S. Influences of in-cloud aerosol scavenging parameterizations on aerosol concentrations and wet deposition in ECHAM5-HAM. Atmos. Chem. Phys. 2010, 10, 1511–1543. [Google Scholar] [CrossRef] [Green Version]
  54. Lau, W.K.M.; Yuan, C.; Li, Z. Origin, Maintenance and Variability of the Asian Tropopause Aerosol Layer (ATAL): The Roles of Monsoon Dynamics. Sci. Rep. 2018, 8, 3960. [Google Scholar] [CrossRef]
  55. Martins, E.; Noel, V.; Chepfer, H. Properties of cirrus and subvisible cirrus from nighttime Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), related to atmospheric dynamics and water vapor. J. Geophys. Res. Atmos. 2011, 116. [Google Scholar] [CrossRef] [Green Version]
  56. Adcock, K.E.; Fraser, P.J.; Hall, B.D.; Langenfelds, R.L.; Lee, G.; Montzka, S.A.; Oram, D.E.; Röckmann, T.; Stroh, F.; Sturges, W.T. Aircraft-based observations of ozone-depleting substances in the upper troposphere and lower stratosphere in and above the Asian summer monsoon. J. Geophys. Res. Atmos. 2021, 126, e2020JD033137. [Google Scholar] [CrossRef]
  57. Bhat, G.; Kumar, S. Vertical structure of cumulonimbus towers and intense convective clouds over the South Asian region during the summer monsoon season. J. Geophys. Res. Atmos. 2015, 120, 1710–1722. [Google Scholar] [CrossRef]
  58. Minnis, P.; Harrison, E.F.; Stowe, L.L.; Gibson, G.G.; Denn, F.M.; Doelling, D.R.; Smith, W.L., Jr. Radiative climate forcing by the mount pinatubo eruption. Science 1993, 259, 1411–1415. [Google Scholar] [CrossRef]
  59. Soden, B.J.; Wetherald, R.T.; Stenchikov, G.L.; Robock, A. Global cooling after the eruption of Mount Pinatubo: A test of climate feedback by water vapor. Science 2002, 296, 727–730. [Google Scholar] [CrossRef] [Green Version]
  60. Bossolasco, A.; Jegou, F.; Sellitto, P.; Berthet, G.; Kloss, C.; Legras, B. Global modeling studies of composition and decadal trends of the Asian Tropopause Aerosol Layer. Atmos. Chem. Phys. 2021, 21, 2745–2764. [Google Scholar] [CrossRef]
  61. Yuan, C.; Lau, W.K.; Li, Z.; Cribb, M. Relationship between Asian monsoon strength and transport of surface aerosols to the Asian Tropopause Aerosol Layer (ATAL): Interannual variability and decadal changes. Atmos. Chem. Phys. 2019, 19, 1901–1913. [Google Scholar] [CrossRef] [Green Version]
  62. Zhao, C.; Yang, Y.; Fan, H.; Huang, J.; Fu, Y.; Zhang, X.; Kang, S.; Cong, Z.; Letu, H.; Menenti, M. Aerosol characteristics and impacts on weather and climate over the Tibetan Plateau. Natl. Sci. Rev. 2020, 7, 492–495. [Google Scholar] [CrossRef] [PubMed]
  63. Lau, K.; Kim, M.; Kim, K. Asian summer monsoon anomalies induced by aerosol direct forcing: The role of the Tibetan Plateau. Clim. Dyn. 2006, 26, 855–864. [Google Scholar] [CrossRef] [Green Version]
  64. Ma, J.; Li, R.; Liu, H.; Huang, Z.; Wu, T.; Hu, G.; Xiao, Y.; Zhao, L.; Du, Y.; Yang, S. The Surface Energy Budget and Its Impact on the Freeze-thaw Processes of Active Layer in Permafrost Regions of the Qinghai-Tibetan Plateau. Adv. Atmos. Sci. 2022, 39, 189–200. [Google Scholar] [CrossRef]
  65. Ma, J.; Li, R.; Huang, Z.; Wu, T.; Wu, X.; Zhao, L.; Liu, H.; Hu, G.; Xiao, Y.; Du, Y. Evaluation and spatio-temporal analysis of surface energy flux in permafrost regions over the Qinghai-Tibet Plateau and Arctic using CMIP6 models. Int. J. Digit. Earth 2022, 15, 1948–1966. [Google Scholar] [CrossRef]
  66. Duan, A.; Wu, G.; Liu, Y.; Ma, Y.; Zhao, P. Weather and climate effects of the Tibetan Plateau. Adv. Atmos. Sci. 2012, 29, 978–992. [Google Scholar] [CrossRef]
  67. Wu, G.; Liu, Y.; He, B.; Bao, Q.; Duan, A.; Jin, F.-F. Thermal controls on the Asian summer monsoon. Sci. Rep. 2012, 2, 404. [Google Scholar] [CrossRef] [Green Version]
  68. Fu, R.; Hu, Y.; Wright, J.S.; Jiang, J.H.; Dickinson, R.E.; Chen, M.; Filipiak, M.; Read, W.G.; Waters, J.W.; Wu, D.L. Short circuit of water vapor and polluted air to the global stratosphere by convective transport over the Tibetan Plateau. Proc. Natl. Acad. Sci. USA 2006, 103, 5664–5669. [Google Scholar] [CrossRef] [Green Version]
  69. Tissier, A.-S.; Legras, B. Convective sources of trajectories traversing the tropical tropopause layer. Atmos. Chem. Phys. 2016, 16, 3383–3398. [Google Scholar] [CrossRef] [Green Version]
  70. Park, M.; Randel, W.J.; Emmons, L.K.; Livesey, N.J. Transport pathways of carbon monoxide in the Asian summer monsoon diagnosed from Model of Ozone and Related Tracers (MOZART). J. Geophys. Res. Atmos. 2009, 114. [Google Scholar] [CrossRef] [Green Version]
  71. Zhang, J.; Wu, X.; Liu, S.; Bai, Z.; Xia, X.; Chen, B.; Zong, X.; Bian, J. In situ measurements and backward-trajectory analysis of high-concentration, fine-mode aerosols in the UTLS over the Tibetan Plateau. Environ. Res. Lett. 2019, 14, 124068. [Google Scholar] [CrossRef] [Green Version]
  72. Qiu, J. China: The third pole. Nature 2008, 454, 393–396. [Google Scholar] [CrossRef] [Green Version]
  73. Bian, J.; Li, D.; Bai, Z.; Li, Q.; Lyu, D.; Zhou, X. Transport of Asian surface pollutants to the global stratosphere from the Tibetan Plateau region during the Asian summer monsoon. Natl. Sci. Rev. 2020, 7, 516–533. [Google Scholar] [CrossRef] [Green Version]
  74. He, Q.; Ma, J.; Zheng, X.; Wang, Y.; Wang, Y.; Mu, H.; Cheng, T.; He, R.; Huang, G.; Liu, D. Formation and dissipation dynamics of the Asian tropopause aerosol layer. Environ. Res. Lett. 2020, 16, 014015. [Google Scholar] [CrossRef]
  75. Khain, A.; Rosenfeld, D.; Pokrovsky, A. Aerosol impact on the dynamics and microphysics of deep convective clouds. Q. J. R. Meteorol. Soc. A J. Atmos. Sci. Appl. Meteorol. Phys. Oceanogr. 2005, 131, 2639–2663. [Google Scholar] [CrossRef] [Green Version]
  76. Tao, W.K.; Chen, J.P.; Li, Z.; Wang, C.; Zhang, C. Impact of aerosols on convective clouds and precipitation. Rev. Geophys. 2012, 50. [Google Scholar] [CrossRef] [Green Version]
  77. Sprenger, M.; Croci Maspoli, M.; Wernli, H. Tropopause folds and cross-tropopause exchange: A global investigation based upon ECMWF analyses for the time period March 2000 to February 2001. J. Geophys. Res. Atmos. 2003, 108. [Google Scholar] [CrossRef]
  78. Shapiro, M. Turbulent mixing within tropopause folds as a mechanism for the exchange of chemical constituents between the stratosphere and troposphere. J. Atmos. Sci. 1980, 37, 994–1004. [Google Scholar] [CrossRef]
Figure 1. Vertical distribution of aerosol in 2016-07-04T20-22-54 determined by CALIPSO (a) projection of orbit, (b) vertical distribution of total attenuated backscatter at 532 nm (TAB) (unit: km−1 steradian−1) from 0 to 50°N.
Figure 1. Vertical distribution of aerosol in 2016-07-04T20-22-54 determined by CALIPSO (a) projection of orbit, (b) vertical distribution of total attenuated backscatter at 532 nm (TAB) (unit: km−1 steradian−1) from 0 to 50°N.
Remotesensing 15 01315 g001
Figure 2. Average pattern of Asian tropopause aerosol layer (ATAL) in (a) June, (b) July, and (c) August from 2006 to 2019 using TAB determined by CALIPSO, with 100-hPa geopotential height (unit: m) and wind field (unit: m s−1). The red rectangles represent the northern region of the QTP (NQTP).
Figure 2. Average pattern of Asian tropopause aerosol layer (ATAL) in (a) June, (b) July, and (c) August from 2006 to 2019 using TAB determined by CALIPSO, with 100-hPa geopotential height (unit: m) and wind field (unit: m s−1). The red rectangles represent the northern region of the QTP (NQTP).
Remotesensing 15 01315 g002
Figure 3. Temporal trend of ATAL in (a) June, (b) July, and (c) August from 2006 to 2019 using TAB determined by CALIPSO. The dotted area indicates that the linear trend passes the 99% significance test. Contours are 100-hPa geopotential height (unit: m). The red rectangles represent the northern region of the QTP (NQTP).
Figure 3. Temporal trend of ATAL in (a) June, (b) July, and (c) August from 2006 to 2019 using TAB determined by CALIPSO. The dotted area indicates that the linear trend passes the 99% significance test. Contours are 100-hPa geopotential height (unit: m). The red rectangles represent the northern region of the QTP (NQTP).
Remotesensing 15 01315 g003
Figure 4. Vertical distribution of summer-to-winter ratio (SWR) in ATAL region from 1985 to 2005 determined by SAGEII.
Figure 4. Vertical distribution of summer-to-winter ratio (SWR) in ATAL region from 1985 to 2005 determined by SAGEII.
Remotesensing 15 01315 g004
Figure 5. Calculated ATAL time series of SAGEII, CALIPSO, and MERRA-2 from 1994 to 2019. Variables: standardized SWR, TAB, and total aerosol mass mixing ratio for SAGEII, CALIPSO, and MERRA-2, respectively.
Figure 5. Calculated ATAL time series of SAGEII, CALIPSO, and MERRA-2 from 1994 to 2019. Variables: standardized SWR, TAB, and total aerosol mass mixing ratio for SAGEII, CALIPSO, and MERRA-2, respectively.
Remotesensing 15 01315 g005
Figure 6. Calculated time series of standardized TAB by 10−4 for NQTP and the entire ATAL region from 2006 to 2019.
Figure 6. Calculated time series of standardized TAB by 10−4 for NQTP and the entire ATAL region from 2006 to 2019.
Remotesensing 15 01315 g006
Figure 7. Difference between NQTP and the entire ATAL region, NQTP to ATAL ratio (NAR), in the vertical direction.
Figure 7. Difference between NQTP and the entire ATAL region, NQTP to ATAL ratio (NAR), in the vertical direction.
Remotesensing 15 01315 g007
Figure 8. ATAL patterns in the vertical direction (color-filled contour: TAB anomaly (ratio of TAB to the average value), dimensionless quantity), tropopause height, and zonal winds (unit: m s−1).
Figure 8. ATAL patterns in the vertical direction (color-filled contour: TAB anomaly (ratio of TAB to the average value), dimensionless quantity), tropopause height, and zonal winds (unit: m s−1).
Remotesensing 15 01315 g008
Figure 9. Distribution of omega at different altitudes. The vertical direction is the pressure coordinate, and the bottom is the terrain.
Figure 9. Distribution of omega at different altitudes. The vertical direction is the pressure coordinate, and the bottom is the terrain.
Remotesensing 15 01315 g009
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Liu, H.; Li, R.; Ma, J. Spatiotemporal and Vertical Distribution of Asian Tropopause Aerosol Layer Using Long-Term Multi-Source Data. Remote Sens. 2023, 15, 1315. https://doi.org/10.3390/rs15051315

AMA Style

Liu H, Li R, Ma J. Spatiotemporal and Vertical Distribution of Asian Tropopause Aerosol Layer Using Long-Term Multi-Source Data. Remote Sensing. 2023; 15(5):1315. https://doi.org/10.3390/rs15051315

Chicago/Turabian Style

Liu, Hongchao, Ren Li, and Junjie Ma. 2023. "Spatiotemporal and Vertical Distribution of Asian Tropopause Aerosol Layer Using Long-Term Multi-Source Data" Remote Sensing 15, no. 5: 1315. https://doi.org/10.3390/rs15051315

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop