Introduction
Airborne mineral dust is a major category of particles in the Earth's
atmosphere that influences climate on a local and regional to a global
scale (Huang et al., 2006). Dust aerosols have a significant role in climate through
the direct radiative effect of absorption and scattering of solar and
thermal terrestrial radiation (Ramanathan et al., 2001; Tegen et al.,
1996; Huang et al., 2009). Moreover, dust aerosols, depending on the
atmospheric conditions and on the dust composition, act either as
effective CCN (cloud condensation nuclei) (Hatch et al., 2008) or as
IN (ice nuclei) (DeMott et al., 2009; Chou et al., 2011), modifying
cloud albedo, coverage and precipitation (Rosenfeld et al.,
2008). Hence, the indirect effect of dust on the Earth's climate lies
in the modulation of the solar radiation forcing by altering the cloud
microphysical and macrophysical properties (Twomey, 1977; Albrecht,
1989; Haywood and Boucher, 2000; Huang et al., 2006). Besides the
direct and the indirect effects and the effect on meteorological
processes, dust transported over large distances has a significant
impact on human health and life expectancy due to the degradation of air
quality (Griffin, 2007; Goudie, 2014). In addition to its impact on human life
quality, the aeolian transport of dust is crucial for the
sustainability of marine and terrestrial ecosystems through the
deposition of mineral inputs and nutrients (Martin et al., 1994; Okin
et al., 2004; Jickells et al., 2005).
Over Asia, airborne mineral dust is considered a significant
atmospheric aerosol contributor. Major Asian dust sources include the
deserts of the Arabian Peninsula in southwest Asia and the Middle
East, the Thar Desert (Pakistan/India), the sandy Taklimakan region across
northwest China, and the vast arid and semi-arid Gobi in north
China and southern Mongolia (Fig. 1). The dust aerosol
load generated in the Gobi and Taklimakan deserts is estimated to
be 800 Tgyr-1 (Zhang et al., 1997). Airborne dust
originating from Asian deserts is frequently transported eastward
across China (Zhang et al., 2003), over the north Pacific Ocean (Shaw,
1980; Duce et al., 1980) to the western coast of north America (Uno
et al., 2001; Huang et al., 2008) and in extreme cases over even longer
distances, completing full global circuits (Clarke et al., 2001; Uno
et al., 2009).
Illustration of the study domain, confined to between longitudes
65–155∘ E and latitudes 5–55∘ N. Major dust
aerosol sources (yellow colour) are included (Taklimakan, Gobi and Thar
deserts). Dashed black lines delineate domains of high surface
elevation (Tibetan Plateau, Himalayan Ridge). The grey lines
delineate the domains of regional statistics provided in Table 1:
(1) Taklimakan and Gobi deserts, (2) Tibetan Plateau, (3) southeast China,
(4) Indian Peninsula, (5) North Pacific Ocean,
(6) mainland Southeast Asia/Indonesia.
In order to examine the composition, properties and radiative effect
of Asian dust, several field campaigns have been conducted. Regional
aircraft and ground-based campaigns, such as the Indian Ocean
Experiment (INDOEX) (Rasch et al., 2001), the Asian Aerosol
Characterization Experiment (ACE-Asia) (Huebert et al., 2003), the
Intercontinental Chemical Transport Experiment (INTEX-B) (McKendry
et al., 2008), the Aeolian Dust Experiment on Climate Impact (ADEC)
(Mikami et al., 2006), the NASA Transport and Chemical Evolution over
the Pacific (TRACE-P) (Jacob et al., 2003), and the Pacific Dust
Experiment (PACDEX) (Stith et al., 2009) have contributed considerably
to our knowledge and understanding of Asian dust. In addition to the
field campaigns, extensive measurements on the spatial variability and
temporal evolution of dust aerosols are required in order to assess
their contribution to climate. To this end, several passive remote-sensing satellite instruments, such as the Advanced Very High
Resolution Radiometer (AVHRR) (Husar et al., 1997), the MODerate
resolution Imaging Spectroradiometer (MODIS) (Remer et al., 2005) on
board both Terra and Aqua platforms, the Total Ozone Mapping
Spectrometer (TOMS) on board Nimbus 7 (Prospero et al., 2002), and the
Ozone Mapping Instrument (OMI) onboard Aura have been used (Chimot
et al., 2017). Although passive satellite sensors provide information
on the columnar properties of aerosols with adequate spatial and
temporal resolution, they are bound by certain limitations. The major
limitation is the lack of information on the vertical distribution of
aerosols and clouds, an important parameter for the assessment of the
aerosol radiative forcing on climate as well as their contribution as
IN and CCN (IPCC, 2013).
The vertical structure of aerosols and clouds is provided through
ground-based and satellite-based lidar systems (Liu et al., 2002). Regarding satellite-based lidar
observations, CALIOP (Cloud Aerosol Lidar with Orthogonal
Polarization) has been providing vertically resolved information on both
aerosols and clouds on a global scale since June 2006. CALIOP is the main instrument onboard the NASA/CNES (Centre National d'Etudes Spatiales) CALIPSO
(Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations)
satellite (Winker et al., 2007). CALIOP measures total attenuated
backscatter at 532 and 1064 nm and the linear depolarization
ratio at 532 nm. The depolarization ratio is a crucial
parameter in the dust aerosol classification (Ansmann et al., 2003;
Liu et al., 2008b), since dust particles are strongly depolarizing, as
opposed to other aerosol types. Thus, CALIOP is an ideal instrument to
detect dust aerosols and study the dust 3-D spatial distribution and
temporal evolution (Yang et al., 2012; Amiridis et al., 2013; Marinou
et al., 2017).
Over the past decade CALIOP retrievals have been frequently utilized
in dust aerosol studies with a focus on South and East Asia. Huang
et al. (2007) examined summertime dust plumes appearing over the
Tibetan Plateau and found that they originate from the nearby
Taklimakan Desert. Liu et al. (2008a) examined the spatial
distribution of dust over the Tibetan Plateau and the surrounding
areas on a seasonal basis. Huang et al. (2008), using CALIOP,
a micropulse lidar and surface meteorological data from the Gansu
Meteorological Bureau studied the long-range transport of dust from
the Taklimakan and Gobi deserts over east China and the Pacific Ocean
during the PACDEX campaign (March to
May 2007). They also showed that the dust storms over the Gobi region
are more intense but less frequent than the dust storms over the
Taklimakan region. The passage of dust from the Taklimakan and Gobi to Japan
(covering 1000–1500 km day-1) and consequently over the
Pacific Ocean is in addition reported by Uno et al. (2008), using
extinction coefficient profiles from CALIOP, a dust transport model
and forward trajectory analysis. Huang et al. (2010) used CALIOP dust
data along with other A-Train satellite observations to study the
climatic effect of dust on the semi-arid areas of northwest China. Ge
et al. (2014) combined dust CALIOP data with observations from the
Multiangle Imaging Spectroradiometer (MISR) showing that the
Taklimakan dust can be injected vertically up to 10 km height
above sea level, as a result of the local topography and synoptic
conditions. He and Yi (2015) utilized data from CALIOP and ground-based lidar observations over China to examine 13 dust events
within the period 2010–2012, while Xu et al. (2016) studied the
horizontal, vertical and temporal variability in dust aerosols over
China based on CALIOP. More recently, Tan et al. (2016 and 2017)
combined CALIPSO, Terra MODIS, OMI data and ground-based dust records
and studied the transport processes of five dust storms in spring 2010
from the Taklimakan and Gobi deserts to the Pacific and their impacts
on the ocean. Satellite-based observations from CALIPSO have also
been utilized to study the effect of dust transport from the Thar Desert
to the Indian subcontinent (Gautam, 2009; Das et al., 2013; Kumar
et al., 2014).
The aforementioned studies used the standard CALIPSO product and aerosol
subtype classification (Omar et al., 2009). Using this classification the
studies either did not take into consideration the pure-dust component of
polluted-dust aerosol subtype or they defined as “dust” both the dust and
polluted-dust aerosol subtypes (hence considering the non-dust component of
polluted dust to be dust). In the present study we use a separation methodology
developed in the framework of EARLINET (European Aerosol Research Lidar
Network), which makes use of the particle linear depolarization ratio and
updated lidar ratio values suitable for Asian dust, in order to distinguish
the pure-dust component from the dust and polluted-dust aerosol subtypes. In
this paper we use this new pure-dust product in order to provide the
three-dimensional seasonal distribution and the short-term temporal evolution
of dust over South and East Asia, based on 9 years of CALIOP observations
(January 2007–December 2015). The domain of the study is confined to between
65–155∘ E and 5–55∘ N (Fig. 1). South and East Asia
encompasses three major sources of dust aerosols, namely the deserts of
Taklimakan, Gobi and Thar. Regarding the dust aerosol transport, the
transport pathways depend greatly on the atmospheric circulation, which in the
case of Asia is heavily affected by the Himalaya orographic barrier. The area
southwest of the Himalayas (Arabic Peninsula, India, Indian Ocean) is mostly
affected by the long-range transport of dust generated by the Arabian Desert
and from the arid areas of Somalia and Ethiopia (Prospero et al., 2002).
Local dust sources in arid areas of Iran, Iraq and Afghanistan additionally
contribute to the regional dust load. By contrast, the areas located to
the east of the Himalaya barrier (Mongolia, China, southeast Asian Peninsula,
Pacific Ocean) are mostly affected by dust originating from the Gobi and
Taklimakan deserts (Prospero et al., 2002). The pure-dust aerosol product can
be used to study the horizontal and vertical distribution of dust aerosols
over the major sources along with the intercontinental transport, the
temporal evolution of dust aerosols and the intensity of the dust events
(Marinou et al., 2017).
The paper is organized as follows. Sect. 2 provides a description of
CALIOP, the data used and the methodology followed in the
study. Section 3 provides the main results. In Sect. 3.1 results on
the horizontal distribution of aerosols over South and East Asia (AOD, aerosol optical depth;
D_AOD, non-dust AOD) and of the observed dust centre of mass and dust
top height over South and East Asia are presented and discussed. The
vertical distribution of dust aerosols is presented and discussed in
Sects. 3.2–3.3 through the dust climatological and conditional
extinction coefficient profiles, while the short-term temporal
evolutions of AOD and D_AOD during the study period are examined in
Sect. 3.4. Finally, Sect. 4 provides a summary of the study along with
the main concluding remarks.
Data and methodology
CALIPSO is a sun-synchronous polar orbit satellite with an equatorial
crossing time around 13:30 LT and approximately 16 days
repetition orbit. CALIPSO, the collaborative NASA and CNES project,
joined the A-Train formation of satellites in April 2006 (Winker
et al., 2007). CALIOP, the primary instrument onboard CALIPSO,
consists of an Nd:YAG elastic backscatter and polarization lidar (Hunt
et al., 2009). CALIOP transmits linear polarized light, while
a telescope of 1 m diameter collects the backscattered
component by the atmosphere. Utilizing the total backscatter signals
and the polarization of the backscattered light CALIOP provides almost
continuously height-resolved information on the vertical structure of
aerosols and clouds (Winker et al., 2009), from the ground up to
30 km height.
Three levels of CALIPSO products are provided by NASA and CNES. The
Level-1 (L1) product consists of the raw range-corrected signals at the
highest spatio-temporal resolution. The Level-2 (L2) products are high-level quality products. More specifically, the CALIPSO L2 algorithm
classifies the detected layers into characteristic classes (Vaughan
et al., 2009), namely into clear air, cloud, aerosol, stratospheric,
surface, subsurface, totally attenuated or invalid feature types. The
classification algorithm (Omar et al., 2009) utilizes the
depolarization ratio and the attenuated backscatter signal, the height
of the aerosol layer and the characteristics of the Earth's surface
along the CALIPSO footprint (desert, ocean, snow/ice) in order to
divide the detected atmospheric features types into aerosol
subtypes (clean marine, dust, clean continental, polluted continental,
polluted dust and smoke). In addition, the CALIPSO L2 algorithm uses
specific lidar ratio (LR) values for each classified aerosol type in
order to derive the profiles of the extinction coefficient (Young and
Vaughan, 2009). The final L2 product is characterized by a 5 km
horizontal resolution and vertical resolution of 60 m in the
altitude range -0.5–20.2 kma.s.l. and 120 m in the
altitude range 20.2–30.1 kma.s.l. The L2 aerosol extinction
product is used to provide the Level 3 (L3) product of CALIPSO,
characterized by a 2∘×5∘ grid resolution (Winker
et al., 2013).
In the framework of this study we use the CALIPSO L2 optimized
profiles (based on the CALIPSO Version 3 dataset), developed as an
intermediate product under the collaborative EARLINET-ESA LIVAS (LIdar
climatology of Vertical Aerosol Structure) project (Amiridis et al.,
2015). This product has a spatial resolution of 1∘×1∘ and is described in detail and compared with AERONET (AErosol RObotic NETwork) in
Amiridis el at. (2015). In brief, for this product, several quality
control filters are applied in the CALIPSO L2 V3 dataset, following
the filtering proposed for the L3 product (Winker et al., 2013).
Moreover, in order to ensure the high quality of the aerosol product,
in addition to the filters described in Winker et al. (2013), L2
profiles with cloud observations are filtered out from the dataset
(Amiridis et al., 2013).
In addition to the CALIPSO L2 optimized profiles, the aerosol
observations categorized through the CALIOP classification scheme as
dust or polluted dust (Omar et al., 2009) are used in order to
retrieve the pure-dust aerosol component. To this end, the particle
depolarization ratio of dust is used. During the SAharan Mineral dUst
experiMent (SAMUM) 1 and 2 campaigns, Saharan dust particle
depolarization ratio values varied between 0.27 and 0.35 at
532 nm (Ansmann et al., 2011). Typical dust particle
depolarization ratio values measured with lidars in field campaigns
around the globe are consistent with these values, showing little
variation independently of the source region (e.g. Sakai et al.,
2000; Liu et al., 2008a; Freudenthaler et al., 2009; Groß et al.,
2011, 2013, 2015; Burton et al., 2013; Illingworth et al.,
2015). According to the methodology proposed by Tesche et al. (2009), the aerosol layers classified as dust or polluted dust and having
a depolarization ratio lower than 0.31 are assumed to be a mixture of
pure-dust and non-dust aerosol components. The particle
depolarization ratio value of the pure-dust component is then
calculated by
δp=β⊥βt-β⊥,
where δp is the particle depolarization value of the “pure dust component” in the
aerosol mixture, β⊥ is the perpendicular component of the
backscatter value and βt is the total backscatter of the
aerosol layer. The backscatter contribution of the pure-dust component is
calculated by
β1=βtδp-δ2(1+δ1)(δ1-δ2)(1+δp),
where δ1 (δ2) is a theoretical depolarization
value of the dust (non-dust) component. For the non-dust aerosols, we
assume particle depolarization ratio values of 0.03±0.02,
computed as a mean value of non-dust spherical particles (Omar et al.,
2009), considering minor contributions to depolarization by dried
marine particles and by anthropogenic particles. Using this
methodology, the CALIPSO pure-dust backscatter coefficient profile at
532 nm is calculated.
In order to retrieve the pure-dust extinction coefficient profile at
532 nm the pure-dust backscatter coefficient profile has to be
multiplied with the appropriate LR for Asian dust. The LRs observed
globally are summarized in the works of Mueller
et al. (2007) and Baars et al. (2016). In
general, different desserts produce dust of different mineralogy, thus
with different properties and different LRs. Typical values of LR of
desert dust aerosols vary between 35 and 55 sr. The LR values
of desert dust originating from the deserts of the Arabian Peninsula,
the Taklimakan region and the vast semi-arid Gobi have been
investigated with ground-based lidars (Sakai et al., 2002; Murayama
et al., 2004; Ansmann et al., 2005; Tesche et al., 2007; Xie et al.,
2008; Haenel et al., 2012; Komppula et al., 2012; Mamouri et al.,
2013), airborne instrumentation (Anderson et al., 2003) and during
intensive campaigns (Liu et al., 2002; Murayama et al., 2003). Based
on the atmospheric circulation over Asia, the dust aerosol transport
and the observed LR values, the domain of this study can be divided in
two subdomains and two different LRs for pure dust can be assigned in
these regions. Southwest of the Himalayas (Arabic Peninsula, India,
Indian Ocean) an LR of 40 sr is assigned to pure dust, while
east of the Himalayas the value of 47 sr is used. The assigned
LR values are used for the retrieval of the pure-dust aerosol
extinction coefficient profiles at 532 nm through the
backscatter coefficient profiles at 532 nm (Tesche et al.,
2009). The discrimination of the pure-dust component from the total
aerosol load is a polarization-based technique; therefore, it is
possible to provide the global horizontal and vertical distribution of
pure-dust only through satellite-based active remote sensing (CALIPSO
CALIOP, ISS-CATS (International Space Station – Cloud Aerosol Transport System); McGill et al., 2015).
In this study, based on the aerosol extinction coefficient profiles at
532 nm and on the pure-dust extinction profiles at
532 nm (from here on referred to as dust extinction), the
following products are discussed:
The seasonal CALIPSO L3 AOD, D_AOD and non-dust aerosol optical depth aggregated in
1∘×1∘ spatial-resolution grids.
The seasonal CALIPSO L3 dust profile top height (TH) and dust centre of mass (CoM) aggregated in 1∘ × 1∘
spatial-resolution grids. The dust TH (km) is defined as the height
below which 98 % of the D_AOD lies, while the dust CoM height is
defined according to Mona et al. (2006), as the backscatter weighted
altitude below which 50 % of the D_AOD lies. The CoM is given by
the following equation:CoM=∫zbztz⋅βzdz∫zbztβzdz[km],where z is the height in the atmosphere, β(z) is the
backscatter coefficient of the dust layer at heights z, and zb and
zt are the base and top heights of the profile respectively.
The seasonal zonal distribution of the climatological and conditional
dust extinction coefficient profiles (Mm-1).
The climatological dust product is a measure of the average dust load
over a geographical domain and is computed acknowledging only the
contribution of the dust component in the atmosphere. Technically,
this is accomplished by setting the extinction coefficient value of
the non-dust aerosols to 0 km-1 when averaging the
profiles over a grid. The dust climatological product can be used for
studies related to the contribution of dust to the total aerosol load
over a period of time. In addition, the climatological dust product
can be used in the evaluation of models related to dust transport and
to radiative transfer models, in studies of dust-related physical
processes (dust transport dynamics, CCN, IN), to investigate the
effect of dust aerosols on ecosystems (dust deposition into the
oceans), and to determine the dust aerosol load over highly
industrialized and densely populated regions.
The conditional dust product is a measure of the average intensity of
dust load over a geographical domain and is based explicitly on the
dust profiles, hence ignoring completely non-dust
aerosols. Technically, this is accomplished by setting the extinction
coefficient value of the non-dust aerosols to not-a-number (NaN) when
averaging the profiles over a grid. The conditional dust product is
related to the intensity of the dust events.
Validation of the pure-dust aerosol product against collocated AERONET
observations over northern Africa and Europe show absolute biases
between CALIPSO and AERONET AODs of -0.03 (Amiridis et al.,
2013). The methodology followed for the calculation of the dust
climatological extinction coefficient profiles and the dust
conditional coefficient profiles is shown in the flowchart of
Fig. 2. In the recent study of Marinou et al. (2017), the
climatological and conditional dust products have been used to study
the dust distribution above Europe and northern Africa. In this study,
the climatological and conditional products are similar to the study
of Marinou et al. (2017), with the only difference in the selection of
the domain being the LR values (55 sr for the Sahara).
Short-term CALIOP time series and trends in AOD and D_AOD for the
study domain, based on 9 years of CALIPSO overpasses (January
2007–December 2015). In addition to the CALIPSO/CALIOP time
series and trends, Aqua MODIS trends for the same period (January
2007–December 2015) are included.
Flowchart of the CALIPSO pure-dust, conditional dust
extinction coefficient and climatological dust extinction coefficient
products.
MODIS onboard the Aqua satellite was launched on 4 May 2002. The sensor has
a daytime equator crossing at 13:30 LT (noon). Due to its wide swath
(2330 km) it is capable of providing almost global coverage on
a daily basis. MODIS measures backscattered radiation in 36 spectral bands,
from 0.415 to 14.235 µm, with a spatial resolution of 250, 500
and 1000 m depending on the band. In this work, monthly AOD550 data
from the Level-3 Aqua MODIS Collection 6 1∘ × 1∘
gridded dataset (MYD08_M3) are used. The MODIS data were acquired from
NASA's Level 1 and Atmosphere Archive and Distribution System (LAADS)
(http://ladsweb.nascom.nasa.gov) covering the period from January 2007
to December 2015. AOD550 is retrieved using two algorithms: Dark Target (DT)
and Deep Blue (DB). There are two separate DT algorithms, one used for land
surfaces and one for water surfaces (Kaufman et al., 1997; Tanré et al.,
1997; Remer et al., 2005; Levy et al., 2010, 2013). The DT expected error for
Collection 6 is ±(0.05+0.15τA) over land and
+(0.04+0.1τA), -(0.02+0.1τA) over ocean
relative to the AERONET aerosol optical thickness (τA)
(Levy et al., 2013). While DT is used over vegetated surfaces and surfaces
covered by dark soil, the DB algorithm is capable of retrieving AOD550 over
bright surfaces such as deserts and arid and semi-arid areas (Hsu et al.,
2004, 2013). The new DB algorithm which is used for the production of
Collection 6 is applicable over all land surfaces (Sayer et al., 2013, 2014;
Hsu et al., 2013). For Collection 6 the DB expected error is ∼±(0.03+0.2τM) relative to the MODIS aerosol optical thickness
(τM) (Hsu et al., 2013; Sayer et al., 2015). In this work,
AOD550 data from the merged (DT and DB) (Levy et al., 2013)
datasets are used.
Regarding the uncertainties in the products, CALIOP L2 V3 is
characterized by a daytime minimum detectable backscatter of 0.0017±0.0003 km-1sr-1, a nighttime minimum detectable
backscatter of 0.0008±0.0001 km-1sr-1 and an AOD of
0.005 in north China (based on the minimum CALIOP 532 nm channel
detection sensitivity; Winker et al., 2009). The reported
underestimation in the CALIPSO AOD (Kittaka et al., 2011; Rogers
et al., 2014; Papagiannopoulos et al., 2016; Tian et al., 2017) is
additionally related to the limitation of CALIOP to collect
backscatter signals lower than the minimum detectable backscatter from
aerosol layers in the free troposphere. The estimation of the
uncertainties in the CALIPSO L2 V3 product is based on the assumptions
that they are random and uncorrelated (Young, 2010). Under these
assumptions the backscatter, depolarization and AOD are characterized
by uncertainties of 30–100, 30–160 and 100 %
respectively. In addition to the inherited uncertainties in the CALIOP
L2 V3 standard product in the AOD and D_AOD optimized products used
in the study, uncertainties are introduced due to the selection of LR
values suitable for Asian dust. An LR of 47±4 sr is used
for dust aerosols emitted from the Taklimakan and Gobi deserts, based
on the literature (Liu et al., 2002; Sakai et al., 2002; Anderson
et al., 2003; Murayama et al., 2003, 2004; Ansmann et al., 2005;
Tesche et al., 2007; Xie et al., 2008; Hänel et al., 2012;
Komppula et al., 2012; Mamouri et al., 2013). The LR introduces an
uncertainty of approximately 20 % in the D_AOD product. In
addition, as has already been mentioned, both aerosol types classified by
CALIPSO as dust or polluted dust are a mixture of a dust component and
a non-dust component. Thus, another source of uncertainty in the
decoupling of the dust component from the total aerosol load is the
lack of information regarding the non-dust component in the aerosol
mixture, due to the low depolarization ratio values of the non-dust
aerosol subtypes (Omar et al., 2009). As already discussed, for the
non-dust component a mean value for the different aerosol subtype
depolarization ratios of 0.03±0.02 is used. An extended analysis on
the way in which uncertainties propagate into the products is presented in
Marinou et al. (2017).
Results and discussion
Horizontal distribution of aerosols and dust
In this section we present and discuss the CALIPSO L3 optimized AOD and the
D_AOD products. Since the mechanisms of dust generation and transport and
the removal processes of aerosols from the atmosphere vary with season in
this section, we present and discuss the horizontal distribution of aerosols
and dust over South and East Asia per season. The seasons are defined as
follows: December–January–February (DJF), March–April–May (MAM),
June–July–August (JJA) and September–October–November (SON). Figure 3 shows
the spatial distribution of the seasonal mean AOD (Fig. 3a, e, i and m),
D_AOD (Fig. 3b, f, j and n) and non-dust AOD computed as the difference
between the total AOD and D_AOD (Fig. 3c, g, k and o) and the corresponding
percentage of D_AOD to the total AOD (Fig. 3d, h, l and p) at a 1∘ × 1∘ grid resolution and based on 9 years of
CALIPSO observations (January 2007–December 2015).
Spatial distribution of the seasonal mean CALIPSO AOD, D_AOD, optical depth
difference between AOD and D_AOD (non-dust AOD), and the
percentage of D_AOD with respect to the total AOD for the domain
between 65–155∘ E and 5–55∘ N and for the period
January 2007–December 2015.
Regarding the horizontal distribution of AOD and non-dust AOD, similar
geographical patterns are evident between all four seasons, although
the observed features vary in magnitude. High values of non-dust AOD
are consistently observed over the heavily industrialized and densely
populated regions of India, Bangladesh and China. Over the
Indo-Gangetic Plain and the entire region extending between New Delhi
and Kolkata, the observed non-dust AOD values are persistently higher
than 0.5 during DJF (Fig. 3a–d), MAM (Fig. 3e–h) and SON
(Fig. 3m–p), while during JJA (Fig. 3i–l) the AOD is suppressed (<0.3). The relatively lower AOD values observed during JJA over the
Bay of Bengal (0.3) are related to the regional meteorology
(monsoons). The high frequency of cloudiness (Fig. 8c and d) results
in biased mean AOD values since extreme aerosol events are less
frequently captured (Winker et al., 2013). Furthermore the wet
deposition rate of aerosols increases during the summer monsoon period
of the year (Lau et al., 1988). The countries of mainland Southeast Asia are
characterized by inhomogeneities in the observed aerosol load, with
larger non-dust AOD values observed during MAM (>0.5) and DJF (>0.3) and lower values during JJA (<0.15) and SON (<0.1). The
lower values during these months are attributed to the monsoon season
in the area, which runs roughly between June and October. Over maritime
southeastern Asia, the AOD values are relatively similar between the
different seasons with mean AOD values of 0.2±0.1. Similar
features have been shown by Campbell et al. (2013), who investigated
the 2-D variability in aerosols over the Indonesia region. Over China
similar geographical patterns in the horizontal distribution of
aerosols are evident between all four seasons, with larger non-dust
AOD values over the major sources of anthropogenic activity such as
urban clusters (Beijing, Shanghai, Guangzhou, Chongqing, Wuhan)
(Kourtidis et al., 2015) and high D_AOD values over the deserts of
Taklimakan and Gobi (Che et al., 2014, 2015).
Regarding the horizontal distribution of dust aerosols over South and East
Asia, the main difference is attributed to the high seasonality of dust
aerosol generation and transport. Moreover, the activation mechanisms of the
desert regions may vary as well (Prospero et al., 2002). Asian dust emission
sources in India (Thar Desert) and China (Taklimakan, Gobi) are clearly
mapped through the systematic high D_AOD values throughout the year
(Fig. 3b, f, j and n). The seasonality of the great Arabian Desert, the Thar
Desert, and the arid regions of Ethiopia and Somalia is mainly related to the
west Indian monsoon activity (Vinoj et al., 2014) and is mostly evident
during the summer months (Fig. 3f). The local dust sources in the arid areas of
Iran, Iraq and Afghanistan additionally contribute to the regional dust load.
However, the activation mechanism of these sources is mainly related to
convective episodes (Karami et al., 2017), and the contribution of these dust
events to long-range transport is limited. By contrast with the desert
regions of southwest Asia, the maximum activity of the Gobi and Taklimakan
deserts is observed during March and May (Husar et al., 2001).
Regarding the transport of dust aerosols, the long-range transport is
usually related to the activation of the major deserts (D. Liu et al.,
2008). Dust aerosols emitted from the great Arabian Desert, the Thar Desert and Somalia are transported eastwards over India and the Indian
Ocean reaching even the west coast of mainland Southeast Asia and Indonesia (Mao
et al., 2011). The feature of dust transport over the Indian Peninsula
and the Bay of Bengal is more prominent during MAM and JJA
(Fig. 3f and j). The transported dust aerosols significantly
contribute to the observed aerosol load over India, although the
magnitude of the contribution varies with season. Over China, for
latitudes north of 35∘ N, a similar pattern with respect
to the features of dust contribution to the total aerosol load due to
the dust aerosols emitted from the Taklimakan and Gobi deserts are
observed. More specifically, a persistent dust aerosol background is
evident during all seasons, with a peak during MAM (Fig. 3f). The
Asian dust generated from the Gobi and Taklimakan deserts is
transported over China, Korea and Japan and across the Pacific Ocean
(Liu et al., 2008a). This dust belt is usually confined to between 25 and
45∘ N (Fig. 3d), extends frequently towards the western coast
of north America and is most prominent during MAM (Clarke et al.,
2001; Uno et al., 2009).
Regions of low AOD and D_AOD values, regardless of the season of the
year, are additionally evident. Climatologically, there is little
evidence of dust transport over the Himalaya orographic barrier and
low AOD over the Tibetan Plateau. This is in line with
previous studies, reporting rare events of dust transport over the Himalayas (Huang et al., 2007; Liu et al., 2008a; Yumimoto et al.,
2009). The region to the north of Taklimakan, Gobi and Mongolia is
also characterized by low values of AOD and D_AOD, except during MAM
(Fig. 3f and h). The high dust aerosol load observed to the east of
the major dust aerosol source of Taklimakan (D_AOD values greater
than 0.3) and the high percentage of D_AOD with respect to the total
AOD indicate a strong eastward transport of both dust (Fig. 3f) and
anthropogenic aerosols (Fig. 3g). Similarly, the maritime region of
the Pacific Ocean south of 25∘ N is also characterized by low
AOD and D_AOD values, an observation which is in line with previous
studies (Huang et al., 2008; Kellogg and Griffin, 2006).
Figure 4 shows the seasonal geographical distribution of dust occurrences
(Fig. 4a, d, g and j), dust CoM (Fig. 4b, e, h and k) and the corresponding
dust TH (Fig. 4c, f, i and l). Please note the different height scale of CoM and TH. The dust occurrences are calculated as the number of CALIPSO
overpasses with dust observations, compared to the total number of CALIPSO
overpasses (percentage). Both the dust CoM and TH are defined as the height
in kilometres above ground level (a.g.l.). The dust occurrences, dust CoM and dust TH
are provided at a spatial resolution of 1∘ × 1∘ and are based on 9 years of CALIOP observations, between January 2007 and
December 2015.
Spatial distribution of dust occurrence (%),
climatological pure-dust CoM and dust TH in km a.g.l., for each season over the domain
between 65–155∘ E and 5–55∘ N for the period
January 2007–December 2015.
The distribution of dust occurrences shows that over the extensive
desert areas of South and East Asia (Tarim Basin, the Thar Desert,
southern Mongolia and Pakistan), the presence of dust is continuously
high (over 80 %) throughout the year. In the Taklimakan, Gobi and
Thar deserts, similar seasonal features are observed. Based on Fig. 4,
the occurrence of dust over these desert regions reaches a maximum
during spring (Fig. 4d), while minimum dust activity is observed
during winter (Fig. 4a). Lower frequencies of dust occurrence, which
still exceed 70 %, are also evident over east China and
southeastern India. Conversely, over mainland Southeast Asia and Indonesia, the
occurrence of dust is particularly low, especially during summer
(Fig. 4g) and autumn (Fig. 4j). More specifically, dust
occurrence percentage is between 50 and 60 % over Thailand and
Cambodia, 40 to 60 % over Laos and Vietnam, ∼60 % over
southeast China, and lower than 40 % over Malaysia and Philippines
during summer and autumn. The patterns of dust frequency
are in good agreement and consistent with the distribution of dust
provided by D. Liu et al. (2008), based on 1 year of CALIPSO
overpasses. Another noticeable feature of Fig. 4 is the two distinct
pathways which are observed: the trans-Pacific belt between 25 and
45∘ N and a second pathway over the Indian subcontinent towards
the Bay of Bengal and the Arabian Sea. The observed values of dust
occurrence over the major pathways decrease with increasing distance
from the dust source regions. Furthermore, the distribution of dust
occurrences shows that the range of dust transport is subject to high
seasonality. Over the dust belt of the Pacific Ocean
(25–45∘ N), values of dust occurrence vary between 30 %
during summer (Fig. 4g) and 90 % during spring (Fig. 4d). To the
south of the dust belt and over the Pacific Ocean, persistent low
values of dust occurrence, which rarely exceed 30 %, are observed
almost all year long. The low dust occurrence over the Pacific Ocean
south of 25∘ N agrees with studies based on CALIPSO
regarding the long-range transport of Asian dust (Huang et al., 2008;
D. Liu et al., 2008).
The distributions of dust CoM and TH show that during DJF (Fig. 4b and c)
dust aerosols are, in general, suppressed below 3 km height, with the
CoM below 2 km. Maximum values of dust TH during DJF are observed
across central and eastern China, with a peak around 3 km a.g.l.
During MAM (Fig. 4e and f) a large dust belt is observed, extending from the
desert regions of Taklimakan and Gobi to the east across central China and
over the Pacific Ocean. Dust is advected from the deserts, which are
1.5–2 kma.s.l. (Taklimakan) and 1–1.5 kma.s.l. (Gobi).
When dust is transported eastward over the Pacific Ocean, the above sea level
distance of the layer remains constant, although due to the change of the
surface elevation the absolute distance above ground level seems to increase.
Thus, the observed differences in CoM and TH between land and ocean and the
high values observed over the Pacific Ocean (more pronounced during MAM) are
an artefact of the change in the terrain elevation. In addition, a decreasing
west-to-east D_AOD gradient is observed over north China, between the dust
sources over the Taklimakan and Gobi and the Pacific Ocean (Fig. 3f). The
decreasing gradient of TH is less pronounced during MAM when dust aerosols
are injected as high as 10 kma.s.l. and are transported over longer
distances over the Pacific Ocean (Fig. 4f). Over the Indian Peninsula and the
Arabian Sea, dust CoM (TH) tends to be observed between 1–2 km
(3–4 km) a.g.l., while both dust TH and dust CoM are decreasing
toward the southeast and over the Bay of Bengal. The Himalaya Mountains are
clearly observed as they act as a physical barrier to the transport of dust
aerosols emitted from the great Arabian and Thar deserts towards the Tibetan
Plateau. Another noticeable feature of Fig. 4 is that the dust aerosols
observed over the Tibetan Plateau during spring are transported from the
Taklimakan Desert. During MAM, dust particles emitted from the Taklimakan and
Gobi deserts are transported over central China and the Pacific Ocean, while
at the same time significant long-range transport of dust aerosols emitted
from the Thar Desert is not observed (Fig. 4f). During JJA, however, the
pattern reverses, with longer-range transport of dust particles from the Thar
Desert over the Indian Peninsula, the Arabian Sea and the Bay of Bengal,
while no significant dust transport of dust aerosols emitted from the
Taklimakan Desert is evident (Fig. 4i). Dust CoM and TH values are higher
over the Indian Peninsula and the source of the Thar Desert than over the
Taklimakan and Gobi deserts. The dust TH over the Thar Desert extends to an
altitude as high as 5 km a.g.l., while over the Taklimakan and Gobi
deserts the corresponding altitude varies around 3.5 km a.g.l.
Furthermore during JJA dust aerosols emitted from the deserts to the
southwest of the Himalayas are transported over longer distances and injected
into higher altitudes. The dust TH over the entire Indian Peninsula during
summertime is in general around 3.5 km, gradually decreasing
eastwards towards the Bay of Bengal. To the north of the Himalayas, over the
Tibetan Plateau and central China large inhomogeneities in dust CoM and TH
are observed. The dust belt during MAM is still evident, although the
magnitude and extent of the dust transport have clearly decreased. The dust
TH during JJA varies over China between 1.5 km in the coastal region
and 4 km a.g.l. over central China. The season with the minimum
observed dust CoM and TH values throughout the year is SON (Fig. 4k and l),
with dust CoM and TH values between 1.5 and 3 km a.g.l. A regional
statistics description of the dust product is provided for six regions of
interest over the domain of South and East Asia in Table 1: Indian Peninsula
(5–30∘ N, 65–95∘ E), Tibetan Plateau (30–36∘ N,
80–103∘ E), Taklimakan and Gobi deserts (36–45∘ N,
77–115∘ E), southeast China, North Pacific Ocean
(20–45∘ N, 125–155∘ E), and the mainland Southeast
Asia/Indonesia region (5–20∘ N, 95–155∘ E). Figure 1
provides a map of the selected domains while the statistical description of
the dataset per domain is provided in Table 1. More specifically, Table 1
provides the mean D_AOD and SD of D_AOD, the maximum observed D_AOD value
and the 95th percentile, the mean dust CoM and the SD of CoM in kilometres
a.g.l., and the dust mean TH and the SD of dust TH in kilometres a.g.l.
Finally the number of profiles where dust or/and polluted-dust aerosol
subtypes to the total number of cloud-free profiles is included. The
statistical representation of the dataset is provided per domain and per
season for the period January 2007–December 2015.
Domain statistics on mean dust optical depth, max
D_AOD/95th percentile, dust CoM and TH (both in km a.g.l.) and number of dust
profiles to the total number of cloud-free profiles, based on the period
January 2007–December 2015.
Mean D_AOD ± SD
D_AOD max/
Dust CoM ± SD
Dust top height ± SD
No. of dust profiles in
(climatological)
percentile 95 %
(km a.g.l.)
(km a.g.l.)
no. of cloud-free profiles
(climatological)
(conditional)
(conditional)
Taklimakan and Gobi
DJF
0.078±0.135
1.802/0.327
2.31±1.39
3.47±2.01
0.74
MAM
0.193±0.308
2.729/0.819
3.06±1.43
5.01±2.17
0.78
JJA
0.113±0.232
2.504/0.529
3.19±1.38
4.94±1.8
0.68
SON
0.095±0.18
2.488/0.401
2.58±1.27
3.92±1.77
0.73
Tibetan Plateau
DJF
0.012±0.037
0.758/0.062
6.01±1.26
6.96±1.42
0.31
MAM
0.028±0.055
0.731/0.127
6.2±1.21
7.76±1.64
0.52
JJA
0.013±0.033
0.676/0.068
5.99±1.05
7.13±1.3
0.39
SON
0.006±0.023
0.631/0.032
6.08±1.37
6.87±1.45
0.24
SE China
DJF
0.062±0.104
1.769/0.254
1.73±1.29
3.03±1.84
0.79
MAM
0.108±0.171
2.39/0.404
2.38±1.47
4.35±2.26
0.85
JJA
0.032±0.064
1.06/0.145
1.9±1.56
3.09±2.13
0.69
SON
0.045±0.081
1.329/0.19
1.6±1.27
2.74±1.71
0.72
Indian Peninsula
DJF
0.043±0.065
1.736/0.147
1.13±0.9
2.13±1.28
0.84
MAM
0.171±0.188
1.944/0.521
1.79±0.99
3.63±1.44
0.93
JJA
0.199±0.167
2.071/0.751
2.05±1.22
3.72±1.55
0.86
SON
0.075±0.106
1.459/0.267
1.29±0.87
2.54±1.25
0.83
N Pacific
DJF
0.026±0.059
1.169/0.113
1.55±1.54
2.49±1.97
0.67
MAM
0.046±0.085
1.596/0.196
2.29±1.86
3.96±2.7
0.79
JJA
0.007±0.02
0.613/0.035
1.55±1.95
2.29±2.38
0.45
SON
0.012±0.032
0.929/0.057
1.26±1.49
2.06±1.89
0.56
mainland Southeast Asia/Indonesia
DJF
0.005±0.016
0.562/0.022
0.84±0.99
1.28±1.11
0.39
MAM
0.005±0.012
0.269/0.024
0.98±1.16
1.47±1.29
0.47
JJA
0.003±0.01
0.383/0.018
1.19±1.96
1.6±2.07
0.33
SON
0.003±0.012
0.712/0.016
1.02±1.57
1.45±1.71
0.38
Climatological dust extinction coefficient
In this section we present and discuss the vertical distribution of
dust aerosols in the atmosphere over South and East Asia; thus, we
present and discuss the vertical dimension of the 3-D dust
distribution and transport. The derivation of a pure-dust product
from CALIOP is of particular importance particularly for the
densely populated areas of India and China where a significant
percentage of the overall observed AOD is related to dust. The term
climatological refers to the computation process, where the mean pure-dust extinction coefficient value is computed based on the cases where
dust aerosols are detected, while the extinction coefficient of
non-dust aerosol types is assigned to 0 Mm-1. Therefore, the zonal vertical distributions discussed in this section correspond
to the horizontal distribution of the D_AOD presented in Sect. 3.1.
The domain of interest, between 5 and 55∘ N, is divided into five
10∘ longitudinal bands. Based on 9 years of CALIPSO observations
(January 2007–December 2015), Fig. 5 shows the vertical distributions of the
dust climatological extinction coefficient (Mm-1) for the four
seasons winter (DJF), spring (MAM), summer (JJA) and autumn (SON). The
surface elevation of the area is denoted with black colour in the plots
(below the minimum elevation, the contour plots are black). The
continuous and dashed lines correspond to the average elevation of the
surface level and to the average maximum elevation respectively. A threshold
of four dust cases is applied to the computation process of the pure-dust
climatological extinction coefficient (arbitrarily selected) in order to
avoid presenting extremely rare events at high altitudes at the same time as climatological values close to the surface level.
Zonal distribution of the climatological dust extinction
coefficient (Mm-1) profiles for the regions with longitude
from 65 to 155∘ E and latitudes 45 to 55∘ N
(a–d), 35 to 45∘ N (e–h), 25 to
35∘ N (i–l), 15 to 25∘ N
(m–p), and 5 to
15∘ N (q–t); profiles are presented as 3-month averages:
December–January–February (a, e, i, m, q),
March–April–May (b, f, j, n, r), June–July–August
(c, j, k, o, s) and September–October–November
(d, h, l, p, t). The minimum terrain elevation is denoted
with black colour. The black continuous (dashed) line refers to the
mean (max) elevation of the surface.
The north of the study domain, i.e. the region between 45 and
55∘ N (Fig. 5a–d), is characterized by relatively low values
of dust extinction coefficient. Dust layers are relatively homogeneous
and constrained to below 4 kma.s.l. The dust climatological
extinction coefficient values are in general below
25 Mm-1. The highest values in this region, as high as
35 Mm-1, are observed during winter over the Manchurian Plain (120–135∘ E) and extend as high as
5 kma.s.l. (Fig. 5a).
To the south of this region, the Taklimakan and Gobi deserts
(77–115∘ E) are the dominant land characteristics of the domain
between 35 and 45∘ N (Fig. 5e–h). Over this belt dust is
ubiquitously present close to the surface throughout the year. The Taklimakan Desert consists of a very arid area encompassed by the Tarim Basin. Due to the local
topography of the Tarim Basin and the cyclonic systems generated over the
Mongolian Plateau (Sun et al., 2001; Gong et al., 2006), the Taklimakan Desert is
active throughout the year (Liu et al., 2008a). Over this region, favourable topographic and meteorological conditions form an elevated layer
of dust aerosols where climatological dust extinction coefficient values
greater than 100 Mm-1 are regularly observed. The vast semi-arid
region to the east of the Tarim Basin, the Gobi, is considered an
additional source of Asian dust. Although the Tarim Basin is the primary
source of Asian dust to the north of the Tibetan Plateau, values of
climatological dust extinction coefficient as high as 100 Mm-1
close to the surface of the Gobi are present throughout the year. During
the period between March and May the strong surface winds which develop over
the Mongolian Plateau create favourable mechanisms of extreme dust events
(Bory et al., 2003; Yu et al., 2008). More specifically, the maximum dust
climatological extinction coefficient values over all of South and East
Asia are observed over the region of the Taklimakan Desert during spring,
reaching values as high as 200 Mm-1 (Fig. 5f). Although the dust
layer is mostly observed between 1.5 and 4 kma.s.l., during MAM
dust aerosol layers are detected as high as 9 km a.s.l. The
observed features of dust transport are consistent with the values of dust
height reported in the literature (Huang et al., 2008; Eguchi et al., 2009).
The elevated dust layers are captured by the strong westerly jet in the upper
troposphere and accordingly transported eastwards across the mainland of
China (Zhang et al., 2003) and the Pacific Ocean (Duce et al., 1980; Shaw,
1980). This feature is evident throughout the year, although more pronounced
during spring. The maximum height of dust transport also varies significantly
with season. Moving from the Taklimakan Desert towards the coastline and over
the Pacific Ocean, the highest altitude where dust layers are observed decreases from 8 kma.s.l. over the Taklimakan Desert to less than
2.5 kma.s.l. over the Pacific Ocean. The decrease in the altitude
of transport of the dust layers is attributed to both dry and wet deposition
processes that remove dust aerosols from the atmosphere (Colarco et al.,
2003). This characteristic is evident through the steep decrease across the
coastline and over the Yellow Sea and the Pacific Ocean, although during MAM
a lofted layer of dust aerosols that yields climatological extinction
coefficients up to 25 Mm-1 is observed up to
10 kma.s.l. (Fig. 5f). Close to the surface, over the densely
populated and highly industrialized provinces of eastern China, a persistent
dust layer with climatological extinction coefficient values as high as
100 Mm-1 is observed throughout the year.
The region between 25 and 35∘ N, hence the area to the south of the
dust belt which encompasses the deserts of Taklimakan and Gobi, is the domain
of Asia which is heavily affected by the Himalaya orographic barrier and the
Tibetan Plateau (Fig. 5i, j, k and l). Dominant sources of dust aerosols in this area are the Thar Desert and the Arabian Peninsula to the west of
the Himalayas, while to the south lies the densely populated Indian subcontinent.
Over the Thar Desert dust is ubiquitously present throughout the year,
although the magnitude of dust activity is characterized by high seasonality.
During the dry season, between March and May, an elevated layer of dust
aerosols forms in the lower altitudes over Afghanistan, Pakistan and the
western part of India (Fig. 5j). Typical values of dust climatological
extinction coefficient are around 100 Mm-1 and high
concentrations of dust are observed close to the surface, although airborne
dust is also frequently observed as high as 4 kma.s.l.
Significantly higher dust climatological extinction coefficient values, as
high as 200 Mm-1, are observed over the Thar Desert during the summer
season (Fig. 5k). Although the dust layer is primarily observed between the
surface and 2.5 km altitude, during JJA elevated layers of dust are
detected over the sources at altitudes as high as 7 km. The elevated
layer of dust is accordingly transported eastwards, over the highly
industrialized and densely populated Indo-Gangetic plains, where dust
interacts with locally generated aerosol particles (Middleton, 1986). Due to
the gravitational settling and to wet deposition (Colarco et al., 2003),
significantly lower values are recorded southeast of the dust sources,
over the Indian subcontinent. The observed dust climatological extinction
coefficient values range between 50 and 100 Mm-1 over the
Indo-Gangetic plains and the foothills of the Himalayas. Dust climatological
extinction coefficient values of 25 Mm-1 indicate the advection
and presence of dust aerosols even as high as the Tibetan Plateau and the Himalayas. The observations regarding the vertical structure of dust over
this domain support the elevated heat pump hypothesis (Lau et al., 2006) of
the accelerating Himalayas warming (Liu and Chen, 2000; Thompson et al.,
2003) due to the presence of dust aerosols coupled with black carbon over the
Tibetan Plateau. Significantly lower values, between 25 and
75 Mm-1 are observed to the east of the Himalayas. The decrease
in the dust climatological values over the Tibetan Plateau is less pronounced
during MAM when the Taklimakan and Gobi deserts to the north of this domain are
characterized by maximum dust activity (Fig. 5j). Additionally, throughout
the year, a steep decrease in the dust climatological extinction coefficient
close to the coastline and the Pacific Ocean is evident. Over southeast China
the values close to the densely populated surface are persistently higher
than 45 Mm-1, while over the Pacific Ocean the dust
climatological extinction coefficient values are decreased to less than
10 Mm-1.
The domain between 15 and 25∘ N encompasses the largest part
of India and of the countries of mainland Southeast Asia and maritime southeastern
Asia (Fig. 5m, n, o and p). This domain is characterized by large
inhomogeneities. High values of dust climatological extinction
coefficient are observed over India and the Arabian Sea (as high as
100 Mm-1), especially during MAM and JJA, and lower values
(Fig. 5n and o; below 50 Mm-1), during SON and DJF
(Fig. 5p and m). Over the Bay of Bengal the dust climatological
extinction coefficient values are drastically decreased compared to
mainland India, and values around 25 Mm-1 are
frequently encountered. The steep decrease over the Bay of Bengal
during MAM and JJA is most probably caused by wet deposition of dust
aerosol particles due to the heavy monsoon rainfall (Lau et al.,
2006).
Similar patterns are observed in the domain between 5 and
15∘ N, although the features vary in magnitude
(Fig. 5q, r, s and t). Over the Arabian Sea, to the south of
15∘ N, values of dust climatological extinction coefficient
between 75 and 100 Mm-1 are observed during MAM and JJA
(Fig. 5r and s). Over south India during JJA, elevated dust is present
at altitudes as high as 5 kma.s.l., while over the Bay of
Bengal the monsoon effect is observed through the steep decrease in
the dust climatological extinction coefficient values (Fig. 5s), as
a result of the wet deposition of aerosols. Values consistently below
25 Mm-1 are observed over the Indonesia region throughout
the year.
Conditional dust extinction coefficient
In this section we present and discuss the intensity of the dust events and
the purity of dust aerosols in the atmosphere over South and East Asia
(three-dimensional). In order to investigate the intensity of the dust
events, the dust conditional extinction coefficient parameter is used, as defined in
Sect. 2. The vertical distributions of the dust conditional extinction
coefficient and the corresponding conditional depolarization ratio are
presented in Figs. 6 and 7 respectively. More specifically, Fig. 6 shows the
seasonal vertical distribution of the dust conditional extinction coefficient
(Mm-1) for 9 years of CALIPSO observations (January
2007–December 2015) and for the five zones of 10∘ latitudinal
interval between 5 and 55∘ N. The vertical structure of the
atmosphere is shown for altitudes higher than the average surface elevation
of the CALIPSO orbits during the 9-year period between January 2007 and
December 2015. The continuous and dashed lines correspond to the average
elevation of the surface level and to the average maximum elevation of the
surface level respectively.
Zonal distribution of the conditional dust extinction
coefficient (Mm-1) profiles for the regions with longitude
from 65 to 155∘ E and latitudes 45 to 55∘ N
(a–d), 35 to 45∘ N (e–h), 25 to
35∘ N (i–l), 15 to 25∘ N
(m–p), and 5 to
15∘ N (q–t); profiles are presented as 3-month averages:
December–January–February (a, e, i, m, q),
March–April–May (b, f, j, n, r), June–July–August
(c, j, k, o, s) and September–October–November
(d, h, l, p, t). The minimum terrain elevation is denoted
with black colour. The black continuous (dashed) line refers to the
mean (max) elevation of the surface.
Zonal distribution of the conditional dust depolarization
ratio profiles for the regions with longitude from 65 to
155∘ E and latitudes 45 to 55∘ N (a–d),
35 to 45∘ N (e–h), 25 to 35∘ N
(i–l), 15 to 25∘ N (m–p), and 5 to 15∘ N (q–t); profiles
are presented as 3-month averages: December–January–February
(a, e, i, m, q), March–April–May (b, f, j, n, r), June–July–August (c, j, k, o, s) and
September–October–November (d, h, l, p, t). The minimum
terrain elevation is denoted with black colour. The black continuous
(dashed) line refers to the mean (max) elevation of the surface.
Distinct sources of dust generation, where dust conditional extinction
coefficient values exceed 200 Mm-1 are revealed. High
values of dust conditional extinction coefficients indicate that
the Taklimakan and Gobi deserts are the most dominant sources of dust
aerosols to the north of the Tibetan Plateau. To the east of the
orographic barrier of the Himalayas, the major source of dust generation is
the Thar Desert. In addition to the natural sources, regions of dust
emissions related to anthropogenic activities are also evident. As
seen in Fig. 6, values that exceed 100 Mm-1 are observed
throughout the year over the highly industrialized and
densely populated regions of southeast China and over the Indian
subcontinent. In the northern part of China, however, the near-surface
dust emissions to the west of the Tarim Basin most probably represent
a mixture of Gobi and anthropogenic dust emissions. These features are
consistent with the observation that close to the sources of dust
generation, the conditional extinction coefficient values are of the
same magnitude as the climatological coefficient values.
Although the spatial and seasonal features between the observed
conditional and climatological values are highly consistent, two major
differences are evident: (1) the climatological values become
significantly lower than the conditional values with increasing
distance from the sources of dust; and (2) the conditional values
observed in the upper troposphere are significantly higher than the
climatological values. The differences are attributed to the
difference between the definitions of the dust conditional and
climatological products. The dust climatological product is related to
the contribution of the dust load to the total aerosol load. By
contrast, the dust conditional coefficient product exclusively describes the dust events. As a consequence, areas of rare dust
events in general yield low climatological extinction coefficient
values. This makes the conditional coefficient value an ideal
parameter in order to realistically describe and study the routes of
transport of the dust plumes.
To the north and east of the Tibetan Plateau two distinct eastward
pathways of dust transport are observed: (1) a northern flow that
propagates from the Taklimakan and Gobi towards the Yellow Sea and the
Pacific Ocean (Uno et al., 2009) and (2) a southern flow that occurs
over central China (Kuhlmann and Quaas, 2010). The northern flow is
mostly evident during winter (Fig. 3d), while the southern transport
pathway over central China is more prominent during spring
(Fig. 3h). Figure 6 provides information on the vertical distribution
and depth of the two dust transport pathways. Both transport pathways
are observed in the middle and upper troposphere, indicated by dust
conditional coefficient values as high as 20 Mm-1,
observed at an altitude up 10 kma.s.l. Another noticeable
feature is that the vertical intensity of the transported dust aerosol
plumes is subject to high spatial and seasonal variability. Decreasing
values of both dust aerosol climatological and conditional values are
observed with increasing distance from the dust sources of the Taklimakan
and Gobi deserts towards and over the Pacific Ocean.
To the south and west of the Tibetan Plateau dust transport that
originates from the Arabian Peninsula and the Thar Desert and propagates
towards the Indian subcontinent (Gautam et al., 2009) and the Indian
Ocean is observed. The maximum altitude and intensity of the flow of
dust aerosols originating from the northwest part of India is subject
to high seasonal oscillation. During the period between May and August
dust events yield values of dust extinction coefficient as high as
200 Mm-1 over the source of the Thar Desert. The layer of
dust over the Indian subcontinent during this period exceeds the
altitude of 5 kma.s.l., while during the period between September
and May the dust aerosols are constrained to lower than
4 kma.s.l. Over Indonesia, mainland Southeast Asia and the Bay of Bengal,
the dust aerosol layer is well-confined within the first
4 kma.s.l. throughout the year, with dust extinction
coefficient values up to 30 Mm-1 for heights greater than
1 kma.s.l. In the first kilometres above sea level, we see relatively
high values that regularly exceed 40 Mm-1. These values are
affected by the selection of the particle depolarization ratio of
the non-dust aerosols in our dust-separation methodology (as discussed
in the “Data and methodology” Section). In this method, we selected the
most dominant value for the depolarization of the non-dust aerosols
(optimal for anthropogenic and marine cases). Furthermore, under
specific conditions the particle depolarization ratio of dry marine
aerosols can exceed these values, reaching up to 0.1, especially close
to the top of the marine boundary layer (Haarig et al., 2017). By
using the generic non-dust depolarization of 0.03, we have to
recognize a bias in the marine boundary layer, extinction values up to
50 % of the mean values of the conditional dust product.
The depolarization ratio is an ideal intensive parameter for the
discrimination between spherical and non-spherical aerosols, hence for
the classification of dust aerosols (Omar et al., 2009). Values of
the particle depolarization ratio at 532 nm that exceed 30 %
denote the presence of pure-dust aerosols (Liu et al., 2008b), while
lower values that range between 10 and 30 % suggest a mixture of
dust with more spherical aerosols (Murayama et al., 2003; Tesche
et al., 2009). Therefore, the depolarization ratio is used here as an
indicator in order to describe the state of the dust mixture and as
a discriminator between pure-dust and polluted-dust cases.
Figure 7 shows the vertical, horizontal and seasonal variability in the
average particle depolarization ratio of the cases classified by CALIOP as
dust or polluted-dust aerosol subtypes based on 9 years of CALIPSO
observations (January 2007–December 2015) and for five zones of 10∘
latitudinal interval, between 5 and 55∘ N. The vertical cross
sections of the mean depolarization ratio correspond to the dusty CALIPSO
observations (dust and polluted-dust cases) and hence correspond to the dust
conditional extinction coefficient parameter described above (Fig. 6). Based
on Fig. 7, dust depolarization ratio values between 30 and 35 % are
regularly observed over the Taklimakan, Gobi and Thar deserts throughout the
year. Intermediate depolarization ratio values, between 25 and 35 %, are
observed close to the dust sources, while even lower values, between 10 and
25 %, are evident over the densely populated and highly industrialized
regions of southeast China and India and over the remote domains of Indonesia
and mainland Southeast Asia.
In general, to the north of the Himalayas, low values of particle
depolarization ratio are observed close to the surface, while the particle
depolarization ratio increases with increasing height (Fig. 7e–h). The low
dust depolarization ratio values observed over the densely populated and
highly industrialized regions suggest the occurrence of a mixture of
non-spherical aerosols with particles of anthropogenic origin (Heese and
Wiegner, 2008). Conversely to the aerosol layers close to the urban or
industrial regions, the elevated layers over China and India are
characterized by intermediate dust depolarization ratio values, from about 15
to 25 %. The higher values of dust depolarization ratio in the middle and
upper troposphere compared to the lower troposphere are consistent with the
characteristics of dust transport. More specifically, to the north of the
Tibetan Plateau, between 25 and 45∘ N, three ranges of dust
depolarization ratio are observed. The air masses below 2 km altitude
are characterized by significantly low dust depolarization ratio values, in
general below 15 %. The observed low values are most probably the effect
of anthropogenic emissions coupled with near-surface dust aerosols. The
altitudinal range between 2 and 4 km height is characterized in
general by depolarization ratio values greater than 15 %, which, however,
rarely exceed the value of 20 %. The third elevated layer, above about
4 km, is characterized by depolarization ratio values greater than
20 %. The dust layers between 2 and 4 km height and above
4 km height have been observed and identified as dust aerosol layers
with different origin, from the Gobi and Taklimakan deserts respectively
(Kwon et al., 1997; Matsuki et al., 2003).
To the west of the Tibetan Plateau, between 25 and 35∘ N,
the Thar Desert is located. Over the Thar Desert the average particle
depolarization ratio of cases classified as dust or polluted dust by
the CALIPSO classification algorithm yield average depolarization
values greater than 25 % throughout the year. Average
depolarization values of the dust cases (dust or polluted dust) that
exceed 30 % are observed during JJA when Thar dust activity is at
its maximum (Fig. 7k). The elevated layer of dust is accordingly
transported eastwards, over the highly industrialized and
densely populated Indo-Gangetic plains. The interaction of dust aerosols
with locally generated aerosol particles (Middleton, 1986) is evident
through the decrease in the dust depolarization ratio over the Indian
subcontinent. The observed depolarization ratio values range between
15 and 20 % over the Indo-Gangetic plains and the foothills of
the Himalayas (Fig. 7i–l). Furthermore, dust depolarization ratio values
observed to the west of the Himalayas are typically larger than the
values observed over the eastern Himalayas and over the Tibetan
Plateau. The observed intermediate values of dust depolarization ratio
values at the windward slopes of the Himalayas are consistent with the
elevated heat pump hypothesis (Lau et al., 2006), which considers the
accelerating Himalayas warming to be the effect of accumulation of dust
aerosols coupled with black carbon over the Tibetan Plateau.
The average particle depolarization ratio of cases classified as dust
or polluted dust by the CALIPSO classification algorithm over
mainland Southeast Asia and Indonesia is significantly different from the
corresponding depolarization ratio features observed over China and
India (Fig. 7m–t). The dust depolarization values to the south of the
Tibetan Plateau and to the east of the Indian subcontinent are in
general below 15 %, indicating that the dust aerosols are coupled
with natural and anthropogenic emissions.
Temporal evolution of AOD and D_AOD
In this section, the CALIPSO AOD and D_AOD time series, based on 9 years of
overpasses, are presented and discussed. In addition to the CALIPSOCALIOP AOD and D_AOD trends, Aqua MODIS AOD trends for the same
period (January 2007–December 2015) are presented. The short-term trends in
this paper are calculated through the method originally proposed by
Weatherhead et al. (1998). The applied method has been widely used to
examine the trends in trace gasses, aerosols and surface solar radiation
(e.g. De Smedt et al., 2010; de Meij et al., 2012; Pozzer et al., 2015;
Georgoulias et al., 2016; Alexandri et al., 2017). Monthly satellite-based
time series are fitted by using a model with a linear trend and
a Fourier-based seasonal component for the annual cycle. According to the
method, the calculated trend (ω) is statistically significant at the
95 % confidence level if the absolute value of the ratio of ω to
its precision (σω) is greater than 2 (ω/σω>2). The approach followed here is extensively described in Alexandri
et al. (2017).
Figure 8 shows the short-term trends in CALIOPCALIPSO and
Aqua MODIS over South and East Asia for the period January 2007–December
2015. Aqua MODIS AOD trends were calculated from the C6 DTDB (Collection 6 Dark Target Deep Blue) merged AOD
dataset and are presented in Fig. 8b. For computing the
CALIOPCALIPSO AOD trends, the methodology includes
additionally a spatial expansion of each grid, in order to increase the
accuracy and representativeness of each AOD value in the sequence of the
monthly mean CALIOPCALIPSO time series. The mean optical
depth value per month is computed based not only on each grid but
additionally on the corresponding eight surrounding neighbour grids. Finally
the methodology proposed by Weatherhead et al. (1998) is applied to examine
the statistical significance of CALIOPCALIPSO AOD (Fig. 8a).
On the trend plots the “+” symbol denotes trends statistically
significant at the 95 % confidence level. Negative trends are shown in
blue, while red colour indicates positive trends. In addition to the
short-term trends the mean Aqua cloud fraction (Fig. 8d) and the number of
months used in the CALIPSO time series (Fig. 8c) are shown.
Trends in Aqua MODIS and CALIOPCALIPSO AOD
at 532 nm over South and East Asia for the period
January 2007–December 2015: CALIOPCALIPSO trends in AOD at 532 nm (a) and the number of months used
in the CALIOPCALIPSO time
series (c); Aqua MODIS C6 AOD at
550 nm trends in DTDB merged datasets (b) and mean
cloud fraction (d). Symbol “+” denotes trends
statistically significant at the 95 % confidence level.
Regarding China, CALIOP shows significantly positive AOD trends over
the northwest and eastern provinces whereas negative statistically
significant trends are mostly found over the southeastern
provinces. MODIS shows statistically significant positive AOD trends
over northwest, central and eastern China, whereas over northeast China
AOD trends are mostly positive. More specifically, both CALIOP and
MODIS sensors quantitatively agree on a statistically significant
increase at the 95 % confidence level over Xinjiang
(0.007 yr-1) and Hebei (0.01 yr-1)
provinces. Towards central China and the Tibetan Plateau, differences
are observed between CALIOP and MODIS. Over the broader Tibetan
Plateau, low positive trends are shown by MODIS, while no trends is
found by CALIOP. However, the disagreement over the region of the Tibetan
Plateau (Tibet; Qinghai, Gansu and Yunnan provinces) is not relevant
considering both the absence of statistical significance and the small
magnitude of the AOD trends. Larger differences between the MODIS and
CALIOP short-term trends are detected over southeast China. MODIS
shows a strong significant increase in AOD (0.01 yr-1), whereas significantly decreasing AOD trends are visible by CALIOP
(-0.007 yr-1). Over the Indian Peninsula, MODIS and CALIOP
trends qualitatively agree over the 9-year period in most
regions. Positive AOD trends (0.01 yr-1) are found by both
sensors over the broader central and eastern Indian Peninsula,
although disagreements are observed over the western regions of
India. The AOD trends in CALIOP and MODIS are very similar over land,
while over ocean (Arabian Sea, Bay of Bengal), discrepancies are
observed. MODIS shows increasing and statistically insignificant AOD trends of the order of 0.002 yr-1 over the Bay of Bengal
and strong positive, statistically significant trends over the Arabian
Sea (0.01 yr-1). The strongly increasing AOD trend over the
Arabian Sea, however, is not corroborated by CALIOP observations. The
observed trends from CALIOP and MODIS should be interpreted and
compared with caution, since the observed discrepancies between MODIS
and CALIOP AOD short-term trends may be attributed to several aspects
such as the different measurement principles and sampling of the two
sensors, among others. Differences between the two sensors have been
reported in the literature (Redemann et al., 2012).
Three domains of interest are selected to perform regional analysis on the
CALIPSO AOD and D_AOD time series: South and East Asia (5–55∘ N,
65–155∘ E), eastern China (22–43∘ N,
105–124∘ E), and South Asia (5–33∘ N, 65–91∘ E).
Figure 9 shows the linear trends in D_AOD (panels a, c and e) and AOD (panels b, d and f) for the selected domains. The continuous black lines represent the
seasonal variability between January 2007 and December 2015, while the dashed
(straight) lines depict statistically significant (statistically non-significant) trends.
The scatter points in grey denote the monthly mean D_AOD and AOD values over
the selected areas, while estimated trends (1 yr-1) and changes in
D_AOD and AOD with respect to 2007 (%yr-1) are also shown.
Short-term time series of CALIPSO/CALIOP AOD
and D_AOD over South and East Asia (AS), eastern China (EC) and
of South Asia (SA) based on observations during the period
January 2007–December 2015. The dashed lines show the multi-annual trend line
while the continuous black lines depict the seasonal
variability. The trends are in units of 1 year-1 and % year-1
relative to 2007.
Focusing on the selected domains, over South and East Asia the D_AOD
trend is negative (-5×10-4yr-1/0.96%yr-1), while despite the
decreasing D_AOD trend, the AOD trend is slightly positive (4×10-4yr-1/0.21%yr-1). Both the D_AOD and
AOD trends over South and East Asia, however, are not statistically
significant. In contrast to the South and East Asia region, the
observed AOD trend over eastern China is statistically significant and
negative (-5×10-3yr-1/-1.38 %yr-1). Similar to the AOD,
the region of eastern China presents a statistically significant, negative D_AOD trend (-2.1×10-3yr-1/-2.28 %yr-1). The negative AOD trend
observed over eastern China is in line with the air quality
regulations and the applied policies promoting the reduction in
emissions over China. Van der A et al. (2017) report on the decrease in aerosol precursor gases (SO2, NO2) based on OMI,
SCIAMACHY (Scanning Imaging Absorption Spectrometer for Atmospheric Cartography) and GOME-2 (Global Ozone Monitoring Experiment) satellite observations, while Yoon and
Pozzer (2014) report on the decrease in biomass-burning-related
emissions. The decline in AOD over China is additionally observed by
several authors (Kang et al., 2016; Zhang et al., 2017), with the
suggested pivot point around 2011 (Zhao et al., 2017). The negative
AOD trend over southeastern China is further enhanced due to the
negative D_AOD trend, which in turn can be attributed to the positive
precipitation trend over southeastern China and the increase in dust
aerosol deposition (Pozzer et al., 2015). Focusing on the South Asia
region, however, similar behaviour to that over South and East Asia is
observed. South Asia is characterized by not statistically significant, decreasing D_AOD trends (-1.5×10-3yr-1/-1.01 %yr-1). In contrast, due to
the increasing emissions of India, which is developing quickly, the observed
AOD trend is positive (3.3×10-3yr-1/0.98 %yr-1, not significant).
Summary and conclusions
In this work, CALIPSO is used to provide a multiyear 4-D climatology of
desert dust aerosols over South and East Asia at a spatial resolution of
1∘ × 1∘ grids. An optimized dust aerosol
product, developed using CALIOP backscatter and a particle depolarization
ratio, along with a regional correction on the dust lidar ratio suitable for
Asian dust is used. The optimized product is utilized to provide the
horizontal and vertical distribution along with the temporal evolution of
dust aerosols over a 9-year period (January 2007–December 2015).
Regarding the horizontal distribution of AOD,
D_AOD and non-dust AOD, our analysis shows similar patterns between all four
seasons, although the magnitude of the observed features varies with
season. High values of non-dust AOD are consistently observed over
the heavily industrialized and densely populated regions of China and
India (non-dust AOD >0.5). In addition to the anthropogenic emissions from the densely populated areas of South and East Asia, the major sources of
dust aerosols, namely the Taklimakan, Gobi and Thar deserts are
clearly mapped through the systematic high D_AOD values throughout
the year. However, the magnitude of the D_AOD observed features is
subject to high seasonality, ranging between D_AOD 0.2 during winter
and more than 0.6 during spring and summer seasons. The maximum activity
of the Gobi and Taklimakan deserts is observed during spring, while the
highest activity of the Thar Desert is observed during summer. The
seasonality of the dust transport pathways is additionally
well-captured. Dust transport over the Indian Peninsula is more
pronounced during spring and summer, while over China similar patterns
of a persistent dust aerosol background are evident throughout the
year, with a peak during spring when the dust transport across the
Pacific Ocean is at its maximum.
Regarding the vertical distribution of dust aerosols, the CoM, TH and the mean dust extinction coefficient
profiles (climatological and conditional),
together with the horizontal distribution, are used to provide the full three-dimensional
structure of dust aerosols and the atmospheric dust transport pathways
over all of South and East Asia. Based on the synergy of CoM, TH
and the CALIPSO dust extinction profiles, two distinct dust transport
pathways over South and East Asia are revealed: a trans-Pacific belt
between 25 and 45∘ N and a second pathway, extending from the Thar Desert towards the Bay of Bengal and the Arabian Sea. Both zones of
dust transport are subject to high seasonality. The highest dust aerosol
transport from the Taklimakan Desert towards the Pacific Ocean is
observed during spring, while dust aerosol transport from the desert
of Thar and across the Indian subcontinent is more pronounced during
summer.
Regarding the temporal evolution of AOD and D_AOD between January 2007 and
December 2015, the analysis showed statistically significant positive
short-term AOD trends over the Indian Peninsula (0.01 yr-1), NW
China (0.007 yr-1) and E China (0.01 yr-1), whereas our
study shows negative short-term AOD trends over southeast China
(-0.007 yr-1). CALIPSO-based positive AOD trends are found over
the broader central and eastern Indian Peninsula (0.01 yr-1). The
CALIOP observed trends between January 2007 and December 2015 are generally
in qualitative agreement with the derived MODIS AOD trends over large domains
of South and East Asia, although the short-term trends disagree over specific
regions. The CALIOP and MODIS trends, however, are interpreted and compared with
caution, since the samples of the datasets are non-uniform.
Observational evidence regarding the vertical distribution of dust
layers is of particular interest for modelling studies and
consequently for assessing the role of airborne dust on radiation
(direct climate effect) and clouds (indirect climate effect). So far,
modelling simulations of dust are evaluated through comparisons with
column dust observations (e.g. AERONET and MODIS AOD) and only
occasionally with lidar or in situ measurements at specific
stations. Moreover, the availability of ground measurements (lidar and
in situ) is limited near the dust sources. Assimilation of dust in
atmospheric models is also problematic since the 2-D initial
observational fields need to be assimilated towards the 3-D prognostic
model variables. Other characteristics than AOD aerosol properties
(e.g. Ångström exponent or aerosol type) are essential for
radiative forcing studies as the spectral dependence of AOD impacts
the RF (radiative forcing) model results for different aerosol cases (e.g. dust or
non-dust cases). Furthermore, studies of transboundary aerosol
transport (e.g. China, Korea and Japan) should include
a quantification of natural (dust) and anthropogenic aerosol
components. In this context, the utilization of the CALIPSO pure-dust
profiles derived here will certainly assist both the evaluation and
assimilation activities in relevant atmospheric simulations and will
provide a better estimation of the climatic impact of dust aerosols.