ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-18-1573-2018Two decades of satellite observations of AOD over mainland China
using ATSR-2, AATSR and MODIS/Terra: data set evaluation and large-scale patternsTwo decades of satellite observations of AOD over mainland Chinade LeeuwGerritgerrit.leeuw@fmi.fihttps://orcid.org/0000-0002-1649-6333SogachevaLarisaRodriguezEdithKourtidisKonstantinoshttps://orcid.org/0000-0002-5753-7074GeorgouliasAristeidis K.AlexandriGeorgiaAmiridisVassilishttps://orcid.org/0000-0002-1544-7812ProestakisEmmanouilhttps://orcid.org/0000-0001-9547-3019MarinouElenihttps://orcid.org/0000-0003-2631-6057XueYonghttps://orcid.org/0000-0003-3091-6637van der ARonaldhttps://orcid.org/0000-0002-0077-5338Finnish Meteorological Institute (FMI), Climate Research Unit, Helsinki, FinlandLaboratory of Atmospheric Pollution and Pollution Control Engineering of
Atmospheric Pollutants, Department of Environmental Engineering, Democritus University of Thrace, Xanthi, GreeceNational Observatory Athens (NOA), Athens, GreeceLaboratory of Atmospheric Physics, Department of Physics, University of Patras, Patras, GreeceDepartment of Electronics, Computing and Mathematics, College of Engineering and Technology, University of Derby, Derby, UKRoyal Netherlands Meteorological Institute (KNMI), De Bilt, the NetherlandsGerrit de Leeuw (gerrit.leeuw@fmi.fi)5February2018183157315927September201721December201721December201719October2017This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/This article is available from https://acp.copernicus.org/articles/18/1573/2018/acp-18-1573-2018.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/18/1573/2018/acp-18-1573-2018.pdf
The retrieval of aerosol properties from satellite observations
provides their spatial distribution over a wide area in cloud-free
conditions. As such, they complement ground-based measurements by providing
information over sparsely instrumented areas, albeit that significant
differences may exist in both the type of information obtained and the
temporal information from satellite and ground-based observations. In this
paper, information from different types of satellite-based instruments is
used to provide a 3-D climatology of aerosol properties over mainland China,
i.e., vertical profiles of extinction coefficients from the Cloud-Aerosol
Lidar with Orthogonal Polarization (CALIOP), a lidar flying
aboard the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation
(CALIPSO) satellite and the column-integrated extinction (aerosol optical depth – AOD)
available from three radiometers: the European Space Agency (ESA)'s Along-Track
Scanning Radiometer version 2 (ATSR-2), Advanced Along-Track Scanning Radiometer (AATSR) (together referred to
as ATSR) and NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Terra satellite, together spanning the period 1995–2015. AOD
data are retrieved from ATSR using the ATSR dual view (ADV) v2.31 algorithm, while for MODIS
Collection 6 (C6) the AOD data set is used that was obtained from merging the
AODs obtained from the dark target (DT) and deep blue (DB) algorithms, further
referred to as the DTDB merged AOD product. These data sets are
validated and differences are compared using Aerosol Robotic Network (AERONET) version 2 L2.0 AOD data
as reference. The results show that, over China, ATSR slightly underestimates
the AOD and MODIS slightly overestimates the AOD. Consequently, ATSR AOD is
overall lower than that from MODIS, and the difference increases with
increasing AOD. The comparison also shows that neither of the ATSR and MODIS AOD
data sets is better than the other one everywhere. However, ATSR ADV has
limitations over bright surfaces which the MODIS DB was designed for. To
allow for comparison of MODIS C6 results with previous analyses where MODIS
Collection 5.1 (C5.1) data were used, also the difference between the C6 and
C5.1 merged DTDB data sets from MODIS/Terra over China is briefly discussed.
The AOD data sets show strong seasonal differences and the seasonal features
vary with latitude and longitude across China. Two-decadal AOD time series,
averaged over all of mainland China, are presented and briefly discussed.
Using the 17 years of ATSR data as the basis and MODIS/Terra to follow
the temporal evolution in recent years when the environmental satellite Envisat was lost requires a
comparison of the data sets for the overlapping period to show their
complementarity. ATSR precedes the MODIS time series between 1995 and 2000
and shows a distinct increase in the AOD over this period. The two data
series show similar variations during the overlapping period between 2000 and
2011, with minima and maxima in the same years. MODIS extends this time
series beyond the end of the Envisat period in 2012, showing decreasing AOD.
Introduction
An aerosol is a suspension of droplets and/or particles in a fluid (Seinfeld
and Pandis, 1997). For atmospheric aerosols, the fluid is the air and the
aerosols are generally referred to as particles. This convention will also be
followed in this paper. The particles usually consist of solid and/or liquid
material, or a mixture of these, depending on their origin and aggregation
state, dissolved in liquid (usually water). These particles can be more or
less hygroscopic, depending on their chemical composition, and water vapor is
released or taken up depending further on the ambient relative humidity (RH),
until they are in an equilibrium state. At very high RH, around 100 %,
hygroscopic particles are activated to cloud condensation nuclei, grow into
the cloud droplet size range and are no longer considered aerosol particles.
When they are transported into a lower RH environment, the water evaporates,
but the remaining aerosol particle may have a different chemical composition
than the original one. The initial chemical composition of an aerosol
particle depends on the original generation mechanism, i.e., on the sources
such as biomass burning, dust lifted up from the surface or sea spray aerosol
generated at the sea surface by the action of wind and waves. Alternatively,
aerosol particles are generated from their precursor gases by nucleation
under the influence of UV radiation or catalysts (e.g., Kulmala and Kerminen,
2008) after which these very small particles, with a radius of a few
nanometers, may grow by condensation and coagulation. In this process, their
chemical composition may change, and hence the atmospheric aerosol is a
complex mixture of chemical components distributed over a wide range of sizes
spanning 5–6 orders of magnitude from a few nanometers to tens of
micrometers and a wide range of concentrations spanning about 10 orders of
magnitude, depending on the particle size. Aerosol particles are important
because of their effects on climate (e.g., Rosenfeld et al., 2008; Koren
et al., 2014; Guo et al., 2016a), health (Pope et al., 2009; Anenberg et al.,
2010), atmospheric chemistry, visibility (Sisler and Malm, 1994), cultural
heritage, etc.
In this paper, we focus on satellite retrieval of aerosol properties, which
requires that the particles are optically active; i.e., their size is of the
same order of magnitude as the wavelength of the incident light. The
radiometers which are commonly used for aerosol measurements from space,
making use of the Earth-reflected solar radiation at the top of the
atmosphere (TOA), are not sensitive to particles smaller than about
100 nm due to their low scattering efficiency at wavelengths in the
UV–visible (UV–VIS) part of the electromagnetic spectrum (Sundström et al., 2015).
Very large particles, larger than several
tens of micrometers, occur in very low concentrations and therefore
contribute little to the radiance measured at TOA. Hence, the particles
observed from satellites, during clear-sky conditions, are in the size range
of about 100 nm to several tens of micrometers and thus do not include
newly formed particles (Sundström et al., 2015), unless proxies are used
(Kulmala et al., 2011; Sundström et al., 2015). On the other hand, cloud
droplets with sizes on the order of 10 µm do affect the thermal infrared
(TIR) radiances, and this is used for cloud detection.
China, with its large variability of aerosol concentrations and a wide range
of emissions of both different types of aerosol particles and precursor gases
as well as different climate regions and meteorological conditions, offers
unique opportunities to study aerosols. Many studies have been published on
aerosols in relation to air quality in the eastern part of China, including
satellite remote sensing, ground-based measurements, modeling and
combinations thereof, which often focus on local or regional aspects (e.g.,
Song et al., 2009; S. Wang et al., 2011; Ma et al., 2016; Zou et al., 2017;
Xue et al., 2017; Miao et al., 2017; Guo et al., 2017). Satellites offer the
opportunity to obtain information, using the same instruments and methods,
over a large area during a longer period of time. In addition, using a lidar
such as the Cloud-Aerosol Lidar with Orthogonal Polarization
(CALIOP) flying aboard the Cloud-Aerosol Lidar and
Infrared Pathfinder Satellite Observation (CALIPSO) (Winker et al., 2007),
complementary information can be obtained on the aerosol vertical structure
(Winker et al., 2009), and hence a 3-D aerosol climatology can be developed
(Winker et al., 2013). CALIOP also provides information on aerosol type (Omar
et al., 2009).
The study area is mainland China, i.e., the area within the Chinese
border, indicated in this elevation map by the blue line. Also indicated are
some major cities (purple dots) and the locations of the Aerosol Robotic Network (AERONET) sites
(yellow diamonds) used in this study for validation. The color of the dot
inside each diamond gives an indication of the length of the data record at
that site (red: years, green: months, blue: days). Areas mentioned in this
paper are the Taklamakan Desert (TD), Gobi Desert (GD) and Tibetan
Plateau (TP). Other areas are the Beijing–Tianjin–Hebei (BTH) area, the
Yangtze River Delta (YRD) including Shanghai and Nanjing, the Pearl River
Delta in the south including Guangzhou and the North China Plain (NCP)
including BTH, YRD and the area in between.
The objective of the current study is to present the aerosol spatial and
temporal distribution over mainland China, using two decades of satellite
observations. The focus is on the use of the European Space Agency (ESA)
Along-Track Scanning Radiometer (ATSR), i.e., on ATSR-2 which flew on the
European Remote Sensing satellite ERS-2
and provided data from 1995 to 2003, and the Advanced ATSR (AATSR) which
flew on the environmental satellite Envisat from 2002 to April 2012 when
contact with the satellite was lost and its mission ended. Hence, a period of
17 years of ATSR data is available. With the focus on aerosols over
land, the aerosol optical depth (AOD) at a wavelength of 550 nm (AOD550, from here on
referred to as AOD, unless specified otherwise) retrieved using the ATSR dual
view algorithm (ADV; Kolmonen et al., 2016; Sogacheva et al., 2017) was used.
This data set is further extended to 2015 by using AOD data available from
Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Terra satellite, Collection 6 (C6)
(Levy et al., 2013). CALIOP data, available
from 2007, were used to obtain information on the vertical distribution of the
AOD (at 532 nm) over part of the study area. Thus, a 3-D aerosol
climatology over China was obtained, and the time dimension was added by
evaluating a two-decadal time series by combining ATSR and MODIS data spanning
the period 1995–2015. The study encompasses the area between
18–54∘ N and 73–135∘ E (see Fig. 1), but the discussion
focuses on AOD and vertical extinction profiles over China, i.e., the area
over land within the Chinese border indicated by the blue line.
The ATSR dual view offers the opportunity to effectively separate the
contributions of the surface and atmospheric reflections to the total
reflection measured at TOA and thus retrieve aerosol properties independent
of a surface correction, assuming that the ratio of the surface reflections
in the forward and nadir views is independent of wavelength (Veefkind
et al., 1998; Kolmonen et al., 2016). However, that approach may fail over
very bright surfaces such as snow, ice or some desert areas. ATSR AOD data
retrieved using the ADV algorithm have been successfully applied in many
studies (e.g., Veefkind et al., 1998, 1999, 2000; Robles-Gonzalez et al.,
2000, 2003, 2006, 2008; Schmid et al., 2003; Sundström et al., 2012;
Holzer-Popp et al., 2013; Virtanen et al., 2014; de Leeuw et al., 2015;
Rodriguez et al., 2015; Sogacheva et al., 2015, 2017; Popp et al., 2016). By
combining the ATSR-2 and AATSR data sets, a unique time series of AOD over
land, from June 1995 to April 2012, offers the opportunity to analyze the
temporal variation of the AOD and possibly detect trends. However, the most
recent changes in response to emission regulations in China (e.g., van der
A et al., 2017) cannot be observed and the analysis of the ATSR time series
remains inconclusive. Therefore, this time series has been extended with
MODIS/Terra C6 AOD data obtained from the dark target (DT) and deep blue (DB)
algorithms and merged into a single data set referred to as DTDB. The
MODIS/Terra AOD data set was selected because of the proximity of the
overpass times of the ERS-2/Envisat and Terra satellites over China within
about 1 h. MODIS/Terra data are available from April 2000. Furthermore, the
most recent MODIS C6 data have been selected because of updates described by
Levy et al. (2013), with further specification for DB updates by Hsu
et al. (2013) and expanded description and evaluation of DTDB by Sayer
et al. (2014). To use these data sets together requires an evaluation of
their similarities and differences across the study area, where it is
possible to include an independent reference data set like the one provided
by the Aerosol Robotic Network (AERONET; Holben et al., 1998), CARSNET (Che
et al., 2015; Che. et al., 2016) or SONET (Li et al., 2017).
Earlier studies on the aerosol climatology and trends over China were made
using ground-based remote sensing, i.e., sun photometers in CARSNET (Che
et al., 2015), hand-held sun photometers in the Chinese Sun Hazemeter Network
(CSHNET; Y. Wang et al., 2011) and solar radiation measurements (e.g., X. Xu
et al., 2015), or satellite data, in particular MODIS (e.g., Li et al., 2003;
Song et al., 2009; S. Wang et al., 2011; Luo et al., 2014; Tan et al., 2015;
H. Xu et al., 2015; Ma et al., 2016; He et al., 2016) but also using multiple
satellite data (Lin et al., 2010; Guo et al., 2011, 2016b; Dong et al., 2017;
Zhao et al., 2017; Zhang et al., 2017). However, these studies used MODIS
C5.1 AOD data and substantial differences exist between C6 and C5.1 (e.g.,
Levy et al., 2013; Sayer et al., 2014; Tao et al., 2015; Xiao et al., 2016).
Furthermore, in addition to data sets over dark and brighter surfaces from
the DT and DB algorithms, respectively, C6 also
provides a merged DTDB data set based on criteria using the quality flags in
each product (Sayer et al., 2013). The merged DTDB data set offers better
coverage but at the expense of a slight decrease in accuracy (Tao et al.,
2015). Direct and systematic comparisons between C5.1 and C6 AOD over China
have been published for the individual DT and DB data but not for the merged
DTDB AOD. This task is undertaken here to support the comparison of the
results from the current study with those from previous work.
In this study, the spatial distribution of AOD over China is presented and
discussed as well as the vertical distribution of the aerosol extinction
coefficients and AOD inferred from CALIOP data. Seasonal variations of AOD
are presented and briefly discussed, as well as the two-decade time series
(1995–2015) of AOD. In a second paper (Sogacheva et al., 2018), the seasonal and long-term
variations for different regions in China will be presented.
Aerosol data sets for China
Different data sources are used in this study, including satellite and
ground-based observations. Satellite data sets include AOD retrieved from
ATSR and MODIS/Terra, and vertical profiles obtained from CALIOP. Independent
and accurate ground-based data sets are used as reference for validation and
evaluation. The study encompasses the area shown in Fig. 1, with a focus on
mainland China.
Satellite data setsATSR (ATSR-2 and AATSR)
ATSR is a dual view instrument
(near nadir and 55∘ forward). The two views facilitate effective
separation of surface and atmospheric contributions to the observed upwelling
radiances, as applied over land in the ATSR ADV algorithm (Kolmonen
et al., 2016). Multiple wavelengths (seven) from VIS to TIR facilitate effective
cloud screening and allow for multi-wavelength retrieval of aerosol
properties. ATSR has a conical scan mechanism with a swath of 512 km,
resulting in daily global coverage of 5–6 days. Data are provided with a
nominal resolution of 1 × 1 km2; sub-nadir and aerosol
data are provided at a default spatial resolution of
10 × 10 km2 on a sinusoidal grid (L2) and at
1∘× 1∘ (L3).
ATSR-2 flew aboard ESA's ERS-2 from 1995 to 2003. AATSR
flew on ESA's environmental satellite Envisat and provided data from May 2002
to April 2012. Both satellites flew in a Sun-synchronous descending orbit
with a daytime Equator crossing time of 10:30 LT (ERS-2) and 10:00 LT
(Envisat). Together, these instruments provided 17 years of global
aerosol data. This time series is planned to be continued with a similar
instrument on Sentinel-3, i.e., the Sea and Land Surface Temperature
Radiometer (SLSTR), launched in the spring of 2016. A detailed description of
the AATSR data processing, using the ADV algorithm
over land and the ATSR single view (ASV) algorithm over the ocean, is provided in
Kolmonen et al. (2016).
The ATSR product used in this paper is the aerosol optical depth at a
wavelength of 550 nm (hereafter referred to as AOD) over the study
area for the full ATSR mission. The data were produced using ADV version 2.31
which includes cloud postprocessing as described in Sogacheva et al. (2017).
MODIS
The MODIS sensor (Salomonson
et al., 1989) aboard NASA's Terra satellite has been flying in a near-polar
Sun-synchronous circular orbit for more than 15 years (launched on
18 December 1999), observing the Earth–atmosphere system. MODIS/Terra has a
daytime Equator crossing time at 10:30 LT (descending orbit), a viewing
swath of 2330 km (cross track) and provides near-global coverage on a
daily basis. One of the most successful products of MODIS, which has been
used in numerous aerosol-related studies, is the aerosol optical depth at
550 nm (hereafter referred to as AOD).
MODIS AOD is retrieved using two separate algorithms, DT and
DB. In fact, two different DT algorithms are utilized: one for
retrieval over land (vegetated and dark-soiled) surfaces (Kaufman et al.,
1997; Remer et al., 2005; Levy et al., 2010, 2013) and one for retrieval over
water surfaces (Tanré et al., 1997; Remer et al., 2005; Levy et al.,
2013). The DB algorithm (Hsu et al., 2004, 2013) was traditionally used over
bright surfaces where DT cannot be used (e.g., deserts, arid and semi-arid
areas). However, the enhanced DB algorithm, which is used in C6, is capable
of returning aerosol measurements over all land types (Sayer et al., 2013,
2014). The C6 DT expected error is
±(0.05 + 0.15τAERONET) over land and
+(0.04 + 0.1τAERONET),
-(0.02 + 0.1τAERONET) over sea relative to the AERONET
optical thickness (τAERONET) (Levy et al., 2013). The C6 DB
expected error is ∼±0.03 + 0.2τMODIS) relative
to the MODIS optical thickness (τMODIS) (Hsu et al., 2013;
Sayer et al., 2015).
Several changes have been made in C6 compared to C5.1. Details about these
updates in C6 DT and DB data can be found in a number of recent studies
(e.g., Levy et al., 2013; Tao et al., 2015; Sayer et al., 2015; Georgoulias et al.,
2016). Corrections for polarization, gain and response-versus-scan corrections
and detrending for MODIS/Terra degradation have been included in DB but not
in DT. Another important update in C6 is the inclusion of a merged (DT and
DB) data set as described in Levy et al. (2013). This includes measurements
from both algorithms, offers a better spatial coverage and can be used in
quantitative scientific applications (Sayer et al., 2014). In this work, the
merged C6 L3 MODIS/Terra (MOD08_M3) monthly 1∘× 1∘
gridded AOD data set is used for the period March 2000–December 2015.
CALIOP
LIght Detection And Ranging (lidar) is a powerful remote sensing technique
for obtaining information related to the vertical distribution of aerosols in
the atmosphere (Liu et al., 2002). On a global scale, lidar data are acquired
by CALIOP which is the primary instrument aboard the CALIPSO satellite
(Winker et al., 2007). CALIPSO, developed as a collaboration project between
NASA and the space agency of France (CNES) has provided altitude-resolved
profiles of aerosols and clouds since June 2006. In addition to the total
attenuated backscatter signal at two wavelengths (532 and 1064 nm),
CALIOP is capable of acquiring polarization measurements at 532 nm.
Since the particle depolarization ratio is considered as the fingerprint of
desert dust particles (Ansmann et al., 2003; Liu et al., 2008), CALIOP is an
ideal instrument for studies related to the three-dimensional distribution
and transport of dust in the atmosphere (Amiridis et al., 2013; Proestakis
et al., 2018).
CALIPSO joined the A-Train constellation of satellites in April 2006 (Winker
et al., 2007). Being an integral part of the A-Train formation, CALIPSO is in
a Sun-synchronous polar orbit, with a local Equator crossing time at
13:30 LT and an orbit repetition frequency of approximately 16 days. Aboard
CALIPSO, the primary instrument is CALIOP, a dual-wavelength and dual-polarization
elastic backscatter Nd:YAG lidar (Hunt et al., 2009). CALIOP transmits
linearly polarized pulses at 532 and 1064 nm, while a telescope of
1 m diameter collects the backscatter signals. Based on the 532 and
1064 nm total backscatter signals and on the parallel and
perpendicular polarization components of the 532 nm backscatter
signal, CALIOP provides global and continuous information on the vertical
distribution of aerosols and clouds (Winker et al., 2009). The product of
CALIOP is provided in different levels of processing. Here, we use the
L2 product, which provides height-resolved information of aerosol and cloud
backscatter and linear depolarization ratio along the CALIPSO track. Based on
a number of parameters, namely the magnitude of the attenuated backscatter,
the cross-to-total ratio of the attenuated backscatter signals, the altitude
of the detected layers and the surface characteristics along the CALIPSO
orbit, the CALIPSO algorithm classifies the detected atmospheric feature
types into subtypes (Omar et al., 2009). In the case of aerosols, the algorithm
assigns aerosol-dependent lidar ratios (LRs) to the different subtypes in
order to convert the L2 backscatter coefficient profiles into profiles of
extinction coefficient (Young and Vaughan, 2009). In this paper, the ESA-LIVAS
(“LIdar climatology of Vertical Aerosol Structure for space-based lidar
simulation studies” project) database is used. LIVAS is developed based on
CALIPSO v3 L2 Aerosol and Cloud Profile products towards a global
multi-wavelength (355, 532, 1064, 1570 and 2050 nm) aerosol and cloud
optical database on a uniform 1∘× 1∘ grid
resolution (Amiridis et al., 2015). Here, the CALIPSO-based ESA-LIVAS product
(via http://lidar.space.noa.gr:8080/livas/) is used to provide the
three-dimensional climatology of the aerosol distribution over China for the
period January 2007–December 2015.
Ground-based reference data: AERONET
For the validation of satellite-retrieved aerosol products, AERONET sun
photometer data (Holben et al., 1998) are most commonly used as an
independent data source which are publicly available at the AERONET website
(http://aeronet.gsfc.nasa.gov/). An extensive description of the
AERONET sites, procedures and data provided is available from this website.
Ground-based sun photometers provide accurate measurements of AOD
(uncertainty ∼ 0.01–0.02, Eck et al., 1999) because they directly
observe the attenuation of solar radiation without interference from land
surface reflections. The parameter used in this study is version 2 L2.0
(cloud screened and quality assured; Smirnov et al., 2000) AOD at
550 nm, obtained from interpolation between AOD retrieved at 440 and
675 nm using the Ångström exponent. The locations of the
AERONET sites used in this study are indicated in Fig. 1, and their
coordinates and periods for which data are available are listed in Table S1
in the Supplement.
Data overviewATSR-retrieved AOD for 1995–2012 using ADV v2.31
ATSR-2-retrieved AOD data are available for the period
June 1995–December 2003, with some gaps in 1995 and 1996, and also toward
the end the data were not reliable. Hence, in this study, ATSR-2 data are only
used until August 2002. AATSR data are available for the period
May 2002–April 2012, but some data are missing in 2002, and therefore we use
these data only from August 2002 on. The consistency between the ATSR-2 and
AATSR data sets has been discussed in Popp et al. (2016). Over the ocean,
significant differences are observed with ATSR-2 consistently somewhat
higher, whereas over land there is no significant shift in AOD from ATSR-2 to
AATSR. In the current study, we only use data over land. Years for which data
are not continuously available for operational purposes are not shown in
multi-year averaged maps or aggregates. Here, the term aggregate is used
instead of average because of missing data for, e.g., cloudy situations,
bright surfaces and other situations where a successful retrieval was not
achieved. Furthermore, satellite data are biased toward clear-sky situations, and
hence no information is available for cloudy or partly cloudy scenes. In
addition, satellite observations offer a snapshot during the overpass at a
certain time of the day and, in the case of ATSR with a limited swath width,
data are available only every 3–5 days, depending on latitude. Lacking
information on the AOD for other days, the data cannot represent a true
average.
A map showing the spatial distribution of the ATSR-retrieved AOD over China,
aggregated for the full years 2000–2011, is shown in Fig. 2. This period was
selected to allow for comparison with MODIS/Terra. Differences with the
aggregated AOD map for the ATSR full-mission period (1995–2012) are very
small (not shown). It is clear that in such aggregate the absolute AOD value
may not be representative for the actual value in a certain area because
systematic temporal and year-to-year variations are hidden in the process. In
the aggregation process, all pixels retrieved and quality controlled were
used to provide monthly averages and these in turn were used to aggregate to
full years. Temporal variations will be discussed below based on time series.
Spatial distribution of the AOD over China, aggregated over the
(full) years 2000–2011. The AOD has been retrieved from ATSR-2 and AATSR
data, using the ADV v2.31 algorithm. The AOD scale is presented in the color
bar to the right of the map. Areas for which no data are available are shown
in white.
The map in Fig. 2 shows the commonly reported high AOD over southeast (SE) and southwest (SW) China
with the highest values (on the order of 0.8) over the North China Plain
(NCP) and the Sichuan province. Also, south of the Himalayas the AOD is high,
with moderately high AOD over the area east of the Himalayas and SW China. Northwest
(NW) of the NCP, the AOD is moderate with values around 0.3. In most other
areas, the AOD is low with values of 0.15 and smaller. In the west, over the
Taklamakan Desert, the multi-year aggregated AOD is high, due to the presence
of wind-blown desert dust in the spring. However, the highest values are not
observed over the bright desert surface where the ADV retrieval was not
achieved and thus no data are available (white area). Along the Chinese
coast, the AOD is high (on the order of 0.5), with overall smooth land–sea
transitions, and it decreases toward the open ocean. Likely, the high AOD is due to
a combination of transport from land and ship emissions along this very busy
shipping route. A very high AOD area is observed at about 34∘ N,
122∘ E, which also occurs in the MODIS data (see below) as well as in
Ozone Monitoring Instrument (OMI)-retrieved NO2 column data (Ding et al., 2017). Likely, ship
emissions are also the reason for the AOD hotspot at 38∘ N,
119∘ E where NO2 concentrations are also high.
It must be kept in mind that these are 12-year aggregated values and
strong deviations may occur in certain years or seasons.
MODIS/Terra 2000–2015: C6 merged DTDB AOD
The MODIS AOD used in this study is the MODIS/Terra C6 L3 merged DTDB AOD
product for the period April 2000 to December 2015. Here, MODIS/Terra has
been chosen because of the morning orbit with an Equator overpass
(descending) time at 10:30 LT, i.e., close to the ATSR Equator overpass times
(ATSR-2 at 10:30 LT; AATSR at 10:00 LT), which allows for comparison of the
data over China within about 1 h. It is noted that the drift in the
MODIS/Terra blue channel has been corrected from C5.1 to C6 (Levy
et al., 2013).
The spatial distribution of the MODIS/Terra DTDB AOD for the period
2000–2011 is presented in Fig. 3. The overall AOD distribution is similar to
that presented in Fig. 2 for ATSR but with some noticeable differences. The
main features in Fig. 3 are the higher AOD provided by MODIS, as compared to
ATSR, over almost the whole study area, as well as the much higher AOD over
most of the Tibetan Plateau and the Taklamakan Desert. Obviously, the latter is
due to the use of the DB algorithm over the bright areas in west China where
DT provides hardly any AOD data (see also Tao et al., 2015), but also the
ADV-retrieved AOD is very low (over the Tibetan Plateau) or not available due
the bright surface. The AOD spatial pattern over SE and north China is
similar for MODIS and ATSR, with MODIS AOD higher, and the same is observed
over northern India. In contrast, the AOD retrieved by MODIS over Vietnam and
Laos is lower than that retrieved using ATSR data. The differences between
ATSR and MODIS will be further addressed in Sect. .
MODIS C5.1 vs. C6
Many studies on the AOD over China have been published, as
mentioned in the introduction. These studies are relevant for comparison with
the current study with regard to both the spatial resolution and the temporal
behavior. In view of the rather recent production of the MODIS C6 data, most
of the earlier studies used C5.1. Levy et al. (2013) made an initial
comparison between C6 and C5.1 for 4 months of MODIS/Aqua AOD data showing
that the C6–C5.1 AOD difference is smaller than 0.1. Levy et al. (2013) also
compared MODIS/Terra aggregated 1∘× 1∘ AOD for
1 month (July 2008) and noted an extra C6–C5 difference over land in the
MODIS/Terra data. Their Fig. 22 shows that over China the Terra C6–C5
difference is larger than 0.1.
Sayer et al. (2014) did not specifically address the AOD over China although
these authors noted that in C6 DB AOD tends to be lower than DT in the
high-AOD region of China throughout the year. They also concluded that the
merged product does not specifically outperform the DT or DB results. The
Terra C6 3 km AOD product was validated by Xiao et al. (2016) using
AERONET data from the DRAGON Asia campaign (2012–2013) (Holben et al., 2017)
and over Beijing using hand-held sun photometers.
Spatial distribution of the MODIS/Terra C6 merged DTDB AOD over
China, aggregated for the full years 2000–2011.
Tao et al. (2015) evaluated MODIS/Aqua C6 AOD over different regions in China
for both DB and DT products, but not for the merged DTDB AOD, using AERONET
data as reference. One handicap is the sparsity of AERONET stations and their
spatial distribution across China and another one is the length of operation
of each station. Tao et al. considered five different regions, which are
indicated as northern China, the Yangtze River Delta (YRD), southern China,
northwestern China and scattered arid areas. It is noted that most AERONET
stations are concentrated in the northern China and YRD regions. The
validation results show the good performance of DB for the northern sites,
whereas over all other sites DB underestimates the AOD. DT overestimates the
AOD over almost all AERONET sites where sufficient quality retrievals are
available to allow for proper evaluation. Seasonal mean DT AOD over eastern
China may be 0.3–0.4 higher than DB. Tao et al. also note that DT misses
haze periods with high AOD and further comment on the use of the normalized
difference vegetation index (NDVI) in the merging procedure.
In view of the regional differences in the behavior of DB and DT, the
regional quality of the merged product cannot be assessed a priori and
depends on whether the DB or DT product has been selected, and when both are
used, on their actual values in the merging process. The merged DTDB AOD from
both Terra and Aqua were evaluated by Zhang et al. (2016) for two CSHNET (Xin
et al., 2007) sites near Beijing, i.e., the city of Beijing and a rural mountain site
west of Beijing (Beijing Forest). The results show the good performance of
the merged DTDB AOD product although it is not better than any of the
individual DT or DB products in all cases. At the rural site, DTDB performs
similar to DT but better than DB, as compared to the CSHNET AOD, with a
slight underestimation of the daily product over the rural site. Over the
urban site, the DB product performs somewhat better than DTDB, with a slight
overestimation of the latter.
In this study, a C5.1 merged DTDB AOD product has been produced over China
following the procedure described in Levy et al. (2013). The difference
between the C6 and C5.1 merged products is presented in Fig. 4. Figure 4
clearly shows the higher C6 AOD over most of eastern China as well as the
Tibetan Plateau with the largest differences, up to 0.2, over the northeast (NE) of the
NCP and Sichuan province and the western part of the Tibetan Plateau. On the
other hand, C6 is lower over the lower part of western China and in
particular the Taklamakan Desert where local differences are observed of
-0.25. These differences need to be taken into account when comparing the
results from using C6, as used in the current paper, with those in earlier
papers using C5.1 data.
Difference between MODIS C6 and C5.1 merged DTDB AOD; see color
scale on the right. Red colors indicate that C6 is higher; blue colors
indicate that C5.1 is higher.
CALIOP 2007–2015: the three-dimensional distribution of aerosols
In addition to the presented and discussed horizontal variability of ATSR and
MODIS columnar properties, CALIOP observations are synergistically used in
this study, in order to provide information on the vertical distribution of
aerosols over China. The vertical distribution of aerosols in the atmosphere
greatly affects aerosol–cloud interaction (DeMott et al., 2009; Hatch et al.,
2008) and is critical to estimations of the aerosol direct and indirect
radiative forcing on climate (Haywood and Boucher, 2000), to human health and
degradation of air quality (Goudie, 2014).
Seasonal maps of AOD at 532 nm derived from CALIOP over the
area 35–45∘ N, 70–150∘ E, including the Taklamakan/Gobi
deserts and the Beijing–Tianjin–Hebei area, derived from 9 years of
CALIPSO overpasses (2007–2015): winter (DJF), spring (MAM), summer (JJA) and
autumn (SON).
Vertical distribution of the climatological extinction coefficient
profiles at 532 nm over the area 35–45∘ N,
70–150∘ E, including the Taklamakan Desert and the
Beijing–Tianjin–Hebei area, derived from 9 years of CALIOP
measurements (2007–2015): winter (DJF), spring (MAM), summer (JJA) and
autumn (SON). The solid and broken lines indicate the mean and maximum
elevation of the aerosol extinction above the surface.
The horizontal variability of the CALIOP-derived AOD at 532 nm is
shown in Fig. 5 for the domain 35–45∘ N and 70–150∘ E,
for winter (DJF), spring (MAM), summer (JJA) and autumn (SON), where the data
for each season have been averaged over the years 2007–2015. The vertical
distribution of aerosols over the same domain (35–45∘ N,
55–155∘ E) and for the same seasons is presented through the
climatological extinction coefficient profiles at 532 nm (Fig. 6).
Through the combination of the vertical dimension (Fig. 6) with the
horizontal AOD distribution (Fig. 5) the full 3-D overview of atmospheric
aerosols over this domain is provided. Similar figures for other areas,
encompassing SE Asia (5–55∘ N, 65–155∘ E) are provided in
Proestakis et al. (2018).
The domain shown in Fig. 5 encompasses the Taklamakan and Gobi deserts and
the densely populated Beijing–Tianjin–Hebei (BTH) area. Over this domain,
similar patterns of AOD are observed throughout the year, with their
intensity varying strongly with the seasons, especially over the dust
sources. The Taklamakan/Gobi deserts and the BTH area are clearly mapped
through the high AOD values. Large mean AOD values, of the order of 0.3–0.8,
are observed over the arid region of the Taklamakan Desert/Tarim Basin and
the BTH area, while the semi-arid Gobi Desert yields significantly lower mean
AOD values, of the order of 0.1–0.3. Over the Taklamakan Desert, the highest
AOD values, of the order of 0.5–0.8, are observed during MAM and JJA, while
during the period between September and February AOD is much lower
(∼ 0.3). The seasonal variation over the Gobi Desert is similar to that
over the Taklamakan Desert, but AOD values are substantially lower, even at
its maximum activity (< 0.3). The observed seasonality and variability of
the AOD over the Taklamakan/Gobi deserts and the high AOD values observed
during MAM over the Taklamakan Desert are strongly related to the activation
mechanisms of the dust sources (Prospero et al., 2002), the local topography
of the Tarim Basin (Yumimoto et al., 2009) and the cyclonic systems developed
over Mongolia (Sun et al., 2001). Downwind from the Taklamakan and Gobi dust
sources, the anthropogenic activity in the densely populated and highly
industrialized BTH area results in a consistently high average AOD which is
present throughout the year. Similar AOD features are observed between all
four seasons with larger AOD values, of the order of 0.7, during JJA and
lower AOD values, of the order of 0.5, during SON.
The CALIOP-derived 9-year averaged vertical distribution of the
climatological extinction coefficients over the same domain as discussed
above is shown in Fig. 6 for each of the four seasons in the period
2007–2015. Over the Taklamakan Desert and the BTH area, similar
climatological extinction coefficient features are observed close to the
surface, with values as high as 200 Mm-1 persistently present
throughout the year. Over the vast semi-arid Gobi Desert, though, the
near-surface climatological extinction coefficient values are significantly
lower (∼ 50 Mm-1). The extinction coefficient values are
altitude dependent and distinctly decrease with height. Over BTH and
eastwards (110–130∘ E), high extinction coefficient values
(∼ 200 Mm-1) are in general suppressed below
1 kma.s.l. (above sea level), while over the Taklamakan Desert
(77–86∘ E) high extinction coefficient values of the order of
200 Mm-1 are observed as high as 3 kma.s.l. The eastward
long-range transport of dust aerosols generated from the Taklamakan and Gobi
deserts is also evident. Over the BTH area, high dust-related extinction
coefficient values (∼ 125 Mm-1, gradually decreasing with
height) are observed at altitudes higher than 3 kma.s.l. This
suggestion is supported by the areas of high extinction values (Taklamakan,
about 200 Mm-1; Gobi, about 75 Mm-1) located to the
west of BTH. Dust extinction coefficient profiles (Proestakis et al., 2018)
suggest that part of the transported aerosol is dust, with a large
contribution of locally produced aerosols over BTH. Over the whole region,
the extinction decreases gradually with height above the surface up to an
altitude of about 8 km, where the extinction coefficient has
decreased to ∼ 10 Mm-1. However, the height of the layer
gradually decreases toward the east, following the surface elevation to some
degree, but also with gradually increasing layer depth. The synergy of the
horizontal (Fig. 5) and vertical (Fig. 6) distributions allows for the
simultaneous study of the emission sources (Taklamakan/Gobi deserts, BTH
area), the aerosol load and the corresponding injection height of dust
aerosols in the atmosphere.
Validation and evaluation
The data overview presented above shows the differences between the AOD
derived from ATSR, MODIS and CALIOP data. To evaluate the quality of the
ATSR- and MODIS-retrieved AOD, these products are compared with reference AOD data
available from AERONET sites in the study area (see Fig. 1 and Table S1 in
the Supplement for locations). For this comparison, collocated data are used;
i.e., satellite data within a circle with a radius of 0.125∘ around
the AERONET site are averaged and compared with averaged AERONET data
measured within ±1 h of the satellite overpass time.
Density scatterplot of ATSR-retrieved AOD, using ADV v2.31, over
China for the years 2002–2012 vs. AOD from AERONET stations in mainland
China (see Fig. 1 and Table S1 in the Supplement). The filled circles are the
averaged ATSR AOD binned in 0.1 AERONET AOD intervals (0.2 for AERONET
AOD > 1.0) and the vertical lines on each circle represent the 1σ SD
of the fits. Statistics in the upper left corner indicate correlation
coefficient r, bias, SD, root mean square (rms) error and number of data
points (N). The color bar on the right indicates the number of data
points.
ATSR
AATSR AOD data sets over China produced by three different algorithms,
including ADV, have been validated by Che et al. (2016) using as reference
the AOD data from selected AERONET and CARSNET (Che et al., 2015) sites for
the years 2007, 2008 and 2010. The results show that the AATSR-retrieved AOD
is underestimated by a factor which increases with increasing AOD. However,
Che et al. (2016) likely used an older version of the
ADV-retrieved data set than the one produced by v2.31 which is used in the
current work. The v2.31 AOD is substantially different from the earlier
version as shown in Sogacheva et al. (2017), especially for high AOD regions.
(Che et al. (2016) do not mention which ADV version was
used, but at their submission date the latest updates described in Sogacheva
et al., 2017 had not been implemented). Sogacheva et al. (2017) present a validation
of the full-mission AATSR AOD over China vs. AERONET AOD. Figure 7 shows a
density scatterplot of the ATSR-retrieved AOD vs. AERONET AOD for the sites
listed in Table S1 in the Supplement. Comparison of the AOD scatterplots in
Che et al. (2016) (their Fig. 2a) with those in Fig. 7
shows the better performance of the newer ADV version v2.31 over China
although a small underestimation of less than 0.1 remains for AOD up to
∼ 0.5, increasing somewhat with increasing AOD between 0.5 and 0.9. For
AOD larger than 1.4, ADV v2.31 overestimates with respect to the AERONET
reference AOD and, in view of the low number of valid data points and their
large scatter, the use of these data is not recommended.
Same as Fig. 7 but for MODIS/Terra C6 merged DTDB AOD data.
MODIS/Terra C6 merged DTDB AOD
MODIS AOD over China has been validated vs. AERONET and CARSNET AOD. However,
as discussed in Sect. , only few publications address the validation of the MODIS C6
data suite and most of these consider only the Aqua DT and DB data set. The
validation of the MODIS/Terra C6 merged DTDB L2 AOD product is shown in the
density scatterplot of Fig. 8, where MODIS/Terra AOD has been plotted vs.
AERONET AOD using the available AERONET sites listed in Table S1 in the
Supplement. The MODIS AOD has been binned in AERONET bins with a width of 0.1
showing the good agreement between MODIS and AERONET data for AOD up to 1.8
but with a slight overestimation on the order of 0.1 for AOD up to 0.5 and
somewhat more for higher AOD.
Difference of ATSR AOD minus MODIS/Terra C6 merged DTDB AOD,
aggregated for the years 2000–2011, over China. The values in increments of
0.05 are given in the color scale to the right.
Intercomparison of AATSR and MODIS/Terra C6 merged DTDB AOD
Having established that both the ADV and the MODIS/Terra C6 merged DTDB AOD
data sets compare well with the AERONET reference AOD data, we can address
the differences observed in the AOD maps in Figs. 2 and 3, with MODIS AOD
overall higher than that from ATSR. The difference map in Fig. 9 shows the
actual differences between the two data sets (ATSR-MODIS) aggregated over the
overlapping years (2000–2011) which are largest over brighter areas, such as
the Taklamakan Desert and the Tibetan Plateau, where MODIS DTDB is governed
by the DB data, which underestimate the AOD with respect to AERONET (Tao
et al., 2015, 2017), whereas ADV and especially DT provide few successful
retrievals. However, also over areas with very high AOD, such as the Sichuan
province, NCP and YRD, the differences are large. In contrast, along the
mountains from the NW to the SW of China and around the Sichuan province, the
AATSR and MODIS AOD values are in very good agreement, within ±0.05, i.e., the
estimated retrieval uncertainty over land (for MODIS). In other areas, the AOD
difference is about 0.1, as may be expected from the validation presented
above, i.e., showing that overall MODIS slightly overestimates and AATSR
slightly underestimates with respect to AERONET which adds up to the AOD
difference of about 0.1. It should be noted here that, as pointed out by a
reviewer (A. Sayer, private communication, 2017), the MODIS team has been working on a
new version, Collection 6.1, which has significantly lower AOD over, e.g.,
the Tibetan Plateau, while over east China the differences between C6 and
C6.1 are very small. (At the time of writing of the current paper, C6.1 was not
available.)
The surprising finding is thus the high AOD difference over SE China, i.e.,
mostly over the low elevation part of China (Liu et al., 2003) classified as
forests and cropland (Bai et al., 2014), i.e., dark surfaces where retrieval
algorithms are expected to perform best. This is also the area where most of
the AERONET sun photometers are located, with clusters in the BTH area and
the YRD. Apparently, the slight under- and overestimations by ATSR and
MODIS, respectively, are not due to the surface correction in the retrieval
algorithms and likely caused by the aerosol types used.
MODIS/Terra C6 merged DTDB AOD vs. AATSR ADV v2.31 AOD, for
collocated AATSR-MODIS-AERONET data, as described in the text of Sect. .
The colors (see scale on the right) indicate the difference between the
MODIS/Terra and ATSR overpass times in minutes.
Figure 10 shows a scatterplot of MODIS AOD vs. AATSR AOD, for collocated
AATSR-MODIS-AERONET data; i.e., only AOD data are shown for AERONET sites
where both MODIS and AATSR have achieved a successful retrieval and the
overpasses were within ±1 h while also AERONET data were available, for
the period 2002–2012. The color code indicates the difference in the exact
overpass time. Figure 10 shows that the difference in exact overpass time,
varying between 20 and 80 min, does not lead to a systematic effect on the
MODIS/AATSR AOD differences. Hence, the somewhat later MODIS/Terra overpass,
which could influence the AOD as a result of a developing atmospheric
boundary layer during the morning hours and associated temperature and
relative humidity difference, does not explain the higher MODIS AOD.
Seasonally averaged maps of the ATSR- (a) and
MODIS-retrieved (b) AOD distributions over China for the years
2000–2011: winter (DJF), spring (MAM), summer (JJA) and autumn (SON). The
AOD color scale is on the right.
CALIOP
The comparison of the CALIOP data over the region 35–45∘ N over
China with ATSR and MODIS data in Figs. 2 and 3 (or better in Fig. 11, which
shows the seasonal variations of the AOD retrieved from ATSR and MODIS data),
shows similar patterns with high AOD over the Taklamakan Desert and the BTH
area (differences between ATSR and MODIS were discussed in Sect. ).
However, differences are also observed, i.e., the underestimation of CALIOP
AOD compared to AOD retrieved by MODIS/Terra. As mentioned above, the CALIOP
AODs refer to the years 2007–2015, while ATSR and MODIS/Terra AODs refer to
the years 2000–2011. Hence, a direct comparison of the spatial distributions
of the AOD should not be made since substantial year-to-year variations may
occur, depending on meteorological and synoptic conditions. Furthermore,
CALIPSO overpasses happen in the afternoon, whereas Envisat and Terra overpasses happen in
the morning. The differences between the ATSR, MODIS/Terra and CALIOP AOD are
likely due to the highly non-uniform data sample and to the fundamentally
different algorithms and operation of the sensors. Following the literature,
the CALIOP aerosol extinction coefficients are slightly underestimated as
compared with EARLINET lidars (Papagiannopoulos et al., 2016). Tian
et al. (2017) obtained similar results from comparison with lidar
measurements in SACOL (China). A comparison of the CALIOP AOD climatological
product against spatially and temporally co-located AERONET observations is
discussed in Amiridis et al. (2015). In their Fig. 15, the absolute bias of
the means between the CALIOP optimized product (named LIVAS) and AERONET
reveals biases within ±0.1 in terms of AOD.
Seasonal variation
The spatial variation presented in the previous chapter is strongly
influenced by emissions and meteorological factors which obviously vary
seasonally due to both natural processes and human activities. As a result,
strong seasonal variations are observed in the AOD distributions as shown in
Fig. 11 where the ATSR- and MODIS-retrieved AOD values over the study area are shown
for winter (DJF), spring (MAM), summer (JJA) and autumn (SON). As mentioned
above, ATSR retrieval is often not successful over bright surfaces and has
hardly any coverage over the Taklamakan and Gobi deserts and the Tibetan
Plateau and also in the north during the winter. Due to the use of the merged DTDB
AOD, MODIS has better coverage over bright surfaces and thus higher
AOD over the Tibetan Plateau and the Taklamakan and Gobi deserts as well as
in the north of China during the winter season. Seasonal AOD maps from CALIOP
for the region 35–45∘ N over China were shown in Fig. 5 and the
vertical climatological extinction coefficient profiles for the same region
were presented in Fig. 6. CALIOP, ATSR and MODIS/Terra are used
synergistically for the analysis of the regional and seasonal variations of
the AOD over China. The horizontal variability of CALIOP AOD (Fig. 5) shows
features similar to the AOD distributions provided by ATSR and MODIS/Terra
(Fig. 11), i.e., high AOD values over the Taklamakan Desert and over the BTH
area, with lower values over the Gobi Desert. However, differences also
exist, as discussed in Sect. .
The AOD spatial distribution over China differs between seasons. The highest
AOD is observed in the summertime over the NCP including the BTH area, with
AOD of the order of 0.9 and somewhat lower values (about 0.6) in the spring
and autumn, and minima in the winter. This seasonal behavior is in contrast
to that of near-surface aerosol concentrations, indicated, for instance, by
PM2.5, i.e., the mass concentration of dry particles with in situ
diameters smaller than 2.5 µm, which peak in the winter and reach
a minimum in the summer (e.g., Wang et al., 2015). These differences can be
explained by seasonal variations in synoptic patterns (Xia et al., 2007; Miao
et al., 2017) and associated boundary layer height and relative humidity,
transport of aerosols, emission of primary aerosols and aerosol precursors
contributing to chemical processes leading to secondary formation of new
aerosol particles and thus higher concentrations (e.g., Tang et al., 2016).
In particular, the seasonal emissions of dust aerosol (spring) and biomass
burning aerosol (season varies with region) have a strong influence. Song
et al. (2009) also noted that the temporal correlation between the monthly
values of the PM10 concentrations and MODIS AOD exhibit regional
seasonality contrasts over China, with correlation coefficients > 0.6
near the southeast coast and -0.6 or lower in the north-central regions.
They attribute this contrast to differences in the aerosol size distributions
and support their argument by MODIS data on the distribution of the
Ångström exponent and fine-mode fraction.
Relatively high AOD is observed over the Sichuan province and Chongqing in
all seasons, with values of about 0.8 and somewhat lower in the autumn. Also,
over the Pearl River Delta (PRD), the AOD is relatively high throughout the
year with values on the order of 0.4–0.6 in winter, summer and autumn and a
maximum in the spring of about 0.8. In the Shanghai/Nanjing region in the
YRD and the area to the SW of YRD toward PRD, the AOD
is relatively high in the spring, summer and autumn, with values of about
0.5–0.6 and lower in the winter (around 0.3). South of the YRD, along the
coast, an area is observed with more moderate AOD of about 0.3 throughout the
whole year, which however stretches inland further in the winter then in the
summer and autumn and is smallest in the spring. Over sea, along the SE coast
of China, the AOD is on the order of 0.5 in the area north of the YRD, while
further south it varies between about 0.3 and 0.5. Also the extent of the
elevated AOD area varies with the season and is largest in the spring when it
reaches east beyond Taiwan. It is noted that the BTH, PRD and YRD regions
host three major urban clusters that constitute huge spatial sources of
anthropogenic aerosols (Kourtidis et al., 2015). This brief discussion
clearly illustrates the seasonal variation of the AOD, as well as the
regional differences in the seasonal variation as earlier shown by, e.g.,
Song et al. (2009), Luo et al. (2014) and Li et al. (2017).
Time series of ATSR- and MODIS-retrieved AOD over China for two
decades: 1995–2015. Note that data are missing in the beginning of the
ATSR-2 observation period in 1995, and also MODIS/Terra data are available
starting in April 2000; AATSR data start in May 2002. Therefore, the ATSR-2
1995 data point includes data for June–December and for 1996 July–December.
ATSR-2 was used from 1995 to July 2002; for August 2002 to 2011
AATSR data were used. AATSR for 2012 is not shown since only 3 months of data
are available, mainly in the winter (January–February–March) with low coverage. The MODIS 2000
data point includes data from April to December.
Decadal time series: ATSR and MODIS (1995–2015)
The combined ATSR-2 and AATSR AOD data set provides a continuous record of
17 years from 1995 to 2012. The ATSR time series, for yearly
averaged AOD over mainland China as defined in Fig. 1, is presented in
Fig. 12. The first two data points, for 1995 and 1996, miss the winter and
spring months and may therefore be not fully representative. This time series
shows a strong initial increase in the AOD by 50 %, from about 0.18 in
1995 to 0.27–0.28 in 2001 and 2003 but with a dip in 2002. It is noted that
2002 is the transition between ATSR-2 and AATSR, and the data point presented
in Fig. 12 is the average of 7 months (January–July) of ATSR-2 AOD and
5 months (August–December) of AATSR AOD.
This initial increase is followed by a strong decrease in 2004 after which
the AOD seems to increase to a plateau of about 0.27 in 2006–2008. In 2009
and 2010, the AOD is lower by about 0.03–0.04 and then increases again to
about 0.27 in 2011. The interruption of the ATSR time series after 2011 is
unfortunate because this year could be a tipping point where the AOD starts
to decrease, as shown by Zhao et al. (2017) for east and central China.
Therefore, to visualize the evolution of AOD over all of China beyond the ATSR
era, the MODIS/Terra time series has been added in Fig. 12. It is noted that
the first MODIS data point for 2000 may not be fully representative since the
first 3 months are missing.
Clearly, as described above, there are differences between ATSR and
MODIS/Terra AOD and the MODIS/Terra yearly averaged AOD is 0.1–0.2 higher
than that of ATSR. However, as Fig. 12 shows, the two curves show similar
features with minima and maxima in the same years. The offset between the two
data sets is similar for most of the period 2003–2011. Hence, in spite of
this offset, the temporal behavior of the AOD is well represented by both
data sets, for the overlapping period. The clear advantage of using both data
sets is that ATSR provides additional information on the pre-EOS (Earth Observing System) period,
showing the initial increase of the AOD before 2000. MODIS/Terra complements
the time series for the post-Envisat period, showing a decrease after 2011.
The observed AOD variations may be in response to the enforcement of policies
to reduce emissions, but they may also be caused by meteorological
influences or a combination of these and other factors. The initial growth
between 1995 and 2001 reflects urbanization and economic growth (e.g., Hao and
Wang, 2005) and was also reported by Guo et al. (2011) using TOMS-retrieved
AOD and by Hsu et al. (2012) using SeaWiFS. The initial decline after 2003
may be due to emission control measures such as those implemented in Beijing
and reducing atmospheric concentrations of PM10, SO2 and
NO2 (Hao and Wang, 2005). The AOD minimum in 2002 is observed in both
the ATSR and MODIS/Terra data in Fig. 12, but the Multi-angle Imaging Spectroradiometer (MISR) AOD over the NCP
presented by Liu et al. (2013) shows a clear maximum. Liu et al. (2013) relate the
variability in the AOD over the NCP to large-scale periodic climate
variability modulated by the El Niño–Southern Oscillation (ENSO) with a
period of 3–4 years.
Several authors identify a pivot point around 2006–2008 (Kang et al., 2016;
Zhang et al., 2017; Zhao et al., 2017) after which the AOD fluctuates before
the decrease sets in from 2011 (Zhao et al., 2017). Using a different
analysis method, Zhang et al. (2017) suggest a decrease starting from about
2006–2008. It is noted that these studies were made for east or SE China
with much higher AOD, as shown in Figs. 2 and 3, than those in Fig. 12 which
are averages over all of China. The data in Fig. 12 show a decrease in the MODIS
data for the period 2006–2009, as observed by Zhang et al. (2017), but the
ATSR AOD does not show a clear decrease in that period. The decrease in both
data sets in 2008–2009 could be the result of the economic recession as
suggested by, e.g., Lin et al. (2010), He et al. (2016) and Zhao
et al. (2017). The data in Fig. 12 do however suggest the onset of a decrease
in 2011, confirming the conclusion by Zhao et al. (2017). This behavior with
pivot points in 2006 and 2011 is in line with the reduction of emissions of
aerosol precursor gases, such as SO2 and NO2 (van der
A et al., 2017), possibly together with large-scale climate variability as
discussed above. The increase/decrease of anthropogenic particles is expected
to increase/decrease the water uptake from the aerosols and hence the
recorded AODs (Pozzer et al., 2015).
Discussion
Satellite data have been used to provide a 3-D aerosol climatology over
mainland China for two decades (1995–2015), describing the spatial variation
of the column-integrated extinction, or AOD, by combining ATSR-2, AATSR (both
using the most recent data set produced by the ADV v2.31 algorithm) and
MODIS/Terra C6 merged DTDB AOD data. The vertical dimension has been provided by
the CALIOP extinction measurements since 2007 and the temporal variation has
been provided by the time series of the yearly AOD. Inspection of the AOD
data from different sources shows strong differences due to the failure of
retrieval algorithms to deal with very bright surfaces, except for the MODIS
DB algorithm which was designed for this purpose. However, DB and DT are
complementary because DT performs better than DB over dark surfaces and the
merged DTDB provides better coverage. Although the ATSR dual view algorithm
was designed to eliminate the effect of surface reflectance on the radiances
measured at TOA, it does not work well over very bright surfaces (Flowerdew
and Haigh, 1995; Kolmonen et al., 2016). The differences between the ATSR- and
MODIS/Terra-retrieved AODs have been addressed in detail by validation and
evaluation of the individual AOD data sets vs. a reference data set provided
by AERONET. This study presents the first extensive validation of MODIS/Terra
C6 merged DTDB and ATSR v2.31 AOD data over China. The results show that both
data sets are of high quality. ATSR slightly underestimates and MODIS/Terra
C6 slightly overestimates AOD, with respect to AERONET AOD, resulting in an
overall higher MODIS than ATSR AOD and the difference increases as AOD is
larger. We have no explanation for this behavior and a more detailed study
on the differences between the MODIS and ATSR algorithms is beyond the scope
of the present study. Likely, they are due to the choice of the aerosol models
and/or cloud screening, while also calibration issues may influence the
results. The data in Fig. 10 are collocated MODIS/Terra, ATSR and AERONET
data, and it is unlikely that the results from these instruments together,
with different cloud-screening criteria, are cloud contaminated. MODIS, with
its wider swath than ATSR, provides a much larger data sample and has global
coverage on a nearly daily basis. Hence, MODIS might also provide a
statistically better sample but the metrics shown in Figs. 7 and 8 are
similar, except that ATSR underestimates by 0.07 and MODIS overestimates by a
similar amount. It is noted that differences in the AOD value such as shown
here between ATSR and MODIS are also observed between the two MODIS
instruments (with a 4 h difference in overpass time which may influence the
results) and between MODIS and MISR (Zhao et al., 2017; Zhang et al., 2017).
MISR flies on Terra and has a swath width which is only a little smaller than
that of ATSR. The comparison of the ATSR and MODIS/Terra AOD data sets over
China with AERONET does not show a clear advantage for one of these, and
statistically the validation results are similar in spite of differences in
AOD values. This conclusion applies to areas where both ATSR and MODIS
provide quality data, and hence over the bright surfaces in western China,
such as the Taklamakan Desert, the ATSR data cannot be used. Further
development is needed to account for the surface effects on the ATSR TOA
radiance. In conclusion, the MODIS/Terra and ATSR AOD values are different, but
there is no clear preference, in regard to data quality, for one or the other.
In view of the slight overestimation of MODIS and the slight underestimation
of ATSR, the complimentary use of the AOD retrieved from these instruments
may provide added value for, e.g., data assimilation in chemical transport
models.
The spatial distribution and the temporal behavior of the ATSR and MODIS AOD
data sets show similar features, with similar covariation in time and space.
The spatial AOD distribution is a 20-year climatology which updates and
extends earlier climatologies derived from MODIS C5.1 data, for different
periods, usually starting in about 2000 (Terra; e.g., Guo et al., 2016b; Luo
et al., 2014) or 2002 (Aqua; e.g., He et al., 2016). The 3-D climatology by
Guo et al. (2016b) focuses on the frequency of occurrence of aerosols over
China for 2006–2014. MODIS C5.1 publications on AOD over China usually
address certain aspects including, e.g., regional studies over SE China or
over regions such as BTH (e.g., Li et al., 2007; Tang et al., 2016), YRD
(e.g., Li et al., 2015; Kang et al., 2016) or PRD (e.g., Bilal et al., 2017),
differences between DT- and DB-retrieved AOD over certain regions or
validation. However, none of them present an overview for all of China, or even
all of eastern China, and addresses differences between different regions.
CSHNET (Y. Wang et al., 2011) or CARSNET (Che et al., 2015) do provide data
all over China; however, these are point measurements with low coverage over
rural areas. Satellite data fill these gaps but with lower temporal
resolution. As briefly discussed in Sect. , some distinct differences
in the AOD seasonal behavior can be observed over different parts of China,
i.e., from south to north and from east to west.
The AOD time series presented in Sect. are for all of China, including
both the relatively clean western China and the relatively polluted SE and SW
China. Clearly, this is not a good representation of differences of
emission abatement policy for either aerosols (e.g., Zhao et al., 2017) or
precursor gases (e.g., van der A et al., 2017) with a complicated effect on
AOD (Lin et al., 2010) and their effects on different parts of
industrialized China. In addition, the evolution of the AOD is not only
determined by policy and economic development but also by the evolution of
living standards and migration of people in China, such as urbanization and
development of agriculture, which may be different across the country.
Furthermore, different sources influence the aerosol content in different
parts of China and in different seasons, i.e., dust emission in the west in
the spring, biomass burning in the summer/autumn seasons in eastern China and
the emissions of VOCs from vegetated areas (e.g., Tan et al., 2015). The
effects of these emissions are augmented by the meteorological conditions
which also vary by region and season (e.g., Ding and Murakami, 1994; Domros
and Peng, 1988; Song et al., 2011; Jiang et al., 2015) and large-scale
periodic climate variability (e.g., Liu et al., 2013).
Conclusions
Two decades of satellite-derived aerosol optical properties provide an
extended aerosol climatology over China (1995–2015), using the most recent
retrieval results. The analysis of the data from different sensors shows
the following:
The MODIS/Terra C6 merged DTDB AOD over China is distinctly higher than that
retrieved from ATSR using the ADV v2.31 algorithm and the difference
increases with increasing AOD.
Validation of both data sets over China shows that both MODIS/Terra C6 DTDB
and ATSR ADV v2.31 AOD compare well with AERONET reference data but MODIS
slightly overestimates and ATSR slightly underestimates with respect to the
AERONET AOD.
AOD time series for ATSR and MODIS AOD show similar features and, although
with a substantial offset, they provide complimentary information in regard
to the AOD increase in the late 1990s (pre-EOS) and the apparent decrease after
the end of the Envisat mission in April 2012.
Seasonal variations in the AOD are evident and vary for different parts of China.
The regional variation of seasonality and long-term behavior of the AOD over
China will be discussed in Sogacheva et al. (2018).
The ATSR data used in this paper are publicly
available (after registration, a password will be issued) at
http://www.icare.univ-lille1.fr/. MODIS data are publicly available at
https://ladsweb.modaps.eosdis.nasa.gov/ and CALIOP data are available
via the LIVAS NetCDF database (after obtaining log-in credentials) at
ftp://lidar.space.noa.gr. A technical description of the LIVAS database
is available under http://lidar.space.noa.gr:8080/livas/. A brief
description of the product can be found in the recently published article in
ACP:
(http://www.atmos-chem-phys.net/15/7127/2015/acp-15-7127-2015.html).
AERONET data are available at AERONET: https://aeronet.gsfc.nasa.gov/.
The supplement related to this article is available online at: https://doi.org/10.5194/acp-18-1573-2018-supplement.
Acknowledgements
Work presented in this contribution was undertaken as part of the MarcoPolo
project supported by the EU, FP7 SPACE grant agreement no. 606953 and as part
of the Globemission project ESA-ESRIN Data Users Element (DUE), project
AO/1-6721/11/I-NB, and contributes to the ESA/MOST DRAGON4 program. The ATSR
algorithm (ADV/ASV) used in this work is improved with support from ESA as
part of the Climate Change Initiative (CCI) project Aerosol_cci (ESA-ESRIN
projects AO/1-6207/09/I-LG and ESRIN/400010987 4/14/1-NB). Further support
was received from the Centre of Excellence in Atmospheric Science funded by
the Finnish Academy of Sciences Excellence (project no. 272041). Emmanouil
Proestakis acknowledges the Stavros Niarchos Foundation for its support. Many
thanks are expressed to NASA Goddard Space Flight Center (GSFC) Level 1 and
Atmosphere Archive and Distribution System (LAADS)
(http://ladsweb.nascom.nasa.gov) for making available the L3
MODIS/Terra C5.1 and C6 aerosol data. We thank the reviewers of this paper
for their valuable comments which helped improve the
manuscript. Edited by: Stelios
Kazadzis Reviewed by: Andrew Sayer and two anonymous referees
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