ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus GmbHGöttingen, Germany10.5194/acp-15-13113-2015The regime of aerosol asymmetry parameter over Europe, the Mediterranean
and the Middle East based on MODIS satellite data: evaluation against surface
AERONET measurementsKorras-CarracaM. B.HatzianastassiouN.nhatzian@cc.uoi.grhttps://orcid.org/0000-0001-6119-1793MatsoukasC.GkikasA.https://orcid.org/0000-0002-4137-0724PapadimasC. D.Department of Environment, University of the Aegean, 81100
Mytilene, GreeceLaboratory of Meteorology, Department of Physics,
University of Ioannina, 45110 Ioannina, GreeceEarth Sciences Department, Barcelona Supercomputing
Center, Barcelona, SpainN. Hatzianastassiou (nhatzian@cc.uoi.gr)26November20151522131131313210May20145September201411September20151October2015This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://www.atmos-chem-phys.net/15/13113/2015/acp-15-13113-2015.htmlThe full text article is available as a PDF file from https://www.atmos-chem-phys.net/15/13113/2015/acp-15-13113-2015.pdf
Atmospheric particulates are a significant forcing agent for the radiative
energy budget of the Earth–atmosphere system. The particulates' interaction
with radiation, which defines their climate effect, is strongly dependent on
their optical properties. In the present work, we study one of the most
important optical properties of aerosols, the asymmetry parameter
(gaer), over sea surfaces of the region comprising North Africa, the
Arabian Peninsula, Europe, and the Mediterranean Basin. These areas are of
great interest, because of the variety of aerosol types they host, both
anthropogenic and natural. Using satellite data from the collection 051 of
MODIS (Moderate Resolution Imaging Spectroradiometer, Terra and Aqua), we
investigate the spatiotemporal characteristics of the asymmetry parameter.
We generally find significant spatial variability, with larger values over
regions dominated by larger size particles, e.g., outside the Atlantic coasts
of northwestern Africa, where desert-dust outflow takes place. The
gaer values tend to decrease with increasing wavelength, especially
over areas dominated by small particulates. The intra-annual variability is
found to be small in desert-dust areas, with maximum values during summer,
while in all other areas larger values are reported during the cold season
and smaller during the warm. Significant intra-annual and inter-annual
variability is observed around the Black Sea. However, the inter-annual
trends of gaer are found to be generally small.
Although satellite data have the advantage of broad geographical coverage,
they have to be validated against reliable surface measurements. Therefore,
we compare satellite-measured values with gaer values measured at
69 stations of the global surface AERONET (Aerosol Robotic Network), located
within our region of interest. This way, we provide some insight on the
quality and reliability of MODIS data. We report generally better agreement
at the wavelength of 860 nm (correlation coefficient R up to 0.47), while
at all wavelengths the results of the comparison were better for spring and
summer.
Introduction
Atmospheric aerosol particles interact with radiation, mainly the short wave
(SW or solar) part of the spectrum, modifying the energy budget of the
Earth–atmosphere system. The aerosol effect is either direct (through the
scattering and absorption of solar radiation), thus reducing the incoming
solar radiation flux at the surface, indirect (through the modification of
cloud properties), or semi-direct (due to the absorption of solar radiation
and consequent modification of the atmospheric temperature profile,
convection, and cloud properties) (e.g., Graßl, 1979; Hansen et al., 1997;
Lohmann and Feichter, 2005).
The interaction of particles with the solar flux, which defines their
climate role, strongly depends on their optical properties (Hatzianastassiou
et al., 2004, 2007), which cannot be covered globally by surface in situ
measurements. Besides the aerosol optical depth (AOD), one of the most
important optical properties of atmospheric particles, which is used in
radiative transfer, climate, and general circulation models, is the
asymmetry parameter (gaer). The asymmetry parameter describes the
angular distribution of the scattered radiation and determines whether the
particles scatter radiation preferentially to the front or back. The
globally available satellite-based AOD data are considered to a great extent
as reliable and adequate, due to significant developments in surface and
satellite measurements during the last 2 decades, and particularly the
arrival of MODIS in 2000, which is regarded as one of the most reliable
data sets (Bréon et al., 2011; Nabat et al., 2013). On the other hand,
despite the important role of the asymmetry parameter, relevant global
coverage data are measured only for the few last years, or are available in
long-term aerosol climatologies such as the Global Aerosol Data Set (GADS,
Koepke et al., 1997) and Max Planck Aerosol Climatology (MAC, Kinne et al.,
2013). Even so, asymmetry parameter data are usually examined for regions
with limited geographical extent and temporal coverage (Di Iorio et al.,
2003), without intercomparison between alternative data platforms.
The goal of the present work is the study of the spatiotemporal distribution
of the aerosol asymmetry parameter, using the most recent data from MODIS
(Moderate Resolution Imaging Spectroradiometer, collection 051). Emphasis is
given to the comparison between the provided MODIS data and respective
reliable surface measurements of the global AERONET, in order to gain
insight on the quality of the former.
For this study we focus on the region defined by latitudes 5 to
70∘ N and longitudes 25∘ W to 60∘ E, including
North Africa, the Arabian Peninsula, Europe, and the greater Mediterranean
Basin (Fig. 1). This area is selected because it is of particular scientific
interest due to the simultaneous presence of a variety of particles, both
natural and anthropogenic (e.g., desert dust, marine, biomass burning,
anthropogenic urban/industrial pollution) as shown in previous studies
(Lelieveld et al., 2002; Smirnov et al., 2002b; Sciare et al., 2003; Pace et
al., 2006; Lyamani et al., 2006; Gerasopoulos et al., 2006; Engelstaedter et
al., 2006; Satheesh et al., 2006; Kalivitis et al., 2007; Rahul et al., 2008;
Kalapureddy et al., 2009; Alonso-Pérez et al., 2012; Zuluaga et al.,
2012; Kischa et al., 2014) which makes this area ideal for aerosol studies.
The presence of a variety of aerosols in the area is due to the fact that two
of the largest deserts of the planet are partly included in our area of
interest, i.e., the Arabian desert and the Sahara, while one finds also
significant sources of anthropogenic pollution from urban and industrial
centers, mainly in the European continent. Moreover, our area of interest and
primarily its desert areas are characterized by a large aerosol load (large
optical depth, Remer et al., 2008; Ginoux et al., 2012). In addition,
significant regions in this area, more specifically the Mediterranean Basin
and North Africa, are considered climatically sensitive, since they are
threatened by desertification (IPCC, 2007, 2013). Finally, one more reason
for the selection of study area is that the present study complements
previous ones made by our team (e.g., Papadimas et al., 2008, 2012;
Hatzianastassiou et al., 2009), analyzing other key aerosol optical
properties, namely AOD for the same region. This is the first study (to our
knowledge) that focuses on asymmetry parameter over a geographically extended
area, while at the same time compares satellite with ground-station data.
The study region (5–70∘ N, 25∘ W–60∘ E)
and the location of 69 AERONET stations used for validation of MODIS
satellite aerosol asymmetry parameter (gaer) data. Solid red
circles denote stations located in Europe and hollow red circles are stations
in Africa, the Middle East and the Arabian Peninsula. Also shown are seven
sub-regions selected for studying the seasonal variation of
gaer.
Data
Before presenting the data used in this study, a short introduction of the
parameter studied is given here for readers more or less unfamiliar with it.
The asymmetry parameter (or factor) is defined by
g=ω¯13=12∫-11PcosΘcosΘdcosΘ,
where P is the phase function, which represents the angular distribution of
the scattered energy as a function of the scattering angle Θ and it
is defined for molecules, cloud particles, and aerosols. The phase function
can be expressed using the Legendre polynomials ω¯l (see
Liou, 2002) and ω¯1 in Eq. (1) stands for l=1. The
asymmetry parameter is the first moment of the phase function, and it is an
important parameter in radiative transfer. For isotropic scattering, g equals zero, which is the case for Rayleigh molecular scattering. The
asymmetry parameter increases as the diffraction peak of the phase function
sharpens. For Lorenz-Mie type particles, namely for aerosols and cloud
droplets, the asymmetry parameter takes positive values denoting a relative
strength of forward scattering, with values increasing with particle size.
It can also take negative values if the phase function peaks in backward
directions (90–180∘). The phase function and its simple
expression, the asymmetry parameter, along with the extinction coefficient
(or equivalently the optical depth) and the single scattering albedo,
constitute the fundamental parameters that drive the transfer of diffuse
intensity (Joseph et al., 1976) and are used in modeling. Hence, the
importance of aerosol asymmetry parameter is easily understood for enabling
computations of aerosol radiative properties and effects (e.g., forcings).
In this work, we use MODIS aerosol asymmetry parameter (gaer) data,
which we compare with in situ measurements at AERONET stations. We provide a
detailed description of the utilized data in the following sections.
Satellite MODIS Terra and Aqua data
MODIS is an instrument (radiometer) placed on the polar-orbiting satellites
of NASA (National Aeronautics and Space Administration) Terra and Aqua, 705 km from the Earth, in the framework of the Earth Observing System (EOS)
program. Terra was launched on 18 December 1999, while Aqua was launched
on 4 May 2002. The two satellites are moving on opposite directions, and
their equatorial crossing times are at 10:30 (Terra) and 13:30 (Aqua). MODIS
is recording data in 36 spectral channels between the visible and the
thermal infrared (0.44–15 µm), while its swath width is of the order
of 2330 km, which results in almost full planetary coverage on a daily
basis.
Aerosol properties are monitored in seven spectral channels between 0.47 and
2.13 µm and final results are derived through algorithms developed for
aerosol quantities both over land and ocean (Kaufman et al., 1997; Tanré
et al., 1997; Ichoku et al., 2002; Remer et al., 2005). MODIS data are
organized in “collections” and “levels”. Collections comprise data
produced by similar versions of the inversion algorithms, with the recent
collection “051” including also outputs from the “Deep Blue” algorithm.
Levels are characterized by data of different quality analysis and spatial
resolution.
In this study we use daily MODIS data for the asymmetry parameter
(gaer) provided on an 1∘× 1∘ grid (namely 100 km × 100 km), from Collection 051, Level 3. These data were measured at wavelengths
470, 660, and 860 nm, only over oceanic regions, since they were derived
through the algorithm “Dark Target” over ocean. These wavelengths were
selected in order to match as much as possible those of the available
corresponding AERONET gaer product (see Sect. 2.2). The period of
analysis stretches from 24 February 2000 to 22 September 2010 for MODIS-Terra and from
4 July 2002 to 18 September 2010 for MODIS-Aqua.
The MODIS C051 gaer data are a derived product of the MODIS algorithm
over ocean. This MODIS algorithm (http://modis.gsfc.nasa.gov/data/atbd/atbd_mod02.pdf, Remer
et al., 2006) retrieves as primary products the AOD at 550 nm, the fine-(mode) weighting (FW, also known as fraction of fine-mode aerosol type, FMF)
and the Fine (f) and Coarse (c) modes used in the retrieval, along with the
fitting error (ε) of the simulated spectral reflectance. The
algorithm reports additional derived parameters, such as the effective
radius (re) of the combined size distribution, the spectral total, fine
and coarse AODs or the columnar aerosol mass concentration. Among them,
gaer is also derived and reported at seven wavelengths: 470, 550,
660, 860, 1200, 1600 and 2120 nm. The derived parameters are calculated
(Levy et al., 2013) from information contained within the look-up table
(LUT) and/or other retrieved products. For example, knowing the resulting
total AOD and FMF, and which aerosol types were selected (or assumed), one
can go back to the LUT, and recover additional information about the
retrieved aerosol, such as the gaer. Hence, it should be noted that the
derived gaer product is dependent on the used aerosol models (modes),
since the algorithm is based on an LUT approach, assuming that one fine and
one coarse lognormal aerosol modes can be combined with appropriate
weightings to represent the ambient aerosol properties over the target
(spectral reflectance from the LUT is compared with MODIS-measured spectral
reflectance to find the “best” – least squares – fit, which is the
solution to the inversion). In the C051 algorithm there are four fine modes
and five coarse modes, for which the spectral (at the aforementioned seven wavelengths) aerosol asymmetry parameter values are given in Remer et al. (2006).
We also used Level 3 daily Ångström exponent data from MODIS-Aqua
C051, and also spectral aerosol optical depth data from MODIS-Aqua C006
data sets, from which we computed C006 Ångström exponent. These data
were used to assess the validity of gaer data and their temporal
tendencies, as discussed in Sect. 3.2.3.
Ground-based AERONET data
AERONET (Aerosol Robotic Network) is a global network of stations focused on
the study of aerosol properties. AERONET currently encompasses about 970
surface stations (number continuously evolving) equipped with sun
photometers of type CIMEL Electronique 318 A (Holben et al., 1998), which
take spectral radiation flux measurements.
The optical properties of aerosols are extracted through the application of
inversion algorithms (Dubovik and King, 2000). Data are provided on three
levels (1.0, 1.5, and 2). In the present work, we use the most reliable
cloud-screened and quality-assured Level 2 data. AERONET calculates the
asymmetry parameter at wavelengths 440, 675, 870, and 1020 nm. We employ
daily Level 2 asymmetry parameter data from 69 stations (Fig. 1) contained
in our study area (North Africa, the Arabian Peninsula, Europe). We choose only
coastal stations in order to maximize the coexistence of satellite marine
gaer data with surface data. Also, in order to compare corresponding
data between the satellite and station platforms, we perform comparisons only
for 440, 675, and 870 nm.
Satellite-based resultsGeographical distributions
The spatial distribution of annual mean values of gaer is given in Fig. 2 separately at the wavelengths 470, 660 and 860 nm. The values are averages
over the common period between Terra and Aqua, namely 4 July 2002 until 18 September 2010. A significant spatial variability is evident, with
MODIS-Terra values varying within the ranges 0.63–0.76, 0.57–0.75, and
0.55–0.74, at 470, 660 and 860 nm, respectively. The results exhibit a
decreasing tendency of gaer with increasing wavelength, consistent with
the theory. Similar results are also obtained from MODIS-Aqua, but with
slightly smaller values than Terra by up to 0.02 on average. More
specifically, the corresponding ranges of wavelengths are 0.63–0.75, 0.57–0.73, and 0.55–0.73. The smaller Aqua than Terra gaer values
could be attributed to smaller sizes of aerosols in midday than morning,
corresponding to passages of Aqua and Terra, respectively, associated with
lower relative humidity values and shrinking of aerosol particles. It should
be reminded that the ability of atmospheric aerosol to absorb water affects
the particle size (hygroscopic growth), as described by Köhler theory in
the early 20th century. It is also well known that relative humidity
significantly affects aerosol optical properties (e.g., Pilinis et al., 1996;
Kondratyev, 1999), namely AOD, single scattering albedo and gaer, by
modifying the aerosol liquid water content, size and hence extinction
coefficient and refractive indices.
In general, the largest gaer values (deep red colors) are observed off
the coasts of West Africa (eastern tropical Atlantic Ocean) at all three
wavelengths. High values are also found over the Red and Arabian seas. These
high values are due to strong dust outflows from the Saharan and Arabian
deserts carrying out coarse aerosol particles (Prospero et al., 2002;
Alonso-Pérez et al., 2012; Miller et al., 2008) and causing strong
forward scattering. Nevertheless, the Persian Gulf region, which is
surrounded by deserts, is characterized by relatively smaller gaer
values. More specifically, values as small as 0.69 (MODIS-Terra) and 0.67
(MODIS-Aqua) are observed in this region at 470 nm, while at the longer
wavelengths (660, 860 nm) the smallest values are equal to 0.66 (Terra) and
0.64 (Aqua). The smaller gaer values over the Persian Gulf can be
attributed to the presence of fine aerosols, which is corroborated by the
low effective radius and large fine-fraction measurements by MODIS over the
Persian Gulf, compared to neighboring areas (not shown here). These fine
particles originate from the industrial activities in the Gulf countries
related to oilfields or refineries (Goloub and Arino, 2000; Smirnov et al.,
2002a, b; Dubovik et al., 2002).
Geographical distribution of MODIS-Terra (-a, left column)
and MODIS-Aqua (-b, right column) gaer values averaged over
2002–2010, at the wavelengths of
470 nm (i-, top row), 660 nm (ii-, middle row) and 860 nm (iii-, bottom row).
The high gaer values over the northeastern tropical Atlantic Ocean as
well as west of the Iberian coasts are possibly related with the presence of
coarse sea salt particles. On the other hand, the asymmetry parameter takes
clearly smaller values over the Black Sea, where according to MODIS-Terra,
values vary between 0.63 and 0.7 at 470 nm, 0.57 and 0.67 at 660 nm, and 0.55 and
0.66 at 860 nm, with the smallest values appearing over the Crimean
Peninsula (corresponding maximum Aqua values are smaller by 0.02). The small
Black Sea gaer values can be associated with industrial activities as well as biomass burning activities in nearby countries. A region of special interest
is the Mediterranean Basin since it hosts a large variety of aerosols like
anthropogenic, desert dust or sea salt (e.g., Barnaba and Gobbi, 2004). The
MODIS results over this region show relatively small gaer values,
secondary to those of the Black Sea, characterized by an increase from north to
south, which is more evident at 660 and 860 nm. More specifically, based on
MODIS-Terra, gaer over the Mediterranean takes values from 0.68 to 0.74
at 470 nm, while at 670 and 860 nm it ranges from 0.64 to 0.73 and 0.62 to
0.72, respectively. According to MODIS-Aqua the gaer values are
slightly smaller again. The observed low values in the northern parts of the
Mediterranean are probably associated with the presence of fine
anthropogenic aerosols transported from adjacent urban and industrial areas
in the north, especially in central Europe. In contrast, the higher
gaer values in the southern Mediterranean, particularly near the North
African coasts, can be explained by the proximity to the Sahara desert and
the frequent transport of significant amounts of coarse dust (e.g., Kalivitis
et al., 2007; Hatzianastassiou et al., 2009; Gkikas et al., 2009, 2011, 2014).
The spatial distributions of climatological monthly mean gaer values
from MODIS-Aqua at 470 nm reveal significant differences in the range and
the patterns of the seasonal variability, depending on the area (Fig. 3).
Thus, in tropical and sub-tropical areas of the Atlantic Ocean (up to about
30∘ N), where dust is exported from the Sahara, gaer keeps high
values throughout the year, which reach or even exceed 0.74 locally. Over
the regions of Arabian and Red seas and the Gulf of Aden, which also
experience desert dust transport, larger gaer values appear in the
period from March to September, with a maximum in August (locally as high as
0.75–0.76). This seasonal behavior is in line with intra-annual changes of
dust production over the Arabian Peninsula indicated by MODIS
Ångström exponent (AE) and Deep Blue aerosol optical depth data
(Ginoux et al., 2012), as well as over southwest Asia through in situ data
(Rashki et al., 2012), aerosol index from various platforms and MODIS Deep
Blue AOD data (Rashki et al., 2014). Indeed, the production of dust there is
relatively poor in winter, increases in March and April and becomes maximum
in June and July (Prospero et al., 2002). Over the Arabian Sea, it is known
that large amounts of desert dust are carried out during spring and early
summer (Prospero et al., 2002; Savoie et al., 1987; Tindale and Pease, 1999;
Satheesh et al., 1999). Nevertheless, according to MODIS, the seasonal
variability of gaer remains relatively small there in line with a small
seasonal variability in MODIS Deep Blue AE data (results of our analysis,
not shown here). This can be explained by the presence of sea salt coarse
particles throughout the year, with which dust particles co-exist.
A greater seasonal variability exists over the Persian Gulf, where gaer
values are higher during spring and in particular in summer (up to 0.74 at
470 nm according to Aqua), and lower in autumn and winter (area-minimum
values smaller than 0.65). This seasonal behavior can be explained taking
into account the meteorological conditions over the greater area of the
Gulf; mainly in spring and summer, dry northwestern winds (Shamal) blow, carrying desert dust from the arid areas of Iraq (Smirnov et al., 2002a, b; Kutiel and Furman, 2003). The transport of dust is
gradually decreased in autumn and reaches its minimum in winter. When the
presence of desert dust is limited, a significant fraction of total aerosol
load in the region consists of fine anthropogenic particles (Smirnov et al., 2002a, b), which can explain the observed relatively small gaer values
in autumn and winter.
Month by month variation of MODIS-Aqua gaer values at
470 nm averaged over the period 2002–2010.
In the Mediterranean Basin, gaer exhibits a relatively small seasonal
variation, with lower values tending to appear in summer, in line with the
presence of fine anthropogenic or biomass burning aerosols in the area,
transported from the Balkans or central Europe (Hatzianastassiou et
al., 2009). On the contrary, over the Black Sea, a clear seasonal cycle is
apparent, with higher values in the cold period of the year and smaller in
the warm one. More specifically, according to MODIS-Aqua, the values at 470 nm drop down to 0.61 in summer months whereas they reach 0.7 in January and
December. This seasonality is in agreement with the summer biomass burning
from agricultural activities and wildfires (Barnaba et al., 2011; Bovchaliuk
et al., 2013), and the resulting abundance of fine particles.
It is also interesting to look at the geographical distribution of monthly
gaer values in latitudes higher than 50∘ N, for which annual
mean values were not given in Fig. 2 because of unavailability of data for
all months. Off-shore northern France (English Channel) and Germany the
asymmetry parameter has small seasonally constant values (note that data do
not exist for January and February). In these areas, the aerosol load
consists mainly of anthropogenic polluted particles, which explains the
small gaer values.
In the Baltic Sea (values available from March to October) gaer shows a
significant spatial and temporal variability. More specifically, it is small
during summer, whereas it increases, locally up to more than 0.7, in March
and October. The smaller summer values can be explained by the presence of
fine aerosols in the Baltic Sea originating from forest fires in Europe and
Russia (Zdun et al., 2011). On the contrary, in autumn the local aerosol
loading consists largely of coarse marine aerosols. It is also important to
note that the Baltic Sea hosts significant amounts of anthropogenic
industrial and urban aerosols throughout the year, but especially in summer
(Zdun et al., 2011).
In the higher latitudes of Atlantic Ocean, where the presence of maritime
aerosols is dominant, we note a remarkable month by month variation of
asymmetry parameter, with low values in summer (values up to 0.59) against
high values (up to 0.75–0.77) in spring (March, April) and autumn (October).
This difference is possibly explained by the seasonal variability of aerosol
size in the northern Atlantic. Apart from the presence of coarse sea salt
throughout the year, in spring and summer small particles are formed through
photochemical reactions of dimethylsulfide (DMS) emitted by phytoplankton
decreasing the aerosol size. Moreover during summer fine anthropogenic
aerosols are transported in the region from North America (Yu, 2003;
Chubarova, 2009). These result in lower gaer values between May and
August.
Based on MODIS-Terra, the patterns of spatial distribution are generally the
same with Aqua, with slightly larger gaer values. At larger wavelengths
(660, 860 nm) a decrease of gaer is observed, especially for its
smallest values. Further details and an overall picture are given in Sect. 3.2.1 which deals with climatological monthly mean values not at the pixel
but at the regional level.
Intra-annual variation of MODIS, Terra (-a, left column) and
Aqua (-b, right column), gaer values averaged over seven selected sub-regions (Fig. 1).
Results are given for gaer values at 470 nm (i-, top row), 660 nm
(ii-, middle row) and 860 nm (iii-, bottom row), averaged
over the period 2002–2010, respectively.
Temporal variabilitySeasonal variability
In order to provide an easier assessment of the seasonal cycle of aerosol
asymmetry parameter and its changes from one region to another, but also
among the different wavelengths (470, 660 and 860 nm), the study region was
divided into seven smaller sub-regions (see Fig. 1). The average values of monthly
mean climatological (2002–2010) data of the pixels found within each sub-region's
geographical limits have been computed and are given in Fig. 4, for every
wavelength, both for Terra and Aqua. It appears that the seasonal cycle
differs between the sub-regions, as it has already been shown in the
geographical map distributions discussed in the previous section.
Slope (in units decade-1) of MODIS gaer
de-seasonalized anomalies over the period 2002-2010 from MODIS-Terra
(-a, top) and MODIS-Aqua (-b, bottom), for the wavelengths
of 470 nm. Results are shown only if the trend is statistically significant
at the 95 % confidence level.
At 470 nm (Fig. 4i), the intra-annual variability of gaer is
greater over the Black Sea, where it is as large as 0.06 according to
MODIS-Terra and 0.05 according to MODIS-Aqua, the northeastern Atlantic Ocean
(0.04 and 0.05 for Terra and Aqua, respectively) and the seas of northern
Europe (0.05 for both Terra and Aqua). In these regions, there is a tendency
for smaller values during summer. More specifically, in the Black Sea the
smallest gaer value (0.64) is observed in June, over the seas of
northern Europe in July and over the northeastern Atlantic Ocean in August.
In these regions, the largest values appear in the cold period of the year.
Reverse seasonality with a large seasonal amplitude is observed over the
Persian Gulf, where the variability is as large as 0.08, according to both
MODIS-Terra and MODIS-Aqua. The seasonal cycle of gaer over the
Middle East exhibits a smaller range of variability (0.02 for MODIS-Terra and
0.03 for Aqua), with maximum values
in summer and minimum in winter. In the other two sub-regions (Mediterranean
and eastern Atlantic Ocean) the annual range of values is small
(< 0.02). It is noteworthy that in the Mediterranean Sea, there is a
weak tendency of appearance of double maxima in winter and spring. The spring
maximum should be associated with the presence of desert dust particles,
which are transported from Sahara, mainly in the eastern Mediterranean in
this season (e.g., Fotiadi et al., 2006; Kalivitis et al., 2007; Papadimas et
al., 2008, Gkikas et al., 2009, 2013; Hatzianastassiou et al., 2009). There
is also a similar transport of Saharan dust in the central and western
Mediterranean during summer and autumn (e.g., Gkikas et al., 2009, 2013), but
then the predominance is not so clear because of the co-existence of fine
anthropogenic aerosols. Regardless of the annual cycle, smaller
gaer values are clearly distinguished over the Black Sea and
northern European seas throughout the whole year.
Inter-annual (2002–2010) variation of monthly mean gaer
values at 470, 660, and 860 nm (in black, red and green colours, respectively), over the sub-regions of (i) Black Sea,
(ii) eastern Atlantic Ocean, (iii) Mediterranean Sea,
(iv) Middle East, (v) northeastern Atlantic Ocean,
(vi) northern Europe and (vii) Persian Gulf. Results are given
based on MODIS-Terra (-a, left column) and MODIS-Aqua (-b,
right column).
At 660 nm, the gaer values are lower than at 470 nm, in particular
over the Black Sea, northern Europe and the northeastern Atlantic, whereas the intra-annual
variability (range of gaer values) increases up to 0.10 (Terra) and
0.08 (Aqua) over the Black Sea. This increase is mainly attributed to the
reduction of summer values due to the strong appearance of fine aerosols in
this season. Also, at 660 nm, there is a clearer double annual variation of
gaer over the Mediterranean Sea than at 470 nm. At 860 nm the general
picture is similar to that of 660 nm though a further increase of month by
month variability is noticeable.
In general, our results indicate that over the regions characterized by a
strong presence of desert dust particles (eastern Atlantic and the Middle
East and the Mediterranean Sea) the annual range of variability of gaer is
smaller than in the other regions. An additional feature above regions with
desert dust is the smaller decrease of gaer values with increasing
wavelengths. This is attributed to the lower gaer spectral dependence
of coarse compared to fine particles (e.g., Dubovik et al., 2002; Jianrong et
al., 2011).
We should note that the MODIS-Terra and Aqua gaer seasonal cycles are
about similar but with generally greater Terra than Aqua values.
Inter-annual variability and changes
Figure 5 displays the geographical distribution of the slope of inter-annual
trend of gaer over the study region, as computed from the application
of the Mann–Kendall test to time series of de-seasonalized monthly anomalies
of gaer at 470 nm. Results are shown in units decade-1 for both
Terra and Aqua over their common time period, namely 2002–2010, only if
the trend is statistically significant at the 95 % confidence level. We
also performed the same analysis for the 660 and 860 nm (not shown), with
similar results to the 470 nm wavelength.
(a) Geographical distribution of MODIS-Aqua 051
Ångström exponent (AE550-865) values averaged over 2002–2010,
at the wavelength pair of 550–865 nm. Winter AE data are missing from the
northernmost areas, and therefore the long-term averages in (a) are
left blank. The correlation coefficients between AE550-865 and
gaer data at 660 and 860 nm are given in (b) and
(c), respectively.
(a) Geographical distribution of MODIS-Aqua C006
Ångström exponent (AE550-865) values averaged over 2002–2010,
at the wavelength pair of 550–865 nm. Winter AE data are missing from the
northernmost areas, and therefore the long-term averages in (a) are left
blank. In (b), (c) and (d) are given the
correlation coefficients, the absolute biases and the relative percent
biases, respectively, between the C006 and corresponding 051 AE550-865
data. In (e) and (f) are given the computed de-seasonalized
trends of MODIS Aqua 051 and C006 AE550-865) slope values for years
2002–2010, respectively.
In general, the estimated changes are relatively small. Terra produces widely
statistically significant positive trends, showing that during the period of
interest, the asymmetry parameter increased over the examined area, with very
few exceptions. The results from Aqua are statistically significant at
considerably fewer cells, while they give a few points with decreasing gaer. Based on Terra data, the
stronger increases are observed in the eastern and southern Black Sea, as
well as over the Baltic and Barents seas. According to MODIS-Aqua, negative
trends are found over few Atlantic Ocean cells. Both Aqua and Terra report
increases of gaer over the Persian Gulf, the Red Sea, southern
Black Sea, the eastern Mediterranean Sea, the coast of the Iberian Peninsula,
and some coastal areas of West Africa. The differences encountered between
the Terra and Aqua gaer trends may be attributed to the different
time of passage of each satellite platform carrying the same MODIS
instrument, given that everything else is the same. Nevertheless, they may
more probably be the result of calibration differences between the two MODIS
sensors. It is known that there is a degradation of MODIS sensor (Levy et
al., 2010; Lyapustin et al., 2014) impacting time series of MODIS products.
More specifically, it is also known that Terra suffers more than Aqua from
optical sensor degradation. These calibration issues are known to affect
MODIS AOD retrievals, producing an offset between Terra and Aqua, and they
are also expected to affect the aerosol asymmetry parameter, which is
probably more sensitive to such calibration uncertainties than AOD. In this
sense, the results of Fig. 5 shown here are not to be taken as truth but
rather they are given as a diagnostic of a problematic situation with MODIS
aerosol asymmetry parameter inter-annual changes. Such calibration issues are
expected to be addressed, at least partly, in the new Collection 006
products. Nevertheless, a preliminary comparison between MODIS Aqua C051 and
C006 Ångström exponent (AE), which is another common aerosol
parameter strongly dependent on size, using data for the 550–865 nm pair of
wavelengths spanning the period 2002–2010, does not reveal significant
modifications in geographical patterns of AE inter-annual changes. This puts
some confidence on the C051 gaer results given in the present
study. The results of this analysis are presented in detail in the next
Sect. 3.2.3.
The overall gaer changes of Fig. 5 may hide smaller timescale
variations of gaer, which are obtained by the time-series shown
in Fig. 6. Results are given for the seven sub-regions defined previously, at the
three different wavelengths and for Terra and Aqua separately. A general
pattern is the decrease of gaer values with increasing
wavelength, in particular from 470 to 660 nm. The largest month-to-month and
year-to-year variation is for the Black Sea (Fig. 6i). Relatively large
variability is also found in the sub-regions of NE Atlantic (6v), northern Europe (6vi) and the Persian Gulf (6vii). On the contrary, small variability
is noticed in the eastern Atlantic, where systematic dust outflows from
Sahara take place, leading to consistently high values of gaer.
There are also some other interesting patterns, like the significant drop of
gaer with wavelength in areas characterized by the presence of
fine aerosols, namely the Black Sea, northern Europe and the Persian Gulf (Fig. 6i, vi, vii, respectively). The specific patterns of inter-annual
changes of gaer are suggested by both Terra and Aqua, though a
slight overestimation by Terra is again apparent in this figure. The obtained
results of our analysis are meaningful and in accordance with the theory,
underlining the ability of satellite observations to reasonably capture the
gaer regime over the studied regions.
Possible uncertainties of MODIS aerosol asymmetry parameter
The MODIS aerosol asymmetry parameter is not a direct product of the MODIS
retrieval algorithm, but it is rather a derived by-product. Since this
parameter is dependent on aerosol modes used and relative weights (see
Sect. 2.1), it is understood that there can be uncertainties associated with
it. Therefore questions may arise about the validity of gaer and
their spatial and temporal patterns presented in the previous sub-sections.
Given that, as already mentioned, it is an aerosol optical parameter that is
valuable and highly required by radiative transfer and climate models, it is
worth assessing it through comparison against another more common aerosol
size parameter, namely the C051 MODIS Ångström exponent at the
550–865 nm wavelength pair (AE550-865) over ocean, which is an
evaluated MODIS aerosol size product (Levy et al., 2010) that is extensively
used in literature. Figure 7a, displays the geographical distribution of
long-term average AE for the whole study period, i.e., 2002–2010. In this
figure, the northernmost areas are blank because there are no data during
winter and a long-term average would be biased. The main geographical
patterns in Fig. 7a are in line with those of asymmetry parameter (Fig. 2).
For example, note the high AE values in the Black Sea (between about 1.3 and
1.8, yellowish-reddish colors), indicative of fine aerosols, the relatively
high values in the Mediterranean Sea (between about 0.7 and 1.2,
greenish-yellowish colors) and the low values (0.1–0.4, deep bluish colors)
off the western African coasts corresponding to exported Saharan dust. Over
the same areas, gaer takes inverse low and high values, for
example smaller than 0.65 over the Black Sea and larger than 0.7–0.75 off
the western African coasts (Fig. 2ii-b and iii-b), indicating the
predominance of fine and coarse aerosols respectively, in accordance with AE.
The consistency between gaer and AE data is shown by the strong
anti-correlation between the MODIS AE550-865 and gaer data
at 660 and 860 nm, shown in Fig. 7b and c, respectively. It should be noted
that correlation coefficients are computed from any available data pairs,
i.e., available data for both gaer and AE550-865 at a given
pixel and day. Note that there are no blank areas in Fig. 7b and c, in
contrast to Fig. 7a. There are both AE and gaer data for all
seasons except winter; therefore, correlation coefficients can be calculated
for these regions. Strong negative correlation coefficients, larger than 0.7
and 0.8 in Fig. 7b and c, respectively, relate inversely high
gaer values with low AE ones and vice-versa, over the same areas.
In both cases (Fig. 7b and c), the correlation is slightly higher over sea
areas characterized by the presence of fine aerosols (e.g., the Black Sea or
the Persian Gulf) and lower over seas undergoing frequent transport of coarse
dust particles (e.g., southern Mediterranean Sea, Arabian Sea, or Atlantic
Ocean off the western African coasts). The overall computed correlation
coefficient between gaer and AE is equal to -0.95 over the
Black Sea, -0.89 over the Mediterranean Sea, -0.87 and -0.94 over the
Arabian Sea and Persian Gulf, respectively and -0.89 off the western
African coasts (values given for AE550-865 and gaer data at
860 nm). These results indicate that the spatial patterns of MODIS C051
gaer product are reasonable as compared to the C051
Ångström exponent data. This shows that the use of gaer
in modeling studies can be considered as reasonably reliable with regards to
the consideration of fine and coarse aerosols over the examined study area,
with slightly more confidence over areas characterized by the presence of
fine particles, such as the Black Sea or Persian Gulf.
Since questions may also arise about possible uncertainties regarding the
long-term variability of MODIS C051 aerosol size products, due to the
calibration issues discussed in the previous section, the corresponding MODIS
C006 AE product is displayed in Fig. 8a. Figures 8a and 7a are similar in the
main geographical patterns of the two collections' AE product. The similarity
between C051 and C006 AE data is also depicted in the computed correlation
coefficients (Fig. 8b), exceeding 0.8, and biases (in absolute and relative
percentage terms, Fig. 8c and d, respectively). For the Mediterranean Sea,
the Arabian Sea and Persian Gulf, biases are smaller than 0.1 or 10 % in
most areas and 0.2 or 20 % almost everywhere. Relative biases larger than
30 % are only observed over the open Atlantic Ocean. The overall
computed correlation coefficient for the entire study region is 0.88 (0.86,
0.89, 0.95 and 0.84 for Mediterranean, Arabian, Persian and Atlantic sea
surfaces off the western African coasts). The corresponding overall relative
percent bias is equal to 15.6 % (9.1, 6.7, 6.1 and 15.7 for the same
sub-areas as above). Our results indicate that the uncertainty related to the
use of C051 AE data is small, especially over the Mediterranean Sea, the
Arabian Sea, the Persian Gulf and the Atlantic Ocean areas not far from the
European, African and Asian coastlines. Our AE results are in line with those
of Levy et al. (2013, Fig. 15) which refer, however, only to 2008 (ours are
for 2002–2010). In addition, a comparison is attempted in Fig. 8e and f
between the computed trends of C051 and C006 AE data over the common period
2002–2010, in order to assess whether changes are detected, which could be
an indication of possible changes in corresponding asymmetry parameter
trends. Figure 8e and f show the computed de-seasonalized trends of slope
values for both C051 and C006 AE. The results reveal similar patterns between
C051 and C006. Small trends are found in both of them, in agreement with the
small trends of asymmetry parameter reported in Fig. 5. We find that the sign
of AE trends basically does not change from C051 to C006. This might be a
signal that no changes of aerosol asymmetry parameter are expected in C006
and puts confidence on the C051 gaer results given in the present
study.
Evaluation against AERONET data
In order to evaluate the satellite-measured aerosol asymmetry parameter, we
identified the AERONET stations inside our area of interest and finally
utilized only the coastal ones, so that both satellite and surface data be
available. The total number of these stations is 69, and their locations are
shown in Fig. 1 (open and full circles).
Table 1 contains the comparison statistical metrics for all wavelengths
(Pearson correlation coefficient, bias, root mean square error (RMSE),
slope, intercept) of the comparison between surface daily mean data from
AERONET and satellite data from MODIS-Terra and MODIS-Aqua, which correspond
to the 1∘× 1∘ cell wherein each station is located. For
this analysis, we use all cells and days with common data between
Terra-AERONET and Aqua-AERONET. The mean differences are calculated as
gaer(AERONET)–gaer(Aqua) and gaer(AERONET)–gaer(Terra).
In general, we may note that on an annual level, the MODIS-Terra and Aqua
asymmetry parameter values at 470 nm are not in very good agreement with the
respective data from AERONET at 440 nm. Results at larger wavelengths are more reassuring, although still not very
satisfactory. We note that R, RMSE and slope generally increase with
wavelength. At 870/860 nm (Table 1 and Fig. 9), correlation coefficients are found
to be the largest and equal to 0.47 (AERONET-Terra) and 0.46 (AERONET-Aqua),
while satellite data are slightly overestimated compared to the surface data
(bias -0.035 or 5.54 % and -0.015 or -2.43 %, respectively).
It is interesting that the correlation coefficient and slope between satellite and surface data is
better in spring and summer, for all studied wavelengths. On the other hand, winter is generally the season with the largest bias, while RMSE seems insensitive to the
season.
Moreover, we find that for all seasons gaer values at 860 and 660 nm, both from MODIS-Terra and MODIS-Aqua, are overestimated compared to
gaer(AERONET) at the corresponding wavelengths (stronger
overestimation at 860 nm and by Terra). Finally we note an underestimation
of gaer at 470 nm from MODIS-Aqua, relative to the data by AERONET at
440 nm, while very small biases (< 0.5 %) are found between Terra
and AERONET at the same wavelengths.
Scatterplot comparison between gaer values at 860 nm
from MODIS Terra (black color) and Aqua (red color) and corresponding values
from AERONET stations at 870 nm (blue squares, Fig. 1). The 95 %
prediction bands as well as the mean bias (AERONET minus MODIS) and root mean
squared error are given.
Correlation coefficients (R), mean bias, root mean squared error
(RMSE) and the slope and intercept values of applied linear regression fits
between MODIS and AERONET gaer data. The statistical parameters
are given separately for the pairs of wavelengths: (i) 470 nm (MODIS) and
440 nm (AERONET), (ii) 660 nm (MODIS) and 675 nm (AERONET) and
(iii) 860 nm (MODIS) and 870 nm (AERONET). The statistical parameters are
also given separately for winter, spring, summer and autumn.
a The reported correlation coefficients and slopes may be
biased low, because we did not include in our
analysis the unknown AERONET errors.bgaer(AERONET)-gaer(MODIS).
Frequency distribution histograms for MODIS-Terra (red colored
lines) MODIS-Aqua (blue-colored lines) and AERONET (black lines)
gaer values at 860 and 870 nm, respectively. The histograms are
given separately for (a) the entire study region, (b)
Europe, and (c) Africa, the Middle East and the Arabian Peninsula.
In Fig. 9 we present a scatterplot comparison between MODIS and AERONET
gaer data pairs. There is bias towards larger gaer values from
both Aqua and Terra compared to AERONET, with Terra overpredicting more than
Aqua. The root mean square error to the fit between MODIS and AERONET is
very similar between Aqua and Terra. There are concerns on the application
of ordinary least squares regression, arising from the assumption that as
the assigned independent variable, AERONET values should be free from error.
We cannot guarantee the validity of this assumption, so we recognize that
the reported R and slope values from Fig. 9 and Table 1, if viewed as
metrics of agreement between MODIS gaer and real g, may be subject to
the effect of regression dilution and consequently biased low. This possible
bias for R and slope could be neglected only if AERONET errors can also be
considered negligible. With the above caveat in mind, the applied
least-squares fit line to the scatterplot comparison between matched
MODIS-AERONET data pairs (Fig. 9) indicates that MODIS overestimates
gaer more in the smaller than larger values, i.e., more for fine than
coarse particles.
We present the frequency distributions of asymmetry parameter daily values
(Fig. 10) on the days when data from all three databases (MODIS-Terra,
MODIS-Aqua and AERONET) were provided. Figure 10a corresponds to the whole
area of interest, while Fig. 10b and c correspond to two broad sub-regions
with basic differences in the aerosol source, namely Europe with great
anthropogenic sources, and Africa, the Middle East and the Arabian Peninsula, with
predominant natural sources and mainly desert dust. There is an apparent
skew in the MODIS-Terra and MODIS-Aqua gaer distributions, while the
AERONET distributions are more symmetrical. Moreover, the satellite data
distributions show larger values and smaller standard deviations compared to
AERONET, with the Terra overestimation being more exaggerated. The
disagreement is more pronounced in the sub-region of Europe, while in the
sub-region of North Africa/ the Arabian Peninsula, the distributions of
satellite and surface data agree more thus confirming the finding of Fig. 9
based on the slope of applied linear regression fit. Values over Europe are
generally smaller than over North Africa/ the Arabian Peninsula (Fig. 3), which
can be attributed to the presence of larger size particles of desert origin
in the latter sub-region, in contrast to Europe, where due to industrial
activity and frequent biomass burning the presence of smaller size particles
is important. Therefore, the smaller gaer values (< 0.6) in the
frequency distributions of the whole area, are overwhelmingly contributed by
the European sub-region, contrasting with larger values (0.7–0.75) being
contributed by both sub-regions and even more by North Africa/ the Arabian Peninsula
at larger gaer values.
The overall comparison between satellite and surface gaer data
performed in the scatterplot of Fig. 9 and Table 1 does not allow one to
have an insight to how the comparison behaves spatially, namely how it
differs from one region to another. This is addressed in Fig. 11, showing
the comparison of satellite and surface data at the wavelength of 860/870 nm
separately between MODIS-Terra – AERONET and MODIS-Aqua – AERONET. For this
comparison, we selected AERONET stations for which there is satisfactory
overlap between the time series from AERONET and the time series from MODIS,
namely the number of common days between AERONET-Terra and AERONET-Aqua is
larger than 100. This criterion is satisfied by 36 stations for
AERONET-Terra and by 34 for AERONET-Aqua shown in Fig. 11. For each AERONET
station we compute the Pearson correlation coefficient between the station
data at 870 nm and the corresponding MODIS-Terra or Aqua data at 860 nm, for the
1∘× 1∘ cell containing the station. Moreover, there is
the information if the trends between AERONET and either MODIS-Terra or Aqua
have the same sign (blue color) or not (red color).
In the case of the gaer(AERONET) – gaer(Terra) comparison, at 5 stations, (i.e., in 14 % of total 36 stations), the
correlation coefficient R is larger than 0.5 (largest R found is 0.64 at
station “Bahrain”), while at 13 stations (36 %) and 26 stations
(72 %) R is larger than 0.4 and 0.3, respectively. With respect to the
agreement on the sign of the trends, at 24 out of 36 stations (67 %) there
is a trend sign match and at 12 stations (33 %) a mismatch. Nevertheless,
it should be noted that no systematic spatial behavior, i.e., homogeneous
spatial patterns, is found concerning the performance of MODIS-Terra
gaer against AERONET in terms of either the magnitude of correlation or
the agreement of trends between the satellite and ground data sets. A similar
picture emerges for the comparison gaer(AERONET) – gaer(Aqua). In this case, there are again 5 stations (15 % of total
34 stations) with R > 0.5 (maximum value R=0.61 again at
“Bahrain”), while at 13 stations (38 %) and 24 stations (71 %), R is
larger than 0.4 and 0.3, respectively. Also, we see that at 22 stations
(65 %) there is a trend sign match and at 12 (35 %) there is a mismatch.
Map distribution of correlation coefficients between (a) MODIS-Terra and AERONET gaer values at 860 and 870 nm,
respectively (top panel) and (b) MODIS-Aqua and AERONET
gaer values at 860 and 870 nm (bottom panel). The size of
circles corresponds to the magnitude of correlation coefficients, while blue
and red colors are used for stations for which MODIS and AERONET indicate
same and opposite tendency of gaer, respectively.
Summary and conclusions
Using satellite collection (051) MODIS-Terra and Aqua data, we
examine the spatiotemporal variations of the aerosol asymmetry parameter
(gaer) over North Africa, the Arabian Peninsula and Europe. To our
knowledge, this is the first time that a satellite-based (MODIS) data set of
gaer, assessed and evaluated (against AERONET data), is used for the
study region. This is important, since such an evaluated satellite data set
is very useful for many applications, like radiative transfer and climate
modeling as well as for remote sensing. The advantages of MODIS gaer
data are the following.
They ensure complete spatial coverage over sea surfaces surrounding Europe,
Mediterranean and Middle East, which is essential for investigating and understanding
physical processes related to aerosols. These processes are strongly dependent on the
aerosol radiative and optical properties, gaer being one of the three key ones
(the other two being aerosol optical thickness and single scattering albedo). Such a
complete spatial coverage is especially required by radiative transfer and climate models.
They provide spectral gaer values, at seven wavelengths from 470 to 2130 nm,
which are of essential importance for radiative transfer models. Such spectrally resolved
aerosol optical properties can induce significant differences in model computations of
aerosol radiative effects (Hatzianastassiou et al., 2007).
They provide a relatively long temporal coverage, i.e., 8 years, which is significant
for examining seasonal and inter-annual cycles and changes of this aerosol optical property,
especially combined with the complete spatial coverage. This is also important since it
provides a reasonable statistical bed for attempting evaluations through comparison
against other gaer data like the AERONET.
They constitute the first known so far satellite-based gaer data set; until
now, the utilized gaer data in modeling or other analyses were taken from in situ
measurements or aerosol models, which both have their own deficiencies, namely limited
spatial coverage or pure theoretical basis, respectively.
According to the obtained results, generally, the largest values of the
asymmetry parameter, indicating the strongest forward scattering of
radiation by atmospheric aerosols, are found over areas with aerosol load
being dominated by large particles of desert dust (tropical Atlantic,
Arabian and Red seas). On the contrary, smaller gaer values are seen
where a significant fraction of aerosol load comes from small size particles
of anthropogenic origin, e.g., over the Black Sea. The results are consistent
with the theory and thus indicate a good performance of the MODIS retrieval of
aerosol asymmetry parameter. Depending on the area of interest, the seasonal
cycle of the asymmetry parameter varies markedly. More specifically, in
areas with abundance of desert dust particles, the range of intra-annual
variation is small, with the largest values during summer, while in other
areas the seasonality is reversed, with the largest values during the cold
season and the smallest during the warm season. The asymmetry parameter
decreases with wavelength, especially when one examines its spatially
minimum values, while this decrease is weaker for the larger gaer
values, corresponding to the presence of coarser particles.
The seasonal fluctuation is more pronounced with increasing wavelength in
the examined regions, which is attributed to the different spectral
behavior of the asymmetry parameter for small and large particles. With
respect to the inter-annual variability of the asymmetry parameter, we did
not discern very important either increasing or decreasing tendencies, with
absolute changes smaller than 0.04 in any case. On the other hand, we found
opposing tendencies for the two satellite data sets. MODIS-Terra observes
mostly increasing tendencies, while Aqua gives also a few regions with
decreasing tendencies. Generally, the largest intra-annual and inter-annual
variations are seen over the Black Sea, while the smallest was seen over the tropical
Atlantic. However, some strong trends (especially from Terra) may be due to
calibration drift errors, which may be addressed in collection 006. Along
these lines, we performed some preliminary comparisons between 051 and 006
Ångström Exponent trends from Aqua, which ensured that AE and
gaer are very closely anti-correlated. These preliminary results, show
that 051 Aqua AE trends resemble very closely the 006 trends, supporting
that the gaer trends from collection 051 (at least for Aqua) reported
in this study are credible.
The 051 MODIS gaer data is not a retrieved but a derived MODIS
parameter. Given that the retrieval is strongly dependent on the assumptions
made, namely on the aerosol modes used, uncertainties can be associated with
its use in radiative transfer modeling. In order to examine these
uncertainties, the gaer data were compared with 051 AE data for the
same period. The results from the comparison showed a strong
anti-correlation (coefficient higher than 0.7–0.8) proving the consistency
and reasonably safe use of gaer data in modeling studies, at least to
the same degree with MODIS AE data in modeling and other analyses. The
correlation is even higher over sea areas characterized by stronger presence
of fine aerosols, like the Black Sea, the Persian Gulf or the North Sea.
This confidence is further strengthened by the small identified
uncertainties related with the use of collection 051 instead of 006 MODIS
gaer data reported in the previous paragraph. This was obtained
indirectly based on the use of AE data of both collections since gaer
data are not yet available in collection 006.
We compare satellite data with surface data from the AERONET, in order to
further validate the reliability of the former. Through the examination of
frequency distributions of daily gaer, a shift of satellite data
towards larger values relative to surface data becomes apparent. This
finding is more pronounced for gaer over Europe, while the North
African and Arabian Peninsula values are more in agreement. Moreover, the
smallest gaer values originate from particles from Europe, because of
the generation of smaller size particles by industrial activities and
biomass burning.
We present scatter plots of daily gaer values between MODIS-Terra,
MODIS-Aqua, and AERONET, which show moderate agreement between satellite
data at 470 nm and surface data at 440 nm, with small correlation
coefficients (R < 0.3) and a slight underestimation by MODIS.
Slightly better agreement was noted at larger wavelengths, but still without
reaching very satisfactory levels (R < 0.47). Nevertheless, during
spring and summer, satellite and surface measurements tend to agree more.
Finally, for the comparisons at 660/675 and 860/870 nm, we report an
overestimation of gaer by MODIS compared to AERONET.
When examined at the local scale, i.e., station by station, the MODIS
gaer data agree reasonably and for some stations better than in
overall, but still not very well, with those of AERONET. This analysis,
based on 36 and 34 AERONET stations ensuring at least 100 common days with
MODIS-Terra and Aqua, respectively, shows that in 36 and 38 % of stations,
respectively, the MODIS data have correlation coefficients larger than 0.4
(reaching values up to 0.64), while in about 65 % of stations the trends
of gaer from MODIS and AERONET have the same sign. Nevertheless, the
magnitude of correlation coefficients or the agreement between trends of
gaer from the satellite and ground data sets do not exhibit a systematic
(homogeneous) spatial pattern.
Our results offer an interesting way to assess the uncertainty induced by
the use of such satellite gaer data in climate and radiative transfer
models that compute aerosol radiative and climate effects. Based on an
overall assessment of satellite MODIS gaer through detailed
comparisons against ground AERONET data, it appears that in overall MODIS
performs satisfactorily in terms of magnitude of gaer values. This is
indicated by the computed biases, which are smaller than 5 % with respect
to MODIS values, with better performance at smaller wavelengths. The root
mean squared errors vary within the range 5–10 % again being smaller for
smaller wavelengths.These results indicate an uncertainty of MODIS gaer data over the study region up to of 10 % at maximum. Previous analyses
and sensitivity studies for the same study region (Papadimas et al., 2012)
have shown that such gaer uncertainties can induce modifications of
aerosol direct radiative effects (DREs) which are equal to 30 % at the
top-of-atmosphere (TOA) and 1 % in the atmosphere and 10 % at the
surface, at maximum. Therefore, the uncertainty associated with the use of
MODIS gaer is larger than any aerosol related physical process taking
place at TOA, namely planetary cooling or warming and its magnitude, smaller
for processes at the Earth's surface, e.g., surface cooling and very small
for aerosol processes and feedbacks in the atmosphere, like the aerosol
semi-direct effect and its implications. Results from the same previous
analysis (Papadimas et al., 2012) proved that the exact magnitude of MODIS
gaer DRE uncertainty can be estimated by simple linear equations
relating DREs and gaer, separately given for TOA, atmosphere and
surface.
The results of the present analysis are useful since they assess for the
first time the performance of satellite-based products of aerosol asymmetry
parameter over broad regions of special climatic interest. The obtained
results are relatively satisfactory given the difficulties encountered by
satellite retrieval algorithms due to the different assumptions they made.
Nevertheless, our results and identified weaknesses remind that users should
be aware of the gaer uncertainties and their consequences. The
identified weaknesses may provide an opportunity to improve such satellite
retrievals of aerosol asymmetry parameter in forthcoming data products like
those of MODIS C006. The increased temporal coverage of gaer data,
combined with the continued operation of MODIS, is expected to make possible
the building of the first real satellite climatology of this important
aerosol optical property.
Acknowledgements
This research has been co-financed by the European Union (European Social
Fund – ESF) and Greek national funds through the operational program
“Education and Lifelong Learning” of the National Strategic Reference
Framework (NSRF) – Research Funding Program: THALES. Investing in knowledge
society through the European Social Fund. The Collection 051 MODIS-Terra
data were obtained from NASA's Level 1 and Atmosphere Archive and
Distribution System (LAADS) website (ftp://ladsweb.nascom.nasa.gov/). We would like to thank the principal
investigators maintaining the AERONET sites used in the present work. We
would also like to thank three anonymous Reviewers and Dr Kaskaoutis for
helping to improve the manuscript with their comments.
Edited by: A. Nenes
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