Introduction
Measurements of aerosol optical depth (AOD) – by
ground-based, airborne, and satellite-borne instruments
– have provided us with a good picture of the highly
variable distribution of aerosols throughout the
globe. The uncertainties in our knowledge of the global
distribution of aerosol loading have become progressively
smaller during the past decade owing to dedicated
satellite-borne aerosol instruments like the Moderate
Resolution Imaging Spectroradiometer and the Multi-angle
Imaging Spectroradiometer MODIS and MISR; see
e.g.,and references
therein. However,
for many applications the aerosol amount tells only half
of the story: to study the interaction between aerosols
and clouds , to determine aerosol
radiative effects, and for the development of mitigation
strategies it is crucial to additionally know the aerosol
type or source e.g.,. For remote
sensing retrievals themselves, aerosol optical properties
or some constraints on particle type are also needed to
aid model selection in the inversion process.
The contribution of aerosols to the top-of-atmosphere
radiance detected by satellite instruments is spectrally
smooth, and due to the interfering signal from the
surface, passive radiometers like MODIS cannot retrieve
more than one or two pieces of information from their
measurements: AOD and the extinction Ångström exponent (EAE). The EAE, as a proxy for the particle size
distribution, turns out to be a very useful metric when
characterizing aerosol types. Naturally emitted primary
aerosols, such as mineral dust and sea salt, consist of
relatively large particles with a size distribution
centered at sizes >1 µm. In contrast,
secondary aerosols – those formed from components
emitted in gaseous form – are generally (much) smaller
than 1 µm (i.e., the extinction is almost
entirely due to small
particles; Seinfeld and Pandis, 2006). The majority of such
“fine” particles is often assumed to be of
anthropogenic origin , although
biomass burning aerosols, which consist mostly of fine
particles, are not all human-induced. In addition, there
are strong biogenic sources of small secondary organic
aerosols. To further discriminate between aerosol types,
differences in absorption can be exploited as
e.g., in. This allows for the distinction
of desert dust (large particles that absorb in the UV
range) from sea salt (large, but non-absorbing), and
smoke (small, absorbing) from industrial pollution
(small, weakly or non-absorbing), for
example. In practice, such simple rules are often
violated: aging of particles (hygroscopic growth, coating
or other processes) or mixing of different aerosol types
change the optical properties. To determine the (most
probable) main aerosol source, more information is
required. We use measurements of trace gas abundances as
a source of this information.
Apart from naturally formed particles (desert dust and
sea salt), aerosols are often accompanied by enhanced
trace gas levels – because they were emitted by the same
source, or were formed from those trace gases or from the
same precursor. Hence, collocated measurements of trace
gases can be used to determine the main source of
aerosols. This has been exploited in a study
by , in which it was shown that the
presence of significant correlation of AOD with trace gas
concentrations, notably NO2 and HCHO, is
an indication of the main source of those aerosols. In
a later publication, also involving data from the Ozone
Monitoring Instrument (OMI),
demonstrated that the use of CO data from the
Atmospheric Infrared Sounder (AIRS) to identify smoke
improves the aerosol retrieval by OMI. In the present
study, we take these findings a step further and
integrate them into an algorithm to determine the main
aerosol type and its source on a global scale. We extend
the analysis initiated by by adding
CO abundance and aerosol optical properties. The
resulting Global Aerosol Classification Algorithm, GACA,
combines the EAE from MODIS and UV Aerosol Index (UVAI)
from GOME-2 (Global Ozone Monitoring Experiment-2) to
determine an aerosol type based on its size and
absorption. Subsequently, trace gas vertical column
densities (VCDs of NO2, HCHO, SO2,
and CO) are used to infer the dominating source of
the aerosols. The main results from this algorithm are
seasonal maps that show the dominating aerosol type and
source at 1∘×1∘ or 2∘×2∘ resolution, respectively.
GACA results are compared to aerosol composition from
MACC (Monitoring Atmospheric Composition and Climate)
reanalysis data on a global and regional scale. The MACC
project provides data on atmospheric composition for the
recent past and makes midterm forecasts by combining
state-of-the-art atmospheric modeling with
satellite-based
measurements e.g.,. The model
assimilates AOD from both MODIS instruments, using it to
scale the total aerosol mixing ratio. The tropospheric
aerosol types (or components) included in MACC are sea
salt, desert dust, organic matter, black carbon, and
sulfate. The comparison with model data highlights an
important application of our algorithm: the improvement
of emissions of both trace gases and aerosols in
models as suggested in e.g.,.
In this paper we present GACA and demonstrate its
capabilities with seasonal global maps of aerosol type
and main source, seasonal cycles of aerosol type and
source in six selected regions, and several other
applications. We find good agreement between results from
GACA and MACC reanalysis in most cases; some important
discrepancies between the data sets are discussed. The
paper is structured as follows: first, we describe the
instruments and data sets used in GACA. The algorithm is
described in detail in Sect. . Global maps
of aerosol type and aerosol source determined by GACA are
presented and compared with maps of aerosol composition
from the MACC reanalysis in Sect. , where
the study of the seasonal cycle in six study regions is
also shown. In Sect. the sensitivity of GACA
to various parameters is discussed, GACA results are
compared to existing aerosol climatologies, and future
improvements to the algorithm are suggested; the closing
Sect. contains our concluding
remarks.
Global Aerosol Classification Algorithm description
GACA is based on the outcome of several tests applied to
the trace gas and aerosol data described in the previous
section, and their correlation with AOD. The algorithm
consists of two main parts: the first part, named
GACA-type, assigns certain aerosol types to each data
point within a grid box based on UVAI (a measure of
aerosol absorption) and EAE (a measure of aerosol
size). The second part, GACA-source, relates trace gas
abundance to the different aerosol types and assigns the
most probable aerosol source to each grid box. Both parts
will be described in detail in Sects.
and , and are summarized in the
decision tree in Fig. .
Speciation of aerosol types based on absorption (UVAI) and size
(EAE). Left, aerosol types color-coded according to size (larger sizes have
darker hues) and absorption (non-absorbing in blue, neutral in green,
absorbing in red): LA, large absorbing; MA, medium-size absorbing; SA, small
absorbing; LN, large, neutral; MN, medium-size, neutral; SN, small, neutral;
LNA, large, non-absorbing; MNA, medium-size, non-absorbing; SNA, small,
non-absorbing. Right, monthly mean UVAI and EAE within grid boxes in regions
dominated by desert dust (red dots), biomass burning smoke (gray crosses),
secondary biogenic aerosols (green circles), and sea salt (light blue
pluses).
Data are from June–August 2007–2011; see the text for the
selected geographical regions.
Schematic decision tree of GACA. The corresponding
threshold values are given in Table . The
mean value of a quantity, e.g., ΔCO, is denoted
ΔCO; the coefficient of correlation between AOD
and a quantity, e.g., HCHO, is denoted
R2(HCHO). Thresholds are denoted as e.g.,
SO2,thresh, Rthresh2,
ratiothresh (for the HCHO : NO2
ratio threshold), or AODSS-thresh (for the
maximum AOD allowed for SS classification). Other
abbreviations are explained in Table .
Data selection
Prior to analysis, GACA performs a selection of data for
each “grid box”. For a final map with a resolution of
2∘×2∘ (which was chosen as
a compromise between spatial resolution and statistics),
each grid box on the globe contains four data points per
month, because the input monthly mean maps (of AOD, UVAI,
EAE, and trace gas column densities) have a resolution of
1∘×1∘. To improve statistics and
stability of the algorithm, the data are grouped by
season and 5 years of data (2007–2011) are combined,
increasing the number of data points to 60. Grid boxes in
which the monthly mean AOD never exceeds 0.05 are
removed, as it is assumed that they cannot be reliably
classified. The obtained data set is screened for
missing values and outliers; the latter because the
intention is to build a climatology of typical
conditions, which should not be influenced by exceptional
events. In addition, faulty retrievals (e.g., due to the
South Atlantic Anomaly) are removed. Outliers are removed
by repeated exclusion of data points exceeding the
mean-plus-3σ criterion until all data fall
within the 3σ range. Whenever an AOD, EAE or
UVAI outlier is encountered, all corresponding values
(collocated AOD, UVAI, EAE, and trace gas columns) are
removed from the data set. Trace gas outliers are also
excluded, but in this case only the affected data point
is removed. Hence, if an NO2 outlier is
encountered, the NO2 value is removed, but
HCHO, SO2, and ΔCO columns
and aerosol data are retained (i.e., in this case the
mean NO2 VCD is calculated with one data point
less than the means of the other trace gases and aerosol
data; the same applies to the calculation of the
correlation with AOD). If outliers are not removed from
the data set, GACA results are not strongly affected, but
the effects of local extreme events (fires, volcanic
eruptions) become apparent. This is discussed in more
detail in Sect. .
Abbreviations of aerosol types and sources used throughout this document.
Acronym
Aerosol type/source/component
Occurrence
LA
Large absorbing
GACA-type
LN
Large neutral
GACA-type
LNA
Large non-absorbing
GACA-type
MA
Medium-size absorbing
GACA-type
MN
Medium-size neutral
GACA-type
MNA
Medium-size non-absorbing
GACA-type
SA
Small absorbing
GACA-type
SN
Small neutral
GACA-type
SNA
Small non-absorbing
GACA-type
BB
Biomass burning smoke
GACA-source
DD
Desert dust
GACA-source and MACC
BIO
Secondary aerosols of biogenic origin
GACA-source
URB
Secondary aerosols of urban/industrial origin
GACA-source
AGED
Aged aerosols
GACA-source
VOG
Volcanic sulfate
GACA-source
SS
Sea salt
GACA-source and MACC
XX
Unknown source
GACA-source
BC
Black carbon
MACC
OM
Organic matter
MACC
SO4
Sulfate
MACC
MIX
Mixture
MACC
na
Not assessed
All
Thresholds used in GACA. Variables are unitless except for the trace gas (excess) VCDs (given in moleccm-2).
Variable
Nominal range
Thresholds
GACA step
AOD
0–3
0.05
Filtering
AOD
0–3
0.15
GACA-source (sea salt)
EAE
0–2
0.75 and 1.25
GACA-type
UVAI
-2.5 to +2.5
-0.5 and 0.25
GACA-type
NO2 column
0–10×1015
1×1015
GACA-source
HCHO column
0–25×1015
7×1015
GACA-source
SO2 column
0–20×1015
1×1015
GACA-source
ΔCO excess column
0–40×1017
4×1017
GACA-source
Ratio HCHO : NO2
0–100
4
GACA-source
Correlation coefficient, R2
0–1
0.25
GACA-source
Aerosol type classification by GACA-type
Each point of the filtered data set is subsequently
assigned one of nine aerosol types based on its UVAI and
EAE values. In this study, aerosol types are defined by
their size – small (S), medium (M), and large (L) – and
the amount of aerosol absorption in the UV range –
non-absorbing (NA), neutral (N), or absorbing (A) – as
shown in the left panel of
Fig. . The acronyms of aerosol
types and sources are explained in
Table .
The choice of UVAI and EAE thresholds is motivated by the
right panel of Fig. , which
displays monthly mean data (June–August 2007–2011) from
regions which we assume to be dominated by one of four
aerosol sources: mineral dust
(14–26∘ N, 16∘ W–8∘ E),
smoke (4–16∘ S, 14–30∘ E), biogenic
secondary organic aerosols
(30–36∘ N, 80–90∘ W), and sea salt
(0–10∘ S, 120–140∘ W). The depicted
aerosols are clearly separated by the EAE thresholds (sea
salt from secondary organic aerosols; desert dust from
smoke) and the UVAI thresholds (desert dust from sea
salt; smoke from secondary organic aerosols). The choice
of nine aerosol types instead of four like in was motivated by the occurrence of
situations where different particle types are mixed.
For each 2∘×2∘ grid box, the
fraction of data points belonging to each aerosol type is
computed and the most frequently observed type, weighted
by AOD, is assumed to be the dominant type. Note that if
the type classification is run on its own (i.e., not as
input for the aerosol source assignment step), the
statistics requirements are less strict and global maps
can be produced on 1∘×1∘
resolution (e.g., Fig. ).
Aerosol source assignment by GACA-source
The results from GACA-type are used as input for the
second part of GACA: the determination of the dominant
aerosol source. The main assumption underlying
GACA-source is that enhancements in trace gas and aerosol
abundance are caused by the same source. The algorithm
computes means over all data points within a grid box (of
AOD, UVAI, and trace gas VCDs) and correlations between
AOD on the one hand, and UVAI and trace gas VCDs on the
other. Together with the dominant aerosol type determined
in the previous step, these data are used to assign
a main aerosol source based on the outcome of two types
of tests: (1) is the mean trace gas abundance or
HCHO:NO2 ratio above the threshold given
in Table ? (2) Is there a linear
correlation (with R2>0.25) between AOD and UVAI or AOD
and trace gas abundance? An overview of GACA-source can
be found in the lower part of the decision tree in
Fig. .
Eight aerosol sources are discriminated in GACA-source:
biomass burning smoke, desert dust, secondary biogenic,
secondary urban/industrial, aged, volcanic sulfate, sea
salt, and unknown sources. Each source and the selected
classification criteria will be described in more detail
in the following sections.
Biomass burning smoke (BB)
Fresh smoke from forest, agricultural, or grassland fires
mainly consists of small
particles e.g., that
absorb light in the UV and visible range. Co-emitted
trace gases are NO2, HCHO and CO, as well
as SO2 but only in very small
amounts . In GACA-source, grid boxes
are always designated BB when the main type is small
absorbing. Biomass burning is also assigned if the
absorbing aerosol criterion is fulfilled and
either (1) mean CO or (2) correlation between
ΔCO and AOD or (3) mean HCHO and
correlation between HCHO and AOD pass the
threshold. The absorbing aerosol criterion requires that
either (a) the dominant aerosol type is absorbing or (b) the dominant type is neutral and a good correlation with
a positive slope is found for UVAI and AOD, and mean
AOD ≥0.15. This allows grid boxes with relatively
small UVAI (e.g., due to lower-lying aerosol layers or
cloud contamination) to be designated as BB.
Desert dust (DD)
Mineral dust consists of large, non-spherical particles
that absorb UV radiation due mainly to their iron oxide
content . The emission and transport
of DD is linked to meteorology (i.e., wind fields) and
land surface conditions and not to trace gas
emissions. GACA-type assigns DD as a source to grid boxes
that are dominated by large absorbing aerosols – unless
they were already characterized as BB. To include aged DD
plumes, medium-size and large neutral aerosol types can
be attributed to DD if the absorbing aerosol criterion is
fulfilled (see above) but, additionally, the correlation
of ΔCO and AOD and means of the other trace
gases (NO2, HCHO, and SO2) should
be below their respective threshold values. The latter
criterion serves to distinguish DD from BB and volcanic
ash but as a negative side effect excludes polluted dust
and cases of mixed desert dust and smoke.
Secondary aerosols biogenic origin (BIO)
The small, non-absorbing aerosols that form by
condensation of (semi-)volatile biogenic precursors are
accompanied by enhanced levels of HCHO, as both
are products of the oxidation of isoprene and other
volatile organic
compounds . To
separate them from urban/industrial aerosols, the ratio
of HCHO/NO2 is required to be above
a certain threshold value (given in Table ).
Secondary aerosols of urban/industrial origin (URB)
Due to the diversity of sources and chemical processing
in industrialized environments, the URB source is very
broadly defined in GACA-source. All grid boxes dominated
by non-absorbing or neutral aerosol types that have
enhanced NO2 columns qualify. The only exception
being grid boxes already characterized as BIO.
Relationship between 1∘×1∘ monthly mean
values of AOD and trace gas columns (in moleccm-2) for a region
in central Africa (2–4∘ S, 18–20∘ E; left panel) and in
the eastern Pacific Ocean (16–18∘ N, 162–164∘ W; right
panel) for July–August 2007–2011. Dots depict NO2 (blue),
HCHO (green), and SO2 (red) VCDs and excess CO VCDs
(light blue, scaled by a factor of 0.01) and their respective thresholds.
The threshold values of NO2 and SO2 are identical (dotted
blue and red lines). Note the differences in y axis scales.
Aged/transported aerosols (AGED)
Air masses with enhanced ΔCO but low
levels of NO2 are assumed to have been
transported away from their sources. The AGED source is
therefore assigned when CO, which has a long lifetime, is enhanced but the shorter-lived NO2 is
not. Aging may change average aerosol properties by
dilution, mixing with other air masses, processing within
clouds, or other mechanisms. Hence, all neutral and
non-absorbing aerosol types qualify as AGED.
Volcanic sulfate (VOG)
Secondary aerosols formed by the reaction of volcanic
SO2 with the atmosphere are named volcanic smog
(VOG) here to distinguish them from anthropogenic
sulfate. GACA-source can only detect VOG in remote
locations, as one requirement for the assignment is the
lack of enhancements in NO2 and
ΔCO. In addition, the SO2 mean and
correlation with AOD need to pass the thresholds. Freshly
formed sulfate aerosols are small, but can grow rapidly
due to their hygroscopicity; therefore small and
medium-sized aerosol types can be assigned to VOG. Both
non-absorbing and neutral aerosol types qualify because
the sensitivity of UVAI to non-absorbing aerosols is not
very high.
Sea salt (SS)
Breaking waves and bursting bubbles cause the release of
sea salt particles. The particles are hygroscopic and
grow readily in the marine boundary layer, forming large,
non-absorbing particles. The emission of SS depends
mainly on wind speed and geography (e.g., coastlines) but
is not associated with the emission of trace
gases. GACA-source attributes SS as a main source to grid
boxes with mean AOD <0.15 and no trace gas enhancements;
only non-absorbing and neutral, large and medium-size
type aerosols are eligible candidates. GACA does not
discriminate between grid boxes located over land and
ocean; therefore, the SS type is also regularly found over
land and may be interpreted as a generic background type.
Unknown source (XX)
If all tests leading to the above-mentioned aerosol
sources fail but significant amounts of aerosols are
detected (mean AOD >0.05), the aerosol source is set to
“unknown”.
Seasonal cycle of global aerosol type distribution according to
GACA. Data are from 2007–2011 and were divided into the four main seasons
(from top to bottom): winter, spring, summer, and fall. The legend is given
on the bottom; see Fig. and
Table for aerosol-type abbreviations.
The yellow box indicates the region investigated in
Fig. .
Source assignment
Means and correlation coefficients are calculated from
all valid data points within a grid box if the fraction
of valid points amounts to at least 25 % of all points
(down to an absolute minimum of five). The tests performed
by GACA-source are based on thresholds (given in Table ), the values of which were chosen
empirically. The source assignment criteria were chosen based on textbook
knowledge (e.g., that biomass burning is associated with HCHO and
CO emissions), as detailed for each source type in
Sects. –, and were adjusted
iteratively to obtain consistent results. The quantitative understanding of
aerosol–trace gas relationships, however, is currently not sufficient to
derive trace gas thresholds in a systematic way, hence the trace gas
thresholds were determined in a more empirical fashion. The thresholds were
empirically chosen high enough to exclude noise (or natural variability), but
low enough that the associated sources are recognized. The ΔCO
threshold, for example, was chosen low enough to include aged air masses. The
SO2 threshold, on the other hand, had to be set sufficiently high to
exclude noise.The thresholds were chosen independent of region and season to
keep the algorithm globally consistent. A future development of GACA may be
the adoption of threshold climatologies to better account for regional and
seasonal variability of trace gas and aerosol emissions (see Sect. 5.4).
In Fig. we demonstrate the
algorithm for two 2∘×2∘ grid
boxes: the first shows data from a region in central
Africa (2–4∘ S, 18–20∘ E) during the
biomass burning season, whereas the second is located
west of Hawaii, in a region of volcanic outflow at
16–18∘ N, 162–164∘ W. The trace
gas columns for June–August 2007–2011 are plotted
together with their respective thresholds (colored
lines) so that, if data points lie above the respective
threshold, the trace gas is assumed to be associated with
the local aerosols. In the left panel (central Africa),
HCHO (green) and ΔCO (light blue)
are strongly enhanced. The level of NO2 (blue)
clearly exceeds 10-15, the threshold for both
NO2 and SO2. This is in contrast to
SO2, which is close to or even below the
detection limit, leading to scatter of data and negative
values. The dominating source is BB,
because (1) the dominating aerosol type is medium-size
absorbing and (2) the correlation between
ΔCO and AOD is high (R2=0.71).
Over the remote eastern Pacific Ocean (right panel) the
trace gas means and correlations usually fall below the
threshold values; however, due to prodigious degassing of
the Kilauea Volcano (especially in 2008) strongly enhanced
SO2 columns can be observed in the selected grid
box. In the atmosphere SO2 is converted to
sulfate aerosols, resulting in a good correlation between
AOD and SO2 of R2=0.53. The dominating
aerosol types are large neutral and large non-absorbing;
the main source assigned to this grid box is volcanic
sulfate (VOG).
Transect showing transport of mineral dust
plumes. Shown are summertime (June–August 2007–2011) data
from 15–20∘ N, a region of Saharan dust
outflow. Upper panel: mean AOD (total of all aerosol types);
the mean wind direction is indicated by an arrow, and the
surface type (land or ocean) is given at the bottom of the
panel. Lower panel: AOD-weighted fraction of all aerosol
types contributing >20 % to AOD.
Results
Aerosol type
We applied GACA-type to the 2007–2011 data set to
study the seasonal cycle of aerosol properties
globally. Figure shows maps of
the dominating aerosol type on a 1∘×1∘ resolution for all four seasons. Focusing
first on the summer (third panel), it can be seen that
the dust belt, at around 10–40∘ N, is dominated
by large particles (dark hues) with strong to moderate
absorption (red and green tones). Smoke plumes from
central Africa consist mostly of small to medium-size
absorbing particles (orange and red), although there
appears to be a significant contribution from large
absorbing (LA) particles, which is probably an artifact
that will be discussed in more detail in the next
section. North America, Europe and large parts of Asia
are dominated by small, non-absorbing aerosols (light
blue). Over ocean, particularly in the southern oceans,
large particles (dark blue and green) dominate. Light
gray areas denote regions where no AOD data were
available (due to e.g., clouds, snow or ice cover, low
sun) or where monthly mean AOD did not exceed 0.05 within
the studied period.
In winter and spring (December–February; March–May) the
contribution of mineral dust to the aerosol mix over
China can be clearly seen: the aerosol type is dominated
by larger, more strongly absorbing particles than in
summer. The burning of cropland and agricultural waste in
Southeast Asia stands out in spring, when aerosol types
are predominantly absorbing (red and orange). The biomass
burning season in South America, which starts in
July–August and peaks in September–October, has a very
different signature than that in southern Africa: the
particles are smaller and appear less absorbing. This may
be a consequence of the difference in fuel
type e.g, which leads to different
trace gas and aerosol emission factors. But the main
causes are probably the increased cloudiness, which leads
to lower UVAI values and more data gaps in the trace gas
products, and the large abundance of (non-absorbing)
secondary organic aerosols. Despite the fact that wildfires occur frequently
in summer in North America, BB is not selected as a major source there. This
is because forest fires occur at irregular intervals, so that their signal is
suppressed as a consequence of averaging data in time and space.
The frequency of occurrence of each aerosol type can be
used to study changes in aerosol composition as
a function of time (or distance to the source). As an
example, the westward transport of Saharan dust over the
Atlantic Ocean is shown in
Fig. . The upper panel displays the
mean total AOD along a longitudinal transect from
10∘ E to 80∘ W, at 15–20∘ N
(see yellow box in panel 3 of
Fig. ). The lower panel presents
the aerosol fraction, weighted by AOD, for the same
transect. Only the three large aerosol types (LNA, LN,
and LA) are shown, the other types never contribute more
than 20 % to the total AOD. Close to the source,
situated at roughly 10∘ E–10∘ W, the
aerosol load is almost completely made up of large
absorbing particles (LA, brown triangles). West of about
25∘ W, the fraction of large neutral aerosols
(LN, green crosses) starts increasing until it becomes
the dominating particle type at 50∘ W, where the
total AOD has decreased to 0.3 (from a maximum of
0.75). This apparent change in absorption is mainly due
to the fact that we use UVAI as a measure for absorption:
as UVAI increases with AOD and aerosol altitude, the
gradual descent of the dust layer
combined with the decreasing AOD causes UVAI to fall
below the upper threshold value of 0.25. This indicates
that GACA underestimates dust abundance far from its
source.
Seasonal cycle of global main aerosol source
distribution according to GACA. Data are from 2007–2011 and
were divided into the four main seasons (from top to
bottom): winter, spring, summer, and fall. Aerosol source
type abbreviations are given in Table ;
gray areas are not analyzed due to lack of data or too small
mean AOD (see text for details). Enumerated yellow boxes in
the third panel mark the regions investigated in
Figs. –,
respectively.
Global aerosol source for each aerosol type according
to GACA for June–August 2007–2011. Aerosol source and type
abbreviations are given in Table ; gray
areas do not contain more than four points belonging to the
relevant aerosol type.
Source type
The results from a run of GACA-source with data from
2007–2011 are shown in the form of seasonal global maps
with 2∘×2∘ resolution in
Fig. . The upper frame shows the
main source type in winter. Most of the continental
Northern Hemisphere aerosols are of urban/industrial
origin (URB, dark blue), except where mineral dust (DD,
red) predominates (in northern Africa, the southern Arabian
Peninsula, and northwestern China). Biomass burning smoke
(BB, dark red) can be found in sub-Sahelian Africa in
this season, as well as over parts of Southeast Asia. The
forested part of South America is a large source of
secondary organic particles (BIO, dark green). Aged
aerosols (AGED, blue-gray) can be seen in the outflow
from Asia (India, China) and are also found in the air
masses transported from equatorial Africa over the
Atlantic. Most of the aerosols over oceans are classified
as sea salt (SS, light blue), although aerosols of
undefined composition (XX, dark gray) are found in the
Asian outflow over the Pacific and the African outflow
over the Atlantic. The band of aerosols at
40–60∘ S (also seen in March–May) is caused by
unrealistically high AOD mainly due to inaccurate wind
speed assumptions and residual cloud contamination in the
MODIS retrieval and may be
ignored. In spring and summer (second and third panels of
Fig. ) more dust is activated
within the global dust belt. The amount of biomass
burning smoke also increases as first the agricultural
fires in Southeast Asia reach their springtime peak and
then the Southern Hemisphere fire season starts in
summer. A conspicuous sulfate (VOG) plume is seen
emerging from Hawaii and is mainly due to prodigious
degassing in April–October 2008 by the Kilauea Volcano
(19.4∘ N, 155.3∘ W) see,
e.g.,. The misclassification of SS
aerosols over continents in the high latitudes is most
apparent in fall (lower-most panel). These grid boxes
show no enhanced trace gas concentrations and have mean
AOD <0.15, corresponding to the definition of SS in
GACA. These aerosols may be regarded as background aerosols of which the source cannot reliably be determined by GACA.
Whereas Fig. depicts the main
aerosol source, determined from all data points within
a grid box, Fig. shows the
aerosol source determined for each of the nine aerosol
types separately. The data are from June–August
2007–2011: the same data set as shown in the third panel
of Fig. . The three absorbing
aerosol types (small, medium-size and large) are shown in
Fig. 7a–c. Medium-sized and large
absorbing aerosols north of the Equator are almost
exclusively attributed to mineral dust; the apparent band of desert dust at
60∘ S is caused by a few data points with unrealistically high AOD,
as mentioned above, in addition to erroneous (high) UVAI values that are
probably caused by small scattering angles (90–100∘)
encountered in this region. The smoke
plume off the southwestern coast of Africa in panel (a) is
rather unusual, as biomass burning particles are usually
small. This is caused by the use of EAE as a measure for
aerosol size; although the EAE is <0.75, the FMF in this
region is on the order of 0.7, indicating a large
fraction of small aerosols (not shown). It is unclear why
EAE and FMF show opposing behavior in this region. We
speculate that it has to do with the persistent low-cloud
cover during the biomass burning season, which may cause
enhanced cloud contamination and, possibly, a wrong
choice of aerosol model. However, as it was pointed out
in various studies, the size information retrieved by
MODIS is not very
reliable e.g., and we do
not pursue the issue further (but see
Sect. for a discussion on EAE and
FMF). GACA assumes that the source of all small absorbing
aerosols is biomass burning; therefore, no other source
type is seen to contribute in Fig. 7c.
Neutral aerosol types, shown in Fig. 7d–f, come from
various sources: URB, SS, AGED, MIX, BB, and some
DD. Large and medium-sized non-absorbing aerosols
(Fig. 7g–h) are dominated by SS with contributions from
URB and MIX. Small non-absorbing (Fig. 7i) is the
dominating aerosol type throughout the eastern United
States, most of Europe and eastern China (compare lower
right panel of Fig. ), where URB is
the main source. Large parts of South America and
southern Africa can be seen to emit BIO aerosols (which
are assumed to be exclusively small non-absorbing
particles), but the AOD-weighted main aerosol source in
those regions is BB – in contrast to Southeast Asia,
where BIO is the dominant aerosol source in this season
(Fig. ). Performing the analysis
by GACA-source on each aerosol type separately
allows for an insight into the aerosol mixture that cannot be
seen when studying the main source map only.
Trace gas composition for grid boxes with URB source
for June–August 2007–2011. The presence of enhanced trace
gas columns (in addition to NO2) is indicated by 1,
2, or 4 for HCHO, SO2, and ΔCO,
respectively: 1 thus indicates enhanced NO2 and
HCHO, 2 enhanced NO2 and SO2, 3
enhanced NO2 and HCHO and SO2,
etc. Gray areas are not dominated by URB.
Seasonal cycle of global main aerosol type
distribution according to MACC. Data are from 2007–2011 and
were divided into the four main seasons (from top to
bottom): winter, spring, summer, and fall. Aerosol types are
black carbon (BC), mineral dust (DD), organic matter (OM),
sulfate (SO4), sea salt (SS), and mixture
(MIX). Light gray areas (na) are not analyzed due to too
small mean AOD. As BC does not dominate anywhere, contours
show mean BC amount (AOD 0.02–0.1) to indicate regions
affected by smoke; see text for details.
Additional information on the sources can be gained by
adding the trace gas information that was not directly
used for source assignment. For example: the URB source
is assigned based only on the presence of enhanced
NO2 (after exclusion of BIO as source type; see
Fig. ), but the information on other
trace gas means is retained. By adding binary coding,
i.e., values of 1, 2, and 4 to grid boxes with enhanced
mean values of HCHO, SO2, and
ΔCO, respectively, we obtain the map
presented in Fig. for June–August
2007–2011. If only NO2 is enhanced, the grid box
has an index of 0 and appears dark blue. If, in addition,
HCHO is enhanced, the grid box obtains an index of
0+1=1 and appears in a lighter shade of
blue. Grid boxes with enhanced NO2, HCHO,
and ΔCO are indexed 0+1+4=5 and are
shown in orange. Grid boxes with a main source other than
URB are shown in light gray. This analysis reveals
a great diversity in urban/industrial emissions. A clear
separation can be seen in Europe, with enhancements of
NO2 in the west but additionally enhanced
HCHO in the east, confirming the findings
of . Further east, urban/industrial
aerosols are again only associated with increased
NO2 columns. Throughout most of the Indian
subcontinent, both NO2 and HCHO are
enhanced. The increased HCHO levels are mainly due
to human activities: industrial and vehicle exhaust,
biogenic emissions from agriculture (direct and indirect,
by CH4 oxidation) and burning of biomass (e.g.,
household fires) . A similar pattern
can be seen over northern Thailand. Northeastern China
emits large quantities of all trace gases investigated
here, in addition to aerosols. It is one of only a few
regions where anthropogenic SO2 can be detected
from satellite – another being the Highveld in South
Africa, which stands out in light blue (enhanced
NO2 and SO2). Grid boxes colored yellow
and orange (increased ΔCO, or increased
ΔCO and HCHO levels) mostly appear
at the edges of regions with intense biomass burning in
this season (central Africa, central South America) and
are influenced by fire emissions or are possibly
misclassified. In southern South America, the South
Atlantic Anomaly causes errors in trace gas retrievals
that show up mainly in erroneously enhanced SO2
values. Although the HCHO retrieval is similarly
affected, the threshold used in GACA-source is high
enough to exclude those outliers. Urban aerosols in North
America are accompanied by enhanced HCHO levels,
which is mostly of biogenic
origin . The filament
with enhanced ΔCO and HCHO seen on
the northeast coast of the United States is possibly due
to transported wildfire smoke from the northwest
(Canada/Alaska). Similar patterns are found for the
other seasons (see Fig. S1 in the Supplement).
Comparison with MACC
Main aerosol types from the MACC reanalysis for
2007–2011 were grouped by season and treated analogously
to the measured data with respect to the minimum AOD
threshold of 0.05 and the removal of outliers. The
dominating aerosol component is shown in
Fig. in a similar fashion to
Fig. , but there are two
important differences.
The aerosol components are
different : black carbon (BC,
black) originates mostly from biomass burning but also
occurs in urban regions due to e.g., vehicle exhaust
or household fires. In Fig. , AOD
due to BC is additionally shown in contours
(AOD =0.02–0.1), to indicate the regions affected by
biomass burning, as BC constitutes only a small
fraction of aerosol emissions by fires. The
contribution of organic matter (OM, green) to biomass
burning smoke is much greater, but OM also has
important biogenic and anthropogenic sources. The types
desert dust (DD) and sea salt (SS) are equivalent to
the source types of the same name in GACA and are
therefore indicated with the same colors (red and light
blue, respectively). Sulfate aerosols (SO4) are
indicated in the same color, blue, as URB aerosols in
GACA, because the sources are assumed to be
similar. The aerosol type is set to MIX when none of
the aerosol components contributes more than 50 %
to the total AOD.
MACC data are also inherently different from GACA
data in that each data point contains contributions of
each aerosol component (BC, OM, DD, SO4, SS), whereas
GACA determines only one dominating aerosol source per
grid box or, at most, one dominating aerosol source
for each aerosol type found in a grid box.
At a first glance, the agreement between GACA-source and
MACC is quite good (compare
Figs. and
): the general spatial and seasonal
patterns of DD and SO4 (or URB) agree well. The biomass
burning regions roughly agree, although the model does
not show BC in South America in summer, where GACA sees
a lot of BB (mostly due to fires in August, as can be
seen in MODIS fire count patterns). In addition, GACA
selects BB as the main source of AOD in sub-Sahelian
Africa in the first half of the year, whereas in MACC DD
dominates. On the other hand, the agricultural fires in
Southeast Asia in spring are well captured by both GACA
and MACC. The main sources of BIO (or OM) agree in GACA
and MACC, but the source in the southeastern USA is missed by
the model. The differences between MACC and GACA will be
discussed in more detail in the following section, where
regional seasonal cycles are investigated.
Seasonal cycles of global aerosol type and source
according to GACA and MACC for 5∘× 5∘ regions in central South America
(10–15∘ S, 60–65∘ W) and central
southern Africa
(0–5∘ S, 15–20∘ E). Data are grouped
into four seasons and separated by year. Panels (a1) and (a2)
mean AOD contribution of each aerosol type; (b1) and (b2) mean
AOD contribution of aerosol source (determined from each
aerosol type); (c1) and (c2) mean AOD contribution of aerosol
types from MACC. Abbreviations are explained in
Table .
Seasonal cycles of global aerosol type and source according to GACA
and MACC for 5∘× 5∘ regions in southeastern USA
(30–35∘ N, 80–85∘ W) and northwestern Europe
(48–53∘ N, 3–8∘ E). See Fig. for
details.
Regional seasonal cycles
Six 5∘×5∘ regions were selected
for the study of the seasonal cycle: central South
America (1), southern Africa (2), southeastern USA (3),
northwestern Europe (4), Thailand (5), and northeastern China (6); the regions are shown as enumerated yellow boxes in
the third panel of Fig. . For
each season of each year (2007–2011), the AOD of every
aerosol type is shown in panels (a1)–(a6) of
Figs. –. The
dominant aerosol source was determined for each
individual aerosol type separately and is shown in
panels (b1)–(b6). The AOD fractions are therefore equal in
panels (a) and (b) of Figs. 10–12. For example, the bar representing the
fall (September–November) of 2007 in Fig. 10a1 contains
contributions from MA, SA, SN, and SNA types. The AOD
fraction corresponding to SNA reappears in Fig. 10b1 in
dark green (BIO), the dominant source of the SN fraction
is URB (blue), and the summed AOD from SA and MA types is
attributed to BB (brown). Panels (c1)–(c6) of Figs. 10–12, finally, display
the AOD corresponding to the MACC aerosol types for the
same regions. All data presented in
Figs. –
can be found in Tables S1–S6 in the Supplement.
The first two regions, central South America and southern
Africa (panels a1–c1 and a2–c2 of
Fig. , respectively), are
characterized by seasonal biomass burning. The fire
season starts in late summer in South America; the
highest number of fires is usually found in fall. The
high year-to-year variability of biomass burning in this
region is clearly reflected in all three panels. Both
GACA and MACC ascribe the larger part of AOD in winter
and spring to secondary organic aerosols (BIO and OM in
GACA and MACC, respectively). Although the DD
contribution in the model appears to be somewhat high (no
DD is detected by GACA), the agreement between GACA and
MACC is good for this example. Good agreement is also
found for southern Africa, where smoke forms the major
part of the aerosol mixture during the fire season in
summer, when the highest AOD are detected. All panels
show that the year-to-year variation is much smaller than
in South America. Urban/industrial aerosols appear to be
overestimated by GACA, whereas MACC shows higher
contributions of DD.
Seasonal cycles of global aerosol type and source according to GACA
and MACC for 5∘× 5∘ regions in Thailand
(15–20∘ N, 100–105∘ E) and northeastern China
(35–40∘ N, 115–120∘ E). See Fig.
for details.
The regions southeastern USA and northwestern Europe are
dominated by non-absorbing aerosols
(Fig. a3, a4). Throughout most of the year, aerosols over
the southeastern USA are of urban/industrial origin (URB and SO4 for
GACA and MACC, respectively). In summer this region is
dominated by secondary organic
aerosols , clearly seen by GACA
(Fig. 11b3), which attributes nearly all AOD to BIO. MACC,
on the other hand, only shows a slight increase in OM
relative to the other seasons. The contributions of dust
and sea salt to the aerosol mixture appear to be too
large in the model in comparison to GACA results, which
points to sources missing in the model: MACC scales the
aerosol amount with MODIS AOD but keeps the mass
fractions of the different aerosol components constant
(see Sect. ). Hence, if a source is
missing, e.g., secondary organic aerosols, the AOD due to
those aerosols is spread over the remaining
components. The small year-to-year variation observed in
MACC aerosol composition is a result of this procedure.
There is no clear aerosol seasonal cycle recognizable in
northwestern Europe (Fig. 11a4–c4): the AOD is rather constant
throughout the year and the composition rarely deviates
from the urban/industrial (URB and SO4) type. In winter
there is a larger contribution of medium-size and large
particles (Fig. 11a4), which GACA-source has trouble
identifying but which MACC attributes to sea salt. As in
all previous regions, the model sees significant amounts
of dust that are not detected by GACA. This can partly be
explained by too low deposition rates in the model but
may also be due to the fact that GACA does not select DD
as a source if any trace gas means are enhanced (unless
the aerosol type is large absorbing).
Figure presents the seasonal
cycle for two regions in Asia. In winter and particularly
in spring, agricultural fires in Thailand release large
quantities of smoke, as seen by both GACA and MACC
(Fig. 12a5–c5). During the rainy season (June–October)
secondary aerosols dominate, both from anthropogenic (URB
and SO4) and biogenic sources (BIO and OM). MACC finds
significant contributions of dust which are not seen by
GACA.
In northeastern China, the seasonal mean AOD is greater than 0.5
throughout the year for each year from 2007 to 2011
(Fig. 12a6–c6). Most of the AOD can be attributed to
aerosols of anthropogenic origin (URB and SO4), but
a large fraction is caused by mineral dust transported
from deserts in Mongolia, northern China, and Kazakhstan,
especially in winter and spring. In view of their sizes
(medium to large), most of the aerosols
characterized as BB by GACA are probably polluted dust or
dust in the presence of pollution, i.e., NO2,
HCHO, SO2 or ΔCO. The
variability of the seasonal cycle of DD appears to be
underestimated by MACC (compare Fig. 12a6 and c6). The
amount of modeled BC in China is as high as for South
America in the biomass burning season (see
Fig. c1), which may
be reflected by the high levels of aerosol absorption found
by GACA for northeastern China. The more probable source
of absorbing aerosols is, however, desert dust.
Discussion
GACA is a threshold-based algorithm for the determination
of dominant aerosol types and sources globally on
a seasonal basis. In this section we investigate the
robustness of the algorithm, motivate our choice of EAE
(as opposed to FMF), and compare results from GACA with
previously reported climatologies from measurements and
models. Although the algorithm can be improved further by
fine-tuning with regional settings and/or additional
(satellite) data, the main objective of the current study
is to explore what can be learned from the combination of
different satellite data sets. We present some
suggestions for future improvements to GACA in
Sect. .
Sensitivity studies
It is clear that GACA results depend on the choice of
thresholds and criteria for aerosol type and source
determination. Most source assignments are rather robust and altering
thresholds only causes small shifts of borders between different sources.
Beyond being rooted in textbook knowledge, our criteria are justified by the
consistency of the obtained results and the good general agreement with MACC
model results. The basic
assumption underlying GACA is that enhancements in trace
gas and aerosol abundance are caused by the same source
and wherever this is not the case, the algorithm
fails. Correctly characterizing mixed air masses
(e.g., dust with smoke or pollution) or transported
aerosols (that may be present above or in addition to
local pollution) thus is beyond the capabilities of GACA.
To investigate how robust GACA is with respect to effects
of clouds, varying time ranges, and the treatment of
outliers, we performed a series of tests. First, we
applied different cloud filters to the GOME-2 data prior
to gridding. Unfortunately, a similar test could not be
performed on MOPITT data, as we used gridded monthly
means that had already been cloud-cleared. MODIS AOD is
only retrieved under clear sky conditions, but because
the field of view of the instrument is small, retrievals
in between cloud patches are often possible in regions
that would be considered cloudy by GOME-2. Setting the
maximum effective cloud fraction (CF) to 0.05, 0.20, or
0.40 does not cause major changes in global maps of
GACA-type and GACA-source (Figs. S2 and S3 in the Supplement,
respectively). Perhaps surprisingly, the results are
still similar if only data with CF >0.40 are selected:
the main difference is the disappearance of non-absorbing
aerosol types due to the increase in data points with
UVAI <0. We conclude that measurements of NO2,
HCHO, SO2, and UVAI in the presence of
clouds contain enough information to be used for
characterization of aerosol (or air mass) sources, at
least on a monthly mean basis. Measurements of other
trace gases, e.g., CO, are expected to be similarly
useful e.g.,.
The effects of varying the time range from the maximum of
15 months per season (5years×3months) are rather trivial: the scatter
increases with decreasing data amount, and so does the
influence of one-time events, such as volcanic
eruptions. We performed tests for the summer
(June–August) and found that GACA-type and GACA-source
results are very similar if data from 2007–2011 or
2008–2010 are used. Decreasing the time window further
to July 2007–2011 (5 months) or to June–August 2009
causes noisy results with large data gaps (particularly
over South America). For source determination of
individual aerosol types (as in
Fig. ), the statistical requirements
are even higher. Changing the resolution of GACA-source
to 1∘×1∘ yields dominant source
maps very similar to those in
Fig. but with several large
data gaps, most notably over South America in summer.
In the standard GACA setup, each data set is screened
for outliers which are then removed (see
Sect. for details). The reason
for this procedure is that GACA is aimed at constructing
a climatology in which exceptional events (large fires,
volcanic eruptions, etc.) should not be
represented. Another reason is the removal of artifacts
which are, however, only rarely encountered in the
monthly averaged, gridded data sets used here – except
in the region affected by the South Atlantic Anomaly. If
GACA is run without removing outliers, the resulting
source maps are very similar to those from the standard
run (compare Fig. with Fig. S4
in the Supplement); in fact, the map for winter does not
change at all. The biggest change is found for the spring
maps, where several volcanic sulfate (VOG) plumes appear, e.g.,
most prominently the one from the Fernandina Volcano on the
Galapagos Islands, which erupted in April 2009. VOG
plumes from degassing (Kilauea, Hawaii, 2008) and
erupting (Nabro, Eritrea, 2011) volcanoes are also seen
more clearly in the summer map when outliers are not
removed. The largest change in summer is caused by the
exceptional fire season that occurred in 2010 in
Russia. Because GACA uses AOD weighting, the thick,
persistent smoke plumes strongly influence the algorithm,
despite the fact that the fires occurred in only 2 out of
15 months considered. In South America more grid boxes
are assigned to BB, replacing URB; the same is seen in
fall, although there BB replaces several assignments of
BIO if outliers are included in the analysis.
Extinction Ångström exponent and fine-mode fraction
Throughout this study, EAE is used as a measure of
aerosol size, instead of the often-used FMF (also denoted
as η in the MODIS literature). The main reason is
consistency among the three MODIS aerosol algorithms: the
Deep Blue algorithm does not output FMF, and although
both dark target algorithms (land and ocean) provide
values of FMF, the definitions are different. The MODIS
over-ocean retrieval adjusts the abundance of two aerosol
types – one fine-mode, one coarse-mode – to best fit
the measured radiance at six wavelength bands. The two
types are chosen from a total of nine aerosol types (four
fine, five coarse), each represented by a single
lognormal size distribution. The over-ocean FMF is the
radiance fraction attributed to the fine-mode aerosol
type . Over (dark) land, the FMF
represents the weighting of fine-dominated and
coarse-dominated models, which each consist of
fine and coarse mode(s). In practice, FMF is essentially
binary, rarely deviating from either 0 or
1 .
The definition of EAE, on the other hand, is unambiguous
(Eq. 1). Throughout the course of the MODIS retrieval, AOD
is determined at each of the wavelengths used in the
retrieval, hence EAE can be computed for several
wavelength combinations. Here, 470 and 660 nm were
chosen, as these are the only two wavelengths used in all
three MODIS retrievals. Despite the fact that like FMF,
EAE is affected by a priori assumptions of aerosol
optical properties and surface reflectance (over land),
the monthly pattern of EAE corresponds to the global
distribution of dust and non-dust and
this is sufficient for the application presented
here. For spatially and temporally higher-resolved
characterization studies, however, a different (or
additional) metric may need to be used, e.g., size and/or
shape from instruments like the MISR or
POLDER Polarization and Directionality of the
Earth's Reflectances;.
Comparison with other climatologies
Different aerosol climatologies of microphysical aerosol
properties (or proxies) have been constructed using
remotely sensed data in the past. The most established
empirical climatologies are derived from AERONET (Aerosol Robotic Network)
data . At a first glance, the
agreement between GACA-source and AERONET-derived climatologies (e.g., Fig. 2
in Omar et al., 2005, Fig. 3 in Levy et al., 2007a, or Fig. 2 in Lee et al.,
2010) is good. However, due to large differences in spatial sampling and the
limited information available from AERONET, the informative value of such a
comparison is limited.
More recently, large-scale collaborations between various
modeling groups have shown that a combination (or mean)
of aerosol properties from different models performs
better (i.e., display smaller differences with
measurements) than the output of any single
model e.g.,. The
resulting climatologies (Fig. 2 in Kinne et al., 2013, and
Fig. 3 in Sessions et al., 2015) are in agreement with
GACA regarding the dominating aerosol type. But, again,
the gain from such a comparison is limited because there
is no separation of aerosol types in the presented model
climatologies apart from that between fine and coarse
modes. It would be more interesting to compare the
aerosol composition from the model climatologies with
GACA-source, but this is beyond the scope of the current
study. Recently published model data of global aerosol
composition allow for a more detailed
comparison with GACA-source results. The agreement
between our
Figs. –
and Chin's Fig. 6a (where regional annual average AOD
composition from 1980–2009 is shown) is good; many of
the discrepancies between GACA-source and GOCART (Goddard
Chemistry Aerosol Radiation and Transport) model results
may be attributed to the differences in geographical
selection. There are, however, some important
differences, two of which point to inaccuracies in the
modeling of secondary organic aerosols. In the regions
of southern USA and South America, GOCART clearly
underestimates the amount of organic matter contributing
to aerosols. This is particularly evident in South
America, where both GACA-source and MACC ascribe the
major part of AOD to secondary organic aerosols
throughout the year, whereas in GOCART sulfate aerosols
contribute almost 50 % to the yearly mean
AOD. Additionally, the amount of desert dust appears to
be high compared to GACA. The general underestimation of
secondary organic and biomass burning aerosols, as well
as the overestimation of desert dust by the GOCART model
is known and might be remedied with the
help of an algorithm like GACA.
Applications and improvements
The presented algorithm is an attempt at determining
dominating aerosol types and sources on a global scale
and mainly intends to show the potential of combined
trace gas and aerosol data sets. The most important
application of an algorithm like GACA is the improvement
of model emissions of aerosols and trace gases, as
suggested in the study by . Not only models
that rely on data assimilation (like MACC, now succeeded
by CAMS) may benefit from comparisons with GACA. The
possibility of selecting certain aerosol types (e.g.,
small non-absorbing aerosols) or sources (e.g.,
urban/industrial) for more detailed investigations of the
relationships between AOD and trace gases is a useful
tool for the assessment of model performance regarding
aerosols and may assist in finding strategies to improve
aerosol parameterization. In addition, GACA is rather
robust despite the flexibility with respect to temporal
and spatial resolution and input data.
There is a multitude of possible adaptations for an
algorithm like GACA, but here we focus on three.
Adaptation of GACA to shorter time periods and
smaller spatial scales. The algorithm as such can be
easily applied to daily Level-2 data (on a single-pixel
scale), with the caveat that co-location of the
measurements then becomes more important. This could be
achieved using data from a single instrument
(e.g., GOME-2 or OMI), from different instruments on the
same platform (GOME-2 and Infrared Atmospheric Sounding
Interferometer (IASI); OMI and Tropospheric Emission
Spectrometer, TES), or from instruments closely
following each other, as in the A-Train. Such an
approach could be directly applied to atmospheric
composition modeling through global data assimilation,
e.g., in CAMS. Using the combined information from
different satellite observations, the aerosol type
could be updated in addition to the total AOD, yielding
a more realistic mix of aerosol composition.
Application of GACA to cloudy data, i.e., aerosol
and trace gas measurements of pixels with high cloud
cover. As shown above, trace gas measurements of cloudy
pixels contain enough information to be used for
aerosol characterization. These would have to be
combined with aerosol retrievals over clouds, e.g., from
MODIS or OMI .
Modification of GACA to ground-based data. For
example, multi-axis-DOAS (MAX-DOAS) measurements of
trace gases could be combined with aerosol data from
a sun photometer (e.g.,
AERONET) to assess local aerosol sources.
Possible future improvements include (a) the use of more
aerosol data, e.g., particle shape and aerosol layer
height (e.g., from POLDER or MISR) or more trace gas data
from GOME-2 (glyoxal) or other instruments; (b) making use
of spatial and/or temporal patterns and correlations,
e.g., by taking into account the results from neighboring
grid boxes or by pattern recognition; and (c) replacing the
fixed thresholds with a threshold climatology that
depends on location and season.
Conclusions
Aerosols and trace gases are frequently co-located, and
often even correlated, because they are (1) emitted by
the same sources, e.g., in the case of biomass burning
smoke; (2) formed from the same precursor, e.g., volatile
organic compounds and secondary organic aerosols; or (3) formed from trace gases in the atmosphere,
e.g., sulfate aerosols from SO2. We exploit this
fact for the assessment of the dominant aerosol source
from satellite observations. In this paper, we introduce
a strategy for the systematic classification of aerosols
using the combination of aerosol optical depth and
extinction Ångström exponent from MODIS with UV
Aerosol Index and trace gas columns (NO2,
HCHO, and SO2) from GOME-2, and CO
columns from MOPITT. Our Global Aerosol Classification
Algorithm, GACA, is separated into two main steps: first,
an aerosol type is determined based on its optical
properties; subsequently, trace gas information is added
to appoint a dominant aerosol source. The obtained
global yearly and seasonal maps are generally in good
agreement with MACC model data, indicating that both are
legitimate. However, systematic differences are also
found: more desert dust and less secondary organic
aerosols are indicated by MACC than by GACA. This
demonstrates the potential of our method – combining
aerosol and trace gas data – to evaluate and investigate
aerosol treatment (parameterization, sources, transport,
aging and removal processes) in air quality and climate
models. One possible application of an algorithm like
GACA is the updating of both aerosol and trace gas
emissions, e.g., in CAMS (successor of MACC) or in
GEOS-Chem, as suggested in the study
by . Since the mix of aerosol types is
currently preserved in models, a combined data
assimilation of aerosol and trace gas observations would
lead to an overall more realistic representation of
aerosols by models.
We find that the rather simple, threshold-based GACA
suffices for very plausible results that are quite robust
with respect to outliers, choice of time range and cloud
fraction thresholds. We emphasize, however, that the
presented study is exploratory in nature. We provide
several suggestions for improvement of the algorithm.
With the coming new generation of space-based DOAS
instruments with high spatial resolution, in particular
TROPOMI (Tropospheric Monitoring Instrument on the
polar-orbiting Sentinel-5p platform; Veefkind et al., 2012)
and the geostationary Sentinel 4 ,
more (cloud-free) data will be available. With such
instruments, global aerosol-type maps with even higher
spatial and temporal resolution become feasible. These
maps may find a wide range of applications: from
modelers, who can use the information to verify emissions
and aerosol processes, to scientists working to update
aerosol climatologies used in the retrieval of aerosol
optical depth (e.g., MODIS) or trace gas columns, and
environmental policy makers, for the development of
effective mitigation strategies.