Improvements in air quality and Earth's climate
predictions require improvements of the aerosol speciation in chemical
transport models, using observational constraints. Aerosol speciation (e.g.,
organic aerosols, black carbon, sulfate, nitrate, ammonium, dust or sea
salt) is typically determined using in situ instrumentation. Continuous, routine
aerosol composition measurements from ground-based networks are not
uniformly widespread over the globe. Satellites, on the other hand, can
provide a maximum coverage of the horizontal and vertical atmosphere but
observe aerosol optical properties (and not aerosol speciation) based on
remote sensing instrumentation. Combinations of satellite-derived aerosol
optical properties can inform on air mass aerosol types (AMTs). However,
these AMTs are subjectively defined, might often be misclassified and are
hard to relate to the critical parameters that need to be refined in models.
In this paper, we derive AMTs that are more directly related to sources and
hence to speciation. They are defined, characterized and derived using
simultaneous in situ gas-phase, chemical and optical instruments on the same
aircraft during the Study of Emissions and Atmospheric Composition, Clouds,
and Climate Coupling by Regional Surveys (SEAC4RS, an airborne field
campaign carried out over the US during the summer of 2013). We find
distinct optical signatures for AMTs such as biomass burning (from
agricultural or wildfires), biogenic and polluted dust. We find that all
four AMTs, studied when prescribed using mostly airborne in situ gas measurements,
can be successfully extracted from a few combinations of airborne in situ aerosol
optical properties (e.g., extinction Ångström exponent, absorption Ångström
exponent and real refractive index). However, we find that the optically
based classifications for biomass burning from agricultural fires and
polluted dust include a large percentage of misclassifications that limit
the usefulness of results related to those classes.
The technique and results presented in this study are suitable to develop a
representative, robust and diverse source-based AMT database. This database
could then be used for widespread retrievals of AMTs using existing and
future remote sensing suborbital instruments/networks. Ultimately, it has
the potential to provide a much broader observational aerosol dataset to
evaluate chemical transport and air quality models than is currently
available by direct in situ measurements. This study illustrates how essential it
is to explore existing airborne datasets to bridge chemical and optical
signatures of different AMTs, before the implementation of future spaceborne
missions (e.g., the next generation of Earth Observing System (EOS)
satellites addressing Aerosols, Cloud, Convection and Precipitation (ACCP)
designated observables).
Introduction
Aerosols have an important yet uncertain impact on the Earth's radiation
budget (e.g., Boucher et al., 2013) and human health (e.g., U.S. Environmental Protection Agency, 2011,
2016; Lim et al., 2012; Lanzi, 2016; Landrigan et al., 2018; Wu et al.,
2020). In particular, aerosols impact human health by increasing the number
of cases of emphysema, lung cancers, diabetes, hypertension and premature
deaths (e.g., Wichmann et al., 2000; Pope et al., 2002; Lim et al., 2012;
Lelieveld et al., 2019, 2015; Stirnberg et al., 2020; Nault et al., 2021);
this particularly holds true for specific species of aerosols with
high oxidative potential (e.g., Daellenbach et al. 2020).
We define aerosol speciation as the inherent chemical composition
of the aerosol, the chemical species that are represented in chemical
transport models (CTMs) (e.g., black carbon (BC), organic aerosol (OA,
typically classified into primary and secondary organic aerosol, SOA),
brown carbon, sulfate, nitrate, ammonium, dust, and sea salt). These are
typically defined to match the operational quantities reported by in situ
instruments.
CTMs derive aerosol optical properties and estimate the radiative forcing
due to aerosol–radiation interactions (RFari), based on simulated water
uptake, simulated aerosol mass concentrations, simplified aerosol size
distributions and assumed aerosol refractive indices per species (Chin et
al., 2002). RFari for individual aerosol species are less certain than the
total RFari (Boucher et al., 2013; Myhre et al., 2013). Myhre et al. (2013)
present a large AeroCom Phase II inter-model spread in the RFari of several
aerosol species. BC, for example, had a 40 % relative standard deviation
in RFari. Inter-model diversity in estimates of RFari is caused in part by
different methods for estimating aerosol properties (e.g., emissions,
transport, chemistry, deposition, optical properties; Loeb and Su, 2010)
and to a lesser extent by surface and cloud albedos, water vapor absorption,
and radiative transfer schemes (e.g., Randles et al., 2013; Myhre et al.,
2013; Stier at al., 2013; Thorsen et al., 2021).
In order to constrain model simulations, and in particular to reduce the
uncertainties associated to RFari per species, data assimilation techniques
have been adopted using optimal estimation methods and observational
constraints that we separate in four main groups. The first group of
constraints consists in column-integrated aerosol optical properties from
passive orbital and/or suborbital instruments (e.g., Collins et al., 2001;
Yu et al., 2003; Generoso et al., 2007; Adhikary et al., 2008; Niu et al.,
2008; Zhang et al., 2008; Benedetti et al., 2009; Schutgens et al., 2010;
Kumar et al., 2019; Tsikerdekis et al., 2021). The second group consists in
fine aerosol mass concentrations from airborne and/or ground-based
instruments (e.g., Lin et al., 2008; Pagowski and Grell, 2012). The third
group consists in a combination of in situ gas-phase measurements (e.g., sulfur
dioxide, nitrogen dioxide (NO2), ozone and carbon monoxide (CO)), fine
aerosol mass concentrations from ground-based instruments and
column-integrated aerosol optical properties from passive orbital
instruments (e.g., Ma et al., 2019). The fourth group consists in surface
(e.g., Kahnert, 2008, Yumimoto et al., 2008; Uno et al., 2008) and
space-based aerosol lidar profiles (e.g., Sekiyama et al., 2010; Zhang et
al., 2011), which are used to constrain aerosol mass and extinction.
Constraining model-predicted aerosol mass concentrations with passive
satellite total column-integrated aerosol properties has been shown to be
useful to constrain model-predicted aerosol optical depth (AOD). This is the
case for the single-channel visible AOD retrievals from the Moderate
Resolution Imaging Spectroradiometer (MODIS) sensor (e.g., Yu et al., 2003;
Zhang et al., 2008; Benedetti et al., 2009; Sessions et al., 2015; Buchard
et al., 2017; Kumar et al., 2019; Ma et al., 2019). However, this process
does not correct the uncertainty associated with the simulated vertical
distribution of aerosols, nor can it derive aerosol chemical speciation. On
the other hand, assimilation of satellite-derived optical properties related
to particle size (e.g., extinction Ångström exponent, EAE) and light
absorption (e.g., single scattering albedo, SSA) represents a step forward
(e.g., Tsikerdekis et al., 2021). Another way to improve estimates of
speciated RFari would be to use satellite-derived total column speciated
aerosol mass concentration to adjust the mass concentration of individual
aerosol masses when applying data assimilation techniques in the model (and
potentially the emission/chemistry/transport processes driving them).
However, currently no satellite-derived retrievals of aerosol chemical
speciation exist.
Let us note an important distinction between what is called aerosol
speciation and air mass aerosol type (AMT). The AMT is representative of
typical aerosol mixes associated with certain seasons and geographical
locations. It is a coarse definition (qualitative) of the aerosol size,
shape and color that dominates an air mass (e.g., clean marine, dust,
polluted continental, clean continental, polluted dust, smoke and
stratospheric in the case of the active spaceborne Cloud-Aerosol Lidar with
Orthogonal Polarization (CALIOP) on board the Cloud-Aerosol Lidar and
Infrared Pathfinder Satellite Observation (CALIPSO); Omar et al., 2009).
In the next paragraphs, we concentrate on A-Train's POLDER (Polarization and
Directionality of Earth's Reflectances) passive satellite observations on
board the PARASOL platform. POLDER measures polarized radiances in 14–16
viewing directions at 443, 670, and 865 nm and retrieves aerosol optical
properties over land (Deuzé et al., 2001) and over ocean (Herman et al.,
2005) using its standard retrieval algorithm. In addition, two alternate
POLDER retrieval algorithms from the Netherlands Institute for Space
Research (SRON) algorithm (Hasekamp et al., 2011, Fu et al., 2020) and generated by
the GRASP (Generalized Retrieval of Atmosphere and Surface Properties)
algorithm (Dubovik, 2014) make full use of multi-angle, multi-spectral
polarimetric data.
On the one hand, recent techniques infer aerosol speciation from POLDER
using an inverse modeling framework, which consists in fitting satellite
observations to model estimates by adjusting aerosol emissions. For example,
Chen et al. (2018, 2019) use POLDER/GRASP spectral AOD and aerosol
absorption optical depth (AAOD) to estimate, e.g., emissions of desert dust
or BC. Similarly, Tsikerdekis et al. (2021) use POLDER/SRON AOD, AAOD, EAE
and SSA but with a different model and assimilation technique, as well as to
estimate the aerosol mass and number mixing ratio of specific aerosol
species.
On the other hand, AMTs inferred by various techniques and using satellite
remote sensing observations are useful to provide spatial context (e.g.,
regional, seasonal, annual trends) to support other observations of aerosols
and clouds or evaluate other aerosol type classifications. These AMTs are
also useful in evaluating models in simple cases where a single aerosol
species is present (e.g., pure dust). For example, Johnson et al. (2012)
demonstrated how CALIOP mineral dust aerosol extinction retrievals were
applied to improve dust emission and size distribution parameterizations in
the global GEOS-Chem model, a global 3-D model of atmospheric chemistry
driven by meteorological input from the Goddard Earth Observing System
(GEOS).
We have inferred qualitative AMTs from passive POLDER/SRON remote sensing
retrievals of EAE between 491 and 863 nm, SSA at 491 nm, a difference in
single scattering albedo (dSSA) between 863 and 491 nm, a real refractive
index (RRI) at 670 nm, and a pre-specified clustering and Mahalanobis
classification method (SCMC) (Russell et al., 2014).
The SCMC method, based on the methodology developed by Burton et al. (2012),
uses the Mahalanobis distance (Mahalanobis, 1936) analysis in
multidimensional space to assign AMTs based on a suite of observed
parameters. The number of parameters is adjustable, as is the nature of the
parameters themselves. Similarly, the AMT definitions are flexible. However,
a key requirement for the SCMC method is that reference values for each AMT
must be defined (i.e., the mean, variances and covariances of the aerosol
variables), typically using prescribed AMTs for a subset of observations. In
practice, when applying SCMC to a new environment, a training dataset is
created by prescribing a set of air masses based on independent
observations. Those pre-specified AMTs from Russell et al. (2014) are based
on dominant aerosol types from AErosol RObotic NETwork (AERONET) stations at
specific locations and times (Holben et al., 1998). In Russell et al. (2014), qualitative AMTs were derived over the island of Crete, Greece,
during a 5-year period using the SCMC method and pre-specified AMTs from
global AERONET observations. We refer the reader to Sect. 2 of Russell et al. (2014) or Burton et al. (2012) for a thorough description of the SCMC
method.
We have extended the methods of Russell et al. (2014) (i.e., over Greece) to
the entire globe for the year 2006. On the one hand, the POLDER-derived AMTs
presented reassuring features such as (i) dust over the Atlantic between the
Saharan coast and Central to South America, predominant from March to
August; (ii) urban industrial aerosols found near industrialized cities such
as the east coast of North America and over Southeast Asia; and (iii) two
different types of biomass burning (BB) over the southeast Atlantic (i.e.,
one illustrating more smoldering combustion and pre-specified using AERONET
stations located in South America and the other one illustrating more
flaming combustion and using AERONET stations in Africa). We found darker BB
(i.e., lower SSA) in August compared to September, due to an increase in
POLDER-retrieved SSA during the season, reflecting either a change in BB
aerosol composition (Eck et al., 2013) or a mix of AMTs (Bond et al., 2013).
On the other hand, many features such as marine aerosols over the Saharan
desert or urban industrial aerosol type in South America were most likely
misclassified. Ambiguities in POLDER-derived AMTs could result from a
combination of four factors:
errors in POLDER reflectance/polarization measurements and aerosol
retrievals (e.g., errors in POLDER retrievals get larger for smaller AODs
and/or a smaller range of scattering angles);
a coarse spatial resolution of the gridded POLDER product (e.g.,
2∘×2∘);
non-optimal AERONET-based pre-specified AMTs used as a training dataset
(e.g., the AMT illustrating more flaming combustion is defined in locations,
such as Mongu in Africa, where smoldering and flaming combustion might be
occurring at the same time, together with other AMTs present in the
atmospheric column); and/or
a restricted number of POLDER-derived aerosol optical parameters – that is,
the relative AMT discriminatory power increases with the number and
diversity of observed parameters.
Unlike in Russell et al. (2014), where we used total column remote-sensing-inferred optical properties which are often representative of a mix
of different AMTs, the AMTs in this study are defined, characterized and
derived using simultaneous gas-phase, chemical and optical instruments on
the same aircraft. This reduces errors in measurements/retrievals and errors
due to spatiotemporal colocation (see i–ii above). It also reduces
ambiguities in the selection of the AMT training dataset (see iii), and we
specifically investigate the strengths and weaknesses of optical properties
used as tools to define AMTs and how much these optical properties can
capture dominant aerosol speciation (see iv).
The objectives of this study are to
prescribe well-informed AMTs that display distinct aerosol chemical and
optical signatures to act as a training AMT dataset and
evaluate the ability of airborne in situ-measured aerosol optical properties that
are suitable to be retrieved from space to successfully extract these AMTs.
We first describe the instruments, observations and methods used in this
study (Sect. 2). We provide additional information on the methods in
Appendix A1. We then present (Sect. 3), conclude (Sect. 4) and discuss
(Sect. 5) our results. We provide additional results in Appendix A2. We
refer the reader to Appendix B for the abbreviations and acronyms used in
this paper.
Data and methodInstruments and observations
We select NASA DC-8 airborne in situ data collected during the Study of Emissions
and Atmospheric Composition, Clouds, and Climate Coupling by Regional
Surveys (SEAC4RS) project (Toon et al., 2016), which was carried out in
August–September 2013 over North America with a strong focus on the
southeastern US (SEUS). Measurements are collected at the altitude of the
aircraft and are not representative of the full column satellite retrieval.
Although these airborne in situ observations lack the widespread coverage of
surface networks or satellite retrievals, their benefits include measuring a
wide variety of gas-phase species, aerosol types and aerosol optical
properties (Toon et al., 2016). A major strength of our study is the use of
in situ gas-phase, chemical and optical instruments on the same NASA DC-8 research
aircraft during the SEAC4RS campaign. Table 1 lists the various
airborne in situ instruments, products used in this study and important references
for each instrument. It also shows that the instruments in Table 1 sample
different aerosol sizes. This is especially true for the DASH-SP instrument,
which sampled particles with dry diameters between 180 and 400 nm during
SEAC4RS (Shingler et al., 2016). In contrast, the sampled air was
provided to the PI-Neph instrument through the NASA LARGE shrouded diffuser
inlet, which sampled isokinetically and is known to have a 50 % passing
efficiency at an aerodynamic diameter of at least 5 µm at low altitude (McNaughton et al., 2007; Espinosa et al., 2017).
Instruments, products, sampled aerosol size and references relevant to this study. More
information on the instruments during SEAC4RS can be found at
https://espo.nasa.gov/home/seac4rs/content/Instruments (last access: 13 March 2022).
Instruments ProductsSampled aerosol sizeReferences1PTR-MS, DACOM, TD-LIF, NOyO3Acetonitrile, isoprene, monoterpene, carbon monoxide (CO), nitrogen dioxide (NO2)–PTR-MS (Mikoviny et al., 2010); DACOM (Fried et al., 2008); TD-LIF (Cleary et al., 2002);NOyO3 (Ryerson et al., 2012)2PALMSInternally mixed sulfate/organic/nitrate (SON), biomass burning (BB), sea salt and dust particle types<5µm dry diameterMurphy et al. (2006) Froyd et al. (2019)3SAGAChloride (Cl), bromide (Br), nitrate (Nit.), sulfate (Sul.), oxalate (C2O4), sodium (Na),ammonium (Amm.), potassium (K), magnesium (Mg), calcium (Ca.)<4µm dry diameterDibb et al. (2003)4AMSOrganic aerosol (OA), sulfate, ammonium, nitrate0.02–0.8 µm (trapezoidal transmission efficiency, D50 at 0.035 and 0.35 µm)DeCarlo et al. (2006); Canagaratna et al. (2007); Hu et al. (2015); Guo et al. (2021)5SP2Black carbon (BC)0.1–0.5 µm (BC component, only)Perring et al. (2017)6LARGE TSI nephelometer and PSAPAbsorption, scattering and extinction coefficient (AC, SC and EC) at 450, 550 and 700 nm<5µm dry diameter for dry total scattering coefficients at 450, 550 and 700 nm (TSI nephelometer) and total absorption coefficients at 467, 530 and 660 nm (PSAP) Ziemba et al. (2013); McNaughton et al. (2007)7DASH-SPReal refractive index (RRI) at 532 nm0.18–0.40 µm dry diameterSorooshian et al. (2008); Shingler et al. (2016)8PI-NephRRI at 532 nm<5µm dry diameterDolgos and Martins (2014); Espinosa (2017, 2018)
In this study, we use the 16 aerosol optical parameters listed in Table 2 (i.e., five first parameters at three wavelengths or three combinations of
wavelengths and last parameter at 532 nm) and derived from the optical
instruments in lines 6–8 of Table 1.
In situ optical parameters used in this study (provided at a given aircraft altitude by the
instruments in lines 6–8 of Table 1), the way we call them in this paper, and how they are computed.
The way we call these parameters is closer to what would be observed from remote sensing
instruments. In the calculations, λ1 and λ2 are two given wavelengths. In this paper, we compute (i) SSA and AC at 450, 550 and 700 nm; (ii) EAE, AAE and dSSA between 450–550, 550–700 and
450–700 nm; and (iii) RRI at 532 nm.
Initial namesWhat we call them in this studyCalculation1ACACACλ1=ECλ1-SCλ12EACExtinction Ångström exponent (EAE)EACλ1,λ2=-ln(ECλ1/ECλ2)/ln(λ1/λ2)3AACAbsorption Ångström exponent (AAE)AACλ1,λ2=-ln(ACλ1/ACλ2)/ln(λ1/λ2)4SSACSingle scattering albedo (SSA)SSACλ1=SCλ1/ECλ15dSSACDifference in SSA at λ1 and λ2 (dSSA)SSACλ1,λ2=SSACλ1-SSACλ16RRIReal refractive index (RRI)–
Instead of simply using the standardized SEAC4RS merged dataset, a lot
of effort was dedicated to carefully collocate, combine, cloud-screen,
filter, and humidify datasets (i.e., convert from dry to ambient conditions), as
well as compute and interpolate/extrapolate optical parameters to specific
wavelengths (see Sect. A1.1 and A1.2).
Method
Figure 1 illustrates the overall method in this study, which involves
following the five steps described below.
Overall method in this study. PS-AMTs: prescribed source-based air
mass types (AMTs); DO-Classes: defined optical-based class definitions;
DO-AMTs: derived optical-based AMTs; EAE: extinction Ångström exponent; SSA:
single scattering albedo; dSSA: difference in SSA; AAE: absorption Ångström
exponent; AC: absorption coefficient; RRI: real refractive index; SCMC:
pre-specified clustering and Mahalanobis classification. The concept of the
wolf and its tracks is based on the dragon and its tracks in Bohren and
Huffman (2008).
Scheme to pre-specify air mass types (PS-AMTs; step 1 of Fig. 1)
using mostly gas measurements and a method based on Espinosa et al. (2018)
and Shingler et al. (2016) but modified to include marine and two different
types of BB AMTs (i.e., BBAg. and BBWild.).
Prescribe source-based aerosol air mass types (called PS-AMTs).
The PS-AMTs are defined using the gas-phase and aerosol instruments in lines
1–2 of Table 1 and a method based on Espinosa et al. (2018) and Shingler et al. (2016) illustrated in Fig. 2. These aerosol and gas measurements better
characterize the aerosol properties in these AMTs compared to observations
of aerosol optical properties. First, we define polluted dust PS-AMT (called
PollDust) using PALMS dust number fraction (i.e., PALMS
MineralFrac_PALMS) above 0.15 and the integrated dry
aerosol volume concentration by the TSI aerodynamic particle sizer (APS)
above 2 µm3 cm-3 (i.e., IntegV_Daero-PSL_APS_LARGE; note that APS
measurements sampled dry aerodynamic diameters ranging from 0.56 to
6.31 µm; Espinosa et al., 2018). Similarly, we define marine PS-AMTs when PALMS sea salt number fraction >0.15 and total
volume >2µm3 cm-3. The remaining
observations may then be evaluated for BB PS-AMTs using PTR-MS
acetonitrile, WAS isoprene_WAS, PTR-MS
isoprene-furan, PTR-MS monoterpenes, WAS CO_WAS
and DACOM CO_DACOM if (i) acetonitrile >250×10-3 ppbv or (ii) (acetonitrile >190×10-3 ppbv)
and (acetonitrile/(isoprene + monoterpene) >2.5) or (iii) CO >250 ppbv. BB PS-AMTs are further differentiated as coming
from agricultural fires (called BBAg.) if the longitude is east
of -95∘ or from wildfires (called BBWild.) if
the longitude is west of -95∘. The -95∘
longitude threshold was selected according to the location of agricultural
fires in Liu et al. (2016). If observations are not classified as PollDust
or BB, we classify them as biogenic (called Bio.) if isoprene + monoterpene >2 ppbv. Finally, remaining observations are
classified as urban if the altitude is below 3 km and NO2>1 ppbv (i.e., using the NOAA nitrogen oxides and ozone
(NOyO3), NO2_ESRL or TD-LIF NO2_TD-LIF). Section 3.1 describes these PS-AMTs, their location and
composition during SEAC4RS.
Determine most useful and well-separated aerosol optical properties.
Once the PS-AMTs are defined, we test whether these PS-AMTs exhibit distinct
aerosol optical properties and then select the most useful and well-separated aerosol optical properties. This step and the following
steps use the optical parameters listed in Table 2 and provided by the
instruments listed in lines 6–8 of Table 1. To select the most useful and
well-separated aerosol optical properties for each PS-AMT, we define a
cluster in multi-dimensional parameter space, which is composed of all the
data points (values of optical properties) in that PS-AMT category. Then,
for each point in the dataset, we calculate the nearest cluster using the
Mahalanobis distance (Mahalanobis, 1936). If the nearest cluster to a point
corresponds to the PS-AMT, then that point is steady. This method was
used in previous studies (e.g., Espinosa et al., 2018) and is described in
further detail in Sect. A1.3. Section 3.2 describes the
results from this step, i.e., the most useful and well-separated aerosol
optical properties in our study.
Define optical-based training classes (called DO-Classes).
We use the set of aerosol optical parameters defined in the second step
above to define optical-based class definitions (called DO-Classes), including
means, variances and covariances. In other terms, in this step, we form the
mathematical definitions of the classes. The DO-Classes use the steady
(i.e., well separated) points from the first half of all valid aerosol
optical observations. Once the training clusters DO-Classes are defined, we
use the Mahalanobis distance to filter outliers from our training dataset
and further purify them. Similar to Russell et al. (2014), we delete
points that have less than 1 % probability of belonging to each
pre-specified DO-Class. We also delete from a specified cluster any points
that are closer (in terms of Mahalanobis distance) to a different cluster.
Note that unlike in Russel et al. (2014), this additional filtering step has
a minimal impact on the training dataset in our study.
Derive optical-based aerosol air mass types (called DO-AMTs).
The DO-AMTs are analyzed and classified using the set of aerosol optical
properties defined in the second step above, the DO-Class defined in the
third step and the SCMC method for a set of observations that was not
included in the training datasets. This test dataset is based on
independent observations and must be of the same nature as the training
dataset. In this study, our test dataset is composed of independent airborne
in situ optical properties. It is the other half of all valid aerosol optical
observations (DO-Classes are defined using the steady portion of the first
half). We derive DO-AMT for each test data point using the SCMC method and
the DO-Class. This is achieved by assigning the test data point to the
DO-Class that shows minimum Mahalanobis distance in a multi-dimensional
space made of the best suited and most separable optical properties. Section 3.3 describes the results from this step.
Compare derived optical-based AMTs (DO-AMTs) and prescribed source-based AMTs (PS-AMTs).
We evaluate the ability of airborne aerosol optical properties to
successfully extract PS-AMTs by comparing PS-AMTs and DO-AMTs. Section 3.4
describes the results of this final step in our study.
In Fig. 1, we illustrate AMTs as wolves and their optical properties as
their tracks. The second and third step consist in describing the optical
properties (or tracks) of each AMT (or wolf). The fourth step consists in
inferring an AMT (or wolf) from its optical properties (or tracks). The
fifth and last step consist in comparing the inferred to the initial AMT
(or wolf).
ResultsPrescribe source-based air mass types (PS-AMTs)
Figure 3 shows the PS-AMTs pre-specified using mostly measured gas-phase
compounds and the method described in Fig. 2.
Air mass types pre-specified (PS-AMT) using mostly gas
measurements and methods based on Espinosa et al. (2018) and Shingler et
al. (2016) (see Fig. 2). The number of data points assigned to each PS-AMTs
are N=31 BBAg., N=382 BBWild., N=646 Bio. and N=46 PollDust PS-AMTs.
PS-AMTs marine and urban were not analyzed in the remainder of this study
due to their limited number of data points (N=9 urban in black and N=7 marine in blue). Green triangles show the location of agricultural fires
according to Liu et al. (2016).
During SEAC4RS, according to Kim et al. (2015) and Wagner et al. (2015), the campaign-averaged aerosol mass was composed of mostly OA that is
internally mixed with sulfate and nitrate at all altitudes over the SEUS
i.e., 55 % OA and 25 % sulfate mass on average according to ground-based
filter-based PM2.5 (particulate matter concentration with an
aerodynamic diameter smaller than 2.5 mum) speciation measurements
from the US EPA Chemical Speciation Network. This is consistent with the
findings of Edgerton et al. (2006), Hu et al. (2015), Xu et al. (2015) and
Weber et al. (2007) which show that PM2.5 is dominated by SOA and
sulfate during the summer in SEUS. Aircraft data show that 60 % of the
aerosol column mass (i.e., mostly OA and sulfate) is contained within the
mixing layer (Kim et al., 2015).
GEOS-Chem attributes OA mass as 60 % from biogenic isoprene and
monoterpenes sources (with a significant role of isoprene in accordance with
Hu et al., 2015; Marais et al., 2016; Zhang et al., 2018; Jo et al., 2019; and Liao et al., 2015), 30 % from anthropogenic sources, and
10 % from open fires (Kim et al., 2015). Espinosa et al. (2018) confirm
the domination of biogenic emissions in the SEUS (see their Fig. 2). Figure 3,
in agreement with these studies, shows a majority of biogenic PS-AMTs (in
green, N=646), mostly in the SEUS.
During SEAC4RS, the air sampled by the DC8 was also affected by both
long-range transport of wildfire from the west (Peterson et al., 2015; Saide
et al., 2015; Forrister et al., 2015; Liu et al., 2017) and local
agricultural fires mostly from the burning of rice straw along the
Mississippi River Valley (Liu et al., 2016). Figure 3, in agreement with these
studies, shows BBWild. PS-AMT in the west (in grey, N=382) and BBAg.
PS-AMT in the east (in salmon, N=31). Both agricultural and wildfire smoke
are mainly composed of OA, which includes a substantial amount of
light-absorbing brown carbon (Liu et al., 2017), produced mostly by
smoldering combustion (Reid et al., 2005; Laskin et al., 2015).
Although Fig. 3 also shows urban and marine PS-AMTs in the SEUS, these
PS-AMTs were not further analyzed in the remainder of this study due to
their limited number of data points (urban in black with N=9 and marine in
blue with N=7 data points).
(a) Average PALMS normalized volume concentration per PS-AMT.
PALMS normalization uses the sum of BB particles, sulfate-, organic- and
nitrate-rich particles from non-BB sources, mineral dust,
sulfate–organic–nitrate (SON) particles without a dominant sub-component,
and sea salt (the latter two PALMS aerosol types are not shown and
constitute the remainder). (b) Averaged and normalized SAGA mass
concentrations per PS-AMT; normalization uses the sum of all the SAGA
components in the x axis; Cl: chloride; Br: bromide; Nit.: nitrate; Sul.:
sulfate; C2O4: oxalate; Na: sodium; Amm.: ammonium; K: potassium; Mg:
magnesium; Ca: calcium. (c) Normalized mass fractions of AMS sulfate,
ammonium, nitrate, OA, SP2 BC, and ratio of SP2 BC and AMS OA per PS-AMT. The
AMS inorganic mass fraction of sulfate, ammonium and nitrate is normalized
to the sum of sulfate, ammonium and nitrate. The AMS and SP2 total
non-refractory (NR) mass fraction of OA and BC is normalized to the sum of
OA, BC, sulfate, ammonium and nitrate. In each blue box, the red horizontal
line indicates the median, and the bottom and top edges of the box indicate
the 25th and 75th percentiles, respectively. The black whiskers extend to
the most extreme data points not considered outliers, and the outliers are
plotted individually using red points. PS-AMTs marine and urban are not
analyzed due to their limited number of data points (N=9 urban and N=7
marine PS-AMTs).
Figure 4 describes the aerosol chemical signatures of the principal PS-AMTs
using the PALMS, SAGA, AMS and SP2 instruments (see lines 2–5 in Table 1 for
more information on these instruments and their products). Note that some
aerosol components (e.g., OA, sulfate, nitrate) are very general chemical
indicators and much less specific than the gas-phase chemistry they are
trying to predict. These aerosol components are nonetheless directly
comparable to aerosol chemical components simulated in CTMs.
Note that the four aerosol instruments in Fig. 4 measure different aerosol
properties. For instance, AMS and SAGA measure bulk concentrations of
chemical sub-components (e.g., sulfate), whereas PALMS classifies individual
particles into several size-resolved types, including mineral dust, BB and
several non-BB types that have varying amounts of internally mixed sulfate,
organic and nitrate.
The PS-AMTs in Fig. 4 show expected chemical features.
The BB PS-AMTs (i.e., BBAg. and BBWild.) record high BB particle
concentrations from PALMS in Fig. 4a; high nitrate (Nit.), ammonium (Amm.),
calcium (Ca.) and potassium (K) concentrations from SAGA in Fig. 4b; high OA
(i.e., >0.8) from AMS; and high BC mass fractions from SP2 in
Fig. 4c, in agreement with many other studies (e.g., Cubison et al., 2011;
Hecobian et al., 2011; Jolleys et al., 2015; Guo et al., 2020). The BB PS-AMTs also record higher AMS ammonium and nitrate, compared to Bio. and
PollDust PS-AMTs in Fig. 4c. This is due to ammonium nitrate forming in
fires by neutralization of freshly formed nitric acid from NOx oxidation
with an excess of primary ammonia (e.g., Guo et al., 2020).
The Bio. PS-AMTs record higher non-BB organic-rich particles from PALMS in
Fig. 4a, higher SAGA sulfate concentrations in Fig. 4b, and smaller nitrate and
ammonium (i.e., relatively acidic) and higher sulfate particle
concentrations (from, e.g., coal plants) from AMS in Fig. 4c, compared to the
BB PS-AMTs. As such, the Bio. PS-AMTs in this study are typical of the SEUS
region (e.g., Kim et al., 2015, and Hu, 2015). When using positive matrix
factorization (Ulbrich et al., 2009) on the AMS measurements, most of the
organic aerosols in the Bio. PS-AMTs are composed of biogenic SOA. The Bio. PS-AMTs also record significantly lower BC concentrations from the SP2 as
well as BC-to-OA ratios from the AMS and SP2 in Fig. 4c, compared to the BB
and PollDust PS-AMTs, in accordance with, e.g., Hodzic et al. (2020).
The PollDust PS-AMTs record, as expected, high dust concentration from PALMS
in Fig. 4a and high calcium (Ca) and magnesium (Mg) from SAGA in Fig. 4b. In
addition, the PollDust PS-AMTs also include BB from PALMS in Fig. 4a and
possibly a minor sea salt component (i.e., high sodium, Na, and chloride, Cl)
from SAGA in Fig. 4b as well as relatively high sulfate from SAGA and AMS in
Fig. 4c. A compositional picture of the PollDust PS-AMTs from PALMS in
Sect. A2.3 shows dust predominately in the coarse mode
but also an accumulation mode that contains a variety of particle types, all
of which contain sulfate and organic material.
The analysis in Fig. 4 confirms that the gas-phase-derived PS-AMTs indeed
have distinct aerosol chemical properties. Therefore, we explore whether
these PS-AMTs can be derived using only aerosol optical properties.
Determine most useful and well-separated aerosol optical properties
As described in Sect. 2.2, we need to test if the PS-AMTs from Sect. 3.1
exhibit distinct aerosol optical properties. This is an essential step to
optimize the final prediction of AMTs using aerosol optical properties
(DO-AMTs).
We start with the 16 aerosol optical parameters in Table 2 (i.e., EAE,
SSA, dSSA, AAE and AC at different combinations of 450, 550 and 700 nm and
RRI at 532 nm). Section A2.1 illustrates the ranges of
these 16 aerosol optical parameters, classified by PS-AMTs. Given that
many of these parameters have similar properties, we select 6 out of these
16 aerosol optical parameters to simplify the analysis and
presentation of results. To do that, we first look at the percentage of
points unambiguously retrieved or steady (i.e., points that are well
separated from other clusters and, hence, remain in their initial clusters)
when using different combinations of 2 out of 16 aerosol optical
parameters across all four PS-AMTs. We first select parameters AAE between
450 and 550 nm and RRI at 532 nm as they form the only combination of two
parameters to achieve >65 % steady points for all four
PS-AMTs (see Fig. A5). The rest of the six optical
parameters are either chosen at 550 nm (i.e., closest wavelength to 532 nm)
or between 450 and 550 nm. As a result, the six parameters we choose for the
remainder of this study are dSSA 450–550 nm, RRI 532 nm, EAE 450–550 nm, AAE
450–550 nm, SSA 550 nm and AC at 550 nm. Among these parameters, the
usefulness of parameters dSSA 450–550 nm, EAE 450–550 nm, SSA 550 nm and AC
at 550 nm only becomes apparent in a 3-D parameter space (see Fig. A6 and its
orange boxes, which record >65 % steady
points for many combinations of three parameters among these six selected
aerosol optical parameters).
Optical characterization of PS-AMTs using the LARGE, PI-Neph and
DASH-SP instruments (see Table 1). In each blue box, the red horizontal line
indicates the median, and the bottom and top edges of the box indicate the
25th and 75th percentiles, respectively. The black whiskers extend to the
most extreme data points not considered outliers, and the outliers are
plotted individually using red points. (a) AAE: absorption Ångström
exponent; (b) AC: absorption coefficient; (c) dSSA: difference in single
scattering albedo; (d) SSA: single scattering albedo; (e) EAE: extinction
Ångström exponent; (f) RRI: real refractive index. Numbers in the title
correspond to the number of points behind each box–whisker plot for the
respective BBAg., BBWild., Bio. and PollDust PS-AMTs.
Figure 5 illustrates the range of these six aerosol optical properties for
each PS-AMT. Fine particles (i.e., BBWild., BBAg. and Bio. PS-AMTs with
higher EAE values) show mostly well-separated variability in RRI, AAE and
dSSA. Coarse particles (i.e., PollDust PS-AMT with lower EAE
values) are optically distinctive from the other PS-AMTs, particularly
showing lower RRI, higher AAE and higher dSSA. In agreement with Selimovic
et al. (2019, 2020) in Missoula, MT, we seem to also observe separate
optical signatures and more specifically different AAE ranges for BBAg.
and BBWild. PS-AMTs during SEAC4RS.
The aerosol optical properties of the PollDust PS-AMTs in this study differ
from the ones of the pure dust AMT in Russel et al. (2014). The pure
dust in Russel et al. (2014) is based on AERONET measurements in various
dusty regions of the world. In this study, PollDust PS-AMTs show a median EAE
of ∼1.3 between 450 and 550 nm and a median RRI of
∼1.4 at 532 nm in Fig. 5, compared to
∼0 between 491 and 864 nm and 1.53 at 670 nm for
AERONET-based pure dust in Russel et al. (2014). We show that the higher
PollDust PS-AMT EAE values in our study are due to the presence of
accumulation-mode non-dust aerosols, which constitute a significant
contribution to the total number and volume concentration of particles (see
Fig. A7 for a compositional picture of PollDust PS-AMT).
Similarly, we also suggest that the low PollDust PS-AMT RRI values are due
to its non-dust accumulation mode, which is generally more hygroscopic than
pure dust and may have a larger contribution to the PollDust total growth
factor. We refer the reader to Fig. A4 for a closer look at
RRI values in the case of PollDust PS-AMTs from the PI-Neph and DASH-SP
instruments separately.
Percentage of steady points (i.e., fraction of cases of a
given type that are correctly identified) in panel (b) when using
different combinations of aerosol optical parameters in panel (a) for
each PS-AMT. Grey boxes and black points depict combinations of optical
parameters showing >65 % steady points for PS-AMTs BBAg.,
BBWild., Bio. and PollDust. RRI: real refractive index; AAE: absorption
Ångström exponent; AC: absorption coefficient; dSSA: difference in single
scattering albedo; SSA: single scattering albedo; EAE: extinction Ångström
exponent.
Figure 6 shows steady values (i.e., fraction of cases of a given type
that are correctly identified) for combinations of two, three and four
optical parameters out of the six selected aerosol optical parameters in
Fig. 5 and four AMTs (i.e., BBAg., BBWild., Bio. and PollDust). Moving
forward, we select the 16 combinations of optical parameters
highlighted by grey boxes and black dots in Fig. 6, as they show
>65 % steady points for PS-AMTs BBAg., BBWild., Bio. and
PollDust. These combinations are numbered in grey at the top of Fig. 6.
Let us note that for some cases, the fraction of steady points seems to
decrease when adding classifying variables. These cases were investigated
and are mostly due to fewer data points that are non-steady when adding
classifying parameters, out of an already small total number of data points
(e.g., a combination of EAE, dSSA, AAE and RRI shows <65 %
steady points for BBAg. PS-AMT, compared to >65 % steady
points for a combination of EAE, AAE and RRI; this is due to four more
steady points (N=18) when using a combination of three parameters,
compared to four parameters (N=14), out of a total of N=26 cases).
Moreover, we suggest that higher aerosol loadings within the air masses
allow for more accurate identification by optical properties, due to higher
accuracy of the aerosol optical properties themselves. For example, we have
seen an increase from ∼80 % to 100 % steady data
points in the BBWild. PS-AMT when using EAE, AAE and RRI when extinction
coefficients increased from 30–40 to 60–70 Mm-1 (number
of data points between N=11 and N=20).
Define optical-based class definitions and derive optical-based air mass types (DO-Classes and DO-AMTs)
Next, we derive AMTs (DO-AMTs) followed by a comparison between DO-AMTs and
the initial PS-AMTs to test the ability of aerosol optical properties alone
to capture PS-AMTs.
As described in Sect. 2.2, to derive DO-AMTs using the SCMC method, we
need (i) a combination of useful and well-separated optical properties
(e.g., EAE, AAE and RRI or combination no. 4 in Fig. 6), (ii) a set of
defined classes of reference (i.e., a training dataset that we call
DO-Class) and (iii) the computation of the Mahalanobis distance between each
observation we want to classify in a test dataset and each of the clusters
from the training dataset.
We introduce Table 3, which records the number of data points behind each
step in our study.
Number of data points per AMT behind each step in our study.
PS-AMTs marine and urban are not analyzed due to their limited number of
data points (N=9 urban and N=7 marine PS-AMTs). EAE: extinction Ångström
exponent; AAE: aerosol absorption exponent; RRI: real refractive index.
Number of data BBAg.BBWild.Bio.PollDustTotalMajor steps (see Fig. 1)1PS-AMTs31382646461105(1) Pre-specify source-based PS-AMTs (see colored points in Fig. 3)2Valid AAE (Fig. 5a)313826414611003Valid RRI (Fig. 5f)2613759033786(2) Determine most useful and well-4Valid EAE, AAE and RRI2613758533781separated aerosol optical propertiesobservations (no. 4 in Fig. 6)5Steady points*18101460256046To define DO-Class391(3) Define optical-based classes,7DO-Class*85223813311DO-Class; use steady portion offirst ∼half of observations8To derive DO-AMTs389(4) Derive optical-based AMTs,9Known DO-AMTs*325521777381DO-AMTs; apply SCMC method,10Unknown DO-AMTs*8using DO-Class, on second ∼halfof observations11PS-AMTs from dataset in l813682921638912DO-AMTs similar to PS-AMTs*105421313290(5) Compare DO-AMTs13DO-AMTs similar to PS-AMTs as31989817–and PS-AMTsa % of assigned DO-AMTs (l9)*14DO-AMTs similar to PS-AMTs as77797381–a % of PS-AMTs (l11)*
* In the case of combination no. 4 in Fig. 6, i.e., EAE, AAE and RRI.
The first line of Table 3 shows the number of data points per PS-AMT (see
Fig. 3). Then, lines 2, 3 and 4 of Table 3 show the valid number of AAE, RRI
and a combination of EAE, AAE and RRI data points. Line 5 of Table 3 shows
the steady number of data points per PS-AMT in the case of a combination
of EAE, AAE and RRI (see Fig. 6). To create the training dataset DO-Class
(line 7 in Table 3), we select the steady portion of half (every other
sample) of the entire set of valid data points (line 6 in Table 3). The test
dataset that we want to classify as DO-AMTs is the other half of the entire
set of valid data points (line 8 in Table 3). This DO-AMT dataset is made of
steady and non-steady data points.
Figure 7 illustrates the separability of the DO-Class in the 3-D space made
of aerosol optical parameters EAE, AAE and RRI. The regions of the DO-Class
are described by colored ellipses representing the mean, variance, and
covariance of the DO-Class training set. It also shows that most of the
DO-Classes represent the original source-based PS-AMTs (represented by colored
triangles in Fig. 7). However, let us note that a distinct portion of the
Bio. PS-AMTs (green triangles) seems to not be represented by the Bio.
DO-Class (green ellipse). These Bio. PS-AMTs show higher AAE and lower EAE
values and mostly fall into the PollDust DO-Class instead (red ellipse).
DO-Class definition (solid and dashed ellipses colored by AMTs
defining boundaries of the DO-Class clusters; DO-Class data points are not
plotted) and prescribed source-based PS-AMTs (triangles colored by AMTs).
A total of 75 % of the DO-Classes are contained in the solid ellipses, and 50 % of the DO-Classes are contained in the dashed ellipses. RRI: real refractive index;
AAE: absorption Ångström exponent; EAE: extinction Ångström exponent. Panels (a)–(c) illustrate PS-AMT and DO-Class in the respective 2-D spaces
made of AAE-RRI, EAE-AAE and EAE-RRI.
Line 9 in Table 3 shows the number of DO-AMTs (correctly and incorrectly)
classified as BBAg., BBWild., Bio. or PollDust AMTs using the combination of
EAE, AAE and RRI as an example, the SCMC method, and the DO-Class reference clusters. Most points from the test dataset were assigned an AMT (see
N=381 assigned DO-AMTs on line 9, compared to N=8 unknown on line 10 of
Table 3). Unclassified/unknown DO-AMTs are those where the 3-D data point is
outside the 99 % probability surface for all four DO-Classes.
Compare optical-based and source-based air mass types (DO- vs. PS-AMTs)
Once we have derived DO-AMTs from optical properties (i.e., inferred our
wolf based on its tracks in Fig. 1), we need to assess how many of the
DO-AMTs agree with those originally assigned as PS-AMTs. Line 11 in Table 3
shows the number of prescribed PS-AMTs in each category when only looking at
the test dataset to derive DO-AMTs on line 8 of Table 3 (N=389). Line 12
in Table 3 shows the number of DO-AMTs that are identical to PS-AMTs. Lines 13 and 14 show the same result but as a percentage of the respectively
derived DO-AMTs or prescribed PS-AMTs in the same category. In Table 3, we
find 77 % BBAg., 79 % BBWild., 73 % Bio. and 81 % PollDust PS-AMTs
are correctly reflected in the DO-AMTs. This result can also be seen for
combination no. 4 in Fig. 8 (i.e., EAE, AAE and RRI).
Identical DO-AMTs and PS-AMTs as a percentage of prescribed PS-AMTs
in each category when using the different combinations of optical parameters
listed in the table to the right (black squares show combination on each
line) and for the four PS-AMTs BBAg. (salmon), BBWild. (grey), Bio. (green)
and PollDust (red). Black horizontal dashed lines show 60 % and 70 %
identical DO-AMT and PS-AMTs.
Figure 8 illustrates the percentage of identical DO-AMTs to PS-AMTs when using
each of the 16 combinations of optical parameters illustrated by black
squares in the table of Fig. 8. These combinations are the same as the ones
in grey at the top of Fig. 6. This percentage, like line 14 in Table 3, is
computed as the number of DO-AMTs that agree with those originally assigned
as PS-AMTs, compared to the total number of prescribed PS-AMTs in each
category in our test dataset (e.g., line 11 in Table 3).
According to Fig. 8, the entire 16 combinations of aerosol optical
properties listed in the Table of Fig. 8 as black squares seem to capture
both the Bio. and BBWild. PS-AMTs (>∼60 %
identical DO-AMT and PS-AMTs in green and grey solid lines in Fig. 8). We
remind the reader that these PS-AMTs are mostly based on gas measurements
(see Fig. 2) and are dominated by different aerosol species (see Fig. 4).
On the other hand, fewer combinations of aerosol optical parameters seem to
adequately capture the BBAg. and PollDust PS-AMTs. Further analysis shows
that, on average, most DO-AMTs assigned to the BBAg. and PollDust categories
are, in fact, misclassified and fail to capture the Bio. PS-AMTs. As
shown earlier in Fig. 7, we suggest these DO-AMTs fail to capture the Bio. PS-AMTs because the Bio. DO-Class might not be entirely representative of
the Bio. PS-AMTs (see green triangles outside of the green ellipses in Fig. 7).
Note that three combinations of aerosol optical parameters, namely no. 4
(EAE, AAE and RRI), no. 12 (EAE, RRI, AC and dSSA) and no. 13 (EAE, AAE, RRI
and AC) in Fig. 8, seem to capture all four PS-AMTs particularly well
(>∼70 % identical DO-AMTs and PS-AMTs). Let us
mention that results linked to the use of the absorption coefficient, AC, an
extensive property that is dependent on aerosol loading, is likely to be
unique to this study and might not be representative of any other field
campaign.
Discussion
We suggest that a similar study should be performed using data from additional
airborne field campaigns which have the necessary, or equivalent, gas-phase
measurements to derive source-based AMTs and many of the critical optical
properties to extract optical-based AMTs. First, this would provide more
robust statistics – e.g., particular attention should be given to revisit the
BB from agricultural fires and polluted dust AMTs in this study. Second,
this would provide more AMTs/sub-AMTs to analyze – e.g., urban and marine AMTs
should be visited during CAMP2EX (Clouds, Aerosol and Monsoon
Processes Philippines Experiment) or KORUS-AQ (An International Cooperative
Air Quality Field Study in Korea), and other types of BB and at different
aging stages should be visited during FIREX-AQ (Fire Influence on Regional
to Global Environments and Air Quality). Finally, this would also help
assess if these chemical and optical signatures are reproducible from one
year to another.
In this study, we obtained in situ aerosol optical signatures. Another essential
step should be to examine optical signatures from a space-based passive remote
sensor(s), which derive total column effective ambient aerosol optical
properties (instead of properties measured at the altitude of the aircraft
in this study). One way to answer this question would be to compare the
defined optical-based classes (DO-Classes) signatures using collocated
airborne in situ aerosol optical properties and total column aerosol optical
properties measured or inferred by sun photometry (e.g., airborne 4STAR,
Spectrometers for Sky-Scanning Sun-Tracking Atmospheric Research, Dunagan et
al., 2013; or ground-based AERONET). This DO-Class database could then be
used as an optical-based training dataset to enable widespread derivation of
optical-based AMTs (DO-AMTs) using existing and future orbital and
suborbital remote sensing instruments and networks.
The space mission addressing the designated observable Aerosols, Cloud,
Convection and Precipitation (ACCP) from the NASA decadal survey (National
Academies of Sciences, Engineering, and Medicine, 2019) is currently designing its
suborbital (airborne and ground-based) component to address science
questions that cannot be addressed from space (e.g., bridging
satellite-inferred aerosol optical properties and aerosol speciation). This
study illustrates how essential it is to explore existing airborne datasets
to bridge chemical and optical signatures of different AMTs before the
implementation of future spaceborne missions and their corresponding
suborbital field campaign(s) – e.g., upcoming spaceborne polarimeters SPEXone
(Hasekamp et al., 2019) and Hyper-Angular Rainbow Polarimeter (HARP-2) on board
the NASA Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) (Werdell et al.,
2019) and the multi-viewing multi-channel multi-polarization imager (3MI)
(Fougnie et al., 2018) to be launched in the next 3 years or the next
generation of Earth Observing System (EOS) satellites addressing NASA's
ACCP.
Most of the six optical properties in this study (i.e., extinction Ångström
exponent, single scattering albedo, difference of single scattering albedo,
absorption coefficient, absorption Ångström exponent and real part of the
refractive index) are routinely derived by in situ and remote sensing
instrumentation/networks (see Table 4). Some optical properties are more
likely to present a higher uncertainty when measured from suborbital field
campaigns and/ or from satellites. The real part of the refractive index,
for example, although generally more uncertain, is highly desirable in many
combinations of optical parameters to capture both the BB from wildfires and
biogenic AMTs in this study. We strongly suggest future airborne campaigns
consider including in situ measurements of AAE and RRI (very few of the campaigns to
date flew PI-Neph and/or DASH-SP instruments), and a special attention should
be given to deriving these parameters accurately from space. Our analysis
has the advantage of providing alternate combinations of optical parameters
when one optical parameter is either not available or too uncertain.
Frequency at which the six aerosol optical parameters in our study
are routinely derived from aircraft and current passive satellite sensors
and importance of these optical parameters in our study. RRI: real
refractive index; AAE: absorption Ångström exponent; AC: absorption
coefficient; dSSA: difference in single scattering albedo; SSA: single
scattering albedo; EAE: extinction Ångström exponent.
Ultimately, this technique and its results has the potential to provide a
much broader observational aerosol dataset to evaluate global transport
models than is currently available. Current satellite-derived AMTs seem to
marginally help models. One way to assess models would be to directly
compare satellite-derived AMTs to AMTs derived from modeled optical
properties (which are, in turn, computed from modeled chemical composition)
using the same classification method (e.g., Taylor et al., 2015; Dawson et al., 2017; Nowottnick et al., 2015; Meskhidze et al., 2021). However, it
would be difficult to define the main source of errors in the case of a
disagreement between model- and observation-based AMTs. Potential causes of
such a disagreement could be a combination of observation and
method-specific errors or model-specific errors (e.g., the assumed model
size distribution, dry refractive index, growth factor per species, mass
extinction efficiency per species, estimated mass per species, RH,
transport, chemical processing, emissions and other physiochemical
variables). Let us emphasize that the technique and results in this study,
alone, will not be able to fully explain any discrepancies between model and
observations. However, we suggest that the use of near-simultaneous
gas-phase, chemical and optical instruments on the same aircraft restrict
the causes of a disagreement between model- and observation-based AMTs to
mostly model-specific errors. Moreover, as the AMTs in this study are less
ambiguously defined (e.g., to each AMT corresponds an averaged distribution
of aerosol chemical composition), we suggest that this may allow the
assessment (and, by extension, improvement) of a few aerosol processes
simulated in CTMs.
Conclusions
One desire of our scientific community is to ultimately translate the
space-based total atmospheric column effective AMTs such as biomass
burning, dust, urban industrial, and polluted marine into chemical species
with defined emission source inventories and formation/aging chemistry such
as sulfate, BC, OA, SOA, nitrate, dust, or sea salt to better improve
models. Fully achieving that goal might not be feasible, and progress can
only be incremental. This study constitutes a first step towards the goal of
translating the space-based total atmospheric column effective aerosol
optical properties and derived optical-based AMTs into source-based AMTs.
Current satellite-derived AMTs inferred by various techniques are useful to
provide spatial context to support other observations of aerosols and clouds
or evaluate other aerosol type classifications. However, these satellite-derived AMTs are ambiguously defined and might often be misclassified.
The AMTs in this study are defined, characterized and derived using
gas-phase, chemical and optical instruments on the same aircraft. This
reduces errors in measurements/retrievals due to spatiotemporal colocation
and ambiguities in the selection of the AMT training dataset. We also
specifically investigate the strengths and weaknesses of various aerosol
optical properties used as tools to define AMTs and how much these optical
properties can capture dominant aerosol speciation.
We first define AMTs using mostly airborne gas-phase measurements during
SEAC4RS. We find distinct optical signatures for biomass burning (from
agricultural/prescribed or wildfires), biogenic and dust-influence AMTs
(marine and urban AMTs show too few data points to analyze). Useful aerosol
optical properties to characterize these signatures are the extinction
Ångström exponent between 450–550 nm, the single scattering albedo at 550 nm,
the difference of single scattering albedo in two wavelengths between
450–550 nm, the absorption coefficient at 550 nm, the absorption Ångström
exponent between 450–550 nm and the real part of the refractive index at
532 nm. We then use these aerosol optical properties, prescribe a
well-separated AMT training dataset, and use the pre-specified clustering
and Mahalanobis classification method to derive optical-based AMTs during
SEAC4RS. We find that by using any of 16 combinations of these six optical parameters, over 65 % of two AMTs (i.e., optical-based wildfire biomass burning and biogenic) agree with their source-based analog. We find that four AMTs (i.e., biogenic, BB from wildfires, BB from agricultural fires, and polluted dust), when prescribed using mostly airborne in situ gas measurements, can be successfully extracted from at least three combinations of airborne in situ aerosol optical properties over the US during SEAC4RS, such that more than 70 % of optical observations are typed consistently with source-based analog. However, we find that misclassifications are not evenly distributed
across the classes, and specifically the optically based classifications for
BB from agricultural fires and polluted dust include a large percentage of
misclassifications that limit the usefulness of results relating to those
classes.
Additional information on methodsMethod to cloud-screen, filter and humidify airborne observations
This section describes the cloud-screening, filtering, humidification and
colocation involved in the computation of the final set of 16 optical
parameters (i.e., EAE, dSSA and AAE between 450–550, 550–700 and 450–700 nm;
AC and SSA at 450, 550 and 700 nm; and the RRI at 532 nm) in this study.
The LARGE TSI nephelometer and PSAP instruments operate under dry
conditions. The only measurement provided at ambient conditions is the EC at
532 nm. In this work, we need LARGE EC and SC at 450, 550 and 700 nm at
ambient conditions. To do that, we use the parameter fRH550_RH20to80 at 550 nm provided by the LARGE f(RH) system (different from the
TSI or PSAP instruments) and an exponential curve to obtain the impact of
hygroscopic growth on the aerosol light scattering coefficient, i.e., the
scattering enhancement factor f(RH) at 450, 550 and 700 nm. Ambient SC at
550 nm, for example, is computed as the product of dry SC at 550 nm and
f(RH) at 550 nm. We filter out any values of LARGE dry SC at 450 nm≤10 Mm-1 and LARGE ambient SSAC at 863 nm≤0.7.
DASH-SP provides measurements of RRIDASH-SP_dry, information on the particle hygroscopicity,
κDASH-SP_dry, and the particle diameter,
DpDASH-SP_dry, in dry conditions. We compute DASH-SP RRI
in ambient conditions, RRIDASH-SP_ambient, using
RRIDASH-SP_dry, κDASH-SP_dry, and
the ambient relative humidity and temperature measurements, RHHSKP and
THSKP, provided by the AIMMS-20 (Aircraft-Integrated Meteorological
Measurement System) or 3D-winds instruments. First, we vary the growth
factor, GFvar, from 1.02 to 1.5 by increments of 0.01 and compute the
particle hygroscopicity, κvar, for given RHHSKP, THSKP and DpDASH-SP_dry measurements as follows:
κvar=GFvar3-1×1-κa/κa,
where
We select the growth factor, GFvar, that provides the closest
κvar value to the κDASH-SP_dry measurement. We call this growth factor GFselect. Finally, we compute
the ambient RRI, RRIDASH-SP_ambient, using
RRIDASH-SP_dry and GFselect obtained in the
previous steps and Eq. (5) of Mallet et al. (2003) (based on Hänel,
1976) as follows:
RRIDASH-SP_ambient=RRIw+RRIDASH-SP_dry-RRIw×GFselect-3,
where RRIw=1.33. Let us note that Aldhaif et al. (2018) demonstrate
the limitations of using the volume-weighted mixing rule approach above,
especially in the presence of OA.
The PI-Neph provides measurements of dry phase function (P11)
and the second element of the scattering phase matrix (P12) at three
wavelengths over an angular range spanning >170∘.
These measurements are fed into the GRASP (Dubovik et al., 2014) algorithm
to obtain retrieved values of spectral complex refractive index, a
parameterized size distribution and derived optical properties like
scattering coefficients. In this work we utilize these optical properties
provided by PI-Neph in dry conditions: the SC at 532 nm,
SCPI-Neph_dry; the dry size distribution,
dNdlnrPI-Neph_dry; and the refractive index,
RIPI-Neph_dry. First, we compute the target ambient
SC at 532 nm, SCPI-Neph_target, as the product of
SCPI-Neph_dry and LARGE f(RH) measurements at 550 nm.
Second, we compute the ambient SC at 532 nm, SCPI-Neph_ambient, corresponding to each GFvar from 1 to 1.5 by increments of
0.01 using (i) a Mie code (Mishchenko et al., 2002) and, as input to the Mie
code, (ii) the ambient size distribution and corresponding radii, computed
from dNdlnrPI-Neph_dry and GFvar, (iii) the
ambient refractive index computed from RIPI-Neph_dry and GFvar (see Eq. A2) and a prescribed geometric
standard deviation (i.e., ∼1.12, which results in similar
computed and provided SCPI-Neph_dry values when using
the same Mie code and initial parameters dNdlnrPI-Neph_dry and RIPI-Neph_dry). Third, we select GFvar (we call this growth factor GFselect) and corresponding
RRIPI-Neph_ambient that record the minimum difference
between SCPI-Neph_ambient and
SCPI-Neph_target.
We compute ambient AMS and SP2 mass concentrations using the
parameter stdPT-to-AMB_Conversion_AMS-60s
reported with the AMS data. SP2 BC standard concentration (referred to as
refractory black carbon, and experimentally equivalent to elemental
carbon at the 15 % level; Petzold et al., 2013; Kondo et al., 2011;
Perring et al., 2017), originally in ng m-3, is converted into µg m-3 and scaled upwards, on a flight-by-flight basis, to represent the
entire accumulation mode (on average by 1.14). The AMS sulfate, ammonium and
nitrate are normalized to the sum of sulfate, ammonium and nitrate. The AMS
OA and SP2 BC are normalized to the sum of OA, BC, sulfate, ammonium and
nitrate. In the case of SAGA, bromide and chloride are set to zero
if under the detection limit of 0.0107 and 0.0391 µg m3. In the
case of PALMS, we use volume-weighted products (Froyd et al.,
2019). In this study, PALMS particle classes include mineral dust, sea salt,
biomass burning and sulfate–organic–nitrate mixtures (SON). The SON class
was further refined into organic-rich, sulfate-rich and nitrate-rich
particle types, plus a remainder of SON particles that did not exhibit a
dominant chemical sub-component. To define the marine and polluted dust
AMTs, PALMS composition was combined with aerosol size distribution data
from LARGE to yield integrated volume fractions of mineral dust and sea salt
particle types from D=0.1-5µm based on the method of Froyd et al. (2019). The average AMT chemical composition is determined as a raw number
fraction of particles observed by PALMS.
Method to collocate airborne observations
All the airborne observations are cloud-screened using wing-mounted cloud
probes. Table A1 defines three datasets used in this study with their
associated number of data points, called AIRBO1, AIRBO2 and
AIRBO3, and their combination, AIRBO. In all four datasets, the LARGE
data are first collocated to housekeeping (HSKP) data (i.e., select same
start_utc in seconds) and humidified/ filtered (see Sect. A1.1).
Definition of three datasets (AIRBO1, AIRBO2,
AIRBO3) and their combination, AIRBO (which is the dataset used in this
study); the airborne instruments involved during SEAC4RS (see Table 1);
the co-located parameters (see Table 2 for a definition of EAE, SSA, dSSA,
AC, AAE and RRI); and the number of data points showing valid aerosol
optical properties and PS-AMT BBAg., BBWild., Bio. or PollDust.
In the AIRBO1 dataset, we compute the mean HSKP and LARGE values in a
±30 s range centered on each collocated AMS–PALMS–SP2
start_time (i.e., the 1 min merged file). We then
record LARGE averaged values if (i) the average is made of at least 20 points and (ii) the standard deviation of the LARGE EAE is below 30 %. In
the AIRBO2 dataset, we compute the mean HSKP and LARGE values between
each DASH-SP start_utc and end_utc. We
record HSKP, humidified LARGE and DASH-SP values if the following four
parameters are below 30 %: (i) the standard deviation of the LARGE EAE,
(ii) the difference between κDASH-SP_dry and κvar
(Eq. A1), (iii) the standard deviation of RHHSKP, and (iv) the
standard deviation of THSKP. In the AIRBO3 dataset, we compute the
mean HSKP and LARGE values between each PI-Neph start_utc
and end_utc. We record HSKP, humidified LARGE and PI-Neph
values if the following four parameters are below 30 %: (i) the standard
deviation of the LARGE EAE, (ii) the standard deviation on
scatPI-Neph_dry, (iii) the standard deviation on
LARGE f(RH), and the difference between PI-Neph SCPI-Neph_target and SCPI-Neph_ambient (see Sect. A1.1). Finally, we
collocate the HSKP–LARGE–DASH-SP (HSKP–LARGE–PI-Neph) to the AMS–PALMS–SP2
datasets in the case of AIRBO2 (AIRBO3). To do that, if there
are multiple AMS–PALMS–SP2 data points between each HSKP–LARGE–DASH-SP
(HSKP–LARGE–PI-Neph) averaged time stamp, we average all AMS–PALMS–SP2 data
between the HSKP–LARGE–DASH-SP (HSKP–LARGE–PI-Neph) averaged time stamps. If
there are no multiple AMS–PALMS–SP2 data points between the
HSKP–LARGE–DASH-SP (HSKP–LARGE–PI-Neph) averaged time stamps, we select the
closest AMS–PALMS–SP2 data in time to the HSKP–LARGE–DASH-SP
(HSKP–LARGE–PI-Neph) averaged time stamps. The dataset in this study, AIRBO,
was obtained by first selecting common 1 min UTC time stamps from all three datasets and then arbitrarily selecting, in order of priority when present,
AIRBO2, AIRBO1 and AIRBO3.
Method to select most useful and well-separated aerosol optical
properties
This section explains the second step of Fig. 1 in more details. Figure A1
is a simplified example to illustrate our method. It shows only two optical
parameters (i.e., SSA and EAE) and three hypothetical PS-AMTs (e.g.,
pure dust in red, marine in blue and BB in green) measured by one
hypothetical optical instrument in two different environments (defined by
different locations and times, Fig. A1a–b vs. A1c–d). Figure A1a–b shows a
smaller hypothetical range of EAE and SSA for the BB PS-AMT (green cluster),
compared to Fig. A1c–d.
Conceptual/hypothetical illustration of how we quantify
separation between different air mass types and select the most useful and well-separated aerosol optical parameters. It shows three hypothetical PS-AMTs
(e.g., dust in red, marine in blue and BB in green) measured by one hypothetical optical instrument in one environment (a–b) and in another environment (c–d). The EAE and SSA values in this illustration are based on AERONET observations (Russell et al., 2014) and are representative of typical pure dust, marine, and BB total column remote-sensing-inferred ground-based EAE and SSA values. Note that it only shows two dimensions even though
some calculations of Mahalanobis distances (e.g., D1, D2, D3)
will be made using more dimensions in this study.
To answer the question of how well these PS-AMTs (i.e., red, blue and
green clusters in either Fig. A1a or c) are separated, we (i) select each
data point separately (e.g., yellow crosses in Fig. A1b and d), (ii) recompute each PS-AMT cluster with the data point excluded (i.e., different blue PS-AMT in Fig. A1b and green PS-AMT in Fig. A1d compared to Fig. A1a and c) and
calculate the Mahalanobis distance (Mahalanobis, 1936; Burton et al., 2012).
The Mahalanobis distance is the distance between the data point in question
(i.e., yellow crosses in Fig. A1b or d) and the position of each cluster
center (i.e., red, blue and green clusters in Fig. A1b or d), which
depends on cluster center, tilt and width in a multi-parameter space. These
distances are called D1, D2 and D3 on either Fig. A1b or d. In the case of the yellow cross in Fig. A1b, distance D1 is the
smallest, and the test point is reassigned to its original cluster. The test
point is by consequence well separated from other clusters and steady.
On the other hand, distance D1 is also the smallest in Fig. A1d, which
means the test point (yellow cross) in Fig. A1d is not reassigned to its
original cluster. The test point is by consequence not well separated from
other clusters in this case and not steady. The steady fraction is
the fraction of cases within each PS-AMT that are correctly identified.
Steady fractions are used to assess separation between PS-AMTs. When
including additional components (e.g., any other aerosol optical parameter
from Table 2 in addition to SSA and EAE in Fig. A1), the additional number
of steady points shows the component's relative importance in separating
the PS-AMTs. The yellow points that are steady in Fig. A1 (i.e.,
correctly classified or well separated) are used to define the most useful
and well-separated aerosol optical properties for each PS-AMT.
Additional information on resultsAerosol optical parameters classified by PS-AMT
This section describes the ranges of the 16 aerosol optical parameters
(i.e., EAE, SSA, dSSA, AAE and AC at different combinations of 450, 550 and
700 nm and RRI at 532 nm from Table 2), classified by PS-AMTs in our study.
EAE (450–700 nm, 450–550 nm, 550–700 nm) and AC (450, 550 and 700 nm)
per PS-AMT. In each blue box, the red horizontal line indicates the median,
and the bottom and top edges of the box indicate the 25th and 75th
percentiles, respectively. The black whiskers extend to the most extreme
data points not considered outliers, and the outliers are plotted
individually using red points. Let us note that the LARGE EC measurements at
700 nm experienced issues during the latter half of SEAC4RS (Yohei Shinozuka, personal communication, 2018). AC: absorption coefficient; EAE: extinction Ångström exponent.
Numbers in the title correspond to the number of points behind each
box–whisker plot for the respective BBAg., BBWild., Bio. and PollDust PS-AMTs.
SSA (450, 550 and 700 nm) and AAE (450–700 nm, 450–550 nm,
550–700 nm) per PS-AMT. In each blue box, the red horizontal line indicates
the median, and the bottom and top edges of the box indicate the 25th and
75th percentiles, respectively. The black whiskers extend to the most
extreme data points not considered outliers, and the outliers are plotted
individually using red points. AAE: absorption Ångström exponent; SSA:
single scattering albedo. Numbers in the title correspond to the number of
points behind each box–whisker plot for the respective BBAg., BBWild., Bio. and
PollDust PS-AMTs.
The dSSA (700–450 nm, 550–450 nm, 700–550 nm), RRI (from DASH-SP and
PI-Neph), RRI from DASH-SP and RRI from PI-Neph at 532 nm per PS-AMT. In
each blue box, the red horizontal line indicates the median, and the bottom
and top edges of the box indicate the 25th and 75th percentiles,
respectively. The black whiskers extend to the most extreme data points not
considered outliers, and the outliers are plotted individually using red
points. RRI: real refractive index; dSSA: difference in single scattering
albedo. Numbers in the title correspond to the number of points behind each
box–whisker plot for the respective BBAg., BBWild., Bio. and PollDust PS-AMTs.
Note the slightly lower RRI values for DASH-SP compared to PI-Neph (i.e.,
respectively 1.41 and 1.43 at 532 nm in Fig. A4) in the case of PollDust
PS-AMTs. We explain this difference in RRI values by different PollDust
PS-AMT growth factor (GF) values. We obtain GF through two methods: (1) through the
values directly measured by DASH-SP for particles in the size range 0.18< Dpdry<0.40µm and (2) through an iterative
procedure matching the output of a Mie code with dry PI-Neph retrievals and
f(RH) measurements made by the LARGE group in parallel (see Sect. A1.1
for more details). We find a respective median PollDust PS-AMT GF value of
∼1.3 and ∼1.2 in the case of DASH-SP and
PI-Neph, which we suggest is due to a smaller sampling size range for
DASH-SP, compared to PI-Neph (see Table 1).
Most useful and well-separated aerosol optical properties – 16 parameters
This section describes the percentage of points unambiguously retrieved or
steady (i.e., points that are well separated from other clusters and,
hence, reassigned to their initial clusters) when using different
combinations of respectively 2 and 3 out of 16 aerosol optical
parameters across all four principal PS-AMTs (i.e., provides more details to
Sect. 3.2).
Composition of our polluted dust (PollDust) PS-AMT
Figure A7 shows a compositional picture of the PollDust PS-AMTs from PALMS.
The accumulation mode is a mixture of particle types, all of which contain
sulfate and organic material. Coarse-mode dust particles account for most of
the aerosol volume, whereas a non-dust accumulation mode contributes most to
the total number concentration of particles.
Percentage of steady points (i.e., fraction of cases of a
given type that are correctly identified; see Sects. 2.2 and A1.3 for more info) in panel (a) when using
different combinations of two aerosol optical parameters in panel (b) for each PS-AMT. The grey box and black points are a combination
of optical parameters showing >65 % steady points for PS-AMTs
BBAg., BBWild., Bio. and PollDust. RRI: real refractive index; AAE:
absorption Ångström exponent; AC: absorption coefficient; dSSA: difference
in single scattering albedo; SSA: single scattering albedo; EAE: extinction
Ångström exponent.
Percentage of steady points (i.e., fraction of cases of
a given type that are correctly identified; see Sects. 2.2 and A1.3 for more info)
in panel (a) when using different combinations of three
aerosol optical parameters in panel (b) for each PS-AMT. Black points
are combinations of optical parameters showing >65 % steady
points for PS-AMTs BBAg., BBWild., Bio. and PollDust. RRI: real refractive
index; AAE: absorption Ångström exponent; AC: absorption coefficient; dSSA:
difference in single scattering albedo; SSA: single scattering albedo; EAE:
extinction Ångström exponent. Horizontal orange boxes show the six aerosol
optical parameters that we have selected in this study.
PALMS particle classes are mapped to the total number (a) and
volume (b) size distribution from LARGE based on the method of Froyd et al. (2019). Data include flight segments representative of the polluted dust
PS-AMT.
Abbreviations and acronyms
3MIMulti-viewing Multi-channel Multi-polarization imager4STARSpectrometers for Sky-Scanning Sun-Tracking Atmospheric ResearchAACAbsorption Ångström coefficientAAEAbsorption Ångström exponentAAODAerosol absorption optical depthACAbsorption coefficientACCPAerosols, Cloud, Convection and PrecipitationAERONETAErosol RObotic NETworkAIMMS-20Aircraft-Integrated Meteorological Measurement SystemAmm.AmmoniumAMSAerosol mass spectroscopyAMTAir mass aerosol typesAODAerosol optical depthAPSTSI aerodynamic particle sizerBBBiomass burning air mass aerosol typesBBAg.Biomass burning agricultural air mass aerosol typesBBWild.Biomass burning wildfire air mass aerosol typesBCBlack carbonBio.Biogenic air mass aerosol typesBr.BromideC2O4OxalateCa.CalciumCALIOPCloud-Aerosol Lidar with Orthogonal PolarizationCALIPSOCloud-Aerosol Lidar and Infrared Pathfinder Satellite ObservationCAMP2EXClouds, Aerosol and Monsoon Processes Philippines ExperimentClChlorideCOCarbon monoxideCTMChemical transport modelsDACOMDifferential absorption carbon monoxide monitorDASH-SPDifferential aerosol sizing and hygroscopicity spectrometer probeDO-ClassesDefined optical-based classes
DO-AMTsOptical-based air mass aerosol typesDpParticle diameterdNdlnrParticle size distributiondSSADifference in SSA at two wavelengthsdSSACDifference in SSAC at two wavelengthsEACExtinction Ångström coefficientEAEExtinction Ångström exponentECExtinction coefficientEOSEarth Observing SystemFIREX-AQFire Influence on Regional to Global Environments and Air QualityGEOS-ChemGoddard Earth Observing System model of atmospheric chemistryGFGrowth factorGRASPGeneralized Retrieval of Atmosphere and Surface PropertiesHARP2Hyper-Angular Rainbow PolarimeterHSKPHousekeeping datasetKPotassiumκParticle hygroscopicityKORUS-AQAn International Cooperative Air Quality Field Study in KoreaLARGENASA Langley Aerosol Research Group Experiment TSI nephelometer and particle soot absorption photometer (PSAP) instrumentsMgMagnesiumMODISModerate Resolution Imaging SpectroradiometerNaSodiumNit.NitrateNO2Nitrogen dioxideNOyO3NOAA nitrogen oxides and ozoneOAOrganic aerosolPACENASA Plankton, Aerosol, Cloud, ocean EcosystemPALMSParticle analysis by laser mass spectrometryPI-NephPolarized Imaging NephelometerPM2.5Particulate matter concentration with an aerodynamic diameter smaller than 2.5 µmPOLDERPolarization and Directionality of Earth's ReflectancesPollDustPolluted dust air mass aerosol typesPS-AMTsPrescribed source-based air mass aerosol typesPTR-MSHigh-temperature proton-transfer-reaction mass spectrometerRFariRadiative forcing due to aerosol–radiation interactionsRHRelative humidityRIComplex refractive indexRRIReal part of the refractive indexSAGASoluble Acidic Gases and AerosolSCScattering coefficientSCMCPre-specified clustering and Mahalanobis classification methodSEAC4RSStudy of Emissions and Atmospheric Composition, Clouds, and Climate Coupling by Regional SurveysSEUSSoutheastern USSOASecondary organic aerosolSONSulfate–organic–nitrateSP2NOAA single particle soot photometerSPEXoneSpectropolarimeter for Planetary Exploration orbitalSSASingle scattering albedoSSACSingle scattering albedo coefficientSul.SulfateTTemperatureTD-LIFThermal dissociation and laser-induced fluorescenceUS EPAUnited Stated (of America) Environmental Protection Agency
Data availability
The SEAC4RS data used in this study are publicly available at the following link: 10.5067/Aircraft/SEAC4RS/Aerosol-TraceGas-Cloud (Chen, 2013).
Author contributions
The overarching research goals were formulated by MSFK. SPB, OPH, KDF, PCJ, MSJ, JR and GLS influenced the evolution of these research goals. Specific co-authors provided specific datasets and valuable help to interpret them (OPH for POLDER/PARASOL; KDF for PALMS; AJB, LZ, KLT and YS for LARGE; JED for SAGA; TS and AS for DASH-SP; RWE and VM for PI-Neph; JLJ and PCJ for AMS; and JPS for SP2). MSFK and QT carried out the formal analyses. MSFK carried out the investigations and visualizations and wrote the original draft. All co-authors have reviewed and edited the multiple drafts of the paper. The methodology behind the SCMC method was first developed by SPB and adapted to in situ data by MSFK.
Competing interests
At least one of the (co-)authors is a member of the editorial board of Atmospheric Chemistry and Physics. The peer-review process was guided by an independent editor, and the authors also have no other competing interests to declare.
Disclaimer
Publisher’s note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
This research was supported by the NASA Atmospheric Composition Modeling and
Analysis Program (ACMAP). We thank Richard Eckman for his support. We appreciate the efforts of all the SEAC4RS in situ instrument
principal investigators involved in this study for obtaining, processing,
documenting and disseminating their respective datasets. We also appreciate
the comments of the reviewers that helped us to improve this article.
Financial support
This research has been supported by the NASA Earth Sciences Division (grant nos. NNH14ZDA001N-ACMAP, 80NSSC21K1451 and 80NSSC19K0124).
Review statement
This paper was edited by Yafang Cheng and reviewed by two anonymous referees.
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