Long-range transport of biogenic emissions from the coast
of Antarctica, precipitation scavenging, and cloud processing are the main
processes that influence the observed variability in Southern Ocean (SO)
marine boundary layer (MBL) condensation nuclei (CN) and cloud condensation
nuclei (CCN) concentrations during the austral summer. Airborne particle
measurements on the HIAPER GV from north–south transects between Hobart,
Tasmania, and 62∘ S during the Southern Ocean Clouds, Radiation
Aerosol Transport Experimental Study (SOCRATES) were separated into four
regimes comprising combinations of high and low concentrations of CCN and
CN. In 5 d HYSPLIT back trajectories, air parcels with elevated CCN
concentrations were almost always shown to have crossed the Antarctic coast,
a location with elevated phytoplankton emissions relative to the rest of the
SO in the region south of Australia. The presence of high CCN concentrations
was also consistent with high cloud fractions over their trajectory,
suggesting there was substantial growth of biogenically formed particles
through cloud processing. Cases with low cloud fraction, due to the presence
of cumulus clouds, had high CN concentrations, consistent with previously
reported new particle formation in cumulus outflow regions. Measurements
associated with elevated precipitation during the previous 1.5 d of their
trajectory had low CCN concentrations indicating CCN were effectively
scavenged by precipitation. A coarse-mode fitting algorithm was used to
determine the primary marine aerosol (PMA) contribution, which accounted for
<20 % of CCN (at 0.3 % supersaturation) and cloud droplet
number concentrations. Vertical profiles of CN and large particle
concentrations (Dp>0.07µm) indicated that particle
formation occurs more frequently above the MBL; however, the growth of
recently formed particles typically occurs in the MBL, consistent with cloud
processing and the condensation of volatile compound oxidation products.
CCN measurements on the R/V Investigator as part of the second Clouds, Aerosols,
Precipitation, Radiation and atmospheric Composition Over the southeRn Ocean
(CAPRICORN-2) campaign were also conducted during the same period as the
SOCRATES study. The R/V Investigator observed elevated CCN concentrations near Australia,
likely due to continental and coastal biogenic emissions. The Antarctic
coastal source of CCN from the south, CCN sources from the midlatitudes, and
enhanced precipitation sink in the cyclonic circulation between the Ferrel
and polar cells (around 60∘ S) create opposing latitudinal
gradients in the CCN concentration with an observed minimum in the SO
between 55 and 60∘ S. The SOCRATES airborne
measurements are not influenced by Australian continental emissions but
still show evidence of elevated CCN concentrations to the south of
60∘ S, consistent with biogenic coastal emissions. In addition, a
latitudinal gradient in the particle composition, south of the Australian
and Tasmanian coasts, is apparent in aerosol hygroscopicity derived from CCN
spectra and aerosol particle size distribution. The particles are more
hygroscopic to the north, consistent with a greater fraction of sea salt
from PMA, and less hygroscopic to the south as there is more sulfate and
organic particles originating from biogenic sources in coastal Antarctica.
Introduction
The marine boundary layer (MBL) over the Southern Ocean (SO) displays some
of the most pristine conditions in the world, with few anthropogenic
influences, making cloud properties and radiative forcing particularly
sensitive to relatively small changes in aerosol source emissions
(Downey et al., 1990; Fossum et al., 2018; Hudson et al., 1998; Li et al., 2018;
McCoy et al., 2015; Murphy et al., 1998b; Pandis et al., 1994; Pierce and
Adams, 2006; Pringle et al., 2009; Whittlestone and Zahorowski, 1998; Wood
et al., 2015; Yoon and Brimblecombe, 2002). In spite of a growing number of
studies, climate models still struggle to represent SO cloud radiative
properties, partly because their representation of available cloud
condensation nuclei (CCN) is not well constrained
(Bodas-Salcedo et al., 2014; Brient et al., 2019; Carslaw et al., 2013; Efraim et al.,
2020; Hyder et al., 2018; Lee et al., 2015; Mace and Protat, 2018; Mccoy et
al., 2014; Ogunro et al., 2018; Schmale et al., 2019; Seinfeld et al., 2016;
Trenberth and Fasullo, 2010). Understanding the impact of SO particle
sources on the cloud system and their variability is required for accurate
prediction of SO cloud properties and to understand the impact of
aerosol–cloud interactions on the Earth's energy budget. These issues
motivated the Southern Ocean Clouds, Radiation Aerosol Transport
Experimental Study (SOCRATES), which conducted in situ measurements of
clouds and aerosol over the SO on board the NSF/NCAR HIAPER Gulfstream V
(GV) (Laursen et al., 2006).
Aerosol in the SO typically originates from natural marine sources and are
rarely influenced by continental or anthropogenic sources. These marine
sources consist of primary marine aerosol (PMA) particles produced from sea
spray and bubble bursting, as well as secondary organic and sulfate
particles formed from biologically emitted volatile organic compounds (VOCs)
such as dimethyl sulfide (DMS)
(Bates et al., 1998b, 2012; Covert et al., 1992; Frossard et al., 2014; Middlebrook
et al., 1998; Murphy et al., 1998a; Pirjola et al., 2000; Quinn et al.,
2000, 2017; Rinaldi et al., 2010; Saliba et al., 2019, 2020). Primary
particles from the Antarctic continent are not a major source of particles
to the SO because it is mostly covered in ice
(Chambers et al.,
2017), leaving the main sources of primary aerosol from Antarctica limited
to local anthropogenic pollution from research stations, blowing snow, frost
flowers, and sea bird emissions
(Frieß
et al., 2004; Huang and Jaeglé, 2017; Liu et al., 2018; Schmale et al.,
2013).
New particle formation (NPF) from the oxidation of marine biologically
emitted VOCs occurs mostly in the free troposphere (FT) where the particle
condensational sink and temperature are lower than in the MBL, which are
prevalent conditions over the SO (Raes
et al., 1997; Yue and Deepak, 1982). While NPF has been observed in the
marine boundary layer, often at coastal locations
(Covert
et al., 1992; Humphries et al., 2015; Kyrö et al., 2013; Pirjola et al.,
2000; Weller et al., 2015), it occurs more commonly in the FT
(Bates
et al., 1998b; Clarke et al., 1998; Humphries et al., 2016; O'Dowd et al.,
1997; De Reus et al., 2000; Sanchez et al., 2018; Yoon and Brimblecombe,
2002) owing to the absence of PMA in the FT
(McCoy et al., 2015). Regions of sea ice
melt on the Antarctic coast have been observed to be a significant source of
methanesulfonic acid (MSA) as well as DMS
(O'Dowd et al., 1997; Vana et al., 2007),
known precursors of NPF
(Almeida et
al., 2013; Dawson et al., 2012). In addition, NPF is commonly associated
with cumulus outflow regions due to the DMS-rich air lofted by the
convection and the high relative humidity, creating an environment allows
binary nucleation between sulfuric acid (a DMS oxidation product) and water
(Bates
et al., 1998b; Clarke et al., 1999; Cotton et al., 1995; Perry and Hobbs,
1994; Twohy et al., 2002). Ternary nucleation with ammonia or amines is also
possible, particularly in Antarctic coastal regions downwind of penguin
colonies (Weber et al.,
1998).
The remote midlatitude SO contains much less biological activity near the
ocean surface relative to the Antarctic continental coast, which creates a
latitudinal gradient in the contribution of particles from biogenic sources,
with the exception of some biological hotspots such as near South Georgia
(Alroe
et al., 2020; Humphries et al., 2016; Kim et al., 2019; O'Dowd et al., 1997;
O'Shea et al., 2017; Schmale et al., 2019; Weller et al., 2018). Shipborne
observations in the region south of Australia show a distinct increase in
aerosol concentrations south of 64∘ S, where CN concentrations are
about 5 times higher during the austral spring months
(Humphries et al., 2016). The seasonal
variability of biogenically derived particles is linked to seasonal
variations in SO biological activity
(Ayers and
Gras, 1991; Korhonen et al., 2008). On the Antarctic peninsula, NPF events
occurred mostly during the austral summer, with CCN concentrations (at
0.4 % supersaturation) increasing on average by 11 %
(Kim et al., 2019).
Similarly, higher average concentrations of cloud droplet number
concentrations (CDNC) are observed in the austral summer
(Mace and Avey, 2017; McCoy et
al., 2015). Some studies suggest biologically productive waters enhance PMA
production (Fuentes et al., 2010),
while other studies find that biogenic content has little to no influence on
PMA production
(Bates
et al., 2020; Collins et al., 2016). In any case, PMA CCN is found to have
little seasonal variability relative to biogenic CCN
(Vallina et al.,
2006), likely driven by small seasonal differences in wind speed
(Saliba et al.,
2019). Organic enrichment of PMA in biologically productive waters may
further reduce their hygroscopicity
(Burrows
et al., 2018; Cravigan et al., 2020; Law et al., 2017; Meskhidze and Nenes,
2010).
Long-range transport of aerosol and gaseous precursors in the MBL and FT
from the Antarctic continental coast plays a significant role in increasing
CN, CCN, and CDNC concentrations in the SO
(Bates
et al., 1998a; Clarke et al., 1998, 2013; Dzepina et al., 2015; Korhonen et
al., 2008; Woodhouse et al., 2010). With substantial growth of newly formed
particles by the uptake of VOC oxidation products through cloud processing,
particles from biogenic sources may grow CCN larger and subsequently
increase CDNC
(Hoppel
et al., 1986; Hudson et al., 2015; Pirjola et al., 2004; Russell et al.,
2007; Sanchez et al., 2018). Cloud processing occurs when small particles
activate to form cloud droplets, leading to enhanced condensation of VOC
oxidation products onto the droplet because the droplet surface area is
larger than that of the unactivated particles. Aqueous-phase oxidation of
absorbed VOCs also results in the formation of less volatile compounds,
which remain in the particle phase upon evaporation of the water
(Hoppel et al., 1986). In the event that
the cloud droplets do not precipitate, the evaporated particles are larger
than their original size since aqueous oxidation of volatile compounds
(i.e., DMS, MSA, SO2 and nitric acid) have formed non-volatile sulfates and
nitrates that remain in the particle phase. This added mass eventually
shifts Aitken-mode particles to the accumulation mode
(Hoppel
et al., 1986; Hudson et al., 2015; Kaufman and Tanré, 1994; Sanchez et
al., 2017; Schmale et al., 2019). Results from McCoy et al. (2015) show that, despite the ambiguous
results from focused modeling and observational studies of such aerosol
processes, their general global model simulations of natural aerosol account
for more than half the spatial and temporal variability in the
satellite-derived CDNC over the SO. These areas of enhanced CDNC also
correlate with areas of high chlorophyll-a, a tracer for phytoplankton
activity, which increases secondary sulfate and organic aerosol
concentrations
(Krüger and
Grabßl, 2011; McCoy et al., 2015). SO satellite-derived cloud properties
such as liquid water content (LWC), effective radius, and cloud fraction
showed seasonal variations that resulted in a difference in cloud radiative
forcing (i.e., surface cooling) between 14 and 23 W m-2
(Mccoy et al., 2014). Increased CDNC is also
shown to correlate with enhanced cloud fraction, significantly increasing
overall cloud shortwave forcing (Rosenfeld et al.,
2019). If cloud droplets precipitate, CN and CCN concentrations are reduced
through precipitation scavenging
(Croft et al., 2010;
Stevens and Feingold, 2009).
In this study, we discuss airborne aerosol measurements in the SO region
south of Australia (Fig. 1) from the SOCRATES campaign and briefly discuss
shipborne CCN measurements made on the R/V Investigator for the CAPRICORN2 campaign, which
was conducted in the same time frame and region as SOCRATES. The SOCRATES
measurements are divided into four categories based on the total CN and
CCN0.3 (CCN concentration at 0.3 % supersaturation) to identify
differences in processes and sources that lead to the observed variability
of measurements. A back-trajectory analysis is performed to identify the
source of air parcels and their history with respect to their proximity to
clouds and precipitation. Additionally, a PMA-mode fitting algorithm
(Saliba et al.,
2019) is utilized to understand the contribution of PMA to CCN and observed
CDNC concentrations. The findings describe the observed spatial gradients
and relative importance of biogenic sulfate and PMA to CDNCs, which
ultimately contribute to improving estimates of the energy budget in the SO.
SOCRATES and CAPRICORN-2 study region. Blue and red lines
represent the SOCRATES flight tracks and CAPRICORN-2 R/V Investigator
tracks, respectively.
MethodsNSF/NCAR HIAPER GV measurements and R/V Investigator CCN measurements
Airborne measurements were collected on the NSF/NCAR Gulfstream-V
High-performance Instrumented Airborne Platform for Environmental Research
(GV HIAPER) observational platform. The GV was stationed at the Hobart
International Airport, Tasmania, during the austral summer between 15 January and 24 February 2018. The flight strategy during SOCRATES involved
ferrying out to a predetermined area of interest followed by a series of
straight vertical profiles and level legs to sample below, in, and above
clouds. The GV HIAPER conducted 15 research flights (RFs) over the SO between
42.5 and 62.1∘ S and between 133.8
and 163.1∘ E at altitudes ranging from 50–7500 m. Flight tracks
are shown in Fig. 1 and flight strategy are discussed in McFarquhar et al.
(2020).
A wing-mounted ultra-high-sensitivity aerosol spectrometer (UHSAS, Droplet
Measurement Technologies, Boulder, CO) measured particle size distribution
between 0.06 and 1.0 µm in diameter; however, the 0.06–0.07 µm
diameter range was not used in this analysis due to instrument noise.
Ambient subsaturated particles collected with the UHSAS were dried through a
de-icing system (designed to vary the temperature and pressure of sampled
air to prevent ice formation in the inlet). A condensation particle counter
(CPC, TSI 3760A) was used to measure total particle concentrations (CN,
diameter >0.01µm). CCN measurements were performed with
two miniature continuous-flow streamwise thermal gradient chambers, one in
scanning supersaturation mode and one in constant supersaturation mode
(Roberts and Nenes, 2005). The miniature
CCN counters are custom-made and operate with the same physical principles
described by Roberts and Nenes (2005).
Empirical calibrations are derived using dried monodisperse ammonium sulfate
particles that are measured by the CCN counter and a CN counter to derive
the activated fraction. The critical supersaturation in this study was
derived by Kohler theory using a Van 't Hoff factor of 3.0 as an upper limit
for ammonium sulfate. Using a Van 't Hoff factor of 2.52
(Petters
and Kreidenweis, 2007; Rose et al., 2008) would shift the CCN spectra to
larger supersaturations by less than 10 %. An instrument model, discussed
in Roberts and Nenes (2005) showed a
standard deviation in the supersaturation estimate of about ±0.01 %.
The supersaturation range of the scanning CCN counter flow rates and
temperature gradients vary from 0.09 to 0.22 L min-1 and 8 to 12 K,
respectively. A sinusoidal pattern from high-flow and high-temperature
gradient to low-flow and low-temperature gradient with a period of 10 min generated a continuous CCN spectra every 5 min that spanned from
0.06 % to 0.87 % supersaturation. The constant supersaturation CCN
counter operated at constant flow and temperature gradient of 0.15 L min-1 and 9 K for a 0.43 % supersaturation (referred to as CCN0.43) at 1 Hz and
was used to identify CCN gradients in vertical profiles (Sect. 3.6). CCN
concentration at 0.3 % supersaturation (CCN0.3, derived from the
scanning CCN counter) was used throughout this study as a reference CCN
concentration because CCN0.3 corresponded best to observed CDNCs
(Sect. 3.3). The internal chamber pressure of both CCN counters was
controlled to 400 hPa. A cloud droplet probe (CDP, DMT, Boulder, CO) was
used to measure cloud droplet concentration (2–50 µm wet diameter).
The CCN spectra and UHSAS number concentrations on the GV were used to
estimate the hygroscopicity parameter at 0.07 µm diameter (κ70) for each MBL leg. For this calculation, the critical
supersaturation is derived from the CCN spectra, where the UHSAS
concentration at 0.07 µm diameter is equivalent to the CCN
concentration. All particle measurements were converted to surface standard
temperature and pressure (see the Supplement, for example). CN and CCN
measurements made in cloud (defined by CDP measurements of LWC >0.1 g kg-1) were excluded from the analysis due to the influence of
droplet shattering within the aerosol inlets. During the research flights,
areas of intense precipitation were avoided, but some measurements were made
under drizzle and light rain conditions; however, there was no evidence of
droplet shattering in the inlets under these conditions.
In addition to the SOCRATES GV HIAPER measurements, the R/V Investigator (CSIRO, Hobart,
Tasmania) also collected aerosol and sea water samples during the second
Clouds, Aerosols, Precipitation, Radiation and atmospheric Composition Over
the southeRn Ocean (CAPRICORN-2) campaign. The CAPRICORN-2 study was
conducted from 10 January to 21 February 2018, overlapping the SOCRATES
study. The R/V Investigator covered a north–south transect over the SO,
starting at Hobart, Tasmania (43∘ S), reaching approximately
66∘ S, and then returning to Hobart (Fig. 1). In this study, CCN
measurements collected on the R/V Investigator were measured with a commercially available
streamwise CCN counter (CCN-100, Droplet Measurement Technologies, Boulder,
CO) that measured CCN concentration between 0.25 % and 1.05 %
supersaturation with a stepwise scan. Each CCN spectrum took approximately
1 h to complete. R/V Investigator CCN at 0.3 % are analyzed and
compared to the GV HIAPER CCN0.3 measurements. The full CCN dataset
collected on the R/V Investigator during CAPRICORN2 are available at
Humphries et al. (2020). Details of the aerosol sampling system
on board the R/V Investigator are presented in Humphries et al. (2019) and Alroe et al. (2020). In short, aerosol sampling occurred
via a common sampling inlet mounted on a mast at the bow of the ship,
located 18 m above sea level. The CCN counter sampled from a manifold
located 8 m below the mast in the ship's bow.
Model dataHYSPLIT-GDAS
In this study, HYSPLIT hourly 5 d back trajectories were performed with
the Global Data Assimilation System (GDAS,
ftp://arlftp.arlhq.noaa.gov/pub/archives/gdas0p5/, last access: 23 January 2020)
(Rolph et al., 2017; Stein
et al., 2015) at 0.5∘ resolution for each CCN spectrum in the MBL leg
(below the cloud layer if clouds are present). The latitude, longitude, and
altitude (50–500 m) averaged for each CCN spectra (∼150 s, ∼15–20 km horizontal distance) collected during the
MBL legs on the GV HIAPER were used as starting points for the back
trajectories. Antarctica is the only continent over which back trajectories
passed; none of the airborne aerosol measurements in the MBL were influenced
by continental Australia. The only anthropogenic influences were potentially
ship tracks and research stations in Antarctica, which we assume to have a
negligible impact in this study.
ECMWF ReAnalysis (ERA5)
ERA5 is the 5th generation of a climate reanalysis dataset from the European
Centre for Medium-Range Weather Forecasts (ECMWF)
(Copernicus
Climate Change Service (C3S), 2017). The ERA5 model assimilates satellite,
ground, and airborne measurements to archive the state of the weather and
climate. The ERA5 total precipitation and low-level cloud fraction was used
for the time period covering the SOCRATES campaign to identify the role of
clouds and precipitation in changing CN and CCN concentrations. The ERA5
time resolution is hourly, and spatial resolution is 0.25∘.
Primary marine aerosol (PMA) fitting algorithm
The PMA concentration was determined by fitting the UHSAS distribution of
particles greater than 0.2 µm diameter to a single lognormal mode. A
single lognormal mode has been found to represent PMA in ambient
measurements
(Modini
et al., 2015; Quinn et al., 2017; Saliba et al., 2019). While this method
was previously used on dry particle number size distributions ranging from
0.02 to 5.0 µm
(Saliba et al.,
2019), the UHSAS measures the particle number size distribution between 0.07
and 1.0 µm diameter. In addition, PMA particles in the SOCRATES
campaign were not fully dried and a relatively narrow deliquesced mode (geometric standard deviation = 1.44 ± 0.25) is present at approximately 0.6 µm diameter.
This deliquesced mode was present despite the findings by Strapp et al. (1992), suggesting the de-icing heaters of the
PCASP-100X (which are identical to those used for the UHSAS) is expected to
dry the particles to less than 40 % relative humidity. We hypothesize that
the low residence time of the aerosol in the instrument (∼0.2 s) prevented the large hygroscopic sea salt from fully drying before
being measured. This 0.6 µm deliquescent mode was consistently fit by
the algorithm. The deliquesced PMA particles affect the mode diameter of the
fitted PMA size distribution but not the retrieved PMA (and CCN0.3)
number concentrations. The concentration of particles in this fitted mode
correlated moderately with wind speed (Sect. 3.5), similar to previous
measurements of PMA estimated with this method
(Modini
et al., 2015; Quinn et al., 2017; Saliba et al., 2019), indicating the
fitted mode is a viable approximation of PMA concentrations. The estimated
PMA mode diameter and geometric width (0.59 ± 0.04 µm,
1.44 ± 0.25, respectively) are consistent with sea salt distributions
(from PMA) observed on size-resolved particles collected in the marine
boundary layer during SOCRATES and analyzed with transmission electron
microscopy (TEM). The TEM analysis showed that ∼70–95 % of
marine boundary layer particles >0.5µm in optical diameter are PMA
sea spray (Twohy et al., 2021).
ResultsParticle regimes
MBL CN and CCN0.3 measured on the GV HIAPER MBL legs ranged from
116 to 1153 and 17 to 264 cm-3 and averaged 540 ± 246 and 123 ± 58 cm-3, respectively. Figure 2b shows the CN
and CCN0.3 concentrations averaged over each CCN spectra scan during GV
HIAPER MBL legs throughout the SOCRATES field project (with the exception of
RF 14 when the scanning CCN counter malfunctioned). To determine which
atmospheric processes drove the variability of nearly an order of magnitude
in CN and CCN0.3, the measurements were divided into four regimes. The
regime thresholds were selected based on the bimodality of observed CN and
CCN0.3 concentrations shown by the histograms and kernel density
functions in Fig. 2a, c. Using this approach, rather than grouping all
values into a single bin, each measurement is represented by a normal
distribution and integrated to produce the kernel density estimate. The
optimal kernel density estimate bandwidth was found to be 28 and 91 for
CCN0.3 and CN, respectively, and calculated using the “ksdensity”
function from MATLAB (2019), derived from theory developed
by DeVeaux et al. (1999). The Hartigan's Dip
test (Hartigan and Hartigan, 1985) determined that the
distribution was not unimodal (p value <0.01) for both CN and
CCN0.3, thereby validating the use of a bimodal distribution for this
analysis. The bimodal distribution minima correspond to 125 and
750 cm-3 for CCN0.3 and CN, respectively. Even though only
CCN0.3 was used to determine the particle regimes, Fig. 3a
illustrates the systematic differences between the averaged CCN spectra and
CN concentrations for each of the regimes. The bimodal CCN0.3 and CN
regimes were combined for a total of four regimes. Table 1 shows the average
CCN0.3 and CN concentrations for each of the four regimes, which are
distinguished by permutations of high and low CCN0.3 and CN
concentrations.
Mean and standard deviation of CN and
CCN0.3 number concentration for the four identified
regimes measured in the GV MBL legs.
Histograms and kernel density estimates of (a) CN
concentrations and (c) MBL CCN0.3 (CCN concentration
at 0.3 % supersaturation). (b) MBL CN and CCN0.3.
Measurements are divided into four particle regimes based on the observed
bimodal distributions of both CN and CCN0.3. Error
bars represent the standard error.
(a) Mean MBL CCN spectra for each regime. The number of
samples (N) at each supersaturation of the CCN spectra varied from the
number of samples in the legend because CCN spectra scans were occasionally
not fully completed by the end of the MBL leg. Error bars represent the
standard error (σ/N).
Correlations of measured CDNC with (b) calculated effective supersaturation
and (c) measured MBL CCN0.3. Empty points did not
have a corresponding CCN0.3 or CN measurement. Solid
lines in (b) and (c) represent linear fits and the dashed line in (c)
represents the 1:1 line. Error bars represent the standard error.
To differentiate the four regimes in the text, we have given them each
abbreviated descriptive names based on their CN and CCN0.3
concentrations, where the regime with high CN and CCN concentrations is
referred to as “Recent particle formation (RPF) + aged”, the regime with
low CN and CCN concentrations is referred to as “scavenged”, the regime
with low CN and high CCN concentrations is referred to as “aged”, and
finally the regime with high CN and low CCN concentrations is referred to as
“RPF + scavenged”. The classification of each regime is based on the
relative concentration of Aitken + accumulation-mode particles (CN) and
accumulation-mode particles (CCN sizes), with a naming convention that
describes the corresponding air mass history. Similar to analyses in previous
studies, the relative contribution of the accumulation mode to the total
particle concentration is used to identify recent particle formation (RPF)
events and growth of small (<0.07µm diameter) particles to
accumulation-mode or CCN sizes
(Kalivitis
et al., 2015; Kleinman et al., 2012; Williamson et al., 2019). The scavenged
regime is named based on evidence indicating the removal of CCN-sized
particles through precipitation scavenging (Sect. 3.3). The aged regime
represents cases in which accumulation mode is prominent and CCN particle
concentrations are relatively high, likely due to atmospheric processes that
increase particle size over time, such as the condensation of VOC oxidation
products or cloud processing (Sect. 3.2 and 3.3, respectively). The RPF
regimes exhibit a high CN concentration (>10 nm diameter),
indicative of recent particle formation (Sect. 3.2).
Back trajectories
Previous studies have shown long-range transport of particles and VOCs can
affect locally observed particle concentrations and chemical properties
(Dzepina
et al., 2015; Korhonen et al., 2008). In addition, atmospheric processes
affecting particle concentrations upstream of the measurement location
reduce the correlation of particle properties to individual (or discrete)
processes, such as precipitation, cloud processing, and NPF
(Albrecht,
1989; Bates et al., 1998b; Russell et al., 2009; Sanchez et al., 2018;
Stevens and Feingold, 2009; Stevens and Seifert, 2008; Vallina et al., 2006;
Wood et al., 2015). Lagrangian HYSPLIT back trajectories initiated at MBL
leg altitudes (50–500 m) were used to determine the path traveled by the
parcel of air for the previous 5 d for each of the MBL legs (Fig. 4). Consistent patterns are apparent for each of the particle regimes.
Specifically, the back trajectories for the aged particle regime (Fig. 4d)
are consistently from the south along the Antarctic coast, which is
associated with the elevated ocean surface emissions of DMS and other VOCs
produced by phytoplankton activity
(Alroe
et al., 2020; Humphries et al., 2016; Kim et al., 2019; O'Dowd et al., 1997;
O'Shea et al., 2017; Weller et al., 2018). In contrast, the high CN regimes
(RPF + aged and RPF + scavenged) exhibit back trajectories generally
from the west from the SO. The scavenged regime consists of back
trajectories from both the west and the south, signifying that atmospheric
processes rather than the parcel paths and origins influence the observed CN and
CCN concentrations.
The 5 d HYSPLIT back trajectories starting from MBL legs
(at 50–500 m a.m.s.l., magenta points) for each particle regime.
Cloud processing
Relating the identified regimes to the observed cloud processes provides
insight into how cloud processes affect CN and CCN concentrations. Figure 3c
shows that CCN0.3 and CDNC correlated moderately (r=0.75), the highest
correlation of CCN concentrations relative to other supersaturations,
indicating that CCN0.3 is a good proxy for CDNC, which is similar to
previous estimates of marine cloud-effective supersaturations
(Martin
et al., 1994; Snider et al., 2003). For this comparison, the 90th percentile
of CDNC from each vertical profile is matched to the nearest below-cloud MBL
leg CCN concentration. The use of the 90th percentile of CDNC excludes
measurements that are heavily influenced by entrainment drying and also
excludes outliers. As expected, the aged particle regime accounted for cases
with the highest CDNCs (192 ± 100), while the scavenged particle regime
accounted for the lowest observed CDNC (111 ± 72). Few CDNC
measurements are associated with the RPF (high CN) regimes, suggesting fewer
clouds are associated with this regime. Figure 3b shows the cloud-effective
supersaturation and its relationship to the CDNC. The cloud-effective
supersaturation is calculated as the supersaturation where the CCN
concentration was equal to the 90th percentile of the measured CDNC.
Typically, clouds contain a range of peak supersaturations, which are controlled by
the strength of the updraft and the cloud droplet number concentration
(Hudson and Svensson, 1995;
Pawlowska and Grabowski, 2006; Siebert and Shaw, 2017). The effective
supersaturation accounts for the CCN that have activated adiabatically near
cloud base and subsequently dried through sub-adiabatic mixing processes
(Sanchez et al., 2017). In
general, the observed CDNC weakly correlate to the effective supersaturation
(Fig. 3b, r=0.47). The two regimes with aged particles (high CCN)
consistently had higher CDNCs than the scavenged regime, highlighting the
role of CCN concentrations as CDNC. It is also important to note that CDNC can
still be relatively high (∼200 cm-3) in regimes with low
CCN under conditions of high in-cloud supersaturations generated by strong
updrafts or with relatively low PMA concentrations, which also allows for the
generation of higher in-cloud supersaturations
(Fossum et al., 2020).
To identify the effect of precipitation on CCN concentrations, CCN0.3
is compared to the total precipitation (obtained from ERA5) integrated over
a 35 h back trajectory, as shown in Fig. 5a. Manton et al. (2020) showed that the ERA5 annual cycle of
precipitation across the SO is consistent with in situ data, but it is
important to note that there is large uncertainty because of the low number
of observations to constrain the ERA5. As expected, the two scavenged
regimes (with lower CCN0.3 concentrations) corresponded to higher total
precipitation. Figure 5b shows the Pearson correlation coefficient comparing
the base 10 logarithm of the integrated total precipitation over back-trajectory times of 0 to 120 h and CCN concentrations between 0.1 %
and 0.8 % supersaturation. The Pearson's coefficient r value peaked for
35 h back trajectories at CCN supersaturations ranging from 0.3 % to 0.5 %
(similar to effective in-cloud supersaturations, Fig. 5b), indicating air
parcel history, particularly in the last 1.5 d, is important for
determining atmospheric processes that affect CCN concentration. The
Pearson's coefficient for CCN0.1 was consistently the lowest, likely
because CCN0.1 is associated with PMA, which is quickly replenished in
the MBL through sea spray emissions. Similarly, the Pearson's coefficient
for CCN0.87 was also low, likely because this CCN size is associated
with RPF particles that are replenished in the FT and subsequently grow to
larger sizes (and lower supersaturation CCN).
(a) Correlation of MBL CCN0.3 and
total precipitation that occurred along a 35 h HYSPLIT back trajectory.
Colors correspond to the legend in (c). (b) CCN and back-trajectory total
precipitation correlation coefficient as a function of back-trajectory
length. The vertical dashed line indicates a peak in correlation with
CCN0.3 at 35 h. (c) Particle regime averaged ERA5
low-level cloud fraction over the 5 d back trajectory. Shaded areas
represent the standard error.
Figure 5c shows the MBL cloud fraction (obtained from ERA5) over the
120 h back trajectory averaged for each particle regime. Similar to ERA5
precipitation, there are also a low number of observations to constrain the
ERA5 cloud fraction product. Ship measurements in the region south of
Australia were recently shown to be consistent with daily averaged
observations and ERA5 cloud fraction values of 0.75 ± 0.23 and
0.71 ± 0.27, respectively, providing some confidence in the ERA5
(Wang et al., 2020). The two regimes
with RPF (RPF and RPF + scavenged; high CN) are associated with lower
cloud fraction (<0.6), which suggests the presence of cumulus
clouds. NPF has previously been observed in cumulus cloud outflow regions
(Bates
et al., 1998b; Clarke et al., 1999; Cotton et al., 1995; Perry and Hobbs,
1994) and is likely the main source of CN in these RPF regimes. In contrast,
the aged particle regimes correspond to high MBL cloud fraction
(>0.6), which is consistent with stratus and stratocumulus
clouds. Stratus and stratocumulus clouds typically include less
precipitation, allowing more cloud processing of CN to CCN sizes
(Flossmann
and Wobrock, 2019; Hoppel et al., 1990; Hudson et al., 2015; Neubauer et
al., 2014). In addition, the concentration of ultrafine particles (Dp<30 nm) also decreases through Brownian scavenging of interstitial
particles onto cloud droplets
(Croft et al., 2010), and thus
higher cloud fractions further reduce CN concentrations. The back
trajectories associated with the aged regime (Fig. 4d) typically originate
from SO storm tracks to the south, which is consistent with the elevated
cloud fraction shown in Fig. 5c. The storm track frequency peaks around
60∘ S (Li et al., 2009),
suggesting parcels of air entering the storm track from the south have also
been influenced by coastal Antarctic biogenic DMS and other VOC emissions,
eventually leading to increases in CCN concentrations via cloud processing in the absence of precipitation. Schmale et al. (2019) and Alroe et al. (2020) also find that the higher fraction of
particles serving as CCN near the coast of Antarctica are also from
biologically derived particles. The trajectories associated with the RPF and
the RPF + aged regimes are typically from the west and have fewer clouds.
While these regimes have elevated CN concentrations, they are not linked to
Antarctic coastal sources within the last 120 h (Fig. 4a, b).
Long-range transport of aerosol particles and their precursors for more than
5 d is possible in the absence of major sinks (i.e., precipitation)
(Feichter and Leisner, 2009). The existence of both
aged and RPF in the same regime suggests particles have experienced some
cloud processing and input from a recent particle formation event.
The cloud fraction for the RPF + aged regime is significantly lower than
the aged regime (Fig. 5c).
Latitudinal gradient
Both the airborne GV HIAPER and shipborne R/V Investigator measurements showed latitudinal
(north–south) gradients in CCN concentrations, although differences in the
sampling strategies between the two platforms do result in slight
differences in the observed latitudinal gradients (discussed in detail
below). Both sets of measurements showed high CCN concentrations near
Antarctica (Fig. 6a–c) consistent with Antarctic coastal biological
emissions as a source of aerosol precursors. Back trajectories (Fig. 4d;
aged regime) show that long-range transport of these Antarctic coastal
emissions generates elevated aerosol concentrations as far north as
∼50∘ S, almost 2000 km away from the Antarctic
coast. The Pearson's coefficient comparing airborne CCN0.3 measurements
with latitude suggests there is not a significant correlation (r=-0.09;
Fig. 6b), unless the particles that were transported 2000 km across the SO
from the Antarctic coast are excluded (r=-0.26). Similarly, there is no
significant trend in airborne CN (Dp>0.01µm) with
latitude (r=0.16) even though previous studies have noted a distinct
increase in CN and CCN concentrations near the Antarctic shelf at
64∘ S
(Alroe et al.,
2020; Humphries et al., 2016). In SOCRATES, however, airborne measurements
on the GV HIAPER reached only 62.1∘ S and did not capture the
expected distinct increase in CN concentrations above the Antarctic coastal
areas.
(a) The 5∘ latitude bin averaged
CCN0.3 from the R/V Investigator. Correlation of latitude to (b) HIAPER GV
CCN0.3,(c) total particle concentration with
Dp>0.07µm (UHSAS), and (d)κ derived at 0.07 µm. White points in (c) and (d)
did not have a corresponding CCN0.3 or CN
measurement. Pearson's coefficient for (b) is r=-0.27 when
excluding the three outliers at ∼46∘ S highlighted
in the black square. Error bars represent the standard error.
A latitudinal gradient is observed in both the GV HIAPER UHSAS particle
(Dp>0.07µm) and CCN concentrations; however, the
differences in their slopes imply a north–south gradient in particle
composition (i.e., hygroscopicity) across the SO, as identified by the
hygroscopicity parameter (κ70) for Dp>0.07µm (Fig. 6d). The presence of a latitudinal gradient in aerosol
concentrations (Dp>0.07µm) and a weak gradient in
the GV HIAPER CCN implies a north–south gradient in particle composition
(i.e., hygroscopicity) across the SO. Figure 6d shows the hygroscopicity
parameter (κ) for Dp>0.07µm derived at each
MBL leg. The lower κ (less hygroscopic aerosol) at high latitudes is
consistent with sulfates and organic aerosol from biogenic emissions, which
have relatively low κ values (κ=0.6–0.9 and κ<0.2, respectively) compared to PMA (κ∼1.0; Christiansen
et al., 2020; Zieger et al., 2017) present in primary emissions at
lower latitudes
(Kreidenweis and
Asa-Awuku, 2013; Petters and Kreidenweis, 2007). These results are
consistent with findings of Schmale et al. (2019) showing MSA, an aerosol component
associated with biogenic emissions, contributed about 2.5 times more mass in
the Antarctic coastal region compared to the remote SO. Furthermore, the
elevated CCN near the Antarctic coast is also consistent with a higher
incidence of cloud processing in the region, despite the lower particle
hygroscopicity
(Alroe et
al., 2020; Schmale et al., 2019). As PMA (mostly comprised of sea salt) is
present all over the SO, relatively high κ values are found north of
∼55∘ S, where there are fewer biologically derived
organic and sulfate particles. The latitudinal trend of decreasing κ
(i.e., more hygroscopic chemical composition toward the lower latitudes)
implies particles further south in the SO will need higher in-cloud
supersaturations to activate particles of the same size compared to middle
regions of the SO where there are fewer biologically derived particles.
Alternatively, particle growth and aging enhances the particle's ability to
be CCN active even with a low hygroscopicity and small initial size. Despite
the lower observed hygroscopicity of particles at high latitudes based on
the airborne measurements, there are a greater number of CCN available
(Fig. 6b) to increase cloud droplet number and potentially enhance cloud
reflectivity at higher latitudes.
Measurements from the R/V Investigator during the CAPRICORN-2 study show minima in CCN
concentrations around 60∘ S (Fig. 6a), which corresponds to the
maximum in SO storm track activity (Li et
al., 2009); however, this minima in CCN is not observed from the GV
measurements and is not as pronounced in similar ship measurements at the
same time (Humphries et al., 2021). As expected, based on the GV measurements, there are elevated CCN
concentrations to the south of 60∘ S related to biogenic emissions
from the Antarctic coastal areas. There are also elevated CCN concentrations
north of 50∘ S measured on the R/V Investigator, probably related
to continental emissions from Australia, elevated biomass emissions of VOCs
(aerosol precursors), as suggested by increasing chlorophyll-a
concentrations north of the subantarctic front
(McCoy et al., 2015), and even long-range
transport of Antarctic coastal emissions
(Ayers and Gillett,
2000; Twohy et al., 2021). The different latitudinal trends in CCN
observed by the GV HIAPER and R/V Investigator are likely a result of the different temporal
and spatial sampling strategies between the aircraft and the ship. The GV
transects were repeated 15 times over 40 d while avoiding actively
precipitating clouds and represent the CCN variability across the SO. The
GV typically started MBL measurements south of 50∘ S, so the trend
in CCN concentrations is not as apparent in the GV measurements compared to
the CCN gradient measured on the R/V Investigator. The R/V Investigator transected the SO twice, with each
transect occurring over 20 d.
Primary marine aerosol (PMA)
To explore the contribution of marine sources to CCN and CDNC, PMA was
estimated from the UHSAS distributions through fitting of the PMA mode using
the algorithm from Saliba et al. (2019). The retrieved PMA concentrations
varied between <1 and 25 cm-3, with an average of 6 ± 3 cm-3.
The mode diameter of the retrieved PMA number size distribution was 0.59 ± 0.04 µm, which is consistent with the average mode diameter
observed in the North Atlantic of 0.54 ± 0.21 µm
(Saliba et al.,
2019). The low geometric width (1.44 ± 0.25) of the PMA mode relative
to Saliba et al. (2019) (ranging from
1.5–4.0) likely reflects the available statistics (N=74) and the
possibility that the PMA particles were not completely dry (Sect. 2.3). The
calculated PMA number concentrations moderately correlated to wind speed (r=0.53, Fig. 7a), as also reported by Saliba et al. (2019) over the
North Atlantic and Bates et al. (1998b) south of
Australia. Using the ratio of the PMA and CCN0.3 concentration (Fig. 7b), the PMA contribution to SO clouds can be estimated. PMA accounts for up
to ∼20 % of CCN0.3 (and CDNC), even for conditions
with precipitation scavenging in the previous 1.5 d (Fig. 5a), and only
a small fraction compared to the biogenically generated aerosol. These
results are consistent with Twohy et al. (2021) who found sea spray
aerosol comprised a minority of cloud droplet residual number in three
SOCRATES cases. Similarly, Quinn et al. (2017)
found that PMA contributed to less than 30 % of CCN number
concentration (at 0.3 % supersaturation) from measurements collected
during other field campaigns conducted between 130∘ E (near
Tasmania) and eastward to 60∘ W (near South America). In addition,
Schmale et al. (2019) showed over
three measurement legs that spanned the entire longitudinal range of the SO that
the average PMA contribution to CCN ranged from 19 %–32 % at a
supersaturation of 0.15 %. However, others have reported higher contributions of >50 % and even up to 100 % at high wind speeds
(>16 m s-1) for supersaturations ≤0.3 %,
during the austral summer
(Fossum
et al., 2018; Yoon and Brimblecombe, 2002).
(a) Correlation of estimated PMA concentration and wind
speed and (b) the fraction of PMA accounting for
CCN0.3 for MBL legs. Exclusion of the outlier in (a)
increases the Pearson coefficient to 0.59.
Vertical transport
High concentrations of aerosol particles in the MBL can be formed during NPF
events in the FT and subsequently entrained downward into the MBL
(Bates
et al., 1998a; Clarke et al., 1996, 2013; Korhonen et al., 2008; Pirjola et
al., 2000; De Reus et al., 2000; Russell et al., 1998; Sanchez et al., 2018;
Thornton et al., 1997; Yoon and Brimblecombe, 2002). The nucleation of new
aerosol particles often occurs in the FT owing to the low total
condensational sink and cold temperatures
(Raes et al., 1997; Yue and Deepak,
1982). It has traditionally been thought that the SO is a possible exception
to this trend because the SO MBL is a pristine environment with few
anthropogenic sources, relatively low particle concentrations
(condensational sink), and low temperatures compared to other MBLs around
the world
(Covert
et al., 1992; Humphries et al., 2015; Pirjola et al., 2000; Yue and Deepak,
1982). To determine if the SO MBL truly is an exception to the trend of NPF
typically occurring in the FT, we compare the concentrations of FT and BL CN
and UHSAS concentrations across the MBL. CN (Dp>0.01µm) and UHSAS (Dp>0.07µm) concentrations in
the MBL (CNMBL; UHSASMBL) and above the MBL inversion (CNInv;
UHSASInv) are shown in Figs. 8d and 9, respectively. To identify if MBL CN
concentrations are higher, similar, or lower than CN concentrations above the
MBL inversion in Fig. 8d, the vertical profiles of CN are divided into
three subsections, corresponding to classification where CNMBL/
CNInv>1.3 (Fig. 8a), 1.3> CNMBL/
CNInv>0.7 (Fig. 8b), and CNMBL/ CNInv<0.7 (Fig. 8c). Figure 8a–c show examples of two CN and
CCN0.43 vertical profiles. Figure 8a and c show profiles of CN
concentrations when CNMBL/CNInv>1 (consistent with
particle formation occurring in the MBL) and CNMBL/ CNInv<1 (consistent with particle formation in the FT or decoupled
layer). When CNMBL/CNInv∼1, particle formation
has not recently occurred in either the MBL or above the inversion (Fig. 8b), and mixing across the inversion homogenizes the aerosol concentrations
between the FT and MBL. During this study, the CNInv is generally
greater than CNMBL, which suggests particle formation occurs more
frequently above the MBL inversion, either in the FT or a decoupled layer
above the marine boundary layer. Despite the lack of influence from
continental and anthropogenic particles as condensational sinks in the SO,
the presence of a small concentration of PMA particles can lead to a high
total particle surface area
(Cainey and
Harvey, 2002; Sanchez et al., 2021; Yoon and Brimblecombe, 2002) and prevent
NPF in the MBL. This is also shown in the histogram of the CNMBL/CNInv ratio (Fig. 10a), which typically has a value of less than
unity. These results are consistent with previous findings that the observed
long-range transport of particles and their precursors from phytoplankton
blooms (Fig. 4d) typically occurs above the MBL
(Hudson
et al., 1998; Korhonen et al., 2008; Meskhidze and Nenes, 2006; Russell et
al., 1998; Sanchez et al., 2018; Thornton et al., 1997; Williamson et al.,
2019; Yoon and Brimblecombe, 2002).
Vertical profiles of CN and CCN at 0.43 %
supersaturation corresponding to (a) elevated CN concentrations in the MBL,
(b) well-mixed CN profiles, and (c) elevated CN concentrations aloft. The
cyan and magenta points in (a–c) represent two different vertical profiles.
(d) Comparison of CN measured in the surface-coupled MBL and decoupled layer
or FT. Error bars represent standard error. Empty markers do not have a
corresponding CCN0.3 measurement.
Comparison of UHSAS concentrations
(Dp>0.07µm) measured in the
surface-coupled MBL and decoupled layer or FT. Error bars represent standard
error. Empty markers do not have a corresponding
CCN0.3 measurement.
Histogram of (a)
CNMBL/CNInv from Fig. 7 and
(b) UHSASMBL/UHSASInv from
Fig. 8.
Similarly, Fig. 9 compares UHSAS concentrations (Dp>0.07µm) in the MBL to those above the MBL inversion. As the UHSAS
provided vertical profiles of the aerosol, we use the UHSAS to complement
the static CCN measurements to assess the vertical extent of cloud-active
aerosol. CCN0.3 and CCN0.4 correlate well with UHSAS (Dp>0.07µm) concentrations (r=0.94). Contrary to the
vertical extent of CN, UHSAS (Dp>0.07µm) and
CCN0.43 concentrations are generally greater in the MBL compared to
above the MBL inversion (Figs. 8a–c, 10b), which suggests that high MBL
UHSAS concentrations resulted from the growth of Aitken mode particles to
CCN sizes through cloud processing (Sect. 3.2.2)
(Hudson et al., 1998) or
gas-phase to particle-phase condensation in the MBL
(Pirjola
et al., 2004; Russell et al., 2007; Sanchez et al., 2018) and is consequently
associated with the aged regime (Fig. 9). Precipitation also likely played
a role in depleting UHSAS and CCN-sized particles (Dp>0.07µm) for the scavenged regimes.
Conclusions
GV HIAPER airborne measurements collected during the Southern Ocean Clouds,
Radiation Aerosol Transport Experimental Study (SOCRATES) of CN and CCN over
the Southern Ocean (SO) during the austral summer were separated into four
regimes based on back trajectories and CN-to-CCN ratios. Airborne CCN
measurements were also compared to shipborne measurements on the R/V Investigator collected
on the second Clouds, Aerosols, Precipitation, Radiation and atmospheric
Composition Over the southeRn Ocean (CAPRICORN-2) campaign. The airborne
measurements on the GV HIAPER show a weak gradient in CCN at 0.3 %
supersaturation (CCN0.3) with increasing CCN concentrations to the
south between 44 to 62.1∘ S, which may be caused by
aerosol precursors from Antarctic coastal biological emissions. Shipborne
CCN measurements on the R/V Investigator also show gradients between 44 to
67∘ S, with a minimum around 60∘ S where the peak
frequency of SO storm tracks occurs (Li
et al., 2009). Enhanced ship-based CCN concentrations north of 50∘ S are likely from Australia. In one case enhanced CCN concentration measured
on the GV near the Australian coast is shown to be from long-range transport
from Antarctic coastal emissions. Elevated CCN concentrations to the south
of 60∘ S originate from biogenic emissions from the Antarctic
coastal area. The differences in the observed trends between airborne and
shipborne CCN concentrations is likely due to the different sampling
strategies.
The particle regimes from the GV measurements were determined from the
observed bimodal distributions in CN and CCN0.3 concentrations, with
minimum values at 750 and 125 cm-3, respectively. CCN0.3
was used for this analysis because concentrations at 0.3 % supersaturation
showed the highest correlation with observed cloud droplet number
concentrations (CDNCs). Four regimes have been identified based on back
trajectories and CN and CCN0.3 concentrations, which ranged from
116–1153 and 17–264 cm-3, respectively. These regimes are
labeled (1) scavenged regime, with low CN and CCN0.3 concentrations;
(2) scavenged + recent particle formation (RPF) regime, with high CN and
low CCN0.3 concentrations; (3) aged regime, with low CN and high
CCN0.3 concentrations; and (4) RPF + aged regime, with high CN and
CCN0.3 concentrations. Back trajectories associated with the aged
regime consistently intersected the Antarctic coast, an area with elevated
phytoplankton biomass relative to the open ocean and a source of biogenic
emissions. The Antarctic coastal emissions generate a latitudinal gradient
in the UHSAS (Dp>0.07µm) and CCN concentrations,
as well as a gradient in particle composition (inferred from
hygroscopicity). The hygroscopicity gradient was derived from aerosol size
distributions (UHSAS) and CCN spectra and resulted in less hygroscopic
aerosol (lower κ) to the south, indicating CCN contained more
biogenic sulfate and organics relative to those further north, which likely
contained a larger fraction of more hygroscopic sea salt. Biogenic emissions
from coastal Antarctic areas accounted for most of the CCN and CDNC
concentrations in the SO during the austral summer, while PMA only accounted
for about 20 % of observed CCN and CDNC.
Precipitation over the ∼1.5 d trajectory inversely
correlates with CCN concentrations, indicating precipitation scavenging is a
major sink of CCN in the SO. The boundary layer cloud fraction was highest
for the aged (high-CCN) regime, suggesting cloud processing significantly
enhanced CCN concentrations (CCN0.3=185± 38 cm-3 for the
aged regime) in non-precipitating clouds. High CN concentrations (Dp>0.01µm), characteristic of recent particle formation
(RPF), corresponded to cases with low cloud fractions, which is consistent
with particle formation in cumulus outflow, also found in previous studies
(Bates
et al., 1998b; Clarke et al., 1999; Cotton et al., 1995; Perry and Hobbs,
1994). RPF is the main eventual source of CCN number concentration in the
SO. In addition, CN concentrations were typically lower in the MBL relative
to concentrations above the MBL, suggesting that RPF typically occurred
above the MBL inversion – either in the FT or a decoupled layer. In
contrast, CCN and particle concentrations with Dp>0.07µm (UHSAS) were higher in the MBL, suggesting growth of recently
formed particles to CCN sizes occurred after mixing into the MBL and
subsequent aging through gas-to-particle conversion and cloud processing.
Due to the remoteness of the SO, biogenic Antarctic coastal emissions appear
to be the main CCN source to the SO MBL. Long-range transport of these
emissions is shown to enhance measured particle concentrations up to 2000 km
away and contribute significantly to the concentration and variability of SO
CCN and CDNC. These results indicate that changes in future coastal
Antarctica SO phytoplankton production caused by climate change
(Deppeler and Davidson, 2017) could have
significant ramifications for CCN concentrations and cloud properties in the
SO. This work provides measurements that are rare for this region of the
globe and may help reduce discrepancies between models and observations of
CN and CCN concentrations.
Data availability
SOCRATES CCN data can be found at
https://doi.org/10.5065/D6Z036XB (Sanchez and Roberts,
2018). The SOCRATES GV
navigation and microphysics data can be found at
https://doi.org/10.5065/D6M32TM9 (UCAR/NCAR, 2017). GDAS data are available at
ftp://arlftp.arlhq.noaa.gov/pub/archives/gdas0p5/ (Kleist et al., 2009). ERA5 data are available
at https://doi.org/10.26023/Z0M2-M6YK-XD12 (ECMWF, 2018). All data and samples acquired on
the CAPRICORN-2 voyage are made publicly available in accordance with CSIRO Marine National Facility
policy. Processed R/V Investigator CCN data for CAPRICORN-2 are available at
https://doi.org/10.25919/2h1c-t753 (Humphries et al., 2020). Raw data are available by contacting the
data librarians (datalibrariansoamnf@csiro.au).
The supplement related to this article is available online at: https://doi.org/10.5194/acp-21-3427-2021-supplement.
Author contributions
KJS and GCR were responsible for the conceptualization, methodology,
software, and the writing of the original draft. KJS performed the formal
analysis and visualization. GCR and LMR were responsible for supervision,
project administration, and funding acquisition. All authors participated in
curating data, writing, review, and editing.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
The authors wish to thank the CSIRO Marine National Facility
(MNF) for its support in the form of sea time on R/V Investigator, support
personnel, scientific equipment, and data management. We thank the
UCAR/NCAR-Earth Observing Laboratory and the flight crew for all the work
done to obtain the measurements used in this paper.
Financial support
This research has been supported by the National Science Foundation (grant nos. AGS-1660374, AGS-1660509, and AGS-1660605).
Review statement
This paper was edited by Paul Zieger and reviewed by Luke Cravigan and two anonymous referees.
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