We study 41 d with daily median surface
accumulation mode aerosol particle concentrations below 50 cm-3
(ultra-clean conditions) observed at Ascension Island (ASI; 7.9∘ S, 14.4∘ W) between June 2016 and October 2017 as part of the
Layered Atlantic Smoke Interactions with Clouds (LASIC) campaign.
Interestingly, these days occur during a period of great relevance for
aerosol–cloud–radiation interactions, the southeast Atlantic (SEATL)
biomass-burning season (approximately June–October). That means that these
critical months can feature both the highest surface aerosol numbers, from
smoke intrusion into the marine boundary layer, as well as the lowest. While
carbon monoxide and refractory black carbon concentrations on ultra-clean
days do not approach those on days with heavy smoke, they also frequently
exceed background concentrations calculated in the non-burning season from
December 2016 to April 2017. This is evidence that even what become
ultra-clean boundary layers can make contact with and entrain from an
overlying SEATL smoke layer before undergoing a process of rapid aerosol
removal. Because many ultra-clean and polluted boundary layers observed at
Ascension Island during the biomass burning season follow similar isobaric
back trajectories, the variability in this entrainment is likely more
closely tied to the variability in the overlying smoke rather than
large-scale horizontal circulation through the boundary layer. Since
exceptionally low accumulation mode aerosol numbers at ASI do not
necessarily indicate the relative lack of other trace pollutants, this
suggests the importance of regional variations in what constitutes an
“ultra-clean” marine boundary layer. Finally, surface drizzle rates,
frequencies and accumulation – as well as retrievals of liquid water path
– all consistently tend toward higher values on ultra-clean days. This
implicates enhanced coalescence scavenging in low clouds as the key driver
of ultra-clean events in the southeast Atlantic marine boundary layer. These
enhancements occur against and are likely mediated by the backdrop of a
seasonal increase in daily mean cloud fraction and daily median liquid water
path over ASI, peaking in September and October in both LASIC years.
Therefore the seasonality in ultra-clean day occurrence seems directly
linked to the seasonality in SEATL cloud properties. These results highlight
the importance of two-way aerosol–cloud interactions in the region.
Introduction
Cloud-mediated aerosol radiative effects remain a significant source of
uncertainty in our understanding of the climate system (Boucher et al., 2013;
Rosenfeld et al., 2014). The southeast Atlantic (SEATL) is a focal point for
studying these effects because biomass-burning aerosol (BBA) particles
transported from central and southern Africa frequently overlie a major
stratocumulus deck between approximately July and October (Devasthale and Thomas, 2011;
Zuidema et al., 2016c). The regional peak in satellite-retrieved cloud
fraction and aerosol optical depth, as well as vertical overlap between the
smoke layer and clouds, tends to occur between September and October (Adebiyi et
al., 2015; Zuidema et al., 2016a). This establishes the potential for a
complex web of aerosol–cloud–radiation interactions on seasonal and regional
scales.
By absorbing solar radiation, BBA can alter the thermodynamic structure of
the lower troposphere, leading to changes in low cloud cover (Gordon
et al., 2018; Johnson et al., 2004; Sakaeda et al., 2011; Tummon et al.,
2010; Yamaguchi et al., 2015; Zhou et al., 2017). If smoke entrains into the
marine boundary layer (MBL) and activates into a cloud droplet, BBA may also
induce indirect effects (Costantino and
Bréon, 2013; Diamond et al., 2018; Zhou et al., 2017). However, contact
between the base of smoke layers and the cloud-topped MBL is highly variable
and difficult to constrain with satellite remote sensing (e.g., Rajapakshe et al., 2017). At Ascension
Island (ASI, details below), there is frequently heavy smoke intrusion into
the MBL earlier in the burning season (June–August) than expected given the
later (September–October) peak in aerosol optical depth (Zuidema et al., 2018). We should note that ASI is situated
further to the west of the “classically” defined (Klein and Hartmann, 1993) SEATL stratocumulus
region. The full role of BBA in the SEATL MBL particle budget and its
subsequent interactions with low clouds remains under investigation.
Generally, aerosol particle number concentrations in the remote MBL exhibit
significant spatiotemporal variability (Allen
et al., 2011; Anderson et al., 2003; Mohrmann et al., 2017). One feature of
this variability observed at the surface is periods of extremely low
(ultra-clean) accumulation mode (approximately 100 nm–1 µm) number
concentrations (Pennypacker and Wood, 2017; Wood et al.,
2017). Wood et al. (2017) noted relative enhancements in satellite
retrievals of cloud liquid water path (LWP), a crucial driver of MBL
coalescence scavenging (Wood, 2006), in ultra-clean air mass back
trajectories several days before arriving over the Azores in the North
Atlantic. Pennypacker and Wood (2017) further explored the properties of the
post-frontal open cellular clouds typically associated with these
ultra-clean conditions over the North Atlantic. Other studies have noted
ultra-clean layers near the top of the MBL, in subtropical pockets of open
cells and during the stratocumulus-to-cumulus transition (Petters
et al., 2006; Terai et al., 2014; Wood et al., 2018). These examples from
both midlatitude and subtropical MBLs point to heavy drizzle-driven
coalescence scavenging in regions of changing low cloud morphology as key
for driving this particular feature of MBL aerosol variability. Drizzle also
plays an important role in setting the mean MBL aerosol state under
subtropical stratocumulus (Wood et al., 2012).
Our goal is to expand these analyses of ultra-clean conditions, as broadly
defined for other regions in prior work noted above, into the SEATL,
especially given the unique potential for influence from BBA. Our study is
structured around the following three questions:
Do ultra-clean conditions occur at the surface in the SEATL, and what is
their place in aerosol variability?
How do concentrations of biomass burning tracers during any ultra-clean
conditions compare to background values from the non-burning season?
Are ultra-clean conditions associated with enhancements in precipitation?
To address these questions, we employ observations from the first
Atmospheric Radiation Measurement (ARM) Mobile Facility (AMF1) deployed to
Ascension Island (7.9333∘ S, 14.41667∘ W) as part of the
Layered Atlantic Smoke Interactions with Clouds (LASIC) campaign (Zuidema et al., 2016c, b).
Data and methodsAerosol and trace gas observations from AMF1
A Droplet Measurement Technologies (DMT) ultra-high-sensitivity aerosol
spectrometer (UHSAS; Uin, 2016; 10.5439/1095587, ARM, 2016a)
provides aerosol number concentrations (NA) at 0.1 Hz for particles
with dry diameters between 60 nm and 1 µm. We define any day in the
1 June 2016–30 October 2017 LASIC UHSAS observational record (460
available days) as ultra clean if the daily median NA falls below 50 cm-3. UHSAS data are currently unavailable for July 2017 due to
unresolved quality control issues. While admittedly somewhat subjective,
this 50 cm-3 threshold is consistent with the upper bound of
near-surface and below-cloud observations in MBL environments routinely
featuring exceptionally low NA such as subtropical pockets of open
cells (Abel
et al., 2019; Sharon et al., 2006; Terai et al., 2014), midlatitude
open-cellular convection (Abel et al., 2017; Pennypacker
and Wood, 2017) and the trade wind stratocumulus-to-cumulus
transition (Bretherton et al., 2019). It
is also well situated within the typical range (∼30–60 cm-3) of number concentrations used for the lowest aerosol cases in
large-eddy simulation studies of MBL aerosol–cloud interactions (Wang
et al., 2010; Wang and Feingold, 2009; Yamaguchi and Feingold, 2015; Zhou et
al., 2017). Prior work defined ultra-clean layers near the top of the MBL,
often observed in the stratocumulus-to-cumulus transition, with NA<10 cm-3 (Wood et al., 2018). We argue it
is reasonable to set a higher threshold near the surface, where aerosol
number concentrations are generally higher due to proximity to the sea spray
source. Furthermore, Wood et al. (2018) focused on these layers primarily as
a mesoscale feature within larger cloud systems, whereas our interest is in
studying ultra-clean conditions as daily-scale events. Defining ultra-clean
conditions using daily median NA<50 cm-3 balances the
need to reasonably capture conditions with exceptionally low near-surface
NA in the remote MBL while maintaining a robust sample of cases to
study. We take daily medians as a better indication of the aerosol number
concentration over the course of a day since they are more robust to any
outlier observations than daily means, though this choice only leads to a
discrepancy over 1 d identified as ultra clean. Observations of total
particle concentrations from a TSI Incorporated ultrafine (>3 nm, NCN3) condensation particle counter (CPC) complement the UHSAS
observations (Kuang, 2016; 10.5439/1046186, ARM. 2016b).
We also consider measurements of carbon monoxide (CO) and refractory black
carbon (rBC) mass concentrations from AMF1. CO concentrations are measured
at 1 Hz by a Los Gatos Research trace gas analyzer, while a DMT single-particle soot photometer (SP2; Sedlacek, 2017) measures the
rBC. The black carbon concentrations are calculated on 10 s intervals
with a sensitivity of 10 ng m-3. Our primary goal with these data
(Question 2) is to determine whether ultra-clean days represent the absence
of any biomass burning influence in the MBL, relative to the regional
background. This background is calculated from the non-burning season from
December 2016 to April 2017. Both CO and black carbon act as biomass burning
signatures, but since precipitation scavenging does not impact CO, it can
reveal prior smoke contact even if aerosol concentrations are low. Again, we
report median concentrations in order to minimize the potential impact of
any outlier observations. We also report and compare inter-quartile ranges
since a long tail on the distribution often skews the variability about
these medians.
Back trajectories
Systematic differences in surface aerosol concentrations and composition at
ASI, like those between ultra-clean and smoky days, could be explained by
upwind differences in MBL entrainment from the free troposphere. The
frequency of contact between the smoke base and MBL top over the SEATL is
notoriously difficult to constrain because the aerosol often significantly
attenuates lidar beams (Rajapakshe et al.,
2017). Zuidema et al. (2018) also posited that changes in transport pathway
from the African continent, illuminated by three-dimensional
back trajectories, were key to explaining the smokiest conditions in the MBL
near ASI. We take a complementary approach by analyzing 7 d isobaric
boundary layer back trajectories initialized at approximately 500 m over ASI
at 12:00 UTC as computed by the NOAA Hybrid Single Particle Lagrangian
Integrated Trajectory Model (HYSPLIT) with Global Data Assimilation System
meteorology on a 0.5∘ by 0.5∘ grid (Stein et al., 2015). We compare the
behavior of these trajectories on ultra-clean days and days within the same
months that exceed their monthly 90th percentile of daily median NA,
which we label as polluted. See Table S1 in the Supplement for a complete listing of the
specific dates. Isobaric trajectories specifically reveal the origins and
paths of the boundary layers that would be entraining smoke from the free
troposphere.
Clouds and precipitation
Based on prior analysis of ultra-clean conditions in the midlatitude
(Pennypacker and Wood, 2017; Wood et al., 2017) and
subtropical (Petters et al., 2006; Wood
et al., 2018) MBL, we hypothesize that enhanced drizzle also plays an
important role in driving aerosol variability over the SEATL. Local surface
precipitation rates at ASI are measured over a 1 min averaging period
using a Parsivel2 laser disdrometer (Delamere et
al., 2016; 10.5439/1150252). We calculate a daily precipitation
frequency as the ratio of these averaging periods with a detected
precipitation rate to the total within a day. This metric will of course not
be a total drizzle frequency because it cannot include periods when
precipitation evaporates before reaching the ground. We also examine
differences in nonzero (i.e., only when clouds are detected) best-estimate
retrievals of liquid water path from the AMF two-channel microwave radiometer (Cadeddu et al., 2013; Gaustad et
al., 2016; 10.5439/1027369, ARM, 2016c) between ultra-clean and all other
days in the LASIC record. These retrievals are reported in 40 min
averaging windows. LWP is a key driver of MBL aerosol loss through
coalescence scavenging even when drizzle does not reach the surface. In
particular, we examine the statistics of retrieved LWP across bins of daily
median NA (by 50 cm-3 from 0 to 400 cm-3, by 100 cm-3
from 400 to 700 cm-3 and then by 300 cm-3 from 700 to 1000 cm-3).
Finally, we place all of our observations in the context of the full LASIC record of both daily median LWP retrieved by the microwave radiometer (MWR) and daily mean cloud fraction as estimated by the ASI Total Sky Imager (Morris, 2005; 10.5439/1025308, ARM, 2016d).
ResultsUltra-clean days
A total of 41 d meet our criteria for ultra-clean conditions (daily median
NA<50 cm-3) in the available LASIC data. The 28 events
from 2016 and the 13 events from 2017 all occur between July and November
(Fig. 1a, Table S1). The distribution of events within these months
varies, with August 2016 (12 d) and October 2017 (9 d) having the
highest number of ultra-clean days in their respective years. As expected
from Zuidema et al. (2018), we observe the highest daily NA peaks in
the early 2016 and 2017 biomass burning seasons (June–August).
Understanding SEATL MBL aerosol variability in this crucial period thus
requires an understanding of both smoke intrusions and ultra-clean
conditions. In months with few or none of these extremes (October 2016–April 2017), the daily and monthly median particle concentrations vary more
consistently around 200 cm-3.
Time series of daily and monthly median (a)NA and (b)NCN3 measured during LASIC, with ultra-clean days marked in cyan. In
panels (a) and (b), vertical grid lines mark the first of each month labeled on the
tick. Daily median NCN3 is then regressed against daily median NA
for both (c) non-ultra-clean days and (d) ultra-clean days.
Median NCN3 mostly follows the same seasonal pattern as NA across
the LASIC record (Fig. 1b). This leads us to expect that the accumulation
mode is generally an important driver of the variability in the total
particle population. On ultra-clean days, however, the accumulation is by
definition mostly depleted, while daily median NCN3 ranges from a 115 cm-3 minimum to a 374 cm-3 maximum. The range of total particle
concentrations is therefore much higher than the range within the
accumulation mode. On all except ultra-clean days, daily median NA
explains more than half (r2= 0.65) of the variance in the total
particle concentration, as expected (Fig. 1c). This relationship is
substantially weaker (r2=0.06), with the 95 % confidence interval
for this correlation including zero, on ultra-clean days (Fig. 1d). While
ultra-clean days tend to have lower NCN3 than other days, certainly
those with smoke intrusions, the weakened correlation with NA further
confirms that different processes are responsible for governing the range of
total particle concentrations outside of the accumulation mode. A similar
difference in correlation strength between ultra-clean and other days holds
at hourly timescales as well (Fig. S1).
Biomass burning signatures
Perhaps unsurprisingly, CO generally tracks the accumulation mode aerosol
number concentrations in Fig. 1, correlating with daily median NA
most strongly (r2>0.65) in the early biomass burning
seasons (June–August 2016 and 2017) when smoke influence in the boundary
layer is highest (Fig. 2a). Outside of the primary burning season
(December 2016–April 2017), the day-to-day NA–CO correlation
strength varies with r2 values between 0.04 and 0.49, depending on the
month. The rBC also generally follows the same patterns as aerosol number and CO
(Fig. 2b), with day-to-day NA–rBC correlation again strongest
(r2>0.55) in the early burning season. The 2017
observations again confirm the analysis of Zuidema et al. (2018), which,
based on the 2016 data, found that the signature of smoke in the ASI MBL is
strongest earlier in the traditional SEATL biomass burning season. There is
a smaller but noticeable peak in black carbon in January–February 2017 (Fig. 2b) that is oddly not evident in the CO observations. We leave a full
diagnosis of this secondary peak for future work.
Time series of daily and monthly median (a) CO and (b) rBC
measured during LASIC, with ultra-clean days marked in cyan. In panels (a) and (b),
vertical grid lines mark the first of each month labeled on the tick. We
then compare the PDF of hourly median (c) CO and (d) rBC from ultra-clean
days to the PDF of hourly median concentrations from each tracer's
respective non-burning background.
Of primary interest to this study is the range of BBA signature observations
during ultra-clean events, relative to a background value. Prior
observations in the subtropical Southern Hemisphere have put background CO
concentrations between 50 and 60 ppb (Allen
et al., 2008, 2011; Shank et al., 2012). The median of hourly median CO
concentration on ultra-clean days is 69 ppb, with an interquartile range of
62–74 ppb, and the full distribution of ultra-clean CO concentrations
exhibits some moderate bimodality (Fig. 2c). In the non-burning season
(December 2016–April 2017), the distribution shifts to generally lower CO
concentrations. The background median CO concentration is 59 ppb and the
inter-quartile range is between 55 and 65 ppb, consistent with the prior
estimates noted above. The first mode of ultra-clean CO concentrations (Fig. 2c) overlaps more with the background distribution and is consistent with
the background statistics. However, the second mode and longer tail of the
distribution highlight the larger range of possible concentrations on
ultra-clean days. This pulls the overall statistics toward higher
concentrations on ultra-clean days relative to the non-burning background.
There is also some overlap in the distributions of ultra-clean and
non-burning background SP2 rBC (December 2016, March–April 2017, Fig. 2d).
However, as with CO, the statistics do indicate a shift toward overall
higher concentrations on ultra-clean days. The median of hourly median SP2
rBC is 51 ng m-3 with an inter-quartile range of 23–120 ng m-3 on ultra-clean days, compared to the background median of 20 ng m-3
and inter-quartile range of 12–45 ng m-3. Even the hourly extremes
captured by the 5th and 95th percentiles are higher on ultra-clean days (12
and 312 ng m-3) than across the non-burning background (10 and 135 ng m-3). In summary, there is no indication that ultra-clean days are
devoid of BBA signatures or even exhibit the same distribution of smoke
tracer concentrations as the non-burning season background at ASI. We will
return to the implication of these results for the characterization of
extremely low aerosol number events as ultra clean in the Discussion.
Relative to the polluted extremes (recall these are defined by daily median
NA above the monthly 95th percentile), there are somewhat more ultra-clean
boundary layer isobaric back trajectories that originate farther toward the
midlatitudes and the Southern Ocean (Fig. 3a). We might expect lower
background aerosol concentrations and weaker influence from African biomass
burning in these air masses than in those spending more time in the
subtropics, helping explain the subset of ultra-clean days with burning
tracer concentrations closer to background levels. However, trajectory
latitude at 7 d back from ASI only explains 25 % of the variance in
daily median CO concentrations across ultra-clean days. Trajectories from
days with daily median CO ≤59 ppb (n=6), the non-burning
background median concentration, can be anywhere between 40 and 60∘ S at 7 d back from ASI (Fig. S2). Overall, boundary
layer trajectory origin is a relatively weak predictor of downwind
variability in CO concentration on ultra-clean days. Furthermore, there are
many polluted and ultra-clean boundary layers that follow similar isobaric
trajectories on their way toward ASI (Fig. 3b). By 3 d away from
ASI, most trajectories have converged to within 3 to 4∘
latitude and longitude of each other. In other words, the boundary layers
that would be entraining smoke from the free troposphere often follow very
similar horizontal circulation patterns for both the highest and lowest
upstream extremes of NA. This all points to a smaller role for
variations in large-scale horizontal circulation in the SEATL MBL in driving
aerosol and trace gas variability observed at ASI.
Isobaric 7 d HYSPLIT back trajectories at 500 m for (a) ultra-clean and (b) polluted days from ASI.
Precipitation and cloud liquid water
Ultra-clean days exhibit markedly different surface precipitation
characteristics, as measured by the ASI Parsivel2. The distribution of
precipitation rates shifts toward higher intensities on ultra-clean days
(Fig. 4a). Precipitation is also much more common on ultra-clean days
(Fig. 4b), with almost 90 % of non-ultra-clean days having a precipitation
frequency of less than 0.05. The tendency for more frequent and more intense
precipitation inevitably leads to higher total accumulation on ultra-clean
days (Fig. 4c). The difference mostly stems from the shift toward more
frequent drizzle conditions in ultra-clean conditions. These data are all
presented with cumulative distributions in order to concisely highlight the
generally different behavior of precipitation across ultra-clean days, as
well as to clearly visualize the parts of the distributions that contribute
most to these differences. However, the increase in drizzle intensity,
frequency and accumulation also holds for ultra-clean days relative only to
the distribution within their respective months (not shown).
Cumulative distributions of (a) instantaneous precipitation rate, (b) daily precipitation frequency and (c) daily precipitation accumulation
as measured by the ASI Parsivel2 laser disdrometer.
(a) Comparison of the cumulative distributions of best-estimate
LWP retrieval from the ASI MWR between ultra-clean and all other days and
(b) medians and standard deviations of daily median MWR LWP across bins of daily
median accumulation mode aerosol for the entire LASIC record. In panel (b), bin
widths were selected to account for varying density of days across the range
of aerosol concentrations while still visualizing the broader pattern.
The median LWP retrieved by MWR measurements is higher on ultra-clean days
(110 g m-2) compared to other days (76 g m-2). The
inter-quartile spreads are actually larger than the median LWP whether
within ultra-clean days (41–235 g m-2) or not (26–192 g m-2). These statistics are further illustrated by the difference in the
LWP cumulative distributions (Fig. 5a). The shift is noted across most of
the sampled range of LWP, though the distributions do overlap at the very
highest values. While the shift toward higher LWP on ultra-clean days may
not appear substantial, recall that coalescence scavenging is nonlinearly
dependent on LWP (Wood, 2006). The approximately 35 % increase
in median LWP on ultra-clean days would strengthen the coalescence
scavenging aerosol sink by 70 %.
Below a daily median NA of about 150 cm-3, daily median LWP
generally increases with decreasing NA (Fig. 5b), still accompanied
by high variability. This is indicative of higher LWP driving reductions in
accumulation mode aerosol through drizzle production and scavenging. Over a
wide range of intermediate daily median NA (∼150–500 cm-3), there is no discernible variation in binned LWP that would point
to a dominant process. At higher number concentrations, however, daily
median LWP continues to drop with increasing NA, implicating at least
some role for a relative lack of thick, drizzling clouds in sustaining the
highest accumulation mode number concentrations.
Discussion and conclusions
The SEATL remains the focus of intensive study because of the potential for
direct, indirect and semi-direct radiative effects arising from extensive
biomass burning aerosol layers overlying a major stratocumulus deck. We
utilize data collected from an ARM Mobile Facility deployed (June 2016–October 2017) to Ascension Island during the LASIC campaign to study 41 d
with daily median accumulation mode aerosol concentrations below 50 cm-3. Perhaps counterintuitively, all of these observed ultra-clean
days occur between July and November, the season when BBA concentrations in
the SEATL region generally peak. In the 2016 observations, ultra-clean days
are particularly prevalent in July and August and frequently both precede
and follow the periods of heavy smoke intrusion into the MBL around ASI
examined in Zuidema et al. (2018). In 2017, most of the ultra-clean days
occur in October, but we hesitate to comment on the robustness of any
interannual variability given the relatively infrequent sampling of these
events and the 2-year observational record. Satellite retrievals of cloud
droplet number concentration (Grosvenor et al., 2018) may
provide a tool for extending our analysis with both a longer temporal record
and greater spatial context of extreme depletion events in the SEATL MBL.
However, these retrievals remain far more uncertain in the broken and/or
heavily drizzling cloud scenes that often coincide with ultra-clean
conditions. Nonetheless, both years of the LASIC deployment situate
ultra-clean days as a feature of surface aerosol variability at ASI during
the broader SEATL biomass burning season. This naturally leads to the
question of what might drive this seasonality in the occurrence of
ultra-clean days.
Monthly boxplots of (a) daily mean Total Sky Imager cloud fraction and (b) daily median MWR best-estimate LWP for each month in the LASIC record.
Surface precipitation rates, frequency and accumulation, as well as
retrieved cloud LWP, are all systematically enhanced on ultra-clean days
relative to non-ultra clean days. These observations are indicative of
vigorous coalescence scavenging being a key driver of ultra-clean days at
ASI. Clouds capable of initiating and sustaining this scavenging process are
therefore likely precursors for these conditions. The months featuring
ultra-clean days in the LASIC record are also the months leading up to and
including the seasonal maximum in daily mean cloud fraction recorded by the
ASI Total Sky Imager (Fig. 6a). These same months also tend to be
associated with the seasonal peak in daily median MWR best-estimate LWP
values, though there is substantial spread in the monthly distributions in
both 2016 and 2017 (Fig. 6b). While limited to the 2-year LASIC
deployment period, these local observations are broadly consistent with
prior work that has noted a seasonal maximum in satellite-retrieved SEATL
regional cloud fraction (Zuidema et al.,
2016a) and LWP (O'Dell et
al., 2008; Zuidema et al., 2016a) between August and October. And though
these satellite-based analyses tend to consider data from an area to the
southeast of ASI, scavenging upwind of our observations is also likely
important. Thus, the seasonality in ultra-clean-day occurrence appears
broadly tied to the seasonality of SEATL clouds. The increase toward the
seasonal maximum in cloud cover and LWP likely provides the necessary
backdrop for enhancements in coalescence scavenging needed to nearly fully
deplete the accumulation mode in the MBL around ASI.
However, our results further show that using the term “ultra-clean”
incompletely describes conditions with extremely low accumulation mode
particle number concentrations over the SEATL. Accumulation mode and total
particle concentrations are generally well correlated at ASI, though much
less so on ultra-clean days, on both daily and hourly scales. Even when
NA<50 cm-3, smaller particles can have 2–4 times the
number concentrations than in the accumulation mode. The variability of
Aitken and nucleation mode particle concentrations deserves more attention
in future work, including any possibility of contributions from new particle
formation. Carbon monoxide and refractory black carbon mass are also not
necessarily at non-burning season (December–April) background levels
despite the depletion of the accumulation mode. This points to the
possibility of more frequent but subtler influence of smoke in the ASI MBL
outside of the most extreme intrusions like those examined by Zuidema et al. (2018). The wide range of trace pollutant concentrations observed over our
sample of 41 d at ASI with exceptionally low NA highlights the
importance of carefully considering what constitutes an ultra-clean MBL in
a particular region. More work is needed on systematically comparing the
variability of pollutants like CO and rBC during periods of otherwise low
accumulation mode aerosol number both within and between regions.
This analysis highlights an additional layer of complexity in the SEATL
aerosol–cloud system. The months featuring the highest daily concentrations
of aerosol particles in the MBL around Ascension Island also feature the
lowest, likely due to multiday timescale enhancements in coalescence
scavenging on top of a pre-existing seasonal cycle. Ultra-clean MBL
conditions present an important test for large-eddy simulation (LES) physics
and provide a tool for further probing underlying processes and their
associated timescales. The initiation, evolution and persistence of these
conditions could make particularly interesting case studies for LES
modeling of the Lagrangian evolution of the SEATL MBL. More broadly, air
mass history is an important factor in the interpretation of aerosol–cloud
interactions over the SEATL given the typical timescales associated with
entrainment of free tropospheric aerosol into the MBL and loss from
precipitation (Diamond et al., 2018). The broad similarities
in isobaric boundary layer back trajectories even between ultra-clean and
the most polluted days at ASI suggest that systematic differences in
large-scale horizontal circulation in the boundary layer may play less of a
role in downwind (e.g., at ASI) aerosol variability. Instead, the vertical
separation between smoke and the MBL along air-mass trajectories, in
addition to the co-evolution of clouds and precipitation, could set a
balance between entrainment and scavenging. The transport and consequent
three-dimensional structure of BBA layers certainly varies with circulation
patterns above the boundary layer (Adebiyi and Zuidema, 2016). Abel et al. (2019) also noted relative reductions in the entrainment of overlying smoke
tracers into the MBL in a pocket of heavily drizzling open cells near ASI,
potentially driven by cloud dynamical differences noted in other previous
work (Berner et al., 2011). The
progression of clouds and precipitation along trajectories in the SEATL will
depend on a number of factors, including potential influence of overlying
smoke layers (Yamaguchi et al., 2015; Zhou et al.,
2017) and buffering feedbacks (Stevens and Feingold,
2009). The detailed evolution of how all of this might lead to downwind
ultra-clean conditions and the variations in other trace pollutant
concentrations observed during these events should be further explored in a
Lagrangian LES framework. Coarser-resolution models used to study
aerosol–cloud interactions across the broader SEATL region should also test
their capability of reproducing these events and their place in the aerosol,
cloud and meteorological seasonal cycles.
Data availability
All LASIC ARM data are publicly available at https://www.archive.arm.gov/discovery/ (last access: 21 February 2020). ARM dataset DOI references are
included in the text.
The supplement related to this article is available online at: https://doi.org/10.5194/acp-20-2341-2020-supplement.
Author contributions
SP and RW conceived the study design and analysis. SP performed the analysis
of the LASIC data. MD ran the HYSPLIT transport and dispersion model to
produce the back trajectories. SP wrote the paper and all authors reviewed
the manuscript.
Competing interests
The authors declare that they have no conflict of interest.
Special issue statement
This article is part of the special issue “New observations and related modelling studies of the aerosol–cloud–climate system in the Southeast Atlantic and southern Africa regions (ACP/AMT inter-journal SI)”. It is not associated with a conference.
Acknowledgements
The U.S.
Department of Energy, Office of Science, Office of Biological and
Environmental Research, Climate and Environmental Sciences Division sponsors
the Atmospheric Radiation Measurement (ARM) program. We are indebted to the
scientists and staff who make these data possible by taking and quality
controlling the measurements. The authors gratefully acknowledge the NOAA
Air Resources Laboratory (ARL) for the provision of the HYSPLIT transport
and dispersion model and/or READY website (http://www.ready.noaa.gov, last access: 21 February 2020) used in this publication. Particular thanks are due to
Paquita Zuidema for helpful discussions about the LASIC campaign and to
Arthur Sedlacek for providing access to and discussing the SP2 dataset.
Financial support
Sam Pennypacker's work was supported by the United States Department of Energy (DOE) award DE-SC0013489. Michael Diamond's work was supported by NASA headquarters under the NASA Earth and Space Science Fellowship Program, grant NNX-80NSSC17K0404. Robert Wood's work was supported by NASA Earth Venture Suborbital-2 grant NNX-15AF98G.
Review statement
This paper was edited by Paquita Zuidema and reviewed by Jianhao Zhang and three anonymous referees.
ReferencesAbel, S. J., Boutle, I. A., Waite, K., Fox, S., Brown, P. R. A., Lloyd, G.,
and Choularton, T. W.: The role of precipitation in controlling the
transition from stratocumulus to cumulus clouds in a northern hemisphere
cold-air outbreak, J. Atmos. Sci., 74, 2293–2314,
10.1175/JAS-D-16-0362.1, 2017.Abel, S. J., Barrett, P. A., Zuidema, P., Zhang, J., Christensen, M., Peers, F., Taylor, J. W., Crawford, I., Bower, K. N., and Flynn, M.: Open cells can decrease the mixing of free-tropospheric biomass burning aerosol into the south-east Atlantic boundary layer, Atmos. Chem. Phys. Discuss., 10.5194/acp-2019-738, in review, 2019.Adebiyi, A. A. and Zuidema, P.: The role of the southern African easterly
jet in modifying the southeast Atlantic aerosol and cloud environments, Q.
J. Roy. Meteor. Soc., 142, 1574–1589, 10.1002/qj.2765, 2016.Adebiyi, A. A., Zuidema, P., and Abel, S. J.: The convolution of dynamics and
moisture with the presence of shortwave absorbing aerosols over the
southeast Atlantic, J. Climate, 28, 1997–2024,
10.1175/JCLI-D-14-00352.1, 2015.Allen, G., Vaughan, G., Bower, K. N., Williams, P. I., Crosier, J., Flynn,
M., Connolly, P., Hamilton, J. F., Lee, J. D., Saxton, J. E., Watson, N. M.,
Gallagher, M., Coe, H., Allan, J., Choularton, T. W., and Lewis, A. C.:
Aerosol and trace-gas measurements in the Darwin area during the wet season,
J. Geophys. Res.-Atmos., 113, 1–19, 10.1029/2007JD008706, 2008.Allen, G., Coe, H., Clarke, A., Bretherton, C., Wood, R., Abel, S. J., Barrett, P., Brown, P., George, R., Freitag, S., McNaughton, C., Howell, S., Shank, L., Kapustin, V., Brekhovskikh, V., Kleinman, L., Lee, Y.-N., Springston, S., Toniazzo, T., Krejci, R., Fochesatto, J., Shaw, G., Krecl, P., Brooks, B., McMeeking, G., Bower, K. N., Williams, P. I., Crosier, J., Crawford, I., Connolly, P., Allan, J. D., Covert, D., Bandy, A. R., Russell, L. M., Trembath, J., Bart, M., McQuaid, J. B., Wang, J., and Chand, D.: South East Pacific atmospheric composition and variability sampled along 20∘ S during VOCALS-REx, Atmos. Chem. Phys., 11, 5237–5262, 10.5194/acp-11-5237-2011, 2011.Anderson, T. L., Charlson, R. J., Winker, D. M., Ogren, J. A., and
Holmén, K.: Mesoscale Variations of Tropospheric Aerosols, J. Atmos.
Sci., 60, 119–136, 10.1175/1520-0469(2003)060<0119:MVOTA>2.0.CO;2, 2003.Atmospheric Radiation Measurement (ARM): Ultra-High Sensitivity Aerosol Spectrometer (AOSUHSAS). 2016-04-23 to 2017-11-01, updated hourly, ARM Mobile Facility (ASI) Ascension Island, South Atlantic Ocean; AMF1 (M1), compiled by: Uin, J., Salwen, C., and Senum, G., ARM Data Center, 10.5439/1095587, 2016a.Atmospheric Radiation Measurement (ARM): Condensation Particle Counter (AOSCPCU). 2016-05-20 to 2017-11-03, updated hourly, ARM Mobile Facility (ASI) Ascension Island, South Atlantic Ocean; AMF1 (M1), compiled by: Kuang, C., Salwen, C., and Boyer, M., ARM Data Center, 10.5439/1046186, 2016b.Atmospheric Radiation Measurement (ARM): MWR Retrievals (MWRRET1LILJCLOU). 2016-05-21 to 2017-11-01, updated hourly, ARM Mobile Facility (ASI) Ascension Island, South Atlantic Ocean; AMF1 (M1), compiled by: Gaustad, K. and Riihimaki, L. ARM Data Center, 10.5439/1027369, 2016c.Atmospheric Radiation Measurement (ARM): Total Sky Imager (TSISKYCOVER). 2016-05-02 to 2017-10-31, updated hourly, ARM Mobile Facility (ASI) Ascension Island, South Atlantic Ocean; AMF1 (M1), compiled by: Morris, V., ARM Data Center, 10.5439/1025308, 2016d.Berner, A. H., Bretherton, C. S., and Wood, R.: Large-eddy simulation of mesoscale dynamics and entrainment around a pocket of open cells observed in VOCALS-REx RF06, Atmos. Chem. Phys., 11, 10525–10540, 10.5194/acp-11-10525-2011, 2011.Boucher, O., Randall, D., Artaxo, P., Bretherton, C., Feingold, G., Forster,
P., Kerminen, V.-M. V.-M., Kondo, Y., Liao, H., Lohmann, U., Rasch, P.,
Satheesh, S. K., Sherwood, S., Stevens, B., Zhang, X. Y., and Zhan, X. Y.:
Clouds and Aerosols, Clim. Chang. 2013 Phys. Sci. Basis. Contrib. Work. Gr. I to Fifth Assess. Rep. Intergov. Panel Clim. Chang., 571–657,
10.1017/CBO9781107415324.016, 2013.Bretherton, C. S., McCoy, I. L., Mohrmann, J., Wood, R., Ghate, V.,
Gettelman, A., Bardeen, C. G., Albrecht, B. A., and Zuidema, P.: Cloud,
aerosol, and boundary layer structure across the northeast Pacific
stratocumulus-cumulus transition as observed during CSET, Mon. Weather Rev.,
147, 2083–2103, 10.1175/MWR-D-18-0281.1, 2019.Cadeddu, M. P., Liljegren, J. C., and Turner, D. D.: The Atmospheric radiation measurement (ARM) program network of microwave radiometers: instrumentation, data, and retrievals, Atmos. Meas. Tech., 6, 2359–2372, 10.5194/amt-6-2359-2013, 2013.Costantino, L. and Bréon, F.-M.: Aerosol indirect effect on warm clouds over South-East Atlantic, from co-located MODIS and CALIPSO observations, Atmos. Chem. Phys., 13, 69–88, 10.5194/acp-13-69-2013, 2013.Delamere, J., Bartholomew, M., and Shi, Y.: Laser Disdrometer (PARS2), ARM
Mobile Facility (ASI) Ascension Island, South Atlantic Ocean; AMF1 (M1),
Atmos. Radiat. Meas. Clim. Res. Facil. Data Arch, 10.5439/1150252, 2016.Devasthale, A. and Thomas, M. A.: A global survey of aerosol-liquid water cloud overlap based on four years of CALIPSO-CALIOP data, Atmos. Chem. Phys., 11, 1143–1154, 10.5194/acp-11-1143-2011, 2011.Diamond, M. S., Dobracki, A., Freitag, S., Small Griswold, J. D., Heikkila, A., Howell, S. G., Kacarab, M. E., Podolske, J. R., Saide, P. E., and Wood, R.: Time-dependent entrainment of smoke presents an observational challenge for assessing aerosol–cloud interactions over the southeast Atlantic Ocean, Atmos. Chem. Phys., 18, 14623–14636, 10.5194/acp-18-14623-2018, 2018.
Gaustad, K. L., Turner, D. D., and McFarlane, S.: MWRRET Value-Added Product:
The Retrieval of Liquid Water Path and Precipitable Water Vapor from
Microwave Radiometer (MWR) Datasets, U.S. Department of Energy Office of Science Atmospheric Radiation Measurement (ARM) Program, Washington, D.C., USA, 2016.Gordon, H., Field, P. R., Abel, S. J., Dalvi, M., Grosvenor, D. P., Hill, A. A., Johnson, B. T., Miltenberger, A. K., Yoshioka, M., and Carslaw, K. S.: Large simulated radiative effects of smoke in the south-east Atlantic, Atmos. Chem. Phys., 18, 15261–15289, 10.5194/acp-18-15261-2018, 2018.Grosvenor, D. P., Sourdeval, O., Zuidema, P., Ackerman, A., Alexandrov, M.
D., Bennartz, R., Boers, R., Cairns, B., Chiu, J. C., Christensen, M.,
Deneke, H., Diamond, M., Feingold, G., Fridlind, A., Hünerbein, A.,
Knist, C., Kollias, P., Marshak, A., McCoy, D., Merk, D., Painemal, D.,
Rausch, J., Rosenfeld, D., Russchenberg, H., Seifert, P., Sinclair, K.,
Stier, P., van Diedenhoven, B., Wendisch, M., Werner, F., Wood, R., Zhang,
Z., and Quaas, J.: Remote Sensing of Droplet Number Concentration in Warm
Clouds: A Review of the Current State of Knowledge and Perspectives, Rev.
Geophys., 56, 409–453, 10.1029/2017RG000593, 2018.Johnson, B. T., Shine, K. P., and Forster, P. M.: The semi-direct aerosol
effect: Impact of absorbing aerosols on marine stratocumulus, Q. J. Roy.
Meteor. Soc., 130, 1407–1422, 10.1256/qj.03.61, 2004.Klein, S. A. and Hartmann, D. L.: The seasonal cycle of low stratiform
clouds, J. Climate, 6, 1587–1606, 10.1175/1520-0442(1993)006<1587:TSCOLS>2.0.CO;2, 1993.
Kuang, C.: Ultrafine Condensation Particle Counter Instrument Handbook, U.S. Department of Energy Office of Science Atmospheric Radiation Measurement (ARM) Program,
Washington, D.C., USA, 2016.Mohrmann, J., Wood, R., McGibbon, J., Eastman, R., and Luke, E.: Drivers of
seasonal variability in marine boundary layer aerosol number concentration
investigated using a steady-state approach, J. Geophys. Res.-Atmos., 123, 1097–1112,
10.1002/2017JD027443, 2017.
Morris, V.: Total Sky Imager (TSI) Instrument Handbook, U.S. Department of Energy Office of Science Atmospheric Radiation Measurement (ARM) Program, Washington, D.C., USA,
2005.O'Dell, C. W., Wentz, F. J., and Bennartz, R.: Cloud liquid water path from
satellite-based passive microwave observations: A new climatology over the
global oceans, J. Climate, 21, 1721–1739, 10.1175/2007JCLI1958.1, 2008.Pennypacker, S. and Wood, R.: A Case Study in Low Aerosol Number
Concentrations Over the Eastern North Atlantic: Implications for Pristine
Conditions in the Remote Marine Boundary Layer, J. Geophys. Res.-Atmos.,
122, 12393–12415, 10.1002/2017JD027493, 2017.Petters, M. D., Snider, J. R., Stevens, B., Vali, G., Faloona, I., and
Russell, L. M.: Accumulation mode aerosol, pockets of open cells, and
particle nucleation in the remote subtropical Pacific marine boundary layer,
J. Geophys. Res.-Atmos., 111, 1–15, 10.1029/2004JD005694, 2006.Rajapakshe, C., Zhang, Z., Yorks, J. E., Yu, H., Tan, Q., Meyer, K.,
Platnick, S., and Winker, D. M.: Seasonally transported aerosol layers over
southeast Atlantic are closer to underlying clouds than previously reported,
Geophys. Res. Lett., 44, 5818–5825, 10.1002/2017GL073559, 2017.Rosenfeld, D., Sherwood, S., Wood, R., and Donner, L.: Climate Effects of
Aerosol-Cloud Interactions, Science, 343, 379–380,
10.1126/science.1247490, 2014.Sakaeda, N., Wood, R., and Rasch, P. J.: Direct and semidirect aerosol
effects of southern African biomass burning aerosol, J. Geophys. Res.-Atmos., 116, 1–20, 10.1029/2010JD015540, 2011.
Sedlacek, A. J.: Single-Particle Soot Photometer (SP2) Instrument
Handbook, U.S. Department of Energy Office of Science Atmospheric Radiation Measurement (ARM) Program, Washington, D.C., USA, 2017.Shank, L. M., Howell, S., Clarke, A. D., Freitag, S., Brekhovskikh, V., Kapustin, V., McNaughton, C., Campos, T., and Wood, R.: Organic matter and non-refractory aerosol over the remote Southeast Pacific: oceanic and combustion sources, Atmos. Chem. Phys., 12, 557–576, 10.5194/acp-12-557-2012, 2012.Sharon, T. M., Albrecht, B. A., Jonsson, H. H., Minnis, P., Khaiyer, M. M.,
van Reken, T. M., Seinfeld, J., and Flagan, R.: Aerosol and Cloud
Microphysical Characteristics of Rifts and Gradients in Maritime
Stratocumulus Clouds, J. Atmos. Sci., 63, 983–997,
10.1175/JAS3667.1, 2006.Stein, A. F., Draxler, R. R., Rolph, G. D., Stunder, B. J. B., Cohen, M. D.,
and Ngan, F.: NOAA's HYSPLIT atmospheric transport and dispersion modeling
system, B. Am. Meteorol. Soc., 96, 2059–2077,
10.1175/BAMS-D-14-00110.1, 2015.Stevens, B. and Feingold, G.: Untangling aerosol effects on clouds and
precipitation in a buffered system, Nature, 461, 607–613, 10.1038/nature08281, 2009.Terai, C. R., Bretherton, C. S., Wood, R., and Painter, G.: Aircraft observations of aerosol, cloud, precipitation, and boundary layer properties in pockets of open cells over the southeast Pacific, Atmos. Chem. Phys., 14, 8071–8088, 10.5194/acp-14-8071-2014, 2014.Tummon, F., Solmon, F., Liousse, C., and Tadross, M.: Simulation of the
direct and semidirect aerosol effects on the southern Africa regional
climate during the biomass burning season, J. Geophys. Res.-Atmos., 115,
1–20, 10.1029/2009JD013738, 2010.
Uin, J.: Ultra-High-Sensitivity Aerosol Spectrometer Instrument Handbook, U.S. Department of Energy Office of Science Atmospheric Radiation Measurement (ARM) Program,
Washington, D.C., USA, 2016.Wang, H. and Feingold, G.: Modeling Mesoscale Cellular Structures and
Drizzle in Marine Stratocumulus. Part II: The Microphysics and Dynamics of
the Boundary Region between Open and Closed Cells, J. Atmos. Sci., 66,
3257–3275, 10.1175/2009JAS3120.1, 2009.Wang, H., Feingold, G., Wood, R., and Kazil, J.: Modelling microphysical and meteorological controls on precipitation and cloud cellular structures in Southeast Pacific stratocumulus, Atmos. Chem. Phys., 10, 6347–6362, 10.5194/acp-10-6347-2010, 2010.Wood, R.: Rate of loss of cloud droplets by coalescence in warm clouds, J.
Geophys. Res.-Atmos., 111, 1–6, 10.1029/2006JD007553, 2006.Wood, R., Leon, D., Lebsock, M., Snider, J., and Clarke, A. D.: Precipitation
driving of droplet concentration variability in marine low clouds, J.
Geophys. Res.-Atmos., 117, D19210, 10.1029/2012JD018305, 2012.Wood, R., Stemmler, J. D., Rémillard, J., and Jefferson, A.: Low CCN
concentration air masses over the eastern North Atlantic: seasonality,
meteorology and drivers, J. Geophys. Res.-Atmos., 122, 1203–1223,
10.1002/2016JD025557, 2017.Wood, R., O, K.-T., Bretherton, C. S., Mohrmann, J., Albrecht, B. A.,
Zuidema, P., Ghate, V., Schwartz, C., Eloranta, E., Glienke, S., Shaw, R.,
and Fugal, J.: Ultraclean layers and optically thin clouds in the
stratocumulus to cumulus transition: part I. Observations, J. Atmos. Sci.,
75, 1631–1652, 10.1175/JAS-D-17-0213.1, 2018.Yamaguchi, T. and Feingold, G.: On the relationship between open cellular convective cloud patterns and the spatial distribution of precipitation, Atmos. Chem. Phys., 15, 1237–1251, 10.5194/acp-15-1237-2015, 2015.Yamaguchi, T., Feingold, G., Kazil, J., and McComiskey, A.: Stratocumulus to
cumulus transition in the presence of elevated smoke layers, Geophys. Res.
Lett., 42, 10478–10485, 10.1002/2015GL066544, 2015.Zhou, X., Ackerman, A. S., Fridlind, A. M., Wood, R., and Kollias, P.: Impacts of solar-absorbing aerosol layers on the transition of stratocumulus to trade cumulus clouds, Atmos. Chem. Phys., 17, 12725–12742, 10.5194/acp-17-12725-2017, 2017.
Zuidema, P., Chang, P., Medeiros, B., Kirtman, B. P., Mechoso, R., Schneider, E. K., Toniazzo, T., Richter, I., Small, R. J., Bellomo, K., Brandt, P., De Szoeke, S., Farrar, J. T., Jung, E., Kato, S., Li, M., Patricola, C., Wang, Z., Wood, R., and Xu, Z.: Challenges and prospects for reducing coupled climate model SST biases in the eastern tropical atlantic and pacific oceans: The U.S. Clivar eastern tropical oceans synthesis working group, B. Am. Meteorol. Soc., 97, 2305–2327, 10.1175/BAMS-D-15-00274.1, 2016a.Zuidema, P., Alvarado, M., Chiu, C., Deszoeke, S., Fairall, C., Feingold,
G., Ghan, S., Haywood, J., Kollias, P., Lewis, E., Mcfarguhar, G.,
Mccomiskey, A., Mechem, D., Redemann, J., Romps, D., Turner, D., Wang, H.,
Wood, R., Yuter, S., and Zhu, P.: Layered Atlantic Smoke Interactions with
Clouds (LASIC) Science Plan, DOE/SC-ARM-14-037, U.S. Dep. Energy, Off. Sci., available at: https://www.osti.gov/biblio/1232658-layered-atlantic-smoke-interactions-clouds-lasic-science-plan (last access: 21 February 2020), 2016b.Zuidema, P., Redemann, J., Haywood, J., Wood, R., Piketh, S., Hipondoka, M.,
and Formenti, P.: Smoke and clouds above the southeast Atlantic: Upcoming
field campaigns probe absorbing aerosol's impact on climate, B. Am.
Meteorol. Soc., 97, 1131–1135, 10.1175/BAMS-D-15-00082.1, 2016c.Zuidema, P., Sedlacek III, A. J., Flynn, C., Springston, S., Delgadillo, R.,
Zhang, J., Aiken, A. C., Koontz, A., Muradyan, P., and Zuidema, P.: The
Ascension Island boundary layer in the remote southeast Atlantic is often
smoky, Geophys. Res. Lett., 45, 4456–4465, 10.1002/2017GL076926, 2018.