Data are presented from intercomparisons between two
research aircraft, the FAAM BAe-146 and the NASA Lockheed P3, and between
the BAe-146 and the surface-based DOE (Department of Energy) ARM
(Atmospheric Radiation Measurement) Mobile Facility at Ascension Island
(8∘ S, 14.5∘ W; a remote island in the mid-Atlantic).
These took place from 17 August to 5 September 2017, during the African
biomass burning (BB) season. The primary motivation was to give confidence in the
use of data from multiple platforms with which to evaluate numerical climate
models. The three platforms were involved in the CLouds–Aerosol–Radiation
Interaction and Forcing for Year 2017 (CLARIFY-2017), ObseRvations of
Aerosols above CLouds and their intEractionS (ORACLES), and Layered Atlantic
Smoke and Interactions with Clouds (LASIC) field experiments. Comparisons
from flight segments on 6 d where the BAe-146 flew alongside the ARM
facility on Ascension Island are presented, along with comparisons from the
wing-tip-to-wing-tip flight of the P3 and BAe-146 on 18 August 2017.
The intercomparison flight sampled a relatively clean atmosphere overlying a
moderately polluted boundary layer, while the six fly-bys of the ARM site
sampled both clean and polluted conditions 2–4 km upwind. We compare and
validate characterisations of aerosol physical, chemical and optical
properties as well as atmospheric radiation and cloud microphysics between platforms.
We assess the performance of measurement instrumentation in the field, under
conditions where sampling conditions are not as tightly controlled as in
laboratory measurements where calibrations are performed. Solar radiation
measurements compared well enough to permit radiative closure studies.
Optical absorption coefficient measurements from all three platforms were
within uncertainty limits, although absolute magnitudes were too low
(<10 Mm-1) to fully support a comparison of the absorption
Ångström exponents. Aerosol optical absorption measurements from
airborne platforms were more comparable than aircraft-to-ground
observations. Scattering coefficient observations compared adequately
between airborne platforms, but agreement with ground-based measurements was
worse, potentially caused by small differences in sampling conditions or
actual aerosol population differences over land. Chemical composition
measurements followed a similar pattern, with better comparisons between the
airborne platforms. Thermodynamics, aerosol and cloud microphysical
properties generally agreed given uncertainties.
Introduction
A number of in situ and remote sensing observational field campaigns
involving multiple airborne and ground-based measurement platforms operated
in the south-eastern Atlantic region from 2016 to 2018 (Figure 1, Table 1). The
overarching aim of this unprecedented observational effort was to provide
constraints with which to address the disparity in radiative forcing
estimates due to cloud and aerosol processes between leading climate models,
such as those contributing to the AeroCom intercomparison exercise (Stier et
al., 2013). The uncertainty in radiative forcing estimates in the south-eastern
Atlantic is related to poorly constrained optical properties of the
absorbing biomass burning aerosols (BBAs), discrepancies between the
representation of marine boundary layer clouds, the location in the vertical
of the aerosols relative to these clouds and the interaction of these
aerosols with oceanic boundary layer clouds (Zuidema et al., 2016).
The observation platforms during (a) CLARIFY: the FAAM BAe-146;
(b) ORACLES: the NASA P3; and (c) LASIC: ARM Mobile Facility no. 1. (d) The location of the ARM Mobile Facility no. 1 on Nasa
Road, Ascension Island. This photograph was taken looking approximately north-north-east,
showing the site exposed to the prevailing south-westerly winds.
Deployments of ground-based and airborne measurements in the
south-eastern Atlantic during three biomass burning seasons from 2016 to 2018.
CampaignPlatform201620172018ORACLES (Redemann et al., 2021)NASA P3 (350 h) 44 flightsAug Namibia (115.2)Aug/Sep São Tomé* (112.0)Oct São Tomé (121.4)ORACLES (Redemann et al., 2021)NASA ER2 (97 h) 12 flightsAug NamibiaCLARIFY (Haywood et al., 2021)FAAM BAe-146 (99 h)Aug/Sep Ascension IslandLASICARM Mobile1 Jun 2016 to 31 Oct 2017 (Zuidema et al., 2018a, b)Facility no. 1Ascension Island AEROCLO-SA (Formenti et al., 2019)Sapphire ATR-42 30 h 10 flightsSep 2017 Namibia
* The NASA P3 relocated to Ascension Island temporarily to conduct the
intercomparison flight in this study.
International projects (Zuidema et al., 2016) including CLouds–Aerosol–Radiation
Interaction and Forcing for Year 2017 (CLARIFY-2017;
Haywood et al., 2021), ObseRvations of
Aerosols above CLouds and their intEractionS (ORACLES; Redemann et al., 2021), Layered Atlantic
Smoke and Interactions with Clouds (LASIC; Zuidema et
al., 2018a, b), and AEROCLO-SA (AErosol, RadiatiOn, and CLouds in Southern
Africa; Formenti et al., 2019) had many overlapping objectives, aiming to
determine the optical, chemical and physical properties of BBAs and thus the
radiative impacts of those aerosols on climate, through both direct
radiative effects and impacts on the properties of clouds. Figure 2 shows
the flight tracks over the 3 years of sampling between 2016 and 2018 for
the airborne platforms. CLARIFY and ORACLES focussed on measurements over
the south-eastern Atlantic Ocean, and AEROCLO-SA supplemented this with
observations over Namibia and the near-coastal ocean. Direct comparisons
with the AEROCLO-SA were not possible due to the separation in space and
time between it and the other campaigns. Here we focus on observations from
the CLARIFY, ORACLES and LASIC components as side-by-side intercomparison
data are available.
Most measurements of relatively fresh BBA close to the coast of Africa were
taken with the P3 during ORACLES, while more aged BBA was measured from the
LASIC and CLARIFY-2017 platforms. Flight tracks for the airborne sampling
from all years are shown in Fig. 2. Confidence that the contrasts between
the measurement sets are not simply a result of instrument biases is
critical for understanding aerosol ageing. A key benefit of this
collaboration is that it provides information regarding the comparability of
measurements made from the various platforms, provided the instrumentation
remains well calibrated. This facilitates more reliable assessment of
spatiotemporal gradients made by compositing data from the different
platforms.
Here we present results from a wing-tip-to-wing-tip airborne intercomparison
flight between the NASA P3 (Flight PRF05Y17) and the FAAM BAe-146 (Flight
C031) on 18 August 2017, with both aircraft departing from the
Wideawake Airfield on Ascension Island. The intercomparison was composed of
flight segments in the pristine free troposphere, within a moderately
polluted marine boundary layer and through a vertically elevated pollution layer.
Additional comparisons were made by FAAM flying adjacent to an ARM (Atmospheric Radiation Measurement) site on
Ascension Island following this airborne intercomparison and on five further
flights throughout August and September 2017 (Table 2). FAAM–LASIC
intercomparisons took place at nominally the same altitude as the ARM site
with the FAAM BAe-146 operating between 2 and 4 km offshore and upwind of
the LASIC observation site.
We offer the results of this study as a “transfer standard” upon which
other comparisons and scientific conclusions can be baselined. A key aim is
to provide comparisons of parameters that are required to determine aerosol
optical, physical and chemical properties as well as cloud microphysics, atmospheric
radiation and tracers for air mass characterisation.
The following section provides an overview of the instrumentation from each
platform that is considered in this intercomparison. Section 3 describes the
methods employed in executing the intercomparisons and the processing of
resulting measurement data. Results presented in Sect. 4 are discussed in
Sect. 5. Conclusions are presented in Sect. 6. A key to acronyms is found in
Table 8.
Instruments
A brief introduction follows for each of the instruments and inlets under
study here along with the calibration procedures undertaken. When multiple
instruments providing a given measurement were available on a particular
platform, we chose to focus primarily on what would be considered the
standard, routine data product. However, in some cases, datasets are
included from supplementary instruments where this proves informative. We
provide sufficient information for the reader to understand instrument
operation and its installation configuration on the platform, and the reader
is directed to the references provided for full descriptions of
instrumentation characteristics. Parameters depending on sample
concentration or flow rates, such as particulate measurements and gas
concentrations, are converted to standard temperature and pressure (STP)
conditions of 273.15 K and 1013.25 hPa. Timing offsets between instruments,
introduced for example by flow-rate offsets, were first corrected for.
Particle and gas inlets
Gas samples were drawn into the BAe-146 aircraft through dedicated whole-air
sample pipes, and samples containing aerosol particles were drawn into the
aircraft through modified Rosemount Aerospace Inc. Type 102 total
temperature housings, which, while aspirated, operate at sub-isokinetic
flow velocities. The Rosemount inlets are mounted in pairs at three
locations towards the front of the aircraft, the inlets in each pair offset
from one another to avoid interference. The EXtinction SCattering and
Absorption of Light for AirBorne Aerosol Research rack (EXSCALABAR; Sect. 2.5.2) of instrumentation was fed by the Rosemount pair located above the
starboard doorway towards the front of the aircraft. The Single Particle
Soot Photometer (SP2; Sect. 2.4.1) took its feed from the other of this
forward-starboard Rosemount inlet pair. The aerosol mass spectrometer (AMS)
rack (Sect. 2.4.2), which includes a scanning mobility particle sizer (SMPS; Sect. 2.6), was fed from the lower Rosemount pair on the port side. On the
port side of the FAAM BAe-146 is a blister pod that houses large
radiometers. This feature sits just upstream (in terms of airborne
streamlines around the fuselage) of the Rosemount particle inlets for AMS,
SMPS and the condensation particle counter (CPC; Sect. 2.6) and may provide
a potential barrier to the airflow and shadow a certain portion of the
particle size distribution. However, the transmission efficiency for
submicron low-density aerosols (i.e. not dust) has been demonstrated to be
close to unity for individual Rosemount inlets (Trembath et al., 2012;
Trembath, 2013), with agreement demonstrated between two pairs of Rosemount
inlets on the port side of the aircraft.
Aerosol particles were brought into the P3 through the solid diffuser inlet
(SDI), which was operated isokinetically, with the flow rate matched to
external airflow velocity to within 5 % (Dobracki et al., 2022). The
inlet has been shown to efficiently transmit particles at dry diameters up
to 4.0 µm (McNaughton et al., 2007), with good agreement (10 % to 30 %)
for submicron-sized scattering aerosols between this and ground-based tower
observations. Internal pipe work was designed to minimise transport losses
for particles up to 4.0 µm, using open-source software from Baron (2001), although additional complications associated with airborne sampling
mean that not all losses may be well accounted for, and differences may
exist owing to different flow rate and pathways to different instruments
(Dobracki et al., 2022).
Aerosol sampling during LASIC at the ARM facility on Ascension Island took
place within shipping containers fed by a centrally located community inlet
at the top of a 10 m mast and delivered to a five-way distribution port through
a 2′′ polished stainless steel pipe. This nominally transmitted aerosols as
large as 10 µm (PM10), but a selectable impactor was used periodically
to select only those particles smaller than 1 µm (PM1; at 50 %
efficiency; Uin et al., 2020). The latter data stream is available only to
the nephelometers. The switching regime tended to be 5 min on PM1, 1 min off (i.e. PM10), 4 min on, 1 min off and 1 min on, followed by
the inverse with 5 min on PM10, etc.
Meteorological parameters
On the FAAM BAe-146, aircraft position and attitude are provided by an
Applanix POS AV 410 global-positioning-system-aided inertial navigation
system with static pressure taken from the aircraft's air data computer (BAe
Systems 2000). Vertical wind data were produced by combining data from
pressure sensors in a nose-mounted five-port turbulence probe and aircraft
position and attitude data, recorded at 32 Hz, and analysed here at 1 Hz
(Barrett et al., 2020). Temperature was provided by a Rosemount Aerospace
Inc. Type 102 non-de-iced total temperature housing fitted with an open-wire
platinum resistance thermometer sensing element located on the nose cone of
the aircraft. Temperature data were reported at 32 Hz, averaged to 1 Hz. The
uncertainty in temperature was computed by combining in quadrature the
uncertainties associated with sensor drift, the data acquisition system, the
calibration standard itself and the digital voltmeter used in the
calibration. For flight C031 (Sect. 3.1) non-de-iced temperature sensor
uncertainties were smaller than 0.4 K.
Humidity data were recorded by a Buck Research Instruments CR2 chilled-mirror dew point hygrometer with heated inlet (Price, 2022). The Buck
CR2 has computed in-flight uncertainty in dew point temperature (when
conditions were suitable) of a mean value of 0.2 K, with 99 % of values
below 1.0 K. When converted to water vapour concentrations the uncertainty
was below 2 % across the range encountered during the intercomparison
flight. Whilst this humidity sensor is stable and calibrated to traceable
standards it is combined with a tunable diode laser (TDL) hygrometer where
faster response measurements are required. The TDL, a water vapour sensing
system (WVSS-II, SpectraSensors), recorded data at 0.4 Hz, which was linearly
interpolated to 1 Hz, fed by the standard flush-mounted inlet as described
by Vance et al. (2015). The wet bias noted by Vance et al. (2015) was
subsequently shown not to result from the performance of the flush-mounted
inlet (Vance et al., 2018), which is expected to perform well in the humidity
range encountered during the measurements in this study. The WVSS-II is an
absolute measure of water vapour concentration with an uncertainty of
±5 % (above a minimum of ±50 ppmv; Vance et al., 2015),
but the sample-cell temperature and pressure are not known, and so data are
subject to unknown uncertainties. Therefore, data were first baselined
against the Buck CR2 to known good data using the method detailed in Price (2020). This WVSS-II data product is deemed the primary humidity
measurement provided by FAAM, in part due to the combination of a stable
calibrated sensor, the Buck CR2 and the faster response time of the WVSS-II
TDL sensor.
On the NASA P3, a Honeywell Sperry AZ-800 air data system provided static
pressure, pressure altitude and true airspeed, with aircraft position,
attitude, ground speed and vertical speed coming from a Universal Avionics
UNS-1Fw (NASA, 2010). Vertical wind data were provided by this
system and reported at 1 Hz, where the uncertainty was ±0.15 m s-1. The magnitude of the vertical wind velocities and the fluctuations
about the run mean values were interrogated. Total air temperature was
provided by a Rosemount 102 type non-deiced probe with a manufacturer-reported uncertainty of approximately 0.35 K over 1 s. Water vapour
concentrations were measured with the “WISPER” system comprised of a
Picarro L2120-i fed from the SDI (Pistone et al., 2021), nominally “TOT2”,
with a similar measurement made by a second Picarro L2210-i instrument fed
from the counterflow virtual impactor inlet (CVI) when out of cloud,
nominally “TOT1”. A secondary measurement from the COMA system (see Sect. 2.3) came from a Los Gatos Research 23r, also fed from the SDI. Comparisons
during ORACLES-2016 showed agreement between the COMA and WISPER systems,
with the slope of linear regressions within 2 %, with COMA detecting
slightly higher concentrations in general, although lower concentrations at
altitudes greater than 1.3 km. The airborne humidity instruments under test
here reported values of water vapour volume mixing ratio (vmr) with NASA
operating the WISPER TOT2 as the primary instrument. WISPER TOT1 is employed
as a support measurement (it sometimes made cloud measurements from the CVI
inlet) along with the COMA instrument (which also measured CO). All three
are considered here.
LASIC ARM site observations of temperature, pressure and relative humidity
(RH) were supplied from a Vaisala weather transmitter WXT520B (Campbell
Scientific) at a frequency of 1 Hz. Measurements of temperature were
obtained using a capacitive ceramic THERMOCAP® sensor with
manufacturer-quoted instrumental accuracy of ±0.3 K and RH with a
HUMICAP® thin-film polymer sensor accurate to ±3 % RH (below 90 % RH).
Gaseous constituents
Carbon monoxide (CO) concentrations from the FAAM aircraft were provided by
an inboard Aero-Laser GmbH model AL5002 VUV resonance fluorescence
spectrometer (Gerbig et al., 1999). The instrument was calibrated
periodically during flights with reference gases with CO =500 ppb and CO =0 ppb.
CO concentrations on board the NASA P3 were provided with a gas-phase
CO-CO2-H2O analyser (ABB/Los Gatos Research CO/CO2/H2O analyser (907-0029)) modified for aircraft use and referred to as the
“COMA” system. The analyser uses patented integrated cavity output
spectroscopy (ICOS) technology to make stable cavity-enhanced absorption
measurements of CO, CO2 and H2O in the infrared spectral region.
The instrument reports mixing ratio (mole fraction) at a 1 Hz rate based on
measured absorption, gas temperature and pressure using Beer's Law. The
technology has been demonstrated to operate with a precision of 0.5 ppbv if
averaged over 10 s on other airborne research platforms (Liu et al., 2017).
Quoted uncertainty for CO is 6 % ±1 ppb. Altitude-dependent
sample line timing offsets were corrected for.
Likewise, the instrument responsible for CO concentrations at the LASIC ARM
site was a Los Gatos Research instrument, with a quoted uncertainty in the
measurement of ±2 ppb.
Ozone concentrations on the BAe-146 were provided by an inboard Core Thermo
Fisher Scientific Inc. model 49i UV absorption ozone photometer with a
manufacturer-quoted instrumental uncertainty of 1 % ±1 ppb. NASA
ozone measurements were made with a 2B Technologies Model 205 instrument
with an uncertainty of 6 % ±1 ppb. The LASIC ozone measurements
were provided by a Thermo Fisher Scientific Inc. model 49i UV absorption
photometer with uncertainty of ±2 ppb (or 5 %, whichever is
greater).
The FAAM BAe-146 flew an SP2 instrument manufactured by Droplet Measurements
Technologies Inc. (DMT) to monitor refractory black carbon number (BCn)
and mass concentrations (BCm; Schwarz et al., 2006). The SP2 detects
black carbon (BC) for particles between ∼80 and 500 nm volume-equivalent diameter (assuming BC density of 1.8 kg m-3). The instrument
was located on the starboard side of the aircraft behind a Rosemount inlet
(Taylor et al., 2020). Calibrations were performed using nebulised
mass-selected Aquadag (using a centrifugal particle mass analyser) and
corrected by a factor of 0.75 as recommended by Laborde et al. (2012). An
SP2 was also installed at the LASIC ARM site, with this instrument calibrated
using fullerene following Laborde et al. (2012) and Gysel et al. (2011),
giving accuracy of 10 % and precision of 30 % (Sedlacek, 2017).
The NASA P3 SP2 instrument was affected by a leak on the supply rack during
the part of the flight immediately before the intercomparison segments, and
so data are compromised. Nonetheless, data are presented in Sect. S5 in the Supplement for completeness. The P3 SP2 instrument was calibrated in the same manner
as the one at the ARM site, and its data are expected to be of good quality
at other times in the ORACLES campaign. The installation location was on the
front rack some 8 m behind the SDI inlet.
Aerosol mass spectrometers
The FAAM BAe-146 flew an Aerodyne compact time-of-flight aerosol mass
spectrometer (AMS; Aerodyne Research Inc, Billerica, MA, USA; Drewnick et
al., 2005) to measure the chemical composition of non-refractory aerosols
in the 50 to 600 nm vacuum aerodynamic diameter range. According to
Morgan et al. (2009) for a particle with a density of 1600 kg m-3, 600 nm
equates to an upper mobility diameter of 440 nm. Morgan et al. (2009)
describe the operation of the AMS on the FAAM aircraft, including
calibration and corrections, while Wu et al. (2020) outline its use during
CLARIFY. The aerosol samples entered the aircraft through a modified
Rosemount inlet on the port side of the aircraft above the radiometer
blister. Data were processed using the SeQUential Igor data RetRiEvaL,
v.1.60N (Allan et al., 2003, 2004) algorithm (SQUIRREL) to return unit
masses of ion fragments in the mass–charge (m/z) range 10–500 (Wu et al.,
2020). The AMS was calibrated using monodisperse ammonium nitrate, and the
relative ionisation efficiencies (RIEs) of ammonium and sulfate were
calculated by varying concentrations of ammonium nitrate and ammonium
sulfate. The RIE of sulfate was found to be 1.0834, while the RIE of
ammonium was 4.0516. Organics and nitrate RIEs were kept as the SQUIRREL
defaults of 1.4 and 1.1, respectively. Limits of detection for species were
0.3 µg m-3 (organics), 0.1 µg m-3 (sulfate) and 0.03 µg m-3 (nitrate and ammonium).
The NASA P3 flew a high-resolution time-of-flight AMS (HR-AMS), also
manufactured by Aerodyne Research Inc. (Dobracki et al., 2022). Particles
between 70 and 700 nm vacuum aerodynamic diameter were analysed with the AMS
peaks processed using the Particle Integration by Key v.1.16 (PIKA)
algorithm (DeCarlo et al., 2006). The nitrate ionisation efficiency values
for the HR-AMS centred on 1.31×10-7, with a nominal 10 % uncertainty
assigned to it following Bahreini et al. (2009). The ionisation efficiencies
for ammonium, sulfate and organics relative to those for nitrate are
thereafter determined within SQUIRREL as 4 for ammonium, 1.1 for measured
nitrate relative to the calibration value, 1.2 for sulfate and 1.4 for
organics, following Jimenez (2009). Overall uncertainties for components of
the composition are between 33 % and 37 % (Dobracki et al., 2022). The
instrument sat 8 m downstream of the SDI. Sampling transit times of 6 s due
to pipe work transit times were accounted for by comparison to wing-mounted
passive cavity aerosol spectrometer probe (PCASP; Sect. 2.6) measurements.
Cloud shatter events were screened out by considering number concentrations
of (nominal) 10 µm sized cloud particles from a wing-mounted phase
doppler interferometer cloud microphysics probe (Chuang et al., 2008),
including screening of data from 10 s post-event. The limit of detection for
organics was 0.15, 0.03 µg m-3 for sulfate, 0.04 µg m-3 for nitrate and 0.01 µg m-3 for ammonium.
During CLARIFY, a time- and composition-dependent collection efficiency (CE)
was applied to the data based on the algorithm by Middlebrook et al. (2012).
The CE for each airborne AMS during the airborne comparisons was 0.5. This
was demonstrated in the free troposphere for ORACLES data (Dobracki et al.,
2022) and for the CLARIFY boundary layer and free-troposphere measurements
more relevant to the region of these tests (Fig. S3 in the Supplement). Differences between
the SQUIRREL and PIKA algorithms only accounted for 7 % differences
between estimates of sulfate mass concentrations (Sect. S4).
LASIC operated an Aerodyne aerosol chemical speciation monitor (ACSM) to
measure mass loading and chemical composition of non-refractory aerosol
particles in real time, with data taken from the C2 dataset. The aerosol size
range spans 40 to 700 nm (nominal) vacuum aerodynamic diameter (Liu et
al., 2007). The ACSM was calibrated against a dedicated scanning mobility
particle sizer (SMPS) both before and after the LASIC campaign, using
monodisperse ammonium nitrate and ammonium sulfate. The nitrate ionisation
efficiency (IE) and relative ionisation efficiencies (RIEs) for ammonium and
sulfate were calculated using varying concentrations of ammonium nitrate
and ammonium sulfate. The calibrated nitrate IE was found to be
3.88×10-11, and ammonium and sulfate RIEs were 5.51 and 0.75,
respectively. Composition-dependent collection efficiency (Middlebrook et
al., 2012) was unity on all comparison days, at the closest time point, but
not for all days during the preceding or subsequent hours. Once the correct
collection efficiency is applied the ACSM can obtain mass concentrations of
particulates to within a detection limit of organics (0.148 µg m-3), sulfate (0.024 µg m-3), nitrate (0.012 µg m-3), ammonium (0.284 µg m-3) and chloride (0.011 µg m-3) for 30 min of signal averaging (Ng et al., 2011). Results are
presented for the closest 30 min sample to the FAAM fly-past, with the range
given as the standard deviation for the time span 1 h before and after.
Data were not available for 5 September. Overall accuracy is ±30 % (Watson, 2017).
Aerosol optical propertiesNASA P3 nephelometer and PSAP
Aerosol optical properties on the P3 were obtained by measuring optical
scattering coefficients (σSP) with a TSI 3563 nephelometer and
optical absorption coefficients (σAP) with a Radiance Research
tri-wavelength particle soot absorption photometer (PSAP). The PSAP measured
σAP at 470 nm (blue), 530 nm (green) and 660 nm (red). Data
were corrected as per Pistone et al. (2019) following the method of Virkkula (2010; further details in Sect. 11.2). This has been shown to provide a
good level of correction for BBAs over the south-eastern Atlantic region,
mitigating against the impacts of scattering and absorption artefacts on the
filter-based measurement (e.g. Davies et al., 2019). The instrument optics
were heated to 30 ∘C during the 2017 ORACLES campaign, resulting in
a “dried” sample while minimising vaporisation of volatile components.
Errors of 0.5 Mm-1 remain when averaging for 240 to 300 s, as
shown by McNaughton et al. (2009, 2011). The limited sampling time of
∼120 s available in this work and low aerosol concentrations
encountered will result in larger errors. The particular PSAP unit employed
here was the “rear” instrument as the “front” instrument suffered
problems during sampling.
A TSI 3563 nephelometer recorded σSP at 450 nm (blue), 550 nm
(green) and 700 nm (red) wavelengths, corrected according to Anderson and
Ogren (1998). Blue and red channel data were then interpolated to 470 and
660 nm, respectively, using an interpolation based on linear regression
between the logarithms of scattering optical depths (τ0(σSP) and τ1(σSP)) and wavelengths (λ0 and λ1; Eq. 2). First the scattering Ångström
exponent, ÅSP, was derived from observations at the native
wavelengths, prior to use of Eq. (2) again to determine scattering at the
desired wavelength for amalgamation with PSAP data. Calibrations were
performed in the field with refrigerant R-134A (1,1,1,2-tetrafluoroethane).
RH data are measured within the nephelometer, but outside the sensing chamber,
so estimates of sample RH are made by using laboratory calibrations to
correct the real-time data. During boundary layer sampling, the RH was above
60 % and often at the threshold maximum reported value of 70 % (not
shown). Overall uncertainty is of the order of 10 % when averaged over 240 s, so errors at the shorter comparison times available for this study will
be greater than this. The optical extinction coefficient (σEP)
was computed from the sum of the nephelometer-measured σSP and
PSAP-measured σAP at 470 and 660 nm wavelengths using Eq. (1).
Note that humidity may be different in each instrument.
1σEP=σSP+σAP2ÅAP,SP,EP=logτ0σAP,SP,EPτ1σAP,SP,EP/logλ0/λ1
Flow supplied to aerosol optical instruments on the P3 was from the port-side SDI and switched through either a PM1 impactor or directly through the
PM10 (nominal) sampling line. The nephelometer drew at 30 L min-1 and
the PSAP at 2 L min-1. Timing offsets were corrected for by comparing
against aerosol particle measurements from a wing-mounted outboard PCASP
(Sect. 2.6). Although data are output at 1 Hz, the effective sample temporal
resolution is 6 s, and data are first smoothed with a 10 s moving
average to reduce the impact of additional transit pipe work to the rear PSAP
instrument and to facilitate comparison with other instruments under test.
Periods where shattering of cloud particles may have degraded the quality of
the P3 measurements were removed by consulting liquid water content (LWC)
data from a King hot-wire probe and cloud particle number concentration data
from a cloud droplet probe (CDP; Sect. 2.6).
FAAM BAe-146 EXSCALABAR
FAAM flew state-of-the art instrumentation for measurement of aerosol
optical properties: EXtinction SCattering and Absorption of Light for
AirBorne Aerosol Research (EXSCALABAR). The bespoke instrument was developed
by the Met Office and University of Exeter for use on the BAe-146 aircraft
(Davies et al., 2018a, 2019). Cavity ring-down spectroscopy (CRDS;
Langridge et al., 2011) was employed to measure σEP and
photoacoustic spectroscopy (PAS; Davies et al., 2018a, 2019) to measure
σAP. CLARIFY was the first major campaign for EXSCALABAR
following initial work during the Methane Observations and Yearly
Assessments (MOYA) experiment (Wu et al., 2021), which
comprised a limited number of flights sampling West African BBA close to the
source of emissions.
The instrument racks are located towards the front of the BAe-146 on the
starboard side, supplied by a Rosemount aerosol inlet. The 8 L min-1
total sample flow first passed through a Nafion™ dryer
(Permapure, PD-200T-12-MSR) and a custom-built activated carbon
“honeycomb” scrubber to remove ozone and NOx. The sample then passed
through a custom-made impactor (Brechtel Manufacturing Inc.) with nominal
aerodynamic diameter cut size, D50, of 1.3 µm (50 % of particles
of this diameter are captured). All EXSCALABAR sampling occurred with the
impactor in line. Custom-built splitters then feed eight parallel 1 L min-1
sample lines. Transmission losses between the instrument inlet and sample
cells (i.e. through the sample conditioning) have been characterised and
corrected for, as have time lags between measurement cells. Transit through
the airflow system and detection cells results in an effective temporal
resolution of 6 s, and here 1 s reported data are smoothed using a 10 s moving
average prior to further analysis and for direct comparability with
measurements from P3.
Dry σEP (RH below 10 %) is provided by CRDS channels for
blue (405 nm) and red (660 nm) wavelengths (Davies et al., 2018a). Given
aerosol loadings between 10 and 100 Mm-1, the measurement precision
dominates total extinction uncertainty. The precision of 1 Hz data has been
characterised in ground-based tests from Allan–Werle deviation analyses as
being better than 0.4 Mm-1 for the CRDS spectrometers used in this
work. Assessments of the CRDS measurement accuracy demonstrated that the
measured aerosol extinction cross sections are within 3.6 % of expected
values (Cotterell et al., 2020); indeed, this excellent accuracy is expected
given that CRDS is a direct, calibration-free approach to aerosol optical
property characterisations and is not subject to the artefacts that degrade
characterisations from nephelometry or filter-based approaches.
Dry σAP at 405 nm (blue), 515 nm (green) and 660 nm (red)
wavelengths is measured by PAS. Blue and red PAS cells are each positioned
in series downstream of the blue and red dry CRDS cells. The green dry PAS
cell operates in parallel with these blue and red sample lines. The PAS
cells were calibrated either before or after each flight using ozone at
concentrations determined using the CRDS cells (Davies et al., 2018a).
Calibrations were stable throughout the campaign for all channels except PAS
red dry, for which the optics were adjusted slightly mid-campaign. For all
except the PAS red dry cell, an average of all calibrations was applied to each flight. For the red dry channel, calibrations before and after the
adjustment were averaged and applied to all flights during their respective
periods. Various pressure dependencies were corrected for using methods
described by Cotterell et al. (2021).
Measurements of the aerosol-free background are required for both CRDS and
PAS data analysis. A filtered-air stream is passed through the sample
chambers and the response measured for ∼45 s every 10 min
during flight, with additional background measurements following large
pressure (i.e. altitude) changes. From these filtered-air measurements,
background corrections were determined. Absorption coefficients encountered
during the intercomparison flight were low. As such, they were especially
sensitive to variations in acoustic background signal that occurred.
Absolute measurement uncertainties (i.e. the combined uncertainties
associated with measurement sensitivity and sources of bias) in the range of 8 %
to 55 % can be achieved with the upper end of absolute uncertainty
corresponding to the limit of absorption tending to 1 Mm-1 (Davies et
al., 2019). The background signal varies with pressure. During this
campaign, it was also affected by recent previous exposure to BBA, which
complicated the derivation of a background signal. The cell design has
subsequently been improved to minimise this effect (Cotterell et al., 2019a, b).
For comparison with P3 data, the values of σEP and σAP from the blue (405 nm) EXSCALABAR channels were interpolated to a
common wavelength of 470 nm, to avoid extrapolation of data outside of any
instrument's sampled range of wavelengths. This is done for σEP
and σAP by determining the extinction or absorption
Ångström exponent (ÅEP, ÅAP) between the red and
blue CRDS cells and blue and green PAS cells (Eq. 2), before interpolating
the 405 nm CRDS data to the 470 nm wavelength using Eq. (2). The red cell
wavelength of 660 nm already matches that of the P3 PSAP. The absorption
Ångström exponent, σAP, was computed using Eq. (2) for
all combinations of wavelength pairs.
A TAP (tri-wavelength absorption photometer) was also installed in parallel
with EXSCALABAR's PAS cells and has previously been used to compare
absorption instrument filter-based correction schemes (Davies, 2018b; Davies et al., 2019).
This filter-based technique operates at wavelengths of 476 nm (blue), 528 nm
(green) and 652 nm (red) and was subjected to the same sample conditioning
as the sample entering the PAS cells. Data are presented here after
undergoing filtering and processing as described by Davies et al. (2019),
who provide σAP at a sampling rate of 30 s (which is a
longer averaging time than used for other measurements in this paper), and
as they are supplementary, data are left at the native wavelengths. Here, we
take data from the airborne intercomparison for more direct comparison with
the filter-based measurement on board the NASA P3 and utilise the Virkkula (2010)-corrected data. ÅAP was computed using Eq. (2) for all
combinations of wavelength pairs.
LASIC ARM site nephelometer and PSAP and CAPS PMSSA
Aerosol-laden air samples entered the LASIC cabin through the roof-mounted
inlet. Scattering observations took place using a TSI 3563 nephelometer,
which reported at 450 nm (blue), 550 nm (green) and 700 nm (red)
wavelengths. The sample was not actively dried, but the RH of the sample in
the measurement cell was estimated to be between 45 % and 60 %
(Supplement of Zuidema et al., 2018a). Data were corrected
according to Anderson and Ogren (1998). Prior to use in this study the data
from the blue and red channels were interpolated to 470 and 660 nm, the
native wavelengths of the PSAP. Dilution of the sample stream was accounted
for.
A Radiance Research tri-wavelength PSAP measured σAP at 464 nm
(blue), 529 nm (green) and 648 nm (red). The wavelengths differed from those
detailed in Sect. 2.5.1 for NASA P3 (470, 530 and 660 nm) because they had
been empirically determined with an Ocean Optics grating spectrometer
registered to a mercury pen lamp (Springston, 2018a). The sample was actively
dried by a Nafion™ dryer, and further dilution with a clean, dry
airstream occurred. Whilst the RH was not measured, it is estimated to be
below 25 % (Supplement of Zuidema et al., 2018a). PSAP data were
constructed as the average of the Ogren (2010) corrections and Virkkula (2010) wavelength-averaged corrections. Flow rate was calibrated against a
Gilibrator instrument and measurements. Prior to use in this study the data
from blue and red channels were interpolated to 470 and 660 nm to be
comparable with data from the aforementioned spectroscopy instruments.
A cavity-attenuated phase shift single-scattering albedo (CAPS PMSSA) monitor operating at a wavelength of 530 nm was deployed
on Ascension from 4 August to 22 September 2017, overlapping with the
CLARIFY time period, for the express purpose of assessing the filter-based
LASIC single-scattering albedo (ω0) calculation. The CAPS PMSSA monitor provides a
direct measurement of the particle single-scattering albedo by
simultaneously measuring σSP and σEP, calculating
ω0 from their ratio. Absolute particle extinction is measured
using the cavity-attenuated phase shift technique, and particle scattering
is derived from the light collected using an integrating sphere within the
same optical path (Onasch et al., 2015), with absorption calculated from the
difference. The total extinction was calibrated at Aerodyne prior to LASIC
using 600 nm diameter polystyrene latex (PSL) particles, and another
calibration was done in the field on 20 August 2017. The scattering was
calibrated to the extinction for white (non-absorbing) particles (by
definition, ω0=1.0). A 2 % truncation correction was
applied to the scattering channel, based on ultra-high-sensitivity aerosol
probe (UHSAS) size distribution data. The uncertainty in the ω0
measurements is estimated at ±0.03 (Onasch et al., 2015). Early
assessments found excellent agreement (within 1 %) between the PSAP and
CAPS PMSSA absorption measurements, with the nephelometer scattering
exceeding the CAPS PMSSA scattering measurements (within 10 %). The
monitor sampled from both the PM1 and PM10 inlets. The CAPS PMSSA
measured from the same inlet as the UHSAS and PSAP, behind the nephelometer,
which measured air with a relative humidity of 46 %–65 %. Here we use the
data to estimate σAP by inputting the measured quantities into
Eq. (1). The CAPS PMSSA measurement uncertainties for absorption
coefficients are estimated in Onasch et al. (2015). For a typical ω0∼0.8 during LASIC, a conservative uncertainty estimate for
the absorption coefficient is ∼20 %.
Aerosol and cloud microphysical and bulk properties
Total aerosol particle number concentrations in the form of measurements of
condensation nuclei (CN) particle number concentrations were provided on all
three platforms by CPC instruments. The NASA P3 flew a TSI 3010 instrument,
which has a nominal lower size threshold of 10 nm and flow rate of 1.0 L min-1. Uncertainty in concentration of 5 % is primarily due to flow
rate uncertainty. Data are multiplied by a constant factor of 1.02 following
laboratory intercomparisons with other TSI 3010 CN counters used in the
ORACLES campaign. On board the BAe-146 was a TSI 3776 with a lower size
threshold of 2.5 nm and 5 % flow rate uncertainty. LASIC used a TSI 3776,
an ultrafine CPC with a lower size threshold of 2.5 nm, which was operated
without dilution flow. TSI 3776 instruments operate with a flow rate of 0.05 L min-1.
Both FAAM and LASIC had access to scanning mobility particle sizer (SMPS)
data, which provided aerosol particle number concentrations for fixed
particle mobility diameter. In the case of LASIC a TSI 3081 differential
mobility analyser (DMA) associated with a TSI 3080 column supplied a full
scan of data at 5 min intervals following a 260 s scan period. The
instrument was located behind an impactor with D50=700 nm and has a
lower size threshold of 10 nm. FAAM data were provided by a similar system
with a TSI DMA 3081 connected to a TSI CPC 3786 (Wu et al., 2020) and
reported particle mobility diameter in the size range of 20 to 350 nm.
Previously a comparison was made for CLARIFY data between estimated volume
concentrations derived from AMS + SP2 total mass concentrations and PM1
volume concentrations from PCASP (assuming spherical particles). Estimated
AMS + SP2 volumes were approximately 80 % of the PCASP-derived values,
which was considered reasonable and within the uncertainty in the volume
calculations (Wu et al., 2020), demonstrating consistency between inboard and
outboard measurements. Discrepancies between SMPS (inboard) and PCASP
(outboard) number concentrations remained however, and so the SMPS
concentrations were reduced by a collection efficiency factor of 1.8 to give
better correspondence in the overlap region of the particle size
distributions (PSDs). The cause remains unknown.
UHSASs were operated by both LASIC and NASA (located within the aircraft).
These instruments have been shown to undersize particles where BBAs are
present (Howell et al., 2021). The high-power laser modifies the measured
size distribution through heating and evaporation of brown carbon, thus
reducing particle size at the time of measurement. Reductions (up to 35 %) were observed for the larger particles of BC. NASA P3 data are first
corrected using the power law introduced by Howell et al. (2021), which
scales the default bin dimensions to be closer to mobility diameters as
determined in real time in flight by size-selecting particles with a DMA. Moore
et al. (2021) noticed similar behaviour in laboratory tests of a UHSAS for
highly absorbing aerosols. Here we use the NASA P3 UHSAS data for comparison
with the outboard FAAM BAe-146 PCASPs.
FAAM and NASA flew wing-mounted DMT PCASPs (Liu et al., 1992) with updated
electronics (nominally SPP200; DMT, 2021), which were exposed to the free
airstream. NASA operated a single unit located in the inner position of the
inner pylon located under the port wing. FAAM flew two units mounted
externally: PCASP1 and PCASP2. A third probe, PCASP3 (also with SPP200
electronics), was located within the fuselage as part of the EXSCALABAR suite
of instruments, fed by a Rosemount inlet. PCASPs measure aerosol particle
sizes in 30 channels in the nominal size range 0.1 to 3 µm
optical diameter (polystyrene latex sphere (PSL)-equivalent). Data are
reported at a frequency of 1 Hz. Concentrations from the NASA PCASP channels
were calibrated in the laboratory by comparison with an SMPS and a scaling
factor applied to certain channels to ensure comparability. For all PCASPs,
channels that bracket gain-stage crossovers were merged following the method
in Ryder (2013), and the smallest size bin was rejected as the lower size
threshold is unbounded, resulting in 26 usable channels. Errors include
Poisson counting uncertainties (square root of the number of counts) and
flow rate errors (assumed to be 10 %), combined in quadrature. The air
intake of an external PCASP is designed to decelerate the particle flow,
resulting in sample heating and some reduction in RH of the sample compared
to ambient, which may affect particle size. The inboard BAe-146 PCASP sample
was subjected to the same conditioning as that for EXSCALABAR cells – most
notably dried to <10 % RH and behind the impactor – and adjusted
for transmission losses through that conditioning section.
Data for externally mounted PCASPs for the airborne comparisons are
presented in manufacturer nominal bin boundary diameters, and no adjustment
has been made for the absorbing characteristics of BBA-laden air masses or
refractive index (RI) of other materials. All external instruments sample the same material without the complication of inlets, and so when
instruments employ the same measurement technique, i.e. optical
detection, this should not impact the results of this comparison.
Comparisons with the NASA UHSAS should be approached with caution as this
instrument is effectively calibrated to particle mobility diameter. The
internally mounted FAAM PCASP3 is compared against the outboard PCASP2 and
against the internally mounted SMPS instrument (which measures mobility
diameter). The purpose of this comparison is, in part, to assess the
performance of the Rosemount inlets and transmission loss corrections. A RI
correction was applied to the nominal bin boundaries for PCASP2 (outboard)
and PCASP3 (inboard) using the observationally derived value of 1.54-i0.027,
appropriate for the BBA-laden air masses (following Peers et al., 2019, and using an updated calculation).
This correction was applied to bin boundaries for diameters smaller than 800 nm. Differences between the nominal and BBA bins were as large as 25 %
for the smallest bin but typically 10 % for particle diameters smaller
than 800 nm. At sizes larger than this, the nominal bin dimensions (at PSL-equivalent RI) were used.
Both aircraft operated cloud droplet probes (CDPs; Lance et al., 2010), which
detect and size cloud particles in the size range 3 to 50 µm diameter
in 30 particle size bins. The FAAM BAe-146 instrument was located on the
inner lower position of the port pylon, and the NASA P3 instrument was
located on the outer location of the outer port pylon. The pylon holding the
CDP during ORACLES 2017 and 2018 was further ahead and lower relative to the
aircraft wing compared to the pylon used in ORACLES 2016. These forward-scattering probes have size bins defined using the RI for water of 1.33. The
CDP on the NASA P3 used the manufacturer default sample area of 0.26±0.05 mm2 and optics collection angle of 4 to 12∘.
The sample area of the BAe-146 CDP has been experimentally determined by DMT as
0.252±0.05 mm2, with the collection angle for the optics found to
be 1.7 to 14∘ (after Lance et al., 2012). BAe-146 CDP
performance was observed to be stable throughout the campaign as monitored
through daily pre-flight, glass bead calibrations. A linear fit between the
median calibration response to these daily tests showed that the BAe-146 CDP
with nominal bin dimensions undersized cloud particles by ∼7 %. This linear fit was applied to the nominal bin boundaries (Sect. S3). Nominal bin dimensions applicable to BAe-146 and P3 CDPs along
with calibrated bin dimensions for BAe-146 are given in Table S1 in the Supplement. Gupta et
al. (2022) compared data from the P3 CDP against those collected by a cloud
and aerosol spectrometer (CAS) also installed on the P3, concluding that the
CDP provided data most consistent with bulk water contents measured by a
King probe and less than calculated adiabatic water contents. Errors are
comprised of Poisson counting uncertainties, true airspeed uncertainties
assumed to be 5 % and sample area uncertainty of 5 %, all combined in
quadrature.
Larger cloud particles and drizzle drops were sampled on both aircraft using
Stratton Park Engineering Company (SPEC) 2DS optical array probes (OAPs;
Lawson et al., 2006), which measure the sizes of particles between 10 and 1280 µm as they cast shadows on a 128-element
charged-coupled-device (CCD) array illuminated by a laser. FAAM BAe-146 OAP
data were processed using the Optical Array Shadow Imaging Software (OASIS)
software package (Crosier et al., 2011; Taylor et al., 2016) and presented
at a native bin resolution of 10 µm. P3 data were processed using the
University of Illinois/Oklahoma Optical Array Probe Processing Software
(McFarquhar et al., 2018) as described by Gupta et al. (2021). Errors in
channel concentrations were estimated by combining Poisson counting
uncertainty values and size-dependent sample volume uncertainties in
quadrature.
Bulk condensed water properties on FAAM were measured with a Nevzorov
hot-wire probe (Abel et al., 2014). Bulk water content on board the NASA P3
was identified with a King hot-wire probe (King et al., 1981; Strapp et al., 2003). LWC derived from the Picarro L2120-i hygrometer (Sect. 2.3) fitted
downstream of the counterflow virtual impactor inlet (CVI) was used to
determine when the NASA P3 was under cloud-free conditions by locating times
when the bulk water content was determined to be zero. Closure tests between
the LWC derived from the P3 cloud probe spectra and the King hot-wire were
conducted for in-cloud measurements from each ORACLES deployment (Gupta et
al., 2022).
When out-of-cloud, the CDP from BAe-146 and the 2DS probes from both
platforms were used to measure the coarse mode aerosol particle size
distributions and identify the presence of supermicron aerosol particles
(Miller et al., 2021). However, when out-of-cloud the NASA P3 CDP did not
report data, and so aerosol observations are not available.
The altitude of the ARM site at 341 m above mean sea level was low in the
boundary layer, and always below cloud base.
Derived microphysical parameters
Aerosol and cloud particle number concentrations per size channel (Ni)
were reported at 1 Hz from microphysics probes. Particle size distributions
(PSDs) as a function of particle diameter Ni(Di) were computed from
these data using Eq. (3). For CDP and 2DS the individual channel
concentrations were scaled by the size-dependent sample volume (SVi;
Eq. 3.1), which is a function of the sample area (SA(i)) and the aircraft
true airspeed (TAS). For PCASP and UHSAS the sample volume is internally
determined by the sample flow rate and is uniform across size channels.
Aerosol (NA) and cloud drop (NC) number concentrations were
generated using Eq. (4) by summation of the individual discrete channel
concentrations, excluding the smallest size channel, which is susceptible to
electrical noise and has an unbounded lower size threshold. This results in
the smallest reported bin edge of diameter (D) greater than 3 µm for
the CDP and greater than 105 nm for the PCASP. Count median diameters of the
particle size distributions were computed as the diameter where 50 % of
the observations were above and below the given size. Effective radius
(Re) and mean volume radius (Rv) were computed for individual
probes by summation across the particle size channels using Eqs. (5) and (6). For
aerosol observations this was done for the accumulation mode only, by
selecting only particles smaller than 800 nm (PSL-equivalent) to compare
probe performance in the optically important BBA mode (e.g. Peers et al.,
2019). The restrictions on these computations of Re and Rv mean
that the values should not be compared to those from other field campaigns
– the values are representative of probe response only. Full scientific
comparisons require detailed analysis of the material composition and
size-dependent refractive index. Bulk LWC values for cloud particle
spectrometers were computed using Eq. (7).
3NiDi=Ni/SVi3.1whereSVi=SAi⋅TAS4NA,C=∑i=1nbinNiDi5Re=∑i=1nbinDi3NiDidD/2∑i=1nbinDi2NiDidD6Rv=∑i=1nbinDi4NiDidD/2∑i=1nbinDi3NiDidD7LWC=π6ρw∑i=1nbinDi3NiDidD
Cloudy and clear-sky masks
Cloudy periods are readily identified from the airborne datasets by taking
CDP observations of LWC and setting the lower threshold to 0.05 g m-3
at times when Nc>3 cm-3.
Cloud-free periods were identified more rigorously to avoid cloud-contaminating the aerosol measurements. A clear-sky mask was generated for
P3 data by taking LWC data from behind the CVI probe and cloud particle
concentrations from CDP. A threshold number concentration of 2 cm-3
from CDP and times when zero LWC was reported serve as the raw mask. To
account for sporadic sampling of low-concentration events a 2 s safety
margin (approximately 200 m) was applied around any positively identified
cloudy points to generate the final clear-sky mask. The FAAM clear-sky mask
employed bulk water content data from the three Nevzorov probe elements and
the particle number concentrations from CDP as detailed in Barrett et al. (2020). To summarise here, the high-resolution 32 Hz raw power data from
the three Nevzorov sensing elements show a bimodal distribution during
cloudy- and clear-sky sampling with the lower-power mode arising from
clear skies. The threshold between the cloudy- and clear-sky modes depends
on a number of environmental factors and must be chosen empirically on a
case-by-case basis. Here an upper limit of ∼3.1 mW was
chosen, below which the Nevzorov was deemed to be in clear skies. A second
constraint of particle number concentration from CDP below 1 cm-3 was
specified, being less strict than the limit on P3 by virtue of the higher
sensitivity of the Nevzorov flag catching more of the cloudy data points.
The same 2 s safety window was applied.
The ARM site, located within the surface mixed layer at 340 m, did not
suffer from cloud occurrence in situ since cloud bases were consistently
higher.
Atmospheric radiation
The radiation measurements equipment on the FAAM BAe-146 during CLARIFY that
will be compared to the measurements from the NASA P3 include the following.
(a) Two upward- and two downward-facing Eppley broadband radiometers (BBRs)
were fitted with clear and red domes covering the 0.3–3.0 and
0.7–3.0 µm spectral regions (e.g. Haywood et al., 2003). Degradation
of the upper red domes owing to scouring of the leading face of the domes
when flying in mineral dust during previous campaigns based close to the
Sahara Desert (e.g. DABEX, GERBILS and FENNEC campaigns; Haywood et al.,
2008, 2011; Ryder et al., 2013) was evident, and thus data from the upper red
domes were considered unsatisfactory and are not presented in the following
analysis. Data from red-domed Eppley lower radiometers were satisfactory. The
BBRs are installed at a 3∘ pitched-forward angle to the airframe, which
partially accounts for the nominal pitch of the aircraft when under standard
operating conditions of 6∘ nose-up. Owing to the non-perfect
alignment of the radiometers with the horizontal plane when mounted on the
aircraft, box-pattern and pirouette manoeuvres are performed to correct any
alignment discrepancies in the upper BBRs as described in Sect. S1 in the Supplement. The fluxes measured by the BBRs have an estimated error of ±5 W m-2 for upward fluxes (Haywood et al., 2001) and 3 %–5 % for
downward fluxes, the higher uncertainty in the downwelling fluxes being due
to aircraft pitch and roll correction uncertainties, which vary as a function
of the diffuse fraction and hence the altitude of the aircraft (Foot et al.,
1986).
(b) The Shortwave Hemispheric Irradiance Measurement System (SHIMS) measures
the upward and downward spectrally resolved solar irradiances. Each of the
upper and lower SHIMS uses two temperature-controlled Carl Zeiss
spectrometer modules operating across the visible (VIS) spectral range
0.30–1.15 µm and near-infrared (NIR) range 0.95–1.70 µm. Data
from the VIS module were truncated at 0.95 µm to match up with the IR
module at the short wavelength end. The pixel separation is approximately
0.0033 µm in the VIS module and 0.006 µm in the NIR module, giving
approximate spectral resolutions of 0.010 and 0.018 µm with an
in-house-designed integrating head. The SHIMS instrument provides counts per
millisecond. During this measurement campaign, laboratory and transfer
calibrations were performed. The combination of lab work and this knowledge
of the uncertainties associated with the BBRs suggests a likely uncertainty
for SHIMS of ±10 % (Vance et al., 2017). However, when operated on
the aircraft a bias of up to 30 % between the SHIMS and BBR observations
is apparent. An additional spectrally invariant adjustment based on
idealised model radiative transfer data was used to adjust the SHIMS
observations to account for this, as described in Sect. S1 in the Supplement.
Comparable shortwave spectrally resolved irradiances were provided on the
NASA P3 by the Solar Spectral Flux Radiometer (SSFR) in zenith and nadir
directions (Pilewskie et al., 2003). A mechanical levelling platform ensured
correct orientation of the sensors, and data were corrected for aircraft
altitude and the angular response of light collectors (Cochrane et al.,
2019, 2021). The nominally visible wavelength range 0.35–1.0 µm is monitored with a Zeiss grating spectrometer with a silicon linear diode
array and the near-infrared range 0.95–2.10 µm with a Zeiss
grating spectrometer with an InGaAs linear diode array. The devices have
moderate spectral resolution of 0.008 to 0.012 µm with radiometric
uncertainty of 3 % to 5 % for both zenith and nadir and precision of 0.5 % (Cochrane et al., 2019, 2021). A National Institute of Standards and
Technology (NIST) traceable lamp was used to calibrate the instrument before
and after the campaign, and portable field calibrators monitored the
performance of the instrument during the campaign.
One semi-permanent cloud feature that occurs on Ascension Island is the
generation of orographically forced cloud over Green Mountain, whose altitude
reaches 859 m. This cloud frequently impacted LASIC radiation measurements.
As FAAM measurements were limited to a minimum distance of 2–4 km offshore
of Ascension Island, the local impact of the orographically generated cloud
hampered direct comparisons of downwelling solar irradiances, and these are
therefore not pursued further in this study.
Case studiesAirborne and side-by-side intercomparison
Both aircraft departed from Wideawake Airfield on Ascension Island on
18 August 2017 within a few minutes of one another; climbed out of
the boundary layer; and transited approximately 400 km ESE to a rendezvous
point located close to 9∘ S, 11∘ W. The location for the
flight intercomparison segments was chosen based on numerical weather
prediction and aerosol forecasts to give the best possibility of
encountering good conditions for sampling aerosol and cloud (Fig. 3).
Overall, the two aircraft collected co-located data for a period of 75 min between 12:50 and 14:05 UTC, over a horizontal distance in excess of
450 km. Aerosol optical depth measured over Ascension Island using a
handheld sun photometer indicated a column aerosol optical depth at 500 nm
of 0.16, suggesting that the conditions on the day were relatively lightly
polluted (Haywood et al., 2021). Satellite imagery on the day identified a
region of broken cumulus clouds to the south of the island that was a
suitable target (Fig. 3a). The flight intercomparison segments were
located along the 9∘ S latitude line, offset ∼100 km south of the island and the ground-based ARM site to maximise the chances
of sampling adequate clouds.
(a) Flight tracks for both the FAAM BAe-146 and NASA P3 flights
with the intercomparison flight segment marked (green box), overlaid on
Visible Infrared Imaging Radiometer Suite (VIIRS) corrected-reflectance (true colour) imagery from 18 August 2017 (the
imagery was obtained from NASA Worldview). (b) Flight vertical cross
sections as a function of longitude for the intercomparison segment for FAAM
BAe-146 and NASA P3, which commenced at 5.8 km. Run names are indicated (see
Table 2), along with horizontal bands which mark out the vertically elevated pollution
plume (yellow) and boundary layer (light orange).
Following rendezvous in the free troposphere (FT) at ∼5.8 km
(Fig. 3), the two aircraft performed a wing-tip-to-wing-tip flight leg
(hereafter: runFT) for 10 min, from 12:51:19 UTC along the 9∘ S
latitude line (Table 2), with the BAe-146 to the starboard side of the P3.
The flight leg, runFT, was conducted under clean FT conditions characterised by
low aerosol number concentrations and clean conditions (NA<30 cm-3 and CO <90 ppb; Fig. 4). While remaining in
formation, the two aircraft made a profile descent from 5.8 km (runPRO),
through a vertically elevated pollution layer (runELEV) where lidar depolarisation
observations indicated a small number of dust particles, and into the
boundary layer to finish at 330 m pressure altitude, which is nominally
the same altitude as the ARM site. The vertically elevated pollution layer was located
between ∼2.7 and 4 km. Neither aircraft passed through
cloud during the descent. Upon reaching the lower altitude both aircraft
commenced a wing-tip-to-wing-tip straight-and-level run (SLR), hereafter
runBL, flown at the same constant altitude, sampling cloud-free boundary
layer air for 19 min. During SLRs, the FAAM BAe-146 sat between 7 and 13 m lower than the NASA P3. For runBL many instruments operated independently
or had bespoke averaging times as documented in Table 2. Following runBL
both aircraft climbed to 1.7 km and implemented a 14 min cloud sampling
leg at this altitude – hereafter runCLD. For safety reasons, when performing
this cloud sampling flight leg, the BAe-146 trailed behind the P3 by 5 min in time but followed the same track. Flying across wind meant that
any turbulence or exhaust from the lead aircraft will have advected away
from the region before the arrival of the second aircraft. Afterwards, the
FAAM BAe-146 returned to Ascension Island to perform an intercomparison with
the ARM site, while the NASA P3 continued to make measurements remotely from
the island. Only the most relevant and appropriate measurement sections of
the intercomparison flight as indicated in Fig. 3b are analysed here.
Event timing markers during FAAM C031/NASA PRF05Y17
intercomparison flight on 18 August 2017 and FAAM-LASIC ARM site
intercomparison flight legs on 6 d between 17 August and
5 September 2017. FAAM Altitudes are GPS-corrected to WGS84 geoid.
AltitudeCodeStartEndEndNotes[m][UTC][UTC][UTC](all)(aircraft)(LASIC)5800runFT12:51:1913:02:22Upper level5800 to 330runPRO13:02:2213:20:01Profile descent3972 to 2678runELEV13:07:5513:12:22Elevated polluted plume segment330runBL13:20:1813:39:11Full runFAAM C03133013:20:3013:39:30FAAM AMSand NASA13:20:3013:34:20Low-level P3 normal inletPRF05Y1713:34:5013:39:40Low-level P3 CVI inletintercomparisonrunBL_A13:20:1813:29:29P3: PM10flightrunBL_113:30:0113:32:16P3: PM1runBL_B13:32:2013:35:59P3: PM10runBL_213:36:0113:38:16P3: PM1runBL_C13:38:2013:39:11P3: PM101722runCLD13:43:0013:57:00Cloud leg BAe-146173113:49:0014:04:30Cloud leg P3316C030-ARM16:37:5316:51:5317:07:5317 AugFAAM–LASIC309C031-ARM14:46:5314:58:5315:16:5318 AugARM site fly-past318C033-ARM10:13:5310:25:4510:43:5322 Augintercomparison309C036-ARM09:37:5309:51:0010:07:5324 Auglegs316C039-ARM15:37:5415:47:1516:07:5425 Aug326C051-ARM11:37:5211:44:5212:07:525 Sep
Vertical profiles of data from FAAM BAe-146 and NASA P3 for
intercomparison “runPRO” descent from 5.8 km to 300 m. Horizontal bands
mark out the vertically elevated pollution plume (yellow) and boundary layer (light
orange). (a) Temperature; (b) water vapour mixing ratio; (c) RH; (d) CO; (e)NA from PCASP, with BCn from FAAM SP2; (f) optical extinction,
σEP, from FAAM CRDS and NASA PSAP+nephelometer; (g) optical
absorption, σAP, from FAAM PAS and NASA PSAP; (h) optical
scattering, σSP, from FAAM CRDS-PAS and NASA nephelometer. The
legend in panel (b) applies to panels (a)–(e). The legend in panel (f) applies only to panels (f)–(h) for wavelengths of 470 nm (blue) and 660 nm
(red).
Meteorological parameters
The meteorological conditions encountered during the airborne
intercomparison between FAAM BAe-146 and NASA P3 are summarised in the
vertical profiles from runPRO, shown in Fig. 4. The temperature profiles
(Fig. 4a) show the decoupled stability profile expected for this location
with a surface mixed layer in the lowest 600 m of the atmosphere,
characterised by high RH >70 % (Fig. 4c) and a well-mixed
temperature profile. Above the surface mixed layer and beneath the
trade-wind inversion located close to 1.7 km sat a cloud-containing layer
characterised by increasing RH with altitude. Broken cumulus clouds were
present at this altitude throughout the period of the intercomparison.
Moderate levels of pollution due to BBAs mixing into the boundary layer were
found through the depth of the decoupled boundary layer system with CO >100 ppb (Fig. 4d). Concentrations close to the surface were
NA>600 cm-3 (Fig. 4e) and 400 cm-3 just
beneath the inversion. A time series of CO data measured by LASIC at the ARM
site is presented in Zhang and Zuidema (2019) for both August periods, 2016 and
2017, showing that concentrations ranged between 50 and 150 ppb during 2017
and reached somewhat higher to >200 ppb in 2016. Ultraclean
conditions in the Ascension Island region during the biomass burning (BB) season
are defined by NA<50 cm-3 and typically have median
concentrations of CO =69 ppb and an inter-quartile range (IQR) of 62 to
74 ppb (Pennypacker et al., 2020), with almost all cases having CO
concentration levels <80 ppb.
For the first 800 m above the trade inversion, the free troposphere was
pristine and dry, with NA<30 cm-3, CO <60 ppb
(using FAAM measurements) and low vmr (Fig. 4b). During the runELEV
segment of the profile descent, the aircraft passed through a
thermodynamically stable, slightly polluted layer between 2.7 and 4.0 km,
with NA>50 cm-3 and CO >85 ppb. Water
vapour concentrations were also higher than the layers immediately above and
below, leading to slightly increased RH locally, as is typical of the
continental pollution plume (Pistone et al., 2021).
At 5.8 km conditions were relatively pristine and dry, with NA<30 cm-3 and CO <85 ppb and a vmr of 168 ppb reported by FAAM.
Back trajectory calculations using the Met Office Unified Model (not shown)
were used to estimate source regions for air masses arriving at 9∘ S, 12∘ W, at 12:00 UTC on 18 August 2017, chosen to be
representative of the time and location of the airborne intercomparison.
Boundary layer trajectories, ending at 500 and 1500 m, showed air mass
histories predominantly over the ocean to the south-east for the previous 10 d, with the 1500 m trajectory over land for 10 to 12
August. Back trajectory calculations presented by Diamond et al. (2022)
showed that air mass had likely been sampled by ORACLES P3 flight PRF03Y17 on
15 August 2017 to the south-east between 12 and
15∘ S within 1∘ longitude of 5∘ E. A
trajectory ending at 3.5 km was located over Africa at altitudes between 6
and 8 km, from 10 to 13 August, where it may have encountered
BBA in plumes or else lofted to that altitude through convection. Other
trajectories ending in the free troposphere were exclusively over ocean for
at least the previous 7 d. The large-scale synoptic conditions of the day
were typical of the region with broken cumulus clouds.
FAAM – LASIC ARM site fly-pasts
FAAM flew sections upwind of the ARM site on six occasions (Table 2) between
17 August and 5 September, providing a wide dynamic range of
pollution parameters. One such flight leg took place following the
FAAM—NASA intercomparison on 18 August as the BAe-146 returned to
base. The aircraft flew at a nominal altitude of ∼330 m, a similar
altitude to the ARM site (340 m), and was displaced from the coast by between
2 and 4 km at the pilot's discretion depending on local flying conditions.
Flight segments took place across the mean wind direction and were between 7
and 15 min duration (40 to 90 km long). LASIC run times are 30 min
long from the start of the aircraft run. The mean wind speed at the ARM site
was of the order of 7 m s-1, meaning that sampling took place over a
distance equivalent to 12 km. This approach assumes that local variability
is negligible across the aircraft track.
Results
When comparing measurements from two instruments, it is useful to explicitly
consider statistical uncertainties, which differ between individual data
points, and systematic uncertainties, which affect all data points from an
instrument. Statistical uncertainties are large when instrument noise is
large compared to the measured signal, and/or the measured property exhibits
a high degree of variability within the sampling period. The effect of
instrument noise can be minimised by choosing a longer averaging time, and
this is the approach we take for the comparisons between the BAe-146 and ARM
site. The straight and level runs were designed to minimise the variability
in measured properties during the comparisons, and we average the data to
one point per run. Conversely, where a large statistical uncertainty is
caused by real variation in the measured property within the measurement
period, a shorter averaging time must be used. This is the approach we use
when comparing the BAe-146 and P3 aircraft, and here we average the data to
0.1 Hz to balance real variation with instrument noise.
Once a set of points for comparison has been gathered, we compare the
variables using orthogonal distance regression (ODR) with results summarised
in Table 3 and shown in more detail in the Supplement (Sect. S7). These
straight-line fits utilise the uncertainty in both the x and y variables
(taken to be the standard error, equal to the standard deviation divided by
the square root of the number of data points), to produce a fit uncertainty
that accounts for the measurement uncertainty in each data point used to
produce the fit. Comparison between the different platforms can then take
place by comparing the slopes of the fits. Where they are different from
unity both the statistical uncertainty in the fit and the systematic
uncertainty in both instruments may contribute. When quoted in the literature,
this systematic uncertainty tends to be the calibration uncertainty,
although other factors such as different inlets tend to make this
uncertainty larger. Summary values of ODR fits for all parameters along with
uncertainties are to be found in Table 3. More completed tabulated results
are available in the Supplement (Table S2).
Summary of orthogonal distance regression or ratios of weighted
means between observations from FAAM : NASA and FAAM : LASIC comparisons.
Particle observations are for PM1 unless otherwise stated.
a PM10
observations. b Particle diameters larger than 120 nm only. ∗ Ratio
of weighted means. Note the change in instruments and platforms for Re
and NA comparisons.
Air mass characteristics
Vertical profiles of the thermodynamic state of the atmosphere during the
airborne intercomparison are presented for temperature (T), vmr and RH
(Fig. 4a–c, respectively). The temperature observations from
NASA and FAAM are essentially unbiased (Fig. 5b with ODR slope of
1.00±0.00018; LASIC data at the ARM site tend to report warmer
temperatures; Fig. 5a), with an ODR slope of 1.14±0.007, which could
be related to an island heat effect or a genuine bias but is likely related to
the narrow dynamic range available on which to perform a fit. The aircraft
tended to fly between 15 and 30 m lower than the ARM site, which does not
account for the differences.
Correlations from various flight segments (Table 2) between
temperatures for (a) FAAM BAe-146 and LASIC ARM site and (b) FAAM BAe-146
and NASA P3 and for humidity vmr for (c) FAAM BAe-146 and LASIC ARM site
and (d) FAAM BAe-146 and NASA P3. In panel (b) the data points are coloured
by altitude. In panel (d) the instruments are given a different colour for
clarity.
During the aircraft descent in Fig. 4b the vmr variations are tracked in
a similar manner by FAAM WVSS-II and the NASA WISPER instrument until
passing through 800 m altitude, where WISPER (both TOT1 and TOT2) reported
drier conditions than both FAAM and the NASA COMA instrument. Correlations
plotted in Fig. 5d show the performance of each NASA instrument relative
to the FAAM WVSS-II, with ODR slopes of 0.938±0.003 (TOT1)
0.945±0.003 (TOT2) and 0.990±0.002 (COMA), respectively. FAAM to LASIC had
an ODR slope of 1.09±0.02 (Fig. 5c), although this is over a much
narrower dynamic range of vmr.
Summary values for derived quantities dew point temperature and RH are
available in the Supplement (Table S3). Possible impacts of any discrepancies in
RH reported by NASA, LASIC and FAAM would be encountered when using the
distributions of boundary layer humidity to estimate CCN (cloud condensation
nuclei) concentrations or when using aerosol growth models to predict
optical scattering from aerosol as a function of RH.
During the boundary layer sampling leg, runBL, the two aircraft measured
turbulent wind components with the standard deviation of vertical velocity
and the skewness of the distribution (Table 6). Vertical winds from the
BAe-146 show a larger standard deviation than data collected by the NASA P3
during this side-by-side sampling leg. The skewness was more positive on the
NASA P3, indicating that it occasionally sampled stronger updraughts than
the FAAM BAe-146 encountered. The two aircraft inevitably encountered
different conditions when sampling at the cloud level (see Sect. 4.5) – a
consequence of the 5 min separation in time.
Gaseous and particulate pollution tracers
Carbon monoxide (CO) has a lifetime of over 1 month in the troposphere and
is not susceptible to removal through precipitation processes. As such it is
a suitable tracer for pollution from combustion and as such an important
parameter for marking out air masses. Figure 4d shows CO concentration data
for the airborne profile descent, and Fig. 6a shows the correlations
between CO from the FAAM aircraft with various flight level data from NASA
and during the six fly-pasts of the ARM site. The FAAM–LASIC comparisons
sampled a range of 60 to 110 ppb, indicative of clean through to moderately
polluted conditions with a similar range encountered during the airborne
intercomparison. LASIC data reported lower concentrations of CO with an ODR
slope of 0.929±0.006, with the ODR slope from the airborne comparison
0.945±0.007 (Table 3). NASA data are offset by +9.5±0.7 ppb
from FAAM data. It is noted that the FAAM instrument was regularly
calibrated with reference gases during flights (Sect. 2.3), giving confidence
in that instrument's performance. The difference between the CO measurements
from the NASA P3 and the LASIC ARM site is expected to be larger than
between the aircraft platforms, something which remains an unresolved issue.
The implications of these measurements on the characterisation of air masses
are discussed in Sect. 5.1. These and other composition data results are
tabulated in the Supplement (Table S5).
Correlations between pollution and aerosol parameters as a function
of those measured on board the FAAM BAe-146 for both the NASA P3 from various
flight segments (Table 2) and LASIC ARM site from six flights for (a) CO, (b) O3, (c) CN, (d) BCn, (e) BCm, (f)σSP at 470 nm
and (g)σSP at 660 nm, and (h)σAP at 470 nm and (i)σAP at 660 nm. The 1:1 ratio line is shown in all panels as a
dashed black line, and linear fit parameters and ratio of weighted means are
shown. Data points from airborne comparisons are coloured by altitude,
except for NASA PM1 data, which are a single colour to aid clarity.
Ozone concentrations for each platform relative to the measurements made
on board FAAM are shown in Fig. 6b, with a slope from the airborne
comparison of 1.171±0.002 and an offset of -9.6±0.1 and for
the ground-to-air comparison a slope of 0.924±0.007 and offset of
10.0±0.02 (Table 3). There are no systematic biases evident in the
gas phase sampling systems that are common between platforms.
SP2 probes systematically reported lower black carbon concentrations at the
LASIC ARM site than on board the FAAM BAe-146 (Fig. 6d and e), with ODR
slopes of 0.775±0.005 (BCn) and 0.848±0.008 (BCm).
Number concentrations from FAAM SP2, BCn, follow similar trends in the
profile descent as the aerosol number concentrations (Fig. 4e). Pollution
events at Ascension Island have been defined by Zhang and Zuidema (2019) using
thresholds of BCm. During August, 100 ng m-3 was set as the upper
limit for clean conditions, and >500 ng m-3 defined the
most polluted tercile of conditions (Zhang and Zuidema, 2019). Data from the
intercomparisons presented are found in both the cleaner lower tercile and
the moderately polluted middle tercile. The data from ARM and FAAM are shown
to be in sufficient agreement to use these to determine the membership of
clean and polluted conditions reliably. However, data from NASA are 50 %
lower than those from FAAM (Table 3). Specifically, during part of the flight
on 18 August 2017, a leak was detected in one of the instrumentation
racks. This limited the data that was recoverable from the flight, and it is
therefore likely that the data from the intercomparison period were also
affected. NASA P3 SP2 comparisons against FAAM BAe-146 are discussed in the
Supplement (Sect. S5), although it is expected that the temporary leak makes
the NASA SP2 data unusable.
Accumulation mode NA concentrations from NASA and FAAM PCASPs during
the profile descent are shown in Fig. 4e along with the BCn values
from the SP2 (FAAM only). Qualitative correlations between NA and
BCn, pollution tracer CO, and thermodynamic properties of temperature
and humidity are apparent along with being closely related to the optical
coefficients shown in Fig. 4f–h. The greatest NA
concentrations were observed during runBL, with NASA P3 reporting 550±61 cm-3, as compared with 516±63 and 484±63 cm-3
from the two FAAM PCASPs. UHSAS data show particle number concentrations for
diameters greater than 0.1 µm of 570±54 cm-3 (See
Table S6). At these concentrations flow rate errors dominate
(assumed to be 10 % for the PCASP), which means that the number
concentrations were comparable, although it is noted that the two NASA
measurements were closer to one another than the FAAM measurements. At the
cloud level (although when out of cloud) the number concentrations were
slightly lower, of the order of 400 cm-3; Fig. 11c). Further
observations were made during runFT and during descent through the vertically elevated
pollution layer and in the clear-sky portions of the cloud sampling leg.
Number concentrations as low as 16±5 cm-3 (FAAM PCASP1) were
recorded on the runFT leg and were of the order of 74±23 cm-3 (FAAM
PCASP1) in the vertically elevated pollution layer. In general NASA and FAAM PCASP1
were within 10 % of one another, while NASA and FAAM PCASP2 were
separated by slightly larger amounts. ODR fits comparing FAAM PCASP2 to NASA
PCASP and UHSAS had slopes of 1.026±0.003 and 1.047±0.04,
respectively (Fig. 7f), with the comparison between FAAM PCASP1 and FAAM
PCASP1 giving a slope of 1.065±0.004.
Aerosol particle size distribution for (a) runBL, (b) runELEV (solid
lines) and runFT (dashed lines) and volume size distribution for (c) runBL, (d) runELEV (solid lines) and runFT (dashed lines). Errors (positive
only) are only shown for FAAM PCASP1, FAAM CDP and FAAM 2DS to aid clarity;
see main text for details. The legend in panel (a) applies to panels (a),
(c) and (d). Correlations from various flight segments (Table 2) between
aerosol number concentration measurements are shown for between FAAM
BAe-146 PCASP2 for (e) ground–airborne comparisons, where dashed lines refer
to LASIC SMPS data restricted to particles with diameter larger than 120 nm,
and solid lines refer to all sizes of particles and (f) airborne comparisons,
where sample altitude is given by the colour bar for the FAAM PCASP2 and
FAAM PCASP1 comparisons and a single colour for other probes to aid clarity.
Comparisons of NA with the ground-based site were performed using the
LASIC SMPS and FAAM PCASP2, which only sampled particles larger than 120 nm.
The slope of an ODR fit, when restricting LASIC SMPS to particles larger
than 120 nm, was 0.78±0.003. A similar fit slope of 0.77±0.01
was observed between the two FAAM instruments, PCASP2 and SMPS (Fig. 7e).
Interestingly the slopes considering all particle sizes are 0.95±0.004 (LASIC) and 1.18±0.02 (FAAM). This suggests that sizing
differences are present, which may be accounted for using detailed RI
corrections, which should be done for detailed science studies. The
differences between the FAAM and LASIC slopes for the SMPS data where all
particle sizes are considered suggest some influence of the sampling
conditions that has not been fully characterised.
Number concentrations of CN are shown in Fig. 6c. An ODR of CN
concentrations showed that NASA P3 data had an ODR slope of 0.91±0.01
relative to the BAe-146 concentrations. This trend is the opposite of that
shown by the PCASP observations, although it is noted that the NASA CPC
instrument only counts particles larger than 10 nm, whereas the FAAM
instrument can detect particles as small as 2.5 nm. CN concentration data
from the ARM site showed an ODR slope of 0.801±0.005 relative to the
BAe-146 data, even though both of these platforms operated the same model of
CPCs, which can detect particles as small as 2.5 nm.
Aerosols Aerosol composition
Comparisons between the airborne AMSs were possible for runBL, where
concentrations were larger than limits of detection. Concentrations on FAAM
were too low during runELEV to be considered for this. Likewise, data from
elsewhere in the FT were also below limits of detection for some parameters.
Table 4 shows that organic aerosol (OA) concentrations from NASA were 80 % of those reported by FAAM. Similarly, ammonium concentrations were
lower, by 90 %, from NASA measurements compared to those sampled from
FAAM. Concentrations of nitrate throughout the profile were low and close to
the FAAM limit of detection, with NASA reporting 80 % of FAAM
concentrations. Conversely, the NASA-reported sulfate concentrations were
40 % higher than those reported by FAAM. Some fragment markers from the
AMS measurements can provide information on the OA composition and oxidation
states, e.g. m/z 43 and m/z 44. The m/z 43 is mainly from the fragments of
saturated hydrocarbon compounds and long alkyl chains and are good
indicators of fresh aerosols (Alfarra et al., 2007). The m/z 44 is the
signal of the CO2+ ion from carboxylic acids and organo-peroxides and
suggests the presence of oxygenated organic compounds (Aiken et al., 2008).
Proportional contributions were calculated as the ratios of these OA
fragment markers to the total OA mass concentration, respectively (f43 and
f44). The f44 values were relatively consistent between two aircraft
measurements for runBL, and the f43 are also within observed standard
deviations (Table 5).
Aerosol composition properties, organic aerosol (OA), sulfate
(SO4), nitrate (NO3) and ammonia (NH4) for FAAM, NASA and
LASIC platforms. Data are not available from 5 September. Missing
data are represented as NaN (not a number).
Data from LASIC ACSM (using the c2 dataset) do not compare well with those
from FAAM (Table 4), with LASIC–FAAM mass ratios in the ranges of 2.1–4.4
(OA), 2.1–4.5 (SO4), 1.4–2.4 (NO3) and 2.0–4.1 (NH4). These
differences remain unexplained.
Aerosol physical properties
Aerosol PSDs are presented as number distributions (dN/dlogD) for runBL in
Fig. 7a and for the runELEV and runFT leg in Fig. 7b, with
corresponding volume distributions (dV/dlogD) in Fig. 7c and d,
respectively. For completeness the surface area distributions are provided
in the Supplement (Fig. S4). The accumulation mode number distribution in
the boundary layer looks to be captured in a similar manner by the NASA
PCASP and FAAM outboard PCASP1 and PCASP2 (Fig. 7a). Data from PCASP
probes here are not adjusted to a composition-specific RI. Poisson counting
uncertainties (e.g. Lance et al., 2010) for individual channels are below 1 % for sub-0.5 µm diameter aerosol particles (available here for FAAM
probes and expected to be of similar magnitude for the NASA probe). Data for
runBL were also available from the NASA UHSAS, first corrected for the
characteristics of BBA as described in Howell et al. (2021), for diameters
up to 0.5 µm (the stated upper size limit for the correction
algorithm). Concentrations are larger than those reported by any of the
PCASPs. By converting the FAAM PCASP2 bin boundaries to those for BBA-equivalent RI it can be seen that the PSD more closely matches that from the
UHSAS, although concentrations are still lower. This demonstrates the
importance of considering the material RI when combining measurements from
multiple probes with differing techniques.
The accumulation mode aerosol effective radius (Re) ODR fits for the
airborne comparison are shown in Table 3 with slopes of 1.31±0.18 for
NASA PCASP and a mean ratio of 0.92±0.04 for the NASA UHSAS, which
only operated at a single altitude. For comparison the FAAM PCASP1 had an
ODR slope of 1.48±0.07 relative to FAAM PCASP2. Correlations are
plotted in Fig. S5 for completeness and tabulated in Table S6.
These numbers do not reflect ambient conditions as this would require
adjustment to the RI of the material under test. There is greater
variability between probes on the same platform than between platforms.
A coarse aerosol mode was also present during runBL. This can be seen most
clearly in Fig. 7c, which shows the volume distribution dV/dlogD for
runBL. The magnitude of the differences between PCASPs is much larger than
the combined uncertainties at supermicron diameters. The largest differences
are apparent between the two probes on the FAAM BAe-146 platform, while FAAM
PCASP2 and the NASA PCASP are in closer agreement. Only the FAAM CDP
reported aerosol data in the particle diameter range 1–5 µm, but, at
larger diameters, data from 2DS probes on both aircraft cross over with CDP
observations and show distributions with similar shapes. The cross-over
between CDP and PCASP is likely dominated by uncertainty in the larger sizes
of the PCASP. This coarse mode will contribute to the total optical
scattering from aerosol particles, as evidenced by the NASA runBL
nephelometer data (Sect. 4.3.3) when switching between PM1 and PM10. At
diameters larger than 0.5 µm, where particle counts are much lower,
Poisson counting uncertainties become significant: 40 % at 1.5 µm
and more than 200 % at 3.0 µm. The bin boundaries of the PCASP and
CDP have not been corrected for the material RI, which is not known. The 2DS
is a shadow imaging probe and so not affected by the RI of the material.
Detailed scientific analysis should account for the material's RI, and not
doing so here does limit the utility of the results in the probe cross-over
regions
A comparison of number PSDs from the vertically elevated pollution layer and the runFT
leg is shown in Fig. 7b. The PCASP probes detected much greater
concentrations of accumulation mode aerosol particles in the vertically elevated
pollution layer than the clean free troposphere during runFT. The PCASP
probes have the ability to distinguish the vertically elevated pollution layer from the
cleaner surrounding free troposphere, when taking instrumental uncertainties
into account. The volume size distribution is not well sampled (Fig. 7d)
in either runFT or runELEV. There is evidence from PCASP (FAAM and NASA) and
2DS (FAAM) that a coarse mode was present in the vertically elevated pollution layer
that was not present in the clean free troposphere. It was possibly composed
of dust particles, although there is limited external information with which
to verify this other than a weak depolarising signal on the lidar (not
shown). The CDP does not sample the coarse mode well; number
concentrations are low, and the sample volume of the CDP is small, resulting
in poor sampling efficiency. NASA CDP and 2DS did not report data here. For
this set of probes to faithfully sample the coarse mode volume distribution
in this environment a much longer sample time would be required in order to
increase the amount of material sampled.
Comparisons between LASIC and FAAM of aerosol PSDs took place on six occasions
shown in Fig. 8, utilising the ARM site SMPS and the BAe-146 PCASP2
(outboard), PCASP3 (inboard) and SMPS (inboard). A dominant accumulation
mode was observed on 17 and 18 August and on 5 September with good correspondence observed in the overlap region between all
PCASP and SMPS instruments. Only the SMPSs can detect the Aitken mode, which
was most evident on 22, 24 and 25 August.
The Aitken mode was dominant or comparable to the accumulation mode in
magnitude on 22 and 25 August, both notable for
accumulation mode max particle number concentrations (in terms of dN/dlogD)
below 100 cm-3. When the Aitken mode max concentration was low on
24 and 25 August (dN/dlogD<200 cm-3), the ARM
SMPS reported higher concentrations than the empirically scaled (Wu et al., 2020) aircraft SMPS and more comparable to the unscaled values. For
22 August the FAAM aircraft SMPS (scaled) and ARM SMPS concentrations
were very similar, as was found for the accumulation mode. Generally, all
instruments reported similar width and mean for both modes. The application
of the empirical scaling factor (Wu et al., 2020) to FAAM SMPS data is
supported by this comparison, although there is evidence that there may be
some size-dependent features, in particular at the Aitken mode size range,
that are not captured by the simple single number correction.
Particle size distribution for six FAAM–LASIC fly-past flight legs
for (a) 17 August 2017, (b) 18 August 2017, (c) 22
August 2017, (d) 24 August 2017, (e) 25 August 2017 and (f) 5 September 2017.
Aerosol optical properties
The vertical profiles of aerosol optical scattering, σSP, (Fig. 4h) show that data from the NASA and FAAM aircraft both identify the
large-scale features of the vertically elevated pollution layer and the aerosol-laden
boundary layer. Results for aerosol optical scattering and absorption are
tabulated in the Supplement (Table S4). The nephelometer on board NASA P3
reported larger scattering magnitudes in the boundary layer below 1.7 km
compared to scattering measurements (the difference between σEP
(CDRS) and σAP (PAS) from FAAM). During the descent, the NASA
P3 instruments sampled the full particle size range (PM10), whereas the FAAM
CRDS instruments sampled behind an impactor with an aerodynamic D50 cut off
at 1.3 µm. During runBL, the NASA P3 alternately sampled downstream of
either a PM10 or PM1 inlet as detailed in Table 2.
Observations of σSP (470 nm) made on board NASA from the three
PM10 periods (runBL_A, runBL_B, runBL_C; Table 2) showed a decreasing trend along the run from values at the start
of 67±2 to 44±3 Mm-1 at the end of the run,
with corresponding data from behind the PM1 impactor for periods
runBL_1 and runBL_2 (Table 2) of 48±4
and 43±3 Mm-1 (not shown). Comparison of measured
PM1 and PM10σSP along runBL shows that the recorded σSP after the PM10 impactor was on average higher by ∼14 Mm-1, indicative of the contribution to scattering from supermicron
particles, most likely of marine origin (Wu et al., 2020).
Comparisons of σSP for red and blue channels for runBL are
shown in Fig. 6f and g as a function of data from FAAM BAe-146 for the
NASA P3 and LASIC ARM site. The intercomparison of σSP
observations from the two aircraft shows that NASA P3 observes 50 % more
scattering than the FAAM BAe-146 for non-size-selected observations
(runBL_A, runBL_B, runBL_C), as given by
the ODR slopes of 1.485±0.005 (blue channel) and 1.52±0.01
(red channel). The two were closer, within 20 %, when the NASA P3 sampled
only submicron aerosols (Fig. 6f and g) with ODR slopes of 1.172±0.008 (blue channel) and 0.971±0.017 (red channel).
Blue channel σSP data from the ARM site has an ODR slope of
0.742±0.004 compared with the BAe-146 data and 0.391±0.003 for
the red channel during the six intercomparison flight legs. While the
EXSCALABAR optical properties from PAS and CDRS are for dry aerosol, the
LASIC nephelometer is reported to operate between 50 % RH–60 % RH. Ideally all
platforms would carry identical instrumentation and operate it under similar
parameters, but the FAAM EXSCALABAR is a state-of-the-art bespoke
instrument, whilst LASIC and NASA use their unique solutions for airborne
and ground-based installations of commercially available technologies, PSAP
and nephelometers. However, it would be preferable to record all data at a
constant humidity for example, and this should be considered for future
campaigns with multiple platforms. Adequate control of humidity does present
challenges however, and so this may not always be possible, giving rise to
the need for intercomparisons such as this.
However, if RH-controlled growth of aerosol were the only difference, the
LASIC σSP would be larger than EXSCALABAR σSP,
even for aerosol dominated by only weakly hygroscopic organics. Two further
possible explanations for these discrepancies in σSP are (1) that the
aerosol population sampled at the ARM site is different to that encountered
by FAAM or (2) that the aerosol sample is modified in some way during sampling.
The ARM site is located on land, which presents an opportunity for
introduction of aerosols not encountered during the airborne sampling over
the ocean. Relative humidity is not thought to be the cause of the
discrepancy because the LASIC data are not actively dried unlike the FAAM
data. Hence, the LASIC data might be expected to produce more scattering,
with the population of aerosols in a more humid environment growing to some
degree based on the hygroscopicities.
There may be important size-dependent transmission efficiency artefacts.
These would have to affect only larger particles as there is good
correspondence between σAP (see below) and BC observations
along with NA, all of which are dominated by aerosol smaller than 600 nm diameter (e.g. Peers et al., 2019). Comparisons of scattering at the ARM
site between the nephelometer and the CAPS PMSSA data (Table S5) show internal consistency, suggesting that the difference between the
airborne and ground-based measurements is not related to a specific
instrument but a systematic issue. Aerosol sampling – in particular inlets
and particle transmission – is discussed further in Sect. 5.5.
The FAAM PAS σAP data from the profile descent (Fig. 4f)
show that absorbing aerosols are present in magnitudes greater than the
lower threshold of the instrument in the boundary layer, runBL, and upper
pollution layer, runELEV. Data follow similar trends from the NASA PSAP in
the boundary layer. In the vertically elevated pollution layer the NASA PAS data look
suspect; for example signals from red and blue are nearly identical,
suggesting an unphysical absorption Ångström exponent
(ÅAP). This is likely because the PSAP is not suitable for
operating in regions where RH (or pressure or other external factors) changes rapidly, such as during descent, especially, as is the case for NASA
PSAP, where the sample is not actively dried. These data should be treated
with caution and are not used in subsequent correlations (Fig. 6h and i).
Consequently, the data for σEP (Fig. 4f) from NASA
(nephelometer + PSAP) should be treated with caution in the vertically elevated
pollution layer when compared against the FAAM CRDS measurement, which
measures optical extinction directly.
The correlations between σAP from FAAM and NASA when sampling
behind the 1 µm impactor for nominal blue (470 nm) and red (660 nm)
wavelengths are shown in Fig. 6h and i. Because data are only present from
the boundary layer leg, the result is shown as a ratio of weighted means,
both of which are within 10 % of one another (0.927±0.003 (blue
channel) and 1.077±0.008 (red channel)), although in opposite
directions. Data at 530 nm show a slope of 0.96±0.008. This
wavelength dependence is explored in more detail. Figure 9a shows
submicron σAP as a function of wavelength for
runBL_1. NASA reported lower magnitudes of σAP
compared to the FAAM PAS data, as can be seen by considering interpolated
values of FAAM PAS and NASA PSAP data. FAAM σAP data were
derived as a function of wavelength between 405 and 660 nm by computing
ÅAP between adjacent wavelengths and interpolating from the nearest
observation in wavelength space. The same was done for NASA σAP
data between 470 and 660 nm, with values extrapolated at wavelengths
shorter than 470 nm. This shows that NASA PSAP data points at native
wavelengths are within 1 Mm-1 of the interpolated FAAM PAS. We also
show σAP data from the FAAM TAP instrument at the three native
wavelengths. The TAP observations were very close to the interpolated values
for the EXSCALABAR PAS data that it shared an inlet with. Filter-based
absorption measurements including NASA PSAP and FAAM TAP are subject to
larger biases and uncertainties than spectroscopic techniques such as those
used in EXSCALABAR (e.g. Davies et al., 2019). However, there is no evidence
of biases related to filter-based techniques impacting these comparisons of
σAP. The extrapolated values of σAP from the NASA
PSAP at wavelengths shorter than 470 nm fall just outside the 1 Mm-1
maximum expected error range from the FAAM PAS data. The wavelength
dependence in FAAM PAS data is seen to steepen here, yet there are no NASA
PSAP observations at wavelengths shorter than 470 nm with which to constrain
this.
Optical absorption coefficient as a function of wavelength for
boundary layer leg runBL_2 (Table 2). Observations are shown
as mean (symbols) and standard deviation (error bars) for FAAM EXSCALABAR
PAS and NASA PSAP data, along with FAAM TAP data. Interpolated values of
σAP are shown, which use ÅAP from observations for FAAM
and NASA. (b) ÅAP as a function of pairs of mean wavelengths for
runBL_1 and the weighted mean of observations from four FAAM
fly-pasts of the LASIC ARM site are shown (17, 18, 22
and 24 August). Full CLARIFY campaign data are reproduced from Taylor
et al. (2020).
The comparison of σAP at the LASIC ARM site with FAAM
measurements shows that the 470 nm data had an ODR slope of 0.98±0.006
during the six intercomparison flight legs (Table 2) with an offset of
-0.303±0.015 Mm-1. Similar performance was found at 660 nm, with
a slope of 1.00±0.008 and offset of -0.288±0.014 Mm-1; note that FAAM reported σAP greater than 1.0 Mm-1 on
only two occasions. For the 530 nm data (not available for σSP)
the ODR between FAAM PAS and LASIC PSAP data had a slope of 1.00±0.01
and an offset of -0.42±0.04 Mm-1, with comparisons available for
17, 18, 22 and 24 August. At the LASIC ARM site
the CAPS PMSSA probe (530 nm) data gave an ODR slope of 0.98±0.03 for PM1 data, consistent with other observations (Fig. S6).
Absorption Ångström exponents, ÅAP, computed from pairs of
wavelengths as a function of mean wavelength are shown in Fig. 9b for
runBL_1 for NASA PSAP, FAAM EXSCALABAR PAS and the FAAM TAP.
A general trend of increasing ÅAP at shorter wavelengths is
apparent in these measurements from the intercomparison data, as would be
expected considering the CLARIFY campaign-mean data from Taylor et al. (2020). Data from NASA PSAP are in better agreement with the CLARIFY
EXSCALABAR PAS campaign-mean values than the FAAM TAP data (which are also
filter-based).
Similar comparisons of ÅAP for the FAAM EXSCALABAR and LASIC PSAP
observations are also shown in Fig. 9b for three segments with σAP>1.0 Mm-1. FAAM ÅAP data over these
segments are shown as the mean and the range and are largest at shortest mean
wavelength, following the trend of the aircraft intercomparison with other
observations. Contrary to this, the LASIC data show a slightly decreasing
trend towards shorter mean wavelength, although within the bounds of the
uncertainties.
Determination of ω0 from observations of optical properties is
hampered by the low magnitude of σAP and the short averaging
times available for this study. There is additional discussion of this in
Sect. 5.4.
Atmospheric radiationComparisons of downwelling spectral irradiances from FAAM SHIMS
against those from the NASA SSFR
Three opportunities to compare the spectral irradiance from the SHIMS and
SSFR radiometers are available for runs with the FAAM BAe-146 and NASA P3
aircraft: (i) runFT, which is the SLR at 5.8 km; (ii) runPRO, which consisted
of the profile descent from 5.8 to 330 m; and (iii) runBL, which is the SLR at
330 m. These manoeuvres were performed wing tip to wing tip.
Figure 10a–i show the downwelling spectral irradiance from SSFR
(NASA; first column) and SHIMS (FAAM; second column). The third column
shows the fractional difference between the measured spectral irradiances.
Similarity between the measurements is apparent. For runBL, the spectral
irradiances are variable at around peak values of 400–2500 W m-2µm-1. This is likely a consequence of the two aircraft operating
below patchy cloud, where solar radiation is generally diminished, but, on
occasion, three-dimensional reflectance effects from the edge of clouds can lead
to a local enhancement of radiation (Marshak and Davies, 2005). The
agreement in the spectral irradiances during runBL when integrated over
wavelength is on average within 0.04 % for the VIS SHIMS module (0.30–1.15 µm) and within 0.57 % for the NIR SHIMS module (0.95–1.70 µm; Table 7).
The agreement between the irradiances when integrated
over wavelength during runFT and runPRO is in somewhat poorer agreement and
is on average some 1.5 %–2 % higher in the VIS SHIMS module, but 0.5 %–1.7 % lower in the NIR SHIMS module.
Intercomparison of downwelling shortwave spectral irradiance from
(a, d, g, j) ORACLES NASA P3 and (b, e, h, k) CLARIFY FAAM BAe-146 and
(c, f, i, l) percentage differences for three wing-tip-to-wing-tip
manoeuvres: (a–c) runFT, (d–f) runPRO and (g–i) runBL. Intercomparison of upwelling shortwave spectral irradiance for runFT (as a–c). Filled black and blue contours in (a), (d), (g), (j), (b), (e), (h) and (k) show the
observed spectral irradiance ranges. Filled grey contours in (c), (f), (i) and (l) show the range of percentage differences in paired measurements,
overlaid with the average percentage difference (red line).
Cloud microphysical and bulk properties; mean and standard
deviations; 75th, 90th and 95th percentiles; and boundary
layer turbulence.
Vertical velocityCMDReRVNcLWCLWC[µm][µm][µm][cm-3][g m-3][g m-3]FAAM: five-portDMTDMTDMTDMTDMTHot wirenose probe;CDPCDPCDPCDPCDPFAAM:NASA:Nezorov;HoneywellNASA:Sperry AZ-King800runBLFAAMStandard deviation0.62NASA[ms-1]0.44FAAMSkewness0.38NASA0.76runCLDFAAMMean ± standard deviation10.927.0±1.57.8±1.6226±690.23±0.150.23±0.1675th2880.350.3790th3080.470.4699th3350.760.57NASAMean ± standard deviation11.357.2±1.57.9±1.5274±1530.37±0.430.20±0.3175th3660.390.2290th5280.680.3799th5952.11.46NASA∗Mean ± standard deviation11.127.0±1.47.7±1.4253±1370.24±0.150.12±0.1075th3510.360.2190th4870.500.2599th5390.630.36
∗ Values without strong updraughts.
The integrated fluxes derived from the SHIMS and SSFR instruments
over the SHIMS module spectral ranges. The measurements in standard font
represent downwelling irradiances, while those in italics represent
upwelling irradiances. Values in brackets denote 2 standard deviations.
FAAM BAe-146 NASA P3 Module spectral range [µm]0.40–0.950.96–1.690.40–0.90.96–1.69[µm][W m-2][W m-2][W m-2][W m-2]SLR: runFT, 12:51–13:02779 (9)303 (4)767 (3)308 (1)Profile: 13:02–13:20771 (37)290 (37)753 (30)291 (38)SLR: runBL: 13:20–13:39567 (357)169 (124)566 (384)168 (136)SLR: runFT, 12:51–13:0285(76)20(29)86(79)21(30)Comparisons of upwelling spectral irradiances from FAAM SHIMS against
those from the NASA SSFR
The upwelling spectral irradiances from the FAAM and NASA aircraft are shown
in Fig. 10j–k for runFT along with instantaneous differences between
them (Fig. 10l). Considerable variability owing to the aircraft passing
over variable amounts of cloud and, to a lesser extent, aerosol is apparent.
Once again the measurements from the BAe-146 and the P3 aircraft are in
reasonable agreement, with differences in the integrated irradiances of just
1 W m-2 (max 5 %) and similar measures of variability (see also Table 7).
Cloud microphysical and bulk properties
The longitudinal cross section of Nc (Fig. 11c) shows that
broken cumulus clouds were sampled in situ by both aircraft, with
concentrations varying across the run. It is worth recalling that due to
safety considerations the sampling by the two aircraft was separated by a
distance equivalent to 5 min travel time. The composite cloud PSDs from
all cloud passes along the runs are shown in Fig. 11a for data from the
CDP and 2DS probes. The errors are presented for the FAAM instrument only
for clarity since the magnitude of errors will be similar between similar
instruments. There are large uncertainties in the sample volume of the 2DS
instrument in the smallest size channels, resulting in large uncertainties in
the bin concentrations there.
(a) Cloud PSD, (b) PDF of cloud particle Re, and (c) time
series of Nc (CDP) and interstitial NA (PCASP) at cloud level.
Errors in PSD as Fig. 7 are shown only for FAAM platform to aid clarity.
The probability distribution functions (PDFs) of cloud drop effective
radius, Re, shown in Fig. 11b, have a bimodal nature from both FAAM
and NASA observations, with modes overlapping well.
Mean NC values were slightly greater and with a larger standard
deviation on the NASA platform (274±153 cm-3) than from FAAM
(226±69 cm-3; Table 6). The 90th percentiles of the
distributions were 528 cm-3 (NASA) and 308 cm-3 (FAAM), and
99th percentiles were 595 cm-3 (NASA) and 335 cm-3 (FAAM). Errors
due to particle coincidence in the sample volume are expected to be minimal
at these concentrations (<1 % at 800 cm-3 according to
Lance et al., 2012). Number concentrations of NA were lower at this
cloud level than encountered along runBL at 402±28 cm-3 (NASA)
and 374±33 cm-3 (FAAM PCASP1; Table S6). These
NA values were below the peak cloud drop number concentrations,
implying that the clouds were nucleated some way below the flight level –
something which was observed visually from the flight deck.
Occasionally the NASA P3 encountered much greater cloud drop number
concentrations, NC>500 cm-3, with the 90th and
99th percentiles some 30 % greater than for FAAM. Inspection of the
time series of in situ vertical wind velocities (not shown) indicated that
the P3 flew through a strong updraught in excess of 6 m s-1, a feature
not encountered by FAAM. Such an updraught would be expected to increase the
supersaturation, nucleate a greater number of cloud particles from the
aerosol population and condense more water. The particle size distributions
(Fig. 11) for cloud (CDP) and small drizzle (2DS) from both platforms exhibit similar shapes at all sizes given the demonstrated magnitudes of the
uncertainties. The NASA 2DS reports slightly larger concentrations of
particles larger than 40 µm, possibly due to the enhanced updraughts
encountered. To investigate the impact of this the derived metrics of the
PSD are computed with the data from the strongest updraughts removed –
chosen to be above a threshold of 2 m s-1, as this was seldom
encountered by FAAM. Away from strong updraughts the NASA mean NC is
253±137 cm-3, which is closer to the values reported by FAAM.
Derived size metrics count median diameter, Re and Rv were
similar across the two platforms; again when the data from within the
strong updraught are excluded the agreement is improved (Table 6). FAAM
employed bulk-water-corrected bin diameters, but the magnitude of
differences between those and nominal bins is less than 5 %, especially at
diameters close to the mode of the PSD. Re is identical away from
strong updraughts as sampled by the CDPs, at 7.0 µm, with Rv also
very similar: 7.7 µm (NASA) and 7.8 µm (FAAM).
LWCs are also very similar away from strong updraughts, at 0.24±0.15 g m-3 (NASA) and 0.23±0.15 g m-3 (FAAM). The 75th, 90th and
99th percentiles of the distribution are also broadly similar, whereas
the LWC from locations including the updraught passage has a higher mean
and 99th percentile values over 2.0 g m-3. Additional LWC data
come from the hot-wire probes. The FAAM Nevzorov reported 0.23±0.16 g m-3, and while this is very similar to the FAAM CDP, recall that these
data were used to effectively baseline the CDP calibration (Sect. S3). Excluding data during strong updraughts, data from the NASA King probe
are low in comparison at 0.12±0.10 g m-3. The expected
uncertainty range for these evaporative probes according to Baumgardner et
al. (2017) is between 10 % and 30 %. The FAAM Nevzorov LWC compares well
with LWC derived from the optical probes on the NASA aircraft, but the NASA
King probe exhibits a low bias. This may be due to a different size-dependent collection efficiency or inadequate baseline removal (e.g. Abel
et al., 2014).
DiscussionAir mass determination
The magnitude of the differences in CO measurements between platforms does
not preclude robust identification of pollution regimes within the South
Atlantic region (Wu et al., 2020; Gupta et al., 2021), although the most
polluted conditions encountered during biomass burning season were not
sampled during the intercomparisons. For August 2017 at Ascension Island the
vast majority of CO concentrations were between 50 and 150 ppb, although
during August 2016 there were multiple days where CO concentrations above
150 ppb and as much as 200 ppb were observed at the ARM site (Zhang and Zuidema 2019). ORACLES 2016 generally operated within 10∘ of the coast
between, 8 and 24∘ S, and encountered CO
concentrations between 60 and 500 ppb (Shinozuka et al., 2020), although
concentrations of CO in the planetary boundary layer rarely exceeded 120 ppb
(Diamond et al., 2018). Outside the BBA season, between December 2016 and
April 2017, conditions observed at the LASIC ARM site had a median value of 59 ppb and an IQR of 55 to 65 ppb (Pennypacker et al., 2020), similar to
pristine oceanic conditions in the Southern Hemisphere that have previously
been observed (between 50 and 60 ppb; Allen et al., 2008, 2011). Ultraclean
days were also observed during the BBA season (typified by NA<50 cm-3), which corresponded to median CO concentrations of
69 ppb and an IQR of 62 to 74 ppb (Pennypacker et al., 2020), with Abel et
al. (2020) observing 70 ppb in the vicinity of pockets-of-open-cells
convection during BBA season.
Aerosol chemical composition
Comparisons between the two airborne AMS instruments are generally within
1 standard deviation for ammonium and nitrate and within the 30 % to
37 % quoted uncertainty in the NASA P3 AMS. NASA P3 reported more
sulfate, and FAAM BAe-146 reported a greater mass of organics. Differences
may arise from low magnitudes of material or differences between retrieval
algorithms, collection efficiencies within the AMS instruments or relative
ionisation efficiencies of the chemical components. These differences,
detailed further below, were not able to explain the differences in the
sulfate masses, ultimately leading to the conclusion that the two
instruments can be meaningfully compared.
Limits of detection were found to be material-specific using ORACLES 2016
flight data (Dobracki et al., 2022). However, during the intercomparison the
mass concentrations were well above those limits, aside from some of the
individual mass fragments of organics, for which mass concentrations were
close to their 0.15 µg m-3 limit of detection.
To explore any potential effect of using different post-analysis algorithms,
data from the NASA P3 high-resolution AMS were also analysed using the
SQUIRREL algorithm used by the FAAM BAe-146 AMS. This demonstrated that the
different algorithms can account for only 7 % of the difference (Dobracki
et al., 2022). Relative ionisation efficiency (RIE) characterisation could
account for only minimal differences between instruments. Calibrations
performed on the FAAM BAe-146 instrument resulted in changes to the RIE
coefficients for ammonium of less than 2 % and for sulfate of 10 %.
Further information is to be found in the Supplement (Sect. S4).
Another possible source of the difference lies in the application of
collection efficiencies. Liquid, primarily acidic, aerosols are collected
more efficiently than neutralised particles (Dobracki et al., 2022).
Collection efficiency values were set at 0.5 for each airborne AMS, since
the aerosol was shown to be sufficiently neutralised in the free troposphere
for the ORACLES dataset (Dobracki et al., 2022) and for both the boundary layer
and free troposphere for the CLARIFY dataset (Fig. S3).
The source of the nitrate in this region may be either ammonium nitrate or
organic in nature (Dobracki et al., 2022). This can be explored to some
extent by considering the ratio of NO+ (m/z 30) to NO2+ (m/z 46), given the observations of Farmer et al. (2010). However, given the low
concentrations of nitrate within the boundary layer, large uncertainties in
the ratio of m/z 30 to m/z 46 are expected. Considering the uncertainties can
exceed 50 % for the m/z values and 75 % for the fractional values, with
larger errors in NASA P3 data in this instance, the measurements of the
ratio are comparable (Table 5).
Ammonium nitrate is semi-volatile under atmospheric conditions, and to
investigate this a model of evaporation of aerosols to the gas phase was
developed after Dassios and Pandis (1999) was run for a range of atmospheric
conditions and a sample temperature of 30 ∘C and a sample
residence time of 2 s. This showed that the worst-case-scenario losses of
aerosol mass to the gas were 7 %, assuming unity accommodation
coefficient, instantaneous heating upon sample collection and a single
aerosol component. Pressure and relative humidity exerted much weaker
controls (<2 %). Sample residence times may well be longer on
the aircraft, but the uncertainty is related to the differences between the
sampling set-ups on the aircraft rather than absolute values, which also
reduces the impact of this on the comparisons.
Uncertainty in OA mass concentrations stems from the determination of
organic nitrates, with greater mass of OA reported by BAe-146. By assessing the
magnitude of the contributions of mass fragments 30 (NO+) and 46
(NO2+) it is possible to assess the balance of organic to
inorganic nitrates. During the airborne intercomparison nitrate
concentrations were low and close to the FAAM limit of detection. While it
is possible to compute and compare values for the ratio of f30 to f46 it is
not clear that in these circumstances that would be particularly instructive
given the low total nitrate mass.
Useful analysis of chemical composition takes place when derived quantities
are computed, for example to give information of the age state of a polluted
air parcel. For example, in the Ascension Island region the BB OA is highly
oxidised and of low volatility, suggesting it is well aged (Wu et al., 2020;
Dang et al., 2022). Closer to the coast, where ORACLES 2017 operated, the
aerosol might be expected to be younger. For OA fragment markers, the f44
compares well between two aircraft measurements, and the f43 is within 1
standard deviation. The difference in f43 may arise from the low magnitude
as the BB OA is highly oxidised in the Ascension Island region, and the
fraction of hydrocarbon-like OA is low. Good performance of the OA fragment
markers (e.g. f44 and f43) between the two instruments and similarity
between calibrated values suggest that the CLARIFY and ORACLES datasets
should be useful in determination of the chemical age of biomass burning
products.
Insight into the conditions at the time of combustion can be gleaned from
ratios BC/ΔCO and OA/ΔCO, where ΔCO is the difference
from the background concentration in the boundary layer of (from CLARIFY
data) COback=66 ppb (Wu et al., 2020). CLARIFY observations of
BC/ΔCO were indicative of flaming combustion in both the free
troposphere and similar in the boundary layer, with perhaps some inefficient
cloud processing (Wu et al., 2020). The 50 % difference between FAAM and
NASA BC mass concentrations (likely due to an SP2 leak; Sect. 2.4.1) drives
discrepancies in BC/ΔCO, where FAAM =14 ng µg-1 and
NASA between 5 and 7 ng µg-1. Accounting for the CO bias makes the
comparison worse. Despite this, the width of the range representative of
flaming combustion is such that conclusions on combustion type would be the
same for each platform. For the six measurements available from the
FAAM–LASIC comparison, the results are more comparable with FAAM =10.6 ng µg-1 and LASIC =10.3 ng µg-1.
Comparisons of OA/ΔCO yield 0.96 µgµg-1 (FAAM) and
0.92 µgµg-1 (NASA). The positive biases in OA and CO
measurements reported by NASA P3 compared to FAAM BAe-146 combine
favourably, although note that the numbers reported here rely on only the
CLARIFY CO values.
The comparison between the FAAM BAe-146 AMS and the LASIC ARM site ACSM is
poor. There is a difference of a factor of between 3 and 4.5 between individual
species mass concentrations, with the larger magnitudes observed at the ARM
site. The cause of this is unknown. To investigate LASIC ACSM, data points
from 30 min either side of the valid time were looked at and the
resultant range compared to the FAAM AMS data. This did not result in better
agreement. Unlike the airborne AMS collection efficiencies of 0.5, at the
time of the comparison all LASIC data points had composition-dependent
collection efficiencies of unity, although adjacent time sometimes had
values below 1.0. The slight difference in quoted upper cut diameters of 600 nm (FAAM) and 700 nm (LASIC) does not explain these differences. The
unexplained differences would benefit from further investigation.
Aerosol physical properties
During the airborne intercomparison PSDs in the accumulation mode compared
well between airborne PCASPs and the UHSAS once the evaporation of absorbing
particles due to the high laser power was accounted for (Howell et al., 2021). Individual studies will be required to assess the probe response to
the particular RI of aerosols encountered (e.g. Peers et al., 2019) and to
conduct optical closure studies with radiometric measurements. It was shown
by Peers et al. (2019) that aerosols were effectively sampled by FAAM in the
optically active region of the accumulation mode, which fell between 0.3 and
0.5 µm diameter (77 % of extinction).
The external PCASPs were able to distinguish between particle number size
distributions in the vertically elevated pollution plume and the cleaner surrounding
free troposphere. Here the performance of the NASA PCASP is more similar to
the FAAM PCASP2. The accumulation mode at runFT is less well defined, and
Poisson counting uncertainty is large at sizes greater than 0.5 µm. The
presence of a coarse mode in the vertically elevated pollution layer fits with back
trajectory calculations, which had the air parcel history over the African
continent (not shown). This is consistent with similar conditions during
ORACLES 2016, where back trajectories showed polluted above-cloud air masses
(Gupta et al., 2021). The volume size distribution was not well sampled in
the vertically elevated pollution plume, where the CDP sample volume is crucial to
measurement of the coarse mode but suffers from a small sample volume, and
the sampling time in this case was short.
A coarse mode of marine aerosols was observed in the boundary layer and
captured by PCASPs, the FAAM CDP and 2DS probes. The source of the
discrepancy between the response of PCASP probes at larger diameters above 2 µm is unknown, but the inlet sampling efficiency of large particles,
low concentrations, inlet jet alignment and possibly instrument RH
differences may all contribute. The CDP cross-over with PCASPs is poor, and
large errors exist from low counting statistics at larger sizes and
correspond to the region where 2DS sample volume uncertainties are largest,
although the cross-over is good, as is comparison between 2DS probes from
NASA and FAAM. Sampling the coarse mode and being able to account for its
scattering is important for optical studies. At larger sizes >600 nm the aerosol composition will not contain a large amount of BBA (e.g.
Wu et al., 2020) and likely consists of purely optically scattering hydrated
salts, meaning comparison with probes such as CDP and OAPs is therefore
likely to be more valid.
Observations of PSDs generally agreed between LASIC and FAAM, when
considering the scaled FAAM SMPS data and either the external PCASP2 or
internal PCASP3 with calibrated bin boundaries corrected to an appropriate
RI for BBA. Condensation particle number concentrations were slightly lower
for the LASIC dataset. The mean ratio of bin concentrations for sizes
smaller than 600 nm (BBA RI-corrected) between PCASP2 and PCASP3 was close
to unity, although individual flights saw differences for the larger sizes up
to 30 % (average of 14 %).
Aerosol optical properties
Observations of σAP from FAAM and NASA agree within
instrumental uncertainties given the low magnitude of the signal and short averaging
time. Likewise, there is comparability between FAAM and LASIC for
observations of σAP, to better than 2 % or 0.55 Mm-1
for the LASIC PSAP. Additional data from the CAPS PMSSA probe support
the observations and suggest no inherent bias between the ground and
airborne measurements or from filter correction schemes. This study does
not attempt to replicate previous work considering filter-based correction
schemes such as Davies et al. (2019). Instead, it compares the data as
published by each group. NASA data were based on the Virkkula (2010)
wavelength-averaged scheme for comparability with other studies (e.g.
Pistone et al., 2019) and the LASIC data using an average of the absorption
calculated using the correction schemes from Virkkula (2010;
wavelength-averaged) and Ogren (2010).
Aerosol ω0 and ÅAP are two important climate-relevant
parameters that are derived from observations of aerosol optical properties
(e.g. Sherman and McComiskey, 2018). ÅAP was compared between the
two aircraft and against the CLARIFY campaign mean (Taylor et al., 2020).
The trend of larger ÅAP at shorter mean wavelength is apparent in
all airborne datasets, including filter-based retrievals. The data from the
LASIC ARM site show different behaviour for the three comparison segments
under consideration, with similar or slightly lower values (accounting for
uncertainties) of ÅAP for shorter mean wavelength. Zuidema et al. (2018a)
noted spectrally flat behaviour for the 2016 BBA season based on LASIC ARM
measurements. The range of values encountered for the blue–green pair during
the season was large during the BBA season of 2016, with extreme values
smaller than 0.8 and greater than 1.4 (Zuidema et al., 2018a). The variability during
that year is not expected to be unusual, and so the range of values
encountered during these short intercomparison segments may just reflect
this natural variability. The short sample time may not be sufficient to
capture that variability.
Campaign-mean ω0 comparisons have been discussed elsewhere for
the CLARIFY and LASIC campaigns, with Wu et al. (2020) noting that the
measurements collected at the ARM site were lower than the measurements made
on board the FAAM BAe-146, especially at longer wavelengths. Airborne ω0 measurements made in the free troposphere during ORACLES 2016
(Pistone et al., 2019) were shown to be slightly larger than those made by
CLARIFY (Wu et al., 2020). While both ORACLES and LASIC used filter-based
absorption in the computation of ω0, in this instance the
filter correction schemes are not thought to be the dominant source of
uncertainty (Haywood et al., 2021). Rather, the differences between
measurements of scattering (or extinction) coefficients are the likely
source of discrepancies in ω0.
Inlets and particle transmission
Here we consider the effects of inlet systems, internal pipe work and
sampling system components such as impactors on the comparisons.
Transmission of a representative sample of aerosol particles into an
aircraft while flying at high speed is challenging. The NASA P3 SDI has been
well characterised and is expected to have a transmission function
approaching unity for submicron aerosols: differences between this and other
inlets were shown to be below 16 % (McNaughton et al., 2007). Likewise,
the Rosemount inlets employed on FAAM have been shown to transmit with a
function reasonably close to unity for submicron particles (Trembath et al.,
2012), although these inlets are less well characterised than the SDI.
The starboard side of the BAe-146 within the vicinity of the Rosemount
inlets for EXSCALABAR and SP2 is aerodynamically clean, with no barriers to
the airflow. Close correspondence was observed between FAAM BAe-146 and NASA
P3 data for σAP and submicron σSP. There is
support from LASIC σAP data, which follow the FAAM measurements
very closely, but not from LASIC σSP measurements, which are
much lower than those from FAAM. However, LASIC BCn is within 20 %
of FAAM and BCm within 10 %, both lower. BC measurements were much
lower from NASA than FAAM, although a leak was identified at other times,
which possibly also affected the data collected during the intercomparison
period. From observations presented here it seems reasonable to conclude
that the starboard-mounted Rosemount inlets adequately sample
submicron aerosols.
The BAe-146 port-side Rosemount inlets are potentially compromised by the
large-radiometer blister pod. CN number concentrations from FAAM and NASA
are within 10 %. However, LASIC CN number concentrations are
approximately 80 %, lower than FAAM. This ratio is similar to the ratios
between BC measurements and suggestive of a small systematic effect. AMS
data from the two aircraft showed generally good agreement within
uncertainties, and some differences were accounted for through CE and RIE.
Organic aerosols have been shown to be contained in particles smaller than
0.4 µm (Wu et al., 2020), and it is here that the largest difference
between FAAM and NASA data occur – with FAAM reporting 40 % greater mass
concentrations. The AMS data (biased to larger particles with greater mass)
and CN concentrations (biased to smaller particles with greater number) are
not suggestive of particle shadowing by the BAe-146 blister pod.
The FAAM SMPS measured aerosol PSDs behind a port-side Rosemount inlet, and
data from the six LASIC fly-past segments mostly compare well with the LASIC
SMPS and FAAM PCASP2 and PCASP3. There are differences, although they do not
appear to be systematic but vary day to day, with concentrations larger in
either the accumulation or Aitken modes from the FAAM SMPS compared the one
at LASIC. It is noted that there is agreement in the overlap region on all 6 d between the LASIC SMPS and the externally mounted FAAM PCASP2 and the
internally mounted FAAM PCASP3. During CLARIFY as a whole, agreement between
the FAAM SMPS and the FAAM PCASPs was demonstrated in the cross-over region
(Wu et al., 2020). Externally mounted PCASPs on FAAM BAe-146 and NASA P3
also show close agreement, along with the internally mounted NASA UHSAS,
once corrected for particle heating and evaporation, although it is important
for individual studies to pay attention to composition-dependent collection
efficiencies.
Overall, there are no observable biases introduced into the datasets by
sampling submicron aerosols through Rosemount inlets on either the
aerodynamically clean starboard side of the FAAM BAe-146 or the port side,
which supports the blister pod. This study does not have sufficient data to
conclusively answer questions relating to the size-dependent collection
efficiencies of Rosemount inlets in various locations on the FAAM BAe-146 platform
(a task begun by Trembath et al., 2012, and Trembath, 2013). Should better
precision be required than that shown here, then an additional study
involving detailed flow modelling will likely be required.
Differences between the platforms may result from transmission losses within
internal plumbing. Careful design of flow paths within pipe work can mitigate
against some of the potential losses of aerosol particles. Sample line
losses can then be modelled, for example Baron (2001). Aerosol particle data
from FAAM EXSCALABAR were corrected for measured sample line losses.
Transmission losses of aerosols in the submicron range from the NASA P3 SDI
to the AMS are demonstrated to be lower than 20 % as an average for the
ORACLES campaign, although this is not explicitly accounted for when
calculating concentrations (Dobracki et al., 2022). Similar losses are to be
expected for other internal FAAM instruments, where concentrations were not
corrected for line losses.
Differences remain between the LASIC ARM site and FAAM BAe-146 σSP
observations. The BAe-146 EXSCALABAR sampled downstream of a 1.3 µm
aerodynamic diameter impactor (Taylor et al., 2020), and the LASIC ARM site
employed a 1.0 µm aerodynamic impactor upstream of instruments.
Assuming the density of the sampled material to be 1.6 kg m-3 (the
mid-point of the range given in Levin et al., 2010, to two significant
figures), the FAAM impactor has a physical cut size diameter of approximately
1.0 µm, to within 3 % (computed using AeroCalc; Baron, 2001).
Ammonium sulfate, having only a slightly higher density (1.77 kg m-3),
therefore has a similar cut size. For the LASIC ARM site impactor, the
physical cut size diameter (assuming spherical particles) is 0.78 µm.
Scattering by coarse mode particles was observed by the NASA nephelometer,
when not sampling behind its impactor. Since the small end of the coarse
mode very probably extends to diameters less than 1.0 µm, these
submicron coarse mode particles are likely to contribute more to the
extinction measured behind the EXSCALABAR impactor than the scattering
measured behind the LASIC impactor. Thus, differences between σSP (and subsequently ω0) from LASIC and FAAM may stem
from this difference in the upper cut size of the impactors, especially
where marine boundary layer aerosols are present. However, closer agreement
between NASA and FAAM was demonstrated for σSP when NASA also
operated behind a nominal 1.0 µm aerodynamic diameter impactor. This
may be a fortuitous result of the conditions encountered during the
airborne intercomparison. It would have been beneficial to use the impactors
with the same cut size for the different campaigns being compared. Caution
should be taken when comparing scattering measurements and derived
parameters across these campaigns. This might take the form of detailed
optical modelling and closure with radiation measurements.
Atmospheric radiation
In cloud-free skies over ocean, where the surface reflectance is relatively
well known, the direct radiative effect can be inferred simply from
measurements of the upwelling-integrated solar irradiance and the spectral
solar irradiance (e.g. Haywood et al., 2003). However, this does not
constitute radiative closure because the additional upwelling flux from the
aerosol layer is a convolution of the aerosol optical depth, the
backscattered fraction and the degree of absorption of the aerosol, and the
solutions are therefore non-unique. Among other studies, Haywood et al. (2011) and Cochrane et al. (2019) demonstrated that measurements of both the
upwelling and downwelling integrated irradiances are needed if a unique
solution relating the aerosol physical and optical properties unambiguously
to the upwelling and downwelling solar irradiances is to be achieved. In
cloudy skies, where the reflectance from clouds varies far more than the
reflectance from the well-characterised sea surface, it is even more
important to understand the accuracy and variability in the upwelling
spectral irradiances if radiative closure is to be achieved.
For downwelling irradiances, the agreement in the radiometric measurements
appears to be better under diffuse sunlight conditions than during direct
illumination conditions. This may be due to inaccuracies in the pitch and
roll correction for the SHIMS instrument, which requires an accurate
partitioning between the pitch- and roll-corrected direct irradiance and the
non-pitch- and non-roll-corrected diffuse irradiance (see Jones et al., 2018).
Other factors such as the directional sensitivity of the two instruments and
the non-perfect cosine response could also be factors in why there are more
significant differences between the measurements when the instruments are
subject to direct illumination. Nevertheless, given the need to apply an
adjustment to the SHIMS instrument calibration based on the BBR and
radiative transfer (Sect. S1 in the Supplement) and uncertainty estimates as high
as 10 %, the agreement in the spectral irradiances (within 2 % for all
cases) is gratifying. This suggests that data from the instruments can be
used for scientific purposes such as assessing the impact of aerosols on the
spectral irradiances.
For upwelling irradiances, which benefitted from a reliable red-dome Eppley
radiometer measurement (Sect. S1 in the Supplement), the agreement between the
measurements from SHIMS and SSFR is within 1 W m-2 (or 5 %).
The general agreement between the instrumentation lends confidence to the
measurements, and the uncertainties in the measurements are small enough to
suggest that radiative closure studies may be pursued using either the
instrumentation on the BAe-146 or P3 platforms.
Generally, intercomparison of radiation measurements made by the LASIC ARM
site was hampered by the frequent occurrence of orographically generated
cloud, which is a persistent feature over Ascension Island.
Conclusions
Central to the purpose of the overlapping field campaigns CLARIFY, ORACLES
and LASIC was to provide combined datasets with which to undertake process
studies and model evaluation work assessing the impact of biomass burning
aerosols on climate. These datasets are distributed in space, being close to
the coast of southern Africa, or in the far field, and in time, across 3
years, as well as from early or later in the biomass burning season. Broad
comparability between the measurements made during the CLARIFY, ORACLES and
LASIC field experiments has been demonstrated. This gives confidence in any
studies of the spatial and temporal evolution in parameters using combined
datasets.
Temperature, humidity and concentrations of CO were found to compare well
enough to be able to confidently categorise air masses by their pollution
state and air mass history. This is important when using data from multiple
regions, seasons and periods. There were differences in CO that would
benefit from further investigation. Black carbon, another pollution tracer,
compared well between CLARIFY and LASIC, but NASA data were compromised
during the intercomparison. Particle number concentrations, condensation
nuclei and the particle size distributions of submicron aerosols are
comparable between all three field campaigns. There are larger differences
between probes on a single platform than between two independent platforms,
suggesting that platform-specific aspects such as mounting location,
aircraft angle of attack and other specifics of installation do not
result in significant biases to the sampling of accumulation mode
aerosols.
Absorption coefficient measurements are comparable across all three
platforms, although magnitudes of σAp were low during the
airborne intercomparison. The wavelength dependence of absorption,
characterised by ÅAP, followed similar trends for both airborne
platforms and indicated an increasing absorption coefficient at shorter
visible wavelengths. Conversely, observations from the LASIC ARM site show a
reduction in absorption at shorter wavelengths. This may be a consequence of
limited sampling time or potentially size-dependent sampling. The low
absorption coefficient magnitude prevented study of the ω0, and
so caution must be exercised when combining data from multiple platforms.
Submicron measurements of σSP are similar between the FAAM
BAe-146 and the NASA P3, suggesting that derived values of ω0
can be trusted when larger amounts of material are present. LASIC and FAAM
showed that the scattering measurements at the ARM site were of much lower
magnitude than those on board the BAe-146 and that the comparison was worse
at the longer red wavelength.
Composition observations are in general agreement between ORACLES and
CLARIFY, leading to the conclusion that study of the evolution of the BBA
plume as it advects away from the coast is possible using a combined
dataset from both campaigns. The masses of chemical components at the LASIC
ARM site were much larger than those reported by CLARIFY, in contrast to
observations such as concentrations of condensation nuclei and black carbon
particles, which tended to be ∼20 % lower, and black carbon
mass concentrations, which were 10 % lower. The cause of the greater
masses recorded at the ARM site is unknown, and so caution is recommended
when interpreting these datasets.
Previous work has shown that the FAAM SHIMS radiometer requires a bias
correction to FAAM BBRs of ∼30 %. Once this is applied,
there is good agreement with the comparable measurements made by the P3 SSFR
instrument. Comparable observations of the aerosol PSDs permit radiometric
closure studies to be undertaken.
Observations of cloud particles were comparable between ORACLES and CLARIFY.
Further work is needed to characterise inlet systems on aircraft and at
ground-based facilities, including improvements in understanding airflow
around airframes, size-dependent particle transmission and
characterisations of the RH within sampling lines.
Key to acronyms.
Campaigns, facilities and organisations AEROCLO-SAAErosol, RadiatiOn, and CLouds in Southern AfricaARMThe DOE Atmospheric Radiation Monitoring programme operated by LASICBAe-146The FAAM large research aircraft operated by CLARIFYCLARIFYCLouds–Aerosol–Radiation Interaction and Forcing for Year 2017DMTDroplet Measurement Technologies, an instrument manufacturerDOEDepartment of EnergyFAAMFAAM Airborne Laboratory
Continued.
LASICLayered Atlantic Smoke and Interactions with CloudsNASANational Aeronautical and Space AgencyORACLESObseRvations of Aerosols above CLouds and their intEractionS (ORACLES)P3The NASA Lockheed P3 research aircraft operated by ORACLESSPECStratton Park Engineering CompanyInstruments 2DSA cloud and precipitation OAP manufactured by SPECACSMAerodyne Aerosol Chemical Speciation Monitor as installed at the ARM site (LASIC)AMSAerosol mass spectrometer as fitted to the FAAM BAe-146 (CLARIFY)HR-AMSHigh-resolution time-of-flight AMS as fitted to NASA P3 (ORACLES)BBRsBroadband radiometersBuck CR2A chilled-mirror hygrometerCAPS PMSSACavity-attenuated phase shift single-scattering albedo (ω0) monitorCDPCloud droplet probeCRDSCavity ring-down spectrometer optical extinction measurement, part of EXSCALABARCOMAThe NASA humidity and CO and O3 gas analyserCPCCondensation particle counterCVICounterflow virtual impactor inletDMADifferential mobility analyserEXSCALABARThe EXtinction SCattering and Absorption of Light for AirBorne Aerosol Research rack on board FAAM BAe-146HUMICAP®A ground-based humidity sensorNafion™A commercially available drying membraneOAPOptical array probePASPhotoacoustic spectrometer optical absorption measurement, part of EXSCALABAR.PCASPPassive cavity aerosol spectrometer probePCASP1A FAAM external PCASPPCASP2A FAAM external PCASP (primary instrument)PCASP3A FAAM internal PCASP, part of EXSCALABARPSAPRadiance Research tri-wavelength particle soot absorption photometerSDISolid diffuser inletSHIMSShortwave Hemispheric Irradiance Measurement System on board FAAM BAe-146SMPSScanning mobility particle sizerSP2The Single Particle Soot PhotometerSSFRSolar Spectral Flux Radiometer on board NASA P3THERMOCAP®A ground-based temperature sensorTDLTunable diode laserUHSASUltra-high-sensitivity aerosol probeWISPERThe NASA P3 hygrometer system comprising two sensors, “TOT1” and “TOT2”WVSS-IIWater vapour sensing system, a TDL hygrometer on board FAAM BAe-146Parameters BCRefractory black carbonBCnRefractory black carbon numberBCmRefractory black carbon massBBBiomass burningBBABiomass burning aerosolCCNCloud condensation nucleiCNCondensation nucleiCOCarbon monoxideCO2Carbon dioxideH2OWater vapourIRInfraredLWCLiquid water contentm/zMass–chargeNAAerosol particle number concentrationNCCloud particle number concentrationNIRNear-infrared
Continued.
OAOrganic aerosolPM1Aerosol particles smaller than 1 µmPM10Aerosol particles smaller than 10 µmPSDParticle size distribution (number)ReEffective radius of particle distributionRvMean volume radiusVISVisible (light)VSDVolume size distributionvmrHumidity volume mixing ratioÅAPAbsorption Ångström exponentÅEPExtinction Ångström exponentÅSPScattering Ångström exponentλWavelengthσAPOptical absorption coefficientσEPOptical extinction coefficientσSPOptical scattering coefficientτOptical depthω0Single-scattering albedoCodes AeroCalcCode to compute particle losses through plumbing (Baron, 2001)OASISOptical Array Shadow Imaging Software (Crosier et al., 2011; Taylor et al., 2016)PIKAParticle Integration by Key v.1.16 algorithm (DeCarlo et al., 2006)SQUIRRELSeQUential Igor data RetRiEvaL, v.1.60N (Allan et al., 2003, 2004) algorithmOther CECollection efficiencyD50Cut diameter of 50 % transmission efficiencyIEIonisation efficiencyODROrthogonal distance regressionPSLPolystyrene latex spheresRIERelative ionisation efficienciesSTPStandard temperature and pressureCode availability
Processing code for the FAAM core measurements suite is available from GitHub (Sproson et al., 2020).
Data availability
Airborne data for the CLARIFY campaign are available from the Centre for Environmental Data Analysis (Facility for Airborne Atmospheric Measurements et al., 2017) and for the ORACLES campaign from NASA Earth Science Project Office (ORACLES Science Team, 2020). The LASIC ground-based data sets are publicly available from the Atmospheric Radiation Measurement Climate Research Facility (Zuidema et al., 2017) with specialist data sets available for the following:
SP2 – https://iop.archive.arm.gov/arm-iop/2016/
(last access: 25 October 2022, Sedlacek, 2017),
CO – 10.5439/1046183 (Springston, 2018b),
CAPS PMSSA – https://adc.arm.gov/discovery/#/results/s::caps-ssa
(Onasch et al., 2015),
ACSM – 10.5439/1763029 (Zawadowicz and Howie, 2021).
The supplement related to this article is available online at: https://doi.org/10.5194/amt-15-6329-2022-supplement.
Author contributions
SJA, PAB, JH, JR, GMM, RW and PZ developed the concept and scope and designed the
flights and ARM site intercomparison data collection. PAB analysed the data
and wrote the initial manuscript with contributions from SJA, HuC, IC, AD,
SH, AJ, JL, GN, HP, YS, KS, JWT, HW and PZ. All authors performed instrument or
data work for one or more instruments or systems on one or more platforms.
All authors reviewed the manuscript.
Competing interests
At least one of the (co-)authors is a guest member of the editorial board of Atmospheric Measurement Techniques for 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)”. The peer-review process was guided by an independent editor, and the authors also have no other competing interests to declare.
Disclaimer
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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
Airborne data were obtained using the BAe-146-301 Atmospheric Research
Aircraft flown by Airtask Ltd and managed by FAAM Airborne Laboratory,
jointly operated by UKRI and the University of Leeds along with the Lockheed
P-3 Orion (N426NA) operated by NASA Goddard Space Flight Center's Wallops
Flight Facility Aircraft Office. Data were obtained from the Atmospheric
Radiation Measurement (ARM) user facility, a US Department of Energy (DOE)
Office of Science user facility managed by the Biological and Environmental
Research programme. We thank David Sproson (FAAM) for reprocessing the FAAM
BAe-146 datasets with the latest version of the decades-ppandas processing
codes.
We acknowledge the use of imagery from the NASA Worldview application
(https://worldview.earthdata.nasa.gov/, last access: 21 February 2022), part of the NASA Earth Observing
System Data and Information System (EOSDIS).
Financial support
Hugh Coe, Ian Crawford, James Haywood, Anthony Jones, Jonathan W. Taylor, Huihui Wu, Keith Bower and Michael Cotterell were supported through the CLARIFY-2017 Natural Environment Research Council large grant proposal (grant nos. NE/L013797/1, NE/L013584/1, and NE/P013406/1). Nicholas Davies was supported by a NERC/Met Office Industrial Case studentship (grant no. 640052003). James Haywood, Anothony Jones and Michael Cotterell were also supported by the Research Council of Norway via the projects AC/BC (grant no. 240372) and NetBC (grant no. 244141).
Paquita Zuidema and Jianhao Zhang were supported by the US Department of Energy, Office of Science (grant nos. DE-SC0018272 and DE-SC0021250), with Paquita Zuidema receiving further support from NASA EVS-2 ORACLES grant NNX15AF98G. Amie Dobracki was supported by grants DESC0018272 and NNX15AF98G. Sabrina Cochrane, Sebastian Schmidt and Hong Chen were supported by NASA (grant no. NNX15AF62G).
Robert Wood was supported by NASA (grant no. NNX15AF96G-S13). Siddhant Gupta and Greg M. McFarquhar were supported by NASA (grant nos. 80NSSC18K0222, NNX15AF93G and NNX16A018H). Maria A. Zawadowicz was supported by the US Department of Energy Atmospheric Systems Research (ASR) programme under contract DE-SC0012704 to Brookhaven National Laboratory.
Review statement
This paper was edited by Frank Eckardt and reviewed by two anonymous referees.
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