To help satellite retrieval of aerosols and studies of their radiative
effects, we demonstrate that daytime aerosol optical depth over low-level
clouds is similar to that in neighboring clear skies at the same heights.
Based on recent airborne lidar and sun photometer observations above the
southeast Atlantic, the mean aerosol optical depth (AOD) difference at 532 nm is between 0 and
-0.01, when comparing the cloudy and clear sides, each up to 20 km wide, of
cloud edges. The difference is not statistically significant according to a
paired t test. Systematic differences in the wavelength dependence of AOD
and in situ single scattering albedo are also minuscule. These results hold
regardless of the vertical distance between cloud top and aerosol layer
bottom. AOD aggregated over ∼2∘ grid boxes for each of
September 2016, August 2017 and October 2018 also shows little correlation
with the presence of low-level clouds. We posit that a satellite retrieval
artifact is entirely responsible for a previous finding of generally smaller
AOD over clouds (Chung et al., 2016), at least for the region and time of
our study. Our results also suggest that the same values can be assumed for
the intensive properties of free-tropospheric biomass-burning aerosol
regardless of whether clouds are present below.
Introduction
A significant amount of atmospheric particles are transported above liquid
water clouds on the global scale (Waquet et al., 2013). Aerosols above
clouds (AACs) may influence the climate in three ways: their light absorption
may be amplified by cloud reflection; the heating of the atmosphere due to
the absorption may stabilize the atmosphere; and the particles may
eventually subside, enter the underlying clouds, and alter their properties.
Estimates of the direct aerosol radiative effect alone see large intermodel
spread for areas with large aerosol optical depth (AOD) over widespread
clouds (Stier et al., 2013; Zuidema et al., 2016).
Since AACs are difficult to see from the ground or a ship, previous studies
have relied on satellite observations (see Tables 1 and 2 of Kacenelenbogen
et al., 2019). Among them is the paper by Chung et al. (2016) which used the level 2
products of Cloud-Aerosol LIdar with Orthogonal Polarization (CALIOP)
(Winker et al., 2009) to calculate the AOD above the maximum
low-cloud-top height in each grid cell in clear sky as well as the AOD above
low clouds on a global 2∘× 5∘ latitude–longitude
grid. Their results indicate that daytime 532 nm AOD above low clouds is
generally lower than that in clear sky at the same heights. The difference
is up to 0.04 over the southeastern Atlantic Ocean (see their Fig. 2).
As Chung et al. (2016) point out, it is conceivable that aerosol amounts
over cloud can be different from those in nearby clear sky. There are two
sets of potential reasons. The first concerns the effects of meteorology.
Large-scale circulation patterns paired with solar reflection from clouds on
aerosols could modify the horizontal and vertical extent of aerosols,
aerosol concentration and chemical composition. For example, the properties
of hygroscopic aerosols might vary if the relative humidity in clear skies
is somehow higher than above clouds. The second set of reasons pertain to
the case of aerosols in close proximity to clouds. That proximity has been
variously defined, e.g., less than 100 m in the vertical direction
(Costantino and Bréon, 2013) and less than 20 km in the horizontal
direction (Várnai and Marshak, 2018). Chung et al. (2016) note that
aerosols were shown to influence underlying cloud by indirect effects and
semidirect effects (Costantino and Bréon, 2010, 2013; Johnson et al.,
2004; Wilcox, 2010) and that these aerosol–cloud interactions and possibly
more might somehow affect the aerosol amount over cloud.
Chung et al. (2016) raise a bias in the CALIOP standard retrieval as another
possible explanation. The retrieval algorithm confines itself to distinct
aerosol layers whose signals are high enough compared to detector noise. The
detection threshold varies with the atmospheric features (e.g., aerosols,
high-altitude cirrus or boundary layer clouds), the horizontal averaging
required by CALIOP for detection, and (importantly) the background lighting
conditions (see Fig. 4 by Winker et al., 2009). If the signal-to-noise
(S/N) ratio of a layer is not high enough, no extinction is reported for the
portion of the aerosol profile; summing up the extinction produces a
low-biased AOD. Because the upward sunlight reflection adds to the
background noise, the AOD underestimate is likely more pronounced above
clouds than in clear skies. Chung et al. (2016) state that their results
“might simply be a result of systematic differences between the detection
thresholds in clear sky and above low bright clouds.” Layer detection and
other sources of uncertainty in the CALIPSO standard algorithm are also
discussed by Kacenelenbogen et al. (2014) and Liu et al. (2015).
The subject warrants further investigation, given the importance of AACs for
climate. An airborne experiment can help by providing direct measurements
that are subject to smaller uncertainty with finer spatial and temporal
resolution, albeit over limited ranges. The NASA
ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES) mission was carried out to
study key processes that determine the climate impacts of African
biomass-burning aerosols above the southeast Atlantic. Of the two deployed
aircraft, the NASA P3, equipped with in situ and remote-sensing instruments,
flew in the lower- to mid-troposphere, mostly in September 2016, August 2017
and October 2018. In September 2016 the NASA ER2 also flew, at about 20 km
altitude with downward-viewing sensors. Extensive stratocumulus clouds were
observed repeatedly throughout the mission; see a sample satellite image in
Sayer et al. (2019). Details of the ORACLES mission can be found in Redemann
et al. (2020), Zuidema et al. (2016) and Shinozuka et al. (2019).
The instrumentation relevant to the present paper is described in Sect. 2
along with sampling and statistical hypothesis testing methods. This is
followed by comparisons of AOD and other aerosol properties above the
height of cloud top between cloudy and clear skies (Sect. 3). Section 4 offers
discussion.
MethodsInstrumentation
The remote-sensing and in situ instruments used in this study are briefly
described below with references to full descriptions. Note that the
each measurement refers to a unique vertical range, as summarized in Table 1.
Properties and instruments used in this study and the altitudes
they refer to.
PropertySeptember 2016 August 2017 and October 2018aon the ER2 aircraft on the P3 aircraft InstrumentAltitudeInstrumentAltitudeCloud top height (CTH)HSRL-2limited to ≤3241 m in this studyHSRL-2no higher than 500 m below the P3 and ≤3241 mAerosol optical depth above cloud top height (AODct)HSRL-2from ∼50 m above the CTH to 14 km4STARfrom the P3 to top of atmosphere (TOA), when the P3 is 500–1500 m above CTHHSRL-2 and 4STARfrom ∼50 m above the CTH to TOA, except 0–1500 m below the P3, when the P3 is >1500 m above CTHExtinction coefficient, single scattering albedo, submicron nonrefractory organic mass, number concentration––nephelometer, PSAP, HR-ToF AMS and condensation particle counterat the P3 when the P3 is 500–1500 m above CTH
a 1 d in September 2017 and 2 d in September 2018 are also
included.The symbol “–” means data are not presented in this study. Observations were made from the P3 and away from
the ER2 for most cases.
The NASA Langley Research Center High Spectral Resolution Lidar (HSRL-2),
deployed from the ER2 in 2016 and from the P3 in 2017 and 2018, measures
calibrated, unattenuated backscatter and aerosol extinction profiles below
the instrument. The data are reported with 10 s intervals. The HSRL-2 S/N
ratio is higher than that of CALIOP, due to the much lower altitude and the
inverse square dependence of light intensity. In addition, by the use of a
second channel to assess aerosol attenuation, the HSRL technique (Shipley et
al., 1983) results in an accurate aerosol extinction product with no
assumptions about lidar ratio and also a more accurate backscatter product,
particularly in the lower atmosphere where attenuation by upper layers can
present difficulties for the spaceborne backscatter lidar. Differences in
algorithms are discussed in Sect. 4. Further details about the instrument,
calibration and uncertainty can be found in Hair et al. (2008), Rogers et
al. (2009) and Burton et al. (2018).
Our analysis utilizes the HSRL-2 standard products of cloud top height
(CTH), 532 nm particulate backscattering and 532 nm aerosol optical
thickness (Burton et al., 2012) in three ways. First, flight segments are
isolated using the CTH product (detailed in Sect. 2.2). Second, the bottom
and top heights of the smoke plumes are defined with a (somewhat arbitrarily
chosen) threshold backscattering coefficient at 0.25 Mm-1sr-1
after Shinozuka et al. (2019).
Third, we evaluate the 532 nm partial-column aerosol optical thickness from
below the aircraft down to ∼50m above the CTH (even for
columns without clouds; see Sect. 2.2). The ∼50m buffer is
designed to reduce the ambiguity associated with the transition at the cloud
top. The upper limit of the integral of extinction is 14 km altitude for the
2016 ER2 flights and a certain depth, 1500 m, for most flights, below the P3
altitude for 2017 and 2018 (Fig. 1). Profiles with possible influences of
mid- and high-level clouds are largely excluded from the product, but
isolated cases of thin clouds may remain.
AOD above cloud top height (AODct). See text and Table 1 for
details.
We also use partial-column AOD observed upward from the P3 with a
sun photometer (Fig. 1b, c). The Spectrometer for Sky-Scanning, Sun-Tracking
Atmospheric Research (4STAR) measures hyper-spectral direct solar beam.
Calculated AOD is reported at 1 Hz. Our analysis excludes data with possible
influences of clouds above the instrument. Further details on the instrument
as well as data acquisition, screening, calibration and reduction can be
found in Dunagan et al. (2013), Shinozuka et al. (2013) and LeBlanc et al. (2020).
For 2017 and 2018, we examine a combination of the 4STAR and HSRL-2 AODs, in
order to cover the free troposphere both upward and downward from the
aircraft that flew in it (Fig. 1b, c). The vertical coverage is compromised
by two limitations intrinsic to the lidar measurements. First, the CTH is
not sought within 500 m of the instrument (not to be confused with the
∼50m lower buffer for the extinction integral). This means
that the flight segments with clouds so close to the aircraft enter our
analysis only if the clouds extended as deep as to reach 500 m away from it.
This is at most a minor fraction of the data, as the fraction with the CTH
within 550 m of the P3 altitude is a mere 3 %. Second, because of the 1500 m upper buffer for the P3-borne HSRL-2 extinction integral, we only have
4STAR above-P3 AOD for the flight segments when the plane was 500–1500 m
above the CTH (Fig. 1b). We add the HSRL-2 AOD to the 4STAR AOD only for the
flight segments when the P3 was >1500m above the CTH (Fig. 1c).
For 2016, we examine the ER2-borne HSRL-2 AOD only, because with the lidar
above the troposphere, two of the missing layers can safely be ignored,
leaving the ∼50m lower buffer as the only missing layer
(Fig. 1a). We refer to all these AODs from the three campaigns collectively
as AODct (see Table 1). The wavelength dependence expressed as Ångström
exponent is calculated for 10 s periods with AODct at 355 and 532 nm
both exceeding 0.1.
In situ aerosol instruments operated from the P3 include a nephelometer (TSI
model 3563) and a particle soot absorption photometer (PSAP, Radiance
Research, three-wavelength version), which measure particulate light scattering
and absorption, respectively. After adjustments are made for factors such as
angular truncations (Anderson and Ogren, 1998) and filter interference
(Virkkula, 2010) for each wavelength, extinction coefficient and single
scattering albedo at 550 nm are derived for an instrument relative humidity
(RH) that is typically below 40 %. See Pistone et al. (2019) and Shinozuka
et al. (2019) for more details. The nonrefractory masses of submicron
particles were measured by a time-of-flight aerosol mass spectrometer
(Aerodyne Inc, HR-ToF AMS; DeCarlo et al., 2006). A condensation particle
counter (TSI model 3010, with ΔT set to 22 ∘C) measured
the number concentration of particles larger than about 10 nm. These in situ
properties refer to the air immediately outside the P3 aircraft not a
vertical column. Only the in situ measurements in 2017 and 2018 at 500–1500 m above the CTH are used in this study (Fig. 1b).
Sampling
Two methods are employed for selecting subsets of the observations for
analysis. In the first (Sect. 2.2.1), we bundle data from areas hundreds of
kilometers wide for each of the three campaigns, in a manner as similar to
the CALIOP-based study (Chung et al., 2016) as the airborne measurements
allow. In the second method (Sect. 2.2.2), we pair cloudy and clear skies
with more stringent spatiotemporal criteria to isolate the impact of
finer-scale phenomena. Note that both methods ignore time periods for which
the 532 nm backscattering product (from which the CTH product is derived) is
masked at all altitudes, as well as transit flights into and out of the
study area. Cases are also excluded where the CTH exceeds 3241 m. This is to
be consistent with the study by Chung et al. (2016), which refers to clouds
at 680 hPa or higher pressure, although we find similar results with or
without this restriction.
The flight paths of ORACLES. The boxes for mesoscale monthly-mean
sampling are superimposed.
Mesoscale monthly-mean sampling
This method separates profiles measured in the three campaigns into two
groups: those concurrent with a presence of low-level clouds as reported by
the HSRL-2 and those concurrent with an absence of any cloud detected by
HSRL-2 in the column. The groups are each aggregated into grid boxes
approximately 2∘ by 2∘, as shown in Fig. 2. This grid is adapted
from Shinozuka et al. (2019) but with additional boxes for the São
Tomé-based 2017 and 2018 campaigns. In total, 109 and 39 h of
flight segments are selected for the cloudy and clear groups, respectively,
including minor double-counting where boxes overlap.
Examples of local-scale near-synchronous sampling, based on the
HSRL-2 cloud top height (CTH) product. (a) In this subset of the ER2 flight
on 12 September 2016, a cloud edge is found at 11:57:56 (denoted by 0 s and
0 km). The cloudy and clear sides, each with horizontal separation of 4–12 km
measured from cloud edge, are marked by red and orange lines, respectively.
The HSRL-2 AOD profiles are given for altitudes from ∼50m
above the clouds (as in Fig. 1a). (b) With the P3 500–1500 m above the CTH
(Fig. 1b), as is the case with this example from 5 October 2018, we use
4STAR AOD only. The 4STAR AOD is indicated at the P3 altitudes just above
2000 m but refers to all altitudes above them. (c) With the P3 aircraft
>1500m above the CTH (Fig. 1c), as is the case for this example
from 12 August 2017, the 4STAR AOD, indicated at the P3 altitudes just
under 5000 m, is added to the HSRL-2 AOD at ∼50m above the
CTH. The upper limit of the integral of extinction is 1500 m below the P3
altitude.
The arithmetic mean of the CTH of the cloudy group is calculated for each
day for each box, and 50 m above it is set as the lowest altitude for
computing AODct for each 10 s period (Sect. 2.1). Then the arithmetic
mean and standard deviation are calculated for the AODct, as well as
other measurements (Sect. 2.1, Table 1), for each group and each box. We
exclude the boxes with fewer than 10 counts of 10 s averages and the
time periods with mid- and high-level clouds and operational issues. And 49 and 26 h of the AODct measurements enter
the analysis for cloudy and clear-sky groups, respectively.
Local-scale near-synchronous sampling
This method identifies cloud edges and demarcates the cloudy side and clear
side of each edge based on the time series of the CTH detected by HSRL-2
for level flight legs only. Cloud edges are defined by the points in time
when a cloud is detected in a profile adjacent to a profile with no cloud
detection.
A clear sky and a cloud are represented by the time period of a certain
length, 60 s in the example illustrated in Fig. 3a, preceding each edge and
the same length following it. To ensure that clear skies and clouds are not
interrupted for the length, we exclude edges for which another one is found
within the length. The longer the length, the smaller the number of
cloudy–clear pairs, because longer continuous clouds and clear skies are
rarer. Furthermore, we set another length, say 20 s, to exclude immediately
before and after the edge in order to reduce ambiguity associated with a
gradual transition from cloud droplets to unactivated particles, the
so-called twilight zone (Koren et al., 2007; Schwarz et al., 2017;
Várnai and Marshak, 2018). We convert the temporal dimensions into
horizontal ones using the mean true horizontal aircraft speed, which are 200 ms-1
for the ER2 (Fig. 3a) and 140 ms-1 for the P3 (Fig. 3b and c).
While Fig. 3a has one set of maximum and minimum limits of separation noted
as an example, we alter them in order to assess scale dependence and
sampling error as much as our airborne data permit. The way the edges are
identified ensures that a measurement cannot be counted more than twice for
a given range of separation. A measurement can, however, enter multiple
ranges of separation. For example, a measurement 4–6 km away from a cloud
edge enters the ranges of 0–6, 2–6, 2–12, 4–12, 4–20 km, and so on. In
total, 5.0 h of horizontal flight are selected, including the
double-counting for a given range but excluding the multiple-counting over
multiple ranges. Exactly half of them are over clouds. Note that these
expressions of separation are only notional; we discuss this in Sect. 4.
As with the mesoscale monthly-mean sampling, we take the arithmetic mean of
the CTH of the cloudy side and add 50 m (red lines in Fig. 3). The height is
extended to the adjacent clear sky (orange lines) for the calculation of
AODct (Sect. 2.1). The in situ measurements (Sect. 2.1, Table 1) are
each averaged over the cloudy sides and over the clear sides. Cases where
aerosol measurements are unavailable for 33 % or more of the time period,
e.g., due to calibration or operation problems, are excluded. This
makes the number of cloudy–clear pairs vary from property to property for a
given range of separation. In total, 3.8 h of AODct measurements
enter the analysis.
Statistical hypothesis testing
We employ the paired t test, also called paired-samples t test or dependent
t test, to determine whether the mean difference in each property, x (e.g.,
AODct), between the presence and absence of low-level clouds is
statistically consistent with the null hypothesis of zero difference. The
procedure entails calculating the t statistic: the ratio of the mean
cloudy–clear differences to their standard error, E.
1t=Δx‾/E,2E=σ/N.
Here the standard error is the standard deviation computed for N-1 degrees of
freedom, σ, divided by the square root of N, where N is the number of
sample pairs. Note that the standard deviation is close to the
root-mean-square deviation (RMSD) for small absolute mean difference unless
N is smaller than five.
For the calculated t statistic, the two-tailed p value is looked up. Small p values are associated with large t statistics and hence generally large mean
differences relative to RMSD. If the p value is smaller than 0.05, we reject
the null hypothesis. If it is greater, we do not.
The procedure makes several assumptions. One is independence of the
differences. Synoptic- and mesoscale phenomena prevalent throughout ORACLES
(e.g., subsidence and anticyclones) reduce the independence of the samples.
The low day-to-day meteorological variability and repeated flight paths
might mean that the same aerosol–cloud conditions were sampled day after
day. It is unclear whether this would reduce the independence of the
cloudy–clear differences – a potential, seemingly untestable, caveat for the
mesoscale monthly-mean sampling (Sect. 2.2.1). In the local scale the
exclusion of contiguous cloud edges (Sect. 2.2.2) should attain a high level
of independence from one another. The procedure also assumes continuous (not
discrete), approximately normally distributed data that are free of outliers.
The mesoscale monthly-mean samples of the AOD above cloud top
height. Each marker represents the mean over a box shown in Fig. 2. The bar
represents the ±1 standard deviation range. The marker size is
proportional to the number (n) of 10 s measurements, the fewer of the cloudy and clear groups, for each combination of
box and month. N refers to the
number of monthly box means with n≥10 in both cloudy and clear cases.
Results
The mesoscale monthly-mean method finds little systematic difference in 532 nm AODct (Fig. 4). Most markers lie near the 1:1 line. The mean
difference, an indicator of systematic differences, is +0.01. This is only
+9 % of the RMSD, an indicator of the total (random and systematic)
variability. The p value from the paired t test is 0.54. Thus, the AOD above
low-level clouds is not significantly different from that at the same
heights above nearby clear skies in this scale. The p value is also greater
than 0.05 for log10 of AODct; this is something we tested just to confirm
that our conclusions do not depend on the choice of linear or log scale. The
same goes for the Ångström exponent and in situ aerosol properties (Table 2,
see the rows labeled “Box means”). The only exception is the organic mass
with a p value just under 0.05 (before rounding).
(a) The local-scale near-synchronous samples of the AOD above
cloud top height. Each marker represents the mean over the cloudy and clear
sides of a cloud edge, with each 2–6 km from the edge. The bars indicate the
standard deviation of the measurements in each side, with almost all of them too
short to be discernible. (b) Same as (a) except for the horizontal
separation of 4–12 km.
The mean values and the statistics on the cloudy–clear differences.
a Either the separation from cloud edges or box means.
The local-scale near-synchronous method finds virtually the same results.
The AODct is compared in Fig. 5a for 2–6 km separation. The time period
corresponds to approximately 10–30 s temporal range on the ER2 (13 data
points from the 2016 campaign) and 14–43 s at the average P3 speed (53 from
2017 and 2018). All data points lie near the 1:1 line. The mean difference,
-0.002, is only -21 % of the RMSD for 2–6 km separation. The p value is
0.08.
(a) The mean and root-mean-square deviations of the AOD above
cloud top between the cloudy and clear sides of cloud edges. Each side is
defined by the horizontal separation from cloud edge. The maximum separation
(e.g., 12 km in Fig. 3) is indicated on the x axis. Each line represents the
minimum temporal separation (e.g., 4 km in Fig. 3) of 0, 2, 4, …, 18 km in descending order of line length. (b) The p values determined
through the paired t test. (c) The number of cloudy–clear pairs.
We run the same calculation for other combinations of minimum and maximum
separation. Figure 6 shows the resulting statistics. The mean difference for
2–6 km separation, for example, is represented in Fig. 6a at a maximum
separation (x axis) of 6 km by the solid orange line that starts after the
minimum separation of 2 km. This line also shows that the mean difference is
-0.01 if the maximum separation is set to 20 km while keeping the minimum at
2 km. The longest blue line represents the calculations for zero minimum
separation (i.e., with the twilight zone included). All other solid lines
represent the results with greater minimum separation. For example, the
green line that is missing data up to 4 km indicates that the mean
difference is closer to -0.01 at 12 km, as shown in Fig. 5b.
For the separation up to 20 km, the mean difference is mostly between 0 and
-0.01. The p value, shown in Fig. 6b, is below 0.05 for only a handful of
the ranges of separation; most of which, with minimum separation of 0–2 km,
are subject to potential ambiguity associated with the so-called twilight
zone (Sect. 2.2.2). Given that a p value of 0.05 simply means that there is
a 1 in 20 chance that the null hypothesis is correct, we expect some low
p values just by chance as we conduct many comparisons. The scarcity of low
p values is also evident for log10 of AODct, the Ångström exponent
and in situ aerosol properties including the organic mass (Table 2). Large p values are also found for the ER2- and P3-borne measurements separately and
for the 4STAR and the HSRL-2 AOD separately for 2017 and 2018.
Discussion and conclusions
Virtually no systematic differences in aerosol properties are found between
the air above low-level clouds and that above nearby clear areas in ORACLES
daytime airborne measurements. The finding holds for a range (0–20 km) of
distances between, and expanses of, the two air masses. Note that the
temporal and horizontal dimensions associated with the local-scale
near-synchronous sampling must be collectively overestimated, because the
aircraft may have been running parallel to cloud edge. There is no easy way
to know how far from the nearest cloud edge the airplane was in reality.
Images from cameras on the plane and satellites may give some context. But
we stop short of examining them, due to the perceived difficulty in unifying
the definition of cloud edges between the cameras and the lidar, among other
image processing issues. Although we do not know what the real distances and
expanses are, that probably does not matter for the region and season of our
study, judging by the consistently large p values across the notional
distances and expanses. The mesoscale monthly-average sampling, relying on a
larger dataset, provides consistent results. We note that this conclusion may
or may not apply to environments elsewhere, especially those with less
uniform clouds.
Our analysis does not support aerosol–cloud interactions, circulation
patterns or anything else as a cause for a significant systematic difference
in aerosol amounts; this is simply because such a difference is not evident. The
lack of obvious sensitivity to the smoke–cloud gap height, indicated by
marker color in Fig. 5, is consistent with this conclusion. The smoke bottom
height minus the mean CTH gives an estimate of whether aerosols may be
physically in contact with clouds and therefore if there is a chance of wet
removal or cloud processing. Our analysis does not detect any sign of local
aerosol removal by the underlying clouds.
An important difference between the present analysis and the CALIOP-based
one (Chung et al., 2016), apart from the spatiotemporal range and
resolution, is that the HSRL algorithm does not use any explicit layer
detection (Hair et al., 2008). The return signal in the molecular signal
provides a measure of the aerosol attenuation and extinction. A very tenuous
aerosol layer still produces a reported extinction with a reported error
bar. If the aerosol extinction is very small, the error bar may exceed the
retrieved value, but there is no cutoff at small values that produces the
kind of bias one gets from a detection threshold. Furthermore, the S/N ratio
is higher than that of CALIOP, and no assumptions about lidar ratio are made,
as explained in Sect. 2.1.
We posit that the systematic differences between above-cloud and clear-sky AODs shown in Chung et al. (2016) are solely a CALIOP retrieval artifact, at least
for the ORACLES region and season. As described in Sect. 1, the CALIOP
standard algorithm has a detection bias that leads to greater AOD
underestimates over clouds than in clear skies by day due to upward sunlight
reflection. The authors emphasize that this bias might explain their
results, pointing to a day–night contrast as evidence: “a corresponding
difference cannot be seen in the ΔAODct derived from
nighttime retrievals [which are free of sunlight reflection]”. The present
study corroborates this hypothesis, by rejecting the other possible
explanations related to aerosol amounts.
We should note that the detection bias due to a low S/N ratio is not the
only known source of error in the daytime CALIOP standard AOD product. The
error can also originate from a misclassified aerosol type and, hence, an
incorrectly assumed lidar ratio in the CALIOP algorithm. Such an aerosol
misclassification can either over- or underestimate CALIOP AOD, unlike an
undetected aerosol layer. Misclassification and low S/N ratio, taken
together, explain the absence of a significant bias between CALIOP and
HSRL-1 above-cloud AODs in a low aerosol-above-cloud environment such as
over North America in Kacenelenbogen et al. (2014). On the other hand,
Liu et al. (2015) describe a CALIOP standard daytime AOD underestimate above
clouds over two regions of high above-cloud AODs. While both
misclassification and low S/N ratio are at play, Liu et al. (2015) mainly
explain the CALIOP above-cloud AOD underestimate by a low S/N ratio
(especially when solar light is reflected on the underlying cloud) in the
case of smoke in southeast Atlantic and an underestimate of the lidar
ratio in the case of Saharan dust (see their Table 2).
In Chung et al. (2016), the lower daytime CALIOP AOD above clouds can be
explained mainly by CALIOP's low S/N ratio as there is no reason to believe
that CALIOP would show a different classification bias above clouds compared
to nearby clear skies. The depolarization ratio method by Hu et al. (2007)
retrieves above-cloud AOD from CALIOP without a layer detection algorithm.
This method may lead to a different result from Chung et al. (2016). A
future study based on the Hu et al. (2007) method and extended to the globe
as in Kacenelenbogen et al. (2019) will also address environments under a
wider variety of synoptic- and mesoscale conditions that produce specific
opaque water clouds.
Going back to the present aircraft-based study, the absence of systematic
differences is good news, because satellite retrievals and studies of
radiative effects do not need to treat these two conditions as different.
Our results on AODct justify, for example, temporal and horizontal
extrapolation of above-cloud AOD to adjacent clear skies and attribution of
the difference from full-column AOD to the planetary boundary layer. Our
results on the aerosol intensive properties suggest that a single set of
aerosol models can be used for the aerosols in the free troposphere
regardless of whether clouds exist below, which may better
characterize the underlying clouds and the radiative effects (Matus
et al., 2015; Meyer et al., 2015). It seems reasonable to use aerosol
properties retrieved in clear skies for estimating the direct radiative
effects of aerosols above nearby clouds, as in Kacenelenbogen et al. (2019).
But challenges remain. Random variability in AOD and other aerosol
properties is significant, as indicated by RMSD in the present study and
quantified for smoke elsewhere (Shinozuka and Redemann, 2011). It may be
problematic to assume the same values for intensive properties for reasons
not investigated here, e.g., form of combustion, degree of aerosol
aging and influence of the boundary layer. These may be tackled more
effectively by combining sensors of various capabilities with improved
spatiotemporal resolution and retrieval algorithms (National Academies of
Sciences, Engineering, and Medicine, 2018). These improved satellite
observations of aerosol properties in clear skies and above clouds are
urgently needed to reduce the uncertainty in total aerosol radiative
forcing. For this, we are looking forward to the next generation of
spaceborne lidars, radars, microwave radiometers, polarimeters and
spectrometers such as the ones that will address joint Aerosol and Cloud,
Convection and Precipitation (ACCP) science goals and objectives
(https://science.nasa.gov/earth-science/decadal-accp, last access: 31 August 2020).
Data availability
The P3 and ER2 observational data (ORACLES Science Team, 2020a, b,
c) are available through
10.5067/Suborbital/ORACLES/ER2/2016_V2,
10.5067/Suborbital/ORACLES/P3/2017_V2 and
10.5067/Suborbital/ORACLES/P3/2018_V2.
Author contributions
All authors participated in the investigation during the ORACLES intensive
observation periods. In addition, MSK led conceptualization, funding
acquisition, methodology, project administration and supervision. YS led
data curation, formal analysis, software and validation, and wrote the
original draft. YS and MSK contributed figures. All but CJF reviewed
and edited the article.
Competing interests
The authors declare that they have no conflict of interest.
Special issue statement
This article is part of the special issue “New observations and related modelling studies of the aerosol–cloud–climate system in the Southeast Atlantic and southern Africa regions (ACP/AMT inter-journal SI)”. It is not associated with a conference.
Acknowledgements
We would like to thank Eric Wilcox and one anonymous referee for reading the
article and providing valuable comments. In addition, we thank Tamás Várnai and Sasha Marshak for discussion. We would like to thank the
personnel and crews of NASA P3 and ER2 for their help in collecting the
datasets.
Financial support
ORACLES is funded by NASA Earth Venture Suborbital-2 (grant no. NNH13ZDA001N-EVS2).
Review statement
This paper was edited by Paola Formenti and reviewed by Eric Wilcox and one anonymous referee.
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