The interactions between aerosols and clouds are among the least
understood climatic processes and were studied over Ascension Island.
A ground-based UV polarization lidar was deployed on Ascension Island, which
is located in the stratocumulus-to-cumulus transition zone of the
southeastern Atlantic Ocean, to infer cloud droplet sizes and droplet
number density near the cloud base of marine boundary layer cumulus
clouds. The aerosol–cloud interaction (ACI) due to the presence of
smoke from the African continent was determined during the monsoonal
dry season. In September 2016, a cloud droplet number density ACIN
of 0.3 ± 0.21 and a cloud effective radius
ACIr of 0.18 ± 0.06 were found, due to the presence of smoke in
and under the clouds. Smaller droplets near the cloud base makes them
more susceptible to evaporation, and smoke in the marine boundary layer
over the southeastern Atlantic Ocean will likely accelerate the
stratocumulus-to-cumulus transition. The lidar retrievals were tested
against more traditional radar–radiometer measurements and shown to be
robust and at least as accurate as the lidar–radiometer measurements.
The lidar estimates of the cloud effective radius are consistent with
previous studies of cloud base droplet sizes. The lidar has the large
advantage of retrieving both cloud and aerosol properties using a
single instrument.
Koninklijke Hollandsche Maatschappij der WetenschappenPieter Langerhuizen Lambertuszoon FondsTechnische Universiteit Delftn/aWageningen University and Researchn/aIntroduction
The importance of low-level marine boundary layer (MBL) clouds for
the earth's radiative energy has long been recognized. Their high albedo
(30 %–40 %) over a dark ocean reduces the flux of solar radiation into
the ocean, while they contribute only slightly to the downward thermal
radiation, due to their low altitude (and thus high temperature)
inside the MBL . An estimated 4 % increase in MBL
cloud cover could offset the warming due to a doubling of CO2. Aerosols are expected to modulate the low-level
cloud cover through an aerosol-induced reduction in precipitation
or change the cloud short-wave albedo
through an increase in cloud condensation nuclei (CCN)
. The increase in CCN could lead to an
increase in cloud droplet number density and a decrease in cloud
droplet size, provided that the moisture content is constant. This
effect is known as the first aerosol indirect effect. Additionally,
the absorption of short-wave radiation by aerosols will locally heat
the atmosphere and may modulate cloud properties by enhancing
evaporation (e.g. ) or changes in
thermodynamic stability.
In the subtropics, extensive stratocumulus cloud decks form over the
pool of cold water created by upwelling ocean currents west of the
continents. The descending branch of the Hadley circulation in the
subtropics creates a strong temperature inversion at the top of the
MBL, which the stratocumulus decks are generally unable to penetrate.
The stratocumulus decks are maintained by radiative cooling at the top
of the MBL. This creates a moist, well-mixed layer over a cold ocean
surface. Trade winds transport this system northwest along a gradient
in sea surface temperature towards the warmer Equator, and a
transition to cumulus clouds is observed, driven by increased
convection from the warmer underlying surface. When the cumulus clouds
penetrate the inversion and entrain warm, dry air from the free
troposphere, the stratocumulus cloud deck breaks up and gradually
dissipates (e.g. ). This
generally accepted thermodynamic theory of stratocumulus-to-cumulus
transition (SCT) observed in the subtropical oceans is complicated
when precipitation or the presence of aerosols are
taken into account .
Aerosols have several reported competing effects on the SCT duration,
depending on the vertical and horizontal distribution of the aerosols,
age and composition of the aerosols, etc. Over Africa, smoke is
injected into the atmosphere during the dry season of the monsoon,
which is July–October in southern Africa
(e.g. ). The vertical distribution of the
smoke changes through the season, from a mean altitude in the MBL in
July to the free troposphere in October, leading to an amplified
low-cloud seasonal cycle . The smoke is transported over
the southeastern Atlantic Ocean (SEAO) in the free troposphere under
influence of the anticyclic circulation over Africa and the southern African easterly jet . Close
to the continent, the smoke in the free troposphere is found well
above the temperature inversion, separated from the cloud top, while
further out over the ocean it is more often mixed with the cloud top
after several days of transport following the subsiding large-scale
circulation. Therefore, near the continent, the smoke in the free
troposphere was found to delay the SCT by strengthening the
temperature inversion at the top of the MBL during the day when smoke
absorbs solar radiation and heats the atmosphere locally. The stronger
inversion results in thicker stratocumulus clouds . Further from the continent, smoke was found entrained
in the cloud layer , changing the
cloud droplet number density and decreasing the
low-level cloud cover , due to a weakening of the
temperature inversion and evaporation of smaller cloud droplets
. The radiative heating by the smoke
in the free troposphere influences the large-scale circulation itself
by reducing the subsidence, leading to lower temperatures and
increased moistening at the plume top .
For precipitating clouds the effects are much more complicated
(e.g. ), and therefore precipitating clouds are not
considered here.
Inside the MBL aerosols are typically mixtures of sea salt and smoke
from the African continent during the biomass burning season. The
composition of the aerosol mixtures changes during its residence
inside the MBL, due to processing inside clouds, interaction with air
and absorption of sunlight . The short-wave radiation
absorption by smoke during the day changes the diurnal thermodynamics
of the MBL .
In this paper, a method is explored to study aerosol–cloud
interactions of smoke around the base of the clouds around Ascension
Island. Here, we focus on the base of a low-level broken cloud deck in
the SEAO following the metrics specified in for
changes in the cloud droplet effective radius Reff and cloud droplet
number density, as a function of changes in aerosol optical thickness
τaer or aerosol extinction, as derived using one specific
instrument, a UV polarization lidar.
Such an instrument was located on Ascension Island during a month in
2016 and a month in 2017 during the dry season in Africa. The
UV lidar was part of the measurement campaign CLARIFY-2017
Clouds and Aerosol Radiative Impacts and Forcing;, partnering with several ground-based and aircraft
campaigns described in Sect. . The
measurements in 2017 were affected by alignment problems, which
resulted in a lower signal-to-noise ratio (SNR) compared to the 2016
measurements. Therefore, the measurements from 2016 are used mainly to
show the aerosol–cloud interactions (Sect. ). The
consistency of the lidar measurements was investigated through comparison
with the abundant additional campaign measurements both in 2016 and
2017, as described in Sect. .
Both aerosol properties from the aerosol layers and cloud properties
from the cloud deck were derived from the lidar data, using a
technique to infer cloud parameters based on polarization change due
to multiple scattering near the cloud base . In this
set-up, only one instrument is needed to study the impact of aerosols
on cloud albedo by relating the aerosol number density to the
cloud droplet number density . The details of
the retrievals of the aerosol and cloud properties are described in
Appendix . The lidar beam will not penetrate deep into
the cloud layer due to the large scattering cross section of the cloud
droplets in the UV. Therefore, the cloud measurement results are
valid near the cloud base. In this study we relate the cloud
properties to an altitude of 100 m above the cloud base height (see
Sect. ).
Map of Ascension
Island, showing the topography and the location of the UV lidar on
Wideawake airfield and the ARM main site. The distance between the
sites is 6.3 km. Georgetown is the island's main settlement.
Measurement campaign
From 3–29 September 2016 and from 15 August to 9 September 2017 the KNMI UV polarization lidar, normally located in Cabauw, the
Netherlands, was relocated to Ascension Island, a remote volcanic
island in the tropical Atlantic Ocean (8∘ S, 14∘ W).
Ascension Island is located 1600 km from the African coast and 2250 km
from the Brazilian coast. Its climate is a tropical desert, with
temperatures ranging from 22 to 31 ∘C and a low annual
rainfall at an average of 142 mm , with the peak rainfall
occurring in April. Ascension Island lies at the terminating stage of
the SEAO SCT, with clouds capping the boundary layer at an altitude
of around 1–2 km. The prevailing trade winds in the
boundary layer are from the southeast and mostly
invariant. Above the boundary layer (>1200 m above sea level) the
wind is coming from the equatorial regions and frequently loaded with
suspended particles like smoke from African vegetation fires or desert
dust .
The Ascension Island Initiative (ASCII) was aimed at identifying
microphysical properties of marine low-level clouds in the
presence of aerosols . During the
same time various other measurement campaigns were operated on and
around Ascension Island, providing a myriad of complementary measurements.
The ground-based campaign LASIC Layered Atlantic Smoke Interactions with Clouds; operated a
fully equipped ARM (Atmospheric Radiation Measurement) research facility in 2016 and 2017, while
airborne measurements were provided in 2017 by
CLARIFY-2017 and in 2016, 2017 and 2018 by
ORACLES ObseRvations of Aerosols above CLouds and their intEractionS;. On the African continent, in situ and
airborne measurements of the smoke near the source were provided by
the AEROCLO-sA (Aerosol Radiation and Clouds in southern Africa) campaign in Namibia .
Figure shows the main locations of the instruments
used in this paper during the campaigns. The UV lidar was located on
the southwestern side of the island throughout the 2016 and 2017
campaigns at Wideawake airfield, at 79 m above sea level. For all of
2016 and 2017, the ARM research facility was located on the southern
slope of Green Mountain, at 365 m altitude and about 6.3 km from
Wideawake airfield. This location, south of the 859 m peak of the
volcanic island, ensured that pristine oceanic air would be sampled
during the prevailing wind direction, which is east-southeast of the
site. Radiosondes were launched from the airfield four times daily.
Lidar measurements
Lidar measurements have a long history of retrieving aerosol
extinction and backscatter profiles in clear-sky scenes
(e.g. ). In aerosol conditions, the lidar signal
is determined by single-scattering events. In clouds, multiple
scattering must be considered. The occurrence of multiple scattering
also has implications for the polarization state of the lidar signal.
Since cloud droplets are spherical, under single-scattering
conditions, the lidar return signal retains its polarization state.
In clouds, multiple scattering becomes more and more important as the
beam penetrates the cloud base and the lidar beam becomes
increasingly depolarized. On Ascension Island, lidar measurements were
performed to study both aerosol and cloud properties, using a
commercial Leosphere ALS-450 lidar operating at 355 nm, with separate
parallel and perpendicular channels. The data were acquired with a
vertical resolution of 15 m and a temporal resolution of about 30 s.
The field of view of the lidar was found to be between 0.5 and 1.5 mrad. The retrieval error in 2016 was 19.75 %, and in 2017 it was 39.05 %, due
to the calibration, retrieval and measurement errors. In 2017,
instrument internal misalignment (likely incurred during transport)
resulted in a lower SNR and uncertain calibration. Therefore, 2016
data are used in this paper, except where noted. The lidar was
operational 24 h d-1 for almost the entire period of the campaign,
except from 24 to 27 September 2016, due to power cuts on the airfield.
Details about the calibration and the campaign can be found in
, and .
The parallel attenuated backscatter from the lidar on 6 September 2016. The red boxes show examples of selected data: (a) a cloud
with varying cloud base and double cloud layers, not appropriate for
analysis; (b) an appropriate selection of clear sky; and (c) an appropriate
selection of a cloud. Please note that the date format in this figure is year month day.
An example of the type of both cloudy and clear-sky observation
selected for analysis is presented in Fig. .
The skies over Ascension Island are typically defined by broken low-level
warm clouds interspersed with clear spells. The lidar measurements
were used to estimate the aerosol and cloud properties during various
circumstances, detailed below. Due to the strong background light from
the overhead sun, the ability to observe aerosols was much better at
night or when no clouds were present.
Aerosol optical thickness
Using cloud-free lidar observations, the daily averaged aerosol optical thickness (AOT)
retrieved from the lidar during the 2016 campaign is shown in
Fig. and compared to AErosol RObotic NETwork
(AERONET) measurements from the station located on Ascension Island at
the ARM main site. AERONET offers quality-assured, cloud-screened
automated direct sun measurements from ground-based, sun-tracking
sun photometers every 15 min at eight wavelengths . The
measurements at 340 nm were used here. The AERONET AOT data at this
wavelength have an uncertainty of 0.021, due to atmospheric pressure
variations assuming a 3 % maximum deviation from the mean surface
pressure . The uncertainty in the lidar retrieval, taking
into account the systematic error arising from the definition of the
extinction-to-backscatter ratios and the random error due to the
definition of the normalization height, was estimated to be about 11 %
.
Aerosol optical thickness retrievals from AERONET at 340 nm
compared to the retrieval from the UV lidar at 355 nm. (a) The daily averaged AERONET AOT (purple dots) during the 2016
campaign and the AOT from the lidar (black triangles), with black error
bars showing the standard deviation. The retrieval uncertainties were
0.021 for AERONET and 11 % for the lidar data. (b) A scatterplot of the measurements on the left, with black
bars showing the variances and red bars showing the retrieval
errors. Pearson's correlation coefficient was 0.761. The dashed line is the
1:1 line, and the full black line is a linear least-squares
fit with a slope of 1.369 and offset of -0.054. Please note that the date format in this figure is month day year.
Example back trajectories during the 2016 ASCII measurement
campaign on 7, 12, 15 and 23 September 2016. All trajectories were run for
240 h and ended over Ascension Island at different heights, as shown by
the altitude (in m). The figures show the stable MBL
east-southeast flow and the advection of air from the African
continent, except on 7 September 2016. GDAS: Global Data Assimilation System. Please note that the date format in this figure is day month year.
Daily averaged retrievals were compared for cloud-free periods for
each instrument. Since the instruments were not at the same position,
the cloud-free periods can differ. However, the AOT distribution is
assumed to be spatially consistent on the spatial scale of around 6 km. The comparison between the AERONET and lidar retrieved AOT is
good, with a correlation coefficient of 0.76.
The daily averaged AOT measurements show low aerosol conditions during
the beginning of the campaign until 11 September 2016 and increasing
values until 17 September 2016. After 17 September the values decrease, although
not to the same very low values as in the beginning of the month, and
there are again higher values towards the end. On 25 and 26 September 2016,
AERONET shows AOT values up to about 0.9, but unfortunately the lidar
was not operational on those days. These values are consistent with
500 nm AERONET results, shown by . AOT at 500 nm
peaked in August 2016 and returned to low background values in the
beginning of September 2016, as does the AOT at 340 nm. The increase
in AOT over Ascension Island from 14–17 and 23–26 September 2016
is consistent with the increase in strength of the southern African
easterly jet, which develops from being weak in the beginning of September 2016
to being strong at the end of the month . This promoted the
advection of black carbon (BC) from the African continent over the
SEAO, suggesting that the AOT over Ascension Island increased due
to the advection of smoke from Africa. This was also checked by
the inspection of daily backscatter trajectories, showing advection of air
in the free troposphere directly from the east during the days with
increased AOT (e.g. 13–17 and 23–26 September) but not during
low-AOT episodes (e.g. 6–10 September 2016). A few example
back trajectories during different episodes of the campaign are shown
in Fig. .
Aerosol–cloud interactions
Aerosol–cloud interactions were determined from the lidar measurements
using the 2016 data only. In 2017, alignment issues resulted in a
lower SNR and large uncertainties, and these data were discarded for
the analyses in this section. Three approaches are presented. First,
a simple comparison of days of low and high aerosol concentration is
made, showing the change in cloud parameters. Next, the aerosol
indirect effect was determined following the metrics developed in
and : the aerosol–cloud interaction (ACI) is quantified, for a constant ambient relative humidity,
by a change in cloud parameters due to a change in the number of
available condensation nuclei. For the cloud effective radius,
ACIr=-dlnReffdlnA,
and for the cloud droplet number density,
ACIN=dlnNddlnA.
In these equations A is the aerosol proxy, which should represent
the aerosol abundance, and can be aerosol extinction, aerosol optical
thickness or another aerosol quantity.
This approach was applied in two ways: first, by using the daily
averaged AOT around the cloud base and comparing it to the cloud
parameters, which are also determined around the cloud base (since the
lidar does not penetrate deep into the cloud), and second, by
determining the aerosol abundance below the cloud, using the lidar-derived aerosol extinction profile below the clouds. Hence, in the
first method the aerosol proxy is determined during cloud-free spells,
while in the second method the aerosol proxy is determined during
cloudy spells, i.e. collocated in time with the cloud parameter
retrievals.
Aerosol optical thickness was retrieved using the
classical Klett–Fernald two-mode method, i.e. applying
Eqs. () and () to clear-sky measurements and cloud
droplet number density, with the effective radius being retrieved by applying
Eqs. () and () to measurements during cloudy periods.
Classification
A first coarse indication of the
change in cloud properties can be obtained from a comparison of
periods with a high aerosol loading over Ascension Island, compared to
periods with low aerosol loading, assuming everything else will be the
same. A classification of the 2016 measurements was made after
defining periods of clear-sky and cloudy periods for each day with
broken clouds, by visual inspection of the lidar
quicklooks .
A classification was made of days when aerosols were expected to mix
with the clouds and days when the aerosol loading was particularly
low. Figure explains the logic: two layers
were discriminated, one from 850 to 2150 m altitude, which was assumed
to be the altitude of the clouds, and one from 2150 to 5000 m, which was
defined as the free troposphere. If the AOT in both layers was low
(below 0.07 was chosen), the day was assigned the label “clean”; if
the AOT in the layer between 850 and 2150 m was high (higher than
0.07), the day was assigned the label “mixed”. If the AOT was high
only in the free troposphere, the day was labelled “separated” and not
considered, which happened in one case. The average aerosol optical
thickness was determined during the cloud-free periods (10 during
clean days and 17 during mixed days), and the average cloud
properties were determined during the cloudy periods (6 during clean
days and 31 during mixed days).
Classification of the average clear-sky AOT during broken
cloud days, at two levels: from 850 to 2150 m, which is assumed to be at the cloud
level, and from 2150 to 5000 m, which is assumed to be in the free
troposphere above the clouds.
Using this crude selection of cases resulted in a clear difference in
the average cloud properties between the different days, as shown in
Fig. . The cloud droplet number density Nd
was 294 ± 91 cm-3 during all clean days, doubling to over
611 ± 191 cm-3 during the mixed days. Conversely, Reff100 was reduced from 3.81 ± 0.6 to 2.85 ± 0.2 µm.
This suggests a change to smaller, more numerous cloud particles with
the availability of a larger number of cloud condensation nuclei.
However, the assumption that the humidity does not change cannot be
guaranteed with such an approach.
The mean value of (a) the cloud droplet number density Nd and
(b) the cloud effective radius at the reference height Reff100
for the clean and mixed cases. The black error bar
represents the standard deviation; the grey bars represent the
sample standard deviation.
Aerosol–cloud interactions around the cloud base
Next, the ACI was computed using AOT from the daily averaged
extinction profile as before but now averaged from 300 m below the
cloud base until 1000 m above the cloud base. This level was chosen to
isolate the MBL aerosol impact on cloud droplets near the cloud base,
the region that the lidar is sensitive to. For each cloudy period the
cloud properties were determined as before and used in
Eqs. () and () to quantify the ACI. The
results are shown in Fig. . The points were
fitted weighted by the associated standard deviation, yielding ACIN= 0.3 ± 0.21 for the cloud droplet number density and ACIr= 0.18 ± 0.06 for the cloud effective radius. ACIr is at the high
end of values found by previous studies. For example,
found values of ACIr ranging from 0.0–0.16 in marine stratus clouds, while found values from
0.04–0.17 in continental stratus. Higher values (0.13–0.19) were
found in the Arctic and for very large ranges of
aerosol concentration including strong pollution (0.21–0.33)
.
(a) Weighted mean of the cloud drop number density versus
daily average AOT for each cloud selection. (b) Weighted mean of
the cloud effective radius versus daily average AOT for each cloud
selection. For both cloud properties a linear fit is plotted and the
ACI is given. The standard deviation was used as weights in the fit.
Aerosols below the cloud
In order to get aerosol and cloud proxies closer together in
time, ACIr and ACIN were also calculated using the aerosol
extinction below the clouds during cloudy periods. For this, the
aerosol extinction profile was computed using Eq. () but
with the normalization height set inside the cloud and the
extinction-to-backscatter ratio set to 20 sr in the cloud and 50 sr
below the cloud, as described in Sect. . Furthermore,
the cloud extinction-to-backscatter ratio was corrected for multiple
scattering using Eq. (). The extinction profile was
determined from 200 m above the lidar to avoid overlap until 300 m
below the cloud base to avoid the mixing region of wet aerosols just below
the cloud. The mean aerosol extinction coefficient was used instead of
the AOT because the height of the range bins changed per cloud
selection. Cloud retrievals of 30 s intervals were averaged, with a
minimum of 3 values and a maximum of 24 values, corresponding to cloud
periods of 1.5 to 12 min. The errors from the lidar inversion were
used as weights in the determination of the ACI values. The results
for the 2016 measurements are plotted in Fig. .
Aerosol–cloud interaction (ACI) for each selected cloud period in
September 2016, using the average aerosol extinction profile below a cloud
and (a) the retrieved cloud droplet number density and (b) the retrieved
cloud droplet effective radius. The error bars indicate the
standard deviation of the measurements during each selected
interval; the colours indicate the duration of the intervals.
The horizontal grey lines indicate the physically feasible bounds of
the ACI values.
The ACI for all cloud periods during the 2016 campaign show varying
results. Many values are beyond the theoretically feasible values,
indicated in the plots by the horizontal grey lines. Theoretically,
the absolute value |ACI|N must be below 1 and the absolute
value |ACI|r must be below 0.33 , reaching the
maximum absolute values if all aerosol particles are activated to
droplets. However, a number of retrievals show much larger values,
characterized by large uncertainties. The theoretical numbers are
based on idealized clouds in a constant atmospheric state. The
retrievals with large numbers and large uncertainties must be
associated with variable meteorological conditions that drive the
changes in cloud and aerosol properties, like a co-varying liquid water path (LWP).
Furthermore, the theoretical bound for ACIN is based on aerosol
number; using a mean extinction coefficient below clouds may lead to
values larger than 1.
Around 12–15 and 21–24 September ACIN and ACIr are
mostly within the physical ranges with small uncertainties. These
episodes correspond to periods of increased AOT over Ascension
Island (see Fig. ). This suggests that during
those periods the interaction of smoke with the cloud base is the
driving mechanism for forming more numerous, smaller droplets.
Discussion
The three presented methods all suggest some indication of the Twomey
effect in the cumulus clouds around Ascension Island in 2016 during various
episodes. However, changing meteorological conditions could affect the
results. An inspection of (back)trajectories during the measurement
period showed that the MBL around Ascension Island is very persistent
(cf. Fig. ). Daily back trajectories of air ending at
600 m altitude over Ascension Island invariably showed MBL air being
transported from the southeast with little to no vertical
displacement for all the days during the 2016 campaign, indicating a
stable of moist well-mixed air in the MBL as expected over this
region. On the other hand, the air transported to the cloud layer,
e.g. at 2100 m altitude, was from the east most of the time (loaded
with smoke) but was also from the west and variable.
In a recent paper, show that during September 2016 the
southern African easterly jet increases develops from being weak in the
beginning of September 2016 to being strong at the end of the month, with
increased relative humidity and BC concentrations over the central southern
Atlantic Ocean at 600 hPa especially around 15–17 and 27 September 2016.
These episodes correspond to the increased AOT in
Fig. , showing the dominance of the large-scale circulation in the free troposphere on the AOT fluctuation over
Ascension Island. However, the correlation between BC concentration
and relative humidity can also explain a positive correlation between
the AOT and cloud droplet number density, as observed in
Figs. and , if more
particles become activated with more available moisture. However, in
that case the observed reduction in the cloud effective radius is
unlikely, and we conclude that the advection of smoke from the African
continent reduces the effective cloud droplet size at the cloud base
through the first aerosol indirect effect.
Cloud parameters
Lidar retrievals of the cloud parameters have been performed
in only a few cases before .
Below, the cloud retrievals from the UV polarization lidar are
compared to retrievals from cloud radars located at the ARM research
facility. Unfortunately, in 2016 the cloud radar was operational only
for a short period during the campaign, so 2017 data are also used to
assess the cloud data from the lidar retrievals.
Reff100 for selected cloud periods in (a) 2016
and (b) 2017 from the lidar (grey) and the cloud radar
(purple). The shading shows the standard deviation or
retrieval error, while the variance in the cloud per measurement
period is given by the error bars. The dashed line gives the mean
Reff100. Please note that the date format in this figure is month day year.
In 2016, the W-Band Scanning ARM Cloud Radar (WSACR) was operated from
the start of the lidar measurement period until 11 September. In 2017, the
Ka-Band Scanning ARM Cloud Radar (KASACR) was operated during the
entire period of the lidar operation. WSACR was operated at a
frequency of 94 GHz, and KASACR was operated at 35.3 GHz. Both radars have a field
of view of 0.3∘. Although the radars were operated with
scanning strategies, here only the vertical pointing modes were
used, taken each hour for a duration of 4 min. The 2D radar
reflectivity factor Z, with a time resolution of 2 s and a vertical
resolution of 30 m, was collected from the ARM website.
The radar reflectivity was used to derive Reff100
following the method described by . Assuming a cloud
with a lognormal droplet size distribution,
n(r)=Nd2πrσxexp-(ln(r)-ln(R0))22σx2,
where is R0 the median radius, σx is the spread of the
lognormal distribution, the effective cloud droplet radius
Reff is related to the median radius by
Reff=R0exp52σx2,
and the radar reflectivity is
Z=26NdR06exp(18σx2).
This gives a relationship for the effective cloud droplet
radius of
Reff(z)=12Z(z)Nd1/6exp(-0.5σx2),
and the value of Reff100 to be compared to the lidar
retrievals is simply given by the above equation with z corresponding
to 100 m above the cloud base with the cloud base supplied by
co-located lidar ceilometer measurements (see
Appendix ). The value for σx was set to 0.34 ± 0.09, which is a typical value for marine, low-level clouds
. An uncertainty of ±3 dBZ in
the reflectivity factor was used to compute the error margins. Nd
can be estimated from the lidar inversions (see Eq. ).
Daily averaged lidar estimates of Nd were around
466 ± 127 cm-3 in 2016 and 540 ± 142 cm-3 in 2017.
The uncertainty in retrieved Nd is between 25 % and 50 %
. The lidar estimates of Nd are higher than earlier
reported values of 100 ± 70 cm-3 for marine, low-level clouds
and used by . However,
Nd is seasonally dependent, with higher values in the boreal summer
over SEAO and the western northern Atlantic Ocean
, and Eq. () shows that relatively
large changes in Nd will produce only small changes in Reff. The use of the literature value of 100 cm-3 in the radar
estimates increased the effective radius by about 3 µm.
Comparison of daily averaged Reff100 from
lidar and cloud radar in (a) 2016 and (b) 2017. The retrieval error is
shown by the red error bars, while the variance of the daily measurements
is shown by the black error bars. The dashed
line shows the 1:1 line, and the full black line is a linear least-squares fit.
The slope and offset of the fit are indicated in the legend, along with
Pearson's correlation coefficient.
Reff100 estimates from lidar and cloud radar are compared
in Figs. and .
Figure shows the lidar retrievals for selected
cloud periods, with the variance in the measurements shown by error
bars and the estimated measurement error shown by the shaded purple
(radar) and grey (lidar) areas. The average retrieved effective
droplet radii (shown by the dashed lines) was 3.63±0.45µm in
2016 and 3.37±0.4µm in 2017 for the lidar retrieval and
5.84±1.95µm in 2016 and 4.41±1.1µm in 2017 for the
radar retrievals. Figure shows scatterplots of
the daily averaged retrievals of Reff100 from lidar and
radar retrievals in 2016 and 2017. In general, the estimates of
Reff100 from the cloud radar are larger than from the
lidar. This will be even larger for lower values for Nd. The
comparison is complicated by the low number of measurements in 2016.
In 2017, the average value is closer, but the alignment problems
complicates the comparison, and no correlation was found between the
radar and lidar estimates.
The dependence on the assumed value of Nd can be removed altogether
using cloud liquid water path (LWP) data from a microwave radiometer
(MWR) . An MWR was operated at 23.8 and 31.4 GHz
alongside the WSACR until 11 September 2016. The radar–MWR
method described in was also applied in addition to
the radar-only method. The radar–MWR method, however,
tended to yield particle size measurements much higher than the radar-only approach. Moreover, the radar–MWR results tended to yield Reff100 values strongly inconsistent with non-drizzling clouds
(e.g. values greater than 15 µm) and unrealistically low values of
number density (e.g. less than 5 cm-3). The reason for this
is unclear but may point to biases in the LWP data used or an error
in the implementation.
The differences in the effective-radius retrieval could be the consequence
of a number of factors. Both the radar-only and radar–MWR methods are
sensitive to the presence of drizzle, while the lidar-only method is
relatively insensitive to the presence of drizzle .
Even small amounts of drizzle may result in radar-reflectivity-based
retrievals overestimating cloud particle sizes
(e.g. ). It should be noted, however,
that the smaller effective radius seen with lidar is consistent
with that reported by (e.g. Fig. 6). Also,
report cloud droplet effective radii from cloud radar
measurements in warm cumulus clouds growing from about 2 µm near
the cloud base to 10 µm at 1000 m above the cloud base.
report radar estimates of Reff100 in stratus clouds
ranging from 4 to 8 µm in close agreement with aircraft
measurements, depending strongly on cloud height.
Conclusions
In this study, aerosol–cloud interactions were studied in the broken
cloud deck over Ascension Island during the African monsoonal dry
season in 2016 and 2017, which is about July to October. During these
months, plumes of smoke from vegetation fires drift over the ocean.
The typical clouds over Ascension Island are cumulus clouds at the
terminating stage of the stratocumulus-to-cumulus (SCT) transition.
Smoke affects this transition is various ways. We found that the
presence of smoke decreases the cloud droplet sizes near the cloud
base and increases the cloud droplet number density, likely due to the
first aerosol indirect effect. On average, the cloud drop number
density was 294 ± 91 cm-3 and the cloud effective radius was
3.81 ± 0.6 µm during smoke-free days, compared to
611 ± 191 cm-3 and 2.85 ± 0.2 µm during days with smoke at
cloud level. Similarly, aerosol–cloud interactions were quantified
using cloud base parameters during cloud periods and daily averaged
AOT at cloud level: the cloud droplet number density ACIN was 0.3 ± 0.21, and the cloud effective radius ACIr was 0.18 ± 0.06.
Lastly, aerosol and cloud properties were retrieved simultaneously by
the lidar during cloudy periods. This was possible by retrieving
aerosol extinction profiles under the clouds. During two episodes,
12–15 September 2016 and 20–24 September 2016 an indirect effect was
found, corresponding to periods with increased transport of air from
the African continent over the SEAO. This increased not only the BC
concentration and AOT over Ascension Island but also the relative humidity.
However, the results show a decrease in droplet size and increase in
droplet number density near the cloud base related to increases in
aerosol concentration, suggesting that the smoke is responsible for
more numerous but smaller cloud droplets, which will shorten the SCT,
both by warming the MBL during the day and by making cloud droplets more
susceptible to evaporation.
The lidar retrieved values of the effective radius were small compared
to many other studies of cloud droplet sizes of warm low-level clouds
(e.g. ).
However, lidar estimates of the cloud droplet effective radius are
restricted to cloud base values, and care should be taken when
comparing estimates from ground-based radars and satellite
retrievals. Vertical profiles of Reff are typically strongly
growing from a few micrometres to over 10 µm and more until the
cloud top. A radar beam can penetrate the cloud completely, and the
average retrieved effective radius depends on the assumed vertical
distribution. Satellite retrievals of cloud droplet sizes are
typically biased to the cloud top retrievals. Therefore, comparisons
between these types of retrievals should be performed only when
corrected for the vertical profile of the cloud droplet sizes
. A comparison with radar estimates of droplet sizes
near the cloud base showed consistent values to within the
measurement uncertainties.
This is the first time a UV polarization lidar was used to determine
cloud parameters at the cloud base of marine cumulus clouds in the SCT
zone over the SEAO. The measured depolarization of the lidar beam was
fitted to lookup tables (LUTs) of precalculated depolarization by cloud droplets using
Monte Carlo (MC) simulations, relating the depolarization to the cloud
droplet effective radius and the cloud extinction parameter at a
reference height using a proper cloud model. This method shows
potential for the monitoring of aerosol–cloud interactions at
strategically positioned locations in climate sensitive areas, like
the SEAO. The simultaneous retrievals of aerosol extinction and cloud
properties from one single instrument can be helpful in the
measurement of aerosol indirect effects, which constitutes the largest
uncertainties in global climate models. However, we found that proper
calibration of the instrument and careful selection of the data are
essential.
Theory
The theory of the applied methods has been described in earlier
papers cited in the text. For completeness, the method
applied to the UV lidar data on Ascension Island is described below.
UV lidar
The total power returned to a lidar by backscattering in the
atmosphere under single-scattering conditions is
P(z)=Clidz2βπ(z)exp-2∫0zα(z′)dz′,
where P is the power received by the instrument, z is
the altitude from the instrument along the line of sight, Clid is the lidar calibration coefficient, α is the atmospheric extinction
coefficient and βπ is the atmospheric
backscatter coefficient. The atmospheric extinction and backscatter
coefficients can be divided into a molecular, aerosol and cloud part,
viz.
α=αm+αa+αc,βπ=βm+βa+βc.
The extinction-to-backscatter ratio (or lidar ratio) S is defined as
S(z)=α/β. The aerosol scattering ratio (Rasca)
is defined as Rasca=(βa+βm)/βm, which is 1 if there are no aerosols.
Molecular scattering
The molecular backscatter coefficient can
be calculated using βm=ρairMλ550-4.0910-32m-1sr-1,
where λ is the wavelength; M is the
average molecular mass of air (4.81×10-26 kg); and the
atmospheric density was determined using
ρair=pT1Rdryair,
where p is the measured pressure, T is the measured
temperature and Rdryair is the gas constant for dry air
with an average value of 287 J kg-1 K-1.
The temperature and pressure were determined from radiosondes,
launched four times daily from Ascension Island. The molecular
extinction coefficient αm can be calculated using the
molecular extinction-to-backscatter ratio Smol=8π/3 sr . At the lidar wavelength of 355 nm molecular scattering is
strong, and this was used to calibrate the lidar. Details can be found
in .
AOT retrieval
For a lidar operating in the UV, molecular scattering is strong and
must be accounted for in the inversion. In this case, a two-mode
method following e.g. and can be
applied using the transformed variables P′(z)=S(z)P(z)exp2∫0zαm(z′)-S(z′)βm(z′)dz′
and
α′(z)=S(z)βm(z)+αa(z).
Now Eq. () can be rewritten as
P′(z)=Clidz2α′(z)exp-2∫0zα′(z′)dz′,
with the analytical solution
α′(z)=P′(z)z2P′(z0)z021α′(z0)+2∫zz0P′(z)z2P′(z0)z02dz′,
where z0 is a normalization height.
From the transformed variable α′, the aerosol extinction is
derived to be αa(z)=α′(z)-S(z)βm(z). The aerosol
backscatter coefficient is now derived by dividing the aerosol
extinction by the height-dependent lidar ratio.
The aerosol optical thickness (τ) of a layer can be obtained
by integrating the aerosol extinction profile over the altitude of
the layer:
τ(z1;z2)=∫z1z2αa(z)dz.
Cloud-free scenes
In clear-sky scenes the normalization
height is set to an altitude at which the aerosol extinction is 0.
From literature (e.g. ) and from
observations on the island, it was concluded that marine aerosols are
always present in the lower boundary layer, up until 1200 m.
Smarine was set to be 25 sr, a good approximation for
marine aerosols ; (aged)
smoke and dust were often, almost always, present above the boundary
layer, in the layer from 1200 to 5000 m, sometimes mixed in the
boundary layer. For this layer the lidar ratio Sdark was
set to 50 sr . Above 5000 m, the air was mostly
clean and clear of aerosols and the lidar ratio reduces to the
molecular extinction-to-backscatter ratio defined above. The
normalization height was set to 7 km. Various tests were performed
varying Smarine and Sdark around their values
of 25 and 50 sr to check the sensitivity of the choices, resulting in
5 % changes in AOT within the expected reasonable ranges of S.
Aerosol below clouds
In order to derive aerosol optical
thickness close to clouds, aerosol extinction profiles were retrieved
for cloudy scenes under the clouds, using Eq. (). However, in
this case the normalization height is not located at an altitude
without aerosols but rather inside the cloud, where the aerosol contribution
can be neglected. The normalization
height was determined by the cloud base height and the cloud
extinction. The extinction-to-backscatter ratio was set to
20 sr in the cloud and 50 sr below the cloud .
Furthermore, multiple scattering, which influences the lidar return
and the cloud extinction, should be taken into account in a cloud.
Therefore, the cloud extinction-to-backscatter ratio, used to
determine α′ in Eq. (), was corrected by
a multiple scattering correction factor η:
Sc=(1-η)αcβc.
The correction factor η
was determined from a sensitivity study over 3 d in 2016 with
broken clouds. Aerosol profiles below clouds during these days were
fitted to aerosol retrievals during clear-sky spells close in time on
these days. The correction factor was varied between 0.3 and 0.5 in
steps of 0.05, resulting in overcorrection and undercorrection. The
best fit was found for 0.35 and 0.4. The difference in aerosol
extinction coefficient at an altitude of 300 m below the cloud base
between η=0.35 and 0.4 is about 2.6×10-5 m-1.
In all subsequent processing a value of η=0.4 was used. See
for details.
Clouds
Although the lidar equation (Eq. ) formally
only applies for single scattering, the derivation of cloud
extinction and backscatter coefficient in this section is based on a
polarization change after multiple scattering, first developed by
. Light returning from a liquid cloud will be
partially depolarized due to multiple scattering by the cloud droplets
. This multiple scattering in a liquid water cloud can
be simulated by a Monte Carlo (MC) model, assuming a cloud model.
This was achieved using the Earth Clouds and Aerosol Radiation
Explorer (EarthCARE) simulator (ECSIM) lidar-specific MC forward
model. The ECSIM lidar MC model is a modular multi-sensor simulation
framework, which was used to calculate the spectral-polarization state
of the lidar signal.
The underlying cloud model is based on clouds with a linear
liquid water content (LWC) profile from the cloud base and a constant cloud droplet number
density (Nd) (e.g. ). Various MC simulations were
carried out for different LWC slopes, number densities and lidar
fields of view, and cloud base values. The MC results were then used
to produce lookup tables which form the basis of a forward model
which is fast enough to serve as the forward retrieval model in an
optimal-estimation retrieval procedure. Details are described in the
remainder of this section.
Measured (solid line) and fitted (dots) vertical profiles for
the parallel attenuated backscatter (black) and perpendicular attenuated
backscatter (red) on 6 September 2016 for the selected cloud (C) in
Fig. . Please note that the date format in this figure is day month year.
The cloud droplet size distribution was defined as a single-mode modified gamma
distribution :
n(r)=NdRm1(γ-1)!rRmγ-1exprRm,
where Nd is the cloud droplet density, defined to be constant
with height; r is the droplet radius; Rm is the mode
radius; and γ is the shape parameter of the distribution.
A linear liquid water content defines a constant liquid water lapse
rate, Γl. When the liquid water content increases with
height and the number density remains constant, the cloud droplet
effective radius, defined as
Reff=∫n(r)r3dr∫n(r)r2dr,
will increase with height. The cloud extinction
coefficient αc also increases with height. This leads to the
prediction that the depolarization ratio is generally increasing
throughout the cloud, while observations show that the depolarization
ratio may exhibit a peak . Furthermore, the model
represents semi-infinite clouds, with a cloud top at infinity.
However, the lidar signal can only penetrate a few hundred metres
into the cloud. Therefore, no information is known about the upper
part of the cloud, and any retrieved parameters are only applicable to
the cloud base region; the parameters were calculated for a
reference height. In this research, 100 m above the cloud base was
assumed. This simple but effective cloud representation reduces the
parameters to describe the cloud to two, the cloud extinction
αc100 at the reference height and the cloud effective
radius Reff100 at the reference height.
MC model simulations were performed for various values of the cloud base
height (CBH), the lidar field of view (FOV), Reff100
and the adiabatic cloud base liquid water lapse rate Γl. The
values are replicated from in Table .
Lookup tables (LUTs) were generated from the simulations and
predefined input parameters, the lidar constants, and initial values
for Reff100 and αc100. These LUTs
contain information on the simulated parallel and perpendicular
attenuated backscatter and therefore the depolarization ratio.
Range of parameters used in the ECSIM MC calculations.
The observed attenuated backscatter and depolarization ratio were
compared to the LUTs to find the best matching values for
Reff100 and αc100, by iteratively
minimizing a cost function . In
Fig. , the observed and fitted attenuated backscatter
profiles from the LUTs are shown, for a cloud
selected on 6 September 2017. The dotted lines correspond to the
fitted values from the LUTs, with the parallel attenuated backscatter
in black, the perpendicular attenuated backscatter in red. The observed profiles are represented
by the corresponding solid lines.
The cloud drop number density Nd follows from the cloud effective
radius and the cloud extinction of
Nd=αc10012π1(Reff100)21k,
where k is 0.75±0.15.
Because multiple scattering occurs in a cloud, the LUTs, the shape of
the attenuated backscatter and the depolarization ratio profiles are
all well-defined functions of the LWC and effective radius profile.
For single scattering the parallel attenuated backscatter profile will
not depend on the effective radius profile.
It is important to note that the CBH is difficult to define from real
observation due to the presence of sub-cloud drizzle and the presence
of growing aerosol particles. The MC-based inversion results would be
very sensitive to the absolute calibration of the attenuated
backscatter if the CBH is used as a reference. Therefore, the peak of
the observed parallel lidar attenuated backscatter is used as a
reference instead of the CBH in the fitting procedure. Consequently, the
CBH is produced as a byproduct, and in Appendix the
derived CBH will be compared to observations of the CBH using different
instruments.
Cloud base height validation
It is important to compare the cloud parameters from the lidar and the
cloud radars at the same relative height, since the effective radius
strongly depends on the height in the cloud. The effective radius was
determined at a reference of 100 m above the cloud base height (CBH),
which was related to the peak of the observed parallel lidar
attenuated backscatter. The accuracy of the backscatter peak as the
CBH cannot directly be compared to the CBH from the cloud radar
because of the different locations of the instrument. The effect of
the spatial distance between the instruments was investigated by
comparing the CBH from two ceilometers that were installed in the airport
and the ARM main site. This is illustrated in the left panel
of Fig. for the day of 26 August 2017. The CBHs from
these instruments, relative to the mean sea level, are highly
correlated in general (Pearson's correlation coefficient was 0.931).
However, on average a higher cloud fraction was found over the ARM main
site compared to the airport, due to the higher elevation of the
site. More low-level clouds were detected over the ARM main site, and
the cloud fraction differed. However, this should not affect the
analyses too much, since the main difference is in the low-level
clouds and the selected cloud periods had CBHs higher than 1000 m.
The CBHs from the lidar and from the ceilometer at the airport were
compared, as shown in the right panel of Fig. . The
correlation was higher than 90 %. Therefore, the relative height of
the peak of the backscatter can be considered a good proxy for the
relative position of the CBH.
(a) The CBH from the ceilometer at the airport (black crosses,
elevation of 79 m) compared to the CBH from the ceilometer at the ARM main site (purple circles, elevation of 365 m) on 26 August 2017. The CBH
is measured relative to the mean sea level. (b) Comparison of the
cloud base height determined from the UV lidar and the ceilometer
located at the airport. The dashed line is the 1:1 line.
Data availability
The UV polarization lidar data acquired on Ascension Island are available on the KNMI Data Platform at 10.21944/5qqy-0c37.
Author contributions
MdG authored the science application, coordinated and managed the measurement campaigns, and wrote the paper.
KS co-authored the science application.
JB performed the 2016 measurements.
EVT analysed the 2016 data.
MS performed and analysed the 2017 measurements.
DPD designed the MC model, calibrated the UV lidar and overlooked the science.
Competing interests
The contact author has declared that none of the authors has any competing interests.
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
We are grateful for the support of the Royal Air Force (RAF) personnel and staff at Wideawake airfield. Special thanks go to Jim Haywood from the UK Met Office and University of Exeter for his leadership and help on numerous occasions of logistical mayhem and Jenna Macgregor of the Ascension Island Met Office for her initiatives and help on the Ascension Island side.
Financial support
This project was financed by the Pieter Langerhuizen Stipendium of the Koninklijke Hollandsche Maatschappij der Wetenschappen (https://www.khmw.nl/, last access: 2 May 2023) in Haarlem, the Netherlands, supplemented with financial support from the Delft University of Technology (TUD) and manpower from TUD, the Royal Netherlands Meteorological Institute (KNMI) and Wageningen University & Research (WUR), for which we are greatly indebted.
Review statement
This paper was edited by Jérôme Riedi and reviewed by two anonymous referees.
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