Introduction
The interactions of low-level liquid water clouds with aerosol are considered
one of the main sources of uncertainty in climate change predictions.
According to the Fifth Assessment Report (AR5) of the Intergovernmental Panel
on Climate Change , clouds and the effects of
aerosol on their macro- and microstructure continue to contribute to the
largest uncertainty in the estimation and interpretation of the Earth's
energy budget. Low-level liquid water clouds mainly impact the short-wave
radiation budget, as it is mostly sensitive to the cloud albedo. The effect
of aerosol concentration on cloud reflectance is often referred to as the
albedo effect . The albedo effect is based on
the close relation between the aerosol concentration and the cloud droplet
concentration.
An ample number of studies have been made to quantify the impact of aerosol
concentration on cloud microphysical properties. Studies focusing on
low-level liquid water clouds are often based on different methods and
instruments. Because of this, the temporal and spatial resolution vary
significantly. Observational studies of the aerosol effect on clouds use
surface remote sensing instruments at specific locations
e.g. or rely on a combination of
both surface remote sensing and aircraft in situ observations
e.g..
To characterise the aerosol effect on a global scale, many research studies
focus on the satellite remote sensing observations
e.g.. summarised
the broad scope of different methods and scales used. They concluded that a
single measure of aerosol–cloud interactions (ACIs) used in climate model
estimates of the radiative forcing yields widely fluctuating results. ACI is
a single measure derived from observational data from varying scales and
different assemblies of instruments. Further, they concluded that
ACIr (defined as ACIr=-dlnre/dlnα, change in cloud droplet effective radius with aerosol
concentration) is only useful in small-scale measurements. That way it can be
measured at a scale of the process it represents, that is, at a microphysical
scale. Microphysical changes in cloud and aerosol can be captured by either
in situ measurements or point-based remote sensing observations from the
ground with a high temporal resolution. Therefore in this paper we focus on a
new methodology that allows ACI to be continuously observed with ground-based
remote sensing instruments over multiple locations.
We present an approach for monitoring aerosol–cloud interactions with
ground-based remote sensing instruments. We use specifically a zenith-pointing
cloud radar, a lidar and a microwave radiometer to characterise cloud
microphysical properties and the aerosol concentration in the same column.
Thanks to the unique capabilities of the ground-based remote sensors, data can
be collected and compared continuously. Due to the fine height and time
resolution available, cloud and aerosol properties are observed in the same
air column. We developed the monitoring scheme on the basis of the
standardised data format from Cloudnet . The
method described here can be implemented on multiple ground-based
observational sites (e.g. the European ACTRIS network – Aerosol, Clouds and
Trace gases Research InfraStructure and the US Atmospheric Radiation
Measurement (ARM) Program – both databases provide the Cloudnet data set), where
a long-term database of measurements already exists. This will allow statistical calculations of ACI to be performed for different locations.
The structure of this paper is as follows: first, we provide a description of
the methodology for estimating the relationship between the aerosol
concentration below the cloud base and the cloud droplet concentration and
the droplet sizes in the cloud base region. We describe the combination of
instruments and proxies used in the method. Then we show two example case studies from the ARM Mobile Facility on Graciosa Island, the Azores,
Portugal. Finally, we discuss the possibilities of implementing this method
over the network of cloud profiling observatories in Europe.
Quantifying interactions between aerosol and cloud
Very often in the literature, the term “aerosol–cloud interactions” is
associated with the quantification of the impact of aerosol on cloud albedo. This
relation was first postulated by . Through
experimental studies he showed that the number concentration of aerosol
(Na) below the cloud is monotonically related to the cloud droplet
number concentration (Nd) :
Nd∝Naγ,
where γ is the proportionality factor.
The value of γ varies between 0.7 and 0.8 in different experimental
studies , and the
theoretical bounds are between 0 and 1. Na and Nd are not
directly proportional. The increase in the concentration of aerosol that can
be activated into cloud droplets can lead to the lowering of the maximum
relative humidity in the cloud base region .
further derived a theoretical relationship
between the aerosol concentration and cloud albedo. He proposed that an
increased aerosol concentration will lead to an increased cloud droplet
concentration and a smaller effective radius of cloud droplets
(re). A smaller effective radius of cloud droplets will result in a
brighter cloud and an increased cloud albedo. This is only true if the amount
of available water, represented by the liquid water path (LWP), is constant.
The cloud optical thickness (τd) is a function of both the cloud
droplet concentration and cloud effective radius. Thus, we can assume that
the optical thickness will rise with the increase of the droplet
concentration ,
τd∝Nd1/3,
and the decrease of the droplet radius ,
τd∝LWPre.
Theoretical relationships between variables in Eqs. (),
() and () led to the formulation of a relation
between the aerosol optical thickness (τa), as τa
is a function of the aerosol number concentration (Na), and the
effective radius of cloud droplets (re)
:
re∝τa-γ/3,
which is a basic theoretical relation used presently to quantify the effect
described by . In order to empirically quantify
the aerosol–cloud interactions, introduced the
indirect effect index (IE), later referred to as ACI (aerosol–cloud
interactions):
IE=ACIr=-dlnredlnαLWP0<ACIr<0.33,
and
IE=ACIτ=dlnτddlnαLWP0<ACIτ<0.33,
or
IE=ACIN=dlnNddlnα0<ACIN<1,
where α is an observed proxy of the aerosol concentration. Parameters
such as aerosol number concentration (Na), aerosol optical
thickness (τa) or aerosol index, which is a product of
τa and Angström exponent, were used to represent the aerosol
concentration in different studies. Note that ACIN is not bounded
by the value of LWP and is derived directly from Eq. ().
In mathematical terms, ACIr, ACIτ and ACIN are
represented by a slope of a linear regression between a logarithm of a cloud
property (dependent variable) and a logarithm of an aerosol property
(independent variable). Thus, we can write ACIr as
ACIr=Raerosol,cloudScloudSaerosol,
where Raerosol,cloud is the Pearson product-moment correlation
coefficient between the logarithm of aerosol property and the logarithm of
the cloud property, Scloud is the standard deviation of the cloud
property and Saerosol is the logarithm of the aerosol property
.
It is important to note that in order to derive Eq. () a
series of assumptions was made. assumed that
cloud is homogeneous. It allowed them to apply properties of the cloud base
area to the whole cloud. For a cloud in an early formation stage the cloud
droplet concentration is decided mainly by the number of cloud condensation
nuclei (CCN) in the cloud base area. By assuming that cloud is homogeneous, the
same is true for the whole cloud. Further, Twomey assumed that both cloud
droplet number concentration and aerosol optical thickness are directly
proportional to an increasing aerosol concentration. This means that he
considered all components in the aerosol to increase together and at the same
proportion. The combination of these assumptions greatly minimises the number
of observational case studies where the relation from Eq. ()
can be applied.
Another important and often omitted factor is that the cloud droplet
concentration (Nd) is modified by mixing, collision, coalescence
and evaporation within the cloud. However, at the area close to the cloud
base, where the cloud is at the early formation stage, the initial
Nd is determined by the number of nuclei able to activate into
cloud droplets at or below the maximum supersaturation in the cloudy air
. This means that the aerosol concentration
should be related to the number concentration of cloud droplets in the cloud
base area in observational studies, as translation of this relationship to
the whole clouds requires the assumption that cloud is homogeneous – and that is
rarely the case.
In this study we focus on the aerosol–cloud interactions as an approximation
of the nucleation process without relating them to the cloud albedo. We
design a method that enables daily monitoring of the microphysical processes
between aerosol and clouds. We quantify the relation between cloud and
aerosol properties with statistical parameters. We assume that the aerosol
concentration below the cloud is monotonically related to the cloud droplet
concentration in the cloud base region (Eq. ) and that the
increase of the cloud droplet concentration leads to a decrease of the cloud
droplet size. We perform a logarithmic transformation of both aerosol and
cloud properties. Thus, the quantities we use for determining the relation
between aerosol concentration and cloud droplet size are the natural
logarithm of the attenuated backscatter coefficient (lnATB) and
the natural logarithm of the cloud droplet effective radius (lnre) – see Sect. . We use the Pearson
product-moment correlation coefficient, R, to establish how dependent the
cloud droplet size is on the aerosol concentration. The sign of the
correlation coefficient will show if the increasing concentration of aerosol
actually decreases with the cloud droplet size. We further calculate
ACIr (Eq. ), which as we mentioned before represents
the slope of the regression line between the cloud droplet effective radius
(re) and the aerosol concentration. ACIr is important to
estimate the proportionality factor γ as defined in
Eqs. () and (). We also calculate the
coefficient of determination, r2, which suggests the percentage of the
variability in cloud droplet size that can be explained by changes in aerosol
concentration. We want to analyse data daily when the specific conditions are
present (see Sect. ) and divide data into small bins
of liquid water path (LWP) to approximate the conditions in each bin to a
constant LWP, as postulated by .
Aerosol and cloud properties' proxies
Clouds are formed when aerosol particles are activated into cloud droplets.
Activation is a change from stable to unstable growth due to the increase of
the ambient humidity. When haze droplets reach a critical radius
, they are transformed into cloud droplets. When a
higher concentration of the aerosol particles is present, the competition for
the excess water vapour will be greater and thus, the resulting cloud
droplets will be smaller .
In low-level liquid water clouds, in particular stratocumulus, the number of
the activated droplets approaches the concentration of the aerosol
accumulation mode (particles between 0.1 and 1 µm), making that
concentration itself the primary determinant of the cloud droplet
concentration e.g.. Based
on an adiabatic cloud parcel model representing the hygroscopic growth of CCN
and droplet condensation, concluded that
aerosol number concentration (Na) contributes most significantly to
aerosol effects on clouds. Other aerosol parameters, such as size, breadth of
the aerosol size distribution and its chemical composition, are of a secondary
importance.
Relation between aerosol and cloud proxies
The strong relation between aerosol concentration and cloud droplet
concentration (Eq. ) is postulated both by theory and
observations. We expect to see an inverse relationship between the aerosol
concentration and cloud droplets' size. With the increase of the aerosol
concentration, the cloud droplet size is expected to decrease, while at the
same time, the cloud droplet concentration is expected to increase. This is
true if the amount of available water, LWP, is kept constant.
Methodology
Instrumentation and data set
Very often, collocated measurements of aerosol and cloud properties are not
available at a similar time resolution. Alternatively, data are only being
collected during specific measurements campaigns. This does not allow
for a continuous monitoring of aerosol–cloud interactions. To gain a better
understanding of the aerosol impact on cloud microphysical properties, we need
to have continuous measurements, in different meteorological conditions and
over multiple locations. Also, to eliminate rapid variation in the
meteorological conditions, we want to evaluate data daily. Ground-based remote
sensing instruments are able to provide continuous measurements. They can
provide measurements of fine temporal and height resolution that can be used
to monitor aerosol–cloud interactions. The goal of our method is to monitor
the interactions between aerosol and clouds. We combine measurements from
three separate instruments: a cloud radar, a lidar and a microwave radiometer. This
combination of instruments can capture and monitor the influence of a
changing aerosol concentration on the cloud microphysical properties.
We used the Cloudnet data set, which provides a set of high-quality
measurements from a radar, a lidar and a microwave radiometer. The
specification of all three instruments may vary slightly per Cloudnet site,
but the retrieval algorithms are always the same. The detailed specification
of instruments used in this study is presented in
Sect. . Additionally, each pixel of the time–height
grid of the Cloudnet data set is categorised in terms of the presence of
liquid droplets (cloud, rain or drizzle), ice, insects or aerosol. This
categorisation is a specific product of the Cloudnet data set
and was designed to facilitate the retrieval
of cloud microphysical properties. This categorisation product allows us to
construct an algorithm that can be applied to specific targets only, liquid
water cloud droplets and aerosol, and provides an easy way of selecting data
based on a set of selection criteria (Sect. ).
Aerosol number concentration
Numerous proxies have been used in the past to represent the aerosol
concentration. In this method we aim to use continuous measurements with a
high spatial and temporal resolution. Such a data set is available from a
lidar, in the set-up of this research, specifically a Vaisala CT25K ceilometer
operating at 905 nm. Several research studies indicate that a ceilometer can
be used as a quantitative aerosol measurement instrument
. Backscatter from
ceilometers (β) can be approximated to
β≈∫0∞Na(Da)Da2dDa,
where Na is the number concentration of aerosol and Da is
the aerosol diameter. The averaged β shows good correlation with the
in situ measurements of the mass concentration of the particulate matter up
to 10 µm (PM10) and smaller than 2.5 µm (PM2.5)
.
In this method we use a column-integrated value of the attenuated backscatter
coefficient (ATB) in order to represent the whole column of aerosol below the
cloud. We only consider well-mixed conditions
(Sect. ). Specifically, we only look into single-layer
clouds on top of the boundary layer with the cloud base below 2000 m.
Data are integrated from the level of a complete overlap (minimum height
where the cross section of the lidar laser beam is completely in the field of
view of the receiver's telescope; ), which is
120 m in our study, up to 300 m below the cloud base. The
distance from the cloud minimises the amount of cloud and haze droplets or
wet aerosol mixed through the considered aerosol background. The specific
distance of 300 m was used in other studies based on ground-based
lidar measurements .
Very often, a set height of the aerosol concentration proxy is used in the
studies of aerosol–cloud interaction (e.g. Raman lidar extinction at
350 m; ). We compared an aerosol
property (ATB) and a cloud property (cloud droplet effective radius –
re) at a set height, 350 m from the ground for the ATB, and
a mean value of re through the cloud, with the ATB and re
set at a specific distance from the cloud base (and the cloud base height is
seldom constant), 300 m below the cloud for ATB and 85 m
above the cloud base for re. We found that by considering the level
of aerosol proxy (ATB) and cloud proxy (re) at a set distance from
the cloud base, the dependence of cloud properties on aerosol concentration
is bigger. Explicitly, the correlation coefficient, R, has a higher
absolute value. Therefore we use a height based on a set distance from the
cloud base for both aerosol and cloud properties in this study.
Note that Cloudnet ceilometers are calibrated in accordance with the
method which introduces a calibration
uncertainty of up to 10 %. The precision of the measurements is difficult
to estimate as the internal processing algorithms are proprietary. A single
value of 0.5 dB is used for all pixels
.
Cloud droplet size and number concentration
Aerosol–cloud interactions are described as the response of the microphysical
properties of the cloud to the change of the aerosol concentration. The cloud
properties that we are specifically interested in are the cloud droplet size
and the number concentration of the droplets. Both these variables are
obtained through a retrieval of cloud microphysical properties from
measurements.
We apply a method according to to retrieve the
cloud droplet concentration (Nd) and the cloud droplet effective
radius (re). This retrieval method uses observations from a cloud
radar and a microwave radiometer (MWR). Assuming that Nd and a gamma
cloud droplet distribution, with a fixed distribution shape (ν), are
constant with height, the re can be derived from the radar
reflectivity factor (Z) and the MWR-retrieved LWP:
re(h)=(ν+2)3(ν+3)(ν+4)(ν+5)13×πρw∑i=1nZ12(hi)Δh48LWP13Z16(h),
where ρw is the density of liquid water (106 g m-3),
Δh is the length of the radar range gate, Z(hi) is the
reflectivity factor at the ith radar measured gate and n represents the
number of the in-cloud radar-measured gates. The cloud droplet number
concentration (Nd) is calculated from the following formula:
Nd=(ν+3)(ν+4)(ν+5)ν(ν+1)(ν+2)×6LWPπρw∑i=1nZ12(hi)Δh.
Both of these retrieved properties have been evaluated against other methods
in . The comparison of different retrieved
microphysical cloud properties revealed that re is the parameter
least affected by the instrumental errors of the MWR and radar. The estimated
uncertainties in re are about 10–15 % and in Nd
around 40–60 %. In both proxies the uncertainties are due to instrument
errors and algorithm assumptions. The main algorithm assumptions include the
following: (1) the droplet size distribution is approximated by a mono-modal gamma
distribution, (2) the moments of the droplet size distribution are correlated
among each other and (3) the droplet concentration and droplet size
distribution shape parameter remain constant with height in each profile.
Following , the gamma cloud droplet distribution
shape parameter is set to 8.7. This value is obtained from the ratio between
the third and second moments of the droplet distribution and has been found
in reanalysis of the in situ observations of stratocumulus clouds
.
Similarly to the aerosol proxy, we compare the re at
a set distance from the cloud base. We set this distance at 85 m above
the cloud base detected from the lidar measurements. Lidar can detect the
cloud base height more precisely than radar; the difference can be up to two
range gates. Hence we use the distance of 85 m, which is equal to two
range gates, to ensure that the cloud is detected by both instruments.
Data selection criteria
Clouds are complicated systems with many processes taking place at the same
time. Singling out a small microphysical process is difficult. Data need to
be limited by implementing a number of filters. Firstly, this monitoring
scheme applies only to liquid water clouds on top of the boundary layer in
well-mixed conditions, where the cloud base is located below 2000 m.
This limitation ensures that the cloud is not decoupled from the boundary
layer and the aerosol background below the cloud
. Secondly, we can only consider data where no
precipitation is present, including drizzle, as it can obscure the formative
stage of a cloud . We use the Cloudnet
categorisation data for the classification of the observed targets. This
scheme relies on the measurements from three separate instruments. Only
profiles where all three instruments provide good-quality data can be
analysed. Data quality is classified in the Cloudnet data set in a similar
way to the categorisation product. We can therefore easily filter data where
a problem with the measurements was detected.
Some larger scale factors, such as boundary layer dynamics or variations in
temperature, pressure or humidity, can influence changes in the cloud. We
ensure similar meteorological conditions by analysing aerosol and cloud
properties on a daily basis. This minimises the influence of variations in
general weather conditions. However, the transition between meteorological
conditions can happen within a day and often even at a smaller timescale. To
account for these kinds of daily changes, we use filters of the meteorological
conditions, namely temperature, pressure and specific humidity. For each
parameter we calculate a mean value and a standard deviation; if the standard
deviation is below 10 % of the mean value, we consider that as similar
meteorological conditions. We use the integrated value of ATB as a proxy of
aerosol concentration. As we mentioned before, we integrate ATB in the column
from 120 m above the ground (level of complete overlap) to
300 m below the cloud base height. This limits the possible cloud
base height to above 500 m above ground level, if the ATB is to
be integrated over at least two ranges.
Cloud and aerosol properties measured or derived from the
observations at the Graciosa Island, Azores.
Measured quantity
Definition
Instrument(s)
Cloud liquid water path
LWP (g m-2)
MWR
Radar reflectivity factor
Z (dBZ or m6 m-3)
WACR
Cloud droplet effective radius
re (µm) (see Eq. )
WACR/MWR
Cloud droplets number concentration
Nd (cm-3) (see Eq. )
WACR/MWR
Attenuated backscatter coefficient
ATB (m-1 sr-1)
Vaisala CT25K
We also apply a constraint on LWP to isolate the aerosol activation process
from different interactions that can happen at the same time. Daily data sets
are divided into profiles where the value of LWP is similar. We divide the
data into bins of LWP of 10 g m-2. Creating even smaller bins is
difficult due to the limited data points. We only consider LWP bins where the
total number of data points is above 20. LWP should be above
30 g m-2 and below 150 g m-2. Values below 30 g m-2 are
disregarded because of the uncertainty of LWP calculated from the MWR, which is
around 15 g m-2 . The values above
150 g m-2 are excluded to avoid precipitating clouds.
The analysis of an aggregated data set grouped by varying meteorological
conditions (as defined above) would be a good way of getting a better
understanding of the drivers of aerosol–cloud interactions. Such a study can be
carried out using the monitoring method presented in this study, but is beyond the scope of
this paper.
Application of the method to observations from Graciosa Island, Azores
We present here two example case studies of the practical application. The
deployment of the Atmospheric Radiation Measurement (ARM) Program Mobile
Facility on Graciosa Island, Azores, in 2009 and 2010 provides a comprehensive
data set for assessing aerosol effects on low-level liquid water clouds.
Boundary-layer clouds were the most frequently observed cloud type
(40–50 %), with the maximum occurrence during the summer and autumn months
under the presence of anticyclonic conditions .
The instruments we use in this study are a W-band ARM Cloud Radar (WACR)
operating at 95 GHz , a laser
ceilometer Vaisala CT25K operating at 905 nm and a two-channel microwave
radiometer (MWR) operating at 23 and 31.4 GHz. Data from this campaign are
available in the standardised Cloudnet format, which is the basis of
calculations presented here. The Cloudnet data set is re-gridded to the
vertical resolution of the radar (42.86 m) and the time resolution of
the radiometer (30 s). Table summarises all
measurements and all products derived for the data analysis.
Based on the data selection criteria presented in the section above, we
identified two case studies for testing the method: 3 and 29 November 2009.
Both cases showed only a small variability of the LWP which enabled
distribution of data into small bins of LWP g m-2. The station was
located at the north-east shore of the island, situated upwind in order to
reduce the impact of the island. The NOAA HYSPLIT back trajectory model
National Oceanic and Atmospheric Administration Hybrid Single Particle
Lagrangian Integrated Trajectory Model indicated
that the aerosol for the selected days came from marine sources. This single
source of aerosol allowed us to test the method without adding the extra
complexity of a multiple aerosol sources background. We chose two case
studies from the same season, with similar meteorological conditions. Cases
vary in the cloud base height and in the aerosol loading.
The time–height cross section of the radar reflectivity factor from
WACR, the attenuated backscatter coefficient from Vaisala CT25K and the
liquid water path from the MWR for a full day of measurements on 3 November
2009.
Study case from 3 November 2009
The conditions on 3 November 2009 were characterised by a northerly wind of
about 2.5 m s-1 in the boundary layer. The cloud cover persisted the
whole day, with periods of drizzle and heavy rain after 18:00 UTC.
Precipitation-free periods were identified between 00:00 and 05:00 UTC, with
a second short period between 13:30 and 15:00 UTC, after a light
precipitation event (Fig. ). Based on the Cloudnet
categorisation and the measurements from WACR and the MWR, only data in these
two periods were analysed on that day. LWPs in the selected periods ranged
from 15 to 130 g m-2. As few data points were available with an LWP
above 90 g m-2, we limit the data analysed to an LWP between 30 and
90 g m-2. The cloud base was located around 800 m above ground
level (a.g.l.) between 00:00 and 05:00 UTC and around 500 m a.g.l.
between 13:30 and 15:00 UTC.
The time–height cross section of the cloud droplet effective radius
(re) calculated from WACR and MWR measurements (Eq. )
and the cloud droplet number concentration (Nd) calculated from
Eq. () from 3 November 2009. Data are only retrieved in the time
steps when the data selection criteria are met.
Figure presents the time–height cross section of the
retrieved microphysical cloud properties. Only data from time steps meeting
the data selection criteria are calculated. In the chosen periods,
re varies from 3 to 7 µm, with a mean radius of
5 µm and a standard deviation of 0.75 µm. Nd
ranges in the selected periods from 150 to 1700 cm-3. Some values are
much higher than the observational data for stratocumulus. Nd
rarely exceeds 500 cm-3 and is generally lower (200–300 cm-3)
for marine stratocumulus .
Aerosol background (represented by ATB) in the selected periods is variable
with the mean value 0.64×10-3 sr-1 and a standard deviation
of 0.18×10-3 sr-1. ATB in the period between 13:30 and
15:00 UTC is significantly lower, mainly because it was followed by a period
of precipitation and the cloud base was located considerably lower than in
the first period.
The time–height cross section of the radar reflectivity from WACR,
the attenuated backscatter coefficient from Vaisala CT25K and the liquid
water path from the MWR for a full day of measurements on 29 November
2009.
All data points available on 3 November 2009 are divided into bins based on
the value of the LWP which ranges from 30 to 90 g m-2. Data were
divided into six separate bins, each covering 10 g m-2.
Figure presents the relation between the integrated
attenuated backscatter ATB and cloud droplet effective radius re.
The calculated values of the correlation coefficient, R, and ACIr
are presented for every bin. Both R and ACIr are calculated for
the lnATB and lnre (Eq. ).
Table summarises values of R, ACIr and the
coefficient of determination, r2, for every LWP bin. The coefficient of
determination, r2, suggests the percentage of the variability in cloud
droplet size that can be explained by changes in aerosol concentrations. Note
that both R and ACIr values are highest for 3 November 2009 in
the LWP range from 40 to 70 g m-2. This may indicate that
aerosol–cloud interactions representing the activation process are more
significant only for the lower LWP values, and for the higher values of LWP,
other processes, such as collision and coalescence of cloud droplets or cloud
top cooling, may play a more important role. Another possible explanation can
be the presence of drizzle when LWP is above 70 g m-2. Some studies
suggest that marine stratocumulus clouds can form drizzle particles at LWP
values as low as 75–100 g m-2 .
ACIr (Eq. ) and the statistical parameters
calculated between ln(re) and ln(ATB), namely Pearson
product-moment correlation coefficient, R, and the coefficient of
determination, r2, and the number of observations within the LWP bins,
n, for two case studies from Graciosa Island, the Azores (3 and 29 November
2009).
3 November 2009
29 November 2009
LWP bin
ACIR
R
r2
n
ACIr
R
r2
n
30 < LWP < 40
0.01
-0.09
0.01
63
0.08
-0.50
0.25
45
40 < LWP < 50
0.06
-0.36
0.13
34
0.08
-0.52
0.27
63
50 < LWP < 60
0.06
-0.41
0.16
49
0.07
-0.56
0.31
67
60 < LWP < 70
0.04
-0.30
0.09
92
0.09
-0.65
0.42
96
70 < LWP < 80
0.00
-0.03
0.00
50
0.05
-0.39
0.16
98
80 < LWP < 90
0.08
-0.26
0.07
32
0.03
-0.27
0.07
39
Figure shows the relation between the integrated
attenuated backscatter, ATB, and the cloud droplet number concentration,
Nd, together with the corresponding R and ACIN
(Eq. ). Cloud droplet number concentration increases with the
increase of aerosol concentration (represented by ATB) as expected by the
aerosol–cloud interactions. Table 3 summarises values of R, ACIN
and the coefficient of determination, r2, for both study cases.
Study case from 29 November 2009
On 29 November 2009 a northerly wind of about 2 m s-1 in the boundary
layer persisted most of the day. Periods of drizzle and rain occurred
throughout the day, with heavy precipitation after 15:00 UTC. Therefore we
only consider data before 15:00 UTC.
The cloud base was located around 1600 m a.g.l.
(Fig. ). Periods between 00:00 and 03:00, 05:30 and
06:00 and 08:30 and 14:00 UTC correspond with the data selection criteria. In
all cases, the categorisation provided by Cloudnet identifies that the cloud
layer consists of liquid water cloud and aerosol only. LWP in the selected
periods varies between 15 and 150 g m-2. As there are few data points
available with an LWP above 90 g m-2, we limit the data analysed to an LWP
between 30 and 90 g m-2.
Figure shows the retrieved properties in periods
corresponding to our data selection criteria. In the selected periods
Nd varies from 55 to 1900 cm-3, with a standard deviation of
380 cm-3 and mean value of 750 cm-3. Values of re range
between 2.5 and 7 µm, with a mean radius 4.6 µm and a
standard deviation of 0.65 µm. ATB in the selected period has a
mean value of 1.53×10-3 sr-1 and a standard deviation of
0.25×10-3 sr-1. It should be noted that on 29 November, ATB
is higher, but, even accounting for the uncertainty of ATB, the variation is
smaller than on 3 November.
Suitable data from 29 November 2009 are divided into bins based on the value
of the LWP which ranges from 30 to 90 g m-2. Data were divided into
six separate bins, each covering 10 g m-2.
Figure presents the relation between the integrated
attenuated backscatter, ATB, and cloud droplet effective radius,
re, together with the correlation coefficient, R, and ACIr calculated
for each bin. It can be observed that data points are less scattered on the
29 November than on the 3 November, and the values of both R and
ACIr are also higher. Similar to the case from the 3 November, R
and ACIr are highest in the LWP range between 40 and
70 g m-2.
The time–height cross section of the cloud droplet effective radius
(re) derived from the WACR and the MWR (Eq. ) and the
cloud droplet number concentration (Nd) calculated from
Eq. () from 29 November 2009. Data are only retrieved in the
time steps when the data selection criteria are met.
The values of the effective radius, re, derived from WACR
and MWR measurements are plotted versus the integrated attenuated backscatter
ATB measured by Vaisala CT25K on 3 November 2009. Data are sorted by the
values of LWP from the MWR. Every panel shows the corresponding value of
ACIr (Eq. ) and the Pearson product-moment
correlation coefficient, R, for that LWP bin.
The values of the effective radius, re, derived from WACR
and MWR measurements are plotted versus the integrated attenuated backscatter
ATB measured by Vaisala CT25K on 29 November 2009. Data are sorted by the
values of LWP from the MWR. Every panel shows the corresponding value of
ACIr (Eq. ) and the Pearson product-moment
correlation coefficient, R, for that LWP bin.
Figure presents the relation between the integrated
attenuated backscatter, ATB, and the cloud droplet number concentration,
Nd, together with the corresponding R and
ACIN.
The cloud droplet number concentration, Nd, derived from
WACR and MWR measurements with Eq. () is plotted versus the
integrated attenuated backscatter ATB measured by Vaisala CT25K on 3 November
2009. The corresponding value of ACIN (Eq. ) and the
Pearson product-moment correlation coefficient, R, is
presented.
The cloud droplet number concentration, Nd, derived from
WACR and MWR measurements with Eq. () is plotted versus the
integrated attenuated backscatter ATB measured by Vaisala CT25K on
29 November 2009. The corresponding value of ACIN (Eq. )
and the Pearson product-moment correlation coefficient, R, is presented.
Comparison of example case studies
Table summarises statistical parameters, including the
number of observations within each LWP bin, for both case studies presented
here. Values of the correlation coefficient, r, are generally higher for the
value of LWP in the range from 40 to 70 g m-2. This suggests that
aerosol–cloud interactions connected to the droplet activation play a more
important role in the lower values of LWP, and that supposedly, drizzle can
obscure the process of the activation of aerosol into cloud droplets. For
both cases the calculated values of ACIN are very high, with a
value on the 29 November of 1.59, which exceeds the theoretical bounds
(from 0 to 1). This is possibly due to an overestimation of the cloud droplet
number concentration (Nd) by the retrieval. As we mentioned before,
the observational values of Nd for marine stratocumulus clouds are
around 200–300 cm-3 and the retrieved values for both case studies presented here exceed this range drastically. Therefore, we think that it is
more reasonable to compare the values of ACIr, which are between 0
and 0.09 in this study. This range of ACIr is comparable to other
studies of aerosol–cloud interactions performed with ground-based remote
sensing instruments (for example, reported values range from 0.04 to 0.15 in
).
ACIN (Eq. ) and the statistical parameters
calculated between ln(Nd) and ln(ATB), namely the
Pearson product-moment correlation coefficient, R, and the coefficient of
determination, r2, and the number of observations, n, for two case
studies from Graciosa Island, the Azores (3 and 29 November 2009).
3 November 2009
29 November 2009
ACIN
R
r2
n
ACIN
R
r2
n
0.78
0.32
0.10
320
1.59
0.43
0.19
408
Summary and outlook
In this paper we present a method for observing aerosol–cloud interactions.
This method enables continuous monitoring of cloud microphysical responses to
the changing aerosol concentration. It utilises high-resolution ground-based
remote sensing instruments. This scheme is developed on the basis of a
standardised data format from Cloudnet. Therefore, this method can be applied
at any ground-based cloud observatory participating in the Cloudnet network.
We used the Cloudnet cloud categorisation product to choose data points with
the specific targets only (liquid water clouds and aerosol). Instead of
aggregating data with the same values of LWP over a longer period, we process data
from each day separately.
Daily data for analysis are selected based on a range of criteria. Data points
complying with all of them are divided into bins of LWP where each bin is
10 g m-2 wide. For every bin we calculate the Pearson product-moment
correlation coefficient, R, ACIr (Eq. ) and the
coefficient of determination, r2. We show that both the statistical
parameters and ACIr can be used to quantify the dependence of the
cloud droplet size on the aerosol concentration. We showed that it is
possible to derive ACIr and the statistical parameters on a daily
basis and with that ensure that no large variation in the meteorological
conditions is present. Collocation of daily data into larger data sets can be
carried out, but should be based on very similar meteorological conditions. In our
study we identified similar meteorological conditions based on
temperature, pressure and specific humidity. We say that the conditions are
similar if the standard deviation of each parameter is less than 10 % of
its mean value.
We showed two example case studies to present this method. Both data sets
come from the deployment of the Atmospheric Radiation Measurement
(ARM) Program Mobile Facility on Graciosa Island, Azores, in 2009 and 2010. The
presented cases both are characterised by marine stratocumulus clouds; both
occur in November and have similar general meteorological conditions. We
show the correlation coefficient, ACIr, and the coefficient of
determination for both cases and all the LWP bins. We observe a higher
correlation of aerosol concentration and cloud properties in the lower values
of LWP (from 40 to 70 g m-2). This suggests that aerosol–cloud
interactions are more significant processes at lower LWPs, and when the LWP increases,
other processes such as collision and coalescence are the dominant cloud microphysical processes for the case studies presented here. A
study based on a bigger data set should be performed to draw more general
conclusions. We also observed an increase of the correlation between the
aerosol and cloud properties when the parameters are compared at a set height
dependent on the cloud base height.
The method we developed is based on a synergy of widely available,
high-resolution ground-based remote sensing instruments. It enables the
interactions of aerosol and clouds to be monitored. Although data need to
comply with restrictive criteria, the use of a Cloudnet data format and the
categorisation product makes data selection possible close to real time. We
showed that using the integrated value of the attenuated backscatter from
lidar enables the monitoring of aerosol–cloud interactions. The measurements
from a radar, a lidar and a microwave radiometer are collected continuously
and can therefore provide a continuous estimate of the effects of aerosol
concentration on cloud properties. This framework of measurements can be
implemented at any observatory where the Cloudnet data set is available and
can be integrated into a Cloudnet framework as one of the standard products.
The software developed for this methodology is available under the GNU
General Public License . Monitoring aerosol–cloud
interactions in the same manner over multiple regions will allow for more
studies of these phenomena and will result in a better understanding of the
interactions between aerosol and clouds.