Introduction
The evolution of the ice phase in initially liquid-water clouds is still
poorly understood and thus not well considered in climate models. Trustworthy
predictions of the overall indirect aerosol effect on climate are impossible
as long as the important branch of heterogeneous ice formation, the
subsequent production of rain, and the associated removal of water from the
atmosphere are not properly described in atmospheric circulation models.
Aircraft-based field campaigns in cloudy environments are performed to
improve the basic process understanding of heterogeneous ice formation under
given meteorological and aerosol conditions. Laboratory studies and in situ
aerosol characterization provide important knowledge on the influence of
a variety of natural and anthropogenic aerosol types on cloud glaciation.
Lidar- and radar-based remote sensing allows for a continuous monitoring of
co-occurring aerosol and cloud fields in their natural environment and thus
a detailed study of the evolution of the ice phase in liquid and mixed-phase
cloud layers and the impact of aerosol layers on these processes.
Ground-based active remote sensing is, in general, of great importance for
observational studies of aerosol-cloud interactions because of its unique
potential to observe aerosol layers and clouds (from base to top) with high
vertical and temporal resolution and this over long time periods
. Continuously
running stations can provide dense aerosol-cloud data sets for statistical
analysis for all seasons of the year. Organized in networks, regional aspects
regarding aerosol conditions (varying mixtures of urban haze, biogenic
aerosols, smoke, marine and dust particles), and orographic and
meteorological influences can be studied in large detail.
However, further efforts are required to improve the retrieval capabilities
of lidar-radar supersites. Regarding heterogeneous ice formation, it is of
interest to explore the potential of polarization lidar to deliver height
profiles of ice nuclei concentration (INC) up to cloud base as well as around
and above cloud layers. Such an INC profiling would open new ways to explore
the evolution of mixed-phase clouds and the role of aerosols in this context.
Applied to spaceborne CALIOP (Cloud Aerosol Lidar with Orthogonal
Polarization) observations, such an INC retrieval technique could help to
establish global 3-D maps of INC. CALIOP is part of the CALIPSO
(Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) mission
.
A first attempt to use particle optical properties measured with lidar for an
INC estimation was undertaken during the Saharan Mineral Dust Experiment
(SAMUM) in May-June 2006 . Based on Aerosol Robotic Network (AERONET) photometer observations
of optical and microphysical dust properties it was demonstrated that the
retrieval of the particle number concentration APC250 of large desert
dust particles with radius r>250 nm from lidar profiles of the dust
extinction coefficient σd measured at 532 nm wavelength is
possible with good accuracy. This is an essential prerequisite for the next
step, the estimation of INC. The large particle fraction is assumed to be the
reservoir for most favorable ice nuclei .
However, to estimate INC from APC250 in observed dust layers, a rather
crude assumption about the ratio of APC250 to INC of 30 ± 20 was used
in this first, preliminary approach of .
After the eruption of the Icelandic volcano Ejyafjallajökull in April 2010,
investigated the role of volcanic particles to serve as
ice nuclei and continued the discussion regarding the relationship between
the particle extinction coefficient, number concentration APC250 and INC
in the case of volcanic particles, which were found to be very efficient ice
nuclei. The authors used the same parameterization to obtain APC250 from
the lidar-derived σd values as for desert
dust and also applied very rough assumptions about the ratio of APC250
to INC to provide estimates for INC in the case of volcanic aerosol layers.
The recently published INC-APC250 parameterization schemes
motivated us to intensify our effort to
develop a quantitative lidar-based method for INC profiling. These
parameterizations are developed for immersion freezing which is the most
important heterogeneous ice nucleation mechanism . They are applicable to desert dust and, more general, also to
not well defined aerosol types .
In this paper, we start with an INC retrieval scheme for desert dust. Mineral dust from
desert areas belongs to the most important aerosol components in the
atmosphere regarding heterogeneous ice formation .
Desert dust particles are advantageous for
the following reasons: as mentioned, for a successful lidar-based INC
retrieval a clear relationship between the lidar-derived extinction
coefficient σd and the particle number concentration
APC250 is of key importance. Such a close correlation is given for
desert dust because desert dust particles always show a pronounced bimodal
volume size distribution consisting of a fine mode (particles with
r<500 nm) and a pronounced coarse mode (particles with r>500 nm). This
feature significantly facilitates the APC250 retrieval and subsequent
INC estimation as will be discussed in Sect 3. The correlation study in
Sect. 3 is based on several combined lidar and AERONET photometer data sets
collected during the two desert dust SAMUM campaigns in Morocco
SAMUM-1, 2006; and Cabo Verde SAMUM-2,
2008; and the Saharan Aerosol Long-range Transport and
Aerosol-Cloud-Interaction Experiment SALTRACE;
conducted in Barbados in the summer of 2013. Furthermore, we include a
4-year AERONET data set collected at Limassol, Cyprus, in our
investigation of the relationship between dust extinction and the number
concentration of large dust particles. Cyprus is frequently crossed by major
desert dust outbreaks from the Sahara and the Middle East deserts
. For the sake of simplicity (see Sect. 3) we will use
APC280 (number concentration of particles with r>280 nm) in our study
instead of APC250 which was introduced by .
After a short introduction into the instrumentation, field activities and
auxiliary meteorological data in Sect. 2, the INC retrieval method is
explained in Sect. 3. Section 4 contains applications of the new INC retrieval
scheme to CALIOP observations performed during overpasses over the European
Aerosol Research Lidar Network (EARLINET) station of Limassol, Cyprus. The
CALIOP and Limassol lidar results (optical properties, APC280, INC) are
compared for a major and unique Saharan dust outbreak with dust layers of up to
10 km height and for another dust outbreak from the Middle East deserts.
Summarizing and concluding remarks are given in Sect. 5.
Instrumentation and auxiliary meteorological data
CUT lidar
The lidar station of the Cyprus University of Technology (CUT) at Limassol
(34.7∘ N, 33∘ E, 50 ma.s.l.) is located about
150 km south of Turkey and 250 km west of Syria and belongs
to EARLINET . The lidar is described by
and enables us to determine height profiles of the
particle backscatter coefficient and particle linear depolarization ratio at
532 nm.
In the analysis of the CUT lidar data as described in Sect. 3.1, actual height
profiles of temperature and pressure profiles are required for the Limassol
region. The operational systems GDAS (Global Data Assimilation System) of the
National Weather Service's National Centers for Environmental Prediction
(NCEP) provide these meteorological parameters. NOAA's Air Resources
Laboratory (ARL, https://www.ready.noaa.gov/gdas1.php) NCEP model GDAS
output archives contain basic meteorological fields for the horizontal wind
components, temperature, and humidity for specific pressure levels.
AERONET sun/sky photometer
The EARLINET lidar site is co-located with an AERONET sun/sky photometer
(AERONET, CUT-TEPAK site, Limassol, Cyprus,
http://aeronet.gsfc.nasa.gov) . The CUT AERONET photometer allows for the retrieval of AOT at
eight wavelengths from 339 to 1638 nm. Sky radiance observations at
four wavelengths complete the AERONET observations. From these measurements
the column-integrated particle size distribution is retrieved
.
Further AERONET sun/sky photometer observations are used in our study. The
measurements were conducted in the framework of SAMUM-1 close to the Sahara
at the airport of Ouarzazate in southern Morocco in May and June 2006, of
SAMUM-2 at the airport of Praia, the capital city of Cabo Verde, in the near
range of the outflow regime of Saharan dust across the tropical Atlantic, in
January and February 2008, and of SALTRACE at the Caribbean Institute for
Meteorology and Hydrology on Barbados, in the far range of Saharan dust
long-range transport across the Atlantic in June and July 2013, about
5000–8000 km west of the main dust sources. Details of these AERONET stations
can be found on the AERONET web page (http://aeronet.nasa.gov), in
and . For these
desert dust campaigns, lidar observations of dust layering are also available .
CALIOP
The spaceborne lidar CALIOP is described by . Numerous
validation projects have been carried out e.g., to
demonstrate the capability of this lidar to provide accurate aerosol
backscatter profiles throughout the troposphere. The spaceborne aerosol/cloud
lidar measures polarization-sensitive backscatter signals at 532 nm.
CALIOP aerosol products include height profiles of the 532 nm
particle backscatter coefficient, extinction coefficient, particle
backscatter coefficient determined from the cross-polarized 532 nm
signal channel, and the particle linear depolarization ratio. We use the
CALIOP level 2 version 3 aerosol profile products. Besides the available
profile of the particle depolarization ratio, we calculated this quantity in
addition from the individual profiles of the cross-polarized and total
532 nm particle backscatter coefficients after smoothing of these
individual profiles as suggested by .
For the CALIPSO level 2 aerosol profiles product, vertical profiles of the
mean values of meteorological parameters along the flight track are also provided. These data are given for the midpoint of each range bin of the
CALIOP profile. The meteorological parameters such as temperature, pressure,
and relative humidity are derived from the Goddard Earth Observing System
Model, Version 5 (GEOS-5) data product and are provided by the GMAO (Global
Modeling and Assimilation Office) Data Assimilation System
(http://gmao.gsfc.nasa.gov).
HYSLPIT
The data analysis includes a careful investigation of the air mass origin and
long-range aerosol transport by means of backward trajectory analysis with
the HYSPLIT (HYbrid Single-Particle Lagrangian Integrated Trajectory) model.
Access is provided via the NOAA ARL READY website
(http://www.arl.noaa.gov/HYSPLIT.php). HYSPLIT is described in detail by
and .
Method
In this section, we present the INC retrieval scheme. The data analysis
consists of three parts. In the first part (Sect. 3.1), height profiles of
the particle backscatter coefficient are determined from the elastic
backscatter lidar data. Afterwards, the polarization lidar technique is used
to separate dust and non-dust backscatter fractions and to estimate the
desert dust extinction coefficients σd. The second part
(Sect. 3.2) deals with the conversion of σd profiles into
APC280 profiles. In this context, a comprehensive study of the
relationship between σd and APC280 is presented, based on
the AERONET observations mentioned above. This investigation demonstrates
that an accurate determination of APC280 from σd is
possible. The third part (Sect. 3.3) covers the conversion of APC280 to
INC. Here we use the recently published parameterizations of
, which are based on comprehensive laboratory
studies and field campaigns conducted during the last 14 years. Table 1
provides an overview of all steps of the method. The different steps will now
be explained in the following subsections.
The five steps required to obtain the dust-related INC profile from
the profile of the particle backscatter coefficient βp
measured with polarization lidar. The determination of βd and
σd is explained in Sect. 3.1. In Sect. 3.2, the retrieval of
the number concentration of large particles APC280 is described, and the
INC estimation is finally outlined in Sect. 3.3.
Step
Computed parameter
Reference
1
Particle backscatter coefficient
βp
2
Dust backscatter coefficient
βd
3
Dust extinction coefficient
σd
4
Dust APC280
nd,280
Eq. (1)
5
Dust INC
nIN
Eq. (2),
Eq. (3),
(Left) The 532 nm particle backscatter coefficient (green) and particle
linear depolarization ratio (black) as a function of height above sea level
(a.s.l.) and (right) dust extinction coefficients computed by means of the
one-step and two-step methods . A major dust outbreak from the Middle East deserts
was observed with the CUT lidar at Limassol, Cyprus, on 29 September 2011,
whereas fine-mode soil dust, probably injected during biomass burning events in Turkey, was
observed on 27 September 2011. The data analysis is explained in detail by .
Part 1: Dust extinction from polarization lidar
Figure presents two examples of dust profiling with the
CUT lidar at Limassol. The retrieval of the particle backscatter coefficient
βp at 532 nm (step 1 in Table 1) and particle linear
depolarization ratio is discussed in detail by and
. Fine-mode soil dust dominated particle
backscattering on 27 September 2011 in Fig. , while a
major dust outbreak from the Middle East deserts was observed on 29 September
2011. The particle linear depolarization ratio at 532 nm is used to separate
the non-dust backscatter coefficient from the dust backscatter coefficient
βd (step 2 in Table 1). As explained in
, two methods are available for the identification
and quantification of the dust contribution to total particle backscattering
and extinction. The so-called one-step method is used to separate non-dust
particle backscattering from backscattering by fine and coarse dust particles. We
assume a particle linear depolarization ratio of 0.05 for non-dust particles
and 0.31 for dust particles so that depolarization ratios <0.05 and > 0.31
indicate pure non-dust and pure dust aerosol backscattering, respectively.
Mixtures are indicated by depolarization ratios from > 0.05 to <0.31 and
are separated by means of Eq. (3) in . The two-step
method distinguishes backscattering from non-dust aerosol, fine-mode dust,
and coarse-mode dust particles. Non-dust particles cause a particle linear
depolarization ratio of 0.05 or less, fine-mode dust a depolarization ratio
of 0.16, and coarse-mode desert dust a particle depolarization ratio of 0.39.
In the first step, we separate the coarse-mode dust backscatter coefficient
and the residual particle backscatter coefficient (non-dust plus fine-dust
particle backscattering) using Eq. (6) in , and in
the second round we separate the non-dust backscatter coefficient from the
fine-dust backscatter coefficient using Eq. (11) in
. In the case of desert dust observation on
29 September 2011, however, both methods lead to almost the same profile for
the total (fine plus coarse-mode) dust backscatter coefficient and the
respective total dust extinction coefficient in the pronounced dust layer
above 500 m height (above the polluted boundary layer). The one-step and
two-step solutions for the dust extinction coefficient are shown in the right
panel of Fig. . On 27 September 2011, we observed a
smoke-dust plume over Limassol with dominating fine-mode smoke as well as
fine-mode soil dust . A pronounced
coarse mode was missing. Under these conditions, only the two-step method
delivers correct results. The one-step and two-step retrieval profiles do not
match in this case. More details are given by . To
avoid such complicated situations in this first paper on lidar-derived INC
retrievals, we concentrate on desert dust plumes only, and use the
well-established one-step method in the case studies presented in Sect. 4.
The source region of the observed dust can easily be identified by means of
backward trajectory analysis.
The dust extinction coefficient σd at 532 nm (step 3 in
Table 1) shown in Fig. is obtained from the dust
backscatter coefficient βd by multiplying βd with
the dust lidar ratio which was obtained before as the optimum dust lidar
ratio from the particle backscatter retrieval after . The
dust lidar ratio is 43 sr for the 27 September soil-dust case, and 39 sr for
the desert dust observation on 29 September 2011. Both dust lidar ratios are
characteristic of the Middle East region .
The uncertainty in the desert dust extinction coefficients is on the order of
20 % with a range from
10 % for strong dust outbreaks and 30 % for moderate to
background dust conditions.
Correlation between aerosol optical thickness (500 nm AOT)
and column-integrated aerosol particle number concentration (column APC280)
considering particles with r>280 nm.
Desert-dust-dominated observations from several field campaigns (SAMUM-1,
large red open squares, SAMUM-2, green circles, SALTRACE, small pink squares)
and from long-term observations at Limassol from 2011 to 2013 are considered.
For Cyprus, coarse-mode (light blue circles) and total (fine + coarse-mode,
deep blue circles) AOTs are correlated with column APC280. In the case
of the stars (four Cyprus observations with total AOT from 1.6–4), AOT and
column APC values are divided by 4. Least squares fitting to all shown data (except the dark blue Cyprus values)
yield the black solid line with the slope of 6.85 × 1011 m-2.
Part 2: From dust extinction to APC280
In the next step (step 4 in Table 1), we estimate the number concentration of
large particles, APC280 (denoted as nd,280 in
Eq. ()) from the 532 nm dust extinction coefficient σd in Mm-1 by means of
nd,280(z)=cd,280⋅σd(z)
with the conversion factor cd,280=0.673 (in Mmcm-3).
This conversion factor is obtained from a comprehensive analysis of AERONET
sun/sky photometer observations during strong Saharan dust outbreaks reaching
Ouarzazate, Morocco, Praia, Cabo Verde, Barbados, and Limassol, Cyprus. An
overview of the AERONET measurements considered in our study is shown in
Fig. . The column-integrated value of the particle number
concentration APC280 is plotted against AOT at 500 nm (column-integrated
extinction coefficient). In the following we assume equal dust extinction at
the laser wavelength of 532 nm and the AERONET photometer wavelength of
500 nm, i.e., we ignore a weak wavelength dependence of dust backscattering
and extinction in the 500–550 nm wavelength range.
Column-integrated particle volume size distribution (a) and respective particle number size distribution
(b) derived from AERONET
sun/sky photometer observations at Limassol, Cyprus. Two Saharan dust cases (11 March
2013, olive, 2 June 2013, green) and two Middle East desert dust
cases (29 September 2011, orange, 1 November 2013, red) are shown.
The dotted vertical lines at 0.28 µm indicate the lower limit of
the particle radius range considered in the calculation of column
APC280, which is defined as the sum of all particles larger than 280 nm in radius.
The column APC280 values for the four measurement cases are given as numbers together
with 500 nm total AOT in panel (a).
The determination of the column APC280 values from the basic AERONET
information (column particle volume size distribution) is illustrated in
Fig. . Particle volume size distributions measured at
Limassol during two dust outbreaks from the Middle East (1 November 2013 and
29 September 2011) and two outbreaks from the Sahara (11 March and 2 June
2013) are shown in Fig. a. The particle volume size
distribution is retrieved for 22 logarithmically equidistant discrete radius
points ri with index i from 1 to 22. The particle radius range from
r1=0.05 µm to r22=15 µm is covered. Each radius
ri represents a radius interval of logarithmically equal width of about
0.27. To obtain the particle number concentration for each individual radius
class shown in Fig. b, we simply divided the volume
concentration of a given radius interval (or for the discrete radius point
ri) by the volume of a single particle with radius ri.
The column APC280 value is then simply given by the sum of all particles
of the radius classes with radii ri≥330 nm and includes
therefore all particles with roughly r>280 nm (the part of the size
spectrum on the right side of the dashed vertical line in
Fig. b), when taking the width of the radius interval
around 330 nm into account. The radius interval for r8=330 nm roughly
represents the radius interval from 280 to 380 nm. As mentioned before, we
use APC280 instead of APC250 as originally suggested by
for the sake of simplicity. We simply add the particle
number concentrations of the radius classes 8–22 and avoid analyzing the
radius class 7 (200–280 nm radius interval) for the contribution of
250–280 nm particles to the class-7 particle number concentration.
APC250 is about 10–15% larger than APC280.
Relationship between dust layer mean 500 nm extinction coefficient
(EC) and dust layer mean APC280 for observations taken during three desert
dust field campaigns at Morocco, Cabo Verde, and Barbados. The linear
regression yields cd,280 = 0.673 ± 0.07 Mm cm-3. The
correlation coefficient is 0.915. cd,280 is used in
Eq. ().
A good correlation between the
500 nm AOT and the column-integrated APC280 was found for all
dust observations in Morocco, Cabo Verde, and Barbados (see Fig. ). For dust outbreaks
towards Limassol, Cyprus, the correlation was lower because of the
contribution of omnipresent anthropogenic aerosol pollution to the total AOT
in the eastern Mediterranean. The correlation between the coarse-mode AOT and
column APC280 is much better. The coarse-mode AOT is largely determined
by light extinction by dust particles. From all the AERONET data in
Fig. we conclude that a clear and close relationship exists between
desert-dust APC280 and the dust-related extinction coefficient.
According to the regression line in Fig. the column
APC280 is approximately given by AOT (500 nm) multiplied by a factor of
6.85 × 1011 m-2.
To translate the column-related findings in Fig. into scales
of particle extinction coefficient (measurable with lidar) and respective
particle number concentration, we simply used the layer depth information
from the lidar observations in Morocco, Cabo Verde, and Barbados. The dust
layers were typically well mixed and reached from the surface to 5–6 km
heights (Morocco, summer 2006), from the surface to 1.0–1.5 km heights
(Cabo Verde, winter 2008), and from 1.0 to 4–6 km heights (Barbados, summer 2013).
We divided the individual AOT and column APC280 pairs by the respective
layer depths and obtained in this way the correlation shown in
Fig. . We ignore here a small contribution of marine particles
(<20 % contribution to the 500 nm AOT during the major dust outbreak
situations) to the dust observations at Cabo Verde and Barbados. The linear
regression yield a clear relationship between the dust extinction coefficient
and APC280 (Eq. (), step 4 in Table 1). The correlation
coefficient is 0.91. The slope of the regression line in Fig.
of 0.685 (in units of 1012 m-2) is slightly steeper than the
respective one in Fig. of 0.673 (in Mmcm-3) because
the regression in Fig. includes the Cyprus data (coarse-mode
AOT values).
The overall uncertainty in the lidar-derived APC280 values is estimated
to be on the order of 30 %, keeping the 20 % uncertainty in the
determination of the dust particle extinction coefficient σd in mind and assuming a further uncertainty on the order of 20 % in the
conversion of σd into APC280 values. This 20 %
uncertainty includes a potential error of 10–15 % of the
AERONET-derived APC280 value obtained by applying an
inversion algorithm to the basic spectral AOT and sky radiance observations.
Studies of and , which
compared AERONET size distributions with the respective airborne in situ
observations during the SAMUM campaigns, show that the uncertainty in
terms of APC280 obtained from the AERONET data is on the order of
<20 %. There may be situations with giant dust particles
(> 15 µm in radius), for example during dust storms close to the Saharan
source region. These giant dust particles are not considered in the AERONET
retrieval of the volume size distribution. However, the impact of these
missing giant particles on the AERONET results seems to be small, given the
high overall correlation between APC280 and σd in
Fig. which includes SAMUM-1 results (Morocco, close to the
dust source) as well as SALTRACE observations (Barbados, 5000–8000 km west
of the main dust sources).
It should also be clearly mentioned in this context that there is no real
alternative to AERONET observations shown in Figs. and
. Alternative measurements could be airborne in situ
observations of aerosol microphysical and optical properties. But airborne
observations are expensive and thus rare from the statistical point of view.
Only AERONET can provide statistically dense, high-quality data sets of
optical and microphysical properties of aerosol particles for the same air
column at undisturbed ambient conditions. Exactly those data are needed for
our correlation study. Well-established and approved methods are available to
derive particle size distributions with high accuracy and uncertainties below
10–20 % . Complementary methods
can be used in addition to check the quality of the microphysical products
and the consistency between the retrieved optical and
microphysical properties.
Part 3: Estimation of INC from APC280
The retrieval of APC280 from σd is the basic lidar
contribution to the estimation of the INC profiles. Part 3 now provides the
link to the published INC parameterizations (step 5 in Table 1) gained from
comprehensive INC laboratory and field studies.
The INC parameterizations introduced by hold
for standard (std) pressure (p0=1013 hPa) and temperature (T0=273.16 K) conditions so that we have to convert each profile value
APC280(pz, Tz) from ambient pressure pz and temperature Tz
at height z to APC280(p0, T0) by using the factor (Tzp0)/(T0pz).
introduced a so-called global (aerosol-type-independent)
INC parameterization:
nIN(p0,T0,Tz)=a(273.16-Tz)b×na,280(p0,T0)c(273.16-Tz)+d,
with na,280 (in stdcm-3) representing APC280,
nIN (in stdL-1) representing INC, a=0.0000594,
b=3.33, c=0.0265, d=0.0033, and temperature T(z) in Kelvin (<273.16 K).
As mentioned, we use nd,280 instead of
nd,250 as given in the original formula of .
recently introduced another INC parameterization scheme
which is explicitly developed for mineral dust:
nIN(p0,T0,Tz)=fdna,280(p0,T0)ad(273.16-Tz)+bd×expcd(273.16-Tz)+dd,
with the so-called atmospheric correction factor fd=3,
ad=0.074, bd=3.8, cd=0.414, and
dd=-9.671. Finally, we transfer the obtained INC values
nIN(p0,T0,Tz) to the ones for ambient pressure and
temperature conditions, nIN(pz,Tz), by multiplying
nIN(p0,T0,Tz) with the factor (T0pz)/(Tzp0).
According to , Eqs. () and
() can be used to estimate INC for immersion freezing
processes and are applicable to the temperature range from -9 to
-35 ∘C (Eq. ) and -21 to
-35 ∘C (Eq. ). In Sect. 4 (results), we use these immersion-freezing-based parameterizations for higher
as well as lower temperatures. According to ice nucleation
for coated mineral dust particles (coated with natural and/or anthropogenic
soluble material) can be described as immersion freezing as well. Above the
deliquescence relative humidity, additional water is added to the coating and
a solution shell forms around the particles, causing them to nucleate ice
from concentrated solutions via the immersion freezing pathway, taking a
freezing point depression into account.
Regarding uncertainties in the INC computation, we estimate that
Eq. () allows for a prediction of dust-related INC within an
uncertainty range of a factor of 5–10 for temperatures from -5 to
-25 ∘C. The parameterization after Eq. () is
based on observational data collected during nine field studies. These field
studies were performed at a variety of locations around the globe over
a 14-year period. further pointed out that an INC
uncertainty of an order of magnitude is still acceptable for cloud process
modeling. The uncertainties are lower and within a factor of 2 when using
Eq. () . From the comparison with the
results obtained with Eq. () in Fig. and in
the next section, we can conclude that Eq. () underestimates
the dust-related INC by up to a factor of 100 for temperatures below
-30 ∘C.
Relationship between dust extinction coefficient and APC280 (grey
line) after Eq. () and INC after Eq. () (thin lines) and
Eq. () (thick lines) for
temperatures of -15, -25, and -35 ∘C. The full range of
dust extinction coefficients from 30 to 400 Mm-1 measured on
29 September 2011 (Fig. ) is shown.
CALIOP measurement (height versus latitude/longitude display) of the
attenuated 532 nm particle backscatter coefficient during an overpass
45 km to the east of Limassol from 1 June 2013, 23:47 UTC (universal time coordinated), to
2 June 2013, 00:01 UTC. Desert dust layers are given in green to
yellow colors and reach up to 4–10 km height. The inserted
height-time display shows the CUT lidar observation of the range-corrected
cross-polarized 532 nm backscatter lidar signal on 1 June 2013,
21:30–23:55 UTC. Dust
(green, yellow, and light blue layers) is observed up to 9–10 km
height. The vertical black line indicates the closest position of CALIOP
(laser footprint) to the ground-based CUT lidar at Limassol, Cyprus.
Regarding the overall uncertainty of our lidar-based INC retrieval we can
summarize that the profile of APC280 can be derived from the dust
extinction coefficients with a relative error of the order of 30 % and that
the estimation of INC profiles is therefore possible within an overall factor
of 3 by applying Eq. ().
Figure provides an overview of the retrieval approach (steps
3–5 in Table 1) for the dust outbreak on 29 September 2011 shown in
Fig. . Dust extinction values range from
30 to 400 Mm-1. The grey APC280 curve in Fig.
describes the linear increase of the large particle number concentration with
increasing dust particle extinction coefficient σd. To
illustrate the large influence of the ambient temperature Tz on INC, INC
curves for -15, -25, and
-35 ∘C are plotted in Fig. . As can be seen,
a temperature decrease by 10∘ causes an increase in the INC
concentration by 2 orders of magnitude when using Eq. () and
by a factor of 20 when using the more general aerosol parameterization
(Eq. ). Thus, a 10∘ decrease in temperature
(equivalent to about 1000–1500 m height change in the free
troposphere) during lifting of air particles in a convective cloud tower
leads to an enormous increase of the potential of a given dust load to
initiate ice nucleation via immersion freezing.
Results
We applied our INC retrieval scheme to two CALIOP observations in the eastern
Mediterranean close to Limassol, Cyprus. One of these overpasses took place
during a strong Saharan dust outbreak on 1–2 June 2013. Traces of dust
reached the cirrus level (8–10 km height). During the second overpass,
mineral dust was advected from the deserts in the Middle East on 1–2 November
2013. This second case can be regarded as representative for typical dust
outbreaks with dust layers mainly in the lower free troposphere at heights
between 1 and 5 km .
HYSPLIT 7-day backward trajectories arriving within the dust layer over Limassol, Cyprus, at
1500 (red), 4500 (blue), and 7500 m height (green) on 2 June 2013, 00:00 UTC.
CALIOP and CUT lidar observations during a Saharan dust outbreak in June 2013
An overview of the dust and cloud observations of the spaceborne CALIOP lidar
in the night of 1–2 June 2013 is shown in Fig. .
The spaceborne lidar crossed eastern Ukraine (52–56∘ N), the Black
Sea area (44–52∘ N), Turkey (36–42∘ N), the eastern
Mediterranean Sea (30–36∘ N), Egypt (22–32∘ N), and Sudan
(<22∘ N) within 12.5 min (corresponding to a distance of
about 5000 km). The backward trajectory analysis in
Fig. indicates the southern parts of the Sahara as
sources for the dust observed in the middle and upper troposphere over the
eastern Mediterranean.
Figure compares the basic optical
properties derived from the ground-based CUT lidar and the CALIOP
observations. The nearest horizontal distance of the CALIOP laser footprint
to Limassol was 45 km. We used HYSPLIT forward and backward
trajectory analysis to identify the air mass which was seen by both lidars.
This air mass crossed Limassol about 1 h before reaching the CALIPSO
flight track. Accordingly we selected the CUT lidar signal averaging period
from 22:28 to 23:28 UTC. CALIPSO crossed the area at 23:53 UTC.
The small deviations between the optical properties derived from the CALIOP
and CUT lidar observations are mainly caused by different data analysis
schemes, assumptions on input parameters, and the different signal averaging
periods (1 h in the case of the CUT lidar, a few seconds in the case of
CALIOP) . Different aerosol conditions in the lowest 500 m
over the city of Limassol and over the open Mediterranean Sea (CALIOP) may
widely explain the differences at heights below 500 m.
Comparison of profiles of particle backscatter coefficient, extinction
coefficient, and particle linear depolarization ratio measured with
ground-based CUT lidar at Limassol (thick green curves, 60 min means) on
1 June 22:28–23:28 UTC, and with spaceborne CALIOP (blue noisy
curves, 45 km east of Limassol, during seconds 38–43 of
23:53 UTC). In the case of CALIOP, 135 signal profiles are averaged
(nine level-2 aerosol profiles, 45 km horizontal resolution). No
vertical smoothing is applied to the CALIOP data. CUT lidar signal profiles
were vertically smoothed by 180 m. A lidar ratio of SCAL=40 sr
was selected in the retrieval of CALIOP data and
SCUT=55 sr (optimum lidar ratio for this Saharan dust
case after )
was applied in the CUT computation of backscatter and extinction profiles.
The CALIOP products (backscatter and extinction coefficients) are determined
by using lidar-ratio look-up tables . For desert
dust scenarios, the lidar ratio is set to 40 sr. Our own measurements yield a
particle (dust plus non-dust) lidar ratio of 55 sr for the total tropospheric
column following the complex data analysis procedure described by
. The lidar ratio of 55 sr for this desert-dust-dominating
scenario is in full agreement with respective SAMUM lidar-ratio
observations .
For the INC retrieval, we smoothed the height profile of the particle
backscatter coefficient measured with CALIOP with a vertical smoothing length
of 600 m. To reduce the noise in the CALIOP depolarization ratios, we
also smoothed the basic cross-polarized and total particle backscatter
coefficient profiles with 600 m vertical window length before we
calculated the volume depolarization ratios and then the particle
depolarization ratios. This procedure was recommended by .
The left panel in Fig. shows the smoothed basic
CALIOP products (backscatter coefficient, depolarization ratio). The right
panel presents the extinction profiles for desert dust particles and for
remaining non-dust aerosol (marine and anthropogenic) particles calculated
from the smoothed CALIOP profiles. The separation of the dust and non-dust
optical properties was explained in Sect. 3.1. In the conversion of the
backscatter coefficients to the respective dust and non-dust extinction
coefficients we used lidar ratios of 50 sr for non-dust particles and 55 sr
for Saharan dust particles.
CALIOP data analysis products based on the 135 signal profile averages shown
in Fig. : (Left) Vertical profiles of
532 nm particle backscatter coefficient (green) and particle linear
depolarization ratio (black), and (right) derived particle extinction
coefficients for non-dust (blue) and
desert dust particles (red). The particle
backscatter coefficients (left, green) are taken from the CALIOP data base
and smoothed with 600 m gliding window length. The particle linear
depolarization ratio is computed from the cross-polarized and total backscatter
coefficient profiles after smoothing the profiles with 600 m vertical window length.
Lidar ratios used in the backscatter-to-extinction conversion are
50 sr for the non-dust particles and 55 sr for Saharan dust.
Figure presents the results for this CALIOP
overpass case in terms of dust extinction coefficient σd,
APC280, and INC. The respective products derived from the ground-based
CUT lidar observations are also shown. Because of the high
temperatures over the eastern Mediterranean in the beginning of June 2013,
with surface temperatures close to 30 ∘C, INC values are only
given for the upper part of the Saharan dust layer where temperatures
<0 ∘C where given.
Profiles of 532 nm desert dust extinction coefficient σd, APC280,
and INC. GDAS profiles of temperature and pressure for the Limassol
region were used in the CUT lidar data analysis (thin lines) and
GMAO Data Assimilation System profiles of the meteorological
parameters along the CALIOP flight track were used in the analysis of the space lidar observations (thick
lines). INC profiles computed with Eq. () are shown as light blue lines
(CALIOP, thick, CUT lidar, thin).
INC profiles computed with Eq. () are shown as dark blue lines (CALIOP,
thick, CUT lidar, thin). Grey horizontal lines indicate the temperature ranges for which the parameterizations
are explicitly valid (-9 to -35 ∘C, Eq. (), -21 to -35 ∘C,
Eq. ()).
Uncertainties are on the order of 20 % (for σd),
30 % (for APC280), and within a factor of 3 (in the case of INC) when using the dust INC
parameterization after .
Significant differences are found for the different INC parameterizations.
Compared to the INC profile using Eq. () (global aerosol INC
parameterization), significantly higher INC values are obtained with
Eq. () (mineral dust parameterization) for temperatures
<-20 ∘C. This reflects that desert dust particles are known
to be very efficient ice nuclei at temperatures <-20 ∘C,
but not at temperatures >-15 ∘C . Consequently, for temperatures from -5 to
-15 ∘C the INC estimates are slightly higher by using
Eq. () compared to the INC values from Eq. ().
For higher temperatures (0 to -5 ∘C) the INC values are not
trustworthy because these temperatures are far outside the
temperature range for which the parameterizations (Eqs. ,
) were developed. Differences between the INC profiles
derived from the CUT lidar and CALIOP observations are likewise small and
mainly caused by differences in the temperature profiles over Limassol and
above the CALIOP laser footprint.
This unique Saharan dust outbreak with dust traces up to 10 km provides a
favorable opportunity to continue the discussion on the rather strong
temperature influence on INC and the consequences for cloud glaciation. As
can be seen, although the dust number concentration APC280 is almost
constant with height in the layer from 6 to 8.5 km height, the INC values
increase by a factor of 1000, from 6 (-12 ∘C) to 8.5 km
(-30 ∘C) when the mineral dust INC parameterization
(Eq. ) is applied. This means that even traces of desert dust
occurring at the base of an evolving convective cumulus tower can develop an
enormous potential to glaciate the cloud system when lifted by updrafts over
several kilometers.
CALIOP measurement (height versus latitude/longitude display) of the
attenuated 532 nm particle backscatter coefficient during an overpass
180 km to the east of Limassol on 1 November 2013,
23:43–23:57 UTC. Desert dust layers are given in green to yellow
colors and reach up to about 4 km height. The inserted height-time
display shows the CUT lidar observation of the cross-polarized
range-corrected 532 nm backscatter signal
from 1 November 2013, 22:30, to 2 November 2013, 02:00 UTC. The vertical
black line indicates the closest position of CALIOP (laser footprint) to the
ground-based CUT lidar at Limassol, Cyprus.
CALIOP overpass during a dust outbreak from the Middle East in November 2013
A Middle East desert dust outbreak was observed on 1–2 November 2013.
Figure provides an overview of the CALIOP attenuated-backscatter observations. Figure presents the
respective HYSPLIT backward trajectories arriving at Limassol on 2 November
2013, 00:00 UTC, and shows the source regions of the dust (deserts in
Saudi Arabia, Iraq, and Syria). In contrast to the foregoing case study, dust
was detected at heights below 4 km only.
The CALIOP and CUT data analysis was performed in the same way as described
in Sect. 4.1. The noisy CALIOP data profiles, averaged over a 45 km
horizontal length, had to be smoothed with a 600 m vertical window
length. We generally used particle lidar ratios around 40 sr (for CALIOP as
well as for the CUT lidar observations) in this case of a major Middle East
desert dust outbreak. These low lidar ratios around 40 sr are representative
for Middle East desert dust .
HYSPLIT 4-day backward trajectories arriving within the dust layer over Limassol, Cyprus, at
750 (red), 1750 (blue), and 2500 m height (green) on 2 November 2013, 00:00 UTC.
Figure compares the CUT and CALIOP lidar
findings for this dust outbreak in terms of dust extinction coefficient
σd, APC280, and INC. Because the CALIOP laser footprint was 180 km east of Limassol and both (CALIOP and CUT) observations were
performed within a relatively small time window of less than 2 h
(23:45 UTC on 1 November to 01:00 UTC on 2 November), different air masses
were definitely observed . This explains the
differences between the two observations in terms of σd and
APC280. Regarding INC, temperatures along the CALIPSO flight track were
up to 2.5 ∘C lower in the free troposphere above 600 m height
compared to Limassol temperatures so that the CALIOP-derived INC values were
considerably higher because of the lower temperatures and the, on average,
higher APC280 values. However, the 0 ∘C level was above 3 km and the
-5 ∘C level was reached at 4 km height so that only a few
INC estimates for the uppermost part of the dust layer could be calculated.
Such conditions were already observed during the SAMUM-1 campaign in southern
Morocco . Ice formation in altocumulus layers developing
at the top of such dust layers was found to be almost impossible because of
the high temperatures throughout the dust layers: only when cumulus
convection was strong enough so that cloud parcels could penetrate deeply
into the free troposphere could ice formation be observed.
Same as Fig. , except for a Middle East desert dust outbreak on 1–2 November 2013.
In the case of CALIOP profiles (180 km east of Limassol), again, 135 signal profiles (45 km horizontal resolution) are averaged, collected during
seconds 8–14 of 23:47 UTC on 1 November 2013. The CUT lidar profiles show average values for the time period from 00:00 to 00:59 UTC on 2 November 2013.
The original GDAS and GMAO temperature and pressure profiles were used in the computation of the INC profiles indicated
by Tsurface=23 ∘C. The GDAS and GMAO temperature profiles were shifted by 22 K (Limassol) and 23 K (CALIOP) in the case of the INC curves
indicated by Tsurface=0 ∘C.
Uncertainties are on the order of 20 % (for σd),
30 % (for APC280), and within a factor of 3 (for INC) when using the dust INC parameterization after .
Figure contains further INC profiles. We shifted
the GDAS and GMAO temperature profiles by 22–23∘C so that the
surface temperature was 0 ∘C at both sites. We simulated
these profiles to visualize the consequences (in terms of INC increase) of a
horizontal transport of air masses towards colder areas, i.e., when such a
warm dust plume is, e.g., advected to the north (towards Turkey, Black Sea,
Russia, and Scandinavia) and gets colder by radiative cooling and mixing with
colder air during the long-range transport. As can be seen, the potential of
a given dust layer to initiate ice formation in water clouds steadily and
strongly increases. As discussed already, the ice nucleation efficiency
increases by 3–4 orders of magnitude if a dusty air mass is cooled
by about 20 ∘C. These INC levels are further increased by the
fact that air masses are usually also lifted by several kilometers during
long-range transport over thousands of kilometers .
Conclusions
A method has been introduced that permits the estimation of
desert-dust-related INC profiles from polarization lidar measurements at
532 nm wavelength. Of key importance for a successful INC retrieval is a
close relationship between the lidar-derived dust extinction coefficient σd and
the number concentration APC280 of large particles with r>280 nm.
Based on unique desert dust field observations and long-term studies with
AERONET photometers we demonstrated that this close link is given for desert
dust. The uncertainties for the different lidar products are on the order of
20 % for the derived dust extinction coefficients, 30 % for
APC280, and within a factor of 3 for INC when using the recently
developed dust INC parameterization of .
The approach paves the way for INC vertical profiling to support ground-based
and airborne in situ IN characterization and to conduct a global, vertically
resolved mapping of dust-related INC in the framework of spaceborne lidar
missions such as CALIPSO (NASA) or EarthCARE European SpaceAgency;. This was demonstrated by two case studies of
spaceborne CALIOP and ground-based CUT lidar measurements during overpasses
of CALIOP over the eastern Mediterranean. Because there are already several
dust-related global studies based on CALIOP observations with focus on geometrical and optical
properties of desert dust, it should be a comparably easy effort to do the
next step towards characterizing the aerosol conditions in terms of
APC280 and INC. Such a global INC characterization may allow for an improved
consideration of heterogeneous ice formation in atmospheric circulation
models. However, we recommend establishing a global aerosol data set in terms
of APC280 rather than of INC, and to combine the APC280 data set
with actual temperature fields from numerical weather prediction models. This
is a more flexible approach to account for the large influence of ambient
temperature conditions on the efficiency of any given aerosol layer to
initiate heterogeneous ice nucleation. Furthermore, the accuracy of
desert-dust APC280 data sets is high, in comparison with the
uncertainties in the INC estimates.
As an outlook, we need to study to what extent and with what uncertainty the
method presented here can also be used for INC profiling during situations
dominated by fine-mode aerosol such as urban haze, biomass burning smoke, or
even fine-mode soil dust injected into the atmosphere during fire events
. Under
such conditions, a good and clear correlation of the particle extinction
coefficient and APC280 may no longer apply so that a good INC
estimation is difficult. We may also test an alternative INC retrieval approach
which relates INC to particle surface area rather than to APC
.