ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-18-4317-2018Ice particle production in mid-level stratiform mixed-phase clouds observed
with collocated A-Train measurementsIce particle production in mid-level stratiform mixed-phase cloudsZhangDamaodzhang@bnl.govhttps://orcid.org/0000-0002-3518-292XWangZhienhttps://orcid.org/0000-0003-3871-3834KolliasPavlosVogelmannAndrew M.https://orcid.org/0000-0003-1918-5423YangKangLuoTaohttps://orcid.org/0000-0001-9959-2453Brookhaven National Laboratory, Upton, New York, USADepartment of Atmospheric Science, University of Wyoming, Laramie, WY
82071, USASchool of Marine and Atmospheric Sciences, Stony Brook University, New
York, USAKey Laboratory of Atmospheric Optics, Chinese Academy of Sciences, Hefei, Anhui, ChinaDamao Zhang (dzhang@bnl.gov)28March2018186431743275October201718October201726January201816February2018This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/This article is available from https://acp.copernicus.org/articles/18/4317/2018/acp-18-4317-2018.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/18/4317/2018/acp-18-4317-2018.pdf
Collocated A-Train CloudSat radar and CALIPSO lidar measurements between 2006
and 2010 are analyzed to study primary ice particle production
characteristics in mid-level stratiform mixed-phase clouds on a global scale.
For similar clouds in terms of cloud top temperature and liquid water path,
Northern Hemisphere latitude bands have layer-maximum radar reflectivity (ZL)
that is ∼ 1 to 8 dBZ larger than their counterparts in the Southern
Hemisphere. The systematically larger ZL under similar cloud conditions
suggests larger ice number concentrations in mid-level stratiform mixed-phase
clouds over the Northern Hemisphere, which is possibly related to higher
background aerosol loadings. Furthermore, we show that springtime northern mid- and high
latitudes have ZL that is larger by up to 6 dBZ (a factor of 4
higher ice number concentration) than other seasons, which might be related
to more dust events that provide effective ice nucleating particles. Our
study suggests that aerosol-dependent ice number concentration
parameterizations are required in climate models to improve mixed-phase cloud
simulations, especially over the Northern Hemisphere.
Introduction
Ice particle production in a supercooled liquid cloud has dramatic impacts on
the cloud's radiative properties, precipitation efficiency and cloud
lifetime due to distinct differences in particle sizes, shapes, fall
velocities and refractive indexes between liquid droplets and ice crystals
(Sun and Shine, 1994; de Boer et al., 2011a). Such clouds significantly
impact global and regional radiation budgets (Matus and L'Ecuyer, 2017)
having a global coverage of more than 34 % and being particularly common
at high latitudes (Shupe et al., 2011; Adhikari et al., 2012; Wang et al., 2013;
Scott and Lubin, 2016). In a mixed-phase cloud, once ice particles are
formed, they grow through water vapor diffusion at the expense of liquid
water because saturation vapor pressure is lower over ice than liquid. This
process, known as the Wegener–Bergeron–Findeisen (WBF) process
(Wegener, 1911; Bergeron, 1935; Findeisen, 1938), creates a thermodynamically
unstable condition in mixed-phase clouds. In the absence of strong vertical
air motions, the WBF process removes liquid droplets quickly, causing a
mixed-phase cloud to glaciate completely (Korolev and Field, 2008; Fan et
al., 2011). Therefore, aerosol might impose a glaciation indirect effect on
clouds by acting as effective ice nucleating particles (INPs; Lohmann,
2002). Global climate model (GCM) simulations show that changing the
glaciation temperature of supercooled clouds from 0 to -40 ∘C
causes differences in the top-of-atmosphere longwave and shortwave cloud
radiative forcing of ∼ 4 and ∼ 8 W m-2, respectively
(Fowler and Randall, 1996).
However, observations indicate that supercooled liquid water in mixed-phase
clouds persists for tens of hours or even days and down to temperatures of as
low as ∼-36 ∘C (Seifert et al., 2010; Zhang et al., 2010; de
Boer et al., 2011b). Over the polar regions where mixed-phase clouds are
commonly observed, cloud condensation nuclei (CCN) concentrations are usually
low, on the order of 10 cm-3 and sometimes less than 1 cm-3
(Mauritsen et al., 2011; Birch et al.,
2012). An increase of aerosol may enhance CCN
concentrations, thereby increasing cloud cover and reducing cloud droplet size.
This aerosol indirect effect leads to a longer mixed-phase cloud lifetime,
which is opposite the aerosol glaciation indirect effect (Lance et al.,
2011). Even more complicated is the coupling of local thermodynamic conditions and
large-scale dynamics contributes greatly to the long persistence of
mixed-phase clouds (Korolev and Isaac, 2003; Morrison et al., 2012). Bühl
et al. (2016) estimated ice mass flux at mixed-phase cloud base using
ground-based radar measurements and showed that when temperatures are above
-15 ∘C the water depletion due to ice formation is small and the
cloud layer is very stable. The WBF process in GCMs is typically too
efficient, causing severe underestimations of supercooled liquid water
fraction on a global scale (Cesana et al., 2015; McCoy et al., 2016). Tan et
al. (2016) show that the equilibrium climate sensitivity (ECS) can be
1.3 ∘C higher in GCM simulations when supercooled liquid fractions
(SLFs) in mixed-phase clouds are constrained by global satellite
observations. Improved SLF parameterizations in GCMs requires better
understanding of ice production processes in supercooled clouds under various
dynamic environments and background aerosol conditions using extensive
observations from cutting-edge instruments.
Heterogeneous nucleation, which dominates ice formation in supercooled
clouds at temperatures warmer than -36 ∘C (Pruppacher and Klett,
1997; Vali, 1996), is not well understood or well parameterized in models
because of the complicated three-phase interactions of water and the largely
unknown properties of ice nucleating particles (Cantrell and Heymsfield,
2005; DeMott et al., 2011; Morrison et al., 2012). There are four
well-recognized heterogeneous ice nucleation models: deposition nucleation,
condensation freezing, immersion freezing and contact freezing (Pruppacher
and Klett, 1997). The immersion freezing mode, which refers to the process
where an INP is immersed in a droplet at a relatively warm temperature and
causes the droplet to freeze at a colder temperature, is suggested to be the dominant
ice formation mechanism in stratiform mixed-phase clouds (de Boer et al.,
2010). This mode provides a pathway for time-dependent ice production in
clouds, which can be used to explain the long persistence of precipitating
stratiform mixed-phase clouds (Westbrook and Illingworth, 2013). Of course,
ice production in clouds also depends on the presence of INP and, for
example, laboratory measurements of INP properties provide fundamental
databases for developing and improving ice nucleation parameterizations in
models (DeMott et al., 2011; Hoose and Möhler, 2012; Murray et al.,
2012). While such databases are valuable, it is also important to observe
ice nucleation processes in the real atmosphere to constrain and evaluate
parameterizations on a global scale.
Observations of aerosol impacts on ice production in supercooled clouds
mainly come from ground-based and satellite remote sensing measurements.
Choi et al. (2010) and Tan et al. (2014) show that supercooled liquid cloud
fraction is negatively correlated with aerosol occurrence (especially dust)
using Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation
(CALIPSO) spaceborne lidar measurements. Unfortunately, because the lidar
signal cannot penetrate the liquid-dominated layer at the top of mixed-phase
clouds, aerosol impacts on ice production are not directly presented in
their studies. Seifert et al. (2010) avoided this issue by using 11 years of
grounded-based lidar depolarization measurements to study relationships
between dust occurrence and ice-containing cloud fractions over central
Europe. Also, Zhang et al. (2012) quantitatively estimated dust impacts on
ice production in mixed-phase clouds using combined CALIPSO lidar and
CloudSat radar measurements over the “dust belt”, a region including
North Africa, the Arabian Peninsula and East Asia.
Our objective in this paper is to better characterize the primary
heterogeneous ice production in clouds on a global scale. We focus on
mid-level stratiform mixed-phase clouds, which provide a relatively simple
target for studying ice generation for the following reasons. Mid-level
supercooled clouds are decoupled from the Earth's surface and therefore are not
affected by strong turbulent vertical mixing within the boundary layer. There
is usually a liquid-dominated layer at the top of mid-level stratiform
mixed-phase clouds (de Boer et al., 2011b; Riihimaki et al., 2012) and, when
the temperature is low enough, ice particles form from liquid droplets, grow
in the water-saturated environment and fall out of the liquid-dominated
layer (Fleishauer et al., 2002; Carrey et al., 2008; Zhang et al., 2014).
Below the liquid-dominated cloud layer, ice crystals continue to grow during
the fall until they reach a level that is sub-saturated with respect to ice.
The less complex dynamic environment and straightforward ice growth
trajectory in mid-level stratiform mixed-phase clouds provides an ideal
scenario for studying cloud thermodynamic-phase partitioning and aerosol
impacts on ice formation in clouds, and for retrieving cloud microphysical
properties with remote sensing measurements (Wang et al., 2004; Larson et
al., 2006; Heymsfield et al., 2011; Zhang et al., 2012, 2014; Bühl et
al., 2016). Zhang et al. (2010) show for the first time the climatology of
mid-level stratiform clouds and their macrophysical properties using A-Train
satellite remote sensing measurements. This study further uses 4 years of
collocated CloudSat radar and CALIPSO lidar measurements together with other
ancillary A-Train products between June 2006 and 2010 to provide a global
statistical analysis of ice production in mid-level stratiform mixed-phase
clouds.
Dataset and methodology
The description of the collocated A-Train measurements follows directly from
Zhang et al. (2010). The main instrument on the CloudSat satellite is a
nadir-viewing 94 GHz Cloud Profiling Radar (CPR) – the first spaceborne cloud
radar. The sensitivity of CPR was approximately -30 dBZ during the period
analyzed here. The CPR has an effective vertical resolution of about 480 m
(oversampled at 240 m vertical resolution) and horizontal resolutions of
between 1.3 and 1.4 km cross-track and between 1.7 and 1.8 km along-track
(depending on latitude; Stephens et al., 2008). The CPR can detect clouds
with large cloud droplets, large ice crystals or precipitating
hydrometers, and shows the vertical structures of clouds (Stephens et al.,
2002). The Cloud–Aerosol LIdar with Orthogonal Polarization (CALIOP) onboard
the CALIPSO satellite is a near-nadir-viewing lidar with two wavelengths, at
532 and 1064 nm, with linear polarization measurements available at 532 nm
(Winker et al., 2007). The CALIOP has vertical resolutions of 30 m below
8.2 km and 60 m between 8.2 and 20.2 km. The horizontal resolutions of
CALIOP are 333 m below 8.2 km and 1000 m between 8.2 and 20.2 km. CALIOP
is able to provide global, high-resolution vertical profiles of aerosols and
optically thin clouds (Winker et al., 2010). Due to differing wavelengths,
the CPR and CALIOP measurements provide complementary capabilities that
enable accurate detection of cloud boundaries and their vertical structures
(Stephens et al., 2008). Their complementary nature is exemplified in the
detection of supercooled liquid-dominated mid-level mixed-phase cloud layers,
where the CPR is more sensitive to the large-sized ice crystals and the
CALIOP is more sensitive to the higher number concentration of small liquid
droplets (Zhang et al., 2010). Because temperature is critical for ice
formation in supercooled clouds, the European Centre for Medium-Range Weather
Forecasts (ECMWF) AUX product is collocated to provide temperature and
pressure profiles with the same vertical resolution as CPR (Partain, 2007).
In addition, MODIS on the Aqua satellite provides cloud liquid water path
(LWP) determined from retrieved cloud droplet effect radius and cloud optical
depth (Platnick et al., 2003). The ancillary CloudSat MODIS-AUX product that includes
cloud LWP is collocated and employed in our analysis. This analysis is
limited to daytime hours since MODIS cloud property retrievals are only
available when sunlit. Previous studies show that MODIS-retrieved LWP have a
positive bias at high latitudes due to the solar zenith angle dependence in
the retrieval algorithms (O'Dell et al., 2008). Through a comparison of MODIS
retrievals with ground-based microwave radiometer (MWR) measurements at the
Atmospheric Radiation Measurement (ARM) Facility's North Slope of Alaska
(NSA) site, Adhikari and Wang (2013) show that MODIS overestimates LWP for
stratiform mixed-phase clouds by 35 and 68 % in the temperature ranges of
-5 to -10 ∘C and -10 to -20 ∘C, respectively.
Algorithms using collocated CALIOP and CPR measurements to identify mid-level
stratiform mixed-phase clouds were developed by Zhang et al. (2010). To
summarize, candidate mid-level clouds are identified when the CALIOP-detected
cloud top height is above 2.5 km from the ground level and cloud top
temperature is greater than -40 ∘C. Of these clouds, many have a
liquid-dominated layer at the top, which is detected by a strong peak in
lidar total attenuated backscatter (TAB) near cloud top (i.e., layer-maximum
TAB greater than 0.06 sr-1 km-1) and a rapid attenuation of the
lidar backscattering such that the lidar-observed layer geometric depth is
less than 500 m. We use the lidar TAB and rapid lidar signal attenuation to
identify the presence of liquid layers, which is a method that has been
widely used for liquid layer identification from spaceborne lidar
measurements (e.g., Hogan et al., 2004; Zhang et al., 2010; Wang et al., 2013). We
note that horizontally oriented ice crystals can also have a large lidar TAB
however they do not attenuate lidar backscattering significantly. Wang et al. (2013,
Fig. 10) shows that this approach correctly determines liquid clouds in terms
of layer-mean depolarization ratio and integrated backscattering coefficient.
In addition, collocated MODIS cloud LWP greater than 10 g m-2 is used
to guarantee the detection of a liquid-dominated layer. The cloud system is
identified as being stratiform when the cloud top height standard deviation
is smaller than 300 m. To calculate the standard deviation, a cloud system
is identified as containing at least 10 continuous cloudy profiles,
corresponding to a horizontal scale of approximately 11 km (the horizontal
distance between two contiguous CPR profiles is 1.1 km). In addition, the
CPR radar reflectivity factor Ze must be smaller than 10 dBZ near the
surface to exclude strongly precipitating mid-level stratiform clouds.
Radar measurements are used to detect the presence of ice particles in
mid-level stratiform mixed-phase clouds. Cloud droplets and pristine ice
crystals are much smaller than the radar wavelength, so they fall within the
Rayleigh scattering regime where Ze is proportional to the sixth power
of the particle size. Ice crystals are typically larger than cloud droplets
such that Ze is dominated by ice crystal scattering (Shupe et al.,
2007). Bühl et al. (2016) use the Ze value closest to the liquid
layer base with ground-based high vertical resolution (30 m) radar
measurements for studying ice particle properties. However, this is
difficult with A-Train satellite measurements as the CPR has a coarse
vertical resolution and the liquid layer at the top quickly attenuates
CALIOP signals, preventing reliable detection of the liquid layer base.
Given that the physical thickness of supercooled liquid layers at the top of
mid-level stratiform mixed-phase clouds are generally smaller than 500 m and
the vertical resolution of the CPR is oversampled to 240 m from the
effective vertical range resolution of 480 m, we use the maximum Ze
(referred to as ZL) within 500 m below the CALIOP-detected
liquid-dominated layer top to ascertain the presence of ice particles for
analysis. Using temperature-dependent ZL thresholds, Zhang et al. (2010)
show that, at temperatures lower than -6 ∘C, approximately
83.3 % of mid-level liquid-topped stratiform clouds are mixed-phased,
revealing the importance of understanding their ice production. Furthermore,
to exclude seeding from upper-level clouds and to enable use of MODIS column-integrated LWP retrievals, only single-layer clouds detected with collocated
CALIOP and CPR measurements are analyzed (Wang et al., 2013). Since we study
ice production in stratiform clouds in this study, we focus on clouds with
top temperatures within the -40 to 0 ∘C range.
Global distribution of single-layer mid-level stratiform cloud
occurrence frequency from 4 years of collocated CALIOP and CPR
measurements.
To illustrate the importance of understanding ice production in these
clouds, Fig. 1 shows the global distribution of single-layer mid-level
stratiform cloud occurrence during daytime based on 4 years of collocated
CALIOP and CPR measurements. The occurrences are smaller than what are
presented in Fig. 3 in Zhang et al. (2010) because we only focus on
single-layer supercooled stratiform clouds here, while they include both
single-layer and multiple-layer clouds with top temperatures warmer than -40 ∘C. Single-layer mid-level supercooled stratiform clouds have an
annual global mean occurrence of approximately 3.3 % with occurrences
greater than 6 % over northeastern China and the northern polar regions,
and greater that 10 % over the southern polar regions.
The probability distribution function (PDF) of LWP for
single-layer mid-level stratiform clouds from MODIS retrievals. The red area
indicates the third of the cumulative distribution that is centered on the
peak of the LWP PDF. Given in the figure is LWP 1, the value for the lower
third, and LWP 2, the value at the upper third.
Results and discussions
The straightforward ice crystal growth pattern in mid-level stratiform
mixed-phase clouds as described above enables using Ze magnitudes to
quantitatively infer ice number concentration variation in stratiform
mixed-phase clouds. It is noted that because Ze is proportional to ice
number concentration and also the sixth power of particle size, differences
in Ze can be either attributed to large changes in ice number
concentration or to small changes in ice crystal size. Based on integrated in
situ measurements and airborne remote sensing, Zhang et al. (2012) suggest
that – for similar clouds in terms of cloud top temperature (CTT) and LWP –
ice crystal growth in mid-level stratiform mixed-phase clouds is similar and
that Ze differences reveal differences in ice number concentration. They
compare ZL differences between dusty and non-dusty mid-level stratiform
mixed-phase clouds and conclude that mineral dust statistically enhances ice
number concentration by a factor of 2 to 6, depending on CTT. It is quite
challenging to retrieve ice number concentration from radar measurements.
Zhang et al. (2014) developed a method to estimate ice number concentration
in stratiform mixed-phase clouds by using combined Ze measurements and
1-D ice-growth model simulations. CTT, LWP and vertical air motion are
required as inputs in their algorithms and sensitivity tests show that they
all have large impacts on ice number concentration estimations. Due to large
uncertainties in the MODIS-derived LWP for mixed-phase clouds (Adhikari and
Wang, 2013), ice number concentration estimations in mixed-phase clouds using
A-Train satellite measurements are not available at this stage. In order to
use the ZL magnitude to infer ice number concentration variations in this
study, a narrow LWP range is selected to remove the impacts of LWP variation
on the measured ZL. Figure 2 shows the probability distribution function
(PDF) of LWP for single-layer mid-level stratiform clouds from MODIS
retrievals. The global mean LWP for single-layer mid-level stratiform clouds
is approximately 119 g m-2 with a standard deviation of
101 g m-2. The PDF of LWP has a peak at approximately 45 g m-2
and values decrease quickly away from the peak. For our statistical analyses,
a narrow LWP range is selected from the third of the cumulative distribution
centered on the LWP peak which is bounded by the values of 20 and
70 g m-2.
Global, annual-average, mid-level mixed-phase stratiform cloud ice
production statistics. Results for six 30∘ latitude bands are shown
covering the northern and southern tropical, mid-latitude and high-latitude regions.
Cases are restricted so that the supercooled liquid water path is within the
range of 20 and 70 g m-2. (a) Cloud distributions are in
terms of cloud top temperature (CTT) and layer-maximum radar reflectivity
(ZL), a proxy for ice production. (b) Mean ZL of clouds as a
function of CTT. Distributions are normalized at each CTT bin. The bin sizes
for CTT and ZL are 1 ∘C and 1 dBZ, respectively.
Figure 3 shows the global, annual-average, mid-level mixed-phase stratiform
cloud ice production statistics. Figure 3a shows the cloud distributions in
terms of CTT and ZL for six latitude bands (northern and southern tropical, mid-latitudes and high latitudes) within the LWP range of 20 and
70 g m-2. Local peaks are seen in the ZL distributions at
∼-15 ∘C, which correspond to the fast-planar ice growth
regimes, and troughs are seen at -10 and -20 ∘C, corresponding
to the relatively slow isometric growth habit (Sulia and Harrington, 2011;
Zhang et al., 2014). Below -20 ∘C, ZL increases steadily as CTT
decreases, probably because of higher ice number concentrations at lower CTTs
(Zhang et al., 2014). At a given CTT, the ZL distribution has approximately
10 dBZ variations, which might be related to different environmental aerosol
loadings and/or cloud LWPs associated with each individual cloud.
Nevertheless, comparing different latitude bands, the northern latitude bands
statistically have larger ZL than their southern counterparts at a given CTT.
The northern mid- and high latitudes have the largest ZL values.
A complementary way to view the latitudinal dependence of the cloud
properties is given in Fig. 3b, which presents the mean ZL of mid-level
stratiform mixed-phase clouds as a function of CTT for the narrow LWP range.
Due to potential drizzle contributions to Ze measurements at relatively
warm CTTs, the mean ZL is only calculated for clouds with CTT lower than
-10 ∘C (Rasmussen et al., 2002; Zhang et al., 2017). Using mean ZL
differences, we can quantitatively estimate ice concentration variations in
mid-level stratiform clouds under similar cloud conditions in terms of CTT
and LWP, similar to that presented in Zhang et al. (2012). From Fig. 3b, the
northern mid- and high latitudes have the largest mean ZL, while the southern
low-latitude band has the smallest values. Consistent with the cloud
distribution statistics in Fig. 3a, Northern Hemisphere latitude bands have
larger mean ZL at a given CTT than their counterparts in the Southern Hemisphere.
Depending on CTT range, the northern mid- and high-latitude bands are
∼ 6 and 8 dBZ larger than their southern counterparts, while the
northern low-latitude band is only ∼ 1 dBZ greater than southern low-latitude band. These results are consistent with the studies of Choi et
al. (2010) and Tan et al. (2014), who show that the Northern Hemisphere has
a smaller supercooled liquid fraction than the Southern Hemisphere for a
given temperature range, and it is also consistent with Zhang et al. (2010),
who show that the Northern Hemisphere mixed-phase clouds have larger ice
water paths (IWPs).
Atmospheric pressure profiles with subfreezing temperatures for
(a) the six latitude bands and (b) their seasonal
variations. MAM stands for March–April–May, JJA for June–July–August, SON
for September–October–November and DJF for December–January–February.
Mean of the layer-maximum radar reflectivity (ZL) of mid-level
stratiform mixed-phase clouds as a function of cloud top temperature and
liquid layer path (LWP). Results shown for six latitude bands as in Fig. 1.
The dashed lines are the narrow range of LWP between 20 and 70 g m-2.
Atmospheric pressure is another factor that could impact ice crystal
diffusional growth and therefore the observed hemispheric and latitudinal ZL
differences. The same subfreezing temperature at low latitudes corresponds to
a higher height above mean sea level and therefore a lower atmospheric
pressure level than mid- and high latitudes. Takahashi et al. (1991) show
that the mass growth rate at 860 mb is approximately 30 % larger than at
1010 mb due to the impact of pressure difference on the diffusivity of water
vapor in air. It is also noted that, from Fig. 20 in their paper, the mass
growth difference due to pressure difference is much smaller than that due to
temperature difference. Within the Rayleigh scattering regime, radar
reflectivity is proportional to the square of ice crystal mass. Therefore,
the 30 % difference in mass causes approximately a 2 dBZ difference in
Ze. We investigated hemispheric and latitudinal differences of
atmospheric pressure at subfreezing temperatures using 4 years of
ECMWF-AUX product between 2006 and 2010. As shown in Fig. 4, for a given
temperature, hemispheric differences in atmospheric pressure profiles are
negligible over mid- and low-latitude bands, and range from 40 to 140 mb
over the high-latitude band. Therefore, pressure-level differences have a
negligible contribution to the hemispheric ZL differences over mid- and low-latitude bands, and contribute less than 2 dBZ to the observed hemispheric
ZL differences over the high-latitude band. After removing the contributions
from atmospheric pressure differences, mid-level stratiform mixed-phase
clouds over northern mid- and high-latitude bands still have ZL that are
approximately 6 dBZ larger than their southern counterparts. By focusing on
mid-level stratiform mixed-phase clouds and carefully isolating the impacts
of CTT, LWP and atmospheric pressure, the systematically larger ZLs suggest
a factor of 4 higher ice number concentrations over northern mid- and high
latitudes than their southern counterparts.
Similar to Fig. 3a, except for the seasonal variation in stratiform
mixed-phase cloud ice production statistics.
Similar to Fig. 3b, except for the seasonal variations in mean ZL of
clouds as a function of CTT.
The systematically larger ZL and higher ice number concentrations over the
Northern Hemisphere for similar mid-level stratiform mixed clouds might be
related to larger background aerosol loadings in the Northern Hemisphere.
Using CALIOP measurements, Tan et al. (2104) show that the Northern
Hemisphere has dramatically larger frequencies of high aerosol occurrence
than the Southern Hemisphere at sub-freezing temperatures. Based on
ground-based lidar and radar remote sensing measurements from sites in both
hemispheres, Kanitz et al. (2011) found that layered
supercooled clouds at northern mid-latitudes have significantly larger
fractions of ice-containing clouds compared with southern mid-latitudes,
which is possibly related to the rather different aerosol conditions. In
addition, larger mean ZL over the northern mid- and high-latitude bands than
the northern low-latitude band could also be connected to larger aerosol
(especially dust) loadings at sub-freezing levels. Using multiple years of
ground-based Raman lidar measurements, Seifert et al. (2010) show that
Leipzig, Germany (northern mid-latitudes), has much higher ice-containing cloud
fraction than Cabo Verde (northern low latitudes) at a given CTT below
0 ∘C, consistent with the results in Fig. 3. They proposed that
possible factors influencing the differences include different sources of
INP, chemical aging and removal of larger aerosol particles by
washout in the tropics. Indeed, although the tropics and sub-tropics have
extensive dust source regions, large dust particles cannot be elevated to
sub-freezing levels without strong convection (Luo et al., 2015).
We next investigate the global impact of LWP on ZL and its latitudinal
variation. At a given CTT, a cloud with a larger LWP has a geometrically
thicker liquid water layer, which allows ice crystals to reside longer in the
liquid-dominated layer and grow larger due to the WBF process. In addition, a cloud
with a larger LWP also has a larger ice growth rate due to accretion (Zhang et
al., 2014). Figure 5 shows the mean ZL as a function of CTT and LWP for
the six latitude bands. As expected, the mean ZL increases gradually with LWP
at a given CTT for all latitude bands. Mean ZL generally increases more than
about 5 dBZ going from thin clouds, which are associated with small LWP, to
very thick clouds, which are associated with large LWP. Therefore,
observations show a dramatic impact of LWP on the measured ZL. However,
within any given narrow LWP range, the mean ZL for northern latitude bands
are still much greater than their southern counterparts, further supporting
our conclusion that the systematic ZL differences between northern and
southern latitude bands are related to aerosol activity.
Distributions of global dust occurrences and their seasonal
variations at different sub-freezing temperature ranges based on the dust
dataset developed by Luo et al. (2015). Temperature ranges are given at the
right. Each column is for a season, with the abbreviations described in
Fig. 4.
To further explore aerosol impacts on ice formation, Fig. 6 shows the
seasonal variations of mid-level stratiform mixed-phase cloud distributions
in terms of CTT and ZL for the six latitude bands and Fig. 7 shows mean ZL
seasonal variations as a function of CTT. From the statistical distributions
in Fig. 6, northern latitude bands have greater ZL than their counterparts in
the Southern Hemisphere at any season for similar clouds in terms of similar
CTT and LWP, probably related to higher background aerosol loadings over the
Northern Hemisphere. Comparing different seasons, southern latitude bands
generally have little seasonal variation in ZL, as is evident in Fig. 7. In
contrast, northern latitude bands have dramatic seasonal variations in ZL,
with the largest ZL occurring in MAM (boreal springtime) and smallest in DJF
(boreal wintertime). The northern mid- and high-latitude bands have the
largest seasonal variations among all latitude bands. At CTTs warmer than
-30 ∘C, ZLs over northern mid- and high-latitude bands are larger
in the boreal springtime than wintertime by approximately 4 and 6 dBZ for
similar clouds in terms of CTT and LWP, respectively. From Fig. 4b, pressure
profile differences between boreal springtime and wintertime are fairly small
over northern mid- and high-latitude bands. Therefore, the systematically
larger ZLs of 4 and 6 dBZ during boreal springtime than wintertime suggest
a factor of 2.5 and 4.0 higher ice number concentrations.
Dust particles are effective INPs and are recognized as one of the dominant
global INP sources (DeMott et al., 2010; Hoose and Möhler, 2012). Choi et
al. (2010) show the seasonal variation of global mineral dust occurrence at
the -20 ∘C isotherm using the CALIOP level 2 vertical feature mask
data. They observed a significant correlation between mineral dust occurrence and reduction in
supercooled cloud fraction, especially over the northern mid-latitudes,
suggesting that elevated mineral dust particles effectively glaciate
supercooled clouds by providing abundant INPs. In their study, the Arctic
regions have dramatic seasonal variations in supercooled cloud fractions,
with the lowest during the springtime. However, no obvious dust activity over
the Arctic regions is shown in their paper. Luo et al. (2015) point out that
the CALIOP level 2 data product often misses the detection of elevated thin
dust layers. They presented improved algorithms to identify thin dust layers
using CALIOP layer-mean particulate depolarization ratios and CPR
measurements. Figure 8 shows the distributions of global dust occurrence and
their seasonal variations at different sub-freezing temperature ranges based
on the dust dataset developed by Luo et al. (2015). It is obvious that during
March–April–May (MAM), the boreal springtime, northern mid- and
high-latitude regions have significantly higher dust occurrences than other
seasons at any given sub-freezing temperature range. Similarly, using
multiple years of ground-based remote sensing measurements at the ARM NSA
Barrow site, Zhao (2011) shows that Arctic mixed-phase clouds in springtime
have larger IWPs and smaller supercooled liquid water fraction than the other
three seasons, which might be related to there being more dust events
observed with lidar depolarization measurements during springtime that
provide effective INPs for ice nucleation in clouds. The significant seasonal
variations of ice production and their correspondence with dust occurrence in
northern mid- and high-latitude mixed-phase clouds suggest that
aerosol-dependent ice concentration parameterizations need to be used in
GCMs, and improved aerosol (especially dust) simulations are required in
order to improve global mixed-phase cloud simulations, especially over the
Northern Hemisphere.
Summary
Four years of collocated CALIPSO lidar and CloudSat radar measurements
together with other ancillary A-Train products during 2006–2010 are analyzed
to study primary ice particle production characteristics in single-layer
mid-level stratiform mixed-phase clouds on a global scale. Mid-level
stratiform mixed-phase clouds have a simple dynamic environment and
straightforward ice growth trajectory that enables using Ze measurements
to quantitatively infer ice number concentration variations. We carefully
isolate factors that impact ice diffusional growth and measured cloud layer
radar Ze by focusing on mid-level stratiform mixed-phase clouds with
the same CTT and similar LWPs. We also analyzed atmospheric pressure impacts.
Together with MODIS LWP retrievals and an improved thin dust layer detection
algorithm, we connect the observed ZL differences and ice concentration
variations to aerosol (especially dust) activities on a global scale.
Using the large dataset, we show that for similar clouds in terms of CTT and
LWP, Northern Hemisphere latitude bands have ZL that are ∼ 1 to 8 dBZ
larger than their counterparts in the Southern Hemisphere for a given CTT. After
removing contributions from atmospheric pressure differences, ZL is still
6 dBZ larger, suggesting ice number concentrations
(on average) over northern mid- and high latitudes of a factor of 4 greater than their southern
counterparts. The systematically larger ZL and higher ice number
concentrations in mid-level stratiform mixed-phase clouds over the Northern
Hemisphere are possibly related to larger background aerosol loadings. LWP
has a significant impact on measured ZL, but we show that within a given
narrow LWP range, mean ZL over northern latitude bands is always larger than
their southern counterparts. Furthermore, we show that the northern mid- and
high latitudes have dramatic seasonal variations in ZL, where ZL can be up to
6 dBZ larger in springtime than in wintertime. This might be related to more
dust events during springtime that provide effective INPs for ice nucleation
in clouds. Since mixed-phase cloud property evolution is strongly dependent
on ice number concentration, our study suggests that aerosol-dependent ice
concentration parameterizations are required in GCMs in order to improve
global mixed-phase cloud simulations. The results in this study can be used
to evaluate global ice concentrations in mixed-phased clouds and aerosol
impacts simulated by GCMs.
The CloudSat data used in this study can
be downloaded from http://www.cloudsat.cira.colostate.edu/ (Stephens et al.,
2008). The CALIPSO data can be downloaded from
https://eosweb.larc.nasa.gov/order-data (Winker, 2016).
The authors declare that they have no conflict of
interest.
Acknowledgements
This study was funded by the U.S. Department of Energy (DOE) under grant
DE-SC0012704 and the NASA under grant NNX13AQ41G. We thank the CloudSat data
group at the CloudSat Data Processing Center and CALIPSO data group at the
NASA Langley Atmospheric Sciences Data Center.
Edited by: Matthias Tesche Reviewed by: Patric Seifert and one
anonymous referee
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