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JOURNAL OF CLIMATE
VOLUME 28
The Characteristics of Ice Cloud Properties Derived from CloudSat and
CALIPSO Measurements
YULAN HONG AND GUOSHENG LIU
Department of Earth, Ocean and Atmospheric Science, Florida State University, Tallahassee, Florida
(Manuscript received 27 September 2014, in final form 9 February 2015)
ABSTRACT
The characteristics of ice clouds with a wide range of optical depths are studied based on satellite retrievals
and radiative transfer modeling. Results show that the global-mean ice cloud optical depth, ice water path, and
effective radius are approximately 2, 109 g m22, and 48 mm, respectively. Ice cloud occurrence frequency varies
depending not only on regions and seasons, but also on the types of ice clouds as defined by optical depth t
values. Ice clouds with different t values show differently preferential locations on the planet; optically thinner
ones (t , 3) are most frequently observed in the tropics around 15 km and in midlatitudes below 5 km, while
thicker ones (t . 3) occur frequently in tropical convective areas and along midlatitude storm tracks. It is also
found that ice water content and effective radius show different temperature dependence among the tropics,
midlatitudes, and high latitudes. Based on analyzed ice cloud frequencies and microphysical properties, cloud
radiative forcing is evaluated using a radiative transfer model. The results show that globally radiative forcing
due to ice clouds introduces a net warming of the earth–atmosphere system. Those with t , 4.0 all have a
positive (warming) net forcing with the largest contribution by ice clouds with t ; 1.2. Regionally, ice clouds in
high latitudes show a warming effect throughout the year, while they cause cooling during warm seasons but
warming during cold seasons in midlatitudes. Ice cloud properties revealed in this study enhance the understanding of ice cloud climatology and can be used for validating climate models.
1. Introduction
Clouds play an important role in modulating the Earth
radiation budget and global hydrological cycle (e.g.,
Stephens et al. 1990; Chen et al. 2000; Haynes et al.
2013). They impact Earth radiation via their albedo and
greenhouse effects, that is, cooling the Earth by reflecting solar incident radiation and at the same time,
warming it by blocking longwave radiation. The net effect of the competition between these two processes
depends on cloud properties, for example, cloud phase,
water amount and thickness, and so on (Stephens and
Webster 1981). For most mid- and low-level clouds, the
solar albedo effect usually dominates so that a net
cooling to the earth–atmosphere system because of
these clouds is observed (Stephens 2005). In contrast,
high and thin ice clouds are known to have a warming
Corresponding author address: Yulan Hong, Department of
Earth, Ocean and Atmospheric Science, Florida State University,
1017 Academic Way, 404 Love Bldg., Tallahassee, FL 32306-4520.
E-mail: yh12c@my.fsu.edu
DOI: 10.1175/JCLI-D-14-00666.1
Ó 2015 American Meteorological Society
net effect since they are nearly transparent to solar incident radiation but opaque to longwave radiation. As a
result, their greenhouse effect outweighs their solar albedo effect (Liou 1986). As optical depth increases,
solar albedo effect would dominate and therefore the
magnitude and sign of the ice cloud radiative forcing
would rely largely on its optical thickness. Where this
warming–cooling separation occurs in natural ice clouds
is an interesting scientific question.
Classifications have been made for ice clouds to better
study their properties. For instance, Sassen and Cho
(1992) defined three categories of cirrus based on visible
optical depth t in their lidar observation studies, that is,
subvisual cirrus (t , 0.03), thin cirrus (0.03 , t , 0.3),
and opaque cirrus 0.3 , t , 3.0. Based on radiative
transfer calculations with inputs of in situ measurement data, subvisual cirrus clouds were found to have a
positive radiative forcing of approximately 1.6 W m22
in the tropics (McFarquhar et al. 2000), while tropical
cirrus clouds with 0.02 , t , 0.3 tend to have a small
influence on shortwave radiation (instantaneous
forcing , 2 W m22) but significantly impact longwave
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instantaneous emission (;20 W m22) based on CloudSat
measurements (Haladay and Stephens 2009). For deep
convections, although their overall albedo effect usually
offsets greenhouse effect (Rajeevan and Srinivasan
2000), their radiative forcing produced by the upper
level of ice clouds has not been adequately investigated.
Although optically thin ice clouds have received much
more attention, the radiative effects of ice clouds over
the entire spectrum of optical depth have not yet been
well documented. Characterization of ice cloud properties over the entire optical depth spectrum is necessary
in order to fully understand the radiative effects of all
naturally occurring ice clouds.
Moreover, in studying the ‘‘climatology’’ simulated by
various global climate models (GCMs), precipitation,
cloud fraction, and precipitable water are reasonably
well compared among different models. However, large
discrepancies of global-mean atmospheric ice water
amount exist with model-to-model results differing in
the orders of magnitudes. The reasons for these discrepancies are that there are no robust global ice cloud
observational data to constrain the GCM models and
that only suspended clouds are considered in most
GCMs (Waliser et al. 2009; Li et al. 2012). While there is
clearly an urgent need for observed global ice water
amount, substantial disagreements exist in current satellite retrievals, which are primarily because of the
sensitivity of various observing techniques to different
part of clouds (Eliasson et al. 2011). For example, passive sensors have difficulties in obtaining vertical ice
water profiles. Although active sensors resolve the vertical structures of clouds, a single active remote-sensing
technique merely detects a segment of clouds. Thin
clouds are invisible to microwave, while visible and infrared are limited to thin clouds or the topmost part of
clouds. As a result, a combination of multiple sensors/
techniques becomes necessary to get a whole spectrum of
atmospheric ice water (Wu et al. 2006, 2009).
New observations in recent years provide unprecedented possibility for studying and improving the
understanding of ice clouds. The Cloud Profiling Radar
(CPR) on CloudSat and the Cloud–Aerosol Lidar with
Orthogonal Polarization (CALIOP) on Cloud–Aerosol
Lidar and Infrared Pathfinder Satellite Observations
(CALIPSO) provide global ice cloud retrievals for the
first time with vertically resolved values of ice water
content (IWC) (Stephens et al. 2002, 2008; Winker et al.
2003). For the CloudSat radar, thin ice clouds with IWC
smaller than approximately 0.4 mg m23 are invisible
(Wu et al. 2009), whereas the CALIPSO lidar can only
detect ice clouds with optical depth smaller than 3.0 (Liu
et al. 2005). With respect to the total mass of ice water,
thin ice clouds make very little contributions. However,
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when it comes to radiative effects, the role of thin ice
clouds becomes vitally important because of their potentially strong warming effects (Liou 1986, 2002;
McFarquhar et al. 2000). The joint observations by the
CloudSat radar and the CALIPSO lidar make it possible
to investigate ice clouds with a wide range of optical
depths, as the former can detect thick clouds while the
latter is better suited to the thin ones.
The approach of combining data from the CloudSat
radar and the CALIPSO lidar has been taken by several
investigators in cloud climate studies. For example,
Sassen et al. (2008, 2009) studied global cirrus cloud
properties and the cirrus’s connection to deep convections
following the Sassen and Cho (1992) definition of cirrus
(t , 3.0 and cloud-top temperature , 2408C). Using
CloudSat and CALIPSO data, Schwartz and Mace (2010)
found that cirrus clouds in the tropopause layer are generally independent to lower clouds, which provides insight
into the formation and dissipation mechanisms of tropopause layer cirrus. Moreover, Mace (2010) surveyed cloud
properties and radiative forcing over storm tracks of the
Southern Ocean and North Atlantic using the CloudSat
radar and the CALIPSO lidar observations, and they
found a mean ice water path (IWP) of ;100 g m22 as well
as a strong net top of the atmosphere (TOA) cooling effect mainly caused by boundary layer clouds. While the
above studies targeted a particular type or types of ice
clouds in certain regions, a global climatology of the
whole spectrum of ice clouds, including both optically thin
and thick ice clouds and in both high and low altitudes,
seems to still be missing.
The purpose of this study is to investigate cloud
properties over the broad spectrum of ice clouds globally, which include 1) the magnitude of global ice cloud
optical depth, IWP, and effective radius re; 2) global ice
cloud spatial and seasonal variations; 3) the differences
between optically thin and thick ice cloud properties;
4) the temperature dependence of ice microphysical
properties; and 5) the ice cloud radiative effects over the
entire spectrum of ice clouds. To achieve this goal, ice
cloud–retrieved properties produced from the combination of the CloudSat radar and the CALIPSO lidar
observations are analyzed in this study, and radiative
transfer modeling is performed to estimate ice cloud
radiative effects.
2. Data and methodology
Many satellite ice cloud retrieval products have been
currently derived from a series of passive or active sensors
(Waliser et al. 2009; Wu et al. 2009; Eliasson et al. 2011,
2013). Using active sensor measurements, CloudSat level2B radar-only cloud water content (2B-CWC-RO; Austin
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2007) provides vertically resolved retrievals of IWC and
effective radius, but this product does not contain those
thin ice clouds undetected by the CloudSat radar. To
reach a wider range of IWP, a combination of the
CloudSat radar and the CALIPSO lidar is used to derive
ice cloud properties. These products include the CloudSat
level-2C ice cloud property product (2C-ICE) (Deng et al.
2010) published by the CloudSat Data Processing Center
and radar/lidar product (DARDAR) developed at the
University of Reading (Delanoë and Hogan 2008, 2010).
Both DARDAR and 2C-ICE products derive ice
cloud properties including ice water content, effective
radius, and the extinction coefficient s via a synthesis of
both CloudSat radar reflectivity Ze and CALIPSO lidar
attenuated backscattering b. During the period of our
study, CloudSat was 17.5 s ahead of CALIPSO. The
CloudSat radar operates at 94 GHz with a minimum
sensitivity of 230 dBZ and has a vertical resolution of
500 m (Stephens et al. 2002). The CALIPSO lidar is
operating at 532 and 1064 nm with a vertically averaging
resolution of 60 m in the troposphere (Winker et al.
2003). Although IWC, re, and s are included in both
DARDAR and 2C-ICE, these two products are distinct
from one another by making different assumptions and
using varied techniques in their reversion algorithms (Li
et al. 2012). Deng et al. (2013) found IWC and s of 2CICE agree well with DARDAR, especially in radar and
radar–lidar regions. While in lidar-only region, DARDAR
algorithms produce larger IWC and s values than 2C-ICE,
but the re values are still in reasonably good agreements. In
this study, we choose one of these two agreeable ice
retrieval products, DARDAR, to investigate global ice
cloud properties. A brief description of the DADAR
algorithm is given as follows.
According to the different sensitivities of lidar and radar to cloud liquid drops and ice particles, one advanced
feature of the DARDAR algorithm is to distinguish between supercooled liquid water and ice by taking the
advantage of lidar backscattering signals. Lidar produces
strong echoes when meeting liquid cloud drops because
of the large liquid particle number concentrations, while
CloudSat radar signals from liquid drops are usually quite
weak (Delanoë and Hogan 2008, 2010). Supercooled
liquid water is identified by the CALIPSO lidar backscatter in regions of wet-bulb temperature lower than 08C
and air temperature higher than 2408C (Delanoë and
Hogan 2008, 2010). Liquid water is assumed when wetbulb temperatures are greater than 08C. When temperatures are lower than 2408C, all hydrometeors are
assumed to be ice. Lidar signals in and below the liquid
water layer are discarded once a supercooled liquid layer
is detected. Instead, radar signals are used to derive ice
cloud properties in these regions by assuming that radar
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echoes are predominated by ice particles because of their
large sizes compared to liquid water drops (Delanoë and
Hogan 2008, 2010).
In performing the retrievals, the DARDAR algorithm
adopts an optimal estimation formulation starting
with a first guess of a state vector (extinction coefficient,
number concentration, and extinction to backscatter
ratio) for a forward model to predict observational parameters. The prediction is then compared to true observations to get the best estimates of the state vector by
minimizing a cost function using Gauss–Newton iteration (Delanoë and Hogan 2008). Lidar multiple scattering is estimated by the fast multiple-scattering model
of Hogan (2006). Even when only observations from one
sensor are available, retrievals can still be performed by
adopting empirical methods in the literature. For example,
when only lidar data are available, IWCs can still be obtained by introducing a mass–size relationship of Brown
and Francis (1995). In the absence of lidar observations,
the area–size relation taken from Francis et al. (1998) is
used to derive the extinction coefficient (Delanoë and
Hogan 2010), and IWCs are then derived as a function of
the radar reflectivity factor Ze and temperature. This
method enables us to derive retrievals without gaps between optically thin and thick ice clouds.
Effective radius is derived by Foot (1988), which is
related to the ice water content and extinction coefficient by
re 5
3IWC
,
2ri an
(1)
where ri is the density of ice and an is the extinction
coefficient at wavenumber n. For the accuracy of retrievals, it is estimated that the extinction coefficient and
IWC have significant uncertainties up to 60%, and those
of effective radius are about 30% depending on the
mass–size relationship (Delanoë and Hogan 2010).
Nevertheless, it is believed that the DARDAR product
provides the arguably best estimates of ice properties
currently available with a wide range of ice water paths
(Stein et al. 2011; Eliasson et al. 2013).
As a demonstration of how cloud forcing depends on
cloud properties, we evaluated the ice cloud radiative effects on the earth–atmosphere system using radiative
transfer modeling. In this evaluation, ice cloud radiative
effects are estimated over the whole ice cloud spectrum
using 4-yr-averaged ice cloud properties. The meteorological conditions (temperature, pressure, water vapor,
and ozone) are averaged in the same period from European Centre for Medium-Range Weather Forecasts
(ECMWF) reanalysis data (Dee et al. 2011). Ice cloud
optical properties are derived from their microphysical
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HONG AND LIU
properties using parameterizations of Yang et al. (2000,
2005). The radiative transfer model libRadtran, developed by Mayer and Kylling (2005), is used in this
study, which has been employed for studying ice cloud
radiative effects (Wendisch et al. 2005, 2007; Ehrlich
et al. 2009). The discrete ordinate radiative transfer
(DISORT) model, version 2.0 (Stamnes et al. 1988),
with six streams is applied, and the correlated-k distribution band parameterization suggested by Fu and Liou
(1992) is used for broadband absorption parameterizations. Tropospheric aerosols in spring–summer conditions with a visibility of 50 km below 2 km have been
used as background aerosols (Shettle 1989). For globalmean simulations, earth surface albedo is set to be 0.15,
while the mean solar zenith angle is calculated to be
approximately 67.58. When considering ice cloud forcing
in different regions and seasons, calculations are performed at different solar zenith angles, and surface albedo is correspondingly set for different regions based
on MODIS MOD43B3 (Moody et al. 2005) surface albedo product.
DARDAR data from 2007 to 2010 are used in this
study. The retrieved variables have a horizontal resolution
of 1.4 km and a vertical resolution of 60 m. To identify ice
clouds, we used DARMASK_Simplified_Categorization,
an index for scene categorizations included in the
DARDAR product. In our data analysis, we only used
those data where DARMASK_Simplified_Categorization
is 1, which indicates no supercooled water was included.
3. Results
a. Occurrence frequency of ice cloud properties
Statistics of ice cloud properties over the entire globe
have been analyzed using the 4-yr-long DARDAR
products. The global ice cloud frequency is approximately 53%; that is, ice clouds are observed in 53% of all
samples. Figure 1 displays the frequency and accumulative frequency distributions of optical depth and ice
water path. Ice cloud optical depths range from smaller
than 0.1 up to over 100 as indicated by Fig. 1a. Thin
clouds are usually related to nonprecipitating clouds,
while those with large values of optical depth may be
associated with precipitation. The results indicate that a
global, conditional-mean optical depth of ice clouds is
around 4, whereas a mean value is about 2 if calculated
by including nonice samples (Fig. 1a). The mode value is
around 1.2 on a logarithmic scale. Ice clouds with optical
depth smaller than 0.03 account for about 9% of the
total ice cloud population, while those with optical
depth smaller than 0.3 and 3.0 make up about 40% and
79%, respectively, as denoted in Fig. 1b. In other words,
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subvisual (t , 0.03), thin (0.03 , t , 0.3), and opaque
(0.3 , t , 3.0) ice clouds compose approximately 9%,
31%, and 39%, respectively, among the whole spectrum
of ice clouds. Classification for the above three types of
ice clouds originates from Sassen and Cho (1992) for
cirrus. The term cirrus is not adopted in this paper since
cloud height or temperature criteria are traditionally set
for cirrus (Rossow and Schiffer 1991; Sassen et al. 2008),
while we use optical depth alone. The portion of thick
ice clouds is quite small. Ice clouds with t . 20 account
for approximately 5%, and those with t . 100 account
for merely 0.5%.
Similarly, IWP frequency and accumulative frequency distributions are shown in Figs. 1c and 1d. The
frequency distribution has a mode value at approximately
30 g m22 on a logarithmic scale. The IWP conditional-mean
value is 205 g m22, and the all sample mean is 109 g m22.
The cumulative frequency distribution shows that ice clouds
with IWP larger than the mode value account for 59%,
while those with IWP values smaller than the mean
(;100 g m22) are about 76%. In other words, the percentage of ice clouds with IWP greater than the mean value is
only 24%, implying thick ice clouds contribute significantly
to the global-mean ice amount even though their frequencies are small. Meanwhile, the frequency distribution of
effective radius has a mode value around 33 mm, and the
global-mean value is around 48 mm (Fig. 1e).
A common feature of ice cloud property distributions in terms of t, re, and IWP is that the distributions
are skewed with their mean values greater than their
modes. In other words, a majority of ice clouds have an
optical depth or an IWP value smaller than their mean
values, indicating that most of the ice clouds are thinner
than ‘‘a global-mean ice cloud.’’ Additionally, most ice
crystals have a smaller effective radius than the global
mean. Therefore, a mean cloud state cannot represent
global ice cloud properties. Accordingly, applying a
global-mean ice cloud state with the mean IWP and the
mean effective radius to climatic models cannot evaluate the mean radiative properties of these clouds because of the nonlinear relationship between these
parameters and radiative effects (Eliasson et al. 2011).
Particularly, Chen et al. (2000) indicated that calculating radiative fluxes using preaveraged inputs would
cause biases in cloud forcing. However, some earlier
studies have used mean profiles to obtain qualitative
analysis of cloud forcing (Schweiger and Key 1994;
Zhang et al. 1995). Although a more adequate estimate
based on profile-by-profile calculations should be carried out, we attended to interpret ice cloud forcing in a
qualitative fashion in this study, so climatological
properties of ice clouds were adopted as inputs to the
radiative transfer model (section 3f).
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FIG. 1. Frequency distributions of ice cloud properties.
(a) Optical depth frequency, (b) optical depth accumulative frequency, (c) ice water path frequency, (d) ice
water path accumulative frequency, and (e) effective radius frequency.
b. Spatial variations of ice cloud properties
Cloud frequency distributions are important in determining the actual cloud radiative importance (Chen
et al. 2000). To investigate the spatial distributions of ice
cloud amount, we calculate ice cloud occurrence frequency, which is defined as
Fice 5 Nice /Nall 3 100%,
(2)
where Nice is the number of ice cloud profiles, that is,
IWP . 0, and Nall refers to the number of all profiles in
a longitude–latitude space (2.58 latitude by 2.58 longitude
in a grid box). In a latitude–altitude space, Nice refers to
the number of IWC . 0, and Nall is all cases in a 2.58
latitude by 60-m grid box. The number of Nall includes
both clear and cloudy (ice and water) conditions. Using
DARDAR data from 2007 to 2010, the global-mean ice
cloud occurrence frequency is approximately 53%, as
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FIG. 2. Global ice cloud occurrence frequency. (a) Horizontal distribution and (b) latitude–height cross section.
stated in section 3a. In Fig. 2, we show the horizontal
and vertical distributions of ice cloud occurrence frequencies. As indicated by the horizontal distributions
(Fig. 2a), large ice cloud occurrences are closely related to
climate regimes, for example, frequently in tropical deep
convection regions, including the Pacific warm pool, South
America, and central Africa. They are also broadly distributed in mid- and high latitudes, such as near 608 in both
hemispheres. The patterns of ice cloud occurrence frequency distributions in northern midlatitudes are modulated by land, whereas those in southern midlatitudes are
quite zonally continuous. Tropical ice clouds are commonly
located above 10 km but are mostly below 5 km in mid- and
high latitudes, as shown by the vertical distributions in
Fig. 2b. Moreover, the frequency of occurrence in Southern
Hemisphere midlatitudes is generally greater than those in
Northern Hemisphere midlatitudes. In particular, high
frequencies around 1–3 km in southern midlatitude regions
(Fig. 2b) agree with findings in Haynes et al. (2011), who
found a high occurrence of low clouds over the Southern
Ocean, with 79% of clouds having tops below 3 km.
The horizontal distributions of IWPs are shown
in Fig. 3a. The global-mean IWP is approximately
109 g m22. They generally display a similar pattern with
their occurrence frequency distributions, in which large
values are shown over equatorial convective regions and
along storm tracks, while small values are mostly in the
subtropics. Vertically, large ice water contents occur in
several kilometers above the freezing levels (Fig. 3b).
However, a high frequency of ice cloud occurrence does
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FIG. 3. (a) Horizontal distributions of ice water path, (b) latitude–
height cross section of ice water content, and (c) latitude–height cross
section of effective radius.
not necessarily collocate a large amount of ice water. For
example, the mean IWPs over northern Eurasia are
generally lower than 100 g m22 (Fig. 3a), but the corresponding occurrence frequencies over the same area are
relatively large (Fig. 2a). Similarly, tropical ice clouds
frequently occur above 10 km (Fig. 2b), but IWCs are
quite small at such high altitudes, suggesting that ice
clouds in those places frequently occur but are usually
thin. Meanwhile, the vertical distributions of effective
radii (Fig. 3c) show that re continually decreases with
altitudes. Large effective radii collocate with large IWCs
in the tropics. Small re values (,20 mm) happen above
15 km in the tropics and above 12 km in South Pole,
while the most common tropical ice clouds located at
10 km through 15 km have re ranging from 20 to 50 mm.
c. Seasonal variations of ice cloud properties
Figure 4 corresponds to global ice cloud occurrence
frequency distributions in four seasons, that is, March–
May (MAM), June–August (JJA), September–November
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(SON), and December–February (DJF). Across all
seasons, ice clouds are frequently observed in deep
convective regions, storm tracks, and midlatitudes
along 608N/S. Seasonal variation of ice clouds in the
tropics is closely associated with the movement of the
intertropical convergence zone (ITCZ) from north of
the equator during JJA to the south during DJF but is
located almost symmetrically around equator during
MAM and SON. These features are also evident in the
vertical distributions, in which large occurrence frequencies in the tropics shift from north to south of the
equator from JJA to DJF (Figs. 4f,h).
Additionally, fewer ice clouds occur in northern midand high latitudes during JJA but can be much more
frequently observed during DJF, especially over the
ocean (Figs. 4b and 4d). In contrast, southern mid- and
high latitudes have consistently large ice cloud frequencies over four seasons, with the frequencies
slightly larger during JJA than DJF (Figs. 4b and 4d).
These features suggest that more ice clouds are produced in cold seasons for extratropical regions. Another region with a large number of ice clouds is
coincident with the Asian summer monsoon, which
moves following the seasonal march of monsoonal
clouds. The vertical distributions reveal high frequencies of ice cloud occurrences in the stratosphere during
JJA in the southern polar region (Fig. 4f). These are
presumably polar stratospheric clouds, which have
been observed during winter and spring times
(Adhikari et al. 2010).
Seasonal variations of atmospheric ice water and effective radius are shown in Figs. 5 and 6. The distributions of IWPs (Fig. 5) in four seasons show similar
patterns to those of ice cloud occurrences; that is, regions with high IWP values follow the movement of
tropical convections and the seasonal march of the
Asian monsoon. Large IWPs are observed in tropical
deep convective areas and in midlatitude storm-track
regions, while in other places, IWPs are relatively small.
Global-mean IWP path values are about 114, 116, 115,
and 111 g m22 in MAM, JJA, SON, and DJF, respectively, being quite constant across all seasons. Vertically, the seasonal variations of IWC and re (Fig. 6) in
the tropics are both associated with the movement of
ITCZ. Effective radii smaller than 20 mm are consistently observed above 15 km in the tropics, while in the
South Pole these small res occur with polar stratospheric
clouds during JJA and SON.
d. Ice cloud properties across the spectrum of optical
depth
Analyses in the previous sections have revealed great
variability of ice clouds with respect to locations and
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FIG. 4. Global ice cloud occurrence distributions in (a) MAM, (b) JJA, (c) SON, and (d) DJF and latitude–height
cross sections in (e) MAM, (f) JJA, (g) SON, and (h) DJF.
seasons. In the following, we investigate how these
features vary with different optical depths. In other
words, we investigate the ice cloud properties across the
spectrum of ice clouds.
To obtain a complete view of the whole spectrum of
ice cloud distributions, five groups of ice clouds are
considered in this study, that is, t , 0.03, 0.03 , t , 0.3,
0.3 , t , 3, 3 , t , 20, and t . 20. The first three groups
correspond to the three types of ice clouds, that is,
subvisual, thin, and opaque, according to Sassen and
Cho (1992). The last two groups are constructed with the
aim of investigating the properties of thick ice clouds.
According to section 3a, the five groups of ice clouds
account for approximately 9%, 31%, 39%, 16%, and
5%, respectively, among all ice cloud samples, and their
global occurrence frequencies among all observed
samples are approximately 5%, 16%, 21%, 9%, and 3%,
respectively.
The occurrence frequency distributions of the five ice
cloud types as defined above are shown in Fig. 7. The
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FIG. 5. Global distributions of ice water path in (a) MAM, (b) JJA, (c) SON, and (d) DJF.
frequency of subvisual ice clouds (Fig. 7a) is generally
low but is widely spread over the globe. This type of
cloud locates preferentially in the midlatitudes, especially in latitudes between 408 and 608 in both hemispheres. The patterns of frequency distributions for thin
(Fig. 7b) and opaque (Fig. 7c) ice clouds are similar, with
high frequencies primarily occurring over tropical deep
convective regions and in mid- and high latitudes around
608N/S, although thin ice clouds extend more in tropical
oceans, while the opaque ones are more concentrated
over land and in the warm pool regions. Ice clouds with
3 , t , 20 are mainly observed in midlatitudes, and
those with t . 20 occur mostly in deep convective and
storm track regions.
The vertical distributions of occurrence frequency for
the above five groups of ice clouds are shown in Fig. 8,
averaged respectively for the following five regions:
tropics (TRP), northern midlatitudes (NML), northern
high latitudes (NHL), southern midlatitudes (SML), and
southern high latitudes (SHL). We define tropics, midlatitudes, and high latitudes using latitudinal boundaries
of 23.58 and 66.58 in both hemispheres. Frequency here is
calculated by the number of ice cloud observations
(IWC . 0) divided by all measurements at a given layer.
Vertical profiles of subvisual ice clouds (Fig. 8a) show
that they are primarily located in the midlatitudes at low
altitudes of 1–2 km, especially in the southern midlatitudes. Another mode in the midlatitude profiles locates around 10 km. In the tropics, the relatively high
frequency is at 10–17 km. The thin ice clouds (Fig. 8b)
are frequently observed in the tropics above 10 km, and
the peak frequency occurs near 16 km. In mid- and high
latitudes, their occurrences are less frequent but with
peak frequency in altitudes near 1–2 km. Opaque ice
clouds (Fig. 8c) most frequently appear near 15 km in
the tropics. Their patterns of frequency profiles in midand high-altitude regions are similar, uniformly occurring between 2 and 10 km in midlatitudes and with a
bell-shaped profile for high latitudes. The frequency in
Northern Hemisphere is larger than that in Southern
Hemisphere. For thick ice clouds with 3 , t , 20
(Fig. 8d), the maximum frequency is in the northern
midlatitudes at an altitude around 5 km. The peak occurrence in the vertical frequency profile in the tropics is
located near 12 km. For even thicker ice clouds with
t . 20 (Fig. 8e), the altitude of peak frequency is around
5 km in midlatitudes and slightly higher in the tropics.
They are almost nonexistent in high latitudes. Results
that subvisual and thin ice clouds dominate low altitudes
in midlatitudes are consistent with the findings of Mace
(2010) and Huang et al. (2012a,b) that boundary clouds
are common in the Southern Ocean and North Atlantic.
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FIG. 6. Latitude–height cross sections of ice water content and effective radius in four seasons.
A summary of the above ice cloud distribution properties is given in Table 1.
Apart from the occurrence frequency, ice cloud microphysical properties also vary with optical depths.
Figure 9 shows IWC and effective radius frequency
distributions over the five groups of ice clouds. IWC
frequency distributions are nearly Gaussian on a logarithmic scale, especially when t , 3.0. Subvisual ice
clouds have the narrowest IWC distributions (Fig. 9a).
As optical depth increases, IWC distributions become
broader and their modes increase as well. That is,
smaller IWCs dominate in ice clouds with t , 3.0, while
thick ice clouds (t . 3.0) contain a wide range of IWCs.
Compared to IWC, the widths of effective radius frequency distributions (Fig. 9b) are similar among all
types of ice clouds but are more complicated than a
Gaussian distribution. Subvisual ice clouds manifest
three modes, that is, 19, 33, and 58 mm. The mode of 33 mm
is also observed in all other types of ice clouds. Bimodal
distributions are shown in distributions of thin ice clouds
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FIG. 7. Global distributions of occurrence frequencies for five ice cloud categories: (a) t , 0.03, (b) 0.03 , t , 0.3,
(c) 0.3 , t , 3, (d) 3 , t , 20, and (e) t . 20. For better visual effect, each category has been scaled so that the
maximum scaled frequency value is around 1.0. The scaling factor is shown on top of each figure.
as well as of those with t . 3.0, while opaque ice clouds
have merely one mode. The mode values in these re distributions are closely related to ice cloud vertical distributions. For example, three modes of subvisual ice clouds are
in fact corresponding to those ice clouds around 15 km in
the tropics and 10 km and below 2 km in midlatitudes
(Fig. 8a), where mean re values are about 19, 33, and 58 mm
as shown in Fig. 3c. Similarly, the mode of 78 mm of ice
clouds with t . 20 corresponds to ice crystals at the base of
thick ice clouds. More details on IWC and effective radius
distributions are also summarized in Table 1.
Modes in the effective radius frequency distributions
indicate that re are highly correlated with altitudes, while
IWC frequencies show nearly Gaussian distributions,
indicating IWC are related to other factors. These features can be more clearly seen in Fig. 10, in which the
global-mean IWC and re are shown as a function of
altitude and optical depth. The results indicate that ice
clouds with large IWC and re mainly occur at low
altitudes when optical depth is large. Generally speaking,
IWC follows mainly the change of optical depth, while re
is more closely related to altitude than optical depth, in
particular where the altitude is above 5 km.
e. The dependence of microphysical properties on
temperatures
In many climate and weather prediction models, temperature is often treated as an independent variable to
parameterize microphysical properties such as the number of ice particles. In the following, we investigate the
temperature dependence of ice water content and effective radius and their seasonal and regional variations. For
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FIG. 8. Vertical frequency distributions for the five
types of ice clouds and for five regions: black for TRP,
red for NML, purple for NHL, blue for SML, and
green for SHL. The x-axis scale for (a) subvisual ice
cloud frequencies is one order lower than (b)–(e).
convenience, in the following we define the warm season
as March through August in the Northern Hemisphere
and September through February in the Southern
Hemisphere, while the cold season is March through
August in the Southern Hemisphere and September
through February in the Northern Hemisphere. Thus, we
divide ice clouds into five groups according to regions and
seasons: TRP, midlatitude warm seasons (ML-W), midlatitude cold seasons (ML-C), high-latitude warm seasons
(HL-W), and high-latitude cold seasons (HL-C). Based
on this grouping, occurrence frequency is examined to
explore the variations of ice cloud microphysical properties over different regions and seasons with regards to
the change of temperature.
Figures 11 and 12 show the occurrence frequencies of
ice clouds (donated by the occurrence number in this
section) in IWC–temperature space, which indicates how
IWC and re are related to temperature. The most evident
features shown in these two figures are that IWC and re
grow as temperature increases, which agrees with previous findings (e.g., Stephens et al. 1990; Ou and Liou
1995). However, the results also indicate that there are
visible differences of the relation among ice clouds in
different regions. Tropical ice clouds are more frequently
observed with relatively small IWC (,0.01 g m23), low
temperature (190–230 K), and small re (20–40 mm). In
midlatitudes, ice clouds have a high frequency of occurrence at relatively high temperature (210–255 K), large re
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TABLE 1. Summary of ice cloud properties.
Mostly observedb
Microphysical propertiesc
Altitude (km)
Cloud type
t , 0.03
Regions
Tropics
Extratropics
9
Midlatitude
High latitude
Tropics
Midlatitude
High latitude
Tropics
Midlatitude
High latitude
Tropics
Storm tracks
Tropics
Storm tracks
10 ; 17
1;2
1
19, 33, 55
16
1;2
2
21, 35
15
2 ; 10
6
33
12
5
32
33, 58
5
316
33, 78
0.03 , t , 0.3
31
0.3 , t , 3
39
3 , t , 20
16
t . 20
Modes
IWC (31023 g m23)
Fractiona (%)
5
5 ; 15
re (mm)
a
Fraction as the conditional-mean frequency of occurrence.
Summary information shown in Figs. 7 and 8.
c
Summary information shown in Fig. 9.
b
(30–60 mm), and high IWC (up to 0.1 g m23). Highlatitude ice cloud occurrence frequencies are similar to
those in midlatitudes but with some additional occurrences at very low temperatures (;180 K) with small
IWCs (Fig. 11e) and res (Fig. 12e) in cold seasons. These
clouds are most likely to be polar stratospheric clouds
(Sassen et al. 2008; Adhikari et al. 2010). The above
analysis of ice cloud occurrence frequency reveals that
the temperature dependence of ice cloud microphysical
properties varies with regions and seasons, which
implies a cloud parameterization in GCMs should treat
ice clouds differently according to climate regimes.
As discussed in section 1, GCMs-produced ice water
contents have large discrepancies among models. Besides the lack of observational constraints and insufficient understanding of ice cloud properties, most
GCMs do not consider falling ice particles/snow, which
would also lead to disagreements between these models
(Waliser et al. 2009; Li et al. 2012). However, the
CloudSat radar and the CALIPSO lidar are sensitive to
both suspending and falling particles, which might offer
implications for improving parameterization schemes
among GCMs. As a utility of satellite ice cloud observations for model verification, we provide in the following mean IWC and re states in a thermodynamic
(pressure–temperature) diagram. Figures 13 and 14
show the global conditional means of IWC and re from
the DARDAR retrievals as a function of temperature
and pressure for five groups: tropics, midlatitude warm
seasons, midlatitude cold seasons, high-latitude warm
seasons, and high-latitude cold seasons. Figure 13 displays that large IWC values are produced in high temperatures of ;270 K around a level of 700 hPa in both
midlatitudes and the tropics. In contrast, large IWCs in
high latitudes are formed in two areas: high temperature
(;270 K) near the surface and low temperatures (,220 K)
with pressure ranging from 400 to 800 hPa. In the former
situation, IWCs grow as temperatures increase, which are
the same as those in the tropics and midlatitudes. This
positive correlation has been adopted in climate models
(Stephens et al. 1990; Ou and Liou 1995). In the latter
situation, large IWCs (e.g., .0.1 g m23) exist in temperatures as low as 220 K between layers of 400–800 hPa.
These cases exist in high-latitude warm and cold seasons.
However, their samples are relatively small, especially in
warm seasons (Figs. 11d,e). In some degree, it is the extremely cold temperatures in the polar region that allow
relatively large IWCs to exist. When investigating these
large IWCs in low temperatures (not shown), we found
these cases mostly exist in the southern polar region in the
winter season, during when Antarctica suffers from very
low temperatures.
Unlike IWCs, effective radii tend to be uncorrelated
with pressure, which are almost exclusively determined
by temperature, especially in mid- and high latitudes
(Fig. 14). In other words, IWC–temperature relationships are more complicated than those of re temperature. The former depends on thermodynamic conditions
including both pressure and temperature, while the latter is nearly always positively correlated with temperature for all pressure levels.
f. Ice cloud radiative forcing
In this section, we demonstrate how ice cloud radiative effects vary with optical depth values. To do so, we
use the seasonally and regionally dependent cloud occurrence frequencies and cloud microphysical properties derived from previous sections as input for radiative
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FIG. 9. Frequency distributions of IWC and effective radius for five
groups of ice clouds.
forcing computations. The radiative transfer model
libRadtran (Mayer and Kylling 2005) is used to calculate
radiative fluxes. The ice cloud forcing is calculated by
CSW,LW 5 [FSW,LW Y 2 FSW,LW []cloudy
2 [FSW,LW Y 2 FSW,LW []clear ,
(3)
where C is cloud forcing, and F is radiative flux with
upward and downward arrows indicating the fluxes’
directions, and subscripts denote shortwave (SW) and
longwave (LW) radiation. The ‘‘cloudy’’ and ‘‘clear’’
subscripts respectively indicate conditions with and
without ice clouds.
The radiative forcing induced by different types of
ice clouds is examined at the TOA, at the surface, and
in the atmosphere. Figure 15 displays the SW, LW, and
net radiative forcing at both the TOA and the surface,
which have been weighted by ice cloud optical depth
frequency distributions (Fig. 1a) to obtain a global-mean
FIG. 10. Global-mean IWC and effective radius as a function of
altitude and cloud optical depth. (a) IWC and (b) effective radius.
forcing. It is shown that ice clouds with t ; 1.2 contribute
the most to the forcing in both the LW and the SW, while
contributions from thick ice clouds are insignificant on
average as these types of ice clouds occur less frequently.
The magnitude of SW forcing at the TOA is very close to
that at the surface (Fig. 15a). In the LW, the forcing is
much larger at the TOA than that at the surface. The net
forcing, that is, the sum of the SW and the LW forcing,
reveals that solar albedo effect dominates the greenhouse
effect at the surface, whereas at the TOA, the greenhouse
effect outweighs the solar albedo effect when optical
depth values are smaller than approximately 4.0
(Fig. 15c). The mean SW forcing at the TOA and at the
surface is 251 and 249 W m22, respectively, while the
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FIG. 11. Relations between ice water content and temperature as shown by ice cloud occurrence frequency for
(a) TRP, (b) ML-W, (c) ML-C, (d) HL-W, and (e) HL-C.
mean LW forcing is 55 and 33 W m22 at the TOA and at
the surface, respectively. As a result, a global net mean
forcing at the TOA is 4 and 216 W m22 at the surface,
implying that ice clouds keep the earth–atmosphere system
warming although they cool Earth’s surface. This warming
effect is generated by ice clouds with t , 4.0 with peak
contribution being produced by those with t ; 1.2.
For ice cloud forcing within the atmosphere, the
difference of heating rates (K day21) between cloudy
and corresponding clear-sky conditions is applied as
the indicator of the impact of ice clouds. Vertical
heating structures induced by ice clouds are shown in
Fig. 16. Generally, within the atmosphere the ice
cloud–induced heating rate change is much larger in
the LW than in the SW. Ice clouds in the SW cause
temperatures to increase for the atmosphere above
10 km and cool below. Conversely, temperatures are
decreased by ice clouds in the LW above 12 km and
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FIG. 12. Relations between effective radius and temperature as shown by ice cloud occurrence frequency for (a) TRP,
(b) ML-W, (c) ML-C, (d) HL-W, and (e) HL-C.
increased below. The net heating rate change shows
that the atmosphere is warmed by ice clouds below
12 km and cooled above. Along the whole spectrum of
ice clouds, strong cooling or warming is primarily because of ice clouds with 0.2 , t , 10.
To study ice cloud forcing in different regions and
seasons, ice cloud forcing as a function of optical depths
is studied for ice clouds for five groups as defined in
section 3e. Results are displayed in Fig. 17. Ice cloud
forcing at the TOA shows a warming effect when optical depth is small (t , 2.0) and a cooling effect as
optical depth increases in the tropics (Fig. 17a). It is,
however, always positive in high latitudes at both the
TOA and the surface, implying ice clouds keep these
regions warming over the whole year (Figs. 17a,b). In
midlatitudes, ice clouds in warming seasons have a
stronger SW forcing but weaker LW forcing than those
in cold seasons, causing a cooling effect during warm
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FIG. 13. Conditional-mean IWC (g m23) as a function of temperature and pressure for (a) TRP, b) ML-W, c) ML-C,
d) HL-W, and e) HL-C.
seasons but a warming effect during cold seasons at
both the TOA and the surface (Figs. 17a,b).
The above cloud forcing evaluation demonstrates the
complexity of the radiative effects of ice clouds on the
earth–atmosphere system, that is, their dependence on regions, seasons, and optical depths. To fully understand the
ice cloud radiative effects, their spatial–temporal distributions and microphysical properties must be characterized
over a global scale. The results presented in the previous
sections serve as one of steps toward that goal.
4. Summary and conclusions
The objective of this research aims at enhancing our
understanding of ice cloud properties using state-of-theart observations from the CloudSat radar and CALIPSO
lidar observations. Emphases are placed on 1) global
and zonal distributions of ice cloud occurrences and ice
water mass; 2) geographical and seasonal variations
of ice cloud properties; 3) differences of ice cloud
properties across a wide range of optical depths;
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3897
FIG. 14. As in Fig. 13, but for effective radius.
4) microphysical properties in relation to thermodynamic
variables, with implications for GCM model verifications;
and 5) ice cloud radiative effects as a function of optical
depth and over different regions and seasons.
Ice clouds have a wide range of optical depths with
peak frequency around t ; 1.2. The global-mean optical
depth and effective radius are about 2 and 48 mm, respectively. The mean global IWP is assessed to be approximately 109 g m22, which is larger than those
produced by most GCMs based on the Intergovernmental
Panel on Climate Change Fourth Assessment Report
(IPCC AR4) (Waliser et al. 2009). Most GCMs do not
include falling particles and convective cores of mass,
which contributes uncertainties to ice cloud simulations
(e.g., Waliser et al. 2009; Li et al. 2012).
Ice clouds frequently occur in deep convective
areas in the tropics as well as in mid- and high latitudes.
Their seasonal variations are associated with seasonal
shift of climate regimes, for example, ITCZ, monsoon,
and midlatitude storm tracks. Also, more ice clouds
are formed during cold seasons than during warm
seasons.
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FIG. 16. Ice cloud–induced (a) SW, (b) LW, and (c) net heating
rates changing over the whole ice cloud spectrum.
FIG. 15. The (a) shortwave, (b) longwave, and (c) net ice cloud
forcing at the top of the atmosphere (black line) and at the surface
(red line) as a function of optical depth. Note that ice cloud forcing
changes signs from positive to negative at t ; 4.
Macroscale distributions and microphysical properties are also analyzed over the entire spectrum of ice
clouds. Ice clouds with t , 0.03 (subvisual) have a low
occurrence frequency (;5%) but widely spread over the
globe. Subvisual ice clouds occur more frequently in the
southern midlatitude ocean with altitudes near 1–2 km
as well as in the tropics above 15 km. The observed occurrence frequency of both thin and opaque ice clouds
(0.03 , t , 3.0) is approximately 37%, which accounts
for 70% within the ice cloud spectrum. Ice cloud occurrence frequency peaks around 15 km in the tropics.
Thick ice clouds (t . 3.0) mainly exist in the tropics and
over the ocean in midlatitudes associated with deep
convections. Thick ice clouds are observed to occur 11%
globally, which is much greater than that of subvisual ice
clouds. However, the former is absent in the subtropics,
while the latter is distributed more broadly.
IWC and effective radius reveal clear differences of
microphysical properties between the five types of ice
clouds defined in this study. For a given ice cloud type,
IWC displays a near-Gaussian distribution on a logarithmic scale, while the effective radius often shows
multiple modes. A mode of 33 mm dominates in effective radius frequency distribution. As optical depth increases with the change of ice cloud type, mode in IWC
distribution increases accordingly. However, there is no
clear trend of variation for the mode of effective radius.
Rather, its mode values are closely related to most
probable altitudes where ice clouds occur.
As for the relation between temperatures and ice
cloud microphysical properties, tropical ice clouds
mostly occur in low temperatures (190–230 K), with
small effective radii (20–40 mm) and low IWCs (0–
0.01 g m23), while mid- and high-latitude ice clouds have
large portions with high temperatures (210–255 K),
large effective radii (30–60 mm), and large IWCs (up to
0.1 g m23). The effective radius is positively correlated
to temperature but almost uncorrelated to pressures. On
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those with higher t show a cooling effect on the earth–
atmospheric system. Ice clouds in high latitudes keep the
earth–atmosphere system warming through the year. The
strongest cooling effect over the globe occurs in midlatitude warm seasons when the solar albedo effect outweighs the greenhouse effect. In the tropics, ice clouds with
t , 2.0 display a warming effect. While these findings revealed some characteristics of ice cloud radiative forcing,
its precise climatological value requires aggregating individual ice cloud forcing calculated from observational
profiles, which will be our future research topic.
This study has presented a comprehensive analysis of
the properties of all types of ice clouds with respect to
their horizontal distributions, vertical structures, regional
and seasonal variations, the dependence on temperature
and pressure, and a qualitative analysis of radiative
forcing over the whole spectrum of ice clouds. These results can, on one hand, advance our understanding of ice
cloud climatology. On the other hand, they are useful for
validating microphysical parameterizations in global climate models.
Acknowledgments. We acknowledge members of the
DARDAR project in the University of Reading for
providing DARDAR data. DARDAR data were
obtained from the data archive at the University of
Reading. The ECMWF and MODIS MOD43B3 datasets are obtained respectively from archives at ECMWF
and at NASA. This research has been supported by
NASA Grants NNX13AQ39G and NNX13G34G.
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FIG. 17. Ice cloud forcing as a function of optical depth at the
(a) top of the atmosphere and at the (b) surface for five groups of
ice clouds as in Fig. 11. Arrow in (a) indicates the location where
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