ADVANCES IN ATMOSPHERIC SCIENCES, VOL. 27, NO. 6, 2010, 1233–1245
Observational Diagnosis of Cloud Phase in the Winter Antarctic
Atmosphere for Parameterizations in Climate Models
Yong-Sang CHOI∗1,2 , Chang-Hoi HO3 , Sang-Woo KIM3 , and Richard S. LINDZEN2
1
Department of Environmental Science and Engineering, Ewha Womans University, Seoul, Korea
2
Department of Earth, Atmospheric and Planetary Sciences,
Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
3
School of Earth and Environmental Sciences, Seoul National University, Seoul, Korea
(Received 19 October 2009; revised 8 March 2010)
ABSTRACT
The cloud phase composition of cold clouds in the Antarctic atmosphere is explored using data from
the Moderate Resolution Imaging Spectroradiometer (MODIS) and Cloud-Aerosol Lidar with Orthogonal
Polarization (CALIOP) instruments for the period 2000–2006. We used the averaged fraction of liquid-phase
clouds out of the total cloud amount at the cloud tops since the value is comparable in the two measurements.
MODIS data for the winter months (June, July, and August) reveal liquid cloud fraction out of the total cloud
amount significantly decreases with decreasing cloud-top temperature below 0◦ C. In addition, the CALIOP
vertical profiles show that below the ice clouds, low-lying liquid clouds are distributed over ∼20% of the area.
With increasing latitude, the liquid cloud fraction decreases as a function of the local temperature. The
MODIS-observed relation between the cloud-top liquid fraction and cloud-top temperature is then applied to
evaluate the cloud phase parameterization in climate models, in which condensed cloud water is repartitioned
between liquid water and ice on the basis of the grid point temperature. It is found that models assuming
overly high cut-offs (À −40◦ C) for the separation of ice clouds from mixed-phase clouds may significantly
underestimate the liquid cloud fraction in the winter Antarctic atmosphere. Correction of the bias in the
liquid cloud fraction would serve to reduce the large uncertainty in cloud radiative effects.
Key words: cloud phase, mixed-phase clouds, polar cloud, cloud radiative effect, cloud parameterization
Citation: Choi, Y.-S., C.-H. Ho, S.-W. Kim, and R. S. Lindzen, 2010: Observational diagnosis of cloud
phase in the winter antarctic atmosphere for parameterizations in climate models. Adv. Atmos. Sci., 27(6),
1233–1245, doi: 10.1007/s00376-010-9175-3.
1.
Introduction
Polar clouds play an important role in global climate since they have large areal extent and persistence, strong interactions with radiation, and an inextricable link with snow and ice albedo feedbacks
(Curry et al., 1996; Holland and Bitz, 2003; Vavrus,
2004). Any study of polar clouds must cope with the
complexities of polar conditions: the highly reflecting
snow and ice, the virtual absence of solar radiation for
a large portion of the year, temperature and humidity
inversions, and the simultaneous presence of liquid and
ice clouds, etc. These issues have motivated numerous
scientific field/aircraft experiments that aim to mea∗
sure the macro/microphysical properties of the clouds,
mostly in the Arctic (Curry et al., 1996; Uttal, 2002;
Verlinde, 2007).
Observations show that ice clouds are ubiquitous
over the polar region in the stable wintertime boundary layer (Curry et al., 1996). Optically thin and lowlying clouds also predominate (Curry et al., 1996),
frequently with mixed-phase droplets (Shupe et al.,
2006). Better quantification of these features over
a wider area and longer period can be accomplished
by further analyses using current satellite measurements. However, polar clouds pose a unique challenge
to satellite-based remote sensing, particularly when
using passive radiometer measurements; in the Inter-
Corresponding author: Yong-Sang CHOI, [email protected]
© China National Committee for International Association of Meteorology and Atmospheric Sciences (IAMAS), Institute of Atmospheric
Physics (IAP) and Science Press and Springer-Verlag Berlin Heidelberg 2010
1234
OBSERVED VS. MODELED CLOUD PHASE IN THE WINTER ANTARCTIC ATMOSPHERE
national Satellite Cloud Climatology Project (ISCCP),
polar cloud properties have the largest errors (Rossow
et al., 1993).
Among the many cloud macro/microphysical properties, the cloud thermodynamic phase (i.e., liquid, ice,
or mixed) is of vital importance in the polar climate
because it determines the cloud radiative effects. In
particular, modeling assumptions controlling the cloud
phase are critical for the prediction of climate sensitivity, but the evaluation of these assumptions is only
now beginning (IPCC, 2007). Theoretical and empirical studies suggest that the liquid cloud fraction decreases with sub-zero decreasing cloud temperatures,
and becomes negligible below −40◦ C due to homogeneous ice nucleation of pure condensates (e.g., Rogers
and Yau, 1989; Houze, 1993; Pruppacher and Klett,
1997). Based on our current understanding of the thermodynamics of clouds, to date most general circulation
models (GCMs) estimate the cloud phase fraction very
simply as a function of the grid point temperature between 0◦ C and −40◦ C; these parameterizations have
no regard for complicated cloud microphysics which
may depend on subgrid-scale phase transitions, ambient humidity, particle size, aerosol type and content, and so on. For this reason, some studies have
attempted to evaluate the simplifications in GCMs using airborne observations (Gultepe and Isaac, 1997)
as well as passive satellite measurements, e.g., by use
of the Polarization and Directionality of the Earth
Reflectances (POLDER-1) mission data (DoutriauxBoucher and Quaas, 2004; Weidle and Wernli, 2008),
combined data from POLDER-1 and the Along Track
Scanning Radiometer (ATSR-2) (Giraud et al., 2001),
and the Earth Observing System’s Microwave Limb
Sounder (MLS) (Li et al., 2005, 2007). Such observations of the distribution of cloud phase in the current climate would provide a substantial constraint on
model cloud feedbacks (Tsushima, 2006).
The Moderate Resolution Imaging Spectroradiometer (MODIS) instrument onboard the Terra
satellite provides cloud phase data that spans seven
years, which can be applied to longer-term validation of modeled cloud phase information (Weidle and
Wernli, 2008). The MODIS cloud phase is retrieved
by an infrared (IR) technique available during both
day and night (Strabala, 1994; Menzel et al., 2006).
This technique is based on the distinct IR-wavelength
dependencies of the refractive indices of liquid water
and ice. There is a great opportunity to validate the
cloud phase retrieved by the MODIS instrument onboard the Aqua satellite by intercomparison with the
VOL. 27
almost coincident measurements by the Cloud-Aerosol
Lidar and Infrared Pathfinder Satellite Observation
(CALIPSO) satellite; both satellites are part of the
so-called “A-train” constellation and follow the same
track with a short interval between them. CALIPSO
carries onboard an active remote-sensing device called
the Cloud-Aerosol Lidar with Orthogonal Polarization
(CALIOP), which provides unique information on the
vertical cloud phase profile by using the depolarization/backscatter properties of cloud particles (Liu et
al., 2005).
This study aims to provide the observed liquid water fraction of cloudsa by analyzing combined MODIS
and CALIOP measurements. Choi et al. (2010) applied this technique globally, but this study focuses
on the Antarctic atmosphere (40◦ –90◦ S) for the winter months (June, July, and August; JJA) using more
detailed analyses. We calculated the fraction of liquid clouds at the cloud tops, as well as for whole cloud
columns, as recognized by the CALIOP measurements.
On the basis of our calculations, the bulk relation between the cloud phase fraction and temperature in
current model parameterizations was evaluated using
MODIS cloud-top temperature retrievals for the same
field-of-view as for the cloud-top phase retrievals.
This paper is organized as follows: Section 2 describes the details of the satellite data and analysis
methods used to calculate the liquid water fraction.
We also present the cloud phase parameterizations in
current GCMs. Section 3 presents space-viewed horizontal and vertical distributions of the liquid water
fraction in the winter Antarctic atmosphere. Section
4 presents the difference between the model and the
satellite-retrieved liquid water fraction, and suggests
an optimal function for the liquid water fraction with
respect to the temperature for the model parameterization. In section 5, we discuss the observed liquid
water fraction, as well as the possible influence of the
biased model estimates of cloud phase on the polar
climate simulation to conclude this paper.
2.
2.1
Data and methods
MODIS cloud phase
We used the MODIS gridded level-3 atmospheric
data (MOD08, Collection 4) from the Terra satellite
compiled for the southern hemispheric (SH) winter
months (JJA) over seven years (2000–2006) to examine the liquid water fraction. MOD08 contains
the 1◦ ×1◦ gridded values calculated from the MODIS
level-2 cloud product (MOD06). We also used MOD06
a Below −20◦ C, the stated liquid water fraction of clouds may partly contain signal from horizontally oriented ice particles
(Hu et al., 2009) and (quasi-) spherical ice particles (that are used to indicate any small, frozen, droxtal- or spheroid-shaped ice
particles) (Choi et al., 2010). However, in this paper, we shall use the term ‘liquid’ as flagged by the current satellite algorithms.
NO. 6
1235
CHOI ET AL.
(Collection 5) data from the Aqua satellite for August
2006 for pixel-scale analyses. Details of the improvements in the Collection 5 data over those of previous
collections can be found elsewhere (Baum et al., 2005;
King et al., 2006; Yang et al., 2007); however, the bispectral IR test remains the same. These MODIS data
contain various cloud property retrievals, such as the
cloud phase and cloud-top temperature at a 5×5-km
nadir resolution (Platnick et al., 2003).
The recent MODIS cloud phase product is derived
in two ways: via a shortwave IR test and a bispectral IR test. The shortwave IR test uses the visible,
shortwave IR, and thermal bands (King et al., 2006),
whereas the bispectral IR test uses the thermal bands
only. The shortwave IR test is known to better realize
the cloud phase by using more bands, but more significant bias may occur in the dry and high-angle scattering atmosphere at high latitudes. Also, the cloud
phase cannot be retrieved by the shortwave IR test at
nighttime. Because solar radiation is virtually absent
over most of the polar region during the months under study, we used the MODIS cloud phase derived
from the bispectral IR test. The cloud phase product
includes four categories: liquid, ice, mixed, and uncertain phases. The bispectral IR test involves only the
8.5- and 11-µm bands for the current MODIS algorithm (Menzel et al., 2006). The current MODIS algorithm (for both collection 4 and 5), therefore, firstly
identifies water by static thresholds: (1) BT11 >285 K
and (BT8.5 − BT11 )6−0.5 K, or (2) BT11 >238 K and
(BT8.5 − BT11 )<−1.0 K, where BT8.5 and BT11 stand
for the brightness temperatures in the 8.5- and 11-µm
bands, respectively. The ice phase is identified last
if the pixels cannot pass all the decision tests in the
MODIS algorithm; it uses BT11 6238 K or (BT8.5 −
BT11 )>0.5 K (Menzel et al., 2006). Note that the relation BT11 > cloud temperature is always true due to
the surface emission, so that liquid cloud temperature
can have a value lower than 238 K.
The underlying physical principle behind the
MODIS bispectral IR test is based on the dependence
of absorption and emission by clouds on the refractive
index. If ice and liquid clouds have the same temperature (or the same altitude) and similar microphysical
sizes and shape distributions, the 8.5-µm cloud radiance may not depend greatly on the thermodynamic
phase, whereas the radiance at 11 µm shows obvious
dependence. This makes the BT difference between
the 8.5- and 11-µm bands an effective method for cloud
phase determination. However, the behavior of the IR
radiances at these bands for both ice and liquid clouds
requires a more complete accounting. In particular,
they are dependent on (1) atmospheric absorption by
gases such as water vapor; (2) the scattering proper-
ties of ice and liquid clouds, which are in turn based
on the particle size distributions (for ice clouds, particle habit distributions); (3) surface emissivity; and (4)
cloud height (Menzel et al., 2006). These factors may
cause uncertainty in the cloud phase identified by the
simple MODIS threshold test that is valid for singlelayer opaque clouds, and it has been noticed that the
uncertainty often arises in the other cloud types such
as broken, thin, and black clouds, especially in the
temperature range between 250 K and 265 K (Baum
et al., 2000; Choi et al., 2005; Menzel et al., 2006;
Chiriaco et al., 2007; Nasiri and Kahn, 2008). Since
thin and low-lying clouds occur predominantly in the
polar regions (Curry et al., 1996), uncertainty in the
MODIS cloud phase may be unavoidable.
In this study, we also used the MODIS cloud-top
temperature to relate with the cloud phase composition in cold clouds. MODIS cloud-top temperature is
independently retrieved by a CO2 slicing method using
CO2 absorption bands within 13.2–14.4 µm (Menzel et
al., 2006). It is noteworthy that there could be inconsistencies between MODIS cloud-top temperature and
cloud phase in the presence of low-lying liquid clouds
below high cirrus clouds; the CO2 slicing method distinguishes the presence of cirrus clouds, while the bispectral IR test cannot do so. MODIS cloud phase can
be liquid in the case of cirrus clouds (Choi et al., 2005).
2.2
CALIOP cloud phase
The payload aboard CALIPSO is comprised of
CALIOP, a three-channel imaging infrared radiometer, and a wide-field camera (Winker et al., 2007);
http://www-calipso.larc.nasa.gov). This study focuses
on the down-looking CALIOP instrument, which emits
polarized light at both 1064 and 532 nm with a pulse
energy of 110 mJ and pulse repetition rate of 20.25 Hz;
polarization discrimination is only performed for the
532 nm channel (Winker et al., 2007). The CALIPSO
lidar cloud-aerosol discrimination algorithm utilizes
the layer mean attenuated backscatter at 532 nm, the
layer-integrated 1064–532-nm volume color ratio, and
the mid-layer altitude to distinguish between clouds
and aerosols on the basis of probability distribution
functions (Liu et al., 2004). The ability of CALIOP
to determine the cloud base height of opaque clouds
is, however, limited by the strong lidar return-signal
attenuation.
The CALIOP cloud phase is determined by the
layer-integrated particle depolarization ratio at 532
nm (δ532 , the ratio between the perpendicular and
parallel backscatter intensities) and the cloud-top and
bottom temperatures. The backscatter signal of a
linearly polarized laser beam from spherical particles
(i.e., liquid clouds) is totally linearly polarized (δ532 ≈
1236
OBSERVED VS. MODELED CLOUD PHASE IN THE WINTER ANTARCTIC ATMOSPHERE
0). If particles are nonspherical (ice crystals, snow
flakes, or dust particles), or the measured backscatter signal has a multiple scattering contribution (e.g.,
optically thick liquid clouds), the backscattered lidar signal will contain a cross-polarized component
(0 < δ532 < 1). Ice crystals typically have δ532 in
the range of 0.3–0.5. The impact of multiple scattering
on cloud phase discrimination is evaluated by a Monte
Carlo simulation scheme (Hu et al., 2001). In cases
where cloud phase discrimination using δ532 is ambiguous, the CALIOP algorithm identifies cloud phase
by additionally using the cloud temperature: cloudbottom temperature < −45◦ C for ice clouds (Pruppacher, 1995), cloud-top temperature > 0◦ C for liquid
clouds, and −45◦ C< cloud-top/bottom temperatures
<0◦ C indicates that either ice, liquid, or a mixture of
the two exists. Complete descriptions of the CALIOP
algorithm can be found in Liu et al. (2005).
In this study, the cloud phase retrieval from the
CALIPSO lidar level-2 vertical feature mask (version
2.01) for August 2006 is compared with the MOD06
data on a pixel scale to examine the vertical features
of the cloud layers. These data currently have two
categories: liquid and ice phase, and the presence of
polar stratospheric clouds (PSCs) is also additionally
indicated where appropriate. Note that mixed phase
information is not available in the current CALIOP
data stream. From the surface to an altitude of 8.2
km, the horizontal and vertical resolutions of the data
are 333 m and 30 m, respectively. For higher altitudes,
from 8.2 to 20.2 km, the resolutions are coarser (1000
m horizontally and 60 m vertically).
An evaluation of CALIOP cloud phase retrievals
was recently made using ground lidar measurements
(Chiriaco et al., 2007), confirming its superior capability to discriminate cloud phase compared to passive
remote sensing. However, very recently, it was found
that the present CALIOP algorithm may not guarantee to distinguish liquid droplets from horizontally oriented ice particles (Hu et al., 2009) and quasi-spherical
ice particles (Choi et al., 2010). We note that this
problem is embedded in the data used in this study.
2.3
Calculation of satellite liquid water fraction
The liquid water fraction of cloud tops fliq,top for
a given 1◦ -grid box is calculated using the aforementioned satellite cloud phase retrievals according to:
nliq
fliq,top =
,
(1)
nliq + nmix + nice
where nliq , nmix , and nice refer to the numbers of
cloudy pixels in a two-dimensional satellite field identified as indicating liquid, mixed, and ice phases, respectively. The undetermined cloud phase flag is not
VOL. 27
taken into account; consideration of this phase results
in a reduced liquid water fraction. In the calculation
with MOD08 level-3 data, all the detected pixels are
taken within grid boxes that contain over 100 cloudy
pixels (nliq +nmix +nice ), i.e., cloudy indications at 16%
of the maximum 625 pixels are required for statistical
confidence. However, in the calculation with MOD06
level-2 data and CALIOP data, the pixels are confined
to the coincident footprints for the two measurements.
For the CALIOP vertical cloud phase profile, the pixels in Eq. (1) are counted at the cloud tops prior to
those at all the other layers.
We also count a given pixel as being in the liquid
phase if any underlying cloud is identified as a liquid
cloud below the ice clouds in the CALIOP vertical profile. Hereafter, we refer to the concept of the liquid water fraction for the total cloud column (fliq,col ). Since
considerably more liquid-phase pixels are counted in
this way, the liquid water fraction for the total cloud
column is higher than or equal to the value at cloud
top (fliq,col > fliq,top ).
2.4
Cloud phase parameterizations in GCMs
In most current GCMs, the total condensate at the
ith level is initially allotted to liquid and ice phases by
assuming that the liquid water fraction increases with
the grid-point ambient temperature at the ith level in
the range between the minimum (Tmin ) and maximum
temperature bounds (Tmax ):
µ
fliq,i =
Ti − Tmin
Tmax − Tmin
¶n
,
for Tmin 6 Ti 6 Tmax ,
(2)
where fliq,i indicates the liquid water fraction of the
condensate at the ith level. This equation indicates
that the condensed water is purely liquid above Tmax
and purely ice below Tmin . The model cloud phase
parameterizations considered in this study include
the Seoul National University GCM (SNU), Smith
(1990) (S90), the Laboratorie de Météorologie Dynamique GCM (LMD), the ECMWF 40-year reanalyses (ERA40), the NCAR Community Atmosphere
Model version 3.0 (CAM3), and Del Genio et al.
(1996) (D96). These models have constant parameters (Tmin , Tmax , n) in Eq. (2): (−15◦ C, 0◦ C, 1)
for SNU (Lee et al., 2001), (−15◦ C, 0◦ C, 2) for S90,
(−15◦ C, 0◦ C, 6) for LMD (Doutriaux-Boucher and
Quaas, 2004), (−23◦ C, 0◦ C, 2) for ERA40 (Weidle
and Wernli, 2008), and (−40◦ C, −10◦ C, 1) for CAM3
(Collins et al., 2004).
However, D96 introduces a slightly different parameterization for the Goddard Institute for Space
NO. 6
Studies (GISS) GCM:
· µ
¶n ¸
Tmax − Ti
fliq,i = exp −
,
15
for Tmin 6 Ti 6 Tmax ,
(3)
where the parameters (Tmin , Tmax , n) are set to
(−40◦ C, −4◦ C, 2) over the ocean and (−40◦ C, −10◦ C,
2) over land. The temperature bounds in D96 are
somewhat similar to the current settings in CAM3.
However, Eq. (3) for D96 implies an equal probability for liquid and ice formation at −16.5◦ C (ocean)
and −22.5◦ C (land), while Eq. (2) for CAM3 implies
an equal probability at −25◦ C. Note that this study
simply uses the parameter for the ocean in D96.
3.
1237
CHOI ET AL.
The mean cloud phase composition in
MODIS and CALIOP observations
E1
20
180
180
Previous studies suggest that a natural cloud can
be composed of unfrozen liquid (i.e., supercooled)
droplets in the temperature range of 0◦ C to about
−40◦ C (Rogers and Yau, 1989; Houze, 1993; Pruppacher and Klett, 1997). Below about −40◦ C, water
droplets freeze spontaneously without the aid of foreign nuclei. Recent field/aircraft observations in the
Arctic have revealed that very few liquid droplets can
exist at temperatures below −35◦ C, with a 90% probability that cloud particles will be composed of ice crystals (Curry et al., 1996; Shupe et al., 2006; Verlinde,
2007). Significant ice crystal nucleation routinely occurs at temperatures as high as −15◦ C to −20◦ C,
according to observations by Curry et al. (1990) in
the Arctic during April 1983 and 1986. Undoubtedly,
clouds with top temperatures above −20◦ C can certainly contain liquid water droplets that coexist with
the ice crystals at the cloud top.
To determine the climatological amount of liquid
cloud water, we calculate the winter-mean horizontal
distribution of the MODIS liquid and ice cloud fractions in percentiles at cloud tops over the extratropics and the Antarctic (40◦ –90◦ S) for the seven years
(Fig. 1). Here, the sum of the ice and liquid water
fractions is 100%. The cloud-top liquid water fraction
fliq,top exhibits an almost zonally symmetric decrease
with increasing latitude, whereas the opposite is observed for the ice fraction (Figs. 1a and 1b). Zonal
means of fraction and temperature (Fig. 1c) indicate
that fliq,top is roughly 50%±20% within the latitudes
65◦ –40◦ S where the mean cloud-top temperature is between −40◦ C and –20◦ C. This is a much higher percentile than expected; presumably, the large fliq,top
may be derived from individual convective clouds that
are highly smoothed in this climatological view. As
20
W1
E1
20
S80
S80
W6
0
0
E6
S60
20
W1
W6
0
0
E6
S60
0
0
S40
S40
Fig. 1. (a) Liquid water and (b) ice fractions at cloud tops for each 1◦ -grid box from
the MODIS data over the extratropics and the Antarctic (40◦ –90◦ S), averaged for
the winter (June–July–August) months of 2000–2006. Green curves show the wintermean cloud-top temperature observed by MODIS. Zonal means of fraction (black)
and temperature (red) in (a) and (b) are given in (c).
OBSERVED VS. MODELED CLOUD PHASE IN THE WINTER ANTARCTIC ATMOSPHERE
E1
20
E1
20
20
W1
S80
20
W1
S80
W6
0
0
E6
W6
0
0
E6
S60
S60
20
W1
S80
W6
0
0
E6
Liquid fraction (%)
0
S40
180
0
S40
E1
20
VOL. 27
180
180
1238
S60
0
S40
Fig. 2. Liquid water fraction for each 1◦ -grid box calculated for coincident footprints
of (a) MODIS and (b) CALIOP for the cloud tops, and (c) for the total cloud columns
for August 2006. Zonal means of the values in (a)–(c) are given in (c).
we discuss later, a more accurate relationship between
cloud-top temperature and fliq,top should be obtained
from a calculation of fliq,top with respect to temperature bins (section 4).
We evaluated the uncertainty in the MODIS fliq,top
by comparing with CALIOP fliq,top values for coincident footprints for a winter month (Figs. 2a and 2b).
The retrieval algorithms for the MODIS and CALIOP
cloud phase are based on completely different physics
(sections 2.1 and 2.2), so there is a reasonable basis for
confidence if the MODIS and CALIOP fliq,top values
agree with each other. The horizontal distributions of
fliq,top in both satellite measurements are clearly similar. The fliq,top values decrease with an increase in
latitude, with values similar to the seven-year wintertime average in Fig. 1. Then, we further calculated
the CALIOP liquid fraction for the total cloud column
(fliq,col ) in Fig. 2c, in which the liquid clouds underlying the ice clouds are considered (see detailed method
in Section 2.3). The result shows that the CALIOP
fliq,col is much larger than the CALIOP fliq,top over
the whole analysis domain (Fig. 2d). The CALIOP
fliq,col also decreases with increasing latitude; it covers as much as 20% of the total cloudy area at 80◦ S.
The MODIS and CALIOP results at the cloud tops
are generally in good agreement (within a range of
10%, see Fig. 3a). This suggests there is ∼10% potential relative uncertainty in the current fliq,top values
on average. However, at some grid points (∼6% of the
total), considerable differences exist (some larger than
70%, see Fig. 3a). This may be due to the presence
of several factors: broken, thin, black, and multilayer
clouds (Baum et al., 2000; Choi et al., 2005; Menzel et
al., 2006), semi-spherical (droxtal) ice particles (Choi
et al., 2009), horizontally oriented ice particles (Hu et
al., 2009), and/or different cloud tops being detected
and represented by the two measurements. The difference between the MODIS and CALIOP results is much
larger for the total cloud column (Fig. 3b). In a zonal
mean sense, the difference in the liquid water fraction
1239
CHOI ET AL.
180°
180°
NO. 6
°
E1
2
20
W1
0°
E1
20
S80°
0°
E6
°
20
W1
°
S80°
W6
0°
S60°
0°
S60°
0°
0°
S40°
'LIIHUHQFH
S40°
W6
0°
E6
/DWLWXGH
Fig. 3. Difference between MODIS and CALIOP liquid water fractions (a) at cloud tops, and (b)
for the total cloud column. Zonal means of the values in (a) and (b) are given in (c).
between the cloud tops and the total column is approximately 20% (Fig. 3c). This implies that clouds with
considerable loadings of liquid water (but overlooked
by passive satellite devices due to contamination by
the upper-level ice clouds) could be ubiquitous in the
winter Antarctic atmosphere.
To clarify the primary conditions that may have
caused the differences between the MODIS and
CALIOP fliq,top values, we present some individual
cases that represent inconsistent cloud-top phase retrievals. Figure 4 shows the MODIS cloud phase (top
and middle) and CALIOP vertical cloud phase (bottom) for different cases. The MODIS cloud phase corresponding to the CALIOP track (sky-blue line) is superimposed on the CALIOP vertical image for better
comparison.
Four exceptional conditions must be addressed.
The first exists when low-lying liquid clouds exist
below ice clouds comprising tropospheric cirrus and
PSCs (top heights: 15–25 km; see Fig. 4a, Case I).
Clearly, MODIS identifies these pixels as corresponding to liquid or mixed phases if the high clouds are
optically moderately or very thin. This is because the
radiation emitted from the high cloud tops is highly
contaminated by that emitted from the underlying low
clouds. A second key situation occurs when low-lying
liquid clouds cannot be identified by the MODIS al-
gorithm (Fig. 4b, Case II). The major causes may include gaseous absorption above low-lying clouds, surface emission, and temperature inversions at the lowest
level. A third case exists when some ice particles that
are often identified as liquid phase in CALIOP [e.g.,
quasi-spherical ice clouds (Choi et al., 2010) or horizontally oriented ice clouds (Hu et al., 2009)] exist at
altitudes of ∼15 km; MODIS identifies these as all-ice
clouds (Fig. 4c, Case III). The fourth condition occurs
when CALIOP identifies a considerable amount of tropospheric (or stratospheric) ice clouds, which cannot
be detected by the MODIS cloud screening algorithm
(Fig. 4d, Case IV). The major reason for this should
be the drawbacks of the MODIS cloud screening algorithm under conditions including snow/ice surfaces
and PSCs (Ackerman et al., 1998) because Case IV
frequently occurs over the Antarctic continent (70◦ –
80◦ S), particularly for PSCs. The opposite case—
where MODIS clouds are undetected by CALIOP—
is present for a negligible fraction of cases. For all
these exceptional situations, it should be noted that
the CALIOP cloud phase retrieval has a large uncertainty for low-lying clouds under opaque clouds and
the lower parts of optically thick clouds because the
CALIOP lidar signal experiences considerable attenuation under such conditions. Finally, it is noted that
Cases I and II are common in the analyzed region.
1240
OBSERVED VS. MODELED CLOUD PHASE IN THE WINTER ANTARCTIC ATMOSPHERE
(a)
W100°
W80°
(b)
E160°
0550 UTC 12 August 2006 S50°
S50° MODIS
180°
MODIS
W160°
1150 UTC 2 August 2006
S60°
S60°
S60°
S60°
S70°
Liquid
Mixed
Ice
Uncertain
W120°
W100°
W60°
Height (Km)
20
15 CALIOP
10
5
0
Lat S55.03 S59.36
Lon W73.78 W76.28
W140°
W80°
(c)
MODIS
S70°
W40°MODIS
Liquid
Mixed
Ice
Uncertain
S70°
E140° E160° 180° W160° W140°
20
15 CALIOP
10
5
0
S69.42
S71.88 Lat S61.00 S65.26
W89.25 Lon W166.99 W170.45 W175.11
180°
W120°
Height (Km)
S70°
S63.65
S67.81
W79.42
W83.50
W120°
(d)
2315 UTC 13 August 2006
MODIS
S73.38
E178.28
W160°
S77.08
E167.95
1050 UTC 12 August 2006
S60°
S60°
S60°
S60°
S70°
S70°
S70°
VOL. 27
Liquid
Mixed
Ice
Uncertain
W160° W140° W120° W100°
W80° MODIS
E120°
20
15 CALIOP
10
5
0
S61.73
S57.42 Lat S66.61 S70.74
W128.63 W131.44 Lon W156.35 W161.55
W120° W100°
Height (Km)
Height (Km)
20
15 CALIOP
10
5
0
Lat S74.03 S70.07
Lon W113.00 W120.14
Liquid
Mixed
Ice
Uncertain
S70°
S65.93
W125.04
S74.66
W169.18
S78.15
E178.80
S80.84
E158.59
Fig. 4. Cloud phase results obtained by the MODIS infrared technique (top and middle) and by
the CALIOP vertical feature mask algorithm (bottom) for (a) 0550 UTC 12, (b) 1150 UTC 2, (c)
2315 UTC 3, and (d) 1050 UTC 12 August 2006. Black shaded area indicates clear skies. The
sky-blue line indicates the CALIOP track corresponding to the bottom image.
Case IV is predominant particularly at 70◦ –80◦ S due
to PSCs.
4.
Evaluation of GCM cloud phase parameterizations
In the previous section, the grid-based calculations
based on both the passive and active satellite measurements (MODIS and CALIOP, respectively) show
the average fraction of liquid clouds in the winter
Antarctic atmosphere. The fraction is, however, varying as a function mainly of cloud temperature. Here,
we show explicit relationships between fliq,top and the
cloud-top temperature for given domains (Fig. 5). To
this end, the grid-mean cloud-top temperature from
MODIS is binned in 1◦ C intervals between −70◦ C and
10◦ C. For a given temperature bin, the mean (thick
curve) and standard error of the corresponding fliq,top
is calculated; the error bars shown are 20 times the
standard error for better representation of statistical
confidence. Our focus is on the winter Antarctic atmosphere, but other regions also demonstrate the regional
discrepancy in the relationship. The region is divided
into high (> 60◦ ), middle (30◦ –60◦ ), and low latitudes
NO. 6
100
MODIS (60-90N, JJA)
MODIS (30-60N, JJA)
MODIS (30S-30N, JJA)
MODIS (30-60S, JJA)
MODIS (60-90S, JJA)
ERA40
CAM3
D96
SNU
S90
LMD
Liquid water fraction (%)
80
60
40
20
0
-70
1241
CHOI ET AL.
-60
-50
-40
-30
-20
-10
Grid-mean cloud temperature (°C)
0
10
Fig. 5. Liquid water fraction at cloud tops versus gridmean cloud-top temperature observed by MODIS for different latitudes for JJA. The error bar corresponds to
20 times the standard error of the mean. The functions
(black and gray) assumed for the cloud phase parameterizations in the GCMs include CAM3, the NCAR Community Atmosphere Model version 3.0, Del Genio et al.
(1996; D96), the ECMWF 40-year reanalyses (ERA40),
Smith (1990; S90), the Laboratorie de Météorologie Dynamique GCM (LMD), and the Seoul National University
GCM (SNU). Thicker lines indicate the observations and
reanalysis estimate.
(<30◦ ) for the period of June through August.
For all the regions, fliq,top has a nearly linear relationship with the cloud-top temperature in the range
of mixed ice and liquid clouds. The value of fliq,top at
temperatures colder than −40◦ C, as well as the value
of (100% −fliq,top ) at temperatures warmer than 0◦ C,
indicate that the level of uncertainty is ∼10%, which is
comparable to the level evaluated from the comparison
of the two satellite measurements. The relationship
varies depending on region and season. The discrepancy in the shape of the function and the unexpected
threshold temperature may result from regionally different ice nuclei types and content, cloud convection
intensity, subgrid-scale phase variability (Gultepe and
Isaac, 1997), or relevant retrieval problems.
The observations were compared with the GCM
parameterizations [Eqs. (2) and (3)], which are displayed together in Fig. 5. Similarities are found between CAM3 and MODIS for the Antarctic winter,
and between D96 and MODIS for the Arctic summer.
However, most of the models present a lack of liquid
clouds at temperatures higher than the observational
cut-off of −47◦ C. This discrepancy causes significant
underestimation of the liquid water fraction by the
GCMs.
Figure 6 compares fliq,top from the models and
fliq,top from MODIS for JJA 2000–2006 over the extra-
tropics and the Antarctic (40◦ –90◦ S). The model fields
are calculated from Eqs. (2) and (3) using the MODIS
grid-mean cloud-top temperature. Clearly, S90 and
ERA40 underestimate fliq,top by a large amount (over
40%) particularly in the band 50◦ –60◦ S (Figs. 5a
and 5b). D96 shows a relatively small underestimation (∼10% to 20%) in the same region (Fig. 6c).
CAM3 is in better agreement with the MODIS observations, with slight overestimation by 10% to 20%
over some areas at latitudes of around 60◦ –70◦ S. The
zonal means of the differences in the fractions reconfirm and summarize the aforementioned comparison
results (Fig. 6e).
Additionally, we compare the model fliq,top results
with the MODIS observations for the Arctic summer
(Fig. 7). As in Fig. 6, considerable underestimation (over 10%) is found in S90 and ERA40 (Figs. 7a
and 7b). D96 is generally in better agreement with
the MODIS observations (within 10%, see Fig. 7c),
though some specific regions over land show discrepancies larger than 10%. In contrast, large overestimations (over 10%) are found in CAM3, particularly
over land (e.g., over Greenland and Alaska, see Fig.
7d). The discrepancy increases with latitude (Fig. 7e)
because the temperature of the summer Arctic atmosphere is mostly within the range corresponding to
mixed ice and liquid clouds (i.e., –40◦ C to 0◦ C). Therefore, the largest bias in fliq,top is found in the Arctic
regions. It should be noted that the model biases with
respect to the observations in the summer Arctic atmosphere have quite different spatial patterns from those
in the SH (compare Figs. 6 and 7). This is certainly
due to the region- and season-dependent relationship
between fliq,top and cloud-top temperature, as shown
in Fig. 5.
These results indicate that current models that assume constant thresholds for the repartition of cloud
water would produce significantly biased liquid cloud
fractions for the polar atmosphere. For most of the
GCMs, the assumption of overly high thresholds for
complete ice formation may underestimate or simply
ignore a large proportion of the liquid clouds in the
polar atmosphere. The plausible impact of the abundant liquid clouds on the polar climate is discussed in
the next section.
5.
Conclusions and discussion
This study investigated the distributions of liquid
water fraction in the winter Antarctic clouds using
satellite observations for the purpose of testing current
cloud phase parameterizations in GCMs. We analyzed
the most up-to-date satellite measurements, MODIS
and CALIOP. These represent passive and active de-
OBSERVED VS. MODELED CLOUD PHASE IN THE WINTER ANTARCTIC ATMOSPHERE
E1
°
20
20
°
W1
E1
2
W1
20
°
S80°
0°
E6
W6
0
0°
S60°
0°
180°
180°
0°
S40°
20
W1
0°
°
E1
°
0°
S80°
W6
°
2
W1
20
S80°
0
E6
°
S60°
S40°
E1
2
0°
S80°
W6
0°
E6
VOL. 27
180°
180°
1242
0°
S60°
W6
0°
0°
E6
S60°
0°
S40°
Difference (%)
0°
S40°
Fig. 6. Comparison of the mean liquid water fraction at cloud top for JJA of 2000–2006 over the
extratropics and the Antarctic (40◦ −90◦ S) from the MODIS data versus from the GCM parameterizations in (a) S90, (b) ERA40, (c) D96, and (d) CAM3. Zonal means of the values in (a)–(d)
are given in (e).
vices, respectively, making them complementary to
each other. We calculated the fraction of liquid clouds
at the cloud tops as well as for whole cloud columns.
Our results show that the winter Antarctic atmosphere
has abundant liquid clouds in the range of temperatures between −40◦ C to 0◦ C with space- and timedependent relationships between liquid cloud fraction
and temperature.
We have considered the liquid cloud fraction below
−40◦ C as uncertain in current phase retrievals. The
cloud particles that are identified as liquid at temperatures below −40◦ C in these satellite retrievals may
not necessarily be liquid, but rather frozen spheres
which have not developed crystalline faces (Mason,
1952; Madonna et al., 1961), semi-spherical (droxtal)
ice particles (Choi et al., 2009), and/or horizontally
oriented ice plates (Hu et al., 2009). Recent measurements also reveal that, at extremely cold temperatures
down to −80◦ C, clouds could be composed of nitric
acid compounds that can be found either in ice or
liquid phase (Omar and Gardner, 2001; Noel et al.,
2008). However, the true phase of such particles could
not be confirmed in these satellite retrievals, and accurate determination requires direct sampling of cloud
1243
CHOI ET AL.
E1
20
180°
180°
NO. 6
°
20
W1
°
E1
20
S80°
S80°
W6
0
0°
E6
°
W6
0
0°
E6
S60°
°
S60°
0°
S40°
S40°
180°
180°
0°
E1
0°
2
W1
°
°
20
20
W1
°
E1
S80°
S80°
W6
0
°
60
E
0°
2
W1
20
°
°
S60°
W6
0
°
60
E
°
S60°
0°
S40°
Difference (%)
0°
S40°
Fig. 7. Same as in Fig. 6, except for the Arctic in JJA.
particles.
The observations are used to evaluate the cloud
phase parameterizations in current GCMs that repartition condensed cloud water between liquid water and
ice as a function of the local temperature. The relationship between the MODIS cloud-top phase and
cloud-top temperature in a grid-mean sense is highly
dependent on season and region. Therefore, current
models that assume overly high (low) cut-offs for the
complete glaciation of cold clouds would simulate a
significantly lower (higher) liquid cloud fraction in the
winter Antarctic atmosphere, particularly between the
latitudes of 50◦ –60◦ S. The clouds, identified as liquid
phase by satellite sensors, have radiative properties
different from those of typical ice particles for both
shortwave and longwave frequencies (Liou, 2002); liq-
uid cloud particles generally have a higher extinction
optical thickness than do ice particles. For this reason, the increase in liquid cloud fraction in GCM simulations may lead to increased optical thickness (i.e.,
albedo) of mixed-phase clouds.
We note that the increase in liquid cloud fraction in
GCMs might extend to affecting various climate processes that are associated with polar surface/air temperatures, SH westerlies, and sea-ice feedbacks. A series of climate model runs using a coupled mixed-layer
ocean model showed that the underestimation of liquid clouds may cause significantly biased climate simulations, especially for surface temperatures at high
latitudes (Ho et al., 1998); the temperature change
at high latitudes was mostly associated with sea-ice
feedback. The coupled model simulation also shows
1244
OBSERVED VS. MODELED CLOUD PHASE IN THE WINTER ANTARCTIC ATMOSPHERE
that the underestimation of liquid cloud fraction may
also cause a change in cloud distribution (Ho et al.,
1998), which indicates high sensitivity of cloud feedback to the cloud phase composition. The impact of
the changes in cloud phase composition, however, remains uncertain. Further studies are needed to focus on evaluating the quantitative climatic impacts of
abundant liquid clouds.
Acknowledgements. This work was funded by Korean Center for Atmospheric Sciences and Earthquake Research 2010–1178, and US Department of Energy grant
DE-FG02-01ER63257. The MODIS and CALIOP data
were obtained from LAADS (Level 1 and Atmosphere
Archive and Distribution System) at the NASA Goddard
Space Flight Center and ASDC (Atmospheric Sciences
Data Center) at the NASA Langley Research Center, respectively.
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