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JOURNAL OF CLIMATEVOLUME 20

Cloud Cover over the South Pole from Visual Observations, Satellite Retrievals, andSurface-Based Infrared Radiation Measurements
MICHAEL S. TOWN
Department of Atmospheric Sciences, University of Washington, Seattle, Washington

VON P. WALDEN
Department of Geography, University of Idaho, Moscow, Idaho

STEPHEN G. WARREN
Department of Atmospheric Sciences, University of Washington, Seattle, Washington(Manuscript received 7 February 2006, in final form 30 May 2006)
ABSTRACT
Estimates of cloud cover over the South Pole are presented from five different data sources: routine visualobservations (1957–2004; Cvis), surface-based spectral infrared (IR) data (2001; CPAERI), surface-based
broadband IR data (1994–2003; Cpyr), the Extended Advanced Very High Resolution Radiometer(AVHRR) Polar Pathfinder (APP-x) dataset (1994–99; CAPP-x), and the International Satellite Cloud
Climatology Project (ISCCP) dataset (1994–2003; CISCCP). The seasonal cycle of cloud cover is found torange from 45%–50% during the short summer to a relatively constant 55%–65% during the winter.
Relationships between Cpyr and 2-m temperature, 10-m wind speed and direction, and longwave radiation
are investigated. It is shown that clouds warm the surface in all seasons, 0.5–1 K during summer and 3–4 K
during winter. The annual longwave cloud radiative forcing is 18 W m⫺2 for downwelling radiation and 10
W m⫺2 for net radiation. The cloud cover datasets are intercompared during the time periods in which they
overlap. The nighttime bias of Cvis is worse than previously suspected, by approximately ⫺20%; CISCCP
shows some skill during the polar day, while CAPP-x shows some skill at night. The polar cloud masks for thesatellite data reviewed here are not yet accurate enough to reliably derive surface or cloud properties overthe East Antarctic Plateau. The best surface-based source of cloud cover in terms of the combination of
accuracy and length of record is determined to be Cpyr. The use of the Cpyr dataset for further tests ofsatellite retrievals and for tests of polar models is recommended.

1. Introduction
In spite of nearly 50 yr of routine weather observations at South Pole Station, cloud cover over the South
Pole is still not well known. Estimates of cloud coverfrom visual observations are poor during the polarnight because of the high frequency of optically thinclouds (through which stars can be seen) and inadequate moonlight (Hahn et al. 1995). It is also difficult
to determine cloud cover from satellite data over the
Poles because of the small contrast in both albedo andCorresponding author address: Michael S. Town, Department
of Atmospheric Sciences, University of Washington, Box 351640,
Seattle, WA 98195-1640.
E-mail: mstown@atmos.washington.edu© 2007 American Meteorological Society

JCLI4005

temperature between the snow surface and clouds(Rossow and Schiffer 1999). Cloud cover over theSouth Pole is of particular interest because South Pole
Station is one of only a few sites in the interior of Antarctica that maintain continuous year-round records ofmeteorological and climatological variables. This record extends back to the International GeophysicalYear (IGY) in 1957. Because the East Antarctic Plateau is uniform and flat around the South Pole (surface
slope ⫽ 2.25 ⫻ 10⫺3; Liu et al. 2001), the data collected
at South Pole Station can represent a broad region of
the plateau.
Data collected at the South Pole are not only of climatological interest to the Antarctic community, but
are also useful in evaluating the output of weather andclimate forecast models as well as satellite retrievals of

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TABLE 1. The South Pole cloud cover datasets. The ISCCP and APP-x datasets used for the analysis here are limited by availabilityof the pyrgeometer data used for comparison, as well as their own lengths of record.
Cloud cover

SourceMethodLength of record

Cvis
CPAERICISCCP
CAPP-xCpyr

SPMO
SPARCLE
ISCCP
APP-x
ESRL-GMD

VisualSpectral infrared (3.3–22 ␮m)
0.65 and 11 ␮m
0.65, 0.86, 3.75, 11, and 12 ␮m
Broadband infrared (4–50 ␮m)

1957–2004
2001
1994–2003
1994–1999
1994–2003

climate variables (e.g., Briegleb and Bromwich 1998;
Hines et al. 1999; Pavolonis and Key 2003; Guo et al.
2003; Pavolonis et al. 2004; Hines et al. 2004). In addition, understanding the behavior of clouds and theirproperties is important for understanding Antarctic climate. Lubin et al. (1998) found that changing the specified phase and particle size for Antarctic clouds in the
National Center for Atmospheric Research (NCAR)
Community Climate Model Version 2 (CCM2) caused
significant changes in the atmospheric circulation of theSouthern Hemisphere. Other studies have revealed
that current cloud parameterizations limit the successof Antarctic climate and weather simulations (Hines etal. 1999, 2004). The problems associated with these parameterizations are due in part to the lack of adequate
ground-truth data. Therefore, accurate information
about clouds and cloud radiative forcing could provide
a more solid foundation on which to test the models.
Prior work on the subject of cloud cover over theSouth Pole by Town et al. (2005) confirmed a low wintertime bias in visual observations of cloud cover foundby Hahn et al. (1995). The results of Town et al. (2005)
imply that the low wintertime bias may be much greaterthan that shown in Hahn et al. (1995), but the results ofTown et al. (2005) were based on only 1 yr of data
(2001). Retrievals of cloud cover over the South Polefrom satellite data have also been reported (Fig. 13 ofHahn et al. 1995; Fig. 2 of Pavolonis and Key 2003).
Intercomparison of these results shows that there arestill significant discrepancies in climatological cloud
cover over the South Pole.
In this work we present five different cloud coverdatasets for the South Pole (section 2). The estimates ofcloud cover come from routine visual observations ofcloud cover, retrievals from a surface-based spectral
infrared interferometer, retrievals from climatologicalsatellite datasets, and retrievals from a surface-based
broadband pyrgeometer. We intercompare the monthly
means and seasonal cycles of the different cloud coverdatasets, and examine their trends (section 3). We also
examine the relationships between cloud cover andlongwave radiative fluxes, 10-m wind speed and direction, and 2-m temperature (section 4). The results are
summarized in section 5.

2. Cloud cover datasets
This section presents the following relevant details of
the different cloud cover datasets (Table 1): visual observations of cloud cover from 1957 to 2004 taken by
the South Pole Meteorological Office (SPMO), Cvis;
cloud cover derived from spectral infrared radiances
during 2001 as part of the South Pole Atmospheric Radiation and Cloud Lidar Experiment (SPARCLE),
CPAERI; cloud cover derived from the International Satellite Cloud Climatology Project (ISCCP), CISCCP;
cloud cover derived from the Extended Advanced VeryHigh Resolution Radiometer (AVHRR) Polar Pathfinder dataset (APP-x), CAPP-x; and cloud cover derived
from routine surface-based pyrgeometer measurements
made by the Earth System Research Laboratory(ESRL)-Global Monitoring Division (GMD) of the
National Oceanic and Atmospheric Administration(NOAA), Cpyr.

a. Cloud cover from visual observations
Monthly mean Cvis presented here is computed fromvisual observations archived by the Antarctic Data
Center at the Space Science and Engineering Center(SSEC) at the University of Wisconsin. The monthly
means are computed from two different data formats—an archive of the individual synoptic observations (SOarchive), which are recorded 4 times daily, and an archive of daily averages (DA archive). The DA archive
was calculated by SPMO from hourly observations
from late November to early February and from
6-hourly synoptic observations during the rest of theyear. The daily mean sky cover was averaged androunded to tenths until June 1997; thereafter, octas
were reported. The DA archive runs from 1957 throughthe present. The SO archive runs from 1992 through thepresent. Our analysis only extends through 2004. Alltime series or composite averages that relate to theentire 1957–2004 period are from the DA archive. Alltime series or composite averages that relate to the time
period of 1994–2003 are computed directly from the SOarchive. The Cvis from the SO archive has the advantage over Cvis from the DA archive in that each monthcan be treated the same in terms of accuracy because of

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temporal resolution and sampling. Monthly mean Cvis
are obtained from the SO archive in all cases for which
the synoptic data are available, except when they introduce nonuniformities that may affect trends or seasonal
cycles computed here.
Monthly means computed from the DA archive differ from those computed from the SO archive, with anrms difference of about 5%. No doubt some of thediscrepancy between the two is a result of the daily
averages being calculated from hourly observations
from late November to early February. Some of theremaining discrepancies throughout the year may be
explained by the loss of accuracy resulting from additional averaging and rounding steps that are applied in
producing the DA archive. Some of the discrepancies
between the monthly means computed from the SOarchive and the DA archive are still unexplained, butthey have minimal effects on the long-term averages.
Uncertainty in the monthly means of Cvis resulting
from random error is estimated as in Fig. 5 of Warren
et al. (1986). In that work, the random errors in amonthly mean resulting from inadequate sampling for
grid boxes in northern Europe were estimated throughsubsampling a larger dataset. For 120–124 observations
per month (i.e., 6-hourly data), the uncertainty in themonthly mean is 4% points. Step functions in the Cvis
time series may also be present due to changes in observing practices from one organization to another. The
SPMO was operated by several different organizations
from 1957 to 2004 (the U.S. Navy, National Weather
Service, and independent contractors). The uncertainty
introduced by changes in observing practices is notquantified here.
Figure 1 is a histogram of visual observations madeduring daylight. It shows that Cvis is within 1 octa ofclear or overcast 71% of the time. The World Meteorological Observation (WMO) guidelines for visual observers (WMO 1996) state that if there is the slightest
amount of cloud in the sky, then observers are required
to classify the sky as 1 octa of cloud cover. Similarly, if
there is the slightest bit of clear sky visible between theclouds, then the observers are required to classify thesky as 7 octas of cloud cover. Therefore, calls of either
1 or 7 octas often correspond to essentially clear or
overcast skies, respectively. Figure 1 then shows thatthe sky is essentially clear or overcast 71% of the time
during daylight.
Strengths of the visual observation dataset are its
length and regular sampling frequency. During daylightclouds are easily detected because they are being contrasted with a darker background. Weaknesses of thevisual dataset include the nighttime observations andinconsistent calibration of observers from year to year.

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FIG. 1. A histogram of daytime visual observations of cloud
cover from 1994 through 2003 (7280 observations total). Cloud
cover exhibits a U-shaped distribution, with the result that cloud
cover is within 1 octa of being clear or overcast 71% of the timeat South Pole.

b. Cloud cover from surface-based spectral infraredmeasurements
Cloud cover was derived from downwelling spectral
infrared (IR) data (CPAERI) taken as part of SPARCLE(Walden et al. 2001). The spectral IR radiances wereobserved by the Polar Atmospheric Emitted RadianceInterferometer (PAERI) from January to October
2001. The PAERI has a narrow field of view (FOV;
46 mrad) and a spectral range of 3.3–22 ␮m. The spectral IR dataset and cloud-detection algorithm are detailed by Town et al. (2005). Clouds are detected by
how much the IR “window” (10–12 ␮m) is filled in by
downwelling radiation from clouds. Each monthly
mean CPAERI is based on 1000–2000 determinations ofcloud or no-cloud averaged over a month. The CPAERI
is very sensitive to thin clouds. Adding or subtracting
the equivalent of a thin layer of near-surface ice crystals
to the cloud/no-cloud threshold to every observation ina month can change the monthly mean CPAERI by⫺10% or ⫹18%.
One strength of this dataset is its sensitivity to optically thin clouds, a benefit of the IR spectrum fromwhich CPAERI is retrieved. A weakness of this dataset isits short period of record, just 1 yr.

c. Cloud cover from satellites
1) INTERNATIONAL SATELLITE CLOUD
CLIMATOLOGY PROJECTMonthly mean CISCCP presented here is obtained
from the ISCCP D2 dataset. A general description of

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the ISCCP project is given by Schiffer and Rossow(1983). The cloud-detection algorithms are detailed
by Rossow and Garder (1993a). Changes made to the
ISCCP C series data products to produce the D series
are given by Rossow and Schiffer (1999). The clouddetection algorithm was validated by Rossow and
Garder (1993b), and the D2 dataset has been comparedto numerous other satellite products (e.g., Pavolonis
and Key 2003; Thomas et al. 2004). ISCCP D2 data
products include, but are not limited to, cloud fraction,
cloud pressure, cloud-top temperature, cloud opticaldepth, and cloud water path.
The ISCCP D2 dataset uses AVHRR data polewardof 60°. It uses two channels for cloud detection: a visible
channel at 0.65 ␮m and an IR channel at 11 ␮m. The
visible channel is employed when the sun is higher than
9° above the horizon (November–February at theSouth Pole); otherwise, the cloud-detection algorithm
depends solely on the IR channel. The cloud-detection
algorithms are based on spatial and temporal variations
and absolute thresholds in albedo and brightness temperature. For a pixel to be considered cloudy it must be
significantly colder than the warmest pixel in a given
spatial domain. It must also exhibit variability greaterthan a set threshold over a 3-day period. The D2
dataset consists of average values on a 250-km equalarea grid; the values given are averages of all pixels
within a grid box.
The D2 dataset is composed of monthly averages
from 1983 to 2004. We use data from 1994 to 2003 tomatch the surface-based broadband IR record (section2d). Over the South Pole CISCCP is an average of thethree grid boxes centered around the South Pole at88.5°S. Uncertainty in CISCCP is estimated as being approximately 10% (Rossow and Garder 1993b), resulting from errors in clear-sky radiances. A further 15%–25% low bias over the polar regions during summer anda 10% high bias during winter, based on comparison of
the D2 dataset to visual observations, is expected (Rossow and Schiffer 1999).
Strengths of the ISCCP dataset are its regular sampling frequency and its spatial coverage. The latter
strength will not be exploited in this work; rather,
CISCCP will be evaluated over the South Pole only.
Weaknesses of this dataset are that it uses at most twoof the currently available channels, and that near the
Poles it must attempt to detect clouds over snow andice.

2) EXTENDED AVHRR POLAR PATHFINDERDATASETMonthly mean CAPP-x is calculated from the University of Wisconsin APP-x cloud product dataset. APP-x

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has been used in a number of studies both in the Arctic
and the Antarctic to generate cloud climatologies, estimate cloud radiative forcing, and improve forecast
models (e.g., Wang and Key 2003; Pavolonis and Key
2003; Pavolonis et al. 2004; Wang and Key 2005a,b).
The APP-x dataset is presented on a 25-km polarstereographic grid. It uses all available channels of the
AVHRR (at 0.65, 0.86, 3.75, 11, and 12 ␮m) to identify
clouds and derive climate parameters such as surface
temperature, surface albedo, cloud fraction, cloud optical depth, cloud-top temperature, cloud phase, precipitable water, and cloud radiative forcing. All quantities are reported at 0200 and 1400 LT. Key (2002)
describes the cloud-detection algorithm and extended
data products in detail. We expect CAPP-x to underestimate cloud cover during spring and summer and yieldhigher amounts of cloud cover during winter because of
its sensitivity to near-surface ice crystals (diamonddust) based on previous comparisons of cloud cover inthe Arctic (Meier et al. 1997), although diamond dust
clouds are typically much more optically thin over the
Antarctic interior than over the Arctic.
The monthly means we display from APP-x are averages of the four grid boxes south of 89.8°S. However,
we also determined the CAPP-x time series as an averageof all grid boxes south of 89°S (60 grid boxes); it differed insignificantly from that of the four southernmost
grid boxes.
Strengths of this dataset are the multiple channels
used in its cloud-detection algorithm, its regular sampling frequency, and its spatial coverage. Like ISCCP,
its view is from above, so it must deal with the difficultyof detecting clouds over snow and ice. This algorithm
has been optimized for polar climates (Key 2002).

d. Cloud cover from surface-based pyrgeometermeasurements
We introduce another cloud cover dataset based on
retrievals from surface-based pyrgeometer measurements, Cpyr. The ESRL-GMD, formerly known as the
Climate Monitoring and Diagnostics Laboratory(CMDL), operates a baseline atmospheric monitoringsite at the South Pole. Upward- and downward-looking
Eppley pyrgeometers have been deployed there tomonitor the longwave radiation budget. The ESRLGMD station is part of the Baseline Surface Radiation
Network (BSRN; Ohmura et al. 1998). The data have
3-min temporal resolution from 1994 through 1997 and1-min temporal resolution from 1998 through 2003. Uncertainties in Eppley pyrgeometer measurements havebeen characterized at mid- (Philipona et al. 2001) andhigh latitudes (Marty et al. 2003). We use an operational uncertainty of 8 W m⫺2 for the pyrgeometer

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FIG. 2. Scatterplot of standard deviation of downward irradiance over a 20-min time period plotted against mean downward
irradiance for the same time period for December 2001. The
thresholds of irradiance and standard deviation of irradiance aredescribed in the text. Quadrant I represents the clear-sky observations; observations in all other quadrants are classified as overcast for this month.

measurements made at the South Pole (Town et al.
2005).
The Cpyr is retrieved from the routine pyrgeometermeasurements by a dual-threshold method. Figure 2
illustrates how this method is employed for December
2001. Figure 2 is a scatterplot of standard deviation overa 20-min period plotted against the mean downward
irradiance over the same time period. We use the 20min time period for analysis because it is an acceptable
compromise between temporal resolution and a meaningful standard deviation. The dashed vertical line in
Fig. 2 is a threshold calculated using a line-by-line radiative transfer model (LBLRTM) (Clough et al. 1992)
for clear sky. Clear-sky downwelling irradiance thresholds were calculated for each month based on monthly
mean atmospheric profiles as described in Town et al.
(2005). To allow for interannual variability in clear-sky
downwelling flux and pyrgeometer calibration drift, 22
W m⫺2 is added to each monthly threshold. This tolerance was determined by taking 3 times the annual mean
standard deviation of clear-sky irradiance during 2001,
where clear skies were determined using the CPAERI
time series. The irradiance data have been inspected toensure that each monthly irradiance threshold does not
encroach on the times of overcast cloud cover. Thus, alldata points to the right of the vertical dashed line in Fig.
2, quadrants III and IV, are classified as overcast using
the irradiance threshold.
The standard deviation threshold (the horizontal
dashed line in Fig. 2) was chosen by inspection of theentire time series of the standard deviation of irradi-

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ance. Given the absolute irradiance threshold describedabove, the standard deviation threshold was optimized
to a value of 0.8 W m⫺2 to maximize the correlation
with the daylight visual estimates of monthly mean
cloud cover (r2 ⫽ 0.73), and minimize the mean residual(⫺4.5%) and rms difference (6.1%) between Cpyr andCvis. Slightly smaller mean residual and rms differencewith a slightly lower r2 were achieved with a thresholdof 0.7 W m⫺2. The differences in Cpyr using the twodifferent standard deviation thresholds are within theestimated error of Cpyr, discussed below. Therefore, thetwo standard deviation thresholds of 0.7 and 0.8 W m⫺2
are essentially equivalent. Direct comparisons between
the Cpyr and Cvis datasets are presented in section 3a.
The same standard deviation threshold is used for theentire pyrgeometer dataset because there is no reason
to suspect that a clear atmosphere during summer willshow significantly more radiative variability than during winter. Thus, all data plotted above the horizontal
line in Fig. 2, quadrants II and III, are classified as
cloudy. In union with the absolute irradiance thresholddescribed previously, the standard deviation thresholdessentially prevents thin or patchy clouds from beingclassified as clear skies.
Pyrgeometers have a hemispheric FOV; therefore,
this method classifies the entire sky as being either clear(quadrant I) or overcast (quadrants II, III, and IV) every 20 min. The time series of ones and zeros was averaged for each month to give Cpyr. The reason forthinking that a binary method to determine monthly
mean cloud cover at South Pole can succeed is thatcloud cover there exhibits a U-shaped distribution, withmost cases being either clear or overcast (Fig. 1). Figure
1 is for daytime, but it is assumed that clouds exhibit asimilar distribution during night, and therefore thedual-threshold method is applied to pyrgeometer datataken for all months.
The frequency of visual reports of sky cover between
2 and 6 octas in Fig. 1 is utilized to determine the bias
in our monthly mean Cpyr estimate. Twenty-nine percent of the visual observations in any month are between 2 and 6 octas. Assuming that cases of either 2 or
3 octas are likely misclassified as clear skies, and casesof 4–6 octas are likely misclassified as overcast, the errors resulting from misclassification of partially cloudy
skies as clear or overcast almost exactly offset eachother in the monthly mean, leaving only a small positive
bias of 0.5%, well within the uncertainty of the cloud
fraction retrieval. If no skies of 2–3 octas occur in amonth, and the rest of the cloud cover distribution isunchanged, then the maximum bias possible is ⫹5%.
Similarly, if no skies of 4–6 octas occur in a month, andthe rest of the cloud cover distribution is again un-

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changed, then the maximum negative bias is ⫺4.5%.
The absolute uncertainty in monthly mean Cpyr resulting from the operational uncertainty in the pyrgeometer calibration (8 W m⫺2; Town et al. 2005) is ⫾1%.
Thus, interannual variability in monthly mean cloud
cover is much larger than the uncertainty or bias in theestimate of an individual monthly mean.
The sensitivity of Cpyr to the standard deviationthreshold (0.8 W m⫺2) is greater than the sensitivity ofCpyr to the monthly irradiance threshold. Varying theirradiance threshold by ⫾5 W m⫺2 has almost no effecton Cpyr during the austral summer but changes Cpyr by⬃2%–3% during the austral winter. Increasing thestandard deviation threshold from 0.8 to 1 W m⫺2 decreases the annual mean Cpyr by 1%–2%. However,decreasing the standard deviation threshold to 0.5 Wm⫺2 increases the annual mean Cpyr by 20%–25%. Because of the greater frequency of optically thin clouds
during winter, Cpyr is more sensitive to both thresholds
then.
Other methods using threshold methods have beendeveloped for estimating cloud fraction based on surface temperature, relative humidity, and pyrgeometer
irradiance (Marty and Philipona 2000; Dürr and Philipona 2004; Sutter et al. 2004). However, those methods
are not suitable for the extreme conditions at the South
Pole because the surface inversion often decouples thefree troposphere from the boundary layer, making surface meteorological variables such as temperature andhumidity inadequate indicators of cloud cover. Instantaneous cloud fraction cannot be otherwise estimatedfrom the irradiance measurements because cloud temperature and emissivity would have to be known instantaneously.

549

FIG. 3. Scatterplot of monthly mean Cpyr vs monthly mean Cvis
for 1994–2003. The circles indicate daylight months (October–March); the crosses indicate the months of darkness (April–
September). The daytime monthly means are well correlated, r2 ⫽0.73. The nighttime estimates of Cpyr and Cvis are nearly uncorrelated. The extremely low Cvis value (3%) is from July 1994.

in Hahn et al. (1995). Our results show that the magnitude of the low bias varies substantially from year toyear due in large part to changes in the observer staff,
which makes correction of the visual observation climatology problematic. This is discussed further in section 3b.
We also compare monthly mean Cpyr to monthly
mean CPAERI and Cvis for 2001 (Fig. 4). As stated earlier, the PAERI collects spectral infrared data with anarrow FOV. Also, C PAERI is based on a binary
method, but the narrow FOV of the PAERI frees the

3. Intercomparisons, seasonal cycles, and trends incloud coverIn this section we present intercomparisons between
the five different cloud cover datasets. We then use
results of these intercomparisons to analyze the seasonal cycles and trends derived from the datasets.

a. Intercomparisons
We begin with the surface-based estimates of cloud
cover. Figure 3 shows a scatterplot of monthly mean
Cpyr versus monthly mean Cvis, during both day (graycircles) and night (black crosses). The correlation isgood when there is adequate light, but is poor during
the polar night. The wintertime low bias in the visualobservations noted by Hahn et al. (1995) is also apparent in Fig. 3. However, the magnitude of the low bias ismuch greater than expected based on the moonlightcorrection applied to South Pole surface observations

FIG. 4. The seasonal cycle of monthly mean Cvis, Cpyr, and
CPAERI for 2001. The algorithm to determine CPAERI is given in
Town et al. (2005). The timing of the CPAERI point during Marchis explained in Town et al. (2005). The discrepancy between
CPAERI, Cvis, and Cpyr during February is explained in the text.

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retrieval of CPAERI from the environmental constraintof a U-shaped distribution of cloud cover. Clouds over
the South Pole will not preferentially avoid the FOV of
the PAERI, allowing monthly means of cloud cover
between Cvis, Cpyr, and CPAERI to be considered essentially equivalent spatial and temporal means derived
from different measurement techniques.
During the daytime Cpyr compares well with the visual observations, and throughout the winter of 2001
compares well with CPAERI. The year 2001 had a particularly low bias in Cvis during the polar night. The Cpyris biased low relative to CPAERI during the winter because CPAERI is more sensitive to optically thin clouds
than Cpyr. The discrepancy between CPAERI and Cpyr
during February is due to the fact that in February the
PAERI was employed in other activities in addition to
routine observation of the atmosphere. The PAERI
was therefore unable to get a sample of cloud cover that
was adequate to represent the monthly mean.
A figure similar to Fig. 4 appears in Town et al. (2005,
their Fig. 11). The cloud-detection algorithm applied tothe pyrgeometer data for 2001 in that work employed
only an absolute irradiance threshold. Therefore, it isless sensitive to the thin clouds prevalent over South
Pole and misses 5%–12% of the clouds during winterthat are detected in Cpyr here. While still biased low
relative to CPAERI, Cpyr here agrees much better with
CPAERI than the cloud cover from pyrgeometer measurements in Fig. 11 of Town et al. (2005) because of
the incorporation of the standard deviation threshold.
Because Cpyr agrees well with Cvis during the daylightand CPAERI throughout 2001, we consider Cpyr accurateenough to be used as ground truth against which yearround retrievals of cloud cover from satellites can be
judged. Figure 5 shows scatterplots of CAPP-x versus
Cpyr and CISCCP versus Cpyr. The black crosses are forApril–September (“winter”) and the gray circles are forthe other 6 months (non-winter). During summer,
CAPP-x is biased high and CISCCP is biased low. There isno correlation between CAPP-x and Cpyr during daylight,
but there is a slight correlation between CISCCP and Cpyr
when the ISCCP dataset employs the visible channel in
its algorithm (November–February; filled gray circles).
No correlation between CISCCP and Cpyr was found during the polar winter, but CISCCP does obtain a wintermean that agrees with that of Cpyr. While CAPP-x has
some skill at estimating monthly mean cloud cover during the winter, the correlation is low (r2 ⫽ 0.12).
It is currently believed that the cloud bases over theSouth Pole are often within 1–2 km of the surface (Mahesh et al. 2005). During winter, this would often makethe clouds significantly warmer than the surface because of the persistent strong surface-based tempera-

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FIG. 5. (top) A scatterplot of monthly mean CAPP-x vs Cpyr for
1994–99. The gray circles are for cloud cover during daytime, andthe black crosses are for nighttime. The filled-in gray circles are
for November–February, the open gray circles are for March andOctober. The uncertainty-weighted regression with 1␴ errors of
the nighttime data (solid line) is slope ⫽ 0.4 ⫾ 0.2, intercept ⫽36 ⫾ 11, r2 ⫽ 0.12. (bottom) A scatterplot of monthly mean
CISCCP vs Cpyr for 1994–2003. Symbols are the same as above.
The uncertainty-weighted regression with 1␴ errors of the November–February data is slope ⫽ 0.6 ⫾ 0.2, intercept ⫽ ⫺2 ⫾ 9,
r2 ⫽ 0.22.

ture inversion. The APP-x algorithm is theoretically capable of identifying a cloud that is warmer than thesurface (Key 2002). However, in section 4c we will see
the skill of the APP-x cloud mask is limited by thesimilarity in spectral emissivity of clouds and snow andthe radiative equilibrium reached between clouds andthe snow surface.
The mean rms difference between CAPP-x and Cpyr(not shown) is approximately 10% during winter. The
rms difference grows to 25%–30% during the end of
spring through the beginning of fall. This is likely due tosolar heating of the surface and the subsequent destruction of the temperature inversion. The rms differencetime series of CISCCP and Cpyr is similar to that of CAPP-x

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and Cpyr. The rms difference between CISCCP and Cpyr
grows during the summer because of ISCCP’s underestimates of cloud cover during those months. It is unclear why CISCCP and Cpyr have a low rms difference
during winter; they are uncorrelated during this time.
Nearly identical results for the top panel in Fig. 5 areachieved if we use every grid box in the APP-x dataset
south of 89°S (60 grid boxes), as opposed to the fourgrid boxes used to determine CAPP-x. From this we conclude that the reason for the lack of correlation between Cpyr and CISCCP during winter is not due to spatial resolution. Further evidence for this conclusion isinferred from the U-shaped distribution of cloud cover
over the South Pole (Fig. 1). Because it is clear or overcast 71% of the time, low-resolution satellite data
should yield roughly the same results as the high-reslution satellite data, particularly in the monthly mean.
It is also uncertain what inversion strength would benecessary for the APP-x or ISCCP cloud-detection algorithms to succeed operationally in the future over the
Antarctic ice sheet. Estimates of equilibrium clear-sky
surface temperatures from station data or theoreticalmodels would aid the development of clear-sky radiance thresholds. In terms of meteorological parameters,
the thresholds will be some function of cloud temperature, cloud optical thickness, and clear-sky surface temperature. The South Pole has an average inversion
strength of about 25 K during the winter (Hudson and
Brandt 2005). It is believed that many of the clouds
over the South Pole reside at the top of the temperature
inversion (Mahesh et al. 2001, 2005).
Based on the intercomparison between Cvis, CPAERI,
CAPP-x, CISCCP, and Cpyr, we believe that Cpyr adequately represents cloud cover over the South Polethroughout the year. As stated above, it is well correlated with Cvis during the daylight, when Cvis is known
to be accurate. The results of Cpyr agree well with
CPAERI for the entire 2001 SPARCLE record. We
therefore consider Cpyr to be the best surface-based
cloud cover time series for the South Pole to date andrely primarily on Cpyr to determine the true seasonal
cycle and trends in cloud cover in the following subsections. Also, Cpyr is used to find relationships between
cloud cover and meteorological parameters in section 4.
The following subsections do, however, include someanalysis of the other datasets for intercomparison purposes.

b. Seasonal cycles
Figure 6 shows the seasonal cycles of Cpyr and Cvis,
averaged over 1994–2003. The error bars represent interannual variability in cloud cover (one standard deviation). Interannual variability in Cpyr is due primarily

551

FIG. 6. Seasonal cycles of Cpyr and Cvis based on data from 1994
through 2003. The error bars indicate one standard deviation of
the individual monthly means about the multiyear mean for thatmonth; Cvis is determined from the SO archive for this time period
for reasons explained in the text. The dashed line (“light” curve)
results from averaging a subset of the observations screened by
the illumination criterion of Hahn et al. (1995).

to natural variability, because the pyrgeometer calibration has been stable during the measurement period(E.G. Dutton 2004, personal communication). Interannual variability in Cvis is larger than in Cpyr, particularly
during winter, due not only to natural variability but
also to differences among observers in their interpersonal calibration and their ability to detect thin clouds
at night.
As seen earlier (Fig. 3), there is a low wintertime bias
in Cvis. The visual observations in Fig. 6 were screened
for adequacy of moonlight using the method described
Hahn et al. (1995), but this does not seem to improve
the wintertime estimates of cloud cover significantly
relative to Cpyr. The values for the sunlit months areaverages of approximately 1240 observations each. The
months of May, June, and July each contain about 280
observations after application of the illumination criterion.
Not every winter shows the same low bias, as indicated by the large wintertime error bars. Based on this,
we believe it is possible for visual observers to estimatemonthly mean Cvis during night under adequate moonlight with only a small low bias if the observers are
diligent; they must learn the constellations of the nightsky and allow themselves to become dark-adapted prior
to each observation. The accuracy of monthly mean Cvis
is subject to not only the skill of the observers but also
to whether the time period of adequate moonlight isrepresentative of the true monthly mean cloud cover.
However, even though we believe it is possible for vi-

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FIG. 7. The Cvis estimate daily averages from 1957 through 2004.
The dashed line (“light” curve) results from averaging a subset ofthe observations screened by the illumination criterion of Hahn etal. (1995).

FIG. 8. Seasonal cycle of monthly mean CISCCP (1994–2003) and
CAPP-x (1994–99). Error bars on CISCCP are 1␴ of the composite
monthly mean for 1994–2003. No error bars were computed for
CAPP-x because of the short length of the time series.

sual observers to make estimates of cloud cover with
only a small low bias during the polar night, the current
wintertime record of Cvis is still biased significantly low
even after the moonlight correction.
The seasonal cycle of Cvis for the 48 yr of 1957–2004
is shown in Fig. 7. For this dataset screening for adequate moonlight consistently increases the monthly
mean Cvis during the winter months by approximately
3%. However, this is still not sufficient to bring aboutagreement with Cpyr. In addition, the standard deviations about the monthly means in Fig. 7 are still greater
during winter than those about Cpyr in Fig. 6. [Themoonlight criterion of Hahn et al. (1995) was developed using cloud reports from latitude zones of 10°–50°N, where the clouds are thicker on average thanover the interior of Antarctica.]
Ultimately, we use Cpyr to estimate the lower end of
the range of the true monthly mean cloud cover; Cvis isused to estimate the higher end. We assume that the
low bias in Cpyr relative to Cvis during daylight is stillpresent during the night, so we add 5% to Cpyr during
the winter in estimating the true wintertime cloud
cover. Thus, the three cloud cover datasets presented in
Figs. 6 and 7 show that the overall seasonal cycle incloud cover has a minimum in November and December of 45%–50%, and a plateau from March throughOctober of 55%–65%.
We next present the seasonal cycle of cloud cover asderived from the ISCCP and APP-x datasets (Fig. 8) forcomparison with the other cloud cover datasets. We
find the shape of the seasonal cycle of CISCCP to beconsistent with the seasonal cycle of Cpyr in that cloud
cover is greater in winter than in summer, but the win-ter–summer difference in CISCCP is much greater thanin Cpyr. The wintertime mean from CISCCP agrees with
Cpyr. The wintertime mean of CAPP-x agrees with Cpyrbased on the results shown in the top panel of Fig. 5,
but CAPP-x overestimates cloud cover relative to Cpyr
during the short summer, contrary to expectations
based on Meier et al. (1997), but consistent with theresults of Maslanik et al. (2001).

c. Trends
Table 2 shows trends computed from the visual andpyrgeometer time series. In all seasons except summer
Cpyr shows decreasing trends, but none of the trends are
significant. For the same time period (1994–2003) Cvis
shows a significant increasing trend in mean annual
cloud cover, which is dominated by the dubious wintertime trend. For spring, summer, and autumn, although
the trends are mostly insignificant, the signs and approximate magnitude of the trends of Cvis do agree withthose of Cpyr.
The trends in Cvis for 1957–2004 are reported for the
sunlit months. The winter trends, and therefore annual
trends, are not reliable due to the low wintertime bias invisual observations. The long-term trends in the otherseasons may be affected by changes in observing practices resulting from changes in the SPMO contractor.
On the whole, we trust the historical daytime visualobservations. A topic for further investigation is theeffects of changes in SPMO contractor on seasonal
trends.
Figure 9 shows the time series of seasonal averages
for Cvis (thin curve) and Cpyr (thick curve). Variability
in the low wintertime bias in Cvis is apparent in contrast

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553

TOWN ET AL.

TABLE 2. Annual and seasonal trends in cloud cover for 1957–2004 and 1994–2003 in percentage points per decade (% decade⫺1).
The number in parentheses is one standard deviation of the slope of the trend. The trends from visual observations (Cvis) andpyrgeometer measurements (Cpyr) in bold are statistically significant to 1␴. We do not report the winter or annual trends in Cvis becauseof the uncertainties associated with Cvis during winter. All trends in the visual observations may be a result of changes in observing
practices resulting from changes in SPMO contractor. The seasons at the South Pole are defined here as spring ⫽ October–November,summer ⫽ December–January, autumn ⫽ February–March, and winter ⫽ April–September. The reason for the unorthodox seasonal
definitions can be seen in the annual 2-m temperature cycle in Fig. 14.

Cpyr 1994–2003
Cvis 1994–2003
Cvis 1957–2004

AnnualSpringSummerAutumnWinter⫺1.6 (3.4)⫺3.5 (6.7)⫺5.1 (6.0)
2.6 (0.8)

7.5 (8.7)
8.8 (8.2)
1.1 (0.8)⫺6.1 (7.4)⫺1.3 (8.4)
3.6 (0.9)⫺1.9 (4.5)

with the Cpyr data from 1994 to 2003. Interannual variability in the seasonal averages is approximately thesame for each season, also shown in Fig. 6. It is apparent that the time series of Cpyr and Cvis are correlated
for all seasons that have some sunlight (spring, summer,
autumn), but are uncorrelated in winter.

4. Cloud cover and other meteorologicalparameters at the South PoleWe now examine the relationship between Cpyr andradiative fluxes, 10-m wind speed and direction, and2-m temperature. All of the routine observations usedhere were provided to us by the ESRL-GMD.

a. Longwave cloud radiative forcing
Longwave fluxes collected by the ESRL-GMD areused here to calculate monthly means, annual means,
and trends in longwave downwelling cloud radiative
forcing (LDCRF) and longwave net cloud radiative
forcing (LNCRF). LDCRF and LNCRF are defined by
Eqs. (1) and (2). Here LDF is the longwave downwelling flux and LUF is the longwave upwelling flux.
Clear-sky averages were determined using the Cpyrdataset to identify the clear times. We use the definitionof cloud radiative forcing from Ramanathan et al.
(1989). Clear-sky averages are subtracted from all-sky
averages to obtain the net effect of clouds (and theassociated changes in temperature and humidity) on
the downwelling or net longwave fluxes. Here,LDCRF ⫽ LDFall ⫺ LDFclear

共1兲

LNCRF ⫽ 共LDFall ⫺ LUFall兲 ⫺ 共LDFclear ⫺ LUFclear兲.
共2兲

FIG. 9. Time series of cloud cover by season. The thin curve isCvis (1957–2004); the thick curve is Cpyr (1994–2003). The trends
in the visual observations may be a result of changes in observing
practices resulting from changes in SPMO contractor. The wintertime trend for Cvis is particularly unreliable; see text for discussion.

Figure 10 shows seasonal cycles of LDCRF and
LNCRF for the South Pole. The error bars indicate
interannual variability (1␴). LDCRF has an annual
mean of 18 W m⫺2, while LNCRF has an annual meanof 10 W m⫺2. There does not seem to be any significant
seasonal variation in LDCRF, but there is a slight seasonal cycle in LNCRF. No significant trends were detected in either quantity for 1994–2003.
Our result of 10 W m⫺2 for the annual LNCRF differs from the results of other workers. Hines et al.
(2004) estimated LNCRF ⫽ 1 W m⫺2 for 1986–88 at theSouth Pole using the surface radiation dataset compiled
by Dutton et al. (1989). Rather than attempting to identify all clear scenes within a month, Hines et al. (2004)
chose the lowest values of LDF and LUF that occurredwith the month (LDFmin, LUFmin) and assigned thosevalues to LDFclear and LUFclear. For LNCRF computed
using the method of Hines et al. (2004) we will use thesymbol LNCRF*. Equation (2) then becomes

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FIG. 10. A seasonal cycle of LDCRF (thin black curve) andLNCRF (thick gray curve) using data from 1994–2003. Error bars
are 1␴ about the composite monthly means for 1994–2003.

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FIG. 11. Comparison of LNCRF and LNCRF* from Eq. (3) for
the years 1994–2003. The thin dashed lines are the extreme
monthly values of LNCRF*, included to illustrate the effect ofsolar irradiance on LNCRF*. The method for determining
LNCRF* is from Hines et al. (2004).

LNCRF* ⫽ 共LDFall ⫺ LUFall兲 ⫺ 共LDFmin ⫺ LUFmin兲.
共3兲
Figure 11 illustrates the difference between LNCRF*
and LNCRF for the decade of 1994–2003. The annual
mean of LNCRF* is approximately 7 W m⫺2. The
LDFclear–LUFclear difference is similar to the LDFmin–
LUFmin difference during the winter; however, the 10yr mean and interannual variability of monthly mean
LNCRF* are not similar to those of LNCRF during themonths when insolation becomes significant. In general, the standard deviation about the monthly mean
LNCRF* is greater than that of LNCRF, particularly
during sunlit months. We believe this is because theshortwave cloud radiative forcing affects surface temperature, and therefore the longwave upwelling flux.
The effect of the shortwave cloud radiative forcing is
not directly correlated with the longwave cloud radiative forcing because cloud optical depth differs significantly between visible and infrared wavelengths for thesame liquid water path, particle size, and phase. Therefore, the relationships between LUFmin, clear-sky LUF,
and cloudy-sky LUF are more variable when insolation is substantial than when longwave radiation dominates the radiation budget. The range of variability in
LNCRF* during sunlit months then explains how Hines
et al. (2004) might report an annual mean LNCRF* of
1 W m⫺2 for the 2-yr period recorded by Dutton et al.
(1989) when our 10-yr mean is closer to 7 W m⫺2.
We also compute the relationship between LDCRF
and cloud cover (Fig. 12). Over the entire 10-yr dataset,
LDCRF is related to Cpyr by 0.58 W m⫺2 %⫺1. This
relationship is linear over the range of the monthly

mean cloud cover we observed, but surely becomes
nonlinear as cloud cover decreases below 20%–30%
because LDCRF must be zero if cloud cover is zero.
The slopes of the different lines are listed in the captionof Fig. 12. This relationship should be useful in futureassessments of retrieved and modeled cloud cover overthe East Antarctic Plateau. When we consider net forc-

FIG. 12. Relationship between LDCRF and cloud cover (Cpyr)
over the South Pole. The seasons are labeled; the thick dashedline is the annual relationship. The slopes and 1␴ errors are as
follows: annual ⫽ 0.6 ⫾ 0.1 W m⫺2 %⫺l, summer ⫽ 0.7 ⫾ 0.2 W
m⫺2 %⫺1, autumn ⫽ 0.6 ⫾ 0.2 W m⫺2 %⫺1, winter ⫽ 0.6 ⫾ 0.1 W
m⫺2 %⫺1, and spring ⫽ 0.5 ⫾ 0.2 W m⫺2 %⫺1. Note that none of
the seasonal regressions are significantly different from each otheror the annual regression.

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TOWN ET AL.

555

ing as opposed to downward forcing, there is little correlation between LNCRF and Cpyr. It seems that thethermal inertia of the snowpack puts enough lag into
the system to destroy the correlation of LNCRF withcloud fraction.
We now compare our LNCRF estimates to those derived from satellite data. The annual mean values of
LNCRF from ISCCP (16 W m⫺2, 1985–93) and APP-x(30 W m⫺2, 1982–99) (Hines et al. 2004) are larger thanour estimates of LNCRF. Pavolonis and Key (2003)
also found that the ISCCP and APP-x datasets have
opposite seasonal cycles in LNCRF. LNCRFAPP-x isgreatest in summer and least during winter, as we alsofind (Fig. 10). The mean annual LNCRFISCCP is closer
to our results, but it is clear that this must be a coincidence because the CISCCP is not adequately correlatedwith Cpyr to be useful for this application (Fig. 5, bottom panel). However, it is conceivable that these errors
are due solely to mismatches of annual average timing.

b. Wind speed and direction
Cloud cover shows a significant relationship with thewind direction measured at 10 m (Fig. 13). This is dueto the binary nature of weather at the South Pole. During calm periods the Antarctic Plateau falls into an inversion-wind equilibrium where the surface wind results from a balance between gravitational forces along
the shallow slope of the plateau, the Coriolis force, andfrictional drag across the snow surface (Hudson and
Brandt 2005). The other regime commonly experienced
at the South Pole is when a synoptic storm is able toreach the Antarctic interior. Resulting from the latitude
and the stable near-surface atmosphere, winds spiralcounterclockwise with height. Thus, the 10-m winds are
not necessarily in the same direction as the upper-levelwinds that are responsible for mesoscale advection.
Neff (1999) analyzed upper-level winds based on radiosonde soundings and found that even though there are
two dominant regimes of surface flow (north-northwest
and east-southeast), there are three dominant regimesof upper-level flow. When the upper-level winds arefrom the east-southeast, the surface winds fall into theinversion wind regime. [Directions at the South Poleare in reference to a grid centered on the South Pole.
“North” means parallel to the Greenwich Meridian, inthe direction of Greenwich. “South” is in the opposite
direction. “East” is in the direction of the Indian
Ocean, parallel to 90° longitude]. The north-northwest
10-m winds associated with maximum cloud cover correspond to two different upper-level wind regimes—anenergetic synoptic flow that spirals to the northnorthwest from the northwest as it approaches the sur-

FIG. 13. (a) Mean annual cloud cover (Cpyr) as a function of
10-m wind direction in 10° bins for 1994–2003. (b) Mean annual
wind speed as a function of 10-m wind direction in 10° bins for
1994–2003. (c) Frequency of wind direction in 10° bins for 1994–
2003. Directions are referenced to the South Pole grid, where, forexample, 0° is parallel to the Greenwich meridian, in the directionof Greenwich, and 180° is parallel to the 180° longitude line, and
90° is in the direction of the Indian Ocean.

face, and a slightly less energetic synoptic flow from thesouthwest that must spiral even more to the northnorthwest as it approaches the surface.
The lowest average cloud cover (25%) occurs whenthe 10-m winds are from 60° to 100°, which is the inversion wind regime. The highest average cloud cover(80%) occurs when the 10-m winds are from 300° to20°, which is the synoptic regime. The pattern of cloud
cover is essentially the same in all seasons, but the absolute magnitude varies about 30% from season to season in the synoptic regime. Figure 13c shows the frequency of observations for 1994–2003. The near-surface
winds almost never come from the 150°–300° sector,
which is why the data for this sector in Figs. 13a and 13b
are noisy.
The annual average 10-m wind speed is 4.5 m s⫺1 forthe inversion wind regime, increasing to 7 m s⫺1 for thesynoptic regime (Fig. 13b). The difference is maintained throughout the seasons. The monthly averages
in individual years range from 3.5 to 6 m s⫺1 during
summer and from 5 to 8.5 m s⫺1 during winter. There is
no robust correlation between wind speed and cloud556

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FIG. 14. Seasonal cycle of monthly mean 2-m temperature, using
either all observations or only the observations made under clear
skies, determined by Cpyr, for 1994–2003.

cover, although they are generally related by the same
synoptic and topographical influences.

c. Near-surface air temperature
The annual cycle of 2-m temperature on the East
Antarctic Plateau is a peculiar feature of the climate. It
has been thoroughly studied (e.g., Dalrymple 1966;
Kuhn et al. 1975; Schwerdtfeger 1984; Hudson and
Brandt 2005). The seasonal cycle exhibits no welldefined minimum in winter 2-m temperature; this is
known as the coreless winter. Figure 14 shows the seasonal cycle of 2-m temperature for the South Pole
based on monthly means for 1994–2003 (thin blackline). The nearly constant winter temperature resultsfrom a balance between sensible and latent heat advection aloft from the coast, longwave downwelling radiation from aloft, near-surface turbulent heat fluxes, subsurface heat fluxes, and thermal emission from the surface to space.
The seasonal cycle of clear-sky 2-m temperature, as
determined from Cpyr, is shown as the thick gray line in
Fig. 14. Times of clear skies are 0.5–1 K lower than themonthly mean during summer, and 3–4 K lower thanthe monthly mean during winter.
The difference between mean all-sky winter temperatures and mean clear-sky winter temperatures canbe explained by the magnitude of the LNCRF. The
difference in thermal emission of a radiatively blacksurface at ⫺63°C (the mean clear-sky winter 2-m temperature) and a radiatively black surface at ⫺59°C (themean all-sky winter 2-m temperature) is approximately
8.6 W m⫺2. This is nearly the same as the annual mean
LNCRF of 10 W m⫺2. Cloud radiative forcing can

VOLUME 20

therefore explain all of the increase in monthly mean
2-m temperature from clear skies to all skies during thewinter. Advection of heat and moisture aloft is responsible for maintaining the inversion-top temperature, approximately 400 m above the surface, at ⫺35°C. Thislarge-scale advection provides the energy that theclouds and atmosphere radiate to the surface, raisingnear-surface atmospheric temperatures.
Based on Fig. 14, it seems that clouds are associatedwith warmer surface temperatures at the South Pole, as
expected. Pavolonis and Key (2003) found a similar
result with the APP-x dataset, but we question the basisof the result because of the low accuracy of the APP-x
cloud mask. Using a cloud mask algorithm based on
AVHRR data, Comiso (2000, his Fig. 5) found thatcloud cover over the Antarctic ice sheet has no net
effect on surface temperature. That result is in direct
disagreement with the results presented in Fig. 14. Specifically, Comiso found that January and July clear-sky
monthly mean surface temperature from station data
across Antarctica are not significantly colder than the
all-sky monthly means for January and July. The clearsky times were identified based on the satellite cloudmask. Comiso also found an excellent correlation between the in situ surface temperatures and those derived from the satellite data.
Based on our comparisons of the APP-x and ISCCP
polar cloud masks to the surface-based cloud cover estimates, we believe it is likely that the cloud mask ofComiso (2000) is not able to distinguish between clouds
and the snow surface. This explains why Comiso does
not find any difference between clear- and all-sky
monthly mean surface temperatures as derived fromsatellite data. The correlation between the January andJuly satellite-derived surface temperature and the insitu surface temperature is likely due to the emission
properties of clouds. A thick cloud will raise the surface
temperature by emitting downward as a blackbody
while the cloud top becomes the effective emitting surface to space. If the cloud is not “black,” then emission
to space will be a combination of emission from thecloud and the snow surface. Over the Antarctic Plateau,
clouds come into radiative equilibrium with the surface
within a few hours (Hudson and Brandt 2005, their Fig.
19); therefore, the cloud temperature is often close to
the surface temperature. If not, the sum of the irradiances from cloud and surface will not be far from thatof a blackbody at the surface temperature. Thus, estimates of surface temperature in Comiso (2000) probably should be interpreted as estimates of all-sky
monthly mean surface temperature rather than as clearsky monthly mean surface temperature.

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TOWN ET AL.

5. Conclusions
Cloud cover over the South Pole is presented fromtwo satellite-based datasets and three surface-based
datasets, for varying time periods. The surface-based
cloud cover datasets are from visual observations (Cvis),a short spectral infrared dataset (CPAERI), and routine
broadband infrared measurements (Cpyr). The satellitebased cloud cover datasets are those of APP-x and
ISCCP. The estimates of monthly and seasonal cloud
cover are intercompared. The best estimate of cloud
cover over the South Pole is provided by Cpyr becauseof its record length and stable calibration. Cloud coverfrom visual observations is found to have a worse low
bias during the polar winter than expected, even after
correction for adequate light. Cloud cover from APP-x(CAPP-x) has some skill at estimating cloud cover during
the winter, but overestimates mean summer cloud
cover and is uncorrelated with other estimates of cloud
cover during the summer. Cloud cover from ISCCP(CISCCP) is biased low when the sun is more than 9°
above the horizon. While CISCCP is uncorrelated with
Cpyr during the winter, it obtains the seasonal mean
correctly.
The seasonal cycle of cloud cover over the South
Pole ranges from a minimum of 45%–50% during theshort summer to 55%–65% throughout the autumn,
winter, and spring. Insignificant trends were found inCvis during the 1994–2003 time period, the length of the
Cpyr record. However, during this time period the seasonal trends from visual observations do agree withthose from pyrgeometer analyses, increasing cloud
cover during summer months (December–January) anddecreasing cloud cover during spring (October–
November) and autumn (February–March). Their interannual variations are also correlated. The longest
period of record is that of the visual observations(1957–2004), and significant increasing trends are foundin Cvis for spring, summer, and autumn. However,changes in observer practices have probably contributed to these trends, a possibility that requires further
investigation.
The relation of clouds with surface meteorology wasinvestigated. The mean annual longwave downwelling
cloud radiative forcing is approximately 18 W m⫺2, withno seasonal cycle. The mean annual longwave net cloudradiative forcing is approximately 10 W m⫺2, with aseasonal cycle ranging from 14 W m⫺2 during summer
to 8 W m⫺2 during winter. The Cpyr shows significant
and consistent variability with surface wind speed anddirection, which is related to synoptic flow over the
East Antarctic Plateau. Near-surface air temperature is557

raised by the presence of clouds—0.5–1 K during thesummer and 3–4 K during the winter.
We believe that evaluating the cloud mask is an efficient means by which to improve satellite retrievals. If
the satellite algorithms are unable to reliably estimate
cloud fraction, then cloud properties (e.g., cloud opticaldepth, cloud-top temperature, cloud-top pressure,phase) and other products that depend on the accuracyof the cloud mask (e.g., surface temperature, surface
radiative fluxes, cloud forcing) are also suspect. Unfortunately, false positive results are possible when comparing derived properties (surface albedo, cloud radiative forcing), because of potential offsetting errors in
the derived properties, especially when dealing withclouds over snow and ice. Comparison of the resultspresented here to surface temperature retrievals fromspace, together with our other analyses, lead us to conclude that the APP-x and ISCCP cloud masks are probably not yet able to adequately identify clouds over the
East Antarctic Plateau. Newer sensors, like the Moderate Resolution Imaging Spectroradiometer (MODIS)and Multiangle Imaging Spectroradiometer (MISR),
offer promise in cloud detection but do not yet have along enough record for climatic studies. Satellite measurements in the infrared window (11 ␮m) are likely
useful in determining monthly mean all-sky, rather thanclear-sky, surface temperature because of the similar
spectral emissivity of snow and clouds and the radiativeequilibrium established between clouds and the underlying snow surface.
We recommend this dataset of cloud cover and cloudradiative forcing for use in other evaluations of satellite
retrievals and polar models. Routine surface-based
broadband infrared measurements may be used to estimate cloud cover based on simple irradiance and standard deviation thresholds. This method may be usefulin situations where observation of clouds is compromised by extreme climates or availability of manpower.
The method employed here using routine pyrgeometermeasurements will probably succeed only where instantaneous cloud fractions exhibit a U-shaped distribution.
However, infrared measurements made with a narrow
field of view are not subject to this restriction.
Acknowledgments. Stephen Hudson helped to collectthe SPARCLE dataset, and provided advice and technical assistance. Many ESRL and NOAA employees
were involved in collecting the ESRL data at the South
Pole. Bryan Johnson, Thomas Mefford, and Ellsworth
Dutton provided the data and metadata from the
ESRL. Ellsworth Dutton provided information about
the maintenance and calibration of the pyrgeometer.
Keith Hines provided some cloud forcing data. Jeffrey

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Key and Michael Pavolonis provided us with the APP-x
dataset. The Space Science and Engineering Center atUniversity of Wisconsin provided the archived South
Pole meteorological data. Kathie Hill provided information about the South Pole meteorological data. This
work was supported by NSF Grants OPP-9726676 and
OPP-0230466.
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