15 OCTOBER 2005

TOWN ET AL.

4235

Spectral and Broadband Longwave Downwelling Radiative Fluxes, Cloud Radiative
Forcing, and Fractional Cloud Cover over the South PoleMICHAEL 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 12 July 2004, in final form 30 March 2005)
ABSTRACT
Annual cycles of downwelling broadband infrared radiative flux and spectral downwelling infrared flux
were determined using data collected at the South Pole during 2001. Clear-sky conditions are identified bycomparing radiance ratios of observed and simulated spectra. Clear-sky fluxes are in the range of 110–125
W m⫺2 during summer (December–January) and 60–80 W m⫺2 during winter (April–September). The
variability is due to day-to-day variations in temperature, strength of the surface-based temperature inversion, atmospheric humidity, and the presence of “diamond dust” (near-surface ice crystals). The persistentpresence of diamond dust under clear skies during the winter is evident in monthly averages of clear-sky
radiance.
About two-thirds of the clear-sky flux is due to water vapor, and one-third is due to CO2, both in summer
and winter. The seasonal constancy of this approximately 2:1 ratio is investigated through radiative transfer
modeling. Precipitable water vapor (PWV) amounts were calculated to investigate the H2O/CO2 flux ratio.
Monthly mean PWV during 2001 varied from 1.6 mm during summer to 0.4 mm during winter. Earlier
published estimates of PWV at the South Pole are similar for winter, but are 50% lower for summer.
Possible reasons for low earlier estimates of summertime PWV are that they are based either on inaccurate
hygristor technology or on an invalid assumption that the humidity was limited by saturation with respect
to ice.
The average fractional cloud cover derived from the spectral infrared data is consistent with visualobservations in summer. However, the wintertime average is 0.3–0.5 greater than that obtained from visualobservations. The annual mean of longwave downwelling cloud radiative forcing (LDCRF) for 2001 is about
23 W m⫺2 with no apparent seasonal cycle. This is about half that of the global mean LDCRF; the low value
is attributed to the small optical depths and low temperatures of Antarctic clouds.

1. Introduction
The polar regions are of significant importance toearth’s climate. They emit more energy to space in theform of infrared radiation than they absorb from sunlight, and thus act as heat sinks for the climate system.

Corresponding author address: Michael S. Town, Dept. of Atmospheric Sciences, Box 351640, University of Washington, Seattle, WA 98195-1640.
E-mail: mstown@atmos.washington.edu© 2005 American Meteorological Society

JCLI3525

This cooling helps drive the general circulation of theearth’s atmosphere.
Despite Antarctica’s importance to climate, it is stilla relatively poorly characterized continent, particularlyin the interior. Field studies performed in the late 1950s
and 1960s still represent the current knowledge of theclimate of the Antarctic interior (Dalrymple 1966;
Kuhn et al. 1975, 1977; Schwerdtfeger 1984). Althoughmajor efforts have been undertaken to rectify the Antarctic data void by populating remote parts of Antarctica with automatic weather stations and routine observations at permanent science research stations, some

4236

JOURNAL OF CLIMATEVOLUME 18

TABLE 1. Acronyms of parameters discussed in text.
AcronymDefinition

Units

CSR
FCC
FCCAPP-x
FCCISCCP
FCCPAERI
FCCpyr
FCCvisFOVLDF
LDFclear
LDFall
LDCRF
LNCRF
LNF
LUF
MR
RHw
RR
RU
T

Clear-sky radiance ratio
Fractional cloud cover
FCC derived from APP-x observations
FCC derived from ISCCP observations
FCC derived from PAERI observations
FCC derived from pyrgeometer observations
FCC from visual observations
Field-of-view
Longwave downwelling flux
Longwave downwelling, clear-sky flux
Longwave downwelling, all-sky flux
Longwave downwelling cloud radiative forcing
Longwave net cloud radiative forcing
Longwave net flux
Longwave upward flux
Mixing ratio
Relative humidity with respect to water
Radiance ratio
Radiance unit
Temperature

Fraction
%
%
%
%
%
%
Radians
W m⫺2
W m⫺2
W m⫺2
W m⫺2
W m⫺2
W m⫺2
W m⫺2
Unitless%
FractionmW m⫺2 sr⫺1 (cm⫺1)⫺1°C or K

key parameters of the Antarctic climate are still not
well known.
Basic variables of atmospheric composition such asatmospheric humidity and fractional cloud cover(FCC), as well as the consequent downwelling infrared
radiation spectra, are still poorly known quantities overthe East Antarctic Plateau. Until recently, atmospherichumidity measurements made at low temperatures
were not reliable (Elliott and Gaffen 1991; Miloshevich
et al. 2001). There is some hope that Vaisala’s Humicap
radiosonde sensors are performing better under Antarctic conditions than the previous hygristor technology
historically used on radiosondes (Hudson et al. 2004).
FCC is difficult to estimate at high latitudes, particularly during the polar night (Hahn et al. 1995; Curry etal. 1996). Thus aspects of the climate related to clouds,
such as the effect of clouds on the surface radiationbudget, are also poorly characterized for the Antarctic
interior.
FCC has been identified as an important and poorly
predicted parameter in forecasts of Antarctic climate(Hines et al. 1999, 2004). It is important for closing thesurface heat budget (Pavolonis and Key 2003), but recent efforts have been relatively unsuccessful at estimating the magnitude of any one component of theradiative surface heat budget, probably in large part
because of poor knowledge of FCC. More success has
been made in estimating net surface heat budgets, because of the offsetting effects of clouds on surface energy exchange. However, if the predicted energy exchange is smaller than in reality, then the predictedweather will be weaker than reality. Determining themagnitude of each of the components of the heat budget is therefore important not only for understanding
the climate but also for accurately depicting theweather over Antarctica.
This paper provides estimates of some key parameters of the Antarctic atmosphere. Measurements ofbroadband and spectral longwave downwelling radiative fluxes made at the South Pole Station during 2001
are presented. These longwave fluxes dominate the surface heat budget in Antarctica because of the region’shigh solar albedo and prolonged periods of darkness.
We partition the spectral infrared fluxes into a few
bands so that the importance of different gases andspectral regions to the downwelling radiative flux canbe assessed. FCC is determined objectively based on
both broadband and spectral infrared measurements
and then compared with visual observations. The FCC
is utilized together with measured radiation fluxes todetermine longwave, downwelling, clear-sky fluxes(LDFclear) and longwave downwelling cloud radiative
forcing (LDCRF).
Acronyms used in this paper are given in Tables 1
and 2.

2. Instrumentation and data
The data used in this study come from several
sources: (a) the South Pole Atmospheric Radiation
and Cloud Lidar Experiment (SPARCLE), performed
from January to October 2001 (Walden et al. 2001); (b)15 OCTOBER 2005
TABLE 2. Acronyms of terms used in text.
AcronymDefinition

AERI
APP-x
ARM
AVHRR
CAS
CMDL
FTIR
IR
ISCCP
PAERI

Atmospheric Emitted Radiance Interferometer
AVHRR Polar Pathfinder
Atmospheric Radiation Measurement
Advanced Very High Resolution Radiometer
Clean Air Sector
Climate Monitoring and Diagnostics Laboratory
Fourier Transform Infrared Interferometer
InfraredInternational Satellite Cloud Climatology Project
Polar Atmospheric Emitted RadianceInterferometer
South Pole Atmospheric Radiation and Cloud
Lidar ExperimentSouth Pole Meteorology OfficeUpper Atmospheric Research Satellite

SPARCLE
SPMO
UARS

4237

TOWN ET AL.

a field experiment performed at the South Pole in 1992
(Walden 1995; Walden et al. 1998); (c) routine measurements of temperature and atmospheric composition made by the Climate Monitoring and Diagnostics
Laboratory (CMDL) of the National Oceanic and Atmospheric Administration (NOAA) at the South Poleduring 2001; and (d) the routine visual cloud observations made by the South Pole Meteorology Office(SPMO) during 2001.
All uncertainties in the data presented here are listed
in Table 3.

a. Polar Atmospheric Emitted RadianceInterferometer
One primary objective of SPARCLE was to characterize the downwelling longwave radiation spectrum
over the South Pole. Downwelling infrared spectra
were measured with the Polar Atmospheric Emitted

Radiance Interferometer (Polar AERI, or PAERI). A
brief, general description of the PAERI is given below.
More information on AERI instruments is given by
Knuteson et al. (2004a,b).
The PAERI viewed the sky at several angles during
each day of operation. The instrument was operated
inside an insulated shelter with thermostatically controlled heaters. It was deployed on the border of the“Clean Air Sector” (CAS) upwind of South Pole Station, and viewed out over the CAS. The CAS has borders parallel to 0° and 110° ⌭ longitude. Prevailingwinds come from between 0° and 90° such that views
into the CAS are of a pristine environment. The
PAERI field-of-view (FOV) half-angle of 23 mrad waspointed in a direction parallel to 20°⌭, and regularly
viewed at least three zenith angles. Figure 1 indicatesthe routine zenith angles observed by the PAERI eachmonth. Each angle was observed for about 1.5 min witha repeat time of approximately 10 min, yielding about
240 spectra each day.
Quality control procedures were implemented to reject data contaminated by frost-covered mirrors, attenuation of the instrument signal because of blowing
snow, unstable instrument temperatures, and direct
sunlight. Approximately 20%–25% of spectra were
eliminated each day by the quality-control procedures.
About 1800 spectra remained suitable for analysis ateach angle for each month. During some months, such
as January 2001, the PAERI was used for additionaltasks other than observing downwelling radiances atroutine viewing angles. In the most extreme case, thisreduced the total number of spectra to about 1000 spectra per angle per month, excluding the time period from
14 March to 27 April 2001 when the PAERI was notoperating. By comparison to routine visual cloud observations, which are taken only four times per day, thisTABLE 3. Description of instrumentation and uncertainties associated with each measurement or calculation. The uncertainties for the
CMDL 2- and 22-m temperatures (T2 and T22) are from Hudson and Brandt (2005). The O3 uncertainties are for the surface and arefrom B. Johnson (2004, personal communication). The two uncertainties in the LBLRTM LDFclear are from varying the temperatureand humidity profiles, respectively, by one standard deviation. (All uncertainties quoted in this paper are one standard deviation unlessstated otherwise.)
Instrument

Experiment

Uncertainty (1␴)

Range of sensitivity

PAERI
FTIR
RS80 P
RS80 T
RS80 RHw
RS80 O3
T2, T22
Pyrgeometer
LBLRTM LDFclear
PAERI LDFclear

SPARCLE
Walden (1995)
CMDL
CMDL
CMDL
CMDL
CMDL
CMDL and Walden (1995)
Atmospheric and Environmental Research
SPARCLE

1 RU
1–3 RU
0.5 hPa
0.2 K
3% RHw
4 ppbv
0.1 K
8 W m⫺2
1, 0.5 W m⫺2
0.25 W m⫺2

450–1800 cm⫺1
550–1667 cm⫺1
1060 to 3 hPa
183 to 333 K
0%–100%0–450 ppbv
⫺85° to 50°C
200–2500 cm⫺1
1–2500 cm⫺1
450–1800 cm⫺1

4238

JOURNAL OF CLIMATE

FIG. 1. Timelines of data used in this study. All data were collected during 2001 except for the Fourier Transform Interferometer (FTIR) and pyrgeometer data collected during 1992 (Walden
1995). The black lines indicate data collected by us; the gray lines
indicate data collected by NOAA’s CMDL. The Xs indicate RS80
launch dates. During the southern austral spring, CMDL launched
8–12 sondes per month. The 2- and 22-m temperature and 2-m
frost point temperature are represented as T2, T22, and T2f, respectively. The set of zenith angles viewed are (a) 0°, 60°, 75°, 78°;(b) 0°, 45°, 50°, 60°, 75°, 78°; (c) 0°, 50°, 70°; (d) 0°, 60°, 78°; and(e) 45°, 60°, 75°. Here (e) applies only to the FTIR; the pyrgeometer has a hemispheric FOV.

is a sufficient number of spectra to adequately characterize the monthly mean FCC, the downwelling radiance under clear skies, and the monthly mean LDCRF.
The issue of adequate sampling is addressed further insections 4 and 5. Figure 1 gives the seasonal coverage of
all the data used in this study.
Figure 2 shows a clear-sky, summertime, downwelling
infrared spectrum over the South Pole modeled using
the line-by-line radiative transfer model (LBLRTM).
(This model is discussed in section 3a.) The black vertical dashed lines show the spectral bandwidth of the
PAERI, and the gray vertical dashed lines represent
bands where different atmospheric gases emit. Figure 2
shows that CO2 and H2O are dominant contributors to
the downwelling infrared radiation flux over east Antarctica and that clear-sky infrared radiation beyond the
PAERI spectral bandwidth is dominated by emission
from water vapor.
Clouds composed of ice and liquid water emit acrossthe infrared spectrum like gray bodies of variable emissivity and temperature. The infrared emission fromclouds depends mostly on the optical depth, FCC, andtemperature of the clouds. To assess the accuracy andnatural variability of the downwelling fluxes observedby the PAERI and pyrgeometer, the monthly mean

VOLUME 18

FIG. 2. Clear-sky spectrum of downwelling infrared radiance asmodeled by LBLRTM for Jan 2001 over the South Pole. (Seesection 3a for a discussion of LBLRTM.) The PAERI passively
observes infrared radiances from 450 to 1800 cm⫺1 (5.5 to 22 ␮m)with an unapodized resolution of 0.48 cm⫺1. [Apodization is described in Bell (1972).] The radiances are reported in units of mWm⫺2 sr⫺1 (cm⫺1)⫺1, which is called a radiance unit (RU) in thisstudy. Table 3 contains a summary of uncertainties in the PAERI
radiances and derived fluxes. The black vertical dashed lines denote the spectral limits of the PAERI. The gray dashed lines
separate the spectrum into emission bands based on the primarygaseous emitter in each region, as listed at the top of the figure.
See Table 6 for exact bandwidths of the emission bands.

fluxes, their standard deviations, and standard errors
were computed (Table 4).

b. Pyrgeometer
The NOAA–CMDL pyrgeometer reports broadband
longwave downwelling flux (LDF) with an accuracy of
about 4 W m⫺2 under ideal operating conditions (E. G.
Dutton 2004, personal communication). The operatingprocedures at the South Pole are identical to those described in Marty et al. (2003), who estimate the absolute accuracy of an Eppley pyrgeometer at 2 W m⫺2.
[Marty et al. (2003) is also a concise reference comparing both pyrgeometer broadband calibration and AERI
spectral calibration to the common standard of LBLRTM.] We add an additional 6 W m⫺2 to this estimateof uncertainty due to errors induced by day-to-day operations, for a total uncertainty of 8 W m⫺2. The conclusions of this work are not greatly affected by theuncertainties in individual pyrgeometer measurements
because most of the results presented here are monthly
averages.
The spectral bandwidth of the pyrgeometer is about
4–50 ␮m. It has a temperature-dependent calibration

15 OCTOBER 2005

4239

TOWN ET AL.

TABLE 4. Monthly means, and standard deviations (std dev) and standard errors (SEs) of the monthly means of downwelling flux
measured by the PAERI and pyrgeometer under all conditions and under clear conditions during 2001. The time series of LDFclear(column 4 mean) is shown in Fig. 8 for broadband and partial band, and the time series of LDCRF (column 3 mean minus column 4mean) is shown in Fig. 10. All entries below are in W m⫺2.
Pyrgeometerall (LDFall)

Pyrgeometerclear (LDFclear)

Month

Mean

PAERIallStd devSE

Mean

PAERIclear
Std devSE

Mean

Std devSE

Mean

Std dev

SE

Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec

94.3
79.3
60.4

28.7
30.3
28.5

2.2
0.7
1.0

59.4
53.6
39.6

5.7
6.7
6.2

0.3
0.2
0.3

0.3
0.3
0.4

0.5
0.6
0.3
0.4
0.5
0.4

36.2
35.9
33.6
30.0
36.9
36.6

4.7
4.2
5.9
4.0
6.3
5.1

0.2
0.2
0.2
0.1
0.2
0.2

0.1
0.2
0.1
0.1
0.2
0.1
0.1
0.2
0.2
0.1
0.2
0.2

6.2
7.5
7.9

21.4
21.6
15.9
16.8
22.9
18.7

26.0
30.0
27.7
20.1
30.4
24.6
19.0
24.6
35.4
29.9
30.1
31.0

126
114
89

56.8
55.9
48.0
41.6
58.1
46.2

140
137
107
95
102
99
90
85
103
94
129
133

73
77
74
66
76
78

14.3
4.8
9.4
5.1
21.7
16.1

0.5
0.3
0.3
0.2
0.8
0.5

that compensates for out-of-band fluxes due to emission from the water vapor rotational band so that its
effective bandwidth extends to much longer wavelengths. It was calibrated in June 1999 and again in
February 2004 with no significant drift in its calibration.
The pyrgeometer remained unshaded during times ofdaylight, which can lead to, on average, an additional 3
W m⫺2 in downwelling irradiance at midlatitudes(Philipona et al. 2001). This effect is likely to be smallerat the South Pole and only affects data taken during
summer (December and January) due to the low maximum solar elevation at 90°S.
The temperature dependence of the instrument sensitivity was tested down to ⫺40°C with no significant
deviation from the reference standards. Near-surface
temperatures at the South Pole get much lower thanthis, which calls into question the estimate of the water
vapor rotational band emission using CMDL’s temperature-dependent calibration. However, even at Antarctic winter temperatures, the part of the rotationalband outside the pyrgeometer’s spectral bandwidth isalmost completely saturated, so the calibration should
be fairly accurate. More information on the CMDL
pyrgeometer calibration can be found in Dutton (1993).
As indicated in Fig. 1, the pyrgeometer (an Eppley
PIR) operated continuously throughout the 2001 fieldseason. Figure 3 is a histogram of downwelling fluxes
from the pyrgeometer during January 2001. Mostmonths display a similarly skewed distribution due tothe radiative equilibrium between the surface and theatmosphere reached under clear skies.

c. Atmospheric profiles
Profiles of atmospheric temperature and gaseouscomposition are needed for the radiative transfer cal-culations described below. Profiles of temperature (T),relative humidity with respect to water (RHw) andozone concentration were obtained by CMDL using
Vaisala’s RS80H radiosondes. For most of the year,
CMDL launched four balloons each month. However,during periods when dramatic changes in the stratospheric ozone layer were expected, CMDL launched as

FIG. 3. Histogram of pyrgeometer measurements for Jan 2001.
The vertical dashed line is the clear/cloudy threshold, explained in
section 3b. The largest peak is due to clear-sky observations, while
the long tail toward higher fluxes is due to clouds of variable
coverage, optical thickness, and temperature. The clear-sky
thresholds (different for each month) are derived from LBLRTM
simulations based on monthly mean atmospheric profiles. Ideallythese profiles would be climatological profiles and could be used
operationally, but historical radiosonde humidity measurements
over Antarctica are not accurate enough for this purpose. The
clear-sky thresholds used here are based only on monthly mean
atmospheric profiles from 2001. Bin size ⫽ 1 W m⫺2. The numberof measurements is 44 432.

4240

JOURNAL OF CLIMATE

many as 12 balloons each month. The launch dates for2001 are shown in Fig. 1.
Monthly mean profiles of T, RHw, and O3 were computed for 2001 from the CMDL radiosondes. The humidity profiles were corrected for lag in the humidity
sensors in a manner similar to the way temperature
profiles were corrected by Mahesh et al. (1997). The
radiosonde profiles were also corrected for errors in
observed humidity and pressure induced by thermal
shock during the launch. Thermal shock errors in
Vaisala’s RS80s are described by Hudson et al. (2004).
It was discovered during the course of this study thatthose errors are greatly reduced when the radiosonde isventilated by flight. For modeling purposes, the atmospheric profiles from the RS80 launches were interpolated to a grid of altitudes that is finely spaced near thesurface and becomes gradually coarser with altitude.
The CMDL radiosonde profiles were supplemented
near the surface with temperatures at 2 and 22 m (T2,
T22) and frost point temperatures at 2 m (T2f), whichwere collected by CMDL on a tower located on theborder of the Clean Air Sector. For the upper atmosphere, between ⬃30 and 60 km, profiles of T, H2O,
and O3 were obtained from the Upper Atmosphere Research Satellite (UARS) dataset for the south polar region (Walden et al. 1998). Other trace greenhouse gas
concentrations were estimated from climatological values. Values of N2O and CH4 come from Houghton etal. (2001), while values for CCl4, CFC11, and CFC12
come from Walden et al. (1998). Table 3 gives the uncertainties associated with the atmospheric profiles.
Uncertainties in the UARS data are discussed by
Walden et al. (1998).

d. The 1992 data

VOLUME 18

FIG. 4. Flow diagram of analysis procedures. The white ovals
designate datasets; processing models and methods are in lightgray rectangles; the resulting data products are in dark gray rectangles. The number in the upper right corner of each box denotes
the relevant section of the paper.

entire year. From these we calculated monthly mean
FCC amounts. All “sky-obscured” observations wereclassified as overcast because such reports almost always occurred during times of blowing snow. Our own
visual observations indicate that blowing snow is almost
always accompanied by clouds overhead. These observations are substantiated by photomicroscopy of ice
crystals done at the South Pole during 1992 (Walden etal. 2003), which found falling snow grains along withblowing snow crystals in all blowing snow events. The
uncertainty for FCCvis is ⫾4%. ⌻his is based on a statistical study of Warren et al. (1986), which involves
subsampling of a large dataset to determine the error inmonthly mean cloud cover as a function of the numberof observations.

An additional dataset was collected from January
1992 to January 1993 (Walden 1995; Walden et al.
1998), including downwelling infrared spectra at three
angles (45°, 60°, and 75°), and downwelling broadband
irradiances. Infrared spectra were collected at 12-h intervals, yielding 510 spectra for analysis after quality
control. The spectral mean uncertainty of the 1992 radiance measurements is from 1 to 3 radiance units(RUs), and the uncertainty of the 1992 pyrgeometermeasurements is also set to 8 W m⫺2 (Table 3). Further
details regarding the 1992 experiment can be found in
Walden (1995) and Walden et al. (1998).

The three data products, LDFclear, LDCRF, andFCC, (discussed in sections 4, 5, and 6, respectively)
require various data analysis steps to produce. These
processing steps are illustrated in Fig. 4 and are described below.

e. South Pole Meteorology Officea. Line-by-line radiative transfer model

The South Pole Meteorology Office (SPMO) reported visual observations of FCC every 6 h for the

The line-by-line radiative transfer model (LBLRTM),
developed by Atmospheric and Environmental Re-

3. Models and methods

15 OCTOBER 2005

TOWN ET AL.

search (Clough et al. 1992; Clough and Iacono 1995),
was used to simulate clear-sky downwelling infrared
radiances. These simulations were based on themonthly mean atmospheric profiles described above.
Monochromatic radiances were modeled for zenith
angles of 0°, 30°, 60°, and 78°, and then convolved with
the instrument response function of the PAERI to degrade the spectral resolution to that of the PAERI. The
resulting radiances were used to calculate LDF values
over the South Pole. The procedure for determining
broadband and partial-band LDF from spectral radiances is described in section 3c. The modeled downwelling radiances and fluxes are used to determine FCC
from the PAERI and pyrgeometer data (section 3b).
The modeled LDFclear is also used to explain the relative contributions of H2O and CO2 to the clear-sky flux,
discussed in section 4.
Errors in the fluxes calculated by LBLRTM are estimated by comparing LBLRTM model runs using theatmospheric profiles described in section 2c with modelruns made using perturbed atmospheric profiles. Boththe temperature and humidity profiles are perturbed
separately by the RS80 uncertainties reported by
Vaisala (1997); ⫾0.2 K in temperature and ⫾3% in
RHw. The error induced in the model calculation of
LDF due to a uniform 0.2-K increase in tropospherictemperatures, applied to the monthly mean January
2001 atmospheric profile, is ⫹1 W m⫺2, and that induced by a 3% increase of tropospheric RHw is ⫹0.5 W
m⫺2 (Table 3). The combined error is slightly less than
the sum of the two errors.
Errors in the LBLRTM simulations due to errors in
the line shape parameters are discussed by Rowe (2004)
and Tobin et al. (1999). Those studies are focused onregions of water vapor emission but are useful for
perspective on the maximum spectral errors in the
LBLRTM algorithm due to the present state of knowledge of spectral line shapes.

b. Clear-sky thresholds derived from infrared
radiation measurements
Since cloud cover from visual observations is unreliable during the polar night it was necessary to develop
methods of determining FCC from our radiation measurements. Both the pyrgeometer and the PAERI are
sensitive to variations in infrared radiation that accompany changes in cloud cover.
Monthly theoretical clear-sky thresholds were developed for the purpose of identifying clear skies from theradiation measurements. The thresholds are similar inprinciple to other clear-sky indices developed for downwelling infrared data. Marty and Philipona (2000) used

4241

a clear-sky index that is a broadband atmospheric emissivity threshold based on near-surface temperature andhumidity measurements. They showed that their index
was quite consistent with the visual synoptic observations despite having only seasonal clear-sky parameterizations of broadband atmospheric emissivity for summer and winter. Further analysis in Sutter et al. (2004)shows that while the technique of Marty and Philipona(2000) has some success, it overestimates FCC understrong inversions and is unable to distinguish high thinclouds from clear skies. This relative failure under inversion conditions is due to their parameterization of
the emissivity of the entire atmosphere in terms ofnear-surface properties alone. The clear-sky thresholds
presented here are based on atmospheric profiles oftemperature and humidity, so they should be more reliable.

1) PYRGEOMETER
The theoretical clear-sky thresholds applied to thepyrgeometer data were determined from LBLRTM
simulations of LDFclear for each month of 2001. Downwelling radiances were modeled at four different zenith
angles (0°, 30°, 60°, and 78°) and converted to broadband LDFclear via the procedure outlined in section 3c.
An additional irradiance increment was added to thesimulated LDFclear to account for the effects of diamond dust. This increment was estimated by assuming
an approximate value for the emissivity of a diamonddust layer over the South Pole (␧ ⫽ 0.05) and fitting a
blackbody curve scaled by the emissivity to the window
region of clear-sky PAERI spectra. The estimated window radiances were then converted to fluxes. For January, a diamond-dust temperature of 250 K provided agood fit to the radiances in the window region. For
February, March, and October a temperature of 235 K
sufficiently fit the clear-sky radiances in the window
region. A typical winter diamond dust cloud was simulated with a temperature of 220 K.
The LDFclear from monthly LBLRTM simulations,
augmented by the seasonal radiative effects of diamond
dust, is our monthly, clear-sky threshold for the pyrgeometer data. All LDF values below the threshold wereclassified as clear, while all LDF values higher than thethreshold were classified as cloudy (Fig. 3). A binary,
minute-by-minute clear/cloudy time series was produced. Monthly mean FCC was then calculated fromthe binary time series of cloud detection. It is assumed
that for the monthly mean the pyrgeometer cloud detection frequency will equal FCC and is thus referred
to as FCCpyr. The resulting monthly mean values of
FCCpyr for 2001 are discussed in section 6. Adding or4242

JOURNAL OF CLIMATE

subtracting the irradiance of another diamond-dustlayer to or from the clear-sky threshold caused themonthly FCCpyr to change by an absolute amount of
2%–3% (Table 5).
The calculated thresholds for November and December 2001 based on the November and December
LBLRTM profiles seemed to be low when comparedwith the thresholds from February and January, respectively. These months were compared because they have
similar near-surface temperatures (Warren 1996). We
suspect that the monthly mean profiles used to calculated the November and December thresholds are unrepresentative of those months. An independent
method for determining FCC from pyrgeometer measurements described in Town et al. (2005) confirms thatthe original November and December clear-sky thresholds are too low.
A weakness of this algorithm is that it is susceptible
to mistaking scattered, thin, or cold clouds as clear
skies. It is not certain by how much FCCpyr underestimates the actual FCC due to this miscategorization.
This is assessed by comparison with other methods ofdetermining FCC in section 6.

2) PAERI
The theoretical threshold used to derive FCC fromthe PAERI data (FCCPAERI) is based on a method
analogous to that just described for the pyrgeometer. A
radiance ratio (RR) was computed and used as a test
for clear skies for each zenith-looking spectrum collected during 2001:

冕
冕

1800

RR ⫽

Lobs共␷兲 d␷

450

.

1800

共1兲

Lbb共␷兲 d␷

450

The numerator is simply the sum of PAERI radiancesfrom 450 to 1800 cm⫺1. The denominator was deter-

TABLE 5. Table of confidence in cloud cover estimates. The firsttwo rows are sensitivities of FCCpyr and FCCPAERI to the addition/subtraction of the radiative effects of a single layer of diamond dust to/from the clear-sky threshold. The third row contains
the uncertainty in FCCvis based on rms error in average cloud
cover over land [Fig. 5 of Warren et al. (1986)] for the mean
number of observations each month. This value of 4% is based onjust the sampling error; biases can be much greater during thewinter night.
FCCpyrFCCPAERI
FCCvis⫾3%⫺10/⫹18%⫾4%

VOLUME 18

FIG. 5. Longwave downwelling atmospheric spectra at South
Pole. (top) Solid lines are mean, zenith-looking, clear-sky spectra
for Jan and Jul 2001. The dashed line represents a blackbody
radiance spectrum at 243 K (the significance of which is describedin section 3b). (bottom) One standard deviation (1␴) about themeans for Jan and Jul 2001, in RU.

mined by taking the mean brightness temperature (Tb)between 630 and 640 cm⫺1 and computing the blackbody spectrum of Tb for the spectral range 450–1800cm⫺1. An example of the blackbody curve employed isshown in Fig. 5. The mean brightness temperature between 630 and 640 cm⫺1 is used because it usually represents emission from the inversion top, typically thewarmest part of the troposphere over the South Pole.
Therefore, normalizing by the mean brightness temperature of 630–640 cm⫺1 limits the RR maximum to beapproximately 1.0. A histogram of RRs for January
2001 is shown in Fig. 6.
To use the RR for the purpose of determining
FCCPAERI, zenith-looking spectral radiances were computed for each month with LBLRTM, using the mean
atmospheric profiles described in section 2c. The simulated spectra were computed with the same spectral
limits and resolution as the PAERI observations. The
radiative effects of diamond dust were added to thewindow region to simulate a zenith-looking, clearsky-with-diamond-dust spectrum for each month. An
RR was then computed for each monthly simulation. This clear-sky RR (CSR) was used to calculate
another binary clear/cloudy time series. Monthly mean
FCCPAERI values were calculated through time averaging of the binary time series from the PAERI measurements.
It is clear from Fig. 6 that there is a definite lower
limit to the day-to-day RR that occurs under clear skies.
The vertical line indicates the computed CSR threshold15 OCTOBER 2005

TOWN ET AL.

FIG. 6. Histogram of PAERI radiance ratios (RRs) for Jan 2001.
Dashed vertical line represents the clear-sky-ratio threshold described in section 3b. The RR is usually between about 0.35 and
1.0, depending on atmospheric temperature and humidity, and theoptical depth and temperature of any clouds overhead. Bin size is
0.02. The total number of RRs is 1037.

for January 2001. Any zenith-looking spectrum with an
RR below the monthly CSR is classified as clear; theothers are cloudy. Also labeled on Fig. 6 is the special
case of a “black” cloud. This value, RR ⫽ 1.0, is obtained when the cloud overhead is optically thick andthe atmosphere below the cloud has the same temperature as the cloud. A time series of zenith-looking
PAERI spectra is then used for determining FCCPAERI.
FCCPAERI was ultimately used to categorize thedownwelling flux data as clear or cloudy because it wasdeemed to have the fewest a priori weaknesses. The
radiance ratio method is far less susceptible to mistaking scattered clouds for clear skies than is the pyrgeometer method, because of the narrow FOV of the
PAERI. It is also much less susceptible to mistaking
thin or cold clouds for clear skies since the method isvery sensitive to relative changes in the window region(800–1200 cm⫺1) and “dirty window” (450–550 cm⫺1)
rather than absolute changes in total PAERI-band irradiance. In addition, the process of summing acrossthe entire spectrum and dividing by a blackbody radiance that is specific to the spectrum in question minimizes any uniform biases in the PAERI spectrum.
The monthly means of FCCPAERI have been adjusted
to include cloudy times when the PAERI was not operating. Such times were typically easy to assign visuallyas cloudy since they mainly occurred during spells of
blowing snow. As discussed above, it appears that blowing snow is almost always accompanied by clouds at theSouth Pole. In any case, the effect of blowing snow on4243

LDF is quite similar to that of clouds. Adjustments tomonthly mean values of FCCPAERI were as much as⫹12%, but were typically closer to ⫹5%.
Table 5 shows that adding or subtracting the radiative effects of a layer of diamond dust to the CSR
causes FCCPAERI to decrease by 10% or increase by
18%, respectively.
Monthly means of FCCPAERI for other routinely observed zenith angles were also calculated to assess theeffect of cloud thickness on FCCPAERI. Compared to
the zenith value, monthly FCCPAERI increased by only
1% at a zenith angle of 60° and 2% at an angle of 78°.
This small effect is a result of two climatic factors at theSouth Pole. First, the clouds are predominantly thin
throughout the year, so viewing the sides of clouds does
not increase FCC significantly. Second, daytime visualobservations indicate that 74% of the time the sky is
within one octa of being clear or cloudy. Thus, even an
instrument with as narrow an FOV as the PAERI
would not get much opportunity to see sides of clouds.

c. Radiance-to-flux conversion (integration overangle and wavenumber)
The monthly mean spectral radiances for the routinely observed angles were used to calculate monthly
mean partial-band LDF by the following method (illustrated in Fig. 7). The monthly mean, clear-sky, PAERIband (i.e., 450–1800 cm⫺1) and partial-band radiances(such as shown in Fig. 5) were summed over wavenumber and plotted as functions of the cosine (␮) of the

FIG. 7. Clear-sky PAERI-band radiances (ⵧ) for Jan and Jul
2001 as a function of the cosine of the zenith angle. Also shown
are the clear-sky CO2-band radiances for Jan 2001 (◊). The dashedlines represent quadratic functions that were fit to each of thedatasets to convert PAERI-band radiances to fluxes via Eq. (2).

4244

JOURNAL OF CLIMATELDFH2O ⫽ LDF ⫺ LDFPAERI ⫹ LDF450–550

TABLE 6. Bandwidth limits for analysis of spectral infrared datain cm⫺1 and ␮m.
Bandwidth(cm⫺1)

Bandwidth(␮m)

20–550, 1375–2500
550–800
950–1100
800–950, 1100–1200
1200–1375

18–500, 4–7.3
12.5–18
9.1–10.5
10.5–12.5, 8.3–9.1
7.3–8.3

Absorbing gases
Water vapor (H2O)
Carbon dioxide (CO2)
Ozone (O3)Window region
Methane/nitrous oxide(CH4/N2O)

zenith angle (␪). A quadratic curve was fit to the points,
which was then integrated over ␮ to yield the monthly
mean LDFclear (Fig. 7):
LDFPAERI ⫽

冕 冕 冕
1800

2␲

1

450

0

0

LPAERI共␷, ␮兲␮ d␮ d␾ d␷,VOLUME 18⫹ LDF1375–1800.

共3兲

Since LDFH2O in Eq. (3) is calculated from two different measurements, it is affected by the biases and uncertainties of both instruments.
It is interesting that in January, the CO 2 -band
LDFclear is as large as the entire PAERI-band LDFclear
for July (Fig. 7). The nearly flat curve fit to the January,
CO2-band LDFclear highlights the fact that the boundary layer is nearly isothermal during January (i.e., the
PAERI observed almost equal radiances at all angles).
Thus, most of the curvature in the January PAERIband radiances in the upper curve in Fig. 7 is due to thestratification of water vapor in the lower troposphere.
See Mahesh et al. (2001a) for a discussion of CO2 infrared e-folding distances over the South Pole.

共2兲

4. Clear-sky fluxes
LPAERI is the PAERI radiance at a particular wavenumber (␷) and ␾ is the azimuthal angle; azimuthal
symmetry is assumed here.
The partial-band LDFclear values were calculated using the spectral limits shown in Fig. 2 and the ␮ dependence shown in Fig. 7. The partial-band LDFclear due towater vapor rotational and vibrational emission deserves a more detailed explanation. As stated earlier,
we assume that all significant clear-sky atmospheric infrared radiation outside the spectral bandwidth of the
PAERI was due to emission from water vapor. This
assumption was confirmed by LBLRTM calculations.
To calculate the monthly mean LDFclear due to emission from water vapor, the monthly mean LDFclear forthe entire PAERI bandwidth is subtracted from themonthly mean broadband LDFclear from the pyrgeometer. Then the monthly mean LDFclear for the spectral
regions 450–550 and 1375–1800 cm⫺1 are added to theout-of-band water vapor emission resulting in the total
LDFclear from water vapor:

To quantify the greenhouse effect of the various
gases in the atmosphere, the infrared spectrum wasseparated into bands based on the gas or gases most
responsible for the emission within a given spectral region. The spectral band limits used here are summarized in Table 6 and Fig. 2.
The broadband and partial-band LDFclear are plotted
as functions of month in Fig. 8 and listed by season in
Table 7. The seasonal cycles of LDFclear are similar for
all gases. Water vapor and CO2 are responsible for almost all the emission. The maximum LDFclear values
are observed during the short Antarctic summer when
the lower troposphere is warmest and nearly isothermal. There is not a pronounced minimum in broadband
LDFclear during winter in Fig. 8; this is an illustration of
the coreless winter described by Schwerdtfeger (1984).
Table 8 compares LDFall values from different years,
so as to place the field seasons in the context of theclimatology of the South Pole. Both 1992 and 2001 are

TABLE 7. Seasonal LDFclear from 1992 and 2001. The seasons on the Antarctic Plateau are traditionally defined as spring (Oct,Nov), summer (Dec, Jan), autumn (Feb, Mar), and winter (Apr–Sep) (Schwerdtfeger 1984). Units are W m⫺2.
SummerAutumnWinterSpringBand(Dec–Jan)(Feb–Mar)(Apr–Sep)(Oct–Nov)

Broadband (1992)
Broadband (2001)
H2O (2001)
CO2 (2001)
O3 (2001)
CH4/N2O (2001)
Window region (2001)

119
126
83
35
3
3
1

99
101
66
29
2
2
1

79
74
48
23
1
2
1

94
96
63
28
2
2
1

15 OCTOBER 2005

TOWN ET AL.

FIG. 8. Broadband and partial-band clear-sky, longwave downward flux (LDFclear) for the South Pole in 2001. (See section 3c fora description of how the fluxes were determined.) The lower panel
has an expanded vertical scale. Broadband and partial-band
LDFclear are calculated by integrating Eq. (2) over ␮ and acrossthe spectral band limits described above. The existence of clear
conditions was determined using both spectral and broadband IR
data, using the methods described in section 3b. The tick marks
denote the beginning and end of each month. The data points for
Mar are not centered in the month because the PAERI was only
operating during the early part of the month. There is no PAERI
data for Apr.

within one standard deviation of the nine-year mean inTable 8. Both the 1992 and 2001 winters experienced
less LDFall than average, whereas the springs exhibit
greater LDFall than average. Nevertheless, the twoyears presented here are acceptably close to climatology to be considered normal.
LDFclear values in the window region, due mainly todiamond dust, are seen to be very small in all months(Fig. 8). Emissions from O3, N2O, and CH4 are also
small. Water vapor contributes approximately twothirds of the LDFclear regardless of season while CO2
contributes about one-third. The actual ratio of H2O/
CO2 LDFclear is in the range 2.1–2.3 throughout theyear. This constancy is surprising because precipitablewater vapor (PWV) decreases significantly from summer to winter, but atmospheric CO2 concentration remains constant as the atmospheric temperatures drop
dramatically.

a. Precipitable water vapor
It is relevant at this point to discuss the precise natureof the atmospheric water vapor cycle over the South
Pole to fully address the puzzle of the H2O/CO2 flux
ratio. Chamberlin (2001) estimated that the tropo-

4245

spheric PWV over the South Pole ranges from about
1.1 mm in summer to 0.4 mm in winter. His estimate
was based on temperature soundings from 1961 to 1999
and the assumption that the entire troposphere is saturated with respect to ice. He also made an alternative
estimate of PWV based on historical humidity soundings over the South Pole. Unfortunately, the hygristors
used for those soundings are sluggish and inaccurate at
low temperatures (Hudson et al. 2004).
Our estimates of the tropospheric PWV in 2001 (Fig.
9) are based on RS80 radiosonde soundings collectedby CMDL, discussed in section 2c. The RS80s use significantly improved technology based on capacitance todetermine humidity. Our PWV values for the summer
are about 50% larger than Chamberlin’s because thelower atmosphere is often saturated with respect to liquid water (and therefore supersaturated with respect toice) during the summer. Indeed, other observations wehave made using a video camera on a tethered balloon
indicate that clouds of supercooled liquid droplets
are common in summer, down to temperatures of⫺30°C or so.
Tropospheric ice saturation seems to be a valid assumption in the wintertime inversion layer; this explains why our estimates of PWV are similar to one of
Chamberlin’s estimates, which made this assumption. It
is uncertain why Chamberlin’s other estimate, based on
inaccurate hygristor technology, is also similar. On average, about 70% of the PWV is below the altitude of
5 km above sea level (surface altitude at the South Poleis 2.835 km) (Fig. 9). During the 2001 winter, most of
the atmospheric PWV was located in the 1-km layer
centered at the top of the temperature inversion. Even
though the near-surface atmosphere during winter issaturated with respect to ice, and often slightly supersaturated, this does not have a major effect on mean
PWV amounts because of the low temperatures near
the surface. Similarly, the atmospheric humidities
above the inversion layer do not contribute much to
PWV because the free troposphere is cold. During thesummer, the mean RHw of the atmosphere above theisothermal boundary layer is between 5% and 20%
RHw below ice saturation. During the winter the atmosphere above the inversion top is a more uniform 15%
to 20% RHw below ice saturation.
In summary, the atmospheric PWV varies from ⬃1.6
to 0.4 mm from summer to winter while the climatological mean surface temperature drops from ⫺28° to⫺58°C (Warren 1996). The average PWV during summer is much higher than the clear-sky value (point X
indicated in Fig. 9), as evidenced by the frequent occurrence of liquid water stratocumulus clouds.

4246

JOURNAL OF CLIMATEVOLUME 18

TABLE 8. Broadband pyrgeometer measurements under all conditions in W m⫺2 for 1992, 2001, and 1994–2002. The standard
deviations about the means are in parentheses.
SummerAutumnWinterSpringYear(Dec–Jan)(Feb–Mar)(Apr–Sep)(Oct–Nov)

1992
2001
1994–2002

144 (29)
138 (24)140 (29)113 (21)121 (32)
118 (29)105 (33)
96 (26)114 (30)

121 (29)113 (31)98 (28)b. The relative contributions of H2O and CO2 to
LDFclear
There are several factors to consider in explaining
the seasonal cycle of the H2O/CO2 LDFclear ratio. First,
the shift of the blackbody spectrum with temperatureputs more emphasis on the water vapor rotational band
during winter than during summer. On the other hand,
PWV decreases from summer to winter by over 50%.
Other factors are the saturated nature of the 15-␮mband of CO2 and the variably saturated nature of thewater vapor rotational band.
Because of the complex nature of these relationships
we use LBLRTM to quantify these factors. Clear-sky
profiles from summer and winter were input into
LBLRTM to calculate downwelling infrared radiancesat several angles. Values of LDFclear from the water
vapor and CO2 bands were then calculated from theresulting radiances using the procedure outlined in sec-

FIG. 9. Tropospheric precipitable water vapor for 2001 based onmonthly mean RS80 soundings for all sky conditions. The Xs
denote mean PWV amounts for clear-sky only, for Jan and forwinter. A sensitivity study indicates that increasing RHw by 3%
increases PWV by ⬃0.06 mm during summer and ⬃0.03 mm during winter. Increasing temperature by 0.2 K throughout the troposphere, for fixed RHw, results in an increase in PWV of 0.03
mm in summer and 0.01 mm in winter. Most of the PWV is below
5 km above sea level (thin line), i.e., within 2 km of the surface.

tion 3c. The profiles of T, water vapor mixing ratio, andpressure for the two cases were also permuted and input into LBLRTM to further investigate their relative
importance.
Table 9 shows the results of the permutations on thefluxes emitted by water vapor and CO2. The LBLRTM
results indicate that by decreasing the temperature, but
not water vapor mixing ratio, from summer to wintervalues (row 1 to row 2 in Table 9) emission from theCO2 band drops by a third, almost all the way to observed winter levels. However, water vapor emission
decreases by only 23% (17 W m⫺2).
In contrast, decreasing water vapor mixing ratio fromsummer to winter levels (row 1 to row 3 in Table 9) only
decreases water vapor emission by 12 W m⫺2. This isbecause the water vapor rotational band is alreadymostly saturated even at the low water vapor levels of
winter. Emission in the CO2 band is slightly reduced by
the decrease in water vapor because there is some water vapor–CO2 band overlap in the 550–600 cm⫺1 region.
Row 4 in Table 9 is presented for comparison to row
5 to illustrate that the decrease in atmospheric surfacepressure from summer to winter has very little effect on
LDFclear or the flux ratio.
Thus, the temperature decrease from summer to winter is directly responsible for all of the decrease in theCO2 emission but only approximately half of the decrease in the water vapor emission. The decrease inwater vapor mixing ratio from summer to winter is responsible for the remaining decrease in water vapor
emission. The constancy of the flux ratio can thus beseen as a consequence of the partial saturation of thewater vapor bands, together with the reduced water
vapor amount in winter.
Despite the accurate representation of Antarctic
clear-sky downwelling radiances by LBLRTM in thepast (Walden et al. 1998), there are still some discrepancies between our modeled and observed results. The
modeled CO2 LDFclear in January is close to, butslightly greater than, the observed LDFclear (row 1 ofTable 9). Considering the fairly small uncertainties inboth the PAERI observations and the LBLRTM simu-

15 OCTOBER 2005

4247

TOWN ET AL.

TABLE 9. LBLRTM simulations of clear-sky fluxes due to water vapor and CO2 emission based on clear-sky atmospheric profiles for
Jan 2001 and for winter 2001 at the South Pole. The flux ratio is the flux from water vapor divided by the flux from CO2, computed
both from the model (Mod) and observations (Obs). The rows from top to bottom represent different LBLRTM simulations based on
combinations of profiles of temperature, H2O mixing ratio (MR), and pressure, from Jan and from winter. It was found that atmospheric pressure did not change sufficiently from summer to winter to affect LDFclear significantly (row 4 vs row 5); it is only given here
for the sake of completeness.
CO2 flux (W m⫺2)

H2O flux (W m⫺2)

Flux ratio

Conditions

Mod

Obs

Mod

Obs

Mod

Obs

Jan T, H2O-MR, P
Jan H2O-MR, P; winter T
Jan T, P; winter H2O-MR
Jan P; winter T, H2O-MR
Winter T, H2O-MR, P

36.6
24.7
35.5
23.8
23.5

32.6
—
—
—
22.5

73.0
56.4
61.0
50.1
48.8

83.0
—
—
—
47.4

2.0
2.2
1.7
2.1
2.1

2.3
—
—
—
2.1

lations (Table 3) this difference is significant. The modeled LDFclear from water vapor is less than the observed flux by almost 10 W m⫺2, also a significant difference. However, during winter the modeled fluxes(row 5 of Table 9) agree quite well with observations.
Taken together, the LBLRTM fluxes actually predict aslight increase in the H2O/CO2 flux ratio from 2.0 insummer to 2.1 in winter rather than the slight decreasethat is observed. This is due primarily to a low modeledwater vapor LDFclear for summer, suggesting an inaccuracy in the model or an inaccurate representation of
the atmosphere in the model.
It seems unlikely that model errors could cause thediscrepancy. Walden et al. (1998) compared LBLRTM
simulations of downwelling radiances based on atmospheric profiles similar to those described in section 2cto three clear-sky test cases from 1992. Their overall
conclusion was that LBLRTM was able to accurately
simulate downwelling radiances in the extreme climateof east Antarctica. The largest discrepancies between
the simulations and observations occurred from uncertainty in the temperature and humidity profiles and uncertainties in the foreign-broadened continuum of water vapor. This study uses more accurate radiosonde
soundings than those used in Walden et al. (1998), andalso uses updated water vapor continuum coefficientsin the LBLRTM simulation, so inaccuracies in themodel results are not a likely explanation for the discrepancy between the modeled and observed H2O/CO2
LDFclear ratios.
It is possible that the atmospheric humidity in thisclear-sky January atmospheric profile (RHw ⫽ 60%–70%) does not accurately represent the mean clear-sky
humidity. We therefore increased tropospheric RHw to100% in LBLRTM with an otherwise normal January
atmospheric profile to test the sensitivity of LBLRTM
to dramatic changes in RHw; LDFclear was raised byonly ⬃4.5 W m⫺2.

A phenomenon that could explain these discrepancies is the presence of diamond dust under clear skies
during summer and under all sky conditions during winter; diamond dust was not included in the LBLRTM
calculations. The hypothesis is that LDFclear emitted bywater vapor lines in the boundary layer was absorbedby the near-surface ice crystals and spread across theentire infrared spectrum, including the gaps between
the lines. Since the lower troposphere is nearly isothermal during summer at the South Pole, this results in an
increase in LDFclear in parts of the water vapor rotation
band (as well as in other less strongly absorbed bands).
The emission from diamond dust would not raise theCO2-band fluxes by much because the CO2 band isstrongly absorbing.
In winter, however, the effect of diamond dust seems
to be coincidentally canceled by the existence of thesurface inversion. Most of the LDFclear due to water
vapor is from the top of the inversion layer. This downwelling emission is absorbed lower in the inversion
layer by the ever-present diamond dust. The diamond
dust again reemits the radiation across the entire infrared spectrum, as opposed to the spectral lines associated with gaseous emission, but this time it is emitting
at a much lower temperature than the original emission
temperature of the atmospheric water vapor. Thus, thestrength of the clear-sky temperature inversion and theemission of the diamond dust apparently compensate tocause no net effect on the observed H2O/CO2 LDFclear
ratio in winter.

5. Longwave downwelling cloud radiative forcing
As used here, the longwave downwelling cloud radiative forcing (LDCRF) is the difference between allsky downwelling flux and clear-sky downwelling flux:
LDCRF ⫽ LDFall ⫺ LDFclear.

共4兲

4248

JOURNAL OF CLIMATEVOLUME 18

FIG. 10. Monthly values of (top) broadband and (bottom) partial-band longwave downwelling cloud radiative forcing for the South Pole during 2001. The seasonal values of broadband LDCRF for 1992 are also shown. In Mar, FCCPAERI is available only for the beginningof the month. FCCPAERI is not available for Apr.

(Some published work on longwave cloud radiative
forcing at the surface reports instead the net forcing;i.e., downward minus upward, which we discuss later inthis section.) These fluxes are computed for the broadband and the partial bands indicated in Fig. 2, including
the window region, where LDCRF is typically large.
From Eq. (4) it is clear that partly cloudy skies areincluded in the LDCRF calculation. This is similar tothe definition of cloud radiative forcing at the top of theatmosphere in Ramanathan et al. (1989), except employed at the surface and only for terrestrial, downwelling radiation. Monthly LDCRF is the monthly
mean LDFall minus the monthly mean LDFclear. Table
4 shows the monthly means of LDFall and LDFclear,
their standard deviations and standard errors. Table 4shows that most of the uncertainty in the LDCRF timeseries is due to natural variability in cloud cover rather
than variability in LDFclear.
Figure 10 shows the broadband and partial-band
LDCRF for 2001, plus the seasonal values of broadband LDCRF for 1992. The top panel of Fig. 10 shows
two broadband LDCRF calculations for 2001 and one
for 1992. The thick dashed line is based on pyrgeometermeasurements in 2001 for LDF filtered by FCCPAERI.
The thin curve is LDCRF from pyrgeometer measurements utilizing FCCpyr.
The mean broadband LDCRF for 2001 is 20 or 23 W m⫺2using the FCCpyr or FCCPAERI time series, respectively.

The mean broadband LDCRF for 1992 is 24 W m⫺2.
The two estimates of broadband LDCRF for 2001 areconsistent with each other despite being from slightly
different time series. This lends weight to the representative nature of the PAERI time series.
The LDCRF derived here is compared with those of
several other sites in Table 10. LDCRF at the South
Pole is about half the global average even though theannual mean FCC for 2001 of 60% is nearly equal to
the global annual mean FCC of 64% (Hahn et al. 1995).
[In Table 10, 62% is quoted because it is the value usedby Kiehl and Trenberth (1997) in their simulation.] Thisis because the low temperatures and humidities over
the South Pole result in colder, optically thinner clouds
than the global average. Clouds at the South Pole typically have optical depths less than 1 (Mahesh et al.
2001b).
Table 11 gives values of longwave net flux (LNF) andlongwave net cloud radiative forcing (LNCRF) for various sites, including several estimates for the South Pole.
During 2001 at the South Pole, LNCRF ranged from 14
to 6 W m⫺2 from summer to winter (not shown here).
This was determined from observations of upwellingand downwelling fluxes taken by CMDL. The annual
mean LNCRF for 2001 is 9 W m⫺2. Pavolonis and Key(2003) determined annual mean surface LNCRF fromthe Advanced Very High Resolution Radiometer(AVHRR) Polar Pathfinder (APP-x) and International

15 OCTOBER 2005

4249

TOWN ET AL.

TABLE 10. Comparison of LDCRF (W m⫺2) at various sites: SPO ⫽ South Pole, Antarctica; BRW ⫽ Barrow, Alaska; SHEBA ⫽Surface Heat Budget of the Arctic Ocean; BG ⫽ Bremgarten, Germany; FG ⫽ Feldberg, Germany; SGP ⫽ Southern Great Plains,
United States; U-NSW ⫽ Uardry, New South Wales, Australia. Data on FCC are from Warren et al. (1986) and Hahn et al. (1995),
except for South Pole, which is FCCPAERI. The SHEBA LDCRF was determined from all-sky observations minus clear-sky calculations. The Global LDCRF was determined from clear and cloudy calculations, weighted by the FCC. Global LDCRF amounts to about
14% of LDFall whereas LDCRF at the South Pole is approximately 19% of LDFall.
Location

Lat, Lon

Alt (m)Description

FCC (%)LDCRF

SourceSPO (2001)
SPO (1992)
SHEBA
BRW
BG
FG
SGP
U-NSW
Global90°S
90°S
77°N, 165°W
71.3°N, 156.6°W
47.9°N, 7.6°E
47.9°N, 8°E
36.6°N, 97.5°W
34.39°S, 145.30°E
—

2835
2835
0
0
212
1489
315
94
—

Antarctic; mean (1 yr)
Antarctic; mean (1 yr)
Arctic; mean (1 yr)
Arctic; mean (1 yr)
Grassland; mean (4 yr)
Grassland; mean (4 yr)
Plains; mean (Apr–May)
Semi-arid; mean (1 yr)
Model calculation

60
—
—
59
68
68
56
35
62

20
24
37
44
43
38
47
25
46

This work
This work
Intrieri et al. (2002)
Allan (2000)
Iziomon et al. (2003)
Iziomon et al. (2003)
Morcrette (2002)
Wild and Cechet (2002)
Kiehl and Trenberth (1997)

Satellite Cloud Climatology Project (ISCCP) datasetsthat were greater than our estimates. This is potentially
due to errors in cloud cover estimates in the two products (Town et al. 2005). Briegleb and Bromwich (1998)
used ground-based pyrgeometer data from the South
Pole and calculated an annual mean LNCRF of 1 Wm⫺2. To get the clear-sky fluxes without reference tocloud observations they took minimum values of upward and downward flux for the entire month to represent clear-sky conditions.

6. Fractional cloud cover
As stated earlier, FCC at high latitudes based onnighttime visual observations (FCCvis) is relatively uncertain. To accurately report FCCvis, the observers are
required to remain outside long enough for their eyes
to become dark-adapted, and to learn the night sky indetail and apply their knowledge to potentially scattered or broken cloud cover. When Hahn et al. (1995)
screened the visual observations at the South Pole foradequate moonlight, the computed wintertime FCCvis

rose ⬃10% in comparison with FCC computed using all
observations. However, the lunar brightness thresholdused by Hahn et al. (1995) was developed for latitudes
0°–50°N. Hahn et al. (1995) admit that there may neverbe adequate moonlight at the South Pole for a visualobserver to accurately determine FCCvis due to theprevalence of thin cirrus clouds, as opposed to the more
optically thick clouds prevalent in the regions for which
their threshold was developed.
The FCCvis, FCCpyr, and FCCPAERI for 2001 areshown in Fig. 11. The algorithms on which FCCpyr and
FCCPAERI are based were detailed in section 3b. Relative to the daylight visual observations in January andFebruary, the pyrgeometer underreports cloud cover.
As stated earlier, it may be that the algorithm for deriving cloud cover from the pyrgeometer data is notdetecting scattered, thin, or cold clouds. FCCPAERI is
also lower than the visual observations in summer.
However, the differences from visual reports are within
their combined uncertainties for spring, summer, andautumn (Table 5). It is in winter that there are significant disagreements between cloud amounts from theTABLE 11. Comparison of annual mean observations of LNF and LNCRF for the Antarctic Plateau and other locations. The ISCCP
LNF is for the time period of Apr 1986–Apr 1987, and the ISCCP LNCRF is for the time period 1985–93. LNCRF is the same as
LDCRF in the global case because the upwelling fluxes of that simulation were the same in both the clear and cloudy cases. The otherreferences from Table 10 did not report net or upwelling fluxes.
StationLat, lon

Alt (m)

LNF

LNCRF

Time period

SourceSouth Pole
South Pole
South PoleISCCP

90°S
90°S
90°S

2835
2835
2835

⫺32
⫺26
⫺27

9
1
16

2000–01
1986–88
Apr 1986–Apr 1987, 1985–93

South PoleAPP-x

90°S

2835

⫺24

30

1982–99

0
0

⫺22
⫺66

38
46

Oct 1997–Oct 1998⫺

This work
Briegleb and Bromwich (1998)
Hines et al. (2004); Pavolonis and Key(2003)
Hines et al. (2004); Pavolonis and Key(2003)
Intrieri et al. (2002)
Kiehl and Trenberth (1997)

SHEBA
Global

77°N, 165°W
—

4250

JOURNAL OF CLIMATE

FIG. 11. Monthly mean FCCvis, FCCpyr, and FCCPAERI for 2001.
The correction for adequate moonlight for the 2001 visual observations has been included in Fig. 11 for completeness but severely
restricts the data available. To qualify as “light,” visual observations were used only when the moon was above the horizon andbetween its first and last quarter. At the South Pole, the nature of
the lunar orbit results in two continuous weeks of relative lightand two weeks of darkness as the moon rises and sets during thepolar night. Applying a moonlight filter to the visual observations
on a monthly basis therefore leads to potentially significant sampling errors.

irradiance-based estimates of FCC and visual observations.
The FCCpyr shows no seasonal cycle for 2001, while
FCCPAERI actually shows greater cloud cover in winterthan in summer. However, these two radiation-based
estimates of FCC do show similar month-to-monthvariations. Figure 12 is a scatterplot of radiance ratio
from the PAERI plotted against flux from the pyrgeometer for June 2001. It suggests that these two methods are measuring the same thing even though they
have drastically different fields of view. The month ofJune was chosen as an example because it exhibited thegreatest disagreement between FCCpyr and FCCPAERIin Fig. 11. Despite this disagreement, the scatterplot
shows good correlation, indicating that perhaps we arecorrect in our conclusion that the FCCpyr algorithm isclassifying scattered, thin, or cold clouds as clear sky.
This good correlation is a result of the binary characterof cloud cover over the South Pole mentioned in section 3b. In addition, the level topography of the Antarctic Plateau prevents scattered clouds from preferentially avoiding the FOV of the PAERI, making FCCpyrand FCCPAERI statistically comparable.

7. Conclusions
Clear-sky downward longwave flux, cloud radiative
forcing, and fractional cloud cover were determined for

VOLUME 18

FIG. 12. Scatterplot of Jun radiance ratio (RR) plotted against
Jun pyrgeometer measurements. The horizontal dashed line is the
CSR threshold for Jun. The vertical solid line is the clear-sky flux
threshold for Jun. The number of points is 1384.

the South Pole based on data collected during the 2001
SPARCLE field season. A strong seasonal cycle is evident in the LDFclear due predominantly to the seasonal
cycle of atmospheric temperatures and humidities.
Broadband monthly mean LDFclear ranges from 125 Wm⫺2 in summer to 70 W m⫺2 in winter. Flux fromthe water vapor bands accounts for about two-thirds ofthe broadband LDFclear throughout the year and CO2
accounts approximately for the other one-third.
The approximately 2:1 ratio across the seasons is dueto three factors. Shifting of the frequency of the blackbody emission peak as temperature drops emphasizes
the water vapor rotational band over the CO2 15-␮mband. The decrease in atmospheric humidity from summer to winter only slightly reduces the relative water
vapor contribution to LDFclear because the water vapor
rotational band is mostly saturated. The decrease inatmospheric temperature from summer to winter explains nearly all the decrease in CO2 flux and the remaining flux due to water vapor. Absorption and reemission of radiation by near-surface diamond-dust
crystals during both summer and winter may explain
the remaining discrepancies between modeled results
and observations.
LDCRF over the East Antarctic Plateau has no apparent seasonal cycle. The mean LDCRF was 23 Wm⫺2 in 2001 and 24 W m⫺2 in 1992. These values areabout half those typically found at lower-latitude sites.
Earlier surface-based observations underestimate
LNCRF because of assumptions of LDF and LUF correlations. Satellite-derived estimates of LNCRF are toohigh probably because of inaccurate cloud cover estimates from the satellite retrievals.

15 OCTOBER 2005

TOWN ET AL.

The FCC on which the flux analysis was based wasdetermined from data from an infrared spectrometer(the PAERI). The FCCPAERI agrees with visual observations within the uncertainties of both measurements
during times of adequate daylight, but yields higher
cloud amounts during the polar night. The visual observations are probably inaccurate during the polarnight due to the difficult and subjective nature of theobservation.
Acknowledgments. Stephen Hudson helped to collectthe SPARCLE dataset, and provided advice and technical assistance. Eric Sandberg and Paulene Roberts,
the NOAA 2001 winterover crew, collected the CMDL
data. Bryan Johnson, Thomas Mefford, and Ellsworth
Dutton provided the data and metadata from the
CMDL in a timely manner. Ellsworth Dutton provided
information about the maintenance and calibration of
the CMDL pyrgeometer. Michael Pavolonis and Jeff
Key provided us with APP-x data. Keith Hines provided us with some cloud forcing data, and excellent
comments and suggestions. Penny Rowe assisted us in
using LBLRTM. Qiang Fu provided insightful comments and suggestions. We appreciate the useful comments of two anonymous reviewers. This work was supported by NSF Grants OPP-9726676 and OPP-0230466.
REFERENCES
Allan, R. P., 2000: Evaluation of simulated clear-sky longwave
radiation using ground-based observations. J. Climate, 13,
1951–1964.
Bell, R. J., 1972: Introductory Fourier Transform Spectroscopy.
Academic Press, 382 pp.
Briegleb, B. P., and D. H. Bromwich, 1998: Polar radiation budgets of the NCAR CCM3. J. Climate, 11, 1246–1268.
Chamberlin, R. A., 2001: South Pole submillimeter sky opacity
and correlations with radiosonde observations. J. Geophys.
Res., 106, 20 101–20 113.
Clough, S. A., and M. J. Iacono, 1995: Line-by-line calculations ofatmospheric fluxes and cooling rates. 2: Application to carbon dioxide, ozone, methane, nitrous oxide and the halocarbons. J. Geophys. Res., 100, 16 519–16 535.
——, ——, and J. L. Moncet, 1992: Line-by-line calculations ofatmospheric fluxes and cooling rates: Application to water
vapor. J. Geophys. Res., 97, 15 761–15 785.
Curry, J. A., W. B. Rossow, D. Randall, and J. L. Schramm, 1996:
Overview of Arctic cloud and radiation characteristics. J. Climate, 9, 1731–1764.
Dalrymple, P. C., 1966: A physical climatology of the Antarctic
Plateau. Studies in Antarctic Meteorology, Antarctic Research
Series, M. J. Rubin, Ed., Vol. 9, Amer. Geophys. Union, 195–
231.
Dutton, E. G., 1993: An extended comparison between
LOWTRAN7 computed and observed broadband thermal
irradiances: Global extreme and intermediate surface conditions. J. Atmos. Oceanic Technol., 10, 326–336.
Elliott, W. P., and D. J. Gaffen, 1991: On the utility of radiosonde

4251

humidity archives for climate studies. Bull. Amer. Meteor.
Soc., 72, 1507–1520.
Hahn, C. J., S. G. Warren, and J. London, 1995: The effect ofmoonlight on observation of cloud cover at night, and application to cloud climatology. J. Climate, 8, 1429–1446.
Hines, K. M., R. W. Grumbine, D. H. Bromwich, and R. I. Cullather, 1999: Surface energy balance of the NCEP MRF and
NCEP–NCAR reanalysis in Antarctic latitudes during
FROST. Wea. Forecasting, 14, 851–866.
——, D. H. Bromwich, P. J. Rasch, and M. J. Iacono, 2004: Antarctic clouds and radiation within the NCAR climate models.
J. Climate, 17, 1198–1212.
Houghton, J. T., Y. Ding, D. J. Griggs, M. Noguer, P. J. van der
Linden, X. Dai, K. Maskell, and C. A. Johnson, Eds., 2001:
Climate Change 2001: The Scientific Basis. Cambridge University Press, 881 pp.
Hudson, S. R., and R. E. Brandt, 2005: A look at the surfacebased temperature inversion over the Antarctic Plateau. J.
Climate, 18, 1673–1696.
——, M. S. Town, V. P. Walden, and S. G. Warren, 2004: Temperature, humidity, and pressure response of radiosondes at
low temperatures. J. Atmos. Oceanic Technol., 21, 825–836.
Intrieri, J. M., C. W. Fairall, M. D. Shupe, P. O. G. Persson, E. L
Andreas, P. S. Guest, and R. E. Moritz, 2002: An annual
cycle of Arctic surface cloud forcing at SHEBA. J. Geophys.
Res., 107, 8039, doi:10.1029/2000JC000439.
Iziomon, M. G., H. Mayer, and A. Matzarakis, 2003: Downward
atmospheric longwave irradiance under clear and cloudy
skies: Measurement and parameterization. J. Atmos. SolarTerr. Phys., 65, 1107–1116.
Kiehl, J. T., and K. E. Trenberth, 1997: Earth’s annual global
mean energy budget. Bull. Amer. Meteor. Soc., 78, 197–208.
Knuteson, R. O., and Coauthors, 2004a: Atmospheric Emitted
Radiance Interferometer (AERI). Part I: Instrument design.
J. Atmos. Oceanic Technol., 21, 1763–1776.
——, and Coauthors, 2004b: Atmospheric Emitted Radiance Interferometer (AERI). Part II: Instrument performance. J. Atmos. Oceanic Technol., 21, 1777–1789.
Kuhn, M., A. J. Riordan, and I. A. Wagner, 1975: The climate of
Plateau Station. Climate of the Arctic, G. Weller and S. A.
Bowling, Eds., Geophysical Institute, University of Alaska,
255–267.
——, L. S. Kundla, and L. A. Stroschein, 1977: The radiation budget at Plateau Station, Antarctica, 1966–67. MeteorologicalStudies at Plateau Station, Antarctic Research Series, J. A.
Businger, Ed., Vol. 25, Amer. Geophys. Union, 41–73.
Mahesh, A., V. P. Walden, and S. G. Warren, 1997: Radiosondetemperature measurements in strong inversions: Correction
for thermal lag based on an experiment at the South Pole. J.
Atmos. Oceanic Technol., 14, 45–53.
——, ——, and ——, 2001a: Ground-based infrared remote sensing of cloud properties over the Antarctic Plateau. Part I:
Cloud-base heights. J. Appl. Meteor., 40, 1265–1278.
——, ——, and ——, 2001b: Ground-based infrared remote sensing of cloud properties over the Antarctic Plateau. Part II:
Cloud optical depths and particle sizes. J. Appl. Meteor., 40,
1279–1294.
Marty, C., and R. Philipona, 2000: The clear-sky index to separate
clear-sky from cloudy-sky situations in climate research. Geophys. Res. Lett., 27, 2649–2652.
——, and Coauthors, 2003: Downward longwave irradiance uncertainty under arctic atmospheres: Measurements and modeling. J. Geophys. Res., 108, 4358, doi:10.1029/2002JD002937.

4252

JOURNAL OF CLIMATE

Miloshevich, L. M., H. Vömel, A. Paukkunen, A. J. Heymsfield,
and S. J. Oltmans, 2001: Characterization and correction ofrelative humidity measurements from Vaisala RS80-A radiosondes at cold temperatures. J. Atmos. Oceanic Technol., 18,
135–156.
Morcrette, J.-J., 2002: Assessment of the ECMWF model cloudiness and surface radiation fields at the ARM SGP site. Mon.
Wea. Rev., 130, 257–277.
Pavolonis, M. J., and J. R. Key, 2003: Antarctic cloud radiative
forcing at the surface estimated from the AVHRR Polar
Pathfinder and ISCCP D1 datasets, 1985–1993. J. Appl. Meteor., 42, 827–840.
Philipona, R., and Coauthors, 2001: Atmospheric longwave irradiance uncertainty: Pyrgeometers compared to an absolutesky-scanning radiometer, atmospheric emitted radiance interferometer, and radiative transfer model calculations. J.
Geophys. Res., 106, 28 129–28 141.
Ramanathan, V., R. D. Cess, E. F. Harrison, P. Minnis, B. R.
Barkstrom, E. Ahmad, and D. Hartmann, 1989: Cloudradiative forcing and climate: Results from the Earth Radiation Budget Experiment. Science, 243, 57–63.
Rowe, P. M., 2004: Measurements of the Foreign-Broadened
Continuum of Water Vapor in the 6.3 micron band at ⫺30°
Celsius. Ph.D. thesis, University of Washington, 258 pp.
Schwerdtfeger, W., 1984: Weather and Climate of the Antarctic.
Elsevier, 261 pp.
Sutter, M., B. Dürr and R. Philipona, 2004: Comparison of two
radiation algorithms for surface-based cloud-free sky detection. J. Geophys. Res., 109, D17202, doi:10.1029/
2004JD004582.
Tobin, D. C., and Coauthors, 1999: Downwelling spectral radiance observations at the SHEBA ice station: Water vapor

VOLUME 18

continuum measurements from 17 to 26 ␮m. J. Geophys. Res.,
104, 2081–2092.
Town, M. S., V. P. Walden, and S. G. Warren, 2005: Cloud cover
climatology for the South Pole from surface-based infrared
radiation measurements. Preprints, Eighth Conf. on Polar
Meteorology and Oceanography, San Diego, CA, Amer. Meteor. Soc., CD-ROM, P1.26.
Vaisala, 1997: RS80 manufacturer’s brochure. Vaisala, Tech. Rep.
A571 1997–8, 2 pp.
Walden, V. P., 1995: The downward longwave radiation spectrum
over the Antarctic Plateau. Ph.D. thesis, University of Washington, 267 pp.
——, S. G. Warren, and F. J. Murcray, 1998: Measurements of thedownward longwave radiation spectrum over the Antarctic
Plateau and comparisons with a line-by-line radiative transfer
model for clear skies. J. Geophys. Res., 103, 3825–3845.
——, and Coauthors, 2001: The South Pole atmospheric radiationand cloud lidar experiment (SPARCLE). Preprints, Sixth
Conf. on Polar Meteorology and Oceanography, San Diego,
CA, Amer. Meteor. Soc., 297–299.
——, S. G. Warren, and E. Tuttle, 2003: Atmospheric ice crystals
over the Antarctic Plateau in winter. J. Climate, 42, 1391–
1405.
Warren, S. G., 1996: Antarctica. Encyclopedia of Climate andWeather, S. H. Schneider, Ed., Oxford University Press, 31–
39.
——, C. J. Hahn, J. London, R. M. Chervin, and R. L. Jenne,
1986: Global distribution of total cloud cover and cloud typeamounts over land. NCAR, TN-273⫹STR, 29 pp. ⫹ 200
maps.
Wild, M., and R. Cechet, 2002: Downward longwave radiation in
general circulation models: A case study at a semi-arid continental site. Tellus, 54A, 330–337.