Spectral and Broadband Longwave Downwelling Radiative Fluxes, Cloud Radiative Forcing, and Fractional Cloud Cover over the South Pole

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 by comparing radiance ratios of observed and simulated spectra. Clear-sky fluxes are in the range of 110-125 W m Ϫ2 during summer (December-January) and 60-80 W m Ϫ2 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 persistent presence 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 CO 2 , 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 H 2 O/CO 2 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 visual observations in summer. However, the wintertime average is 0.3-0.5 greater than that obtained from visual observations. The annual mean of longwave downwelling cloud radiative forcing (LDCRF) for 2001 is about 23 W m Ϫ2 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.

Atmospheric Moisture and Cloud Cover Characteristics Forecast by AMPS*

“…However, these changes are much smaller than the large improvements gained in the prediction of overcast conditions, which have the most impact on aircraft operations. Additionally, other tests (not shown) demonstrate that the model rarely predicts CF that falls in the partly cloudy range, but rather tends to predict overcast and clear conditions instead, in agreement with the known U distribution of observed cloud cover at South Pole station (Town et al 2007). Thus, the forecast skill is the lowest for the partly cloudy sky coverage regardless of the empirical algorithm employed.…”

“…Notably, there is a much more marked seasonal cycle at South Pole station; the bias during austral fall is small and positive (0.048), while the bias is much farther from zero during the summer (Ϫ0.250). However, the positive bias in fall may be related to a known negative bias in observed CF compared against independent data (Town et al 2007). Incorporating the modified CF algorithm again substantially reduces the mean bias to Ϫ0.004, effectively centering the error distribution over zero, with these changes being highly statistically significant ( p Ͻ 0.001; Table 1).…”

“…Many problems complicate the ground-based visual cloud cover observations at South Pole station, including the very thin nature of clouds at all levels through which the sun/stars are often seen [these conditions tend to favor partly cloud observations instead of overcast conditions; Mahesh et al (2001); Bernhard et al (2004); Town et al (2007)], a 6-month period of complete darkness, making clouds difficult to observe, and extremely cold temperatures year round with especially strong inversion layers in the winter (Turner and Pendlebury 2007). Town et al (2005) further note the persistent presence of "diamond dust" or clear-sky precipitation during the winter, which may also add to errors in observed cloud fraction. They note that the wintertime average cloud fraction obtained from spectral infrared data is 0.3-0.5 greater than that obtained from visual observations.…”

“…Incorporating the modified CF algorithm again substantially reduces the mean bias to Ϫ0.004, effectively centering the error distribution over zero, with these changes being highly statistically significant ( p Ͻ 0.001; Table 1). The exception is for fall, where a larger positive bias is introduced by switching from the original to the modified CF algorithm; the bias using the modified CF algorithm also becomes positive during winter, but both of these seasons are marked with a negative bias in observed cloud cover (Town et al 2007), so AMPS is likely predicting reliable CF amounts in these seasons. During the other seasons, the biases improve by ϳ0.12 and are closer to zero than when using the previous CF values.…”

An analysis of cloud observations from Vernadsky, Antarctica

“…However, over a high albedo surface, such as the ice or snow cover of Antarctica, low clouds can be warming at any time of year. The study by Town et al (2005) describes the radiative fluxes and cloud radiative forcing over the South Pole in detail. Studies in the Arctic though have found that over a brief period in summer the cooling effect due to cloud shading is stronger than the warming caused by the clouds' greenhouse effect (Shupe and Intrieri, 2004).…”