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Michael S. Town
,
Von P. Walden
, and
Stephen G. Warren

Abstract

Estimates of cloud cover over the South Pole are presented from five different data sources: routine visual observations (1957–2004; C vis), surface-based spectral infrared (IR) data (2001; C PAERI), surface-based broadband IR data (1994–2003; C pyr), the Extended Advanced Very High Resolution Radiometer (AVHRR) Polar Pathfinder (APP-x) dataset (1994–99; C APP-x), and the International Satellite Cloud Climatology Project (ISCCP) dataset (1994–2003; C ISCCP). The seasonal cycle of cloud cover is found to range from 45%–50% during the short summer to a relatively constant 55%–65% during the winter. Relationships between C pyr 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 C vis is worse than previously suspected, by approximately −20%; C ISCCP shows some skill during the polar day, while C APP-x shows some skill at night. The polar cloud masks for the satellite data reviewed here are not yet accurate enough to reliably derive surface or cloud properties over the 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 C pyr. The use of the C pyr dataset for further tests of satellite retrievals and for tests of polar models is recommended.

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Michael S. Town
,
Von P. Walden
, and
Stephen G. Warren

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−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 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 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 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−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.

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Nathaniel B. Miller
,
Matthew D. Shupe
,
Christopher J. Cox
,
Von P. Walden
,
David D. Turner
, and
Konrad Steffen

Abstract

The surface energy budget plays a critical role in determining the mass balance of the Greenland Ice Sheet, which in turn has significant implications for global sea levels. Nearly three years of data (January 2011–October 2013) are used to characterize the annual cycle of surface radiative fluxes and cloud radiative forcing (CRF) from the central Greenland Ice Sheet at Summit Station. The annual average CRF is 33 W m−2, representing a substantial net cloud warming of the central Greenland surface. Unlike at other Arctic sites, clouds warm the surface during the summer. The surface albedo is high at Summit throughout the year, limiting the cooling effect of the shortwave CRF and thus the total CRF is dominated by cloud longwave warming effects in all months. All monthly mean CRF values are positive (warming), as are 98.5% of 3-hourly cases. The annual cycle of CRF is largely driven by the occurrence of liquid-bearing clouds, with a minimum in spring and maximum in late summer. Optically thick liquid-bearing clouds [liquid water path (LWP) > 30 g m−2] produce an average longwave CRF of 85 W m−2. Shortwave CRF is sensitive to solar zenith angle and LWP. When the sun is well above the horizon (solar zenith angle < 65°), a maximum cloud surface warming occurs in the presence of optically thin liquid-bearing clouds. Ice clouds occur frequently above Summit and have mean longwave CRF values ranging from 10 to 60 W m−2, dependent on cloud thickness.

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Robert M. Graham
,
Lana Cohen
,
Nicole Ritzhaupt
,
Benjamin Segger
,
Rune G. Graversen
,
Annette Rinke
,
Von P. Walden
,
Mats A. Granskog
, and
Stephen R. Hudson

Abstract

This study evaluates the performance of six atmospheric reanalyses (ERA-Interim, ERA5, JRA-55, CFSv2, MERRA-2, and ASRv2) over Arctic sea ice from winter to early summer. The reanalyses are evaluated using observations from the Norwegian Young Sea Ice campaign (N-ICE2015), a 5-month ice drift in pack ice north of Svalbard. N-ICE2015 observations include surface meteorology, vertical profiles from radiosondes, as well as radiative and turbulent heat fluxes. The reanalyses simulate surface analysis variables well throughout the campaign, but have difficulties with most forecast variables. Wintertime (January–March) correlation coefficients between the reanalyses and observations are above 0.90 for the surface pressure, 2-m temperature, total column water vapor, and downward longwave flux. However, all reanalyses have a positive wintertime 2-m temperature bias, ranging from 1° to 4°C, and negative (i.e., upward) net longwave bias of 3–19 W m−2. These biases are associated with poorly represented surface inversions and are largest during cold-stable periods. Notably, the recent ERA5 and ASRv2 datasets have some of the largest temperature and net longwave biases, respectively. During spring (April–May), reanalyses fail to simulate observed persistent cloud layers. Therefore they overestimate the net shortwave flux (5–79 W m−2) and underestimate the net longwave flux (8–38 W m−2). Promisingly, ERA5 provides the best estimates of downward radiative fluxes in spring and summer, suggesting improved forecasting of Arctic cloud cover. All reanalyses exhibit large negative (upward) residual heat flux biases during winter, and positive (downward) biases during summer. Turbulent heat fluxes over sea ice are simulated poorly in all seasons.

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