Accounting for Clouds in Sea Ice Models

Over sea ice in winter, the clouds, the surface layer air temperature, and the longwave radiation are closely coupled. This report uses archived data from the Russian North Pole (NP) drifting stations and recent data from Ice Station Weddell (ISW) to investigate this coupling. Both Arctic and Antarc...

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Bibliographic Details
Main Authors: Makshtas, Aleksandr P., Andreas, Edgar L., Svyashchennikov, Pavel N., Timachev, Valery F.
Other Authors: COLD REGIONS RESEARCH AND ENGINEERING LAB HANOVER NH
Format: Text
Language:English
Published: 1998
Subjects:
Meteorology
*WEATHER FORECASTING
*CLOUD COVER
*SEA ICE
ALGORITHMS
ATMOSPHERIC TEMPERATURE
SURFACE TEMPERATURE
ANTARCTIC REGIONS
ARCTIC REGIONS
PE61102A
Antarctic
Arctic
North Pole
Weddell
Antarc*
Russian North
Sea ice
Online Access:http://www.dtic.mil/docs/citations/ADA358288
http://oai.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADA358288
Description
Summary:Over sea ice in winter, the clouds, the surface layer air temperature, and the longwave radiation are closely coupled. This report uses archived data from the Russian North Pole (NP) drifting stations and recent data from Ice Station Weddell (ISW) to investigate this coupling. Both Arctic and Antarctic distributions of total cloud amount are U shaped; that is, observed cloud amounts are typically either 0-2 tenths or 8-10 tenths in the polar regions. These data obey beta distributions; roughly 70 station years of observations from the NP stations yielded fitting parameters for each winter month. Although surface layer air temperature and total cloud amount are correlated, it is not straightforward to predict one from the other, because temperature is normally distributed while cloud amount has a U shaped distribution. Nevertheless, the report presents a statistical algorithm that can predict total cloud amount in winter from surface layer temperature alone and, as required, produces a distribution of cloud amounts that is U shaped. Because sea ice models usually need cloud data to estimate incoming longwave radiation, this algorithm may be useful for estimating cloud amounts and, thus, for computing the surface heat budget where no visual cloud observations are available but temperature is measured from the Arctic buoy network or from automatic weather stations, for example. The incoming longwave radiation in sea ice models is generally highly parameterized. The report evaluates five common parameterizations using data from NP-25 and ISW. The formula for estimating incoming longwave radiation that Koenig-Langlo and Augstein developed using both Arctic and Antarctic data has the best properties but does depend nonlinearly on total cloud amount. This nonlinearity is crucial since cloud distributions are U shaped, while common sources of cloud data tabulate only mean monthly values.

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