through NP-7. These climatic data, which include charts and tables of monthly and
yearly averages of the spatial and temporal distributions of n and nL, are still the
basis for describing radiative energy exchange in climatic and prognostic mod-
els of Arctic sea ice (e.g., Parkinson and Washington 1979, Hibler 1979, Ebert and
Curry 1993).
The creation of a complete and corrected archive of the standard meteorologi-
cal data collected on the Russian North Pole drifting stations within the frame-
work of the Russian-American data rescue project, however, now provides the
opportunity for a more accurate description of cloudiness and its temporal vari-
ability in the central Arctic. The National Snow and Ice Data Center (NSIDC 1996)
at the University of Colorado in Boulder recently issued a compact disk contain-
ing these data, Arctic Ocean Snow and Meteorological Observations from Drifting Sta-
tions: 1937, 19501991, Version 1.0.
Using this large mass of recently available data, we have confirmed Voskresen-
skii and Bryazgin's (1988) conclusion that, in the central part of the Arctic Basin,
cloud amount tends to fall in two ranges 02 tenths and 810 tenths. In other words,
the frequency distribution of cloud amount in the winter in the Arctic is U-shaped.
Our more limited analyses of cloud observations in the Arctic during one year at
NP-4 (Fig. 3) and in the Antarctic during three months on Ice Station Weddell (Fig.
4) show the same bimodal distribution. Such histograms can be described with
the beta distribution (Harr 1977). Clearly, the common practice of quantifying cloud
amount with only its average value is, at least for spatial scales less than a thou-
sand kilometers, not correct: For typical cloud distributions over sea ice, the mean
value is the least likely value.
Our investigation of the correlation between atmospheric surface-layer tempera-
ture and total cloud amount, based on the NSIDC data, has allowed us to develop
a method for partially reconstructing total cloud amount in winter
using only air temperature data. Conveniently, temperature is the most readily
available meteorological parameter; for example, the Arctic buoy network yields
it routinely for much of the Arctic. This method appears to be potentially useful
North Pole 4
1600
1 April 1956 - 15 April 1957
1400
1200
1000
Counts
800
600
400
200
0
0
1
2
3
4
5
6
7
8
9
10
Total Clouds (tenths)
Figure 3. Histogram of total cloud amount observed on North Pole 4 from 1
April 1956 to 15 April 1957.
3
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