reconstructed from temperature using eq 4 lead to predictions that are virtually
identical with experiment 5, which we judge as the experiment closest to reality.
The calculated first day of melting in Table 9, nominally 2426 May, is somewhat
earlier than Yanes (1962, Fig. 1) would predict for 85N, about June 20. But his rela-
tion tracks the onset of "intense snow melting," while we record the first appear-
ance of liquid water in the snowpack. A simulation by Jordan et al. (1998) of the
seasonal cycle on NP-4, which was within 5 latitude of the North Pole in 195657,
also predicts a later date, June 18, for the onset of diurnal melting than we list in
Table 9. But Yanes's results suggest melting is delayed by about 8 day for every 5
increase in latitude. Hence, the NP-25 and NP-4 results are fairly compatible.
The "Cooling" column in Table 9 lists the date when the sea ice begins cooling
again after the summer ablation season. The nominal date that we calculate as the
beginning of cooling is 1112 September. From thermocouples embedded in the
sea ice at NP-4, Jordan et al. (1998) show that in 1956 cooling began at this station
on about 28 August. Again, since NP-4 was 5 farther north than NP-25, our mod-
eled date for the onset of cooling is reasonable.
In experiments 712, to evaluate model sensitivity to the method for describing
the temporal variability in cloud amount, we used just the longwave parame-
terizations of KL&A and Marshunova. In the Marshunova (1961) parameteriza-
tion, we used the empirical coefficients listed in Tables 6 and 7 that derived from
observations on the drifting stations. As we hinted above, it is clear in Table 9 that
the Marshunova parameterization (i.e., experiments 2, 1012) is less sensitive to
the method of obtaining cloud amounts than the KL&A parameterization (experi-
ments 59). Because KL&A's parameterization has a cubic dependence on cloud
amount, the three sensitivity experiments predict maximum ice thicknesses that
range over 0.7 m, depending on the method for determining cloud amount. We
thus reiterate that, because the best model for Fdn was derived from nonaveraged
data and depends nonlinearly on cloud amount, sea ice models employing it will
be quite sensitive to the method of handling the cloud data.
In Table 9, experiments 5, 6, and 9 yield practically the same results. Likewise,
experiments 7 and 8 produce almost identical ice thicknesses, but these differ
essentially from the results in experiments 5, 6, and 9. Table 10, which shows cal-
of 1982-1983 on North Pole 25.
November
December
January
February
March
Daily avg*
163
160
137
149
168
Reconstructed
171 (0.84)
163 (0.86)
149 (0.97)
158 (0.94)
184 (0.89)
Monthly avg, int
160 (0.93)
152 (0.88)
135 (0.97)
145 (0.96)
162 (0.88)
* "Daily avg" values are the observations averaged each day and then averaged for
the month; "Reconstructed" values are the individual estimates computed
using eq 4, averaged daily, and then averaged for the month; "Monthly avg, int"
values are based on monthly data interpolated to daily values using a parabolic
interpolation over three months. Figure 11 shows daily values of these various cloud
amounts for November 1982. For the computed longwave radiation values, we used
Knig-Langlo and Augstein's (1994) formula, (eq 12). Numbers in parentheses are
the correlation coefficients between the daily averaged observed values of longwave
radiation and the respective reconstructed and interpolated values. It is important
to point out here that we calculated the monthly averaged values from daily aver-
ages observed on North Pole 25; these averages thus differ from those shown in
Gorshkov (1983).
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