Data collected by the International Arctic Buoy Programme from 1979 to 1998 are analyzed to obtain statistics of sea level pressure (SLP) and sea ice motion (SIM). The annual and seasonal mean fields agree with those obtained in previous studies of Arctic climatology. The data show a 3-hPa decrease in decadal mean SLP over the central Arctic Ocean between 1979-88 and 1989-98. This decrease in SLP drives a cyclonic trend in SIM, which resembles the structure of the Arctic Oscillation (AO).Regression maps of SIM during the wintertime (January-March) AO index show 1) an increase in ice advection away from the coast of the East Siberian and Laptev Seas, which should have the effect of producing more new thin ice in the coastal flaw leads; 2) a decrease in ice advection from the western Arctic into the eastern Arctic; and 3) a slight increase in ice advection out of the Arctic through Fram Strait. Taken together, these changes suggest that at least part of the thinning of sea ice recently observed over the Arctic Ocean can be attributed to the trend in the AO toward the high-index polarity.Rigor et al. showed that year-to-year variations in the wintertime AO imprint a distinctive signature on surface air temperature (SAT) anomalies over the Arctic, which is reflected in the spatial pattern of temperature change from the 1980s to the 1990s. Here it is shown that the memory of the wintertime AO persists through most of the subsequent year: spring and autumn SAT and summertime sea ice concentration are all strongly correlated with the AO index for the previous winter. It is hypothesized that these delayed responses reflect the dynamical influence of the AO on the thickness of the wintertime sea ice, whose persistent ''footprint'' is reflected in the heat fluxes during the subsequent spring, in the extent of open water during the subsequent summer, and the heat liberated in the freezing of the open water during the subsequent autumn.

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The polar oceans contain sea ice of many thicknesses ranging from open water to thick pressure ridges. Since many of the physical properties of the ice depend upon its thickness, it is natural to expect its large‐scale geophysical properties to depend on the relative abundance of the various ice types. The ice pack is treated as a mixture whose constituents are determined by their thickness and whose composition is determined by the area covered by each constituent. A dimensionless function g(h), the ice thickness distribution, is defined such that g(h) dh is the fraction of a given area covered by ice of thickness greater than h but less than h + dh. A theory is developed to explain how the ice thickness distribution changes in response to thermal and mechanical forcing. The theory models the changes in thickness due to melting and freezing and the rearrangement of existing ice to form leads and pressure ridges. In its present form the model assumes as inputs a growth rate function and the velocity field of the ice pack. The model is tested using strain data derived from the positions of three simultaneous manned drifting stations in the central Arctic during the period 1962–1964 and growth rates inferred from climatological heat flux averages. The results are compared with estimates of g based on submarine measurements of ice thickness.

It is an old rule‐of‐thumb that sea ice moves with a speed of about 2% of the surface wind and about 45° to the right of the wind. A similar relationship between the ice velocity and the geostrophic wind is examined here. It is found that only about half of the long‐term (several month) average ice motion is directly related to the geostrophic wind, the other half being due to the mean ocean circulation. On shorter time scales and in all seasons, more than 70% of the variance of the ice velocity in the central Arctic Ocean is explained by the geostrophic wind. Within about 400 km of the coasts the geostrophic wind is less successful in explaining the ice motion. The spatial variations in ice velocity are also partly explained by the geostrophic wind. About half of the variance in the large‐scale ice vorticity and shear are accounted for. On the other hand, none of the large‐scale ice divergence can be explained by the wind. The long‐term average ocean current is estimated by subtracting the share of the ice motion caused by the wind from the total ice motion.

The statistics of surface air temperature observations obtained from buoys, manned drifting stations, and meteorological land stations in the Arctic during 1979-97 are analyzed. Although the basic statistics agree with what has been published in various climatologies, the seasonal correlation length scales between the observations are shorter than the annual correlation length scales, especially during summer when the inhomogeneity between the ice-covered ocean and the land is most apparent. During autumn, winter, and spring, the monthly mean correlation length scales are approximately constant at about 1000 km; during summer, the length scales are much shorter, that is, as low as 300 km. These revised scales are particularly important in the optimal interpolation of data on surface air temperature (SAT) and are used in the analysis of an improved SAT dataset called International Arctic Buoy Programme/Polar Exchange at the Sea Surface (IABP/POLES). Compared to observations from land stations and the Russian North Pole drift stations, the IABP/POLES dataset has higher correlations and lower rms errors than previous SAT fields and provides better temperature estimates, especially during summer in the marginal ice zones. In addition, the revised correlation length scales allow data taken at interior land stations to be included in the optimal interpretation analysis without introducing land biases to grid points over the ocean. The new analysis provides 12-h fields of air temperatures on a 100-km rectangular grid for all land and ocean areas of the Arctic region for the years 1979-97.The IABP/POLES dataset is then used to study spatial and temporal variations in SAT. This dataset shows that on average melt begins in the marginal seas by the first week of June and advances rapidly over the Arctic Ocean, reaching the pole by 19 June, 2 weeks later. Freeze begins at the pole on 16 August, and the freeze isotherm advances more slowly than the melt isotherm. Freeze returns to the marginal seas a month later than at the pole, on 21 September. Near the North Pole, the melt season length is about 58 days, while near the margin, the melt season is about 100 days. A trend of ϩ1ЊC (decade) Ϫ1 is found during winter in the eastern Arctic Ocean, but a trend of Ϫ1ЊC (decade) Ϫ1 is found in the western Arctic Ocean. During spring, almost the entire Arctic shows significant warming trends. In the eastern Arctic Ocean this warming is as much as 2ЊC (decade) Ϫ1 . The spring warming is associated with a trend toward a lengthening of the melt season in the eastern Arctic. The western Arctic, however, shows a slight shortening of the melt season. These changes in surface air temperature over the Arctic Ocean are related to the Arctic Oscillation, which accounts for more than half of the surface air temperature trends over Alaska, Eurasia, and the eastern Arctic Ocean but less than half in the western Arctic Ocean.