Possible future changes in Arctic sea ice cover and thickness, and consequent changes in the ice-albedo feedback, represent one of the largest uncertainties in the prediction of future temperature rise. Knowledge of the natural variability of sea ice thickness is therefore critical for its representation in global climate models. Numerical simulations suggest that Arctic ice thickness varies primarily on decadal timescales owing to changes in wind and ocean stresses on the ice, but observations have been unable to provide a synoptic view of sea ice thickness, which is required to validate the model results. Here we use an eight-year time-series of Arctic ice thickness, derived from satellite altimeter measurements of ice freeboard, to determine the mean thickness field and its variability from 65 degrees N to 81.5 degrees N. Our data reveal a high-frequency interannual variability in mean Arctic ice thickness that is dominated by changes in the amount of summer melt, rather than by changes in circulation. Our results suggest that a continued increase in melt season length would lead to further thinning of Arctic sea ice.

[1] Accurate sea surface height measurements have been extracted from ERS altimeter data in sea ice-covered regions for the first time. The data have been used to construct a mean sea surface of the Arctic Ocean between the latitudes of 60°N and 81.5°N based on 4 years of ERS-2 data. An RMS value for the crossover differences of mean sea surface profiles of 4.2 cm was observed in the ice-covered Canada Basin, compared with 3.8 cm in the ice-free Greenland-Iceland-Norwegian Seas. Comparisons are made with an existing global mean sea surface (OSUMSS95), highlighting significant differences between the two surfaces in permanently ice-covered seas. In addition, we present the first altimeter-derived sea surface height variability map of the Arctic Ocean. Comparisons with a high-resolution coupled ocean-sea ice general circulation model reveal a good qualitative agreement in the spatial distribution of variability. Quantitatively, we found that the observed variability was on average a factor of 3-4 greater than model predictions.

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