As assessed over the period 1979–2014, the date that sea ice retreats to the shelf break (150 m contour) of the Chukchi Sea has a linear trend of −0.7 days per year. The date of seasonal ice advance back to the shelf break has a steeper trend of about +1.5 days per year, together yielding an increase in the open water period of 80 days. Based on detrended time series, we ask how interannual variability in advance and retreat dates relate to various forcing parameters including radiation fluxes, temperature and wind (from numerical reanalyses), and the oceanic heat inflow through the Bering Strait (from in situ moorings). Of all variables considered, the retreat date is most strongly correlated (r ∼ 0.8) with the April through June Bering Strait heat inflow. After testing a suite of statistical linear models using several potential predictors, the best model for predicting the date of retreat includes only the April through June Bering Strait heat inflow, which explains 68% of retreat date variance. The best model predicting the ice advance date includes the July through September inflow and the date of retreat, explaining 67% of advance date variance. We address these relationships by discussing heat balances within the Chukchi Sea, and the hypothesis of oceanic heat transport triggering ocean heat uptake and ice‐albedo feedback. Developing an operational prediction scheme for seasonal retreat and advance would require timely acquisition of Bering Strait heat inflow data. Predictability will likely always be limited by the chaotic nature of atmospheric circulation patterns.

Extratropical cyclone activity over the central Arctic Ocean reaches its peak in summer. Previous research has argued for the existence of two external source regions for cyclones contributing to this summer maximum: the Eurasian continent interior and a narrow band of strong horizontal temperature gradients along the Arctic coastline known as the Arctic frontal zone (AFZ). This study incorporates data from an atmospheric reanalysis and an advanced cyclone detection and tracking algorithm to critically evaluate the relationship between the summer AFZ and cyclone activity in the central Arctic Ocean. Analysis of both individual cyclone tracks and seasonal fields of cyclone characteristics shows that the Arctic coast (and therefore the AFZ) is not a region of cyclogenesis. Rather, the AFZ acts as an intensification area for systems forming over Eurasia. As these systems migrate toward the Arctic Ocean, they experience greater deepening in situations when the AFZ is strong at midtropospheric levels. On a broader scale, intensity of the summer AFZ at midtropospheric levels has a positive correlation with cyclone intensity in the Arctic Ocean during summer, even when controlling for variability in the northern annular mode. Taken as a whole, these findings suggest that the summer AFZ can intensify cyclones that cross the coast into the Arctic Ocean, but focused modeling studies are needed to disentangle the relative importance of the AFZ, large-scale circulation patterns, and topographic controls.

Reliable forecasts of the timing of sea ice advance are needed in order to reduce risks associated with operating in the Arctic as well as planning of human and environmental emergencies. This study investigates the use of a simple statistical model relating the timing of ice retreat to the timing of ice advance, taking advantage of the inherent predictive power supplied by the seasonal ice‐albedo feedback and ocean heat uptake. Results show that using the last retreat date to predict the first advance date is applicable in some regions, such as Baffin Bay and the Laptev and East Siberian seas, where a predictive skill is found even after accounting for the long‐term trend in both variables. Elsewhere, in the Arctic, there is some predictive skills depending on the year (e.g., Kara and Beaufort seas), but none in regions such as the Barents and Bering seas or the Sea of Okhotsk. While there is some suggestion that the relationship is strengthening over time, this may reflect that higher correlations are expected during periods when the underlying trend is strong.