Meeting
2016 SIPN Workshop
Presentation Type
plenary
Presentation Theme
Predictions and Dynamical Predictive Systems II
Abstract Authors

Bruno Tremblay, McGill University, bruno.tremblay [at] mcgill.ca
Charles Brunette, McGill University, charles.brunette [at] mcgill.ca
James Williams , McGill University , james.williams [at] mail.mcgill.ca
Robert Newton, Lamont Doherty Earth Observatory, bnewton [at] ldeo.columbia.edu
Stephanie Pfirman, Barnard College, spfirman [at] barnard.edu

Abstract

In this contribution we extend the pan-Arctic analysis presented in Williams et al. (2015) to produce a regional forecast of sea-ice conditions for each of the Arctic peripheral seas including the Beaufort, Chukchi, East Siberian, Laptev and Kara seas. The model builds on the finding that the mean large scale sea ice circulation the previous winter determines where the following summer minimum sea ice extent. We show that, in particular, the mid to late winter coastal sea-ice divergence plays a major role. When ice divergence occurs late in the winter, new ice forms but it does not have the time to grow to sufficient thickness to survive the following summer melt. To produce a regional forecast, we use a Lagrangian sea-ice model to backtrack an imaginary line defining the boundary of a given sea, starting from the beginning of the melt season in June and ending in November of the previous year. Results show that the position of the ice edge in each of the peripheral seas of the Arctic is well correlated with the previous winter’s divergence. The maximum correlation is obtained when the synthetic ice edge is backtracked from May to February. Note that, unlike the previous pan-Arctic study of Williams and colleagues in which the net ice divergence could be correlated with the winter mean Arctic Oscillation index, this regional analysis requires sea ice drifts in order to calculate the winter mean ice divergence along the coast. This finding supports the need for continuous production of real-time satellite-derived sea-ice velocity vectors, which can now be used for observation-based regional forecasting.

Time
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