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Due to the growing volume of remote sensing data and the low latency required for safe marine navigation, machine learning (ML) algorithms are being developed to accelerate sea ice chart generation, currently a manual interpretation task. However, the low signal-to-noise ratio of the freely available Sentinel-1 Synthetic Aperture Radar (SAR) imagery, the ambiguity of backscatter signals for ice types, and the scarcity of open-source high-resolution labelled data makes automating sea ice mapping challenging. We use Extreme Earth version 2, a high-resolution benchmark dataset generated for ML training and evaluation, to investigate the effectiveness of ML for automated sea ice mapping. Our customized pipeline combines ResNets and Atrous Spatial Pyramid Pooling for SAR image segmentation. We investigate the performance of our model for: i) binary classification of sea ice and open water in a segmentation framework; and ii) a multiclass segmentation of five sea ice types. For binary ice-water classification, models trained with our largest training set have weighted F1 scores all greater than 0.95 for January and July test scenes. Specifically, the median weighted F1 score was 0.98, indicating high performance for both months. By comparison, a competitive baseline U-Net has a weighted average F1 score of ranging from 0.92 to 0.94 (median 0.93) for July, and 0.97 to 0.98 (median 0.97) for January. Multiclass ice type classification is more challenging, and even though our models achieve 2% improvement in weighted F1 average compared to the baseline U-Net, test weighted F1 is generally between 0.6 and 0.80. Our approach can efficiently segment full SAR scenes in one run, is faster than the baseline U-Net, retains spatial resolution and dimension, and is more robust against noise compared to approaches that rely on patch classification. 

The sensitivity of sea ice to fire emissions highlights climate model uncertainty related to the accuracy of prescribed forcings. 

Given growing interest in extreme high‐latitude weather events, we use records from nine meteorological stations and atmospheric reanalysis data to examine extreme daily precipitation events (leading, 99th and 95th percentile) over Arctic Canada. Leading events span 90 mm at Cape Dyer, along the southeast coast of Baffin Island, to 26 mm at Sachs Harbour, on the southwest coast of Banks Island. The 95th percentiles range from 20 to 30% of leading event sizes. Extreme events are most common on or near the month of climatological peak precipitation. Contrasting with Eurasian continental sites having a July precipitation peak corresponding to the seasonal peak in precipitable water, seasonal cycles in precipitation and the frequency of extremes over Arctic Canada are more varied, reflecting marine influences. At Cape Dyer and Clyde River, mean precipitation and the frequency of extremes peak in October when the atmosphere is quickly cooling, promoting strong evaporation from Baffin Bay. At all stations, leading events involved snowfall and strong winds and were associated with cyclone passages (mostly of relatively strong storms). They also involved strong vapour fluxes, sometimes associated with atmospheric rivers or their remnants. The most unusual sequence of events identified here occurred at Clyde River, where the three largest recorded precipitation events occurred in April of 1977. Obtaining first‐hand accounts of this series of events has proven elusive. Identified links between extreme events and atmospheric rivers demonstrates the need to better understand how the characteristics of such features will change in the future.

 

The Bering Strait oceanic heat transport influences seasonal sea ice retreat and advance in the Chukchi Sea. Monitored since 1990, it depends on water temperature and factors controlling the volume transport, assumed to be local winds in the strait and an oceanic pressure difference between the Pacific and Arctic oceans (the “pressure head”). Recent work suggests that variability in the pressure head, especially during summer, relates to the strength of the zonal wind in the East Siberian Sea that raises or drops sea surface height in this area via Ekman transport. We confirm that westward winds in the East Siberian Sea relate to a broader central Arctic pattern of high sea level pressure and note that anticyclonic winds over the central Arctic Ocean also favor low September sea ice extent for the Arctic as a whole by promoting ice convergence and positive temperature anomalies. Month‐to‐month persistence in the volume transport and atmospheric circulation patterns is low, but the period 1980–2017 had a significant summertime (June–August) trend toward higher sea level pressure over the central Arctic Ocean, favoring increased transports. Some recent large heat transports are associated with high water temperatures, consistent with persistence of open water in the Chukchi Sea into winter and early ice retreat in spring. The highest heat transport recorded, October 2016, resulted from high water temperatures and ideal wind conditions yielding a record‐high volume transport. November and December 2005, the only months with southward volume (and thus heat) transports, were associated with southward winds in the strait.