A large retreat of sea-ice in the ‘stormy’ Atlantic Sector of the Arctic Ocean has become evident through a series of record minima for the winter maximum sea-ice extent since 2015. Results from the Norwegian young sea ICE (N-ICE2015) expedition, a five-month-long (Jan-Jun) drifting ice station in first and second year pack-ice north of Svalbard, showcase how sea-ice in this region is frequently affected by passing winter storms. Here we synthesise the interdisciplinary N-ICE2015 dataset, including independent observations of the atmosphere, snow, sea-ice, ocean, and ecosystem. We build upon recent results and illustrate the different mechanisms through which winter storms impact the coupled Arctic sea-ice system. These short-lived and episodic synoptic-scale events transport pulses of heat and moisture into the Arctic, which temporarily reduce radiative cooling and henceforth ice growth. Cumulative snowfall from each sequential storm deepens the snow pack and insulates the sea-ice, further inhibiting ice growth throughout the remaining winter season. Strong winds fracture the ice cover, enhance ocean-ice-atmosphere heat fluxes, and make the ice more susceptible to lateral melt. In conclusion, the legacy of Arctic winter storms for sea-ice and the ice-associated ecosystem in the Atlantic Sector lasts far beyond their short lifespan.

In recent years, spaceborne synthetic aperture radar (SAR) polarimetry has become a valuable tool for sea ice analysis. Here, we employ an automatic sea ice classification algorithm on two sets of spatially and temporally near coincident fully polarimetric acquisitions from the ALOS-2, Radarsat-2, and TerraSAR-X/TanDEM-X satellites. Overlapping coincident sea ice freeboard measurements from airborne laser scanner data are used to validate the classification results. The automated sea ice classification algorithm consists of two steps. In the first step, we perform a polarimetric feature extraction procedure. Next, the resulting feature vectors are ingested into a trained neural network classifier to arrive at a pixelwise supervised classification. Coherency matrix-based features that require an eigendecomposition are found to be either of low relevance or redundant to other covariance matrix-based features, which makes coherency matrix-based features dispensable for the purpose of sea ice classification. Among the most useful features for classification are matrix invariant-based features (geometric intensity, scattering diversity, and surface scattering fraction). Classification results show that 100% of the open water is separated from the surrounding sea ice and that the sea ice classes have at least 96.9% accuracy. This analysis reveals analogous results for both X-band and C-band frequencies and slightly different for the L-band. The subsequent classification produces similarly promising results for all four acquisitions. In particular, the overlapping image portions exhibit a reasonable congruence of detected sea ice when compared with high-resolution airborne measurements.

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During the N-ICE2015 drift expedition north-west of Svalbard, we observed the establishment and development of algal communities in first-year ice (FYI) ridges and at the snow-ice interface. Despite some indications of being hot spots for biological activity, ridges are under-studied largely because they are complex structures that are difficult to sample. Snow infiltration communities can grow at the snow-ice interface when flooded. They have been commonly observed in the Antarctic, but rarely in the Arctic, where flooding is less common mainly due to a lower snow-to-ice thickness ratio. Combining biomass measurements and algal community analysis with under-ice irradiance and current measurements as well as light modeling, we comprehensively describe these two algal habitats in an Arctic pack ice environment. High biomass accumulation in ridges was facilitated by complex surfaces for algal deposition and attachment, increased light availability, and protection against strong under-ice currents. Notably, specific locations within the ridges were found to host distinct ice algal communities. The pennate diatoms Nitzschia frigida and Navicula species dominated the underside and inclined walls of submerged ice blocks, while the centric diatom Shionodiscus bioculatus dominated the top surfaces of the submerged ice blocks. Higher light levels than those in and below the sea ice, low mesozooplankton grazing, and physical concentration likely contributed to the high algal biomass at the snow-ice interface. These snow infiltration communities were dominated by Phaeocystis pouchetii and chain-forming pelagic diatoms (Fragilariopsis oceanica and Chaetoceros gelidus). Ridges are likely to form more frequently in a thinner and more dynamic ice pack, while the predicted increase in Arctic precipitation in some regions in combination with the thinning Arctic icescape might lead to larger areas of sea ice with negative freeboard and subsequent flooding during the melt season. Therefore, these two habitats are likely to become increasingly important in the new Arctic with implications for carbon export and transfer in the ice-associated ecosystem.

In this study, we compare colocated near‐coincident X‐, C‐, and L‐band fully polarimetry SAR satellite images with helicopter‐borne ice thickness measurements acquired during the Norwegian Young sea ICE 2015 (N‐ICE2015) expedition in the region of the Arctic Ocean north of Svalbard in April 2015. The air‐borne surveys provide near‐coincident snow plus ice thickness, surface roughness data, and photographs. This unique data set allows us to investigate how the different frequencies can complement one another for sea ice studies, but also to raise awareness of limitations. X‐band and L‐band satellite scenes were shown to be a useful complement to the standard SAR frequency for sea ice monitoring (C‐band) for lead ice and newly formed sea ice identification. This may be in part be due to the frequency but also the high spatial resolution of these sensors. We found a relatively low correlation between snow plus ice thickness and surface roughness. Therefore, in our dataset ice thickness cannot directly be observed by SAR which has important implications for operational ice charting based on automatic segmentation.