Abstract. Sea ice thickness information is important for sea ice modelling and ship operations. Here a method to detect the thickness of sea ice up to 50 cm during the freeze-up season based on high incidence angle observations of the Soil Moisture and Ocean Salinity (SMOS) satellite working at 1.4 GHz is suggested. By comparison of thermodynamic ice growth data with SMOS brightness temperatures, a high correlation to intensity and an anticorrelation to the difference between vertically and horizontally polarised brightness temperatures at incidence angles between 40 and 50 • are found and used to develop an empirical retrieval algorithm sensitive to thin sea ice up to 50 cm thickness. The algorithm shows high correlation with ice thickness data from airborne measurements and reasonable ice thickness patterns for the Arctic freeze-up period.

Abstract. The presence of melt ponds on the Arctic sea ice strongly affects the energy balance of the Arctic Ocean in summer. It affects albedo as well as transmittance through the sea ice, which has consequences for the heat balance and mass balance of sea ice. An algorithm to retrieve melt pond fraction and sea ice albedo from Medium Resolution Imaging Spectrometer (MERIS) data is validated against aerial, shipborne and in situ campaign data. The results show the best correlation for landfast and multiyear ice of high ice concentrations. For broadband albedo, R 2 is equal to 0.85, with the RMS (root mean square) being equal to 0.068; for the melt pond fraction, R 2 is equal to 0.36, with the RMS being equal to 0.065. The correlation for lower ice concentrations, subpixel ice floes, blue ice and wet ice is lower due to ice drift and challenging for the retrieval surface conditions. Combining all aerial observations gives a mean albedo RMS of 0.089 and a mean melt pond fraction RMS of 0.22. The in situ melt pond fraction correlation is R 2 = 0.52 with an RMS = 0.14. Ship cruise data might be affected by documentation of varying accuracy within the Antarctic Sea Ice Processes and Climate (ASPeCt) protocol, which may contribute to the discrepancy between the satellite value and the observed value: mean R 2 = 0.044, mean RMS = 0.16. An additional dynamic spatial cloud filter for MERIS over snow and ice has been developed to assist with the validation on swath data.

Year-round observations of the physical snow and ice properties and processes that govern the ice pack evolution and its interaction with the atmosphere and the ocean were conducted during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition of the research vessel Polarstern in the Arctic Ocean from October 2019 to September 2020. This work was embedded into the interdisciplinary design of the 5 MOSAiC teams, studying the atmosphere, the sea ice, the ocean, the ecosystem, and biogeochemical processes. The overall aim of the snow and sea ice observations during MOSAiC was to characterize the physical properties of the snow and ice cover comprehensively in the central Arctic over an entire annual cycle. This objective was achieved by detailed observations of physical properties and of energy and mass balance of snow and ice. By studying snow and sea ice dynamics over nested spatial scales from centimeters to tens of kilometers, the variability across scales can be considered. On-ice observations of in situ and remote sensing properties of the different surface types over all seasons will help to improve numerical process and climate models and to establish and validate novel satellite remote sensing methods; the linkages to accompanying airborne measurements, satellite observations, and results of numerical models are discussed. We found large spatial variabilities of snow metamorphism and thermal regimes impacting sea ice growth. We conclude that the highly variable snow cover needs to be considered in more detail (in observations, remote sensing, and models) to better understand snow-related feedback processes. The ice pack revealed rapid transformations and motions along the drift in all seasons. The number of coupled ice–ocean interface processes observed in detail are expected to guide upcoming research with respect to the changing Arctic sea ice.

This study investigates the distribution of Antarctic minke whales (AMW) in relation to sea ice concentration and variations therein. Information on AMW densities in the sea ice‐covered parts of the Southern Ocean is required to contextualize abundance estimates obtained from circumpolar shipboard surveys in open waters, suggesting a 30% decline in AMW abundance. Conventional line‐transect shipboard surveys for density estimation are impossible in ice‐covered regions, therefore we used icebreaker‐supported helicopter surveys to obtain information on AMW densities along gradients of 0%–100% of ice concentration. We conducted five helicopter surveys in the Southern Ocean, between 2006 and 2013. Distance sampling data, satellite‐derived sea‐ice data, and bathymetric parameters were used in generalized additive models (GAMs) to produce predictions on how the density of AMWs varied over space and time, and with environmental covariates. Ice concentration, distance to the ice edge and distance from the shelf break were found to describe the distribution of AMWs. Highest densities were predicted at the ice edge and through to medium ice concentrations. Medium densities were found up to 500 km into the ice edge in all concentrations of ice. Very low numbers of AMWs were found in the ice‐free waters of the West Antarctic Peninsula (WAP). A consistent relationship between AMW distribution and sea ice concentration weakens the support for the hypothesis that varying numbers of AMWs in ice‐covered waters were responsible for observed changes in estimated abundance. The potential decline in AMW abundance stresses the need for conservation measures and further studies into the AMW population status. Very low numbers of AMWs recorded in the ice‐free waters along the WAP support the hypothesis that this species is strongly dependent on sea ice and that forecasted sea ice changes have the potential of heavily impacting AMWs.