Abstract. Pan-Arctic sea ice thickness has been monitored over recent decades by satellite radar altimeters such as CryoSat-2, which emits Ku-band radar waves that are assumed in publicly available sea ice thickness products to penetrate overlying snow and scatter from the ice–snow interface. Here we examine two expressions for the time delay caused by slower radar wave propagation through the snow layer and related assumptions concerning the time evolution of overlying snow density. Two conventional treatments introduce systematic underestimates of up to 15 cm into ice thickness estimates and up to 10 cm into thermodynamic growth rate estimates over multi-year ice in winter. Correcting these biases would impact a wide variety of model projections, calibrations, validations and reanalyses.

Arctic Ocean properties and processes are highly relevant to the regional and global coupled climate system, yet still scarcely observed, especially in winter. Team OCEAN conducted a full year of physical oceanography observations as part of the Multidisciplinary drifting Observatory for the Study of the Arctic Climate (MOSAiC), a drift with the Arctic sea ice from October 2019 to September 2020. An international team designed and implemented the program to characterize the Arctic Ocean system in unprecedented detail, from the seafloor to the air-sea ice-ocean interface, from sub-mesoscales to pan-Arctic. The oceanographic measurements were coordinated with the other teams to explore the ocean physics and linkages to the climate and ecosystem. This paper introduces the major components of the physical oceanography program and complements the other team overviews of the MOSAiC observational program. Team OCEAN’s sampling strategy was designed around hydrographic ship-, ice- and autonomous platform-based measurements to improve the understanding of regional circulation and mixing processes. Measurements were carried out both routinely, with a regular schedule, and in response to storms or opening leads. Here we present along-drift time series of hydrographic properties, allowing insights into the seasonal and regional evolution of the water column from winter in the Laptev Sea to early summer in Fram Strait: freshening of the surface, deepening of the mixed layer, increase in temperature and salinity of the Atlantic Water. We also highlight the presence of Canada Basin deep water intrusions and a surface meltwater layer in leads. MOSAiC most likely was the most comprehensive program ever conducted over the ice-covered Arctic Ocean. While data analysis and interpretation are ongoing, the acquired datasets will support a wide range of physical oceanography and multi-disciplinary research. They will provide a significant foundation for assessing and advancing modeling capabilities in the Arctic Ocean.

Sea ice thickness is a critical variable, both as a climate indicator and for forecasting sea ice conditions on seasonal and longer time scales. The lack of snow depth and density information is a major source of uncertainty in current thickness retrievals from laser and radar altimetry. In response to this data gap, a new Lagrangian snow evolution model (SnowModel-LG) was developed to simulate snow depth, density, and grain size on a pan-Arctic scale, daily from August 1980 through July 2018. In this study, we evaluate the results from this effort against various data sets, including those from Operation IceBridge, ice mass balance buoys, snow buoys, MagnaProbes, and rulers. We further compare modeled snow depths forced by two reanalysis products (Modern Era Retrospective-Analysis for Research and Applications, Version 2 and European Centre for Medium-Range Weather Forecasts Reanalysis, 5th Generation) with those from two historical climatologies, as well as estimates over first-year and multiyear ice from satellite passive microwave observations. Our results highlight the ability of our SnowModel-LG implementation to capture observed spatial and seasonal variability in Arctic snow depth and density, as well as the sensitivity to the choice of reanalysis system used to simulate snow depths. Since 1980, snow depth is found to decrease throughout most regions of the Arctic Ocean, with statistically significant trends during the cold season months in the marginal ice zones around the Arctic Ocean and slight positive trends north of Greenland and near the pole. Plain Language Summary This study evaluates a new snow accumulation model to simulate both realistic snow depth and snow density distributions over Arctic sea ice, filling a critical data gap for polar science. Running the model from August 1980 onward, snow depth is found to be declining as the melt season has lengthened, shortening the time over which snow can accumulate on the ice. This new daily snow and density product will be useful for climate studies as well as improving sea ice thickness retrievals from Satellite radar and laser altimeters.

The North Atlantic subpolar gyre (SPG) connects tropical and high-latitude waters, playing a leading role in deep-water formation, propagation of Atlantic water into the Arctic, and as habitat for many ecosystems. Instrumental records spanning recent decades document significant decadal variability in SPG circulation, with associated hydrographic and ecological changes. Emerging longer-term records provide circumstantial evidence that the North Atlantic also experienced centennial trends during the 20th century. Here, we use marine sediment records to show that there has been a long-term change in SPG circulation during the industrial era, largely during the 20th century. Moreover, we show that the shift and late 20th century SPG configuration were unprecedented in the last 10,000 years. Recent SPG dynamics resulted in an expansion of subtropical ecosystems into new habitats and likely also altered the transport of heat to high latitudes.Plain Language Summary The Northeast Atlantic is of crucial importance for the global climate system and marine ecosystems. We can use sediment from the bottom of the ocean to reconstruct how the Northeast Atlantic has changed over thousands of years. In this study, we present the first evidence that 20th century Northeast Atlantic surface ocean circulation was unusual compared to the last 10,000 years. This change caused a replacement of cool, subpolar waters with warmer subtropical waters near Iceland and has impacted the distribution of marine organisms. The most striking aspect of our work is the exceptional nature of the shift in the 20th century (in contrast to thousands of years of relative stability), with implications for understanding future change.

Abstract. To improve our understanding of how snow properties influence sea ice thickness retrievals from presently operational and upcoming satellite radar altimeter missions, as well as to investigate the potential for combining dual frequencies to simultaneously map snow depth and sea ice thickness, a new, surface-based, fully polarimetric Ku- and Ka-band radar (KuKa radar) was built and deployed during the 2019–2020 year-long MOSAiC international Arctic drift expedition. This instrument, built to operate both as an altimeter (stare mode) and as a scatterometer (scan mode), provided the first in situ Ku- and Ka-band dual-frequency radar observations from autumn freeze-up through midwinter and covering newly formed ice in leads and first-year and second-year ice floes. Data gathered in the altimeter mode will be used to investigate the potential for estimating snow depth as the difference between dominant radar scattering horizons in the Ka- and Ku-band data. In the scatterometer mode, the Ku- and Ka-band radars operated under a wide range of azimuth and incidence angles, continuously assessing changes in the polarimetric radar backscatter and derived polarimetric parameters, as snow properties varied under varying atmospheric conditions. These observations allow for characterizing radar backscatter responses to changes in atmospheric and surface geophysical conditions. In this paper, we describe the KuKa radar, illustrate examples of its data and demonstrate their potential for these investigations.

Abstract. Mean sea ice thickness is a sensitive indicator of Arctic climate change and is in long-term decline despite significant interannual variability. Current thickness estimations from satellite radar altimeters employ a snow climatology for converting range measurements to sea ice thickness, but this introduces unrealistically low interannual variability and trends. When the sea ice thickness in the period 2002–2018 is calculated using new snow data with more realistic variability and trends, we find mean sea ice thickness in four of the seven marginal seas to be declining between 60 %–100 % faster than when calculated with the conventional climatology. When analysed as an aggregate area, the mean sea ice thickness in the marginal seas is in statistically significant decline for 6 of 7 winter months. This is observed despite a 76 % increase in interannual variability between the methods in the same time period. On a seasonal timescale we find that snow data exert an increasingly strong control on thickness variability over the growth season, contributing 46 % in October but 70 % by April. Higher variability and faster decline in the sea ice thickness of the marginal seas has wide implications for our understanding of the polar climate system and our predictions for its change.

The volume of Arctic sea ice is in decline but exhibits high interannual variability, which is driven primarily by atmospheric circulation. Through analysis of satellite-derived ice products and atmospheric reanalysis data, we show that winter 2020/21 was characterised by anomalously high sea-level pressure over the central Arctic Ocean, which resulted in unprecedented anticyclonic winds over the sea ice. This atmospheric circulation pattern drove older sea ice from the central Arctic Ocean into the lower-latitude Beaufort Sea, where it is more vulnerable to melting in the coming warm season. We suggest that this unusual atmospheric circulation may potentially lead to unusually high summer losses of the Arctic’s remaining store of old ice.

The Arctic's sea ice cover is retreating as the region continues to warm at nearly four times the global average rate (Rantanen et al., 2022). Alongside a decrease in extent and age (Stroeve & Notz, 2018), the sea ice is thinning (Kwok, 2018;Mallett et al., 2021) and snow depth is declining (Stroeve et al., 2020;Webster et al., 2014). A thinning ice pack affects the thermodynamic processes that govern seasonal ice melt and growth, as well as the dynamic processes that control ice mobility (e.g., Rampal et al., 2009). Assimilation of accurate sea ice thickness and snow depth data into models offers an opportunity to improve the prediction of future sea ice state (e.g., Holland et al., 2021;Mignac et al., 2022). Despite the importance of sea ice thickness, the most accurate estimates come from highly localized in situ observations from autonomous ice-buoys or upward-looking sonar instruments. While airborne-or submarine-based campaigns offer greater spatial coverage, they too remain temporally and spatially constrained. Satellite-mounted laser and radar altimeters offer a potential solution by providing year-round, pan-Arctic monitoring.Several studies have demonstrated an approach to convert Ku-band satellite radar altimeter freeboards from CryoSat-2 (CS2) and Sentinel-3 (S3) to sea ice thickness (Lawrence et al., 2019;Laxon et al., 2013). Sea ice freeboard, the height of the snow-ice interface relative to the surrounding ocean surface, is estimated from the return-time of a radar pulse. Thickness can be derived from the freeboard by applying the assumption of hydrostatic equilibrium together with assumptions concerning the snow, ice and water densities and the snow depth.