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.

Rain-on-snow (ROS) events can accelerate the surface ablation of sea ice, thus greatly influencing the ice–albedo feedback. However, the variability of ROS events over the Arctic Ocean is poorly understood due to limited historical station data in this region. In this study early melt season ROS events were investigated based on four widely used reanalysis products (ERA-Interim, JRA-55, MERRA, and ERA5) in conjunction with available observations at Arctic coastal stations. The performance of the reanalysis products in representing the timing of ROS events and the phase change of precipitation was assessed. Our results show that ERA-Interim better represents the onset date of ROS events in spring, and ERA5 better represents the phase change of precipitation associated with ROS events. All reanalyses indicate that ROS event timing has shifted to earlier dates in recent decades (with maximum trends up to −4 to −6 d per decade in some regions in ERA-Interim) and that sea ice melt onset in the Pacific sector and most of the Eurasian marginal seas is correlated with this shift. There has been a clear transition from solid to liquid precipitation, leading to more ROS events in spring, although large discrepancies were found between different reanalysis products. In ERA5, the shift from solid to liquid precipitation phase during the early melt season has directly contributed to a reduction in spring snow depth on sea ice by more than −0.5 cm per decade averaged over the Arctic Ocean since 1980, with the largest contribution (about −2.0 cm per decade) in the Kara–Barents seas and Canadian Arctic Archipelago.

Melt ponds occupy a large fraction of the Arctic sea ice surface during spring and summer. The fraction and distribution of melt ponds have considerable impacts on Arctic climate and ecosystem by reducing the albedo. There is an urgency to obtain improved accuracy and a wider coverage of melt pond fraction (MPF) data for studying these processes. MPF information has generally been acquired from optical imagery. Conventional MPF algorithms based on high‐resolution optical sensors have treated melt ponds as features with constant reflectance; however, the spectral reflectance of ponds can vary greatly, even at a local scale. Here we use Sentinel‐2 imagery to demonstrate those previous algorithms assuming fixed melt pond‐reflectance greatly underestimate MPF. We propose a new algorithm (“LinearPolar”) based on the polar coordinate transformation that treats melt ponds as variable‐reflectance features and calculates MPF across the vector between melt pond and bare ice axes. The angular coordinate θ of the polar coordinate system, which is only associated with pond fraction rather than reflectance, is used to determinate MPF. By comparing the new algorithm and previous methods with IceBridge optical imagery data, across a variety of Sentinel‐2 images with melt ponds at various stages of development, we show that the RMSE value of the LinearPolar algorithm is about 30% lower than for the previous algorithms. Moreover, based on a sensitivity test, the new algorithm is also less sensitive to the subjective threshold for melt pond reflectance than previous algorithms.

The Arctic winter seasonal sea ice (WSSI) concentration from 1979 to 2019 is derived from passive microwave data. Based on Empirical Orthogonal Function (EOF) analysis, the WSSI time series includes regionally different trends, abrupt shifts and interannual variations. The time series of the first EOF mode (PC1) mainly represents the WSSI trend, which is characterized by an increase, particularly in the Pacific sector. PC1 confirms two abrupt shifts in WSSI in 1989 and 2007, with a variance of 31%. After 2007, the large-scale atmospheric circulation anomaly shows a strengthened wavenumber 3 structure at high latitudes associated with a mid-tropospheric low-pressure anomaly in central and western Siberia and a high-pressure anomaly in eastern Siberia in summer and autumn. These patterns have promoted the increased transport of moist static energy to the central Arctic and contributed to increased near-surface air temperatures that may enhance ice melting in summer and reduce ice growth in autumn and winter. The changes in ice melt and growth have had opposite effects in the Pacific and Atlantic sectors: WSSI has increased in the Pacific sector due to the replacement of multi-year ice by WSSI, and decreased in the Atlantic sector due to the replacement of WSSI by open water.

Analyses of landfast ice in Arctic coastal areas provide a comprehensive understanding of the variations in Arctic sea ice and generate data for studies on the utilization of the Arctic passages. Based on our analysis, Arctic landfast ice mainly appears in January–June and is distributed within the narrow straits of the Canadian Archipelago (nearly 40%), the coastal areas of the East Siberian Sea, the Laptev Sea, and the Kara Sea. From 1976–2018, the landfast ice extent gradually decreased at an average rate of −1.1 ± 0.5 × 10⁴ km²/yr (10.5% per decade), while the rate of decrease for entire Arctic sea ice was −6.0 ± 2.4 × 10⁴ km²/yr (5.2% per decade). The annual maximum extent reached 2.3 × 10⁶ km² in the early 1980s, and by 2018, the maximum extent decreased by 0.6 × 10⁶ km², which is an area approximately equivalent the Laptev Sea. The mean duration of Arctic landfast ice is 44 weeks, which has gradually been reduced at a rate of −0.06 ± 0.03 weeks/yr. Regional landfast ice extent decreases in 16 of the 17 subregions except for the Bering Sea, making it the only subregion where both the extent and duration increases. The maximum mean landfast ice thickness appears in the northern Canadian Archipelago (>2.5 m), with the highest increasing trend (0.1 m/yr). In the Northeast Passage, the mean landfast ice thickness is 1.57 m, with a slight decreasing trend of −1.2 cm/yr, which is smaller than that for entire Arctic sea ice (−5.1 cm/yr). The smaller decreasing trend in the landfast ice extent and thickness suggests that the well-known Arctic sea ice decline largely occurred in the pack ice zone, while the larger relative extent loss indicates a faster ice free future in the landfast ice zone.