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Abstract Oceanic mesoscale motions including eddies, meanders, fronts, and filaments comprise a dominant fraction of oceanic kinetic energy and contribute to the redistribution of tracers in the ocean such as heat, salt, and nutrients. This reservoir of mesoscale energy is regulated by the conversion of potential energy and transfers of kinetic energy across spatial scales. Whether and under what circumstances mesoscale turbulence precipitates forward or inverse cascades, and the rates of these cascades, remain difficult to directly observe and quantify despite their impacts on physical and biological processes. Here we use global observations to investigate the seasonality of surface kinetic energy and upper-ocean potential energy. We apply spatial filters to along-track satellite measurements of sea surface height to diagnose surface eddy kinetic energy across 60–300-km scales. A geographic and scale-dependent seasonal cycle appears throughout much of the midlatitudes, with eddy kinetic energy at scales less than 60 km peaking 1–4 months before that at 60–300-km scales. Spatial patterns in this lag align with geographic regions where an Argo-derived estimate of the conversion of potential to kinetic energy is seasonally varying. In midlatitudes, the conversion rate peaks 0–2 months prior to kinetic energy at scales less than 60 km. The consistent geographic patterns between the seasonality of potential energy conversion and kinetic energy across spatial scale provide observational evidence for the inverse cascade and demonstrate that some component of it is seasonally modulated. Implications for mesoscale parameterizations and numerical modeling are discussed. Significance Statement This study investigates the seasonality of upper-ocean potential and kinetic energy in the context of an inverse cascade, consisting of energy transfers to and through the mesoscale. Observations show a scale-dependent cycle in kinetic energy that coincides with temporal variability in mixed layer potential energy and progresses seasonally from smaller to larger scales. This pattern appears dominant over large regions of the ocean. Results are relevant to ocean and climate models, where a large fraction of ocean energy is often parameterized. A customizable code repository and dataset are provided to enable comparisons of model-based resolved and unresolved kinetic energy to observational equivalents. Implications result for a range of processes including mixed layer stratification and vertical structure of ocean currents. 

Central Arctic properties and processes are important to the regional and global coupled climate system. The Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) Distributed Network (DN) of autonomous ice-tethered systems aimed to bridge gaps in our understanding of temporal and spatial scales, in particular with respect to the resolution of Earth system models. By characterizing variability around local measurements made at a Central Observatory, the DN covers both the coupled system interactions involving the ocean-ice-atmosphere interfaces as well as three-dimensional processes in the ocean, sea ice, and atmosphere. The more than 200 autonomous instruments (“buoys”) were of varying complexity and set up at different sites mostly within 50 km of the Central Observatory. During an exemplary midwinter month, the DN observations captured the spatial variability of atmospheric processes on sub-monthly time scales, but less so for monthly means. They show significant variability in snow depth and ice thickness, and provide a temporally and spatially resolved characterization of ice motion and deformation, showing coherency at the DN scale but less at smaller spatial scales. Ocean data show the background gradient across the DN as well as spatially dependent time variability due to local mixed layer sub-mesoscale and mesoscale processes, influenced by a variable ice cover. The second case (May–June 2020) illustrates the utility of the DN during the absence of manually obtained data by providing continuity of physical and biological observations during this key transitional period. We show examples of synergies between the extensive MOSAiC remote sensing observations and numerical modeling, such as estimating the skill of ice drift forecasts and evaluating coupled system modeling. The MOSAiC DN has been proven to enable analysis of local to mesoscale processes in the coupled atmosphere-ice-ocean system and has the potential to improve model parameterizations of important, unresolved processes in the future. 

Abstract Ocean‐to‐ice heat flux (OHF) is important in regulating the variability of sea ice mass balance. Using surface drifting buoy observations, we show that during winter in the Arctic Ocean's Beaufort Gyre region, OHF increased from 0.76 ± 0.05 W/m²over 2006–2012 to 1.63 ± 0.08 W/m²over 2013–2018. We find that this is a result of thinner and less‐compact sea ice that promotes enhanced winter ice growth, stronger ocean vertical convection, and subsurface heat entrainment. In contrast, Ekman upwelling declined over the study period, suggesting it had a secondary contribution to OHF changes. The enhanced ice growth creates a cooler, saltier, and deeper ocean surface mixed layer. In addition, the enhanced vertical temperature gradient near the mixed layer base in later years favors stronger entrainment of subsurface heat. OHF and its increase during 2006–2018 were not geographically uniform, with hot spots found in an upwelling region where ice was most seasonally variable. 

This collection contains repeated vertical profiles of ocean temperature and salinity versus pressure, as well as oxygen and velocity for some instruments. Data were collected between about 7 and 750m of the Arctic Ocean by Ice-Tethered Profilers (ITPs) that crawled up and down a conducting wire suspended from a buoy frozen into the ice pack. The profiles were collected roughly twice a day, depending on ITP programming, over the course of months to years. The vertical resolution of the final processed data is 1m, with horizontal resolution depending on ice drift speeds. ITPs have been released in different parts of the Arctic Ocean including near the North Pole and are intended to cover the whole Arctic ocean with depths exceeding 750m. 

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.