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.

Tropical cyclones (TCs) are strong natural hazards that are important for local and global air–sea interactions. This manuscript briefly reviews the knowledge about the upper ocean responses to TCs, including the current, surface wave, temperature, salinity and biological responses. TCs usually cause upper ocean near-inertial currents, increase strong surface waves, cool the surface ocean, warm subsurface ocean, increase sea surface salinity and decrease subsurface salinity, causing plankton blooms. The upper ocean response to TCs is controlled by TC-induced mixing, advection and surface flux, which usually bias to the right (left) side of the TC track in the Northern (Southern) Hemisphere. The upper ocean response usually recovers in several days to several weeks. The characteristics of the upper ocean response mainly depend on the TC parameters (e.g. TC intensity, translation speed and size) and environmental parameters (e.g. ocean stratification and eddies). In recent decades, our knowledge of the upper ocean response to TCs has improved because of the development of observation methods and numerical models. More processes of the upper ocean response to TCs can be studied by researchers in the future.

In situ observation of a buoys/moorings array and a model simulation were used to study the modulation of upper ocean thermal structure by Typhoon Kalmaegi in September 2014. The inertial period signals were significant after forcing of Kalmaegi, but they did not account for the net heat change. Removing the inertial period signals showed that the net thermal response biased to the right of Kalmaegi's track. Vertical mixing caused surface cooling with an inverted‐cone structure and subsurface warming with a double‐wing structure. Net upwelling converted the left wing of the subsurface warming to cooling, while net downwelling warmed the upper ocean in front and on both sides of the net upwelling zone. Horizontal advection was not as important as vertical mixing and vertical advection in modulating the thermal structure but contributed to the net outward advection of thermal anomaly in the mixed layer during the forced stage and also in the net along‐track recovery of subsurface anomaly during the relaxation stage. In general, horizontal and vertical advection modulated thermal anomalies in the upper ocean across a broader horizontal range and into the deeper ocean compared with the effect of vertical mixing. Our results indicate the need to consider both mixing and advection (rather than only mixing) when studying the effects of tropical cyclones on local ocean heat uptake and global ocean heat transport.

Buoy-based observations of surface waves during three typhoons in the South China Sea were used to obtain the wave characteristics. With the local wind speeds kept below 35 m s⁻¹, the surface waves over an area with a radius 5 times that of the area in which the maximum sustained wind was found were mainly dominated by wind-wave components, and the wave energy distribution was consistent with fetch-limited waves. Swells dominated the surface waves at the front of and outside the central typhoon region. Next, the dynamics of the typhoon waves were studied numerically using a state-of-the-art third-generation wave model. Wind forcing errors made a negligible contribution to the surface wave results obtained using hindcasting. Near-realistic wind fields were constructed by correcting the idealized wind vortex using in situ observational data. If the different sets of source terms were further considered for the forcing stage of the typhoon, which was defined as the half inertial period before and after the typhoon arrival time, the best model performance had mean relative biases and root-mean-square errors of −0.7% and 0.76 m, respectively, for the significant wave height, and −3.4% and 1.115 s, respectively, for the peak wave period. Different sets of source terms for wind inputs and whitecapping breaking dissipation were also used and the results compared. Finally, twin numerical experiments were performed to investigate the importance of nonlinear wave–wave interactions on the spectrum formed. There was evidence that nonlinear wave–wave interactions efficiently transfer wave energy from high frequencies to low frequencies and prevent double-peak structures occurring in the frequency-based spectrum.

Based on mooring array data from the South China Sea (SCS) Internal Wave Experiment, in this paper, five burst events of near‐inertial waves (NIWs) in the northern SCS that occurred during August 2010 to April 2011 are reported. Among these NIWs, three were induced by typhoons Lionrock, Meranti, and Megi in 2010. Due to the differences in typhoon characteristics, mooring measuring ranges, distances between typhoon centers and moorings, and local conditions, the NIWs induced by the three typhoons show various intensities and structures at all three moorings. An interesting phenomenon is that the weaker typhoon Meranti generates stronger NIWs than the stronger typhoon Megi, which is largely due to the larger translation speed of Meranti. The e‐folding time of near‐inertial variance suggests that the damping of typhoon‐induced NIWs is related to local conditions. In addition to the typhoon‐induced NIWs, two NIW burst events were found in December 2010 and March 2011 at one mooring; however, these events were absent from the two adjacent moorings. Through analysis, we ruled out the local winds, lateral propagation, and parametric subharmonic instability as the causes of these NIWs. Although the real mechanisms remain unknown, the NIWs that occurred in December 2010 and March 2011 exhibit different features from the typhoon‐induced NIWs. First, these NIWs have larger vertical wave numbers and smaller wavelengths than the typhoon‐induced NIWs. Second, these NIWs can cause intense shear in deep water, whereas intense shear of typhoon‐induced NIWs mainly exist in the upper ocean (approximately 150–200 m).

The annual sea ice freeze–thaw cycle plays a crucial role in the Arctic atmosphere—ice–ocean system, regulating the seasonal energy balance of sea ice and the underlying upper-ocean. Previous studies of the sea ice freeze–thaw cycle were often based on limited accessible in situ or easily available remotely sensed observations of the surface. To better understand the responses of the sea ice to climate change and its coupling to the upper ocean, we combine measurements of the ice surface and bottom using multisource data to investigate the temporal and spatial variations in the freeze–thaw cycle of Arctic sea ice. Observations by 69 sea ice mass balance buoys (IMBs) collected from 2001 to 2018 revealed that the average ice basal melt onset in the Beaufort Gyre occurred on 23 May (±6 d), approximately 17 d earlier than the surface melt onset. The average ice basal melt onset in the central Arctic Ocean occurred on 17 June (±9 d), which was comparable with the surface melt onset. This difference was mainly attributed to the distinct seasonal variations of oceanic heat available to sea ice melt between the two regions. The overall average onset of basal ice growth of the pan Arctic Ocean occurred on 14 November (±21 d), lagging approximately 3 months behind the surface freeze onset. This temporal delay was caused by a combination of cooling the sea ice, the ocean mixed layer, and the ocean subsurface layer, as well as the thermal buffering of snow atop the ice. In the Beaufort Gyre region, both (Lagrangian) IMB observations (2001–2018) and (Eulerian) moored upward-looking sonar (ULS) observations (2003–2018) revealed a trend towards earlier basal melt onset, mainly linked to the earlier warming of the surface ocean. A trend towards earlier onset of basal ice growth was also identified from the IMB observations (multiyear ice), which we attributed to the overall reduction of ice thickness. In contrast, a trend towards delayed onset of basal ice growth was identified from the ULS observations, which was explained by the fact that the ice cover melted almost entirely by the end of summer in recent years.