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The export of Antarctic Bottom Water (AABW) supplies the bottom cell of the global overturning circulation and plays a key role in regulating climate. This AABW outflow must cross, and is therefore mediated by, the Antarctic Circumpolar Current (ACC). Previous studies present widely varying conceptions of the role of the ACC in directing AABW across the Southern Ocean, suggesting either that AABW may be zonally recirculated by the ACC, or that AABW may flow northward within deep western boundary currents (DWBC) against bathymetry. In this study the authors investigate how the forcing and geometry of the ACC influences the transport and transformation of AABWusing a suite of process-oriented model simulations. The model exhibits a strong dependence on the elevation of bathymetry relative to AABWlayer thickness: higher meridional ridges suppress zonal AABWexchange, increase the strength of flow in the DWBC, and reduce the meridional variation in AABWdensity across the ACC. Furthermore, the transport and transformation vary with density within the AABW layer, with denser varieties of AABW being less efficiently transported between basins. These findings indicate that changes in the thickness of the AABW layer, for example, due to changes in Antarctic shelf processes, and tectonic changes in the sea floor shape may alter the pathways and transformation of AABW across the ACC. [ABSTRACT FROM AUTHOR]

The oceanic Meridional Overturning Circulation (MOC) plays a key role in the climate system, and monitoring its evolution is a scientific priority. Monitoring arrays have been established at several latitudes in the Atlantic Ocean, but other latitudes and oceans remain unmonitored for logistical reasons. This study explores the possibility of inferring the MOC from globally‐available satellite measurements via machine learning (ML) techniques, using the ECCOV4 state estimate as a test bed. The methodological advantages of the present approach include the use purely of available satellite data, its applicability to multiple basins within a single ML framework, and the ML model simplicity (a feed‐forward fully connected neural network (NN) with small number of neurons). The ML model exhibits high skill in MOC reconstruction in the Atlantic, Indo‐Pacific, and Southern Oceans. The approach achieves a higher skill in predicting the model Southern Ocean abyssal MOC than has previously been achieved via a dynamically‐based approach. The skill of the model is quantified as a function of latitude in each ocean basin, and of the time scale of MOC variability. We find that ocean bottom pressure generally has the highest reconstruction skill potential, followed by zonal wind stress. We additionally test which combinations of variables are optimal. Furthermore, ML interpretability techniques are used to show that high reconstruction skill in the Southern Ocean is mainly due to (NN processing of) bottom pressure variability at a few prominent bathymetric ridges. Finally, the potential for reconstructing MOC strength estimates from real satellite measurements is discussed. Plain Language Summary: The Meridional Overturning Circulation (MOC) plays a key role in the exchange of heat and chemical constituents between oceans globally, and between the atmosphere and the deep ocean. However, it is currently directly monitored only at a handful of different latitudes in the Atlantic Ocean. Using an ocean simulation constrained by available measurements, we examine the feasibility of monitoring the MOC indirectly using measurements made by satellites. We train a so‐called "neural network" to learn relations between the MOC and satellite‐observable ocean properties, such as pressure at the sea floor. These relations are used to produce reconstructions of the simulated MOC derived from the satellite‐observable ocean properties alone, and to test which satellite‐observable ocean properties and regions may be most impactful in MOC reconstruction. We find that this approach yields high skill in reconstructing the MOC over much of the ocean, suggesting that this approach could be transferred to indirectly monitor the MOC in nature. Key Points: The Meridional Overturning Circulation (MOC) is a key ocean circulation pattern, which is under‐observedWe use an ocean state estimate to test the feasibility of inferring the MOC based solely on satellite observationsA unified machine learning methodology shows high skill in MOC reconstruction as a function of latitude and basin [ABSTRACT FROM AUTHOR]

The action of the barotropic tide over seafloor topography is the major source of internal waves at the bottom of the ocean. This internal tide has long been recognized to play an important role in ocean mixing. Here it is shown that the internal tide is also associated with a net (domain integrated) momentum flux. The net flux occurs as a result of the Doppler shifting of the internal tide at the point of generation by near-bottom mean flows. Linear theory is presented that predicts the amplitude of the wave momentum flux. The net flux scales with the bottom flow speed and the topographic wavenumber to the fourth power and is directed opposite to the bottom flow. For realistic topography, the predicted peak momentum flux occurs at scales of order 10 km and smaller, with magnitudes of order 10−3–10−2 N m−2. The theory is verified by comparison with a suite of idealized internal wave-resolving simulations. The simulations show that, for the topography considered, the wave momentum flux radiates away from the bottom and enhances mean and eddying flow when the tidal waves dissipate in the upper ocean. Our results suggest that internal tides may play an important role in forcing the upper ocean. [ABSTRACT FROM AUTHOR]