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Antarctic ice shelves are losing mass at drastically different rates, primarily due to differing rates of oceanic heat supply to their bases. However, a generalized theory for the inflow of relatively warm water into ice shelf cavities is lacking. This study proposes such a theory based on a geostrophically constrained inflow, combined with a threshold bathymetric elevation, the Highest Unconnected isoBath (HUB), that obstructs warm water access to ice shelf grounding lines. This theory captures ∼ 90% of the variance in melt rates across a suite of idealized process‐oriented ocean/ice shelf simulations with quasi‐randomized geometries. Applied to observations of ice shelf geometries and offshore hydrography, the theory captures ∼80% of the variance in measured ice shelf melt rates. These findings provide a generalized theoretical framework for melt resulting from buoyancy‐driven warm water access to geometrically complex Antarctic ice shelf cavities. Plain Language Summary: The floating extensions of Antarctic glaciers ("ice shelves") are losing ice at drastically different rates. A large component of this ice loss is due to melting from below by relatively warm ocean waters, which typically lie hundreds of meters below the surface. Previous studies have attempted to predict ice shelf melt rates using knowledge of the interface between the ice and the ocean. However, these relationships struggle to capture the variations in melt rates around Antarctica, in part because they do not account for obstruction of warm water access by variations in the shae of the seafloor. In this study we introduce a theory for the rate at which warm waters access Antarctica's ice shelves, which indirectly predicts how much the ice shelf melts. This theory is grounded in the assumption that the ocean flow beneath cavities is dominated by the rotation of the earth, and utilizes a novel quantification of seafloor obstruction of warm water inflows. We show that this theory is successful at predicting melt in simulations of ice shelves of different shapes, and in observations of real ice shelves. This work provides a theoretical grounding for melt resulting from warm subsurface waters flowing underneath Antarctic ice shelves. Key Points: We introduce a new theoretical framework for inflow of warm water into ice shelf cavities based on geostrophically‐constrained circulationA new metric, the Highest Unconnected Isobath (HUB), quantifies bathymetric barriers to warm water access in complex geometriesOur HUB‐informed theoretical framework is able to accurately predict melt rates across a suite of idealized models and observational data [ABSTRACT FROM AUTHOR]

The Greenland Sea is a key region for open ocean convection and ventilation, which exhibit a large variability with periods of strong convection and shutdowns. After a long period of weak winter convection (from the 1970s to the early 1990s), a recovery has been reported, beginning in the 1990s and intensifying in the early 2000s until 2013. Using ISAS, an optimal interpolation product based on Argo observations, we document a recent significant weakening of deep convection between 2014 and 2020, accompanied by a continuous warming of the mixed layer but also a freshening after 2014. These hydrographic changes likely increase the ocean stratification and precondition the shutdown of winter convection. We suggest that these property changes result from a shift of the large scale atmospheric circulation, affecting the source of Atlantic Water to the Nordic seas, causing a freshening of about −0.1 g kg−1 that spreads into the Greenland Sea. Plain Language Summary: The Greenland Sea is a key region for the climate system. There, during winter, the ocean loses heat to the atmosphere in a process called "deep convection," that results in the formation of dense water masses that ventilate and fill the deeper layers of the ocean. The Greenland Sea exhibits a high variability in the intensity of winter deep convection, but the scientific community is still debating on which processes trigger or stop convection. After a recovery period of convection, mainly in the beginning of the 21st century, convection has stopped again after 2014 and at least until 2020. Here, we suggest that this new shutdown is mainly caused by changes in the ocean upper layer temperature and salinity that increase the upper ocean buoyancy, making it more difficult to trigger deep convection during winter. We further propose that the main mechanism driving the ocean properties changes is a shift of the large scale atmospheric circulation, which affects the amount and properties of Atlantic Water transported to the Greenland Sea. Key Points: After a period of intense deep convection since 2002, convection in the Greenland Sea has weakened from 2014 to 2020The upper salinity and temperature in the months prior of convection are driving the deep convection variabilityThe upper salinity changes are resulting from changes in large scale pattern of advection [ABSTRACT FROM AUTHOR]

Vertical mixing parameterizations in ocean models are formulated on the basis of the physical principles that govern turbulent mixing. However, many parameterizations include ad hoc components that are not well constrained by theory or data. One such component is the eddy diffusivity model, where vertical turbulent fluxes of a quantity are parameterized from a variable eddy diffusion coefficient and the mean vertical gradient of the quantity. In this work, we improve a parameterization of vertical mixing in the ocean surface boundary layer by enhancing its eddy diffusivity model using data‐driven methods, specifically neural networks. The neural networks are designed to take extrinsic and intrinsic forcing parameters as input to predict the eddy diffusivity profile and are trained using output data from a second moment closure turbulent mixing scheme. The modified vertical mixing scheme predicts the eddy diffusivity profile through online inference of neural networks and maintains the conservation principles of the standard ocean model equations, which is particularly important for its targeted use in climate simulations. We describe the development and stable implementation of neural networks in an ocean general circulation model and demonstrate that the enhanced scheme outperforms its predecessor by reducing biases in the mixed‐layer depth and upper ocean stratification. Our results demonstrate the potential for data‐driven physics‐aware parameterizations to improve global climate models. Plain Language Summary: The upper region of the ocean is highly energetic and is responsible for transferring mass, energy and biogeochemical tracers between the atmosphere and the deeper regions of the ocean. This transport takes place because of turbulent swirling motions, which are found to be of varying sizes. Climate models cannot represent all of these motions because smaller‐scale swirls are complex and require additional computational resources. As we cannot neglect those small swirls, we try to approximate their effects on larger‐scale motions using mathematical models. These models have a few ad hoc or empirical assumptions that lead to uncertainty when these climate models are used to project the future climate. To reduce this uncertainty, we augment an existing model of turbulent swirling process with machine learning, which replaces some ad hoc approximations with data‐driven neural networks. Neural networks can learn those missing processes more accurately than a traditional physics‐based model. The neural networks are shown to improve physics in climate simulations. Although we only touch on one component in an ocean climate model, this approach can be replicated to improve any other component that was using ad hoc assumptions and replace them with data‐driven models using techniques from machine learning. Key Points: We improve a parameterization of vertical mixing in the ocean surface boundary layer using neural networksNeural networks are trained to predict the diffusivity of second moment closure and maintain energetic constraints of the original parameterizationThe improved scheme reduces biases of mixed layer depth and thermocline in an atmospherically forced ocean model [ABSTRACT FROM AUTHOR]

For better projections of sea level rise, two things are needed: an improved understanding of the contributing processes and their accurate representation in climate models. A major process is basal melting of ice shelves and glacier tongues by the ocean, which reduces ice sheet stability and increases ice discharge into the ocean. We study marine melting of Greenland's largest floating ice tongue, the 79° North Glacier, using a high‐resolution, 2D‐vertical ocean model. While our fjord model is idealized, the results agree with observations of melt rate and overturning strength. Our setup is the first application of adaptive vertical coordinates to an ice cavity. Their stratification‐zooming allows a vertical resolution finer than 1 m in the entrainment layer of the meltwater plume, which is important for the plume development. We find that the plume development is dominated by entrainment only initially. In the stratified upper part of the cavity, the subglacial plume shows continuous detrainment. It reaches neutral buoyancy near 100 m depth, detaches from the ice, and transports meltwater out of the fjord. Melting almost stops there. In a sensitivity study, we show that the detachment depth depends primarily on stratification. Our results contribute to the understanding of ice–ocean interactions in glacier cavities. Furthermore, we suggest that our modeling approach with stratification‐zooming coordinates will improve the representation of these interactions in global ocean models. Finally, our idealized model topography and forcing are close to a real fjord and completely defined analytically, making the setup an interesting reference case for future model developments. Plain Language Summary: The global increase of sea levels is a consequence of human‐induced climate change. It presents a threat to coastal regions and demands action to protect human life and infrastructure near the coast. Planning protective measures requires projections of sea level rise, computed with climate models. We present an approach to improve the simulation of an important contributor to sea level rise: melting of floating ice shelves by ocean circulation. Our modeling approach uses a vertical model grid that evolves over time. The temporal evolution depends on the density structure of the ocean. Large density differences appear just below an ice shelf, where fresh meltwater mixes with salty seawater. The adaptive grid of our model resolves this mixing process in great detail. This is important for an accurate computation of the melt rate and enables us to study in depth the ice shelf–ocean interactions. We study them at the glacier tongue of the 79° North Glacier, which is Greenland's largest ice shelf. The physical understanding gained from our simulations is also applicable to other floating glacier tongues and ice shelves. We suggest that using the presented model technique in global ocean models can improve projections of melting and sea level rise. Key Points: Melting of the 79° North Glacier ice tongue by turbulent ocean currents is studied with an idealized 2D‐vertical fjord modelThe subglacial plume behaves like an entraining plume close to the grounding line and like a detraining gravity current further downstreamA vertical resolution finer than 1 m is achieved in the subglacial plume by using adaptive vertical coordinates that zoom to stratification [ABSTRACT FROM AUTHOR]

• High-accuracy dynamic modeling is proposed for the ocean temperature field forecast. • Incremental data learning is developed for solving the continuous observation process. • The proposed method can disentanglement the dynamic and stable features. With the development of sensor technology, data-driven modeling methods for physical oceanography have boomed. Existing deep learning studies lack interpretability from the physical mechanisms. This paper proposes a novel feature disentanglement learning model for the ocean temperature field forecast. In this method, feature disentanglement is adopted to decompose the high-dimensional measured temperature information based on Tucker decomposition. The extracted dynamic feature is filtered and predicted by incremental dynamic learning, and the incremental data information is balanced by maximizing the Gaussian likelihood. Then, the dynamic feature can be entangled with the stable features to predict the ocean temperature field. Last, incremental residual learning can compensate for the feature disentanglement learning error. Experiments on the temperature dataset of the Western Pacific have verified that the proposed method is superior to the existing baselines. [ABSTRACT FROM AUTHOR]

In this work we present a unique set of coincident and collocated high‐resolution observations of surface currents and directional properties of surface waves collected from an airborne instrument, the Modular Aerial Sensing System, collected off the coast of Southern California. High‐resolution observations of near surface current profiles and shear are obtained using a new instrument, "DoppVis", capable of capturing horizontal spatial current variability down to 128 m resolution. This data set provides a unique opportunity to examine how currents at scales ranging from 1 to 100 km modulate bulk (e.g., significant wave height), directional and spectral properties of surface gravity waves. Such observations are a step toward developing better understanding of the underlying physics of submesoscale processes (e.g., frontogenesis and frontal arrest) and the nature of transitions between mesoscale and submesoscale dynamics. Plain Language Summary: In recent years, through improvement of computational resolution of global ocean models, scientists have begun to suspect that kilometer‐scale eddies, whirlpools and fronts, called "submesoscale" variability, make important contributions to horizontal and vertical exchange of climate and biological variables in the upper ocean. Such features are challenging to analyze, because of their size (and how quickly they evolve; within hours), they are too large to study from a research vessel but smaller than regions typically studied with satellite measurements. In this work, we use a research aircraft instrumented to characterize ocean currents, temperature, color (in turn chlorophyll concentration) and the properties of surface waves over an area large enough to capture submesoscale processes. This approach is a step forward in understanding and quantifying the underlying physics of submesoscale processes, and in turn develops parameterization that can help improve the fidelity of weather and climate models. Key Points: Unique coincident and collocated airborne observations of Sea Surface Temperature, surface currents and properties of surface waves across submesoscales featuresA new airborne instrument enables observations of surface currents, vertical and horizontal shear to capture quickly evolving ocean featuresSuch observations are crucial to develop better understanding of the physics of submesoscale processes and wave‐current interaction [ABSTRACT FROM AUTHOR]

This study assesses the capability of Surface Quasi-Geostrophy (SQG) to reconstruct the three-dimensional (3D) dynamics in four critical areas of the Arctic Ocean: the Nordic, Barents, East Siberian, and Beaufort Seas. We first reconstruct the upper ocean dynamics from TOPAZ4 reanalysis of sea surface height (SSH), surface buoyancy (SSB), and surface velocities (SSV) and validate the results with the geostrophic and total TOPAZ4 velocities. The reconstruction of upper ocean dynamics using SSH fields is in high agreement with the geostrophic velocities, with correlation coefficients greater than 0.8 for the upper 400 m. SSH reconstructions outperform surface buoyancy reconstructions, even in places near freshwater inputs from river discharges, melting sea ice, and glaciers. Surface buoyancy fails due to the uncorrelation of SSB and subsurface potential vorticity (PV). Reconstruction from surface currents correlates to the total TOPAZ4 velocities with correlation coefficients greater than 0.6 up to 200 m. In the second part, we apply the SQG approach validated with the reanalysis outputs to satellite-derived sea level anomalies and validate the results against in-situ measurements. Due to lower water column stratification, the SQG approach's performance is better in fall and winter than in spring and summer. Our results demonstrate that using surface information from SSH or surface velocities, combined with information on the stratification of the water column, it is possible to effectively reconstruct the upper ocean dynamics in the Arctic and Subarctic Seas up to 400 m. Future remote sensing missions in the Arctic Ocean, such as SWOT, Seastar, WaCM, CIMR, and CRISTAL, will produce enhanced SSH and surface velocity observations, allowing SQG schemes to characterize upper ocean 3D mesoscale dynamics up to 400 m with higher resolutions and lower uncertainties. [ABSTRACT FROM AUTHOR]

Fluid mechanics lies at the heart of many of the physical processes associated with the nascent deep-sea mining industry. The evolution and fate of sediment plumes that would be produced by seabed mining activities, which are central to the assessment of the environmental impact, are entirely determined by transport processes. These processes, which include advection, turbulent mixing, buoyancy, differential particle settling, and flocculation, operate at a multitude of spatiotemporal scales. A combination of historical and recent efforts that combine theory, numerical modeling, laboratory experiments, and field trials has yielded significant progress, including assessing the role of environmental and operational parameters in setting the extent of sediment plumes, but more fundamental and applied fluid mechanics research is needed before models can accurately predict commercial-scale scenarios. Furthermore, fluid mechanics underpins the design and operation of proposed mining technologies, for which there are currently no established best practices. [ABSTRACT FROM AUTHOR]

In the low‐energy mixing environment of the Arctic Ocean halocline, a unique mixing mechanism of temperature is present. It consists of the formation of small‐vertical‐scale (∼1 m) intrusive features, with temperature anomalies up to ∼0.1°C, that create mean‐square vertical temperature gradients that are orders of magnitude greater than the background. This finescale temperature structure results in enhanced heat fluxes in conditions of extremely weak turbulence and is responsible for an irreversible mixing of heat into cold halocline waters. The rates of thermal variance dissipation and heat transport are comparable to turbulent mechanisms in the Arctic Ocean, such as internal wave‐driven mixing and double diffusive convection. We propose that in conditions of low turbulence, and in the presence of lateral thermal variability, the temperature field displays a self‐regulating mechanism by which it is able to enhance its finescale structure to generate enhanced mixing, thus compensating for the lack of turbulent fluxes. Plain Language Summary: Understanding the causes of the ongoing reductions in Arctic sea ice cover requires knowledge of the processes controlling ocean heat transport. The transport caused by ocean turbulence is particularly important since it provides a mechanism by which significant heat that is stored in the Arctic Ocean water column may cause ice melt. However, this turbulent transport is hindered by an extremely stable, near‐surface layer, the halocline, that acts to damp ocean turbulence. In this study, we describe observations made using an autonomous underwater glider of a process within the halocline that is able to transport heat while bypassing the generation of ocean turbulence. We argue that this transport mechanism is a result of the highly stable, low‐turbulence conditions in the halocline, and is thus able to partially self‐regulate the transport of heat in such a stable environment. Key Points: Significant thermal variance from intrusive features is found in the Arctic halocline in conditions of laminar and very weak turbulenceMixing of heat resulting from these features can be comparable to internal wave and diffusive convection‐driven sourcesWe propose that the thermal variance arises from lateral water mass variability, and is a response to the weakly turbulent conditions [ABSTRACT FROM AUTHOR]

The 2020 'Elucidating the role of clouds-circulation coupling in climate-Ocean-Atmosphere' (EUREC4A-OA) and the 'Atlantic Tradewind Ocean-Atmosphere Mesoscale Interaction Campaign' (ATOMIC) campaigns focused on improving our understanding of the interaction between clouds, convection and circulation and their function in our changing climate. The campaign utilized many data collection technologies, some of which are relatively new. In this study, we used saildrone uncrewed surface vehicles, one of the newer cutting edge technologies available for marine data collection, to validate Level 2 and Level 3 Soil Moisture Active Passive (SMAP) satellite and Hybrid Coordinate Ocean Model (HYCOM) sea surface salinity (SSS) products in the Western Tropical Atlantic. The saildrones observed fine-scale salinity variability not present in the lower-spatial resolution satellite and model products. In regions that lacked significant small-scale salinity variability, the satellite and model salinities performed well. However, SMAP Remote Sensing Systems (RSS) 70 km generally outperformed its counterparts outside of areas with submesoscale SSS variation, whereas RSS 40 km performed better within freshening events such as a fresh tongue. HYCOM failed to detect the fresh tongue. These results will allow researchers to make informed decisions regarding the most ideal product and its drawbacks for their applications in this region and aid in the improvement of mesoscale and submesoscale SSS products, which can lead to the refinement of numerical weather prediction (NWP) and climate models. [ABSTRACT FROM AUTHOR]

We investigate a hydrodynamic equation system which—with some approximation—is capable of describing the tsunami propagation in the open ocean with the time-dependent self-similar Ansatz. We found analytic solutions of how the wave height and velocity behave in time and space for constant and linear seabed functions. First, we study waves on open water, where the seabed can be considered relatively constant, sufficiently far from the shore. We found original shape functions for the ocean waves. In the second part of the study, we also consider a seabed which is oblique. Most of the solutions can be expressed with special functions. Finally, we apply the most common traveling wave Ansatz and present relative simple, although instructive solutions as well. [ABSTRACT FROM AUTHOR]

Gösta Walin passed away in September 2022 at an age of 84. He was a highly original thinker, who has been influential on oceanographic research in Sweden and abroad. The present article delineates Gösta Walin's scientific contributions and career. [ABSTRACT FROM AUTHOR]

Studying physical oceanography is one of the important fields of remote sensing applications. Previously, the thermal mapping of seas and oceans relied on primitive methods, such as the use of sensors installed on buoys, extracting contour lines, and deriving the values from the confluence of contour lines. Today's remote sensing provides more advanced methods for extracting sea surface temperature (SST) values for all bodies of water as a continuous raster model, through thermal sensors installed on satellites designated to monitor and observe the Earth. The Landsat program has facilitated a quantum leap by providing its data free for the public. What has become increasingly important is the inclusion, in Landsat 8, of a thermal band on the TIRS sensor through which SST can be extracted with a spatial resolution of 100 m². In this article, the accuracy of the two thermal bands (band 10 and 11) of Landsat 8 was validated in estimating the SST of Kuwaiti and Northwest Arabian Gulf waters, through the use of 62 thermal images and 66 ground-truthing points (GTPs) taken from the field in the period from July 2013 to March 2020. This was achieved through a function provided by the ENVI 5.3 software - “brightness temperature” - to derive the surface temperature. The accuracy of Landsat 8 to monitor the SST of Kuwait and north-west Arabian Gulf waters was validated by calculating the root mean square error (RMSE) and the mean absolute percentage error (MAPE). The accuracy of the thermal band 10 was ± 2.03 degrees (7.9%), while the accuracy of the thermal band 11 was ± 3.13 degrees (13.7%). Therefore, this study demonstrated that the thermal band 10 of Landsat 8 is more accurate than the thermal band 11 in monitoring the SST of Kuwaiti and north-west Arabian Gulf waters, with a difference of ± 1.1 degrees (5.8%). [ABSTRACT FROM AUTHOR]

The overall volume of freshwater entering the Arctic Ocean has been growing as glaciers melt and river runoff increases. Since 1980, a 20% increase in river runoff has been observed in the Arctic system. As the discharges of the Ob, Yenisei, and Lena rivers are an important source of freshwater in the Kara and Laptev Seas, an increase in river discharge might have a significant impact on the upper ocean circulation. The fresh river water mixes with ocean water and forms a large freshened surface layer (FSL), which carries high loads of dissolved organic matter and suspended matter into the Arctic Ocean. Optically active material (e.g., phytoplankton and detrital matter) are spread out into plumes, which are evident in satellite data. Russian river signatures in the Kara and Laptev Seas are also evident in recent SMOS Sea Surface Salinity (SSS) Arctic products. In this study, we compare the new Arctic+ SSS products, produced at the Barcelona Expert Center, with the Ocean Color absorption coefficient of colored detrital matter (CDM) in the Kara and Laptev Seas for the period 2011–2019. The SSS and CDM are found to be strongly negatively correlated in the regions of freshwater influence, with regression coefficients between − 0.72 and − 0.91 in the studied period. Exploiting this linear correlation, we estimate the SSS back to 1998 using two techniques: one assuming that the relationship between the CDM and SSS varies regionally in the river-influenced areas, and another assuming that it does not. We use the 22-year time-series of reconstructed SSS to estimate the interannual variability of the extension of the FSL in the Kara and Laptev Seas as well as their freshwater content. For the Kara and Laptev Seas, we use 32 and 28 psu as reference salinities, and 26 and 24 psu isohalines as FSL boundaries, respectively. The average FSL extension in the Kara Sea is 2089–2611 km 2 , with a typical freshwater content of 11.84–14.02 km 3 . The Laptev Sea has a slightly higher mean FSL extension of 2320–2686 km 2 and a freshwater content of 10.15–12.44 km 3 . The yearly mean freshwater content and extension of the FSL, computed from SMOS SSS and Optical data, is (as expected) found to co-vary with in situ measurements of river discharge from the Arctic Great Rivers Observatory database, demonstrating the potential of SMOS SSS to better monitor the river discharge changes in Eurasia and to understand the Arctic freshwater system during the ice-free season. [ABSTRACT FROM AUTHOR]