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The Northern Adriatic Sea Reanalysis and Forecasting (NARF) system is a high-resolution (1/128°) modelling system designed to perform multi-purpose simulations (e.g. from decadal to operational temporal scales) of the oceanographic properties of the northern Adriatic Sea. Validation and quality assessment of horizontal current fields have been carried out for the 2006-2017 time period by comparing model outputs both with available observations and larger scale Copernicus Marine Service (CMS) reanalyses, showing better agreement with the experimental data, and a reduction of the average bias against observations compared to the lower resolution CMS products. In addition, NARF current speed was encountered to be generally more intense, with an overall increase of up to 50% compared to the lower resolution model. This data set was used to assess the energy potential of marine currents in the northern Adriatic. By analysing the statistics of the velocity fields, five areas, where the currents are more intense, were identified. However, none of the selected sites proved to be suitable for large-scale energy generation. Nevertheless, due to the better description of basin hydrodynamics, a drastic increase in the estimated power flux was observed when switching from lower resolution CMS to NARF reanalyses. This work emphasises the importance of high-resolution models in the field of renewable energy feasibility analysis, planning and design. [ABSTRACT FROM AUTHOR]

The supergyre in the Southern Hemisphere is thought to connect the Atlantic, Indian, and Pacific subtropical gyres together. The aim of the study is to investigate whether the supergyre is identifiable in the Coupled Model Intercomparison Project Phase 6 (CMIP6) models and in the Estimating the Circulation and Climate of the Ocean (ECCO) reanalysis and to evaluate the influence of the supergyre on the properties of Antarctic Intermediate Water (AAIW), the dominant water mass at intermediate depths in the Southern Hemisphere. CMIP6 models and ECCO are in agreement at the surface with supergyres connected across all basins but present some differences at depth in both position and strength. AAIW core properties (temperature and salinity) present a high degree of similarity across basins within the supergyre but not outside of it. By the end of the century, the supergyre reduces in size and intensifies at intermediate depths, and the AAIW core depth warms in all basins and freshens in the Pacific although no clear trend in salinity can be found in the Atlantic and Indian basins in the SSP5‐8.5 scenario. The high degree of similarity across basins within the supergyre is maintained in the future scenario. The results suggest that by connecting the basins together at intermediate depth, the supergyre plays a key role in circulating and homogenizing the AAIW core properties. Our results emphasize the role of the supergyre in circulating water masses at the surface and intermediate depths in CMIP6 models and hence its importance to the global circulation. Plain Language Summary: The Southern Hemisphere subtropical gyres (circular ocean currents driven by wind) in the Atlantic, Indian, and Pacific basins are thought to be connected together via a supergyre. The aim of the study is to assess whether the supergyre is identifiable in the latest state of the art climate models, the Coupled Model Intercomparison Project Phase 6 (CMIP6) models. In addition, the aim is to assess whether the inter basin connection due to the presence of the supergyre has an influence on the properties of Antarctic Intermediate Water (AAIW), the dominant water mass at intermediate depths in the Southern Hemisphere. The results show that the supergyre can be identified in CMIP6 models both at the surface and intermediate depths, although the connectivity between the Pacific and Indian basins varies across models. The AAIW core properties are found to present more similarity within the supergyre, and hence the results suggest that there is an increased homogenization of AAIW due to the water mass being circulated between the three basins. In the future, the supergyre is found to intensify and shift poleward as well as to reduce in size. However, the homogenization of the AAIW properties across the three basins is maintained. Key Points: The supergyre is present at the depth of Antarctic Intermediate Water (AAIW) in CMIP6 models although the connectivity between basins differs across modelsAAIW core properties found within the supergyre tend to be homogenized across the different basinsClimate change results in an intensification poleward shift and area reduction of the supergyre at intermediate depths [ABSTRACT FROM AUTHOR]

The Southern Ocean (SO) plays a crucial role in the process of sequestering heat and carbon dioxide from the atmosphere and transferring them to the deep ocean. This process is intricately linked to the formation of Antarctic Intermediate Water (AAIW) and Subantarctic Mode Water (SAMW), which are pivotal components of the Meridional Overturning Circulation (MOC) and have a substantial impact on the global climate balance. AAIW and SAMW take shape in specific regions of the Southern Ocean due to the influence of strong winds, buoyancy fluxes, and their effects, such as convection, the development of thick mixed layers, and wind‐driven subduction. These water masses subsequently flow northward, contributing to the ventilation of the intermediate layers within the subtropical gyres. In this study, our focus lies on investigating the regional aspects of AAIW and SAMW transformation in CMIP6 models. We accomplish this by analyzing the relationship between the meridional transport of these water masses and air‐sea fluxes, particularly Ekman pumping, freshwater fluxes, and heat fluxes. Our findings reveal that the highest transformation rates occur in the Indian sector of the Southern Ocean, with notable values also observed in the southeast Pacific and south of Africa. Additionally, we assess the potential changes in these formation regions under future scenarios projected for the end of the 21st century. Although the patterns of formation regions remain consistent, there is a significant decrease in the transformation process. Plain Language Summary: The ocean plays a fundamental role in the climate system, serving as a vital reservoir for storing and distributing substantial amounts of heat and carbon. Within this context, the Southern Ocean assumes a critical role, as it is the site where significant volumes of water undergo deepening processes, ultimately giving rise to distinct water masses that occupy the intermediate and deep layers of the ocean. This study employs a suite of climate models to delve into the intricacies of the system governing the formation and transformation of specific water masses, namely Antarctic Intermediate Water (AAIW) and Subantarctic Mode Water (SAMW). Our primary objective is to identify the regions within which these water masses originate, comprehend their variability, and assess potential impacts in a future scenario influenced by climate change. Most climate models accurately replicate these formation areas. Moreover, consistent with observations, climate models depict the northward transport of AAIW/SAMW occurring primarily on the western side of the ocean basins. However, concerning future scenarios, we observe a reduction in the volume of water being formed in the Southern Ocean, signifying potential changes in the dynamics of these critical water masses. Key Points: Intermediate water masses are relatively accurately spatially transformed in the climate modelsIntermediate water masses are primarily transported along the boundaries of the ocean basinsThe transformation of water masses decreases in the future climate change scenarios [ABSTRACT FROM AUTHOR]

Below the surface of the ocean, currents, eddies, and other physical processes redistribute energy and biologically important elements. Measuring this transport can be challenging, and scientists must address a wide spectrum of oceanographic and engineering concepts when designing instruments to observe these processes. In this activity, high-school aged students are asked to consider some of these challenges by using easily-available material to design, assemble, and test their own simple deep-water floats, mimicking a tool used by oceanographers to measure transport below the ocean's surface. Students are asked to build floats that are neutrally buoyant at an interface between oil and water, teaching them key oceanographic concepts such as buoyancy and ocean stratification. Additionally, students manage a budget by selecting cost-effective materials to construct their floats. This activity aims to increase students' understanding of how ocean currents affect our planet's climate and ecosystems, how we observe these phenomena, and some of the practical challenges that oceanographers face. The project supports key educational standards and aims to deepen students' appreciation for marine science. [ABSTRACT FROM AUTHOR]

Greenland's marine‐terminating glaciers connect the ice sheet to the ocean and provide a critical boundary where heat, freshwater, and nutrient exchanges take place. Buoyant freshwater runoff from inland ice sheet melt is discharged at the base of marine‐terminating glaciers, forming vigorous upwelling plumes. It is understood that subglacial plumes modify waters near glacier fronts and increase submarine glacier melt by entraining warm ambient waters at depth. However, ocean observations along Greenland's coastal margins remain biased toward summer months which limits accurate estimation of ocean forcing on glacier retreat and acceleration. Here, we fill a key observational gap in northwest Greenland by describing seasonal hydrographic variation at glacier fronts in Melville Bay using in situ observations from moorings deployed year‐round, CTDs, and profiling floats. We evaluated local and remote forcing using remote sensing and reanalysis data products alongside a high‐resolution ocean model. Analysis of the year‐round hydrographic data revealed consistent above‐sill seasonality in temperature and salinity. The warmest, saltiest waters occurred in spring (April–May) and primed glaciers for enhanced submarine melt in summer when meltwater plumes entrain deep waters. Waters were coldest and freshest in early winter (November–December) after summer melt from sea ice, glacier ice, and icebergs provided cold freshwater along the shelf. Ocean variability was greatest in the summer and fall, coincident with increased freshwater runoff and large wind events before winter sea ice formation. Results increase our mechanistic understanding of Greenland ice‐ocean interactions and enable improvements in ocean model parameterization. Plain Language Summary: Many of Greenland's glaciers terminate in the ocean and form an important boundary between coastal waters and the ice sheet. During summer months, meltwater from the ice sheet flows out below glaciers that terminate in the ocean. The less‐dense meltwater rises to the surface as a plume, increasing near‐glacier ocean mixing and submarine glacier melt. Understanding processes at the glacier‐ocean interface, including how plumes modify nearshore waters, requires sustained observations near glacier termini. However, ocean measurements are largely taken during the summer and do not necessarily represent the total variability observed in the system. Here, we fill an important knowledge gap by presenting year‐round measurements of temperature and salinity near glacier fronts in Northwest Greenland. We found the warmest, saltiest waters in the spring and coldest, freshest waters in early winter at all sites. Warm waters in the spring prime deep glaciers for submarine melt during summer. Increased winds in the fall initiated vertical mixing in nearshore waters before winter sea ice created a surface barrier, stabilizing the water column. Results advance our understanding of the ocean's role in the acceleration and retreat of Greenland's glaciers, which is important for estimating large‐scale ocean circulation and Greenland's ice sheet mass loss. Key Points: Moored hydrographic observations at glacier fronts in NW Greenland revealed consistent above‐sill seasonality in temperature and salinityOcean temperatures reached a maximum in spring, priming deep glaciers for submarine melt during summerRemote forcing supplied the renewal of warm, salty water in the spring and local forcing controlled increased variability in the summer/fall [ABSTRACT FROM AUTHOR]

Retention of surface water parcels can benefit coastal ecosystems by increasing the residence times of nutrient‐rich waters and marine larvae but can also negatively impact marine life and human health by concentrating oil and other pollutants. To investigate the spatial patterns, temporal variability, and drivers of retention, surface water parcel retention is quantified using particle simulations forced by high‐frequency radar surface current observations in the Santa Barbara Channel (SBC), California, USA. Retention is defined here as the time a particle remains within 20 km of its starting location and typical retention times are ∼4 days. However, the mechanisms driving retention differ across the SBC. In the central SBC, high retention times are driven by a persistent cyclonic eddy, while in the eastern SBC, high retention times are due to weak oscillatory flow. Areas in the western SBC outside of the cyclonic eddy exhibit the shortest mean retention times (<2 days) due to sustained horizontal advection. Stepwise regression is used to assess the drivers of interannual retention anomalies at sub‐SBC scales and is able to predict the greatest (least) amount of retention variability in the western (eastern) SBC. The presence of coherent eddies, both cyclonic and anticyclonic, is a dominant driver of high retention channel‐wide, while some factors such as wind stress and along‐channel flow have counteracting effects on retention at sub‐channel scales. Plain Language Summary: Surface water retention, the amount of time water stays within a certain region, is an important physical property which has significant consequences in the ocean. On one hand, the retention of water masses can positively affect coastal ecosystems by retaining marine larvae and high‐nutrient waters. On the other, it can have negative impacts on marine life and human health by concentrating oil and other pollutants. Through the analysis of surface current measurements, we observe distinct spatial patterns of retention in the Santa Barbara Channel, California, USA. We find that high retention occurs in areas where surface waters are recirculated often, and areas where surface transport is slow and variable. Low retention occurs in areas where the export of surface waters is quick and consistent in direction. We find that retention varies from year to year, and that the forces which drive this variability have contradicting effects from one part of the channel to the next. Key Points: Retention is quantified using particle simulations forced by high‐frequency radar surface current observationsBoth cyclonic and anticyclonic mesoscale eddies increase retention times across the Santa Barbara ChannelWind stress, along‐channel flow, and eddy circulation have counteracting effects on retention at sub‐channel scales [ABSTRACT FROM AUTHOR]

Deep learning, a data-driven technology, has attracted widespread attention from various disciplines due to the rapid advancements in the Internet of Things (IoT) big data, machine learning algorithms and computational hardware in recent years. It proves to achieve comparable or even more accurate results than traditional methods in a more flexible manner in existing applications in various fields. In the field of physical oceanography, an important scientific field of oceanography, the abundance of ocean surface data and high dynamic complexity pave the way for an extensive application of deep learning. Moreover, researchers have already conducted a great deal of work to innovate traditional approaches in ocean circulation, ocean dynamics, ocean climate, ocean remote sensing and ocean geophysics, leading oceanographic studies into the "AI ocean era". In our study, we categorize numerous research topics in physical oceanography into four aspects: surface elements, subsurface elements, typical ocean phenomena, and typical weather and climate phenomena. We review the cutting-edge applications of deep learning in physical oceanography over the past three years to provide comprehensive insights into its development. From the perspective of three application scenarios, namely spatial data, temporal data and data generation, three corresponding deep learning model types are introduced, which are convolutional neural networks (CNNs), recurrent neural networks (RNNs) and generative adversarial networks (GANs), and also their principal application tasks. Furthermore, this study discusses the current bottlenecks and future innovative prospects of deep learning in oceanography. Through summarizing and analyzing the existing research, our aim is to delve into the potential and challenges of deep learning in physical oceanography, providing reference and inspiration for researchers in future oceanographic studies. [ABSTRACT FROM AUTHOR]

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]

Abstract It is known that several animals are sensitive to magnetic fields. Magnetoreception in animals is the ability to sense the geomagnetic field of the Earth and to use their vectorial properties for navigation and terrestrial location. Animals can also show magneto-sensitivity, unrelated to navigation and show changes in behavior during the application of magnetic fields. The aim of the present study was to analyze the behavior of the shrimp Litopennaeus vannamei under the presence of artificial magnetic fields of different intensities under laboratory conditions. Shrimps were chosen because of their economic importance and because they have been neglected in the study of magneto-sensitivity in animals. The laboratory experiments were performed with the use of a pair of circular coils and four aquariums (three treatments and a control, with 20 shrimps each). Our results show for the first time that L. vannamei shrimps are magneto-sensitive, showing a preference to swim against the magnetic field lines direction as is done by magnetotactic bacteria from the Southern Hemisphere. Also, it was observed that the shrimps remain still after the application of magnetic field intensities from 44 to 237 µT, which could be useful for maintaining shrimps in their captive areas.

Global estimates of mesoscale vertical velocity remain poorly constrained due to a historical lack of adequate observations on the spatial and temporal scales needed to measure these small magnitude velocities. However, with the wide‐spread and frequent observations collected by the Argo array of autonomous profiling floats, we can now better quantify mesoscale vertical velocities throughout the global ocean. We use the underutilized trajectory data files from the Argo array to estimate the time evolution of isotherm displacement around a float as it drifts at 1,000 m, allowing us to quantify vertical velocity averaged over approximately 4.5 days for that depth level. The resulting estimates have a non‐normal, high‐peak, and heavy‐tail distribution. The vertical velocity distribution has a mean value of (1.9 ± 0.02) × 10−6 m s−1 and a median value of (1.3 ± 0.2) × 10−7 m s−1, but the high‐magnitude events can be up to the order of 10−4 m s−1. We find that vertical velocity is highly spatially variable and is largely associated with a combination of topographic features and horizontal flow. These are some of the first observational estimates of mesoscale vertical velocity to be taken across such large swaths of the ocean without assumptions of uniformity or reliance on horizontal divergence. Plain Language Summary: Vertical velocity in the ocean is a fundamental part of how water circulates throughout the globe. This impacts the temperature, salt, nutrients, and currents that make up the ocean. However, vertical velocities are very small and are, therefore, difficult to measure. In particular, the vertical velocities of ocean events that occur on roughly a weekly to monthly time scale (mesoscale) are poorly understood. We have developed a method for estimating these mesoscale vertical velocities across the globe using an array of autonomous robots called Argo floats. Our results show that vertical velocities vary greatly depending on location, with the largest values occurring where there is a combination of relatively shallow ocean depths and larger horizontal velocities. These estimates are some of the first of their kind to be made from observations across such large swaths of the ocean. Key Points: Five‐day averaged vertical velocities from Argo observations near 1,000 m are non‐normally distributed, with a high peak and heavy tailsMesoscale vertical velocities are on the order of centimeters per day, but high‐magnitude events can be on the order of meters per dayVertical velocities estimated from Argo floats are spatially variable and correlated with topographic features and horizontal surface flow [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]

This review will serve as an entry point for international researchers who would like to tap into the vast scientific potential of Chinese-language literature on oceanic fronts. We focused on observational physical oceanography studies of marine fronts. A thorough bibliographic search netted 95 papers published in 1982–2023, with a sharp increase in the total number of papers from 2006–2010 to 2011–2015, when this number almost tripled. This trend continued unabated through the early 2020s. The sharp increase in Chinese-language publications preceded by several years a rapid increase in English-language publications in the same field. Regionally, the overwhelming majority of papers is focused on the China Seas, particularly the East China Seas and northern South China Sea. Elsewhere, a number of papers were dedicated to the Southern Ocean and North Atlantic. Thematically, papers on remote sensing of ocean fronts dominate the literature, with special attention to the development of front-detection algorithms that can be applied to satellite data on sea surface temperature, salinity, chlorophyll, and sea level anomaly. Numerous papers on marine fronts in the China Seas present important results that have to be considered by international researchers. Overall, this review emphasizes the significant contributions made by Chinese oceanographers, particularly to studies of the frontal oceanography of the China Seas. [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]

With the in-depth implementation of the strategy of maritime power, the state has gradually increased its financial input in marine survey and research, and the number of ocean observation instruments and equipment in maritime research institutes has increased substantially. At the same time, due to the particularity of the use of ocean observation instruments and equipment, damage or even loss of instruments and equipment sometimes occurs, which has brought new challenges to the management of instruments and equipment. Taking the ocean observation instruments related to physical oceanography as an example, this paper classified the ocean observation instruments and equipment, discussed their particularity, explored the establishment of management mechanisms in line with the characteristics of ocean observation instruments and equipment, further improved the scientific nature of asset management, to better serve the needs of teaching and scientific research.

• 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]

The South Atlantic Ocean plays a key role in the heat exchange of the climate system, as it hosts the returning flow of the Atlantic Meridional Overturning Circulation (AMOC). To gain insights on this role, using data from three hydrographic cruises conducted in the South Atlantic Subtropical gyre at 34.5°S, 24°S, and 10°W, we identify water masses and compute absolute geostrophic circulation using inverse modeling. In the upper layers, the currents describe the South Atlantic anticyclonic gyre with the northwest flowing Benguela Current (26.3 ± 2.0 Sv at 34.5°S, and 21.2 ± 1.8 Sv at 24°S) flowing above the Mid‐Atlantic Ridge (MAR) between 22.4°S and 28.4°S (−19.2 ± 1.4 Sv), and the southward flowing Brazil Current (−16.5 ± 1.3 Sv at 34.5°S, and −7.3 ± 0.9 Sv at 24°S); the deep layers feature the southward transports of Deep Western Boundary Current (−13.9 ± 3.0 Sv at 34.5°S, and −8.7 ± 3.8 Sv at 24°S) and Deep Eastern Boundary Current (−15.1 ± 3.5 Sv at 34.5°S, and −16.3 ± 4.7 Sv at 24°S), with the interbasin west‐to‐east flow close to 24°S (7.5 ± 4.4 Sv); the abyssal waters present northward mass transports through the Argentina Basin (5.6 ± 1.1 Sv at 34.5°S, and 5.8 ± 1.5 Sv at 24°S) and Cape Basin (8.6 ± 3.5 Sv at 34.5°S–3.0 ± 0.8 Sv at 24°S) before returning southward (−2.2 ± 0.7 Sv at 24°S to −7.9 ± 3.6 Sv at 34.5°S), without any interbasin exchange across the MAR. In addition, we compute the upper AMOC strength (14.8 ± 1.0 and 17.5 ± 0.9 Sv), the equatorward heat transport (0.30 ± 0.05 and 0.80 ± 0.05 PW), and the freshwater flux (0.18 ± 0.02 and −0.07 ± 0.02 Sv) at 34.5°S and 24°S, respectively. Plain Language Summary: The location of the South Atlantic subtropical gyre plays a critical role in the Atlantic Meridional Overturning Circulation, which controls the Earth's climate system. We have examined the South Atlantic subtropical gyre's circulation between 34.5°S and 24°S and above the Mid‐Atlantic Ridge using data from three hydrographic cruises (34.5°S, 24°S, and 10°W). From these cruises, we have identified the water masses present on this region and computed their transport. Thus, we describe the path of the anticyclonic gyre and the northeast route of the Benguela Current in the upper water masses (<1,440 m depth); the southward flowing western and eastern boundary currents and an eastward interbasin flow close to 24°S above the Mid‐Atlantic Ridge in the deep water masses (those on the depth range from 1,440 to 3,800 m depth); and the northward flow of the abyssal water masses (>3,800 m depth). We have also reported the characteristic heat transport flowing northward across the subtropical South Atlantic Ocean, where evaporation dominates over precipitation. Key Points: The upper layers present the course of the anticyclonic South Atlantic subtropical gyre and the northeast route of the Benguela CurrentThe deep layers present the southward flowing boundary currents and an eastward interbasin flow close to 24°S above the Mid‐Atlantic RidgeThe heat transport flows northward across the subtropical South Atlantic Ocean, where evaporation dominates over precipitation [ABSTRACT FROM AUTHOR]

Marine ecosystem dynamics in the context of climate change is a growing scientific, political and social concern requiring regular monitoring through appropriate observational technologies and studies. Thus, a wide range of tools comprising chemical, biogeochemical, physical, and biological sensors, as well as other platforms exists for marine monitoring. However, their high acquisition and maintenance costs are often a major obstacle, especially in low-income developing countries. We designed an advanced low-cost synoptic marine ecosystem observation system that operates at relatively high temporal frequencies, named PlasPi TDM. This instrument is an improved version of the camera system (PlasPI marine cameras) developed in 2020 by Autun Purser from the Alfred Wegener Institute Helmholtz Center for Polar and Marine Research (Germany), and collaborators. It incorporates several innovative developments such as multispectral (records the spectrum of any object photographed), temperature and pressure sensors. The PlasPi TDM operates to a depth of 200 m. The various field deployments demonstrate the operational capability of the PlasPi TDM for different applications and illustrate its considerable potential for in-situ observations and marine surveillance in Africa. This device is intended as an open-source project and its continued development is encouraged for a more integrated, sustainable and low-cost ocean observing system.

Ocean observations are the foundation of our understanding of ocean processes. Improving these observations has critical implications for our ability to sustainably derive food from the ocean, predict extreme weather events that take a toll on human life, and produce the goods and services that are needed to meet the needs of a vast and growing population. While there have been great leaps forward in sustained operational monitoring of our oceans there are still key data gaps which result in sub-optimal ocean management and policy decisions. The global fishing industry represents a vast opportunity to create a paradigm shift in how ocean data are collected: the spatio-temporal extent of ocean data gaps overlaps significantly with fishers’ activities; fishing vessels are suitable platforms of opportunity to host communications and sensor equipment; and many fishing vessels effectively conduct a depth-profile through the water column in the course of normal fishing activities, representing a powerful subsurface data collection opportunity. Fishing vessel-collected ocean data can complement existing ocean observing networks by enabling the cost-effective collection of vast amounts of subsurface ocean information in data-sparse regions. There is an emerging global network of fishing vessels participating in collaborative efforts to collect oceanographic data accelerated by innovations in enabling technologies. While there are clear opportunities that arise from partnering with fishing vessels, there are also challenges ranging from geographic and cultural differences in fleets, fishing methods and practices, data processing and management for heterogeneous data, as well as long term engagement of the fishers. To advance fishing vessel-based ocean observation on a global scale, the Fishing Vessel Ocean Observing Network (FVON) aims to maximize data value, establish best practices around data collection and management, and facilitate observation uptake. FVON’s ultimate goals are to foster collaborative fishing vessel-based observations, democratize ocean observation, improve ocean predictions and forecasts, promote sustainable fishing, and power a data-driven blue economy.

Marine lakes are emerging ecological and evolutionary natural experimental systems, with genetically isolated resident populations that exhibit extreme population dynamics and rapid phenotypic change. Marine lakes are posited to be marine islands, however, unlike terrestrial islands for which rich models have been developed over the past half-century, we know little of the mechanisms driving changes in marine lakes. This is a critical knowledge gap in efforts to reconcile theory on, or distinguish differences among, island and island-like systems. To reduce this critical knowledge gap, we present a mathematical model describing marine lakes based on a case study of Jellyfish Lake (Ongeim’l Tketau, Mecherchar: OTM), Palau. Empirical data show that marine lakes exhibit delayed and reduced tidal motions, suggesting exchange of a limited amount of water with the neighboring (‘mainland’) ocean. Our model tracks changes in lake level, allowing determination of an exchange rate that is a physical null model for biological colonization and a proxy for colonization distance in island biogeography theory. In addition, we track horizontally averaged in-lake quantities such as salinity and temperature (i.e., marine weather, climate) and stratification (i.e., habitat) — that are known to influence resident species’ distributions and population dynamics — by solving an advection-diffusion equation. We find that weather, ocean conditions, groundwater, and exchanges through tunnels determine the abiotic environment in OTM. By comparing simulations and data, we estimate the difficult-to-measure properties of the surrounding groundwater — the ‘matrix’ in the vernacular of habitat islands — and give a range of realistic values for the effective diffusion coefficient. This coefficient is found to increase in a tropical storm, suggesting that other drivers can be important during perturbations.

Dynamically similar regions of the global ocean are identified using a barotropic vorticity (BV) framework from a 20-year mean of the Estimating the Circulation and Climate of the Ocean state estimate at 1° resolution. An unsupervised machine learning algorithm, K-means, objectively clusters the standardized BV equation, identifying five unambiguous regimes. Cluster 1 covers 43 ± 3.3% of the ocean area. Surface and bottom stress torque are balanced by the bottom pressure torque and the nonlinear torque. Cluster 2 covers 24.8 ± 1.2%, where the beta effect balances the bottom pressure torque. Cluster 3 covers 14.6 ± 1.0%, characterized by a Quasi-Sverdrupian regime where the beta effect is balanced by the wind and bottom stress term. The small region of Cluster 4 has baroclinic dynamics covering 6.9 ± 2.9% of the ocean. Cluster 5 occurs primarily in the Southern Ocean. Residual dominantly nonlinear regions highlight where the BV approach is inadequate, found in areas of rough topography in the Southern Ocean and along western boundaries.

Submesoscale eddies, fronts, and filaments are ubiquitous in the upper ocean and play an important role in biogeochemical and mixing processes as well as in the energy budget. To capture the high spatial variability of submesoscale processes, it is desirable to simultaneously resolve the vertical and horizontal gradients of hydrographic properties on scales of 10 m to 10 km. We present a revised towed CTD chain, for rapid quasi-synoptic in situ measurements of submesoscale oceanographic features, that is lighter, more robust and scientifically more useful than previous towed CTD chains. This new instrument provides a horizontal resolution of O(1 m) and can be towed at speeds of up to 5 ms-1 for measurements of the upper 100 m of the water column while providing a reasonable vertical resolution of O(1 m – 10 m). Individual CTD probes are equipped with temperature, conductivity, pressure and either rapid response dissolved oxygen or fluorescence sensors at multiple depths, enabling both hydrographic and biogeochemical studies at high resolution. A flexible probe hardware allows either real-time data collection or internal data logging for offline post-processing. Finally, we outline the necessary post-processing steps and provide data examples. With the presented data examples we show and conclude that the advanced towed CTD chain is a flexible and lightweight take on the towed CTD chain concept. It can easily be adapted to scientific needs and provides high quality very high resolution oceanographic data.

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]