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The methods used to identify coherent ocean eddies are either Eulerian or Lagrangian in nature, and nearly all existing eddy datasets are based on the Eulerian method. In this study, millions of Lagrangian particles are advected by satellite-derived surface geostrophic velocities over the period of 1993–2019. Using the method of Lagrangian-averaged vorticity deviation (LAVD), we present a global Lagrangian eddy dataset (GLED v1.0, Liu and Abernathey, 2022, https://doi.org/10.5281/zenodo.7349753). This open-source dataset contains not only the general features (eddy center position, equivalent radius, rotation property, etc.) of eddies with lifetimes of 30, 90, and 180 d, but also the trajectories of particles trapped by coherent eddies over the lifetime. We present the statistical features of Lagrangian eddies and compare them with those of the most widely used sea surface height (SSH) eddies, focusing on generation sites, size, and propagation speed. A remarkable feature is that Lagrangian eddies are generally smaller than SSH eddies, with a radius ratio of about 0.5. Also, the validation using Argo floats indicates that coherent eddies from GLED v1.0 exist in the real ocean and have the ability to transport water parcels. Our eddy dataset provides an additional option for oceanographers to understand the interaction between coherent eddies and other physical or biochemical processes in the Earth system.

Computational oceanography is the study of ocean phenomena by numerical simulation, especially dynamical and physical phenomena. Progress in information technology has driven exponential growth in the number of global ocean observations and the fidelity of numerical simulations of the ocean in the past few decades. The growth has been exponentially faster for ocean simulations, however. We argue that this faster growth is shifting the importance of field measurements and numerical simulations for oceanographic research. It is leading to the maturation of computational oceanography as a branch of marine science on par with observational oceanography. One implication is that ultraresolved ocean simulations are only loosely constrained by observations. Another implication is that barriers to analyzing the output of such simulations should be removed. Although some specific limits and challenges exist, many opportunities are identified for the future of computational oceanography. Most important is the prospect of hybrid computational and observational approaches to advance understanding of the ocean.

The geographical variability, frequency content, and vertical structure of near-surface oceanic kinetic energy (KE) are important for air-sea interaction, marine ecosystems, operational oceanography, pollutant tracking, and interpreting remotely sensed velocity measurements. Here, KE in high-resolution global simulations (HYbrid Coordinate Ocean Model; HYCOM, and Massachusetts Institute of Technology general circulation model; MITgcm), at the sea surface (0 m) and at 15 m, are compared with KE from undrogued and drogued surface drifters, respectively. Global maps and zonal averages are computed for low-frequency (

Slowly evolving stratified flow over rough topography is subject to substantial drag due to internal motions, but often numerical simulations are carried out at resolutions where this “wave” drag must be parameterized. Here we highlight the importance of internal drag from topography with scales that cannot radiate internal waves, but may be highly nonlinear, and we propose a simple parameterization of this drag that has a minimum of fit parameters compared to existing schemes. The parameterization smoothly transitions from a quadratic drag law () for low Nh/u0 (linear wave dynamics) to a linear drag law () for high Nh/u0 flows (nonlinear blocking and hydraulic dynamics), where N is the stratification, h is the height of the topography, and u0 is the near-bottom velocity; the parameterization does not have a dependence on Coriolis frequency. Simulations carried out in a channel with synthetic bathymetry and steady body forcing indicate that this parameterization accurately predicts drag across a broad range of forcing parameters when the effect of reduced near-bottom mixing is taken into account by reducing the effective height of the topography. The parameterization is also tested in simulations of wind-driven channel flows that generate mesoscale eddy fields, a setup where the downstream transport is sensitive to the bottom drag parameterization and its effect on the eddies. In these simulations, the parameterization replicates the effect of rough bathymetry on the eddies. If extrapolated globally, the subinertial topographic scales can account for 2.7 TW of work done on the low-frequency circulation, an important sink that is redistributed to mixing in the open ocean.

Paleoproxy observations suggest that deep-ocean water-mass distributions were different at the Last Glacial Maximum than they are today. However, even modern deep-ocean water-mass distributions are not completely explained by observations of the modern ocean circulation. This paper investigates two processes that influence deep-ocean water-mass distributions: 1) interior downwelling caused by vertical mixing that increases in the downward direction and 2) isopycnal mixing. Passive tracers are used to assess how changes in the circulation and in the isopycnal-mixing coefficient impact deep-ocean water-mass distributions in an idealized two-basin model. We compare two circulations, one in which the upper cell of the overturning reaches to 4000-m depth and one in which it shoals to 2500-m depth. Previous work suggests that in the latter case the upper cell and the abyssal cell of the overturning are separate structures. Nonetheless, high concentrations of North Atlantic Water (NAW) are found in our model’s abyssal cell: these tracers are advected into the abyssal cell by interior downwelling caused by our vertical mixing profile, which increases in the downward direction. Further experiments suggest that the NAW concentration in the deep South Atlantic Ocean and in the deep Pacific Ocean is influenced by the isopycnal-mixing coefficient in the top 2000 m of the Southern Ocean. Both the strength and the vertical profile of isopycnal mixing are important for setting deep-ocean tracer concentrations. A 1D advection–diffusion model elucidates how NAW concentration depends on advective and diffusive processes.

Oceanic tracers are transported by oceanic motions of all scales, but only the large-scale motions are resolved by the present-day Earth system models. In these models, the unresolved lateral sub-gridscale tracer transport is generally parameterized through diffusive closures with a scale-independent diffusion coefficient. However, evidence from observations and theory suggests that diffusivity varies spatially and is length-scale dependent. Here we provide new scale-dependent quantification of the global surface diffusivities. To this end we use a recently developed statistical inversion method, MicroInverse, to diagnose horizontal surface diffusivities from observed sea surface temperature and idealized model simulation. We compare the results to theoretical estimates of mixing by the large-scale shear and by the sub-gridscale velocity fluctuations. The diagnosed diffusivity magnitude peaks in the tropics and western boundary currents with minima in the subtropical gyres (~3000 and ~100 m2 s−1) at ~40-km scale, respectively. Focusing on the 40–200-km length scale range, we find that the diffusivity magnitude scales with the length scale to a power n that is between 1.22 and 1.54 (90% confidence) in the tropics and also peaks at values above 1 in the boundary currents. In the midlatitudes we find that 0.58 < n < 0.87 (90% confidence). Comparison to the theory suggests that in regions with n > 1 the horizontal mixing is dominated by large-scale shear, whereas in regions where n < 1 the horizontal mixing is due to processes that are small compared to the 40–200-km length scale range considered in this study.

The Southern Ocean overturning circulation is driven by winds, heat fluxes, and freshwater sources. Among these sources of freshwater, Antarctic sea ice formation and melting play the dominant role. Even though ice-shelf melt is relatively small in magnitude, it is located close to regions of convection, where it may influence dense water formation. Here, we explore the impacts of ice-shelf melting on Southern Ocean water-mass transformation (WMT) using simulations from the Energy Exascale Earth System Model (E3SM) both with and without the explicit representation of melt fluxes from beneath Antarctic ice shelves. We find that ice-shelf melting enhances transformation of Upper Circumpolar Deep Water, converting it to lower density values. While the overall differences in Southern Ocean WMT between the two simulations are moderate, freshwater fluxes produced by ice-shelf melting have a further, indirect impact on the Southern Ocean overturning circulation through their interaction with sea ice formation and melting, which also cause considerable upwelling. We further find that surface freshening and cooling by ice-shelf melting cause increased Antarctic sea ice production and stronger density stratification near the Antarctic coast. In addition, ice-shelf melting causes decreasing air temperature, which may be directly related to sea ice expansion. The increased stratification reduces vertical heat transport from the deeper ocean. Although the addition of ice-shelf melting processes leads to no significant changes in Southern Ocean WMT, the simulations and analysis conducted here point to a relationship between increased Antarctic ice-shelf melting and the increased role of sea ice in Southern Ocean overturning.

LEAP's Strategic Plan articulates the Center'svision, mission, values, and goals. LEAP’s Strategic Plan will serve as a guide for setting priorities, allocating resources, assessing capabilities, and identifying needs to define the new discipline of Climate Data Science over the next five years. LEAP's Strategic Plan will be updated and versioned on a periodic basis. The current version is V1, dated April 28, 2022.

With the increase in computational power, ocean models with kilometer-scale resolution have emerged over the last decade. These models have been used for quantifying the energetic exchanges between spatial scales, informing the design of eddy parametrizations and preparing observing networks. The increase in resolution, however, has drastically increased the size of model outputs, making it difficult to transfer and analyze the data. Nonetheless, it is of primary importance to assess more systematically the realism of these models. Here, we showcase a cloud-based analysis framework proposed by the Pangeo Project that aims to tackle such distribution and analysis challenges. We analyze the output of eight submesoscale-permitting simulations, all on the cloud, for a crossover region of the upcoming Surface Water and Ocean Topography (SWOT) altimeter mission near the Gulf Stream separation. The models used in this study are run with the NEMO, CROCO, MITgcm, HYCOM, FESOM and FIO-COM code bases. The cloud-based analysis framework: i) minimizes the cost of duplicating and storing ghost copies of data, and ii) allows for seamless sharing of analysis results amongst collaborators. We describe the framework and provide example analyses (e.g., sea-surface height variability, submesoscale vertical buoyancy fluxes, and comparison to predictions from the mixed-layer instability parametrization). Basin-to-global scale, submesoscale-permitting models are still at their early stage of development; their cost and carbon footprints are also rather large. It would, therefore, benefit the community to document the different model configurations for future best practices. We also argue that an emphasis on data analysis strategies would be crucial for improving the models themselves.

The response of severe convective storms to a warming climate is poorly understood outside of a few well studied regions. Here, projections from seven global climate models from the CMIP6 archive, for both historical and future scenarios, are used to explore the global response in variables that describe favorability of conditions for the development of severe storms. The variables include convective available potential energy (CAPE), convection inhibition (CIN), 0–6 km vertical wind shear (S06), storm relative helicity (SRH), and covariate indices (i.e., severe weather proxies) that combine them. To better quantify uncertainty, understand variable sensitivity to increasing temperature, and present results independent from a specific scenario, we consider changes in convective variables as a function of global average temperature increase across each ensemble member. Increases to favorable convective environments show an overall frequency increases on the order of 5%–20% per °C of global temperature increase, but are not regionally uniform, with higher latitudes, particularly in the Northern Hemisphere, showing much larger relative changes. The driving mechanism of these changes is a strong increase in CAPE that is not offset by factors that either resist convection (CIN), or modify the likelihood of storm organization (S06, SRH). Severe weather proxies are not the same as severe weather events. Hence, their projected increases will not necessarily translate to severe weather occurrences, but they allow us to quantify how increases in global temperature will affect the occurrence of conditions favorable to severe weather.

Oceanic mesoscale motions including eddies, meanders, fronts, and filaments comprise a dominant fraction of oceanic kinetic energy and contribute to the redistribution of tracers in the ocean such as heat, salt, and nutrients. This reservoir of mesoscale energy is regulated by the conversion of potential energy and transfers of kinetic energy across spatial scales. Whether and under what circumstances mesoscale turbulence precipitates forward or inverse cascades, and the rates of these cascades, remain difficult to directly observe and quantify despite their impacts on physical and biological processes. Here we use global observations to investigate the seasonality of surface kinetic energy and upper ocean potential energy. We apply spatial filters to along-track satellite measurements of sea surface height to diagnose surface eddy kinetic energy across 60-300 km scales. A geographic and scale dependent seasonal cycle appears throughout much of the mid-latitudes, with eddy kinetic energy at scales less than 60 km peaking 1-4 months before that at 60-300 km scales. Spatial patterns in this lag align with geographic regions where the conversion of potential to kinetic energy are seasonally varying. In mid-latitudes, the conversion rate peaks 0-2 months prior to kinetic energy at scales less than 60 km. The consistent geographic patterns between the seasonality of potential energy conversion and kinetic energy across spatial scale provide observational evidence for the inverse cascade, and demonstrate that some component of it is seasonally modulated. Implications for mesoscale parameterizations and numerical modeling are discussed.

We describe a new way to apply a spatial filter to gridded data from models or observations, focusing on low‐pass filters. The new method is analogous to smoothing via diffusion, and its implementation requires only a discrete Laplacian operator appropriate to the data. The new method can approximate arbitrary filter shapes, including Gaussian filters, and can be extended to spatially varying and anisotropic filters. The new diffusion‐based smoother's properties are illustrated with examples from ocean model data and ocean observational products. An open‐source Python package implementing this algorithm, called gcm‐filters, is currently under development.

Ocean ventilation is the transfer of tracers and young water from the surface down into the ocean interior. The tracers that can be transported to depth include anthropogenic heat and carbon, both of which are critical to understanding future climate trajectories. Ventilation occurs in both high- and midlatitude regions, but it is the southern midlatitudes that are responsible for the largest fraction of anthropogenic heat and carbon uptake; such Southern Ocean ventilation is the focus of this review. Southern Ocean ventilation occurs through a chain of interconnected mechanisms, including the zonally averaged meridional overturning circulation, localized subduction, eddy-driven mixing along isopycnals, and lateral transport by subtropical gyres. To unravel the complex pathways of ventilation and reconcile conflicting results, here we assess the relative contribution of each of thesemechanisms, emphasizing the three-dimensional and temporally varying nature of the ventilation of the Southern Ocean pycnocline. We conclude that Southern Ocean ventilation depends on multiple processes and that simplified frameworks that explain ventilation changes through a single process are insufficient.

The Atlantic Meridional Overturning Circulation, a key constituent of the climate system, is projected to slow down in the twenty-first century due to a weakening of the Labrador Sea convection, itself a response to greenhouse gas warming and/or enhanced freshwater flux from the Arctic. However, the first observations from the Overturning in the Subpolar North Atlantic Program reveal a minimal response of the Meridional Overturning Circulation to the strong Labrador Sea convection during the winters of 2015–2016. From an analysis of the observational and reanalysis data, we show here that this weak response can be explained by a strong density compensation in the Labrador Sea. Although convection induces important changes of temperature and salinity in the basin interior, the export of the thermal and haline anomalies to the boundary current largely takes place along density surfaces. As a result, the transformation across density surfaces, that is, the imprint on the overturning circulation, is relatively small. This finding highlights the critical relationship between temperature and salinity in determining the overturning strength in the Labrador Sea and underlines the necessity of accurate freshwater flux estimates for improved Meridional Overturning Circulation predictions. Compensation of the effects of salt and temperature changes on water density in the Labrador Sea can explain the minimal response of the Atlantic overturning circulation to convection, according to analyses of observations and reanalysis data.

Geostrophic eddies contribute to the mixing of heat, carbon, and other climatically important tracers. A passive tracer driven by satellite-derived surface velocity fields is used to study the regional and temporal variability of lateral eddy mixing in the North Atlantic. Using a quasi-Lagrangian diffusivity diagnostic, we show that the upstream region (80°–50°W) of the Gulf Stream jet exhibits a significant mixing barrier (with diffusivity of ≈1 × 103 m2 s−1), compared to the downstream region (50°–10°W), which displays no mixing suppression (≈10 × 103 m2 s−1). The interannual variability is 10%–20% of the time mean in both regions. By analyzing linear perturbations of mixing-length diffusivity expression, we show that the across-jet mixing in the upstream region is driven by variations in the mean flow, rather than eddy velocity. In the downstream region, both the mean flow and eddy velocity contribute to the temporal variability. Our results suggest that an eddy parameterization must take into account the along-jet variation of mixing, and within jets such diffusivities may be a simple function of jet strength.

The surface kinetic energy of a 1/48° global ocean simulation and its distribution as a function of frequency and location are compared with the one estimated from 15,329 globally distributed surface drifter observations at hourly resolution. These distributions follow similar patterns with a dominant low‐frequency component and well‐defined tidal and near‐inertial peaks globally. Quantitative differences are identified with deficits of low‐frequency energy near the equator (factor 2) and at near‐inertial frequencies (factor 3) and an excess of energy at semidiurnal frequencies (factor 4) for the model. Owing to its hourly resolution and its near‐global spatial coverage, the array of surface drifters is an invaluable tool to evaluate the realism of tide‐resolving high‐resolution ocean simulations used in observing system simulation experiments. Sources of bias between model and drifter data are discussed, and associated leads for future work highlighted. Plain Language Summary Ocean currents predominately control the transport and distribution of physical properties (such as heat and momentum) and biochemical tracers (such as carbon, oxygen, and nutrients) in the global oceans. These currents are generally most energetic at the ocean surface and distributed across a broad range of temporal and spatial scales. Global ocean models simulate ocean variability from large‐scale circulations to tidal motions and are used to provide guidance for new global oceanographic observations, especially for future high‐resolution satellite missions. Therefore, it is crucial to assess the realism of global ocean models. In this study, we use drifter observations of global surface currents to compare with the output from a 1/48° global ocean simulation. Our results show that the model exhibits a broad qualitative consistency with the surface drifter data but also displays some key differences at high‐frequency dynamics. The model shows a 3 times deficit of near‐inertial variance and a 4 times excess of semidiurnal variance when compared with surface drifter data. Our findings demonstrate that the global drifter data set with hourly temporal resolution provides a resource for assessment of the surface circulation predicted by state‐of‐the‐art global ocean models.

Mesoscale eddies stir along the neutral plane, and the resulting neutral diffusion is a fundamental aspect of subgrid‐scale tracer transport in ocean models. Calculating neutral diffusion traditionally involves calculating neutral slopes and three‐dimensional tracer gradients. The calculation of the neutral slope traditionally occurs by computing the ratio of the horizontal to vertical locally referenced potential density derivative. However, this approach is problematic in regions of weak vertical stratification, prompting the use of a variety of ad hoc regularization methods that can lead to rather nonphysical dependencies for the resulting neutral tracer gradients. Here we use a VErtical Non‐local Method “VENM,” a search algorithm that requires no ad hoc regularization and significantly improves the numerical accuracy of calculating neutral slopes, neutral tracer gradients, and associated neutral diffusive fluxes. We compare and contrast VENM against a more traditional method, using an independent objective neutrality condition combined with estimates of spurious diffusion, heat transport, and water mass transformation rates. VENM is more accurate, both physically and numerically, and should form the basis for future efforts involving neutral diffusion calculations from observations and possibly numerical model simulations., Key Points We provide a vertically nonlocal method (VENM) to calculate neutral slopes and gradientsA VENM‐like method is numerically and physically more accurate than most used methodsVENM can fundamentally improve physics for data analyses and numerical modeling

We investigate the role of small‐scale, high‐frequency motions on lateral transport in the ocean, by using velocity fields and particle trajectories from an ocean general circulation model (MITgcm‐llc4320) that permits submesoscale flows, inertia‐gravity waves, and tides. Temporal averaging/filtering removes most of the submesoscale turbulence, inertia‐gravity waves, and tides, resulting in a largely geostrophic flow, with a rapid drop‐off in energy at scales smaller than the mesoscales. We advect two types of Lagrangian particles: (a) 2‐D particles (surface restricted) and (b) 3‐D particles (advected in full three dimensions) with the filtered and unfiltered velocities and calculate Lagrangian diagnostics. At large length/time scales, Lagrangian diffusivity is comparable for filtered and unfiltered velocities, while at short scales, unfiltered velocities disperse particles much faster. We also calculate diagnostics of Lagrangian coherent structures:rotationally coherent Lagrangian vortices detected from closed contours of the Lagrangian‐averaged vorticity deviation and material transport barriers formed by ridges of maximum finite‐time Lyapunov exponent. For temporally filtered velocities, we observe strong material coherence, which breaks down when the level of temporal filtering is reduced/removed, due to vigorous small‐scale mixing. In addition, for the lowest temporal resolution, the 3‐D particles experience very little vertical motion, suggesting that degrading temporal resolution greatly reduces vertical advection by high‐frequency motions. Our study suggests that Lagrangian diagnostics based on satellite‐derived surface geostrophic velocity fields, even with higher spatial resolutions as in the upcoming Surface Water and Ocean Topography mission, may overestimate the presence of mesoscale coherent structures and underestimate dispersion.

The core tools of science (data, software, and computers) are undergoing a rapid and historic evolution, changing what questions scientists ask and how they find answers. Earth science data are bei...

The core tools of science (data, software, and computers) are undergoing a rapid and historic evolution, changing what questions scientists ask and how they find answers. Earth science data are being transformed into new formats optimized for cloud storage that enable rapid analysis of multi-petabyte datasets. Datasets are moving from archive centers to vast cloud data storage, adjacent to massive server farms. Open source cloud-based data science platforms, accessed through a web-browser window, are enabling advanced, collaborative, interdisciplinary science to be performed wherever scientists can connect to the internet. Specialized software and hardware for machine learning and artificial intelligence (AI/ML) are being integrated into data science platforms, making them more accessible to average scientists. Increasing amounts of data and computational power in the cloud are unlocking new approaches for data-driven discovery. For the first time, it is truly feasible for scientists to bring their analysis to the data without specialized cloud computing knowledge. Practically, for scientists, the effect of these changes is to vastly shrink the amount of time spent acquiring and processing data, freeing up more time for science. This shift in paradigm is lowering the threshold for entry, expanding the science community, and increasing opportunities for collaboration, while promoting scientific innovation, transparency, and reproducibility. These changes are increasing the speed of science, broadening the possibilities of what questions science can answer, and expanding participation in science.

Transport and mixing properties of the ocean's circulation is crucial to dynamical analyses, and often have to be carried out with limited observed information. Finite-time coherent sets are regions of the ocean that minimally mix (in the presence of small diffusion) with the rest of the ocean domain over the finite period of time considered. In the purely advective setting (in the zero diffusion limit) this is equivalent to identifying regions whose boundary interfaces remain small throughout their finite-time evolution. Finite-time coherent sets thus provide a skeleton of distinct regions around which more turbulent flow occurs. Well known manifestations of finite-time coherent sets in geophysical systems include rotational objects like ocean eddies, ocean gyres, and atmospheric vortices. In real-world settings, often observational data is scattered and sparse, which makes the difficult problem of coherent set identification and tracking challenging. I will describe mesh-based numerical methods [3] to efficiently approximate the recently defined dynamic Laplace operator [1,2], and rapidly and reliably extract finite-time coherent sets from models or scattered, possibly sparse, and possibly incomplete observed data. From these results we can infer new chemical and physical ocean connectivities at global and intra-basin scales (at the surface and at depth), track series of eddies, and determine new oceanic barriers.[1] G. Froyland. Dynamic isoperimetry and the geometry of Lagrangian coherent structures. Nonlinearity, 28:3587-3622, 2015[2] G. Froyland and E. Kwok. A dynamic Laplacian for identifying Lagrangian coherent structures on weighted Riemannian manifolds. Journal of Nonlinear Science, 30:1889–1971, 2020.[3] Gary Froyland and Oliver Junge. Robust FEM-based extraction of finite-time coherent sets using scattered, sparse, and incomplete trajectories. SIAM J. Applied Dynamical Systems, 17:1891–1924, 2018.

It has been hypothesized that submesoscale flows play an important role in the vertical transport of climatically important tracers, due to their strong associated vertical velocities. However, the multi-scale, non-linear, and Lagrangian nature of transport makes it challenging to attribute proportions of the tracer fluxes to certain processes, scales, regions, or features. Here we show that criteria based on the surface vorticity and strain joint probability distribution function (JPDF) effectively decomposes the surface velocity field into distinguishable flow regions, and different flow features, like fronts or eddies, are contained in different flow regions. The JPDF has a distinct shape and approximately parses the flow into different scales, as stronger velocity gradients are usually associated with smaller scales. Conditioning the vertical tracer transport on the vorticity-strain JPDF can therefore help to attribute the transport to different types of flows and scales. Applied to a set of idealized Antarctic Circumpolar Current simulations that vary only in horizontal resolution, this diagnostic approach demonstrates that small-scale strain dominated regions that are generally associated with submesoscale fronts, despite their minuscule spatial footprint, play an outsized role in exchanging tracers across the mixed layer base and are an important contributor to the large-scale tracer budgets. Resolving these flows not only adds extra flux at the small scales, but also enhances the flux due to the larger-scale flows.

Scientific data has traditionally been distributed via downloads from data server to local computer. This way of working suffers from limitations as scientific datasets grow towards the petabyte scale. A “cloud-native data repository,” as defined in this paper, offers several advantages over traditional data repositories—performance, reliability, cost-effectiveness, collaboration, reproducibility, creativity, downstream impacts, and access & inclusion. These objectives motivate a set of best practices for cloud-native data repositories: analysis-ready data, cloud-optimized (ARCO) formats, and loose coupling with data-proximate computing. The Pangeo Project has developed a prototype implementation of these principles by using open-source scientific Python tools. By providing an ARCO data catalog together with on-demand, scalable distributed computing, Pangeo enables users to process big data at rates exceeding 10 GB/s. Several challenges must be resolved in order to realize cloud computing’s full potential for scientific research, such as organizing funding, training users, and enforcing data privacy requirements.

Computational Oceanography is the study of ocean phenomena by numerical simulation, especially dynamical and physical phenomena. Progress in information technology has driven exponential growth in ...