We investigate the importance of model resolution in identifying the nature of mixing and dispersion in the Gulf of Mexico, by comparing two data-assimilative, high-resolution simulations, one of which is submesoscale-resolving. By employing both Eulerian and Lagrangian metrics, upper-ocean differences between the mesoscale- and submesoscale-resolving simulations are examined. Focusing on regions characterized by high submesoscale activity, we approach the notion of mixing by tracking the generation of Lagrangian Coherent Structures (LCSs) and transport barriers. Finite-time Lyapunov exponents (FTLE) fields reveal higher separation rates of fluid particles in the submesoscale-resolving case which indicates more vigorous mixing. Using probability density functions (PDFs), the extent of mixing homogeneity is also explored, with preliminary results suggesting that mixing is more homogeneous in the submesosclae-resolving case. Finally, we aim to identify regions of convergence in the areas of interest by advecting passive tracers that tend to organize themselves along attracting LCSs. Applications of passive tracer advection are then translated to extreme event situations, such as the Deepwater Horizon.  

How to cite: Ntaganou, N., Chassignet, E., and Bozec, A.: Impact of Model Resolution on Mixing and Dispersion in the Gulf of Mexico, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7220, https://doi.org/10.5194/egusphere-egu23-7220, 2023.

Lagrangian particle dispersal simulations are widely used for studying directed connectivity between different locations in the ocean. They are used, both, for the understanding of ocean physics and for interdisciplinary questions. One biological example is the dispersal of passively drifting marine organisms.

The typical modus operandi of such “bio-physical” studies is to design an underlying Lagrangian simulation in close synchronisation with a specific biological research question. This leads to a conflation of concerns between physical and biological aspects of the study. This conflation might result in repeated and slow development cycles of re-calculation for different scenarios and hence inhibit scientific progress.

We aim at improving the separation of concerns between biological and physical components for bio-physical Lagrangian studies, by aggregating physical Lagrangian data into directed multigraphs encoding locations as nodes and multiple parallel pathways as directed edges. Those graphs condense the physics-based information on directed oceanic relations and thus serve as a basis for simultaneously answering various biological questions on connectivity. As the proposed aggregation retains the distinction of different pathways between locations, it can, to some extent, also provide information of underway environmental conditions. This greatly enhances the range of applications of our approach over existing aggregations of Lagrangian data as connectivity probability graphs.

We present a specific set of biological case studies — the multi-year spreading of two oyster diseases in the North Sea — and develop a framework that facilitates efficiently and simultaneously testing multiple biological hypotheses for marine diseases of various species based on the same processed physical data set.

How to cite: Rath, W., Schmittmann, L., Trahms, C., Kirch, F., Mock, L., and Biastoch, A.: A versatile Lagrangian-data aggregation framework for marine biological dispersal studies, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6036, https://doi.org/10.5194/egusphere-egu23-6036, 2023.

Lagrangian eddies, generally referred to as elliptic Lagrangian coherent structures (LCS) in the dynamical systems literature, are material objects that trap and transport floating particles over large distances in the ocean in a coherent fashion. In order to expand our understanding of the transport of marine tracers, we need to accurately and reliably track the evolution of vortical flow structures. Drifter trajectories represent a valuable but sparse source of information for this purpose. We employ a recently developed single-trajectory Lagrangian diagnostic tool, the trajectory rotation average (TRA), to visualize oceanic vortices (or eddies) from sparse drifter data in a quasi-objective fashion. We apply the TRA to two drifter data sets that cover various oceanographic scales: the Grand Lagrangian Deployment (GLAD) and the Global Drifter Program (GDP). Based on the TRA, we develop a general algorithm that extracts approximate eddy boundaries. We find that the TRA outperforms other available single-trajectory-based eddy detection methodologies on sparse drifter data and identifies eddies on scales that are unresolved by satellite-altimetry.

How to cite: Encinas Bartos, A. P., O. Aksamit, N., and Haller, G.: Quasi-Objective Eddy Visualization from Sparse Drifter Data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4003, https://doi.org/10.5194/egusphere-egu23-4003, 2023.

Ocean submesoscales are characterized by horizontal scales smaller than approximately 10 km that evolve with timescales of O(1) day. Due to their small size and rapid temporal evolution, they are notoriously difficult to measure. In particular, the associated velocity field is not resolved in current satellite altimetry products. At these scales, surface ocean flows are populated by small eddies, and filaments linked with strong gradients of physical properties, such as temperature. Several recent studies indicate that submesoscale fronts are associated with important vertical velocities, thus playing a significant role in vertical transport. On that account, these fine-scale flows are key to the dynamical coupling between the interior and the surface of the ocean, as well as to plankton dynamics and marine ecology. In spite of their importance, the understanding of submesoscale ocean dynamics is still incomplete. In particular, a relevant open question concerns the role played by the ageostrophic components of the surface velocity field that manifest at these scales.

By means of numerical simulations, we investigate ocean submesoscale turbulence in the SQG⁺¹ model, which accounts for ageostrophic motions generated at fronts, and which is obtained as a small-Rossby-number approximation of the primitive equations. In the limit of vanishing Rossby number, this system gives surface quasi-geostrophic (SQG) dynamics. In this study, we explore the effect of the ageostrophic flow components on the spreading process of Lagrangian tracer particles on the horizontal. We particularly focus on the characterization of pair-dispersion regimes and particle clustering, as a function of the Rossby number, using different indicators. The observed Lagrangian behaviours are further related to the structure of the underlying turbulent flow. We find that relative dispersion is essentially unaffected by the ageostrophic flow components. However, these components are found to be responsible for (temporary) particle aggregation in cyclonic frontal regions. These results appear interesting for the modelling of submesoscale dynamics and for comparison purposes with the new high-resolution surface current data that will be soon provided by the satellite SWOT.

How to cite: Maalouly, M., Mompean, G., and Berti, S.: Lagrangian tracer spreading in surface ocean turbulence with ageostrophic dynamics, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7212, https://doi.org/10.5194/egusphere-egu23-7212, 2023.

Vertical motions across the ocean are central to processes, like CO₂ fixation, heat removal or pollutant transport, which are essential to the Earth’s climate. This presentation describes 3D conveyor routes across the Atlantic Meridional Overturning Circulation (AMOC), with the support of Lagrangian Coherent Structures. Our findings show the geometry of mixing structures in the upper and deep ocean layers. We identify among others, zones linked to vertical transport and characterize vertical transport time scales.

Acknowledgments: RB acknowledges support of a CSIC JAE intro fellowship.  AMM and GGS acknowledge the support of a CSIC PIE project Ref. 202250E001 and MICINN grants PID2021-123348OB-I00 and EIN2020-112235. AMM is an active member of the CSIC Interdisciplinary Thematic Platforms POLARCSIC. JC acknowledges the support of the RyC project RYC2018-025169, the Spanish grant PID2020-114043GB-I00 and the Catalan Grant No. 2017SGR1049 and the ``Beca Leonardo a Investigadores y Creadores Culturales 2022 de la Fundación BBVA''.

How to cite: Mancho, A. M., Bruera, R., Curbelo, J., and Garcia-Sanchez, G.: Mixing and transport across the Atlantic Meridional Overturning Circulation: a 3D geometrical perspective, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8646, https://doi.org/10.5194/egusphere-egu23-8646, 2023.

We study the transportation and rotational dynamics of a finite-sized spheroidal particle in a linear monochromatic surface gravity wave to better understand the transport dynamics of microplastics in oceanic flows. A spheroidal particle, modeled as an anisotropic tracer, attains preferential alignment in a linear wavy flow. We analyze the drift of a finite-size anisotropic particle and find that the horizontal drift of such particles can either increase or decrease depending on the initial orientation and the ratio of the size of the particle to the wavelength of the background wave field. Next, we derive the finite-size modification to the preferred alignment of the spheroidal particle with the flow propagation direction of the wave. In most scenarios, particles in the ocean can have a wide range of densities and are classified into positively and negatively buoyant particles. Negatively buoyant particles settle in a wavy flow with complex trajectories. We study the effect of the orientation and size of such particles on settling and show that the aspect ratio of the particle could alter the trajectory in the wave propagation direction. We also obtain a non-zero vertical Stokes drift. Finally, we consider the effects of fluid and particle inertia in our coupled evolution equations and study the drift and the orientation of an anisotropic particle in a wavy flow field. We demonstrate that considering such an effect could provide a complete picture of the transport and dynamics of microplastics in the upper part of the ocean that can be described more accurately. 

How to cite: Mishra, H. and Roy, A.: Inertial effects on the transport of an anisotropic particle in surface gravity waves, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13939, https://doi.org/10.5194/egusphere-egu23-13939, 2023.

The Salish Sea is a semi-enclosed coastal sea between Vancouver Island and the coast of British Columbia and Washington State, invaluable from both an economic and ecologic perspective. Pacific inflow to the Sea is the main contributor of many biologically important constituents. The contribution of Pacific water masses to the flow through Juan de Fuca Strait (JdF), the Salish Sea’s primary connection to the Pacific Ocean, is explored. Quantitative Lagrangian particle tracking using Ariane was applied to two numerical ocean models (CIOPS-W in the shelf region, and SalishSeaCast in the Salish Sea) matched together within JdF. Water parcels seeded near the entrance of JdF were integrated forwards and backwards in time to assess water mass path (and properties while on this path) from the shelf region and once within the Salish Sea in more detail than previously possible. During summer upwelling, intermediate flow from the north shelf and offshore dominate inflow, while during winter downwelling, intermediate flow from the south shelf and surface flow from the Columbia River plume are the dominant sources. A weaker and less consistent estuarine flow regime in the winter led to less Pacific inflow overall and a smaller percentage of said inflow reaching the Salish Sea's inner basins than in the summer. Nevertheless, it was found that winter dynamics are the main driver of interannual variability, in part due to the strongly anti-correlated behaviour and distinct properties of the two dominant winter sources. This analysis extends the knowledge on the dynamics of Pacific inflow to the Salish Sea and highlights the importance of winter inflow to the interannual variability in biogeochemical conditions in the region.

How to cite: Beutel, B. and Allen, S.: Interannual and seasonal water mass analysis in the Salish Sea using Lagrangian particle tracking, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-316, https://doi.org/10.5194/egusphere-egu23-316, 2023.

On 13 August 2021, the Fukutoku-Okanoba submarine volcano in the North Pacific Ocean was erupted. Satellites detected many pumice rafts that drifted westward to reach southern Japan in about two months. To cope with potential danger due to the pumice rafts, it is crucial to predict their trajectories. Using a Lagrangian particle tracking model, the trajectories of the rafts were investigated. The model results showed strong sensitivity to the windage coefficient of pumice rafts, which is uncertain and could cause large errors. By comparing the model results with satellite images using a skill score, the distance between a simulated particle and the nearest observed raft divided by the travel distance of the particle, an optimal windage coefficient was estimated. The optimal windage coefficients ranging between 2 to 3% produced pathways comparable to the obervation using satellites. The pumice rafts  moved from Fukutoku-Okanoba, toward the Ryukyu Islands for approximately two months before being pushed toward Taiwan by the intensified wind. The techniques presented here may become helpful in managing coastal hazards due to diverse marine debris.

How to cite: Park, Y.-G., Iskandar, M. R., kim, K., and Jin, H.: Tracking the pumice rafts from the recent eruption of the submarine volcano Fukutoku-Okanoba, Japan using Satellites and Lagrangian Particles tracking, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10296, https://doi.org/10.5194/egusphere-egu23-10296, 2023.

The implementation of continuous operational forecast systems using numerical models for coastal environments are scarce, computationally expensive, and difficult to maintain. As an alternative, computationally cheaper tools such as machine learning models can be employed. This is especially relevant when the time to produce a forecast is paramount like in oil spills, marine litter spread due to container-ship accidents, and search and rescue operations. Working in this direction, we tested the skill of an advanced deep learning model, namely a convolutional long short-term memory network (ConvLSTM), to predict the Lagrangian advection (the displacement vector of the center of mass) and the dispersion (the spread described by a covariance matrix) of patches of passive tracers. This model was trained with data from a realistic numerical simulation of the Dutch Wadden Sea: a multiple-inlet system of great ecological importance. Using the relevant drivers (wind, tidal amplitude, and atmospheric pressure), the model was set to learn the advection and dispersion after one tidal period of clouds of particles released on a 200 x 200 m grid, covering the entire DWS. Our results show that the model learned the system-wide temporal variability of both advection and dispersion, while the local spatial features were better reproduced for advection than for dispersion. We use the predicted advection and dispersion as inputs to a set of stochastic differential equations for the reconstruction of particle trajectories, as it is commonly done in particle tracking applications that employ diffusion instead of dispersion. We were able to predict the temporal evolution over several tidal periods of particle patches released from specific locations under contrasting cases like calm and stormy conditions. Our method was employed to predict only the horizontal spreading, but it can be extended to predict the 3D evolution of the particle clouds. Finally, our approach requires simulation data and relevant drivers (e.g. atmospheric forcing and tidal amplitudes) for training and the same drivers from any typical forecast systems for forecasting the evolution of particle patches, which makes it a promising operational tool.

How to cite: Fajardo-Urbina, J. M., Liu, Y., Gräwe, U., Georgievska, S., Grootes, M. W., Clercx, H. J. H., Gerkema, T., and Duran-Matute, M.: Forecast of Particle Spreading Using Machine Learning in a Complex Multiple-Inlet Coastal System, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6537, https://doi.org/10.5194/egusphere-egu23-6537, 2023.