Articles | Volume 21, issue 1
https://doi.org/10.5194/os-21-401-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/os-21-401-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Uncertainties in the finite-time Lyapunov exponent in an ocean ensemble prediction model
Norwegian Meteorological Institute, Henrik Mohns Plass 1, 0371 Oslo, Norway
Johannes Röhrs
Norwegian Meteorological Institute, Henrik Mohns Plass 1, 0371 Oslo, Norway
Pål Erik Isachsen
Norwegian Meteorological Institute, Henrik Mohns Plass 1, 0371 Oslo, Norway
Department of Geosciences, University of Oslo, P.O. Box 1022, Blindern, 0315 Oslo, Norway
Martina Idžanović
Norwegian Meteorological Institute, Henrik Mohns Plass 1, 0371 Oslo, Norway
Related authors
No articles found.
Jean Rabault, Trygve Halsne, Ana Carrasco, Anton Korosov, Joey Voermans, Patrik Bohlinger, Jens Boldingh Debernard, Malte Müller, Øyvind Breivik, Takehiko Nose, Gaute Hope, Fabrice Collard, Sylvain Herlédan, Tsubasa Kodaira, Nick Hughes, Qin Zhang, Kai Haakon Christensen, Alexander Babanin, Lars Willas Dreyer, Cyril Palerme, Lotfi Aouf, Konstantinos Christakos, Atle Jensen, Johannes Röhrs, Aleksey Marchenko, Graig Sutherland, Trygve Kvåle Løken, and Takuji Waseda
EGUsphere, https://doi.org/10.48550/arXiv.2401.07619, https://doi.org/10.48550/arXiv.2401.07619, 2024
Short summary
Short summary
We observe strongly modulated waves-in-ice significant wave height using buoys deployed East of Svalbard. We show that these observations likely cannot be explained by wave-current interaction or tide-induced modulation alone. We also demonstrate a strong correlation between the waves height modulation, and the rate of sea ice convergence. Therefore, our data suggest that the rate of sea ice convergence and divergence may modulate wave in ice energy dissipation.
Håvard Espenes, Pål Erik Isachsen, and Ole Anders Nøst
Ocean Sci., 19, 1633–1648, https://doi.org/10.5194/os-19-1633-2023, https://doi.org/10.5194/os-19-1633-2023, 2023
Short summary
Short summary
We show that tidally generated eddies generated near the constriction of a channel can drive a strong and fluctuating flow field far downstream of the channel constriction itself. The velocity signal has been observed in other studies, but this is the first study linking it to a physical process. Eddies such as those we found are generated because of complex coastal geometry, suggesting that, for example, land-reclamation projects in channels may enhance current shear over a large area.
Johannes Röhrs, Yvonne Gusdal, Edel S. U. Rikardsen, Marina Durán Moro, Jostein Brændshøi, Nils Melsom Kristensen, Sindre Fritzner, Keguang Wang, Ann Kristin Sperrevik, Martina Idžanović, Thomas Lavergne, Jens Boldingh Debernard, and Kai H. Christensen
Geosci. Model Dev., 16, 5401–5426, https://doi.org/10.5194/gmd-16-5401-2023, https://doi.org/10.5194/gmd-16-5401-2023, 2023
Short summary
Short summary
A model to predict ocean currents, temperature, and sea ice is presented, covering the Barents Sea and northern Norway. To quantify forecast uncertainties, the model calculates ensemble forecasts with 24 realizations of ocean and ice conditions. Observations from satellites, buoys, and ships are ingested by the model. The model forecasts are compared with observations, and we show that the ocean model has skill in predicting sea surface temperatures.
Eli Børve, Pål Erik Isachsen, and Ole Anders Nøst
Ocean Sci., 17, 1753–1773, https://doi.org/10.5194/os-17-1753-2021, https://doi.org/10.5194/os-17-1753-2021, 2021
Short summary
Short summary
Non-linear tidal dynamics can produce prominent time-mean transport in coastal regions where strong tidal currents interact with topography. We investigate tidal-induced transport using a tidally driven ocean model for Lofoten–Vesterålen in northern Norway and find that both tidal pumping and tidal rectification can play an important role for time-mean transport in the region. The study emphasizes the importance of non-linear tidal dynamics for time-mean transport in complex coastal regions.
Johannes S. Dugstad, Pål Erik Isachsen, and Ilker Fer
Ocean Sci., 17, 651–674, https://doi.org/10.5194/os-17-651-2021, https://doi.org/10.5194/os-17-651-2021, 2021
Short summary
Short summary
We quantify the mesoscale eddy field in the Lofoten Basin using Lagrangian model trajectories and aim to estimate the relative importance of eddies compared to the ambient flow in transporting warm Atlantic Water to the Lofoten Basin as well as modifying it. Water properties are largely changed in eddies compared to the ambient flow. However, only a relatively small fraction of eddies is detected in the basin. The ambient flow therefore dominates the heat transport to the Lofoten Basin.
Johannes Röhrs, Knut-Frode Dagestad, Helene Asbjørnsen, Tor Nordam, Jørgen Skancke, Cathleen E. Jones, and Camilla Brekke
Ocean Sci., 14, 1581–1601, https://doi.org/10.5194/os-14-1581-2018, https://doi.org/10.5194/os-14-1581-2018, 2018
Short summary
Short summary
Simulations of hypothetical oil spills are presented to investigate how the vertical mixing of oil affects transport towards various directions. It is shown that the horizontal transport of oil greatly varies for different oil types and weather conditions. These differences are a consequence of the entrainment of oil from the surface into the ocean. While oil spills often get entrained into the water by waves, we show that submerged oil typically resurfaces after a few hours or days.
Lars R. Hole, Knut-Frode Dagestad, Johannes Röhrs, Cecilie Wettre, Vassiliki H. Kourafalou, Ioannis Androulidakis, Matthieu Le Hénaff, Heesook Kang, and Oscar Garcia-Pineda
Ocean Sci. Discuss., https://doi.org/10.5194/os-2018-130, https://doi.org/10.5194/os-2018-130, 2018
Revised manuscript not accepted
Short summary
Short summary
This study shows how the Mississippi river influenced the spreading of oil in the Gulf of Mexico after the DeepWater Horizon disaster. High resolution numerical models for ocean and atmosphere circulation are used to force an oil drift model. The circulation is totally different when river input is removed in the ocean model. The study also showcase the importance of the choice of oil droplet size distribution. Model output is compared with satellite observation of surface oil.
Knut-Frode Dagestad, Johannes Röhrs, Øyvind Breivik, and Bjørn Ådlandsvik
Geosci. Model Dev., 11, 1405–1420, https://doi.org/10.5194/gmd-11-1405-2018, https://doi.org/10.5194/gmd-11-1405-2018, 2018
Short summary
Short summary
We have developed a computer code with ability to predict how various substances and objects drift in the ocean. This may be used to, e.g. predict the drift of oil to aid cleanup operations, the drift of man-over-board or lifeboats to aid search and rescue operations, or the drift of fish eggs and larvae to understand and manage fish stocks. This new code merges all such applications into one software tool, allowing to optimise and channel any available resources and developments.
A. K. Sperrevik, K. H. Christensen, and J. Röhrs
Ocean Sci., 11, 237–249, https://doi.org/10.5194/os-11-237-2015, https://doi.org/10.5194/os-11-237-2015, 2015
Related subject area
Approach: Operational Oceanography | Properties and processes: Mesoscale to submesoscale dynamics
Assessing the impact of future altimeter constellations in the Met Office global ocean forecasting system
Transient Attracting Profiles in the Great Pacific Garbage Patch
Machine learning methods to predict sea surface temperature and marine heatwave occurrence: a case study of the Mediterranean Sea
Robert R. King, Matthew J. Martin, Lucile Gaultier, Jennifer Waters, Clément Ubelmann, and Craig Donlon
Ocean Sci., 20, 1657–1676, https://doi.org/10.5194/os-20-1657-2024, https://doi.org/10.5194/os-20-1657-2024, 2024
Short summary
Short summary
We use simulations of our ocean forecasting system to compare the impact of additional altimeter observations from two proposed future satellite constellations. We found that, in our system, an altimeter constellation of 12 nadir altimeters produces improved predictions of sea surface height, surface currents, temperature, and salinity compared to a constellation of 2 wide-swath altimeters.
Luca Kunz, Alexa Griesel, Carsten Eden, Rodrigo Duran, and Bruno Sainte-Rose
Ocean Sci., 20, 1611–1630, https://doi.org/10.5194/os-20-1611-2024, https://doi.org/10.5194/os-20-1611-2024, 2024
Short summary
Short summary
Transient Attracting Profiles (TRAPs) indicate the most attracting regions of the flow and have the potential to facilitate offshore cleanups in the Great Pacific Garbage Patch. We study the characteristics of TRAPs and the prospects for predicting debris transport from a mesoscale-permitting dataset. Our findings show the relevance of TRAP lifetime estimations to an operational application, and our TRAP tracking algorithm may even benefit other challenges that are related to search at sea.
Giulia Bonino, Giuliano Galimberti, Simona Masina, Ronan McAdam, and Emanuela Clementi
Ocean Sci., 20, 417–432, https://doi.org/10.5194/os-20-417-2024, https://doi.org/10.5194/os-20-417-2024, 2024
Short summary
Short summary
This study employs machine learning to predict marine heatwaves (MHWs) in the Mediterranean Sea. MHWs have far-reaching impacts on society and ecosystems. Using data from ESA and ECMWF, the research develops accurate prediction models for sea surface temperature (SST) and MHWs across the region. Notably, machine learning methods outperform existing forecasting systems, showing promise in early MHW predictions. The study also highlights the importance of solar radiation as a predictor of SST.
Cited articles
Adlandsvik, B. and Sundby, S.: Modelling the transport of cod larvae from the Lofoten area, ICES Mar. Sc., 198, 379–392, 1994. a
Allshouse, M., Ivey, G., Lowe, R., Jones, N., Beegle-Krause, C., Xu, J., and Peacock, T.: Impact of windage on ocean surface Lagrangian coherent structures, Environ. Fluid Mech., 17, 473–483, https://doi.org/10.1007/s10652-016-9499-3, 2017. a
Balasuriya, S.: Explicit invariant manifolds and specialised trajectories in a class of unsteady flows, Phys. Fluids, 24, 127101, https://doi.org/10.1063/1.4769979, 2012. a
Balasuriya, S.: Uncertainty in finite-time Lyapunov exponent computations, J. Comput. Dynam., 7, 313–337, https://doi.org/10.3934/jcd.2020013, 2020. a
Balasuriya, S., Kalampattel, R., and Ouellette, N. T.: Hyperbolic neighbourhoods as organizers of finite-time exponential stretching, J. Fluid Mech., 807, 509–545, https://doi.org/10.1017/jfm.2016.633, 2016. a, b
Beegle-Krause, C. J., Peacock, T., and Allshouse, M.: Exploiting Lagrangian coherent structures (LCS) for the calculation of oil spill and search-and-rescue drift patterns in the ocean, 34. AMOP technical seminar on environmental contamination and response 2011, 4–6 October 2011, Banff, AB (Canada), https://www.osti.gov/etdeweb/biblio/21547684 (last access: 14 October 2024), 2011. a
Branicki, M. and Wiggins, S.: Finite-time Lagrangian transport analysis: stable and unstable manifolds of hyperbolic trajectories and finite-time Lyapunov exponents, Nonlin. Processes Geophys., 17, 1–36, https://doi.org/10.5194/npg-17-1-2010, 2010. a
Brunton, S. L. and Rowley, C. W.: Fast computation of finite-time Lyapunov exponent fields for unsteady flows, Chaos, 20, 017503, https://doi.org/10.1063/1.3270044, 2010. a
Bøe, R., Bellec, V. K., Dolan, M. F. J., Buhl-Mortensen, P., Rise, L., and Buhl-Mortensen, L.: Cold-water coral reefs in the Hola glacial trough off Vesterålen, North Norway, Geo. Soc. Mem., 46, 309–310, https://doi.org/10.1144/M46.8, 2016. a
Børve, E., Isachsen, P. E., and Nøst, O. A.: Rectified tidal transport in Lofoten–Vesterålen, northern Norway, Ocean Sci., 17, 1753–1773, https://doi.org/10.5194/os-17-1753-2021, 2021. a, b
Callies, J., Callies, J., Ferrari, R., Klymak, J. M., and Gula, J.: Seasonality in submesoscale turbulence, Nat. Commun., 6, 6862, https://doi.org/10.1038/ncomms7862, 2015. a
Christensen, K. H., Sperrevik, A. K., and Broström, G.: On the Variability in the Onset of the Norwegian Coastal Current, J. Phys. Oceanogr., 48, 723–738, https://doi.org/10.1175/JPO-D-17-0117.1, 2018. a
Dagestad, K.-F., Röhrs, J., Breivik, Ø., and Ådlandsvik, B.: OpenDrift v1.0: a generic framework for trajectory modelling, Geosci. Model Dev., 11, 1405–1420, https://doi.org/10.5194/gmd-11-1405-2018, 2018a. a
Dagestad, K.-F., Röhrs, J., Asbjørnsen, H., Kristensen, N. M., Kristiansen, T., Skeie, P., and Brodtkorb, A. R.: OpenDrift/opendrift: OpenDrift compliant with both Python2 and Python3, Zenodo [code], https://doi.org/10.5281/zenodo.1300358, 2018b. a
de Aguiar, V., Röhrs, J., Johansson, A. M., and Eltoft, T.: Assessing ocean ensemble drift predictions by comparison with observed oil slicks, Front. Mar. Sci., 10, 1122192, https://doi.org/10.3389/fmars.2023.1122192, 2023. a, b
Denis, B., Côté, J., and Laprise, R.: Spectral Decomposition of Two-Dimensional Atmospheric Fields on Limited-Area Domains Using the Discrete Cosine Transform (DCT), Mon. Weather Rev., 130, 1812–1829, https://doi.org/10.1175/1520-0493(2002)130<1812:SDOTDA>2.0.CO;2, 2002. a
d'Ovidio, F., Fernández, V., Hernández-García, E., and López, C.: Mixing structures in the Mediterranean Sea from finite-size Lyapunov exponents, Geophys. Res. Lett., 31, L17203, https://doi.org/10.1029/2004GL020328, 2004. a
Duran, R., Beron-Vera, F., and Olascoaga, M.: Extracting quasi-steady Lagrangian transport patterns from the ocean circulation: An application to the Gulf of Mexico, Sci. Rep., 8, 5218, https://doi.org/10.1038/s41598-018-23121-y, 2018. a, b
Evensen, G.: Inverse methods and data assimilation in nonlinear ocean models, Physica D, 77, 108–129, https://doi.org/10.1016/0167-2789(94)90130-9, 1994. a
Furnes, G. and Sundby, S.: Upwelling and wind induced circulation in Vestfjorden, The Norwegian Coastal Current, Proceedings from the Norwegian Coastal Current Symposium, 1, 152–177, 1981. a
Gascard, J.-C., Raisbeck, G., Sequeira, S., Yiou, F., and Mork, K. A.: The Norwegian Atlantic Current in the Lofoten basin inferred from hydrological and tracer data (129I) and its interaction with the Norwegian Coastal Current, Geophys. Res. Lett., 31, L01308, https://doi.org/10.1029/2003GL018303, 2004. a
Ghosh, A., Suara, K., McCue, S. W., Yu, Y., Soomere, T., and Brown, R. J.: Persistency of debris accumulation in tidal estuaries using Lagrangian coherent structures, Sci. Total Environ., 781, 146808, https://doi.org/10.1016/j.scitotenv.2021.146808, 2021. a
Gille, S., Metzger, J., and Tokmakian, R.: Seafloor Topography and Ocean Circulation, Oceanography, 17, 47–54, https://doi.org/10.5670/oceanog.2004.66, 2004. a
Giudici, A., Suara, K. A., Soomere, T., and Brown, R.: Tracking areas with increased likelihood of surface particle aggregation in the Gulf of Finland: A first look at persistent Lagrangian Coherent Structures (LCS), J. Marine Syst., 217, 103514, https://doi.org/10.1016/j.jmarsys.2021.103514, 2021. a
Gouveia, M., Duran, R., Lorenzzetti, J., Assireu, A., Toste, R., de F Assad, L., and Gherardi, D.: Persistent meanders and eddies lead to quasi-steady Lagrangian transport patterns in a weak western boundary current, Sci. Rep., 11, 497, https://doi.org/10.1038/s41598-020-79386-9, 2021. a, b, c
Guo, H., He, W., Peterka, T., Shen, H.-W., Collis, S. M., and Helmus, J. J.: Finite-Time Lyapunov Exponents and Lagrangian Coherent Structures in Uncertain Unsteady Flows, IEEE T. Vis. Comput. Gr., 22, 1672–1682, https://doi.org/10.1109/TVCG.2016.2534560, 2016. a, b
Hadjighasem, A., Farazmand, M., Blazevski, D., Froyland, G., and Haller, G.: A critical comparison of Lagrangian methods for coherent structure detection, Chaos, 27, 053104, https://doi.org/10.1063/1.4982720, 2017. a
Haller, G.: Distinguished material surfaces and coherent structures in three-dimensional fluid flows, Physica D, 149, 248–277, https://doi.org/10.1016/S0167-2789(00)00199-8, 2001. a, b
Haller, G.: Lagrangian coherent structures from approximate velocity data, Phys. Fluids, 14, 1851–1861, https://doi.org/10.1063/1.1477449, 2002. a
Haller, G.: A variational theory of hyperbolic Lagrangian Coherent Structures, Physica D, 240, 574–598, https://doi.org/10.1016/j.physd.2010.11.010, 2011. a, b
Haller, G.: Lagrangian Coherent Structures, Annu. Rev. Fluid Mech., 47, 137–162, https://doi.org/10.1146/annurev-fluid-010313-141322, 2015. a, b
Haller, G. and Sapsis, T.: Lagrangian coherent structures and the smallest finite-time Lyapunov exponent, Chaos, 21, 023115, https://doi.org/10.1063/1.3579597, 2011. a
Haller, G. and Yuan, G.: Lagrangian coherent structures and mixing in two-dimensional turbulence, Physica D, 147, 352–370, https://doi.org/10.1016/S0167-2789(00)00142-1, 2000. a, b, c
Harrison, C. S. and Glatzmaier, G. A.: Lagrangian coherent structures in the California Current System – sensitivities and limitations, Geophys. Astro. Fluid, 106, 22–44, https://doi.org/10.1080/03091929.2010.532793, 2012. a
Hussain, A. K. M. F.: Coherent structures—reality and myth, Phys. Fluids, 26, 2816–2850, https://doi.org/10.1063/1.864048, 1983. a
Idžanović, M., Rikardsen, E. S. U., and Röhrs, J.: Forecast uncertainty and ensemble spread in surface currents from a regional ocean model, Frontiers in Marine Science, 10, 1177337, https://doi.org/10.3389/fmars.2023.1177337, 2023. a, b
Isachsen, P. E.: Baroclinic instability and the mesoscale eddy field around the Lofoten Basin, J. Geophys. Res.-Oceans, 120, 2884–2903, https://doi.org/10.1002/2014JC010448, 2015. a, b
Karrasch, D. and Haller, G.: Do Finite-Size Lyapunov Exponents detect coherent structures?, Chaos, 23, 043126, https://doi.org/10.1063/1.4837075, 2013. a, b
Koszalka, I., LaCasce, J. H., and Mauritzen, C.: In pursuit of anomalies – Analyzing the poleward transport of Atlantic Water with surface drifters, Deep-Sea Res. Pt. II, 85, 96–108, https://doi.org/10.1016/j.dsr2.2012.07.035, 2013. a
Krishna, K., Brunton, S. L., and Song, Z.: Finite Time Lyapunov Exponent Analysis of Model Predictive Control and Reinforcement Learning, IEEE Access, 11, 118916–118930, https://doi.org/10.1109/ACCESS.2023.3326424, 2023. a
Lebreton, L. C. M., Greer, S. D., and Borrero, J. C.: Numerical modelling of floating debris in the world’s oceans, Mar. Pollut. Bull., 64, 653–661, https://doi.org/10.1016/j.marpolbul.2011.10.027, 2012. a
Lee, Y.-K., Shih, C., Tabeling, P., and Ho, C.-M.: Experimental study and nonlinear dynamic analysis of time-periodic micro chaotic mixers, J. Fluid Mech., 575, 425–448, https://doi.org/10.1017/S0022112006004289, 2007. a
Lekien, F., Coulliette, C., Mariano, A. J., Ryan, E. H., Shay, L. K., Haller, G., and Marsden, J.: Pollution release tied to invariant manifolds: A case study for the coast of Florida, Physica D, 210, 1–20, https://doi.org/10.1016/j.physd.2005.06.023, 2005. a
Lorenz, E. N.: Deterministic Nonperiodic Flow, J. Atmos. Sci., 20, 130–141, https://doi.org/10.1175/1520-0469(1963)020<0130:DNF>2.0.CO;2, 1963. a
Lou, Q., Li, Z., Zhang, X., Xiang, X., and Cao, Z.: Lagrangian analysis of material transport around the headland in the Yellow River Estuary, Front. Mar. Sci., 9, 999367, https://doi.org/10.3389/fmars.2022.999367, 2022. a
Matuszak, M.: mateuszmatu/LCS: FTLE computation software release for article, Zenodo [code], https://doi.org/10.5281/zenodo.10797134, 2024. a
Mitchelson-Jacob, G. and Sundby, S.: Eddies of Vestfjorden, Norway, Cont. Shelf Res., 21, 1901–1918, https://doi.org/10.1016/S0278-4343(01)00030-9, 2001. a
Müller, M., Homleid, M., Ivarsson, K.-I., Køltzow, M. A. O., Lindskog, M., Midtbø, K. H., Andrae, U., Aspelien, T., Berggren, L., Bjørge, D., Dahlgren, P., Kristiansen, J., Randriamampianina, R., Ridal, M., and Vignes, O.: AROME-MetCoOp: A Nordic Convective-Scale Operational Weather Prediction Model, Weather Forecast., 32, 609–627, https://doi.org/10.1175/WAF-D-16-0099.1, 2017. a
Norwegian Meteorological Institute: Barents-2.5 ocean and ice forecast model archive, Norwegian Meteorological Institute [data set], https://thredds.met.no/thredds/fou-hi/barents_eps.html, last acess: 29 April 2024. a
Olascoaga, M. J. and Haller, G.: Forecasting sudden changes in environmental pollution patterns, P. Natl. Acad. Sci., 109, 4738–4743, https://doi.org/10.1073/pnas.1118574109, 2012. a
Olascoaga, M. J., Rypina, I. I., Brown, M. G., Beron-Vera, F. J., Koçak, H., Brand, L. E., Halliwell, G. R., and Shay, L. K.: Persistent transport barrier on the West Florida Shelf, Geophys. Res. Lett., 33, L22603, https://doi.org/10.1029/2006GL027800, 2006. a
Peacock, T. and Haller, G.: Lagrangian coherent structures: The hidden skeleton of fluid flows, Phys. Today, 66, 41–47, https://doi.org/10.1063/PT.3.1886, 2013. a
Peng, J. and Dabiri, J. O.: The “upstream wake” of swimming and flying animals and its correlation with propulsive efficiency, J. Exp. Biol., 211, 2669–2677, https://doi.org/10.1242/jeb.015883, 2008. a
Pierrehumbert, R. T. and Yang, H.: Global Chaotic Mixing on Isentropic Surfaces, J. Atmos. Sci., 50, 2462–2480, https://doi.org/10.1175/1520-0469(1993)050<2462:GCMOIS>2.0.CO;2, 1993. a
Raj, R. P., Chafik, L., Nilsen, J. E. O., Eldevik, T., and Halo, I.: The Lofoten Vortex of the Nordic Seas, Deep-Sea Res. Pt. I, 96, 1–14, https://doi.org/10.1016/j.dsr.2014.10.011, 2015. a
Ramos, A. G., García-Garrido, V. J., Mancho, A. M., Wiggins, S., Coca, J., Glenn, S., Schofield, O., Kohut, J., Aragon, D., Kerfoot, J., Haskins, T., Miles, T., Haldeman, C., Strandskov, N., Allsup, B., Jones, C., and Shapiro, J.: Lagrangian coherent structure assisted path planning for transoceanic autonomous underwater vehicle missions, Sci. Rep., 8, 4575, https://doi.org/10.1038/s41598-018-23028-8, 2018. a
Rosenstein, M. T., Collins, J. J., and De Luca, C. J.: A practical method for calculating largest Lyapunov exponents from small data sets, Physica D, 65, 117–134, https://doi.org/10.1016/0167-2789(93)90009-P, 1993. a
Rossby, T., Ozhigin, V., Ivshin, V., and Bacon, S.: An isopycnal view of the Nordic Seas hydrography with focus on properties of the Lofoten Basin, Deep-Sea Res. Pt. I, 56, 1955–1971, https://doi.org/10.1016/j.dsr.2009.07.005, 2009. a
Röhrs, J., Christensen, K. H., Vikebø, F., Sundby, S., Saetra, O., and Broström, G.: Wave-induced transport and vertical mixing of pelagic eggs and larvae, Limnol. Oceanogr., 59, 1213–1227, https://doi.org/10.4319/lo.2014.59.4.1213, 2014. a
Röhrs, J., Gusdal, Y., Rikardsen, E. S. U., Durán Moro, M., Brændshøi, J., Kristensen, N. M., Fritzner, S., Wang, K., Sperrevik, A. K., Idžanović, M., Lavergne, T., Debernard, J. B., and Christensen, K. H.: Barents-2.5km v2.0: an operational data-assimilative coupled ocean and sea ice ensemble prediction model for the Barents Sea and Svalbard, Geosci. Model Dev., 16, 5401–5426, https://doi.org/10.5194/gmd-16-5401-2023, 2023. a, b, c
Samelson, R.: Lagrangian Motion, Coherent Structures, and Lines of Persistent Material Strain, Annu. Rev. Mar. Sci., 5, 137–163, https://doi.org/10.1146/annurev-marine-120710-100819, 2013. a
Serra, M. and Haller, G.: Objective Eulerian coherent structures, Chaos, 26, 053110, https://doi.org/10.1063/1.4951720, 2016. a
Serra, M., Sathe, P., Rypina, I., Kirincich, A., Ross, S. D., Lermusiaux, P., Allen, A., Peacock, T., and Haller, G.: Search and rescue at sea aided by hidden flow structures, Nat. Commun., 11, 2525, https://doi.org/10.1038/s41467-020-16281-x, 2020. a, b, c
Shadden, S. C., Lekien, F., and Marsden, J. E.: Definition and properties of Lagrangian coherent structures from finite-time Lyapunov exponents in two-dimensional aperiodic flows, Physica D, 212, 271–304, https://doi.org/10.1016/j.physd.2005.10.007, 2005. a, b, c, d
Shchepetkin, A. F. and McWilliams, J. C.: The regional oceanic modeling system (ROMS): a split-explicit, free-surface, topography-following-coordinate oceanic model, Ocean Model., 9, 347–404, https://doi.org/10.1016/j.ocemod.2004.08.002, 2005. a
Sperrevik, A. K., Röhrs, J., and Christensen, K. H.: Impact of data assimilation on Eulerian versus Lagrangian estimates of upper ocean transport, J. Geophys. Res.-Oceans, 122, 5445–5457, https://doi.org/10.1002/2016JC012640, 2017. a
Sundby, S.: Influence of bottom topography on the circulation at the continental shelf off northern Norway, Fiskeridirektoratets Skrifter Serie Havundersokelser, 17, 501–519, 1984. a
Sundby, S. and Bratland, P.: Spatial distribution and production of eggs from northeast-arctic cod at the coast of northern Norway 1983–1985, Fisken og Havet, 1–58, 1987 (in Norwegian). a
Sundby, S., Fossum, P., Sandvik, A. D., Vikebø, F., Aglen, A., Buhl-Mortensen, L., Folkvord, A., Bakkeplass, K., Buhl-Mortensen, P., Johannessen, M., Jørgensen, M. S., Kristiansen, T., Landra, C. S., Myksvoll, M. S., and Nash, R. D. M.: KunnskapsInnhenting Barentshavet–Lofoten–Vesterålen (KILO), 188 pp., Havforskningsinstituttet, https://imr.brage.unit.no/imr-xmlui/handle/11250/113923 (last access: 17 April 2024), 2013. a, b
Tang, W., Chan, P. W., and Haller, G.: Accurate extraction of Lagrangian coherent structures over finite domains with application to flight data analysis over Hong Kong International Airport, Chaos, 20, 017502, https://doi.org/10.1063/1.3276061, 2010. a
Thoppil, P. G., Frolov, S., Rowley, C. D., Reynolds, C. A., Jacobs, G. A., Joseph Metzger, E., Hogan, P. J., Barton, N., Wallcraft, A. J., Smedstad, O. M., and Shriver, J. F.: Ensemble forecasting greatly expands the prediction horizon for ocean mesoscale variability, Communications Earth and Environment, 2, 89, https://doi.org/10.1038/s43247-021-00151-5, 2021. a
Trodahl, M. and Isachsen, P. E.: Topographic Influence on Baroclinic Instability and the Mesoscale Eddy Field in the Northern North Atlantic Ocean and the Nordic Seas, J. Phys. Oceanogr., 48, 2593–2607, https://doi.org/10.1175/JPO-D-17-0220.1, 2018. a
Truesdell, C. and Noll, W.: The Non-Linear Field Theories of Mechanics, in: The Non-Linear Field Theories of Mechanics, edited by: Truesdell, C., Noll, W., and Antman, S. S., 57–73, Springer, Berlin, Heidelberg, ISBN 978-3-662-10388-3, https://doi.org/10.1007/978-3-662-10388-3_1, 2004. a
van Sebille, E., Griffies, S. M., Abernathey, R., Adams, T. P., Berloff, P., Biastoch, A., Blanke, B., Chassignet, E. P., Cheng, Y., Cotter, C. J., Deleersnijder, E., Döös, K., Drake, H. F., Drijfhout, S., Gary, S. F., Heemink, A. W., Kjellsson, J., Koszalka, I. M., Lange, M., Lique, C., MacGilchrist, G. A., Marsh, R., Mayorga Adame, C. G., McAdam, R., Nencioli, F., Paris, C. B., Piggott, M. D., Polton, J. A., Rühs, S., Shah, S. H. A. M., Thomas, M. D., Wang, J., Wolfram, P. J., Zanna, L., and Zika, J. D.: Lagrangian ocean analysis: Fundamentals and practices, Ocean Model., 121, 49–75, https://doi.org/10.1016/j.ocemod.2017.11.008, 2018. a
Wei, M., Jacobs, G., Rowley, C., Barron, C. N., Hogan, P., Spence, P., Smedstad, O. M., Martin, P., Muscarella, P., and Coelho, E.: The impact of initial spread calibration on the RELO ensemble and its application to Lagrangian dynamics, Nonlin. Processes Geophys., 20, 621–641, https://doi.org/10.5194/npg-20-621-2013, 2013. a
Wei, M., Jacobs, G., Rowley, C., Barron, C. N., Hogan, P., Spence, P., Smedstad, O. M., Martin, P., Muscarella, P., and Coelho, E.: The performance of the US Navy's RELO ensemble, NCOM, HYCOM during the period of GLAD at-sea experiment in the Gulf of Mexico, Deep-Sea Res. Pt. II, 129, 374–393, https://doi.org/10.1016/j.dsr2.2013.09.002, 2016. a
Wilde, T., Rössl, C., and Theisel, H.: FTLE Ridge Lines for Long Integration Times, in: 2018 IEEE Scientific Visualization Conference (SciVis), IEEE, 21–26 October 2018, Berlin, Germany, 57–61, https://doi.org/10.1109/SciVis.2018.8823761, 2018. a
Zhong, X., Wu, Y., Hannah, C., Li, S., and Niu, H.: Applying finite-time lyapunov exponent to study the tidal dispersion on oil spill trajectory in Burrard Inlet, J. Hazard. Mater., 437, 129404, https://doi.org/10.1016/j.jhazmat.2022.129404, 2022. a
Zimmerman, J.: The tidal whirlpool: A review of horizontal dispersion by tidal and residual currents, Neth. J. Sea Res., 20, 133–154, https://doi.org/10.1016/0077-7579(86)90037-2, 1986. a
Zimmermann, J., Motejat, M., Rössl, C., and Theisel, H.: FTLE for Flow Ensembles by Optimal Domain Displacement, arXiv [preprint], https://doi.org/10.48550/arXiv.2401.04153, 8 January 2024. a
Short summary
Lagrangian coherent structures (LCSs) describe material transport in ocean flow by describing transport and accumulation regions. We discuss the implications of model flow field uncertainty for finite-time Lyapunov exponents (FTLEs), which under certain conditions approximate LCSs. FTLEs add value to forecasting when they are certain and long-lived. Averaging FTLEs reveals where they are more certain and long-lived, often influenced by bottom topography.
Lagrangian coherent structures (LCSs) describe material transport in ocean flow by describing...