Articles | Volume 17, issue 6
https://doi.org/10.5194/os-17-1545-2021
© Author(s) 2021. 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-17-1545-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
02 Nov 2021
Research article |
| 02 Nov 2021
| 02 Nov 2021
Defining Southern Ocean fronts using unsupervised classification
British Antarctic Survey, NERC, UKRI, Cambridge, UK
Department of Physics, University of Cambridge, Cambridge, UK
Daniel C. Jones
British Antarctic Survey, NERC, UKRI, Cambridge, UK
Anita Faul
British Antarctic Survey, NERC, UKRI, Cambridge, UK
Erik Mackie
Cambridge Zero, University of Cambridge, Cambridge, UK
British Antarctic Survey, NERC, UKRI, Cambridge, UK
Etienne Pauthenet
LOCEAN-IPSL, UPMC Université, Sorbonne Universités, Paris, France
Related authors
Lea Poropat, Dani Jones, Simon D. A. Thomas, and Céline Heuzé
Ocean Sci., 20, 201–215, https://doi.org/10.5194/os-20-201-2024, https://doi.org/10.5194/os-20-201-2024, 2024
Short summary
Short summary
In this study we use a machine learning method called a Gaussian mixture model to divide part of the ocean (northwestern European seas and part of the Atlantic Ocean) into regions based on satellite observations of sea level. This helps us study each of these regions separately and learn more about what causes sea level changes there. We find that the ocean is first divided based on bathymetry and then based on other features such as water masses and typical atmospheric conditions.
Lea Poropat, Dani Jones, Simon D. A. Thomas, and Céline Heuzé
Ocean Sci., 20, 201–215, https://doi.org/10.5194/os-20-201-2024, https://doi.org/10.5194/os-20-201-2024, 2024
Short summary
Short summary
In this study we use a machine learning method called a Gaussian mixture model to divide part of the ocean (northwestern European seas and part of the Atlantic Ocean) into regions based on satellite observations of sea level. This helps us study each of these regions separately and learn more about what causes sea level changes there. We find that the ocean is first divided based on bathymetry and then based on other features such as water masses and typical atmospheric conditions.
Dani C. Jones, Maike Sonnewald, Shenjie Zhou, Ute Hausmann, Andrew J. S. Meijers, Isabella Rosso, Lars Boehme, Michael P. Meredith, and Alberto C. Naveira Garabato
Ocean Sci., 19, 857–885, https://doi.org/10.5194/os-19-857-2023, https://doi.org/10.5194/os-19-857-2023, 2023
Short summary
Short summary
Machine learning is transforming oceanography. For example, unsupervised classification approaches help researchers identify underappreciated structures in ocean data, helping to generate new hypotheses. In this work, we use a type of unsupervised classification to identify structures in the temperature and salinity structure of the Weddell Gyre, which is an important region for global ocean circulation and for climate. We use our method to generate new ideas about mixing in the Weddell Gyre.
Fouzia Fahrin, Daniel C. Jones, Yan Wu, James Keeble, and Alexander T. Archibald
Atmos. Chem. Phys., 23, 3609–3627, https://doi.org/10.5194/acp-23-3609-2023, https://doi.org/10.5194/acp-23-3609-2023, 2023
Short summary
Short summary
We use a machine learning technique called Gaussian mixture modeling (GMM) to classify vertical ozone profiles into groups based on how the ozone concentration changes with pressure. Even though the GMM algorithm was not provided with spatial information, the classes are geographically coherent. We also detect signatures of tropical broadening in UKESM1 future climate scenarios. GMM may be useful for understanding ozone structures in modeled and observed datasets.
Rachael N. C. Sanders, Daniel C. Jones, Simon A. Josey, Bablu Sinha, and Gael Forget
Ocean Sci., 18, 953–978, https://doi.org/10.5194/os-18-953-2022, https://doi.org/10.5194/os-18-953-2022, 2022
Short summary
Short summary
In 2015, record low temperatures were observed in the North Atlantic. Using an ocean model, we show that surface heat loss in December 2013 caused 75 % of the initial cooling before this "cold blob" was trapped below the surface. The following summer, the cold blob re-emerged due to a strong temperature difference between the surface ocean and below, driving vertical diffusion of heat. Lower than average surface warming then led to the coldest temperature anomalies in August 2015.
Rachel Furner, Peter Haynes, Dave Munday, Brooks Paige, Daniel C. Jones, and Emily Shuckburgh
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2021-132, https://doi.org/10.5194/gmd-2021-132, 2021
Revised manuscript not accepted
Short summary
Short summary
Traditional weather & climate models are built from physics-based equations, while data-driven models are built from patterns found in datasets using Machine Learning or statistics. There is growing interest in using data-driven models for weather & climate prediction, but confidence in their use depends on understanding the patterns they're finding. We look at this with a simple regression model of ocean temperature and see the patterns found by the regression model are similar to the physics.
David Ian Duncan, Patrick Eriksson, Simon Pfreundschuh, Christian Klepp, and Daniel C. Jones
Atmos. Chem. Phys., 19, 6969–6984, https://doi.org/10.5194/acp-19-6969-2019, https://doi.org/10.5194/acp-19-6969-2019, 2019
Short summary
Short summary
Raindrop size distributions have not been systematically studied over the oceans but are significant for remotely sensing, assimilating, and modeling rain. Here we investigate raindrop populations with new global in situ data, compare them against satellite estimates, and explore a new technique to classify the shapes of these distributions. The results indicate the inadequacy of a commonly assumed shape in some regions and the sizable impact of shape variability on satellite measurements.
Cited articles
Amante, C. and Eakins, B. W.: ETOPO1 Global Relief Model converted to
PanMap layer format, PANGAEA [data set], https://doi.org/10.1594/PANGAEA.769615, 2009. a
Campin, J.-M., Heimbach, P., Losch, M., Forget, G.,
edhill3; Adcroft, A., amolod, Menemenlis, D., dfer22, Hill, C., Jahn, O.,
Scott, J., stephdut, Mazloff, M., Fox-Kemper, B., antnguyen13, Doddridge, E.,
Fenty, I., Bates, M., AndrewEichmann-NOAA, Smith, T., Martin, T.,
Lauderdale, J., Abernathey, R., samarkhatiwala, hongandyan,
Deremble, B., dngoldberg, Bourgault, P., and Dussin, R.: MITgcm/MITgcm: checkpoint67z, Zenodo [code],
https://doi.org/10.5281/zenodo.4968496, 2021. a
Chapman, C. C.: New perspectives on frontal variability in the Southern Ocean,
J. Phys. Oceanogr., 47, 1151–1168,
https://doi.org/10.1175/JPO-D-16-0222.1, 2017. a, b
Cushman-Roisin, B. and Beckers, J.-M.: Introduction to geophysical fluid
dynamics: physical and numerical aspects, Academic Press, ISBN 9780120887590, 2011. a
Dai, A. and Trenberth, K. E.: Estimates of Freshwater Discharge from
Continents: Latitudinal and Seasonal Variations, J.
Hydrometeorol., 3, 660–687,
https://doi.org/10.1175/1525-7541(2002)003<0660:EOFDFC>2.0.CO;2, 2002. a
Deacon, G.: The hydrology of the Southern Ocean, Cambridge University Press, Macmillan, London, New York, 1937. a
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi,
S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P., Bechtold, P.,
Beljaars, A. C. M., van de Berg, L., Bidlot, J., Bormann, N., Delsol, C.,
Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S. B.,
Hersbach, H., Halm, E. V., Isaksen, L., Kållberg, P.,
Köhler, M., Matricardi, M., McNally, A. P., Monge Sanz, B. M., Morcrette,
J. J., Park, B. K., Peubey, C., de Rosnay, P., Tavolato, C., Thépaut,
J. N., and Vitart, F.: The ERA-Interim reanalysis: configuration and
performance of the data assimilation system, Q. J. Roy.
Meteor. Soc., 137, 553–597, https://doi.org/10.1002/qj.828, 2011. a
Donohue, K. A., Tracey, K. L., Watts, D. R., Chidichimo, M. P., and Chereskin,
T. K.: Mean Antarctic Circumpolar Current transport measured in Drake
Passage: MEAN ACC TRANSPORT, Geophys. Res. Lett., 43,
11760–11767, https://doi.org/10.1002/2016GL070319, 2016. a
Duda, R. and Hart P.: Pattern Classification and Scene Analysis (1st
Edition), John Wiley and Sons, New York, ISBN: 9780471223610,
271–272, 1973. a
Fenty, I. and Heimbach, P.: Coupled sea ice–ocean-state estimation in the
Labrador Sea and Baffin Bay, J. Phys. Oceanogr., 43, 884–904,
https://doi.org/10.1175/JPO-D-12-065.1, 2013. a
Forget, G., Campin, J.-M., Heimbach, P., Hill, C. N., Ponte, R. M., and Wunsch, C.: ECCO version 4: an integrated framework for non-linear inverse modeling and global ocean state estimation, Geosci. Model Dev., 8, 3071–3104, https://doi.org/10.5194/gmd-8-3071-2015, 2015. a
Frenger, I., Munnich, M., Gruber, N., and Knutti, R.: Southern Ocean eddy
phenomenology, J. Geophys. Res.-Oceans, 120, 7413–7449,
https://doi.org/10.1002/2015JC011047, 2015. a
Frolicher, T. L., Sarmiento, J. L., Paynter, D. J., Dunne, J. P., Krasting,
J. P., and Winton, M.: Dominance of the Southern Ocean in Anthropogenic
Carbon and Heat Uptake in CMIP5 Models, J. Climate, 28,
862–886, https://doi.org/10.1175/JCLI-D-14-00117.1, 2015. a
Gaspar, P., Grégoris, Y., and Lefevre, J. M.: A simple eddy kinetic energy
model for simulations of the oceanic vertical mixing: Tests at station
Papa and long-term upper ocean study site, J. Geophys. Res.-Atmos., 95, 16179–16193, https://doi.org/10.1029/JC095iC09p16179, 1990. a
Graham, R. M., Boer, A. M. d., Heywood, K. J., Chapman, M. R., and Stevens,
D. P.: Southern Ocean fronts: Controlled by wind or topography?, J.
Geophys. Res.-Oceans, 117, 2156–2202, https://doi.org/10.1029/2012JC007887, 2012. a
Hallberg, R.: Using a resolution function to regulate parameterizations of
oceanic mesoscale eddy effects, Ocean Model., 72, 92–103,
https://doi.org/10.1016/j.ocemod.2013.08.007, 2013. a, b
Hammond, M. D. and Jones, D. C.: Freshwater flux from ice sheet melting and
iceberg calving in the Southern Ocean, Geosci. Data J., 3,
60–62, https://doi.org/10.1002/gdj3.43, 2016. a
Herraiz-Borreguero, L. and Rintoul, S. R.: Subantarctic mode water:
distribution and circulation, Ocean Dynam., 61, 103–126,
https://doi.org/10.1007/s10236-010-0352-9, 2011. a
Hjelmervik, K. B. and Hjelmervik, K. T.: Detection of oceanographic fronts on
variable water depths using empirical orthogonal functions, IEEE J.
Oceanic Eng., 45, 915–926, https://doi.org/10.1109/JOE.2019.2917456, 2019. a, b, c
Houghton, I. A. and Wilson, J. D.: El Niño Detection Via Unsupervised
Clustering of Argo Temperature Profiles, J. Geophys.
Res.-Oceans, 125, e2019JC015947, https://doi.org/10.1029/2019JC015947, 2020. a
Iudicone, D., Rodgers, K., Schopp, R., and Madec, G.: An exchange window for
the injection of Antarctic Intermediate Water into the South
Pacific, J. Phys. Oceanogr., 37, 31–49,
https://doi.org/10.1175/JPO2985.1, 2007. a, b, c, d
Jones, D. C. and Ito, T.: Gaussian mixture modeling describes the geography of
the surface ocean carbon budget, NCAR, 108–113, https://doi.org/10.5065/y82j-f154,
2019. a
Jones, D. C., Meijers, A. J. S., Shuckburgh, E., Sallée, J.-B., Haynes, P.,
McAufield, E. K., and Mazloff, M. R.: How does Subantarctic Mode Water
ventilate the Southern Hemisphere subtropics?, J. Geophys. Res.-Oceans, 121, 6558–6582, https://doi.org/10.1002/2016jc011680, 2016. a, b, c
Kim, Y. S.: ACC fronts, ResearchGate [data set],
available at: https://www.researchgate.net/publication/338420242_ACC_fronts, last
access: 4 May 2021. a
Large, W. and Yeager, S.: The global climatology of an interannually varying
air‚Äìsea flux data set, Clim. Dynam., 33, 341–364,
https://doi.org/10.1007/s00382-008-0441-3, 2009. a
Le Bras, I. A.-A., Sonnewald, M., and Toole, J. M.: A Barotropic Vorticity
Budget for the Subtropical North Atlantic Based on Observations,
J. Phys. Oceanogr., 49, 2781–2797,
https://doi.org/10.1175/JPO-D-19-0111.1,
2019. a
Losch, M., Menemenlis, D., Campin, J.-M., Heimbach, P., and Hill, C.: On the
formulation of sea-ice models. Part 1: Effects of different solver
implementations and parameterizations, Ocean Model., 33, 129–144,
https://doi.org/10.1016/j.ocemod.2009.12.008, 2010. a
Mackie, E. J. B.: Southern Ocean Circulation and Frontal Dynamics from
Cryosat-2 Along-track Radar Altimetry, PhD thesis, University of Bristol,
School of Geographical Sciences, available at: https://research-information.bris.ac.uk/en/studentTheses/southern-ocean-circulati
on-and-frontal-dynamics-from-cryosat-2-al (last access: 4 May 2021), 2018. a
Marshall, J. and Speer, K.: Closure of the meridional overturning circulation
through Southern Ocean upwelling, Nat. Geosci., 5, 171–180,
https://doi.org/10.1038/ngeo1391, 2012. a
Marshall, J., Adcroft, A., Hill, C., and Perelman, L.: A finite-volume,
incompressible Navier Stokes model for studies of the ocean on parallel
computers, J. Geophys. Res., 102, 5753–5766,
https://doi.org/10.1029/96JC02775, 1997a. a
Marshall, J., Hill, C., Perelman, L., and Adcroft, A.: Hydrostatic,
quasi-hydrostatic, and nonhydrostatic ocean modeling, J. Geophys.
Res., 102, 5733–5752, https://doi.org/10.1029/96JC02776, 1997b. a
Maze, G.: Ocean Profile Classification Model in python, Zenodo [code],
https://doi.org/10.5281/zenodo.3906236, 2020. a
Maze, G., Mercier, H., Fablet, R., Tandeo, P., Radcenco, M. L., Lenca, P.,
Feucher, C., and Le Goff, C.: Coherent heat patterns revealed by unsupervised
classification of Argo temperature profiles in the North Atlantic Ocean,
Prog. Oceanogr., 151, 275–292, https://doi.org/10.1016/j.pocean.2016.12.008, 2017. a, b, c, d, e
Mazloff, M. R., Heimbach, P., and Wunsch, C.: An Eddy-Permitting Southern
Ocean State Estimate, J. Phys. Oceanogr., 40, 880–899,
https://doi.org/10.1175/2009jpo4236.1, 2010. a
McLachlan, G. J. and Basford, K. E.: Mixture models: Inference and applications
to clustering, vol. 38, M. Dekker, New York, 1988. a
Met Office: Cartopy: a cartographic python library with a matplotlib
interface, Exeter, Devon, Cartopy [code], available at: https://scitools.org.uk/cartopy/docs/latest/ (last access:
5 May 2021), 2010–2021. a
Mikaloff-Fletcher, S., Gruber, N., Jacobson, A. R., Doney, S. C., Dutkiewicz,
S., Gerber, M., Follows, M., Joos, F., Lindsay, K., Menemenlis, D., Mouchet,
A., Müller, S. A., and Sarmiento, J. L.: Inverse estimates of anthropogenic
CO2 uptake, transport, and storage by the ocean, Global Biogeochem.
Cy., 20, 1–16, https://doi.org/10.1029/2005gb002530, 2006. a
Morrison, A. K., Frölicher, T. L., and Sarmiento, J. L.: Upwelling in the
Southern Ocean, Phys. Today, 68, 27–32, https://doi.org/10.1063/PT.3.2654, 2015. a
Nakayama, Y., Menemenlis, D., Zhang, H., Schodlok, M., and Rignot, E.: Origin
of Circumpolar Deep Water intruding onto the Amundsen and
Bellingshausen Sea continental shelves, Nat. Commun., 9, 3403,
https://doi.org/10.1038/s41467-018-05813-1, 2018. a
Orsi, A., Whitworth, T., and Nowlin, W.: On the meridional extent and fronts of
the Antarctic Circumpolar Current, Deep-Sea Res. Pt. I, 42,
641–673, https://doi.org/10.1016/0967-0637(95)00021-W, 1995. a, b
Pauthenet, E., Roquet, F., Madec, G., Guinet, C., Hindell, M., McMahon, C. R.,
Harcourt, R., and Nerini, D.: Seasonal Meandering of the Polar Front
Upstream of the Kerguelen Plateau, Geophys. Res. Lett., 45,
9774–9781, https://doi.org/10.1029/2018GL079614, 2018. a, b, c
Pauthenet, E., Roquet, F., Madec, G., Sallee, J.-B., and Nerini, D.: The
Thermohaline Modes of the Global Ocean, J. Phys.
Oceanogr., 49, 2535–2552, https://doi.org/10.1175/JPO-D-19-0120.1, 2019. a
Pauthenet, E., Sallee, J.-B., Schmidtko, S., and Nerini, D.: Seasonal
Variation of the Antarctic Slope Front Occurrence and Position
Estimated from an Interpolated Hydrographic Climatology, J.
Phys. Oceanogr., 51, 1539–1557, https://doi.org/10.1175/JPO-D-20-0186.1, 2021. a
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel,
O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J.,
Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E.:
Scikit-learn: Machine Learning in Python, J. Mach. Learn.
Res., 12, 2825–2830, 2011. a
Pollard, R. T., Lucas, M. I., and Read, J. F.: Physical controls on biogeochemical zonation
in the Southern Ocean, Deep-Sea Res. Pt. II, 49, 3289–3305,
https://doi.org/10.1016/S0967-0645(02)00084-X, 2002. a
Rintoul, S., Hughes, C., and Olbers, D.: The Antarctic Circumpolar
Current system, International Geophysics Series, 77, 271–302, 2001. a
Rosso, I., Mazloff, M. R., Talley, L. D., Purkey, S. G., Freeman, N. M., and
Maze, G.: Water Mass and Biogeochemical Variability in the Kerguelen Sector
of the Southern Ocean: A Machine Learning Approach for a Mixing Hot Spot,
J. Geophys. Res.-Oceans, 125, e2019JC015877, https://doi.org/10.1029/2019JC015877,
2020. a, b, c, d, e
Sallee, J., Speer, K., Rintoul, S., and Wijffels, S.: Southern Ocean
thermocline ventilation, J. Phys. Oceanogr., 40, 509–529,
https://doi.org/10.1175/2009JPO4291.1, 2010. a, b, c
Sallee, J.-B., Matear, R. J., Rintoul, S. R., and Lenton, A.: Localized
subduction of anthropogenic carbon dioxide in the Southern Hemisphere
oceans, Nat. Geosci., 5, 579–584, https://doi.org/10.1038/ngeo1523, 2012. a, b
Siegelman, L., OToole, M., Flexas, M., Riviere, P., and Klein, P.: Submesoscale
ocean fronts act as biological hotspot for southern elephant seal, Sci.
Rep.-UK, 9, 5588, https://doi.org/10.1038/s41598-019-42117-w, 2019. a
Sokolov, S. and Rintoul, S. R.: Structure of Southern Ocean fronts at
140∘ E, J. Marine Syst., 37, 151–184,
https://doi.org/10.1016/S0924-7963(02)00200-2, 2002. a
Sokolov, S. and Rintoul, S. R.: Circumpolar structure and distribution of the
Antarctic Circumpolar Current fronts: 1. Mean circumpolar paths,
J. Geophys. Res.-Atmos., 114, 3675,
https://doi.org/10.1029/2008JC005108, 2009. a
Sonnewald, M., Wunsch, C., and Heimbach, P.: Unsupervised Learning Reveals
Geography of Global Ocean Dynamical Regions, Earth Space
Sci., 6, 784–794, https://doi.org/10.1029/2018EA000519, 2019. a, b
Sonnewald, M., Dutkiewicz, S., Hill, C., and Forget, G.: Elucidating ecological
complexity: Unsupervised learning determines global marine eco-provinces,
Science Advances, 6, eaay4740, https://doi.org/10.1126/sciadv.aay4740, 2020. a
Stammer, D., Wunsch, C., Giering, R., Eckert, C., Heimbach, P., Marotzke, J.,
Adcroft, A., Hill, C. N., and Marshall, J.: Global ocean circulation during
1992–1997, estimated from ocean observations and a general circulation model,
J. Geophys. Res.-Oceans, 107, 1-1–1-27, https://doi.org/10.1029/2001JC000888,
2002. a
Talley, L.: Closure of the Global Overturning Circulation Through the
Indian, Pacific, and Southern Oceans: Schematics and Transports,
Oceanography, 26, 80–97, https://doi.org/10.5670/oceanog.2013.07, 2013. a
Thompson, A. F. and Sallée, J.-B.: Jets and topography: Jet transitions and
the impact on transport in the Antarctic Circumpolar Current, J.
Phys. Oceanogr., 42, 956–972, https://doi.org/10.1175/JPO-D-11-0135.1, 2012. a
Thompson, A. F., Haynes, P. H., Wilson, C., and Richards, K. J.: Rapid
Southern Ocean front transitions in an eddy-resolving ocean GCM,
Geophys. Res. Lett., 37, L23602, https://doi.org/10.1029/2010GL045386, 2010. a
Thompson, A. F., Stewart, A. L., Spence, P., and Heywood, K. J.: The
Antarctic Slope Current in a Changing Climate, Rev.
Geophys., 56, 741–770, https://doi.org/10.1029/2018RG000624, 2018. a
Thompson, A. F., Speer, K. G., and Schulze Chretien, L. M.: Genesis of the
Antarctic Slope Current in West Antarctica, Geophys. Res. Lett., 47, e2020GL087802,
https://doi.org/10.1029/2020GL087802, 2020. a
Verdy, A. and Mazloff, M. R.: A data assimilating model for estimating Southern
Ocean biogeochemistry, J. Geophys. Res.-Oceans, 122,
6968–6988, https://doi.org/10.1002/2016jc012650, 2017. a, b, c
Verdy, A. and Mazloff, M.:
B-SOSE iteration 106, [data set], available at:
http://sose.ucsd.edu/BSOSE6_iter106_solution.html,
last access: 4 May 2021. a
Vernet, M., Geibert, W., Hoppema, M., Brown, P. J., Haas, C., Hellmer, H. H.,
Jokat, W., Jullion, L., Mazloff, M., Bakker, D. C. E., Brearley, J. A.,
Croot, P., Hattermann, T., Hauck, J., Hillenbrand, C.-D., Hoppe, C. J. M.,
Huhn, O., Koch, B. P., Lechtenfeld, O. J., Meredith, M. P., Naveira Garabato,
A. C., Nothig, E.-M., Peeken, I., Rutgers van der Loeff, M. M., Schmidtko,
S., Schroder, M., Strass, V. H., Torres-Valdes, S., and Verdy, A.: The
Weddell Gyre, Southern Ocean: present knowledge and future challenges,
Rev. Geophys., 57, 623–708, https://doi.org/10.1029/2018RG000604, 2019. a
Vincent, O. R. and Folorunso, O.: A descriptive algorithm for sobel image
edge detection, in: Proceedings of informing science & IT education
conference (InSITE), Informing Science Institute
California, 40, 97–107, https://doi.org/10.28945/3351, 2009.
a
Wunsch, C. and Heimbach, P.: Practical global oceanic state estimation, Physica D, 230, 197–208, https://doi.org/10.1016/j.physd.2006.09.040,
2007. a
Short summary
We propose a probabilistic method and a new inter-class comparison metric for highlighting fronts in the Southern Ocean. We compare it with an image processing method that provides a more localised view of fronts that effectively highlights sharp jets. These two complementary approaches offer two views of Southern Ocean structure: the probabilistic method highlights boundaries between coherent thermohaline structures across the entire Southern Ocean, whereas edge detection highlights local jets.
We propose a probabilistic method and a new inter-class comparison metric for highlighting...
