Abstract
This work introduces a new method for ocean eddy detection that applies concepts from stationary dynamical systems theory. The method is composed of three steps: first, the centers of eddies are obtained from fixed points and their linear stability analysis; second, the size of the eddies is estimated from the vorticity between the eddy center and its neighboring fixed points, and, third, a tracking algorithm connects the different time frames. The tracking algorithm has been designed to avoid mismatching connections between eddies at different frames. Eddies are detected for the period between 1992 and 2012 using geostrophic velocities derived from AVISO altimetry and a new database is provided for the global ocean.
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Arrowsmith DK, Place CM (1990) An introduction to dynamical systems. Cambridge University Press
Aurell E, Boffetta G, Crisanti A, Paladin G, Vulpiani A (1997) Predictability in the large: an extension of the concept of lyapunov exponent. J Phys A: Math Gen 30(1):1
Beron-Vera F, Olascoaga M, Goni G (2008) Oceanic mesoscale eddies as revealed by lagrangian coherent structures. Geophys Res Lett 35(12)
Carton X (2001) Hydrodynamical modeling of oceanic vortices. Surv Geophys 22(3):179–263. doi:10.1023/A.1013779219578
Chaigneau A, Pizarro O (2005) Eddy characteristics in the eastern south pacific. J Geophys Res Oceans (1978–2012) 110(C6)
Chelton DB, Schlax MG, Samelson RM, De Szoeke RA (2007) Global observations of large oceanic eddies. Geophys Res Lett 34(15)
Chelton DB, Schlax MG, Samelson RM (2011) Global observations of nonlinear mesoscale eddies. Progress Oceanogr 91(2): 167–216
Faghmous JH, Frenger I, Yao Y, Warmka R, Lindell A, Kumar V (2015) A daily global mesoscale ocean eddy dataset from satellite altimetry. Scientific Data 2:150,028 EP–
Fang F, Morrow R (2003) Evolution, movement and decay of warm-core leeuwin current eddies. Deep Sea Res Part II: Top Stud Oceanogr 50(12):2245–2261
Frenger I, Gruber N, Knutti R, Munnich M (2013) Imprint of southern ocean eddies on winds, clouds and rainfall. Nat Geosci 6(8):608–612
Griffies SM, Winton M, Anderson WG, Benson R, Delworth TL, Dufour CO, Dunne JP, Goddard P, Morrison AK, Rosati A et al (2015) Impacts on ocean heat from transient mesoscale eddies in a hierarchy of climate models. J Clim 28(3):952–977
Haller G (2002) Lagrangian coherent structures from approximate velocity data. Phys Fluids 14(6):1851–1861
Hernández-Carrasco I, López C, Hernández-García E, Turiel A (2011) How reliable are finite-size lyapunov exponents for the assessment of ocean dynamics?. Ocean Modell 36(3):208–218
Isern-Fontanet J, García-Ladona E, Font J (2006a) Vortices of the mediterranean sea: an altimetric perspective. J Phys Oceanogr 36(1):87–103
Isern-Fontanet J, García-Ladona E, Font J (2006b) Vortices of the mediterranean sea: an altimetric perspective. J Phys Oceanogr 36(1):87–103
Lapeyre G (2002) Characterization of finite-time lyapunov exponents and vectors in two-dimensional turbulence. Chaos: Interdiscip J Nonlinear Sci 12(3):688–698
Lilly JM, Scott RK, Olhede SC (2011) Extracting waves and vortices from lagrangian trajectories. Geophys Res Lett 38(23). doi:10.1029/2011GL049727,l23605
Mancho AM, Hernández-García E, Small D, Wiggins S, Fernánde V (2008) Lagrangian transport through an ocean front in the northwestern mediterranean sea. J Phys Oceanogr 38(6):1222–1237
Mason E, Pascual A, McWilliams JC (2014) A new sea surface height–based code for oceanic mesoscale eddy tracking. J Atmos Ocean Technol 31(5):1181–1188
Mendoza C, Mancho AM (2010) Hidden geometry of ocean flows. Phys Rev Lett 105:038,501. doi:10.1103/PhysRevLett.105.038501
Munk W, Armi L (2001) Spirals on the sea: A manifestation of upper-ocean stirring. In: Proceedings of the 12th’ Aha Huliko’a Hawaiian Winter Workshop, DTIC Document, pp 81–86
Nencioli F, D’Ovidio F, Doglioli AM, Petrenko AA (2011) Surface coastal circulation patterns by in-situ detection of lagrangian coherent structures. Geophys Res Lett 38(17). doi:10.1029/2011GL048815.l17604
Özgökmen T M, Griffa A, Mariano AJ, Piterbarg LI (2000) On the predictability of lagrangian trajectories in the ocean. J Atmos Ocean Technol 17(3):366–383
Penven P, Echevin V, Pasapera J, Colas F, Tam J (2005) Average circulation, seasonal cycle, and mesoscale dynamics of the Peru current system: a modeling approach. J Geophys Res Oceans 110(C10)
Petelin B, Kononenko I, Malačič V, Kukar M (2015) Dynamic fuzzy paths and cycles in multi-level directed graphs. Eng Appl Artif Intell 37:194–206
Petersen MR, Julien K, Weiss JB (2006) Vortex cores, strain cells, and filaments in quasigeostrophic turbulence. Phys Fluids (1994-present) 026(2):601
Petersen MR, Williams SJ, Maltrud ME, Hecht MW, Hamann B (2013) A three-dimensional eddy census of a high-resolution global ocean simulation. J Geophys Res Oceans 118(4):1759–1774. doi:10.1002/jgrc.20155
Robinson RC (2012) An introduction to dynamical systems: continuous and discrete, vol 19. American Mathematical Society
Sadarjoen IA, Post FH (2000) Detection, quantification, and tracking of vortices using streamline geometry. Comput Graph 24(3):333–341
Sayol JM, Orfila A, Simarro G, López C, Renault L, Galán A, Conti D (2013) Sea surface transport in the western mediterranean sea: a lagrangian perspective. J Geophys Res Oceans 118(12):6371–6384. doi:10.1002/2013JC009243
Strogatz SH (2014) Nonlinear dynamics and chaos: with applications to physics, biology, chemistry, and engineering. Westview press
Stuart J (1967) On finite amplitude oscillations in laminar mixing layers. J Fluid Mech 29(3):417–440
Zhang Z, Wang W, Qiu B (2014) Oceanic mass transport by mesoscale eddies. Science 345(6194):322–324
Acknowledgments
D. Conti is currently a PhD fellowship (FPI/1543/2013) granted by the Conselleria d’Educaci, Cultura i Universitats from the Government of the Balearic Islands co-financed by the European Social Fund. A. Orfila acknowledges support from ENAP-Colombian Army through different grants. E. Mason is supported by a post-doctoral grant from the Conselleria dEducacio, Cultura i Universitats del Govern de les Illes Balears (Mallorca, Spain) and the European Social Fund. J.M. Sayol is supported by the JAE-Pre scholarships co-funded by CSIC and ESF. G.Simarro is supported by the Spanish Government through the Ramon y Cajal program. This work has been done thanks to financial support from H2020-EU JERICO-NEXT project. Comments from Prof. D. Chelton are greatly appreciated. Comments from three anonymous referees are greatly acknowledged.
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Appendix: Tracking algorithm
Appendix: Tracking algorithm
Description of existing tracking methods
A new tracking method is developed that is intended to increase the overall robustness of the eddy matching process by minimizing the mismatching of eddy pairs. In general, all eddy-tracking methods follow a two-step approach (Penven et al. 2005; Chelton et al. 2011; Mason et al. 2014):
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Preselection. Given an eddy with a determined sense of rotation (C/A) at frame k, the method searches for eddies at frame k + 1 that have the same sense of rotation and within a distance of δ s = c max Δt, where c max is a predefined limit for the propagation velocity and Δt is the time step between two consecutive frames. Some additional preselection criteria may be applied at this point, rejecting eddies at frame k + 1 having extremely different characteristics (i.e., radius, amplitude, etc.).
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Tracking criteria. If an eddy at frame k does not have any preselected eddy at frame k + 1, the eddy is terminated and the search stops. Furthermore, if only a single eddy at frame k + 1 is found in the preselection, it is assumed straightforwardly to be the same structure. If multiple eddies the frame k + 1 are preselected, an additional criterion is applied to identify the correct match. In such cases, Isern-Fontanet et al. (2006a) and Chelton et al. (2011) assume that the successful preselected eddy at frame k + 1 is the nearest one. In order to improve the matching decision, some authors take into account the spatial distances and also the similarity in other eddy properties, such as eddy radii and vertical component of the vorticity (Penven et al. 2005), or the area covered bye the eddy and its amplitude (Mason et al. 2014). Finally, eddies at frame k + 1 that are not linked to any eddy at frame k are labeled as being new eddies.
A new approach to matching of multiple eddy pairs
We implement the preselection as usual: we search for a detected eddy at frame k, eddies at frame k + 1 that are within a prescribed distance (considering distances between eddies as the distance between their centers). Here, we follow the approach of Chelton et al. (2011) where the maximum eddy celerity is 1.75 times the celerity of a long baroclinic Rossby wave. From all eddies satisfying this condition, we discard those satisfying \(r_{\max }/r_{\min } > \sqrt {10}\), where r max (r min) is the radius of the largest (smallest) eddy of the considered pair. This value eliminates eddies with very different radii, and is flexible enough to allow small differences between consecutive frames. Up to this point, nothing new has been introduced with respect to other methods. However, the main difference with other existing tracking algorithms is that we get the preselecting information forwards and backwards. For each detected eddy i at frame k, we store the preselected eddies at frame k + 1 (hereinafter called descendants, D k (i) ), and for each eddy j at frame k + 1 we store the list of ascendants, A k + 1(j). By symmetry in the criteria, if an eddy i at frame k is an ascendant of an eddy j at frame k + 1, then eddy j is a descendant of eddy i.
Figure 12 displays some possible combination treated in the tracking criteria. First, if an eddy at frame k does not have any descendants (Fig. 12, first case), the eddy dies. This case includes both the situation of a real eddy vanishing or when the system is not able to detect the descendant due to both inaccuracies in the algorithm or poorly resolved SSH data (sampling errors and noise in the measurements).
Second, if an eddy (i) at k has a single descendant (j) at k + 1, a link is not established directly; instead the algorithm looks through the list ascendants of j. If eddy j has a single ascendant, then A k (j) = i and the link is made (Fig. 12, third case where eddy #1 at k is linked with eddy 2 at k + 1). If eddy j at k + 1 has several ascendants, the link is not trivial and the method follows the general case described below. This is the situation shown in Fig. 12, third case, where eddies #2 and #3 at k are ascendants of eddy #1 at k + 1. This situation (and others of higher complexity where multiple pairs are found) are not discussed in the above presented tracking methods.
This multi-pair scenario embraces several possibilities: an eddy i at k having multiple descendants; an eddy having a single descendant but the former with multiple ascendants, etc. In the general case, the problem can be expressed as an eddy having multiple descendants, each of them having single or multiple ascendants and, further, each of them may have single or multiple descendants and so on. In these situations, the tracking algorithm considers all involved eddies at frames k and k + 1 in a coupled configuration since each single link might influence the other ones. Starting from an eddy at frame k, the configuration must be constructed iteratively, first listing its descendants (at k + 1) and, then, for each descendant adding its non repeated ascendants (at k). In a third iteration, for every new (non-repeated) ascendant, we add its (non-listed) descendants to the whole configuration. The iteration ends when no new eddies are found. A simple example is shown in Fig. 12, third case, where eddies #3 and #2 at frame k are connected sharing the same descendant (#1 at k + 1). A more complex configuration is shown in Fig. 12, last case, where eddies #1, #2, and #3 at k are linked with eddies #2, #1, and #4 at k + 1. For this example, Table 1 lists the ascendants and descendants and Table 2 shows the iteration process where black lines indicate previous connections and blue lines new connections. In the first iteration, D k (#1) = #2(new); in the second iteration A k + 1(#2)={#1(old),#2(new)}; the third iteration is D k (#2)={#1(new),#2(old)}; the fourth iteration is A k + 1(#1)={#2(old),#3(new)}; the fifth iteration is D k (#3)={#1(old),#4(new)}. The last iteration, A k + 1(#4) = #3(old) does not provide any new eddy at k and therefore the iterative process finishes, returning connected eddies #1, #2, and #3 at k and eddies #1, #2, and #4 at k + 1 (Table 3).
Once connections are established, all permutations of allowed pairs at k and k + 1 are computed including permutations with unmatched eddies. If there is a single permutation with the maximum number of links, it is the one chosen. Otherwise, the criteria is to choose the permutation which minimizes the collective distance that is defined as the sum of distances between the centers of eddy-pairs. If there are unmatched eddies at k in the selected permutation, they are considered to disappear, while unmatched eddies at k + 1 are considered to be newborns. In the example shown in Fig. 12, third case, two permutations are allowed, i.e., #3 at k with #1 at k + 1 leaving eddy #2 at k unmatched, and eddy #2 at k linked with eddy #1 at k + 1 leaving now #3 at k unmatched. Both permutations have the same number of links (one), and the collective distance criteria is reduced to link #1 at k + 1 with its closest eddy at k. It is worth noting that this very simple multi-pair case cannot be solved properly with methods that just perform the pre-selection forwards (and not backwards). In the example shown in Fig. 12, fourth case, there is a single permutation that allows all eddies to be linked, [#1(k)−#2(k + 1)],[#2(k),#1(k + 1)],[#3(k),#4(k + 1)], so this is the one the is chosen. Again, using a tracking system that is based only on minimization distances between individual pairs would match together [#2(k),#2(k + 1)],[#3(k), #1(k + 1)], leaving unpaired eddies #1 at frame k and #4 at frame k + 1.
The above methodology is applied iteratively to all frames to obtain the evolution of the eddies. Figure 13 shows for illustration purposes the trajectory of an eddy detected in the North Atlantic. The panels correspond to snapshots at 0, 10, 20, and 30 weeks from its birth. The detected SNs and CTs are plotted as circles and crossed circles, respectively, while the red circle represents the eddy traveling to the west in a path represented by the dashed red line.
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Conti, D., Orfila, A., Mason, E. et al. An eddy tracking algorithm based on dynamical systems theory. Ocean Dynamics 66, 1415–1427 (2016). https://doi.org/10.1007/s10236-016-0990-7
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DOI: https://doi.org/10.1007/s10236-016-0990-7