Empirical flow field data evaluation in a well-studied ocean region along
the US west coast revealed a surprisingly strong
relationship between the surface integrals of kinetic energy and enstrophy (squared vorticity). This
relationship defines a single isolated Gaussian super-vortex, whose fitted size parameter
is related to the mean eddy size, and the square of the fitted height parameter is proportional to the sum of the square of all individual eddy amplitudes obtained by standard vortex census.
This finding allows very effective coarse-grained eddy statistics with minimal computational efforts. As an illustrative example, the westward
drift velocity of eddies is determined from a simple cross-correlation analysis of kinetic energy integrals.
Introduction
Mesoscale eddies (MEs) are energetic, swirling, time-dependent circulatory flows on a characteristic scale
of around 100 km (see Fig. ), which are observed almost everywhere in satellite altimetry data of
global sea surface height .
The total volume transport by drifting
eddies is comparable in magnitude to that of the large-scale wind-driven and thermohaline circulations ;
therefore, MEs play a crucial role in global material and heat transport and mixing of oceans.
In spite of their importance, it is
far from trivial to identify and characterize MEs from remote sensing data.
The vast majority of the ME studies is based on some automatic algorithm that identifies and tracks the
eddies from gridded maps of sea level anomaly (SLA). Various Eulerian methods were developed and deployed in practice, such
as detecting closed contours of SLA , evaluating the geometry of the velocity vectors ,
determining contours of the Okubo–Weiss parameter or using
wavelet analysis to identify coherent eddy-like structures . Critical comparisons show that
none of the Eulerian methods are superior to the others .
The algorithms based on searching for finite-time Lagrangian coherent structures obey
a better theoretical foundation ; nevertheless, a recent test of 12 different approaches
revealed that the various methods often produce very different predictions for coherent structures. In addition, false positives and
negatives can be produced too . Apart from the difficulties of identifying MEs,
pointed out that the spatial resolution of gridded fields is also a critical limiting factor.
It is not surprising that small vortices are detected in large numbers at fine grid sizes.
However, it is somewhat unexpected that many large eddies remain unidentified by close contour
searching when the velocity field is represented at lower resolutions
.
Visualization of the geostrophic flow field on a randomly chosen day (13 October 2013)
from the data set over the US west coast by . Sea level anomalies (η) are color coded; blue
stream lines indicate flow directions. The centers of cyclonic (yellow dots) and anticyclonic (black dots) eddies are determined by
a standard algorithm .
The original aim of our work was a detailed analysis of kinetic energy budget of the oceanic surface
flow field along the US west coast. At the evaluation of integrated kinetic energy and enstrophy (squared vorticity), we found a non-trivial
strong temporal correlation between these quantities. Since the dominating flow features are obviously mesoscale eddies (Fig. ), it is rather
straightforward to formulate an explanation related to the description of individual ocean vortices. One of the basic models is the Gaussian geostrophic
vortex exhibiting the attractive features of finite total energy and total enstrophy over an infinite domain, and a simple closed relationship between them.
We demonstrate here that a single Gaussian super-vortex properly describes the empirical energy/enstrophy ratio over an extended region; furthermore,
the height and radius of such super-vortex are strongly related to the mean values over the same area obtained by classical vortex census.
Shielded Gaussian vortices
As for the shape of ocean MEs, the common picture is that they are close to Gaussian humps or troughs .
A detailed fitting procedure of about 5 million SLA profiles by revealed that around 50 % of MEs are indeed
Gaussian, another ∼40 % are Gaussian over a sloping background or merger of two close Gaussian eddies, and the rest have a quadratic core resembling Rankine vortices.
Characteristics of a shielded Gaussian geostrophic vortex with peak height η0=1 m and size parameter
R=10 km at an approximate location of 45∘ N latitude (Coriolis parameter f=10-4 s-1).
(a) Amplitude (see Eq. 1),
(b) tangential velocity (see Eq. 2) and
(c) vertical vorticity (see Eq. 3) as a function of radial distance r. Note that R is the radial distance of maximum tangential velocity (vertical red line), and 2R is
the distance of maximal vorticity in the shielding ring (dashed vertical red line). The “visual” radius based on closed contours of zero height anomaly is around 2.5–3 R.
An isolated Gaussian circular eddy in geostrophic equilibrium (where the hydrostatic pressure gradient force is balanced by the local Coriolis force) can be characterized by
the following radial profiles of height η, tangential velocity v and vertical vorticity ξ (in cylindrical coordinates):
1η(r)=η0exp-r22R2,2v(r)=-η0gfR2rexp-r22R2,3ξ(r)=η0gfR2r2R2-2exp-r22R2.
Here, η0 and R are the height and size parameters for the vortex, respectively,
g is the gravitational acceleration, and f=2Ωsin(φ) is the local Coriolis parameter at latitude φ with Ω=7.292×10-5 s-1 for the Earth. The label “shielded” in the title of this section refers to the core of such a vortex being surrounded by a ring of opposite vorticity ; see Fig. c.
The simplest model of planetary-scale dynamics of the ocean is a single layer of homogeneous fluid, described by the
two-dimensional (2-D) barotropic Navier–Stokes equations in a co-rotating frame of reference .
In the absence of dissipative processes, such a model conserves the total kinetic energy ∬KE=12∬v2dA and
total enstrophy ∬Z=12∬ξ2dA. An appealing property of an isolated Gaussian vortex is that its total kinetic energy and
enstrophy are finite over an infinite domain of integration:
4IKE=12∫0∞2πrv2(r)dr=g2πη022f2,5IZ=12∫0∞2πrξ2(r)dr=g2πη02f2R2.
Note that the total kinetic energy integral IKE depends only on the height parameter η0, reflecting self-similarity in the velocity field,
and that the ratio of the two integrals is simply IKE/IZ=12R2.
This very relationship was utilized in a recent paper by , in a different context of studying viscous decay
of individual MEs.
(a) The 10 years of daily values for total enstrophy (red) and rescaled total kinetic energy (blue) integrated over the offshore region
(westward from 125.0∘ W longitude; see Fig. ) and (b) correlation plot of the two quantities. The rescaling factor for the kinetic energy integral is 7.97×10-10 (see text).
Data analysis
Simple visual inspection of a reconstructed geostrophic flow field (Fig. ) reveals that MEs are indeed the dominating features.
The area shown in Fig. is an extremely well-studied region of the California Current System (CCS) both by observations and calibrated
high-resolution numerical simulations . Openly available data compiled by
comprise a set of fields of sea level anomalies by combining gridded daily altimeter fields with coastal tide gauge data .
The geographic area covers 32.0–48.5∘ N (latitude) and
135.0–111.25∘ W (longitude) with a spatial resolution of
0.25∘× 0.25∘. Daily mean geostrophic velocity fields are produced
for the period 1 January 1993–31 December 2014 (8035 d). The primary validation
compares geostrophic velocities calculated from the SLA values and
velocities measured at four mooring sites in the test region .
Visualization of the geostrophic flow field on the same day as in Fig. (13 October 2013)
from the data set over the US west coast by . Empirical vertical vorticity (ξ) is color coded; blue
stream lines indicate flow directions. The color mesh illustrates the spatial resolution well. The heavy dashed frame indicates the offshore region, where the
integrated quantities in Fig. are determined, and the yellow circle demonstrates the size of the hypothetical “super-vortex” related to mean
vortex statistics on the given day over the offshore region (see text).
Black squares illustrate the first 15 growing integration frames centered at the location
40.125∘ N, 130.125∘ W (see Fig. ).
Figure illustrates the total enstrophy (squared vorticity) and total kinetic energy (sum of squared velocity components) integrated over
the offshore region (see the dashed frame in Fig. ) for each day of the record. The correlation is strikingly strong, and it is not trivial. When the shore region is included,
much larger differences appear, especially when the area of integration is restricted to a narrow band along the shoreline.
Figure a clearly demonstrates that large correlation coefficients require large enough areas of integration; a value of 0.95
is reached around A=2.7×105 km2 (∼202 grid cells or 5∘× 5∘). Nevertheless, the geometry
of the area must not be a square.
The red and black symbols in Fig. a belong to meridional
stripes of width of 1 and 2∘ longitudes (smaller areas are stripes eastward from 125.0∘ W where the meridional
length is restricted by the land). Their apparent scatter, however, is not random; the correlation coefficients in equal areas of integration
(symbols lined up vertically in Fig. a) systematically increase with the distance from the shoreline.
(a) Pearson correlation coefficient for the total kinetic energy and enstrophy as a function of the area of integration. Blue
circles indicate growing correlations for square-shaped areas around a central grid cell in the offshore region (40.125∘ N, 130.125∘ W); see
Fig. .
Red crosses (black squares) denote correlation coefficients for meridional stripes of width of 1∘ (2∘) longitude.
(b) Fitted mean scale parameter Reff for a super-vortex determined from the ratio of integrated kinetic energy and enstrophy (in kilometers). Notations are the same as in panel (a).
(c) Fitted mean height parameter η0eff normalized by the square root of the area of integration L
(and rescaled for the sake of convenience)
for a super-vortex determined from the integrated kinetic energy; see Eq. ().
Notations are the same as in panel (a).
By exploiting the strong correlations, the ratio of integrated kinetic energy and
integrated enstrophy provides an effective
size parameter of a hypothetical Gaussian super-vortex as Reff=2∬KE/∬Z. Results for the temporal mean values of this quantity are shown in Fig. b.
Note that the obtained Reff≈50 km scale belongs to the 1σ width of a Gaussian profile given by Eq. (1).
A visual contour of the super-vortex on a SLA map would have a radius closer to
∼2.5–3 Reff≈125–150 km (see Figs. a and ).
As for the height parameter of the super-vortex, Eq. (4) is used for an estimate of η0eff shown in Fig. c.
Since it is obtained from the total kinetic energy integrated over various areas A, an
appropriate comparison requires a proper normalization. A practical choice somewhat correcting shape differences is the
characteristic length scale L=A.
The error bars are much larger than the ones in Fig. b as a consequence of the marked annual
oscillations shown in Fig. a. These oscillations are canceled when the ratio of strongly correlated kinetic energy
and enstrophy is considered. Similarly to the correlation coefficients in Fig. a, the fitted height values of η0eff
for the meridional stripes (red crosses and black squares) exhibit systematic changes with the distance from the shoreline, as discussed below.
(a) Normalized eddy-scale distributions obtained by individual eddy census with the
closed contour SLA method at three different level spacing parameters Δl; see legends.
Vertical dotted lines indicate the mean values of the histograms. Black curve denotes the normalized histogram of Reff
parameter of the super-vortex. The logarithm of frequencies is scaled on the vertical scale.
(b) Normalized eddy height distributions obtained by individual vortex census as in panel (a). The inset shows the histogram for the
height parameter of the super-vortex fit η0eff in meters. Both the eddy census and super-vortex fit were performed
over the offshore region (westward from 125.0∘ W longitude; see Fig. ).
Eddy census
The super-vortex fit makes only sense when the parameters have some relationship with the existing MEs. In order
to make such a comparison, we implemented the eddy census procedure of based on closed SLA contour searches.
The methodology is described in and ; here, we emphasize three particular details. (i) The SLA fields
in the data bank exhibit marked annual oscillations; daily spatial mean values are changing between -8.6 and 10.1 cm. Since
this range is comparable to the most common amplitude of the individual eddies (see below), we removed daily means
before the eddy census. (ii) In order to avoid differences due to various definitions of the eddy amplitude, we
adopted the following rule: when the algorithm identified the location (lat, long) of an eddy center, the amplitude value is
imported directly from the (corrected) SLA field. (iii) We adopted the “equivalent radius” as scale parameter for an eddy ;
that is, S=π-1Atot, where Atot is the total sum of grid cell areas identified inside closed SLA contours.
(a) Fitted mean super-vortex radius Reff and mean eddy scale 〈S〉 from eddy census, determined
in meridional bands and plotted as a function of mean distance from the shore.
(b) Square of fitted mean super-vortex height η0eff2 and the sum of squares of all individual eddy heights
∑η02 normalized by the area of integration or eddy census A.
(c) Estimated westward drift velocities by evaluating the cross-correlation function (Eq. 6) and from vortex tracking of MEs living at least 60 d.
Figure shows the results of eddy census. The histograms are very similar to previous statistics at the same
spatial resolution . Note that the eddy-scale histograms in Fig. a
are sensitive to the level spacing parameter Δl of the closed contour search; fine-scale scans identify smaller eddies in
a larger number. The oscillations at smaller eddy scales are due to the discretization error; the area of an eddy is composed
of an integer number of grid cells. It is clear that the fitted super-vortex parameter Reff fluctuates around the
mean values of eddy-scale histograms (black curve in Fig. a).
We reiterate here that Reff is an 1σ radius of a Gaussian
vortex, while S is closer to a “real” visual radius based on a closed contour estimate of zero height anomaly.
As for the super-vortex height η0eff, Fig. b illustrates that it is
much larger than the height of individual eddies, as expected, because it is related to the total kinetic energy over the test area
(the offshore region, in the particular case). For this reason, we compare the square of eddy amplitudes in what follows.
The significant advantage of using the super-vortex picture emerges when the fits are
performed over subregions of the test area.
We have shown already results for meridional stripes of widths of 1 and 2∘ in Fig. b and c.
Figure a illustrates local mean Reff values compared with local mean eddy scale 〈S〉 as a function of the mean distance from the shore.
Both quantities exhibit very good agreement and a clear tendency of growth
when eddies move away from the shore. The error bars reflect temporal fluctuations over the whole period of 8035 d
which are much larger for the eddy census data, because their frequency fluctuates strongly from day to day in
a given narrow meridional band.
Figure b is a comparison of the height parameters of the super-vortex and eddy census. It is reasonable to
consider a relationship between η0eff2 and ∑η02. The former measures
the total kinetic energy (see Eq. 4), while the latter is proportional to the sum of kinetic energies of all individual eddies
when we assume that all of them are Gaussian vortices. The sum of kinetic energies based on ∑η02 agrees
pretty well with direct counting, when the kinetic energy is determined by adding up squared velocity components for each
grid cell assigned to an eddy. Figure b illustrates that an empirical ratio of around 2 arises in each meridional stripe;
that is, the long-term mean value of kinetic energy for individually identified eddies is ∼50 % of the total kinetic energy
in the test region. Interestingly, reported on a partition ratio between 1 and 5 fluctuating strongly in time;
however, they note that the total kinetic energy obtained for satellite altimetry accounts only for half of the real value.
The tendency of initial growth up to ∼150 km (see Fig. b) might be related to the fact that eddies are generated
mostly along the shore, and later they slowly decay during the drift in open water.
A well-known characteristic of eddy trajectories is the strong tendency for purely
westward propagation . found globally that only about 0.25 % of the eddies have mean
drift directions that deviated by more than 10∘ from pure zonal;
however, and
obtained stronger
dispersion in the CCS study area. Together with the traditional eddy-tracking algorithm,
we used our approach to evaluate the cross-correlations of total kinetic energy I(t)=12∬v2dA between neighboring meridional
bands of width of a single grid cell (0.25∘):
X(τ)=〈[I(t)i-I‾i][I(t±τ)i-1-I‾i-1]〉σiσi-1,
where the time lag τ represents a temporal shift between the two time series by τ days, overbar denotes temporal mean, and σ is the
standard deviation in the given band.
Indeed, we find clear maxima at nonzero time lags (actual values are between 5 and 8 d) indicating that total kinetic energy and enstrophy are mostly advected in the offshore region;
production or loss is almost negligible (considering geostrophic flow). The time lag and distance of neighboring bands permit
an easy estimate of westward drift velocities; the results are shown in Fig. c. Drift velocity values in the literature
are of the same order of magnitude , similarly to our test. As for a direct validation, all individual eddy tracks are evaluated which had longer lifetime than 60 d (432 cyclonic and 422 anticyclonic MEs are identified).
The cut at 60 d is somewhat arbitrary; however, we think that the detection error from both the limited spatial and temporal resolutions is larger for short living vortices (note that
the typical westward traveling distance during 60 d is ∼155–200 km).
Drift values estimated from vortex tracking belong to the centers of eddies, and as expected from a stable β drift, no spatial dependence in the zonal direction is revealed.
Theoretical considerations suggest that anticyclonic eddies might drift faster than cyclonic ones ;
however, we could not detect statistically significant differences between the two subgroups of trajectories.
Conclusions
We proposed a simple description of geostrophic ocean surface flow fields by exploiting the following results. Firstly, a shielded Gaussian vortex has a finite total kinetic energy
and finite total enstrophy; the ratio of them is proportional to
the square of the radius of the vortex. Secondly, these two quantities determined from empirical velocity data are strongly correlated, and their ratio correlates with the
mean eddy size obtained from traditional eddy census. Thirdly, the fitted amplitude parameter is strongly related to the sum of all squared eddy amplitudes. While this description cannot replace traditional eddy census algorithms, it is certainly able to extract coarse-grained eddy statistics in order to follow temporal and regional changes of eddy activity.
Data availability
The ocean flow field data are accessible at 10.1038/sdata.2016.13. A direct link to the data download page is 10.7267/N9639MWJ.
Author contributions
IMJ designed the research;
IMJ and MV performed the research;
GT and JACG contributed new numerical/analytical tools;
IMJ and GT analyzed data; and IMJ, MV and JACG wrote
the paper.
Competing interests
The authors declare no conflict of interest.
Acknowledgements
This work was supported by the Max-Planck Institute for the Physics
of Complex Systems in the
framework of an Advanced Study Group on “Forecasting with Lyapunov Vectors”. Jason A. C. Gallas was supported by CNPq, Brazil.
Financial support
This research has been supported by the Hungarian National Research, Development and Innovation Office (grant nos. FK-125024 and K-125171).The article processing charges for this open-access publication were covered by the Max Planck Society.
Review statement
This paper was edited by Eric J. M. Delhez and reviewed by two anonymous referees.
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