1. Introduction
Considerable effort has recently been devoted to investigating the routes to dissipation of geostrophic mesoscale eddy kinetic energy (EKE), the largest reservoir of kinetic energy in the ocean (Ferrari and Wunsch 2009). The focus has been on identifying and quantifying possible energy transfer mechanisms from the balanced geostrophic eddy field to unbalanced ageostrophic motions, and the subsequent forward energy cascade to dissipation. Potential transfer mechanisms include spontaneous imbalance via submesoscale instabilities (Molemaker et al. 2005; Capet et al. 2008; Molemaker et al. 2010) that are predominantly confined to the surface boundary layer (SBL; D’Asaro et al. 2011; McWilliams 2016), spontaneous emission of internal waves (IWs; Vanneste 2013), wave capture (Bühler and McIntyre 2003, 2005), and wave excitation due to deep geostrophic flow encountering rough topography (Nikurashin et al. 2013).
Several studies have also examined different mechanisms through which externally forced near-inertial IWs (NIWs) can extract energy from the balanced flow. Xie and Vanneste (2015, hereinafter XV15) and Wagner and Young (2016) developed an asymptotic theory for the interaction between low Rossby number quasigeostrophic (QG) flow and NIWs. They derived energy and action conservation laws and illustrated a mechanism through which NIWs can directly extract energy from the balanced QG flow. Using mesoscale eddy-permitting numerical simulations of wind-forced channel flow, Taylor and Straub (2016) demonstrated that near-inertial Reynolds stresses, associated with forced NIWs, can provide a kinetic energy sink for mesoscale geostrophic motions. Focusing on submesoscale processes with Rossby number of O(1), Thomas (2012) investigated the effects of frontogenetic strain on IWs in an idealized, two-dimensional model and suggested that IWs can extract energy from a geostrophically balanced deformation field and transfer it to ageostrophic motion, thereby catalyzing the forward energy cascade. Later, Whitt and Thomas (2015, hereinafter WT15) employed a slab mixed layer model and two-dimensional numerical simulations to demonstrate that inertial oscillations can indeed extract energy from a unidirectional, laterally sheared geostrophic current. Finally, Grisouard and Thomas (2015) showed how upward-propagating IWs experience critical reflection against the ocean surface in a geostrophically balanced front and, subject to viscous effects, can exchange energy with the front eventually leading to its dissipation.
In the present study, we use three-dimensional, submesoscale-resolving numerical simulations of wind-forced energetic mesoscale and submesoscale flow as well as IWs in order to examine the mechanisms leading to the enhancement of EKE dissipation by externally forced IWs, either near the surface or in the interior. We aim to distinguish between direct IW extraction mechanisms followed by an IW forward cascade to dissipation and stimulated imbalance, a process through which externally forced IWs stimulate the forward mesoscale to submesoscale energy cascade to dissipation. We choose a channel configuration, centered at high latitudes, similar to the Antarctic Circumpolar Current (ACC). Previous studies of energy transfers in the ACC suggest that topographically generated internal waves (Nikurashin et al. 2013) as well as submesoscale instabilities (Barkan et al. 2015) can provide important pathways to dissipation for EKE. Both studies, however, considered steady wind forcing only, thereby excluding wind generated IWs and thus ignoring any possible interactions. We show that externally forced IWs enhance EKE dissipation and catalyze energy transfer from low-frequency (LF), large-scale motions. The ratio of the enhanced EKE dissipation to the IW wind work in our solutions is about 1.3, demonstrating the efficiency of the IW-mediated routes to dissipation.
2. Experimental setup
We consider an idealized channel flow on an f plane configured to resemble the dynamics of the ACC (although without loss of generality we take f > 0). The zonal, meridional, and vertical 
A schematic illustrating the experimental setup discussed in section 2. The underlying plotted field is the Rossby number Ro = ζ/f, where ζ is the vertical component of vorticity, and f is the Coriolis frequency.
Citation: Journal of Physical Oceanography 47, 1; 10.1175/JPO-D-16-0117.1
The LFW simulation is run for 4 months starting from a lower-resolution equilibrated solution until a steady state in the volume-averaged kinetic energy equation is reached. The COMB solution is run for 4 months starting from the equilibrated high-resolution LFW solution and the HFW solution is run for 4 months starting from rest. In all cases only the last 3 months are analyzed. Figure 2 shows the initial stratification profile for all solutions, and the time- and zonal-averaged perturbation density and zonal velocity from the COMB solution.
(left) The initial stratification profile for the three numerical solutions. (right) The zonally averaged zonal velocity (color) and perturbation density field (contour lines). Isopycnal interval is 0.04 kg m−3 starting from 27.58 kg m−3 (bottommost contour).
Citation: Journal of Physical Oceanography 47, 1; 10.1175/JPO-D-16-0117.1
3. Overview of results, comparison with observations
Representative snapshots of horizontal slices of the Rossby number Ro, the vertical component of relative vorticity normalized by f, at the surface and vertical velocity w at z = −150 m are shown in Fig. 3. The elongated zonal structures of both fields in the HFW solution are IWs that propagate northward and southward throughout the domain. The Rossby numbers are small compared with the LFW and COMB solutions. In both the LFW and COMB solutions, there is one large anticyclone and two midsize cyclones along with smaller eddies, fronts, and filaments. These flow features have Ro ~ O(1), evidence of ageostrophic dynamics across a range of spatial scales. The COMB solution exhibits smaller-scale features both within and between the larger eddies compared with the LFW solution, a first indication for the interactions among mesoscales, submesoscales, and IWs. The vertical velocity field shows that smaller scales are already apparent within the cyclones and anticyclones in the LFW solution. However, in the COMB solution the magnitude of the vertical velocity is larger than the sum of HFW and LFW (bottom right).
Snapshots of horizontal slices of Rossby number, vertical vorticity normalized by the (top) Coriolis frequency at the surface and (bottom) vertical velocity at 150-m depth for (left) HFW, (center) LFW, and (right) COMB solutions. Note the different colorbar ranges.
Citation: Journal of Physical Oceanography 47, 1; 10.1175/JPO-D-16-0117.1
Snapshots of the small-scale kinetic energy dissipation rate [(5)] for the LFW and COMB solutions are shown in Fig. 4. In LFW most of the dissipation is observed in the anticyclone and midsize cyclones, suggesting that without HF forcing a kinetic energy transfer to smaller scales is predominantly active in these coherent structures. In COMB, one can observe an overall enhancement in dissipation at the surface in the vicinity of frontal regions as well as in the interior of the anticyclonic and cyclonic eddies.
Snapshots of small-scale kinetic energy dissipation [(5); W kg−1] for the (top) LFW and (bottom) COMB solutions.
Citation: Journal of Physical Oceanography 47, 1; 10.1175/JPO-D-16-0117.1
The frequency and wavenumber spectra of the horizontal velocity at z = −150 and −1000 m for the three solutions are shown in Fig. 5. The energy fraction in the IW frequency band in the HFW solution (brown curve, Fig. 5a) contains ~90% of the total energy. The energy fraction in the frequency band between f and 1.1f (near-inertial frequency band) contains ~77% of the total energy. This is a good indication that although the stochastic forcing [(2)] acts on all frequencies it predominantly excites IWs. Comparing the LFW and COMB solutions (Figs. 5a,b) shows that the frequency spectral slopes are shallower for COMB at both depths. The COMB spectral slope at z = −1000 m (blue) matches observations from the Southern Ocean (Phillips and Rintoul 2000; Ferrari and Wunsch 2009, their Fig. 2b) in both sub- and superinertial frequencies as well as the generic Garrett–Munk (GM) spectrum (Garrett and Munk 1972) in the IW band (dashed magenta line in Fig. 5b). Similarly, the wavenumber spectral slope at 1000-m depth in the COMB solution is in good agreement with that of the GM spectra (dashed magenta line in Fig. 5d). In addition, the wavenumber spectral slopes in the COMB solution are much shallower than in the LFW solution at both depths (Figs. 5c,d), illustrating that more energy is found at smaller scales in the COMB solution, as suggested by Figs. 3 and 4. Figure 6 shows that the COMB energy content in the near- to superinertial frequency band is larger by an order of magnitude than that associated with the linear superposition of the HFW and LFW solutions. Furthermore, the similar energy content in LFW and COMB at lower frequencies illustrates that the amount of LF energy that is injected by the HF forcing [(2)] is negligible. Because the wavenumber spectral slope of both the HFW and LFW solutions is steeper than that of the COMB solution (Figs. 5c,d), it is evident that the energy levels of the latter are higher than the linear superposition of the first two.
(a),(b) Horizontal velocity frequency spectra and (c),(d) meridionally averaged wavenumber spectra at z = −150 m (black) and z = −1000 m (blue). (a) and (c) are for LFW, and (b) and (d) are for COMB. Brown lines in (a) and (c) show the HFW spectra at z = −150 m. The dashed magenta line in (b) and (d) corresponds to the Garret Munk spectrum. Dashed red lines denote the inertial frequency.
Citation: Journal of Physical Oceanography 47, 1; 10.1175/JPO-D-16-0117.1
Horizontal velocity frequency spectra of the LFW solution, COMB solution, and HFW + LFW solutions. Compare with Fig. 5. The values of the LFW and HFW + LFW spectra in the superinertial frequency range of the top panel are similar.
Citation: Journal of Physical Oceanography 47, 1; 10.1175/JPO-D-16-0117.1
4. Energetics
a. Volume-averaged kinetic energy
In what follows, we distinguish the eddy contributions to ϵ, εd, 〈wb〉, where eddy components, denoted by primes, are defined as perturbation from a zonal mean 
The terms in (4), computed for the LFW and COMB solutions, are shown in Fig. 7. In the LFW case almost all of the wind work is dissipated via bottom drag (Fig. 7a). The eddy decomposition (Fig. 7c) shows that most of the energy dissipated by bottom drag is associated with the mean flow (~90%). This is an expected result for a flat-bottom channel configuration, where the mean ACC is unrealistically strong (Munk and Palmén 1951; Tréguier and McWilliams 1990). The eddy conversion term 
Volume-averaged terms in the Ek evolution equation [(4); mW m−2] for the (a),(c) LFW solution and (b),(d) COMB solution. (a) and (b) display total quantities while (c) and (d) display eddy quantities. Values in the legend denote time-averaged quantities and standard errors.
Citation: Journal of Physical Oceanography 47, 1; 10.1175/JPO-D-16-0117.1
In the COMB solution (Figs. 7b,d), 
b. Low-frequency/high-frequency energy conversions
Using (9), (10), and (11), we aim to quantify energy transfers between the different temporal scales in the solutions and distinguish between transfers from low to intermediate frequencies and transfers from low to high frequencies. To this end, we apply Gaussian filters with two filter widths: 
Frequency spectra of the horizontal unfiltered velocity (solid black), the (a) 
Citation: Journal of Physical Oceanography 47, 1; 10.1175/JPO-D-16-0117.1
The time- and volume-averaged shear production (SP), APE production (APEP), and buoyancy conversion (BP) for the LFW and COMB solutions are shown in Table 1. For all filter widths, SP and APEP increase in the COMB solution relative to the LFW solution, indicating an enhanced LF to HF kinetic and APE energy transfer. BP is decreased in the COMB solution for the 
Time- and volume-averaged HF to LF SP [(10)], HF APE to HF Ek BP [(9)], and HF to LF APEP [(11)] (mW m−2) computed using Gaussian filters with filter widths of 0.3f (
To examine the vertical structure of the conversion terms we compute the horizontal- and time-averaged values of SP, APEP, and BP (Fig. 9). There is a substantial increase in SPver [(14)] of the COMB solution in the upper ocean (Fig. 9a) for both filter widths. This indicates that a substantial fraction of the Ek that is transferred to the IW frequency band is associated with SPver. This is in agreement with the increased ratio of vertical to horizontal EKE dissipation discussed in section 4a. On the contrary, there is significant increase in SPhor of the COMB solution in the upper ocean for the 
Time- and horizontal-averaged vertical [(14)] and horizontal [(13)] components (10−9 W kg−1) of (a),(b) SP, (c) BP, and (d) APEP for the LFW (black) and COMB (green) solutions computed using 0.3f (dashed) and 0.9f (solid) Gaussian filter widths.
Citation: Journal of Physical Oceanography 47, 1; 10.1175/JPO-D-16-0117.1
(a),(d) Time- and zonal-averaged HF–LF SP [(10)], (b),(e) HF–HF BP [(9)], and (c),(f) HF–LF APEP [(11)] for the (top) LFW case and (bottom) COMB case (10−9 W kg−1). See (8) and appendix B for details. Dashed vertical lines denote the regions dominated by the anticyclonic eddy and midsize cyclonic eddies (see Fig. 3).
Citation: Journal of Physical Oceanography 47, 1; 10.1175/JPO-D-16-0117.1
5. Internal wave effects on coherent eddies
We now focus on the effects the HF wind forcing and the excited IWs have on the large, coherent, anticyclonic eddy and midsize cyclonic eddies seen in Figs. 1, 3, and 4. These coherent eddies are not to be confused with the “eddies” discussed in section 4a, which were defined as a perturbation from a zonal mean.
The time- and zonally averaged EKE dissipation for the LFW and COMB solutions are shown in Fig. 11. There is a significant increase in dissipation in the COMB case both near the surface where submesoscale fronts and filaments are most active and at depth in the regions where the coherent eddies are found. This is in agreement with Fig. 4, the volume-averaged quantities in Fig. 7, and the spatial patterns of LF–HF energy conversion in Fig. 10. To compare the level of enhanced dissipation within the cyclonic and anticyclonic eddies we use the eddy tracking algorithm described in appendix C. There is a four/thirtyfold increase in the dissipation rate inside the anticyclonic/cyclonic eddies, respectively (Table 2). Accordingly, a representative pattern of the azimuthal- and radial-averaged dissipation within the cyclonic and anticyclonic eddies is much larger in the COMB solution (Fig. 11f). Inside the anticyclonic eddy, the largest dissipation increase is closer to the bottom, whereas inside the cyclonic eddies the dissipation increase is roughly constant with depth. These dissipation profiles imply that the interactions with the externally forced IWs extend to depth and that they are manifested differently in cyclones and anticyclones. There is a corresponding increase in the root-mean-square (RMS) perturbation vertical velocity and Rossby number in the COMB solution for both cyclonic and anticyclonic eddies (Figs. 11d,e). The increase in RMS w is largest between 300- and 1000-m depth for the cyclonic eddies and between 1000 and 1500 m for the anticyclonic eddy. The azimuthal velocity profile remains largely unchanged between LFW and COMB solutions in the anticyclonic eddy (Fig. 11c). Conversely, an approximately depth-uniform azimuthal velocity decrease is evident in the cyclonic eddy for the COMB solution.
Time- and zonal-averaged EKE dissipation for the (a) LFW solution and (b) COMB solution. Dashed vertical lines denote the regions dominated by the anticyclonic eddy and midsize cyclonic eddies (see Figs. 3, 10). Azimuthal- and radial-averaged (c) azimuthal velocity, (d) RMS perturbation vertical velocity, (e) RMS perturbation Rossby number, and (f) log dissipation for representative snapshots of an anticyclonic eddy (solid lines) and cyclonic eddy (dashed lines) for the LFW (black lines) and COMB (green lines) solutions. Perturbations are from an azimuthal average.
Citation: Journal of Physical Oceanography 47, 1; 10.1175/JPO-D-16-0117.1
Time- and volume-averaged kinetic energy dissipation computed in the interior of the larger anticyclonic and cyclonic eddies and elsewhere in the top 1000 m (see appendix C for detail). All values are in mW m−2.
Next, we compute the time- and eddy volume–averaged energy conversion terms (9), (10), and (11) from mesoscale to IW frequencies (Table 3) in order to assess the significance of direct extraction in the coherent eddies. Despite the large standard errors, the following quantitative statements can be made: In the anticyclonic eddy total, horizontal, and vertical shear productions are positive in both the LFW and COMB solutions. There is an increase in the vertical shear production for the COMB solution; however, the large standard errors make it impossible to confirm that the reductions in the total and horizontal shear productions are statistically significant. BP in the COMB solution decreases to a negative value, indicating a conversion from HF Ek to HF APE. The LF to HF APE transfer is marginally positive in both cases without any statistically significant change. There is thus little evidence in favor of direct extraction within the anticyclonic eddy.
Time- and volume-averaged temporal conversion terms (mW m−2) computed using 
Conversely, in the cyclonic eddies, total and horizontal shear productions are positive for the COMB solution, whereas the same quantities have large standard errors for the LFW solution indicating that the LF to HF Ek transfer is often reversed. The vertical shear production is negative in both cases with no clear difference in magnitude. APE production is positive for both solutions and larger in COMB. This suggests that direct extraction is more prevalent within the cyclonic eddies.
Finally, we evaluate whether temporal energy transfers correspond to spatial energy transfers and compute the spatial conversion terms SPS, APEPS, and BPS in the interior of the eddies (Table 4). The terms SPS, APEPS, and BPS are similar to their temporal counterparts; however, instead of a temporal filter we apply a spatial two-dimensional symmetric Gaussian filter with filter widths of 7.8 km. The filter widths were chosen to give low-pass spatial scales larger than ~30 km, in agreement with the large mesoscale structures shown in Figs. 1 and 3. Similarly to the temporal conversion terms, positive SPS denotes Ek transfer from large scales to small scales, positive APEPS denotes large-scale to small-scale APE transfer, and positive BPS denotes an energy conversion from small-scale APE to small-scale Ek.
Time- and volume-averaged spatial conversion terms (mW m−2) computed in the interior of the larger anticyclonic and cyclonic eddies over the top 850 m (see appendixes B and C for detail). A horizontally symmetric Gaussian filter was used with a 7.8 km × 7.8 km filter area. Bold values have large standard errors and are therefore not statistically significant.
In the anticyclonic eddy, there is an increase in SPS and 
In both the anticyclone and the cyclones BPS of the COMB solution is reduced, implying less small-scale APE is converted to small-scale Ek. The large standard errors indicate a continuous back and forth exchange of small-scale kinetic and small-scale available potential energies.
6. Summary and discussion
Submesoscale-resolving numerical simulations of an idealized ACC are used to investigate the interaction between energetic mesoscale and submesoscale flows, generated by steady [low frequency (LF)] wind forcing and an internal wave (IW) field, excited by stochastic [high frequency (HF)] wind forcing. In particular, we aim to distinguish between stimulated imbalance, a mechanism through which externally forced IWs trigger a forward mesoscale to submesoscale eddy kinetic energy (EKE) cascade to dissipation, and direct extraction mechanisms of mesoscale EKE by IWs, followed by a forward IW energy cascade to dissipation. To this end we examine three solutions: one forced by HF wind only (HFW), one forced by LF wind only (LFW), and one by a combination of the two (COMB).
The HFW solution is characterized by an active IW field predominately at near-inertial frequencies. The LFW solution is characterized by a larger anticyclonic eddy, two midsize cyclonic eddies along with smaller fronts, filaments, and eddies, all of which have Ro ~ O(1). The COMB solution combines the characteristic phenomena in the HFW and LFW solutions, resulting in a more active submesoscale field, with smaller spatial and temporal scales and more abundant fronts and filaments (Figs. 1, 3–6).
Although neither the LFW nor the HFW frequency spectral slopes independently match observations at either sub- or superinertial frequencies, the COMB solution does (Fig. 5). This suggests that the interactions among IWs mesoscales and submesoscales are significant at a wide range of temporal and spatial scales, as is manifested by the resulting shallower frequency and wavenumber spectra. In addition, it implies that more energy is being transferred from LF and large-scale motions to higher frequencies and smaller scales.
The fact that the COMB frequency and wavenumber spectral slopes match the GM ones, whereas the HFW spectral slopes do not, suggests that mesoscale and submesoscale motions can affect the HF energy distribution. Previous studies (Van Meurs 1998; Ponte and Klein 2015) have discussed the possibility for the scattering of IWs by mesoscale features; however, they did not consider the full nonlinear interactions between the scattered IWs and the balanced motions. More work is required to establish how much of the HF small-scale energy content is associated with IWs versus other unbalanced motions.
a. Stimulated imbalance versus direct extraction mechanisms
One can estimate the importance of the forward EKE cascade in these solutions, either by stimulated imbalance or direct extraction mechanisms, by comparing the ratio between the small-scale EKE dissipation ϵ′ to the total EKE dissipation 
To assess whether the IW-induced geostrophic EKE transfer to dissipation is a result of direct extraction mechanisms or stimulated imbalance we apply a filtering approach (section 4b) to analyze the LF to HF Ek and APE transfers. Using filter widths of 48 (
A schematic illustrating the LF to HF energy pathways in the COMB solution associated with the 
Citation: Journal of Physical Oceanography 47, 1; 10.1175/JPO-D-16-0117.1
We find an enhanced LF to HF transfer of Ek in the COMB solution, where about half of the LF Ek is transferred to submesoscale frequencies, predominantly due to SPhor [(13)], and another half is transferred all the way to IW frequencies, predominantly due to SPver [(14); Table 1 and Figs. 9 and 10]. This implies that both direct extraction mechanisms and stimulated imbalance are important in this case. Taylor and Straub (2016) have also observed an induced LF to HF Ek transfer in the presence of IWs. Their mesoscale-permitting resolution, however, suggests that stimulated imbalance could not have been resolved in their solutions. The reduction in BP [(9)] for the COMB solution implies that less HF APE is converted to HF Ek, and the filter width comparison suggests the reduction occurs at submesoscale frequencies. We associate this reduction with the, apparently, more efficient forward route for submesoscale APE provided by stimulated imbalance and direct extraction mechanisms, as indicated by the increase in LF to HF transfer of APE (APEP) in the COMB solution. This increase in APEP is to submesoscale frequencies in the upper ocean and to IW frequencies at depth. It illustrates another pathway for Ek dissipation whereby LF APE is converted to HF APE, which, in turn, is converted to HF Ek via BP and is eventually dissipated. Note that a careful quantitative analysis of LF–HF APE transfers for high Ro flows requires a more general definition of APE (Winters et al. 1995; Winters and Barkan 2013) and is beyond the scope of this paper.
b. Comparison with existing theories
We can thus estimate an upper bound on the amount of excess dissipation due to stimulated emission by estimating Rog locally in our solutions as 
c. The interaction of IWs with coherent cyclones and anticyclones
To investigate the interactions between IWs and the larger coherent anticyclone and cyclones, we computed the time- and volume-averaged temporal (SP, BP, and APEP) and spatial (SPS, BPS, APEPS) energy conversion terms in the interior of the eddies (Tables 3, 4). This allowed us to determine whether there is a connection between forward energy transfers in temporal and spatial scales and assess the differences between cyclones and anticyclones. The temporal conversion terms were computed using the 
Inside the cyclonic eddies, Ek and APE are transferred to IW frequencies and smaller scales in the COMB solution. The large standard errors associated with this computation (appendix C) make it difficult to quantify which of the energy transfers, spatial or temporal, is amplified more compared with the LFW solution. Nevertheless, this analysis suggests that there is a connection between the two and that direct extraction may be important.
Inside the anticyclonic eddy LF Ek and APE are transferred to HF Ek and APE in the IW frequency band for both LFW and COMB solutions, without any statistically significant enhancement in the latter. Spatially, there is an inverse horizontal Ek transfer to large scales in the LFW solution that is arrested in the COMB solution. This implies that direct extraction is less prevalent in this case and illustrates the difference between IW–cyclone and IW–anticyclone interactions.
d. Final remarks
We hypothesize that stimulated imbalance may be associated with IW-triggered instabilities. Further understanding of the nature of submesoscale instabilities of high Ro eddies, fronts, and filaments is required to understand the way in which IWs can stimulate them, thereby leading to a forward energy cascade. Another possibility is that stimulated imbalance is associated with LF to IW extraction followed by an IW to submesoscale transfer, as suggested by Thomas (2012).
In our solution analysis it was presumed that time scales of O(2–4) days are associated with submesoscale motions and that time scales shorter than a day are associated with IWs; however, Doppler shifting of IWs by lower-frequency motions is also a possibility. Removing the effects of Doppler shifting by applying Lagrangian frame temporal filters, generalizing the methodology described in Nagai et al. (2015), was not attempted in this study and is left to future work. Note that an accurate quantification of IW–submesoscale energy exchanges relies on having a clear scale separation between the two motions, even in a Lagrangian reference frame, but submesoscale advective features of O(f) and higher are not entirely implausible. Furthermore, it is not obvious how linear are the IWs in these solutions and therefore whether they are expected to obey the classical IW dispersion and polarization relations. These outstanding issues have strong implications for any theory of wave–vortex interaction.
Acknowledgments
This work was supported by the National Science Foundation (OCE-1061027 and ONR N00014141062). XSEDE computing resources at the National Institute of Computational Sciences, University of Tennessee, were made available under Grants TG-OCE120004 and TG-OCE150010. We thank the editor and two anonymous reviewers for helpful suggestions to improve the presentation and interpretation of our results.
APPENDIX A
Details of Numerical Simulations
The output files consist of three-dimensional velocity, buoyancy, and pressure fields saved once every 12 h and two-dimensional velocity, buoyancy, and pressure cross sections at selected locations saved once every hour. In addition, during 1 month of the simulation after equilibrium has been reached the full three-dimensional fields are saved once every hour.
APPENDIX B
Derivation of the Filtered Energy Equations
In section 5 and Table 3, we compute SPS, APEPS, and BPS, which are identical to (10), (11), and (9), respectively, for spatial as opposed to temporal filters.
APPENDIX C
Eddy Tracking Algorithm
To compute volume-averaged quantities in the interior of the large anticyclonic eddy and the midsize cyclonic eddies, we used the following eddy tracking algorithm. For each snapshot, we applied at the surface a symmetric, two-dimensional Gaussian filter with filter area of 7.8 km × 7.8 km. Cyclonic eddies were defined as the regions with low-pass filtered Ro > 0.35 in the northern half of the domain (y > 100 km), and the anticyclonic eddy was defined as the region with low-pass filtered Ro < −0.35 in the southern half of the domain (y < 100 km). This worked well because throughout the simulations the large anticyclonic/cyclonic eddies were indeed confined to the southern/northern half of the domain, respectively. The threshold Ro was chosen such that the fraction of the domain in which an eddy is wrongly identified (i.e., the fraction of the domain outside of the large anticyclonic eddy and midsize cyclonic eddies that satisfies the threshold criterion) is less than 0.01%.
The eddy volume–averaged dissipation in Table 2 is computed using the above algorithm over the top half of the domain (z > −1000 m) once per day and then time averaged. The eddy volume–averaged energy conversion terms shown in Tables 3 and 4 are computed using the above algorithm over the top 850 m once per day and then time averaged. We picked a smaller domain for the energy conversion terms in order to reduce standard errors. The large computational cost and disk space requirements of computing three-dimensional conversion fields did not allow us to do it more frequently and resulted in considerable standard errors (bold numbers in Tables 3 and 4 indicate where the standard errors are too large and computed values are not statistically significant). The differences between the identified cyclonic and anticyclonic volumes in the LFW and COMB solutions were less than 5%, indicating that a comparison between them is robust.
REFERENCES
Alford, M. H., 2003: Improved global maps and 54-year history of wind-work on ocean inertial motions. Geophys. Res. Lett., 30, 1424, doi:10.1029/2002GL016614.
Barkan, R., K. B. Winters, and S. G. L. Smith, 2013: Rotating horizontal convection. J. Fluid Mech., 723, 556–586, doi:10.1017/jfm.2013.136.
Barkan, R., K. B. Winters, and S. G. L. Smith, 2015: Energy cascades and loss of balance in a reentrant channel forced by wind stress and buoyancy fluxes. J. Phys. Oceanogr., 45, 272–293, doi:10.1175/JPO-D-14-0068.1.
Bühler, O., and M. E. McIntyre, 2003: Remote recoil: A new wave–mean interaction effect. J. Fluid Mech., 492, 207–230, doi:10.1017/S0022112003005639.
Bühler, O., and M. E. McIntyre, 2005: Wave capture and wave–vortex duality. J. Fluid Mech., 534, 67–95, doi:10.1017/S0022112005004374.
Capet, X., J. C. McWilliams, M. J. Molemaker, and A. F. Shchepetkin, 2008: Mesoscale to submesoscale transition in the California Current System. Part III: Energy balance and flux. J. Phys. Oceanogr., 38, 2256–2269, doi:10.1175/2008JPO3810.1.
D’Asaro, E., C. Lee, L. Rainville, R. Harcourt, and L. Thomas, 2011: Enhanced turbulence and energy dissipation at ocean fronts. Science, 332, 318–322, doi:10.1126/science.1201515.
Ferrari, R., and C. Wunsch, 2009: Ocean circulation kinetic energy: Reservoirs, sources, and sinks. Annu. Rev. Fluid Mech., 41, 253–282, doi:10.1146/annurev.fluid.40.111406.102139.
Garabato, A. N., K. L. Polzin, B. A. King, K. J. Heywood, and M. Visbeck, 2004: Widespread intense turbulent mixing in the Southern Ocean. Science, 303, 210–213, doi:10.1126/science.1090929.
Garrett, C. J. R., and W. H. Munk, 1972: Space-time scales of internal waves. Geophys. Fluid Dyn., 3, 225–264, doi:10.1080/03091927208236082.
Grisouard, N., and L. N. Thomas, 2015: Energy exchanges between density fronts and near-inertial waves reflecting off the ocean surface. J. Phys. Oceanogr., 46, 501–516, doi:10.1175/JPO-D-15-0072.1.
Jiang, J., Y. Lu, and W. Perrie, 2005: Estimating the energy flux from the wind to ocean inertial motions: The sensitivity to surface wind fields. Geophys. Res. Lett., 32, L15610, doi:10.1029/2005GL023289.
Large, W. G., J. C. McWilliams, and S. C. Doney, 1994: Oceanic vertical mixing: A review and a model with a nonlocal boundary layer parameterization. Rev. Geophys., 32, 363–403, doi:10.1029/94RG01872.
Leonard, A., 1974: Energy cascade in large-eddy simulations of turbulent fluid flows. Advances in Geophysics, Vol. 18, Academic Press, 237–248, doi:10.1016/S0065-2687(08)60464-1.
McWilliams, J. C., 2016: Submesoscale currents in the ocean. Proc. Roy. Soc. London, 472A, 20160117, doi:10.1098/rspa.2016.0117.
Molemaker, M. J., J. C. McWilliams, and I. Yavneh, 2005: Baroclinic instability and loss of balance. J. Phys. Oceanogr., 35, 1505–1517, doi:10.1175/JPO2770.1.
Molemaker, M. J., J. C. McWilliams, and X. Capet, 2010: Balanced and unbalanced routes to dissipation in an equilibrated Eady flow. J. Fluid Mech., 654, 35–63, doi:10.1017/S0022112009993272.
Munk, W. H., and E. Palmén, 1951: Note on the dynamics of the Antarctic Circumpolar Current. Tellus, 3, 53–55, doi:10.1111/j.2153-3490.1951.tb00776.x.
Nagai, T., A. Tandon, E. Kunze, and A. Mahadevan, 2015: Spontaneous generation of near-inertial waves by the Kuroshio Front. J. Phys. Oceanogr., 45, 2381–2406, doi:10.1175/JPO-D-14-0086.1.
Nikurashin, M., G. K. Vallis, and A. Adcroft, 2013: Routes to energy dissipation for geostrophic flows in the Southern Ocean. Nat. Geosci., 6, 48–51, doi:10.1038/ngeo1657.
Phillips, H. E., and S. R. Rintoul, 2000: Eddy variability and energetics from direct current measurements in the Antarctic Circumpolar Current south of Australia. J. Phys. Oceanogr., 30, 3050–3076, doi:10.1175/1520-0485(2000)030<3050:EVAEFD>2.0.CO;2.
Ponte, A. L., and P. Klein, 2015: Incoherent signature of internal tides on sea level in idealized numerical simulations. Geophys. Res. Lett., 42, 1520–1526, doi:10.1002/2014GL062583.
Rimac, A., J.-S. Storch, C. Eden, and H. Haak, 2013: The influence of high-resolution wind stress field on the power input to near-inertial motions in the ocean. Geophys. Res. Lett., 40, 4882–4886, doi:10.1002/grl.50929.
Taylor, S., and D. Straub, 2016: Forced near-inertial motion and dissipation of low-frequency kinetic energy in a wind-driven channel flow. J. Phys. Oceanogr., 46, 79–93, doi:10.1175/JPO-D-15-0060.1.
Thomas, L. N., 2012: On the effects of frontogenetic strain on symmetric instability and inertia–gravity waves. J. Fluid Mech., 711, 620–640, doi:10.1017/jfm.2012.416.
Tréguier, A.-M., and J. McWilliams, 1990: Topographic influences on wind-driven, stratified flow in a β-plane channel: An idealized model for the Antarctic Circumpolar Current. J. Phys. Oceanogr., 20, 321–343, doi:10.1175/1520-0485(1990)020<0321:TIOWDS>2.0.CO;2.
Ulloa, H. N., K. B. Winters, A. de la Fuente, and Y. Niño, 2015: Degeneration of internal Kelvin waves in a continuous two-layer stratification. J. Fluid Mech., 777, 68–96, doi:10.1017/jfm.2015.311.
Van Meurs, P., 1998: Interactions between near-inertial mixed layer currents and the mesoscale: The importance of spatial variabilities in the vorticity field. J. Phys. Oceanogr., 28, 1363–1388, doi:10.1175/1520-0485(1998)028<1363:IBNIML>2.0.CO;2.
Vanneste, J., 2013: Balance and spontaneous wave generation in geophysical flows. Annu. Rev. Fluid Mech., 45, 147–172, doi:10.1146/annurev-fluid-011212-140730.
Wagner, G., and W. Young, 2016: A three-component model for the coupled evolution of near-inertial waves, quasi-geostrophic flow and the near-inertial second harmonic. J. Fluid Mech., 802, 806–837, doi:10.1017/jfm.2016.487.
Whitt, D. B., and L. N. Thomas, 2015: Resonant generation and energetics of wind-forced near-inertial motions in a geostrophic flow. J. Phys. Oceanogr., 45, 181–208, doi:10.1175/JPO-D-14-0168.1.
Winters, K. B., 2015: Tidally driven mixing and dissipation in the stratified boundary layer above steep submarine topography. Geophys. Res. Lett., 42, 7123–7130, doi:10.1002/2015GL064676.
Winters, K. B., and A. de la Fuente, 2012: Modelling rotating stratified flows at laboratory-scale using spectrally-based DNS. Ocean Modell., 49–50, 47–59, doi:10.1016/j.ocemod.2012.04.001.
Winters, K. B., and R. Barkan, 2013: Available potential energy density for Boussinesq fluid flow. J. Fluid Mech., 714, 476–488, doi:10.1017/jfm.2012.493.
Winters, K. B., P. Lombard, J. Riley, and E. A. D’Asaro, 1995: Available potential energy and mixing in density stratified fluids. J. Fluid Mech., 289, 115–128, doi:10.1017/S002211209500125X.
Xie, J. H., and J. Vanneste, 2015: A generalised-Lagrangian-mean model of the interactions between near-inertial waves and mean flow. J. Fluid Mech., 774, 143–169, doi:10.1017/jfm.2015.251.
Young, W., and M. Ben-Jelloul, 1997: Propagation of near-inertial oscillations through a geostrophic flow. J. Mar. Res., 55, 735–766, doi:10.1357/0022240973224283.
