June 2025

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June 2025 | Oceanography

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greater sound speed variability. A simulation with tidal forcing

undulates the thermocline leading to greater temperature and

salinity (and thus sound speed) variability at a given depth. Data

assimilation brought the simulations closer to observations;

however, it can also abruptly alter the temperature and salin­

ity during an assimilation window, causing implausible jumps

in sound speed. The elevated sound speed variability in the DA

simulations could be caused by natural ocean variability or this

“shock.” For these reasons, and those discussed in the earlier sec­

tion, Internal Gravity Waves, we chose to use ocean simulations

without DA while studying the sensitivity of acoustics to IGWs.

Acoustic Case Studies at IGW Hotspots

At IGW hotspots, such as the Luzon Strait, the Amazon Shelf,

and the Mascarene Ridge, tidal forcing strongly undulates the

upper ocean, and there is IGW energy transfer among modes

(see the section, From Global to Regional: Supertidal Energy).

Across the Luzon Strait, we compared the depth variability of

a single sound speed surface between the tidally forced and

non-tidally forced global HYCOM simulations (Figure 5b,c). In

the tidally forced simulation, depth striations radiated from the

Luzon Ridge, located at 1,000 km distance, and other ridges with

steep topography (e.g., 4,800 km) as tides propagated in both

directions (Figure 5b). These were largely absent in the simula­

tion without tides (Figure 5c).

We hypothesized that such differences in sound speed between

the tidal and non-tidally forced simulations would cause notable

differences in acoustic propagation. To test this idea, we turned

to the Amazon region, where semidiurnal internal tides propa­

gate northeastward away from the coast (Figure 3). The mean

sound speed along the transect was similar between the tides

and no-tides simulations (Figure 3g), but they differed in sound

speed variability (Figure 3h). The tidal simulation had peri­

odic “banding” in sound speed variability in the thermocline

(~150 m depth) at locations near where there was greater IGW

energy transfer (Figure 3a).

A 1,500 Hz virtual acoustic source was placed at 20 m depth

at 4.1°N, 44.8°W, a location of enhanced sound speed variabil­

ity and IGW energy transfer (yellow star in Figure 3a). The

sound speed, vertical sound speed gradient, and transmission

loss were examined along the 30° radial (clockwise from north).

In the tidal case, there were undulations in sound speed and

SLD (Figure 6a). Without tidal forcing, the sound speed was

more uniform, and SLD was deeper. A deeper SLD will also

typically improve transmission in the surface layer. Tidal forc­

ing also introduced changes to vertical sound speed gradients

(Figure 6a,b) and can be inferred to introduce them in the hori­

zontal as well. Surface layer transmission occurred in both cases

but was stronger in the simulation without tidal forcing. Turning

to time series (Figure 6c), TL tended to be greater in simulations

with tidal forcing than without and often fluctuated at semidiur­

nal timescales (i.e., every 12 hours), such as from May 20 to 23.

The semidiurnal variability extended to both SLD and BLG. In

the nontidal case, TL varied with eddies and currents but not at

semidiurnal frequencies (Figure 6c).

Because the horizontal and vertical structures of the sound

speed determine the path of the sound, the introduction of ver­

tical and horizontal gradients in sound speed in the simulation

with tides could have resulted in more scattering and refraction

of sound throughout the waveguide. However, the mesoscale

differences between the tidal and non-tidal simulations made it

difficult to directly compare their acoustic properties. Some of

the simulation variability was caused by tidal interaction with

the mesoscale field and atmospheric forcing. Correlation coef­

ficients between wind and mixed layer depths in the Amazon

region were similar between the tidally forced and non-tidal

simulations, but with greater differences near the coast where

currents and tidal variability were strongest.

Sound Speed and Grid Spacing

Like IGWs, sound speed is also affected by simulation grid spac­

ing. A finer grid may resolve more processes and have differ­

ent temperature and salinity gradients. As an example, we com­

pared two tidally forced simulations with different model setups

to see how model grid-spacing and boundary conditions may

affect sound speed structure: the hydrostatic tidally forced

global HYCOM simulation (Experiment [Exp.] 19.0; 1/25° res­

olution; Figure 5d) and a two-dimensional nonhydrostatic sim­

ulation of the MITgcm (Figure 5e), with a horizontal grid spac­

ing of 100 m. The Mascarene Ridge, where the simulations are

compared, is known for nonlinear wave interactions; solitons are

generated and propagate away from the ridge (Figure 3b,d,f).

Because the simulations were initialized with an offset in tem­

perature, they couldn’t be compared directly; however, a rela­

tive comparison of SLD and BLG was insightful. The HYCOM

simulation had organized semidiurnal fluctuations of the SLD

and BLG, each oscillating twice a day (Figure 5d). In contrast,

the MITgcm simulation had a periodic signal, but it appeared

disorganized, with a more variable SLD and BLG (Figure 5e).

The finer grid spacing of the MITgcm simulation likely allowed

for nonlinear interactions to occur, which in turn impacted

the sound speed structure. This structure is likely closer to real

ocean variability, showing the difficulties of predicting sound

speed using coarser-resolution ocean models.

To address the confounding challenges of the divergent meso­

scale eddy fields and initialization states, we turned to the ide­

alized model (section on Vertical Grid Spacing in Idealized

Models) to isolate the impact of vertical grid spacing on sound

speed. Hourly output from each of the idealized simulations

with 8, 16, 32, 48, and 96 isopycnal layers was interpolated to

a uniform depth coordinate for a 72-hour period. From this

we calculated the sound speed means and standard deviations

(Figure 4c,d). The mean sound speeds were greater in simu­

lations with 32 or fewer layers (Figure 4c) and did not resolve

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