Early Online Release | Oceanography
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. Te 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). Tese were largely absent in the simula-
tion without tides (Figure 5c).
We hypothesized that such diferences in sound speed between
the tidal and non-tidally forced simulations would cause notable
diferences 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). Te mean
sound speed along the transect was similar between the tides
and no-tides simulations (Figure 3g), but they difered in sound
speed variability (Figure 3h). Te 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). Te
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 ofen fuctuated at semidiur-
nal timescales (i.e., every 12 hours), such as from May 20 to 23.
Te 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
diferences between the tidal and non-tidal simulations made it
difcult to directly compare their acoustic properties. Some of
the simulation variability was caused by tidal interaction with
the mesoscale feld and atmospheric forcing. Correlation coef-
fcients between wind and mixed layer depths in the Amazon
region were similar between the tidally forced and non-tidal
simulations, but with greater diferences near the coast where
currents and tidal variability were strongest.
Sound Speed and Grid Spacing
Like IGWs, sound speed is also afected by simulation grid spac-
ing. A fner grid may resolve more processes and have difer-
ent temperature and salinity gradients. As an example, we com-
pared two tidally forced simulations with diferent model setups
to see how model grid-spacing and boundary conditions may
afect 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. Te 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 ofset in tem-
perature, they couldn’t be compared directly; however, a rela-
tive comparison of SLD and BLG was insightful. Te HYCOM
simulation had organized semidiurnal fuctuations 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).
Te fner grid spacing of the MITgcm simulation likely allowed
for nonlinear interactions to occur, which in turn impacted
the sound speed structure. Tis structure is likely closer to real
ocean variability, showing the difculties of predicting sound
speed using coarser-resolution ocean models.
To address the confounding challenges of the divergent meso-
scale eddy felds 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). Te mean sound speeds were greater in simu-
lations with 32 or fewer layers (Figure 4c) and did not resolve