June 2025

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Oceanography | Vol. 38, No. 2

24

FEATURE ARTICLE

HOW DO TIDES AFFECT

UNDERWATER ACOUSTIC PROPAGATION?

A COLLABORATIVE APPROACH TO IMPROVE INTERNAL WAVE MODELING

AT BASIN TO GLOBAL SCALES

By Martha C. Schönau, Luna Hiron, John Ragland, Keshav J. Raja, Joseph Skitka, Miguel S. Solano, Xiaobiao Xu,

Brian K. Arbic, Maarten C. Buijsman, Eric P. Chassignet, Emanuel Coelho, Robert W. Helber, William Peria, Jay F. Shriver,

Jason E. Summers, Kathryn L. Verlinden, and Alan J. Wallcraft

INTRODUCTION

The underwater soundscape encompasses a range of ambi­

ent, anthropogenic, and biological sound, with research span­

ning acoustic communications to passive acoustic monitoring.

The density of water allows sound, which is a pressure wave,

to travel short distances and across ocean basins. The speed of

sound is set by water temperature and salinity, and pressure.

As it travels, sound scatters from the bathymetry, the surface,

animals, or other objects. Sound refracts when it encounters a

difference in sound speed, which can be introduced by fronts,

eddies, currents, vertical stratification, internal tides, and gravity

waves and mixing.

Soundscape modeling, such as that used to trace the impacts

of anthropogenic noise on marine mammals, is dependent on

the sound speed structure employed in the ocean model. The

vertical motions of internal tides and internal gravity waves

(IGWs) bring cold water up and push warm water down, chang­

ing the sound speed (Gill, 1982). Internal tides and IGWs dissi­

pate energy to both smaller and larger scales. The sound speed

in tidally forced simulations may differ drastically from simula­

tions without tidal forcing. Simulations are also highly sensitive

to grid spacing, mixing parameterizations, and boundary condi­

tions. Identifying the differences of tidally driven ocean models

from their non-tidal counterparts and the actual ocean, and the

length scales that resolve IGW processes, may in turn inform

how internal wave models should be used for diverse acoustic

and biological studies.

This paper presents progress in the modeling of internal tides

and IGWs, the effect of these advances on modeling sound speed

and sound propagation in underwater ray-tracing acoustic mod­

els, and the use of deep learning (DL) to predict the ocean state.

The research stems from a coordinated project funded under the

Office of Naval Research (ONR) Task Force Ocean (TFO) initia­

tive designed to train early career scientists in cross-​disciplinary

oceanography, underwater acoustics, and machine learning

techniques. The project was dubbed “TFO-HYCOM” after

the US Navy’s operational HYbrid Coordinate Ocean Model

(HYCOM), which featured prominently in the research project.

BACKGROUND AND APPROACH

Internal Gravity Waves

Internal gravity waves exist as undulations along constant den­

sity ocean surfaces (isopycnals) with a restoring force of grav­

ity. As IGWs displace isopycnals, they create a profile of depth-​

dependent velocities. Internal tides, a special type of IGWs,

exist at tidal frequencies and are generated by tidal flow over

ABSTRACT. Accurate prediction of underwater sound speed and acoustic propagation is dependent on realistic representation

of the ocean state and its underlying dynamics within ocean models. Stratified, high-resolution global ocean models that include

tidal forcing better capture the ocean state by introducing internal tides that generate higher frequency (supertidal) internal waves.

Through the disciplines of internal wave modeling, acoustics, and machine learning, we examined how internal wave energy moves

through numerical simulations, how this energy alters the ocean state and sound speed, and how machine learning could aid the

modeling of these impacts. The project used global, basin-scale, and idealized HYbrid Coordinate Ocean Model (HYCOM) simu­

lations as well as regional Massachusetts Institute of Technology general circulation model (MITgcm) simulations to examine how

tidal inclusion affects sea surface height variability, the propagation and dissipation of internal wave energy, and the sensitivity of

internal wave modeling to vertical and horizontal grid spacing. Sound speed, acoustic parameters, and modeled acoustic propaga­

tion were compared between simulations with and without tidal forcing, and deep learning algorithms were used to examine how a

tidally forced ocean state could be generated while reducing computational costs.

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