Oceanography | Vol. 38, No. 2
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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.