Oceanography | Vol. 38, No. 2
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refracted upward from the SLD and reflected downward by the
surface, allowing acoustic energy to travel long distances. The
sound speed gradient below the SLD, called the below-layer gra
dient (BLG), can influence the potential of this surface-layer duct
to trap energy.
For this project, sound speed, its variability, the SLD, and
the BLG were compared between simulations with and with
out tidal forcing. Acoustic transmission loss (TL), an estimate
of acoustic pressure, was calculated from a virtual source using
a three-dimensional ray-tracing acoustic model, Bellhop 3D
(Porter, 2011). TL exemplifies how the differences in sound
speed between differently forced ocean simulations can affect
acoustic propagation models.
PROGRESS IN IGW MODELING
Bringing Models Closer to Observations
Realistically capturing ocean variability at different length scales,
from large-scale eddies to smaller coastal features, is a central
goal of global ocean models. Sea surface height (SSH) variability
is a useful proxy for mesoscale ocean variability. The SSH wave
number spectrum was used as a single descriptor of the rela
tive strength of ocean variability as a function of length scale.
Wavenumber, defined as one divided by wavelength, is large
where spatial scales are small. Figure 1f shows an example of
the wavenumber spectra and the spectral slope of the mesoscale
variability (the steepness of the spectrum from 250 km to 70 km
wavelength). The SSH spectral slope varies greatly by location
(Figure 1e; Zhou et al., 2015). The slope is steepest (–5) along
the western boundary current (Gulf Stream), which has large-
scale currents and high mesoscale eddy variability. The slopes
are flatter (close to –3) in the mid-latitude interior, such as the
eastern North Atlantic, and much flatter (close to –1) in the
equatorial region.
The inclusion of tidal forcing in ocean models is paramount
to bringing SSH variability in simulations closer to observations.
Figure 1 compares a series of high-resolution regional 1/50°
North Atlantic HYCOM simulations to satellite altimetry obser
vations. Without tidal forcing, high-resolution models could not
replicate this spatial SSH variability (e.g., Figure 1a,b; Chassignet
and Xu, 2017). With tidal forcing (Figure 1c,d), the SSH spec
tral slope in the equatorial Atlantic and the eastern subtropi
cal North Atlantic began to match observations. Here, there are
strong barotropic tides and strong stratification in the upper layer
of the water column. In these regions, SSH variability at length
scales of 70–120 km increased, flattening the spectral slope in the
70–250 km mesoscale range (Figure 1f). High-resolution bathym
etry (Figure 1b) and high-frequency wind variability (Figure 7b
in Xu et al., 2022) had comparably minor impacts on the spec
tral slope, except at local scales where internal tides are generated
along topography, such as near the shelf break (Xu et al., 2022).
NEATL
NEATL-T-HB
Zhou et al. (2015)
Wavenumber Spectra
NEATL-HB
NEATL-T
FIGURE 1. (a–e) Mesoscale sea surface height (SSH) wavenumber spectral slope in the
Atlantic Ocean based on a series of 1/50° numerical simulations and observations: (a) NEATL
(no tides), (b) NEATL-HB (no tides, with high-resolution bathymetry), (c) NEATL-T (with tides),
(d) NEATL-T-HB (with tides, high-resolution bathymetry), and (e) satellite observations from
Zhou et al. (2015). (f) Example of the wavenumber spectra averaged from 10°S–10°N and
35°–15ºW from observations and four model configurations. The mesoscale spectral slope
in panels a–e was calculated between 70 km and 250 km. Modified from Xu et al. (2022;
their Figures 7 and 11)