The Arctic seas are in a period of transition as they adjust to stimuli from anthropogenic climate change. The acoustic response to this adjustment is of fundamental interest, as acoustics provide an important means for Arctic remote sensing, communication and navigation, and there are important biological implications for marine mammals and other organisms that use sound. The Canada Basin Acoustic Propagation Experiment (CANAPE) is an effort to study Arctic acoustics; this paper reports on ocean sound-speed measurements from a pilot study undertaken between 30 July and 16 August 2015. Moored and shipborne observations of temperature and salinity were made in the upper 600 m of the ocean, allowing analysis along isopycnals (surfaces of constant density) to separate sound-speed structure due to internal-wave-induced vertical displacements from those originating from density-compensated temperature and salinity variations termed spice. Frequency spectra and vertical covariance functions were used to describe the space/time scales of displacements and spice. Internal-wave frequency spectra show a spectral slope much lower than the Garrett-Munk model, with the energy level roughly 4% of the standard Garrett-Munk value. Frequency spectra of spice show a form similar to the internal-wave spectra but with a slightly steeper spectral slope, presumably due to the horizontal advection of the spice by internal-wave currents. The root mean square sound-speed fluctuations from internal waves were small with values less than 0.1 m s–1. Spicy sound-speed fluctuations were much stronger, particularly in the upper 100 m where a maximum of 0.25 m s–1 was observed. Both processes have vertical decorrelation lengths less than 100 m. The observed strong variations in vertical and horizontal sound-speed structure will have significant impacts on acoustic applications, especially in the realm of communications, navigation, and remote sensing.
Analyzing sound speed fluctuations in shallow water from group-velocity versus phase-velocity data representation