Characterizing horizontal variability and energy spectra in the Arctic Ocean halocline

Abstract

Energy transfer from the atmosphere into the upper Arctic Ocean is expected to become more efficient as summer sea-ice coverage decreases and multiyear ice thins due to recent atmospheric warming. However, relatively little is known about how energy is transferred within the ocean by turbulent processes from large to small scales in the presence of ice and how these pathways might change in future. This study characterizes horizontal variability in several regions of the Eurasian Arctic Ocean under differing sea-ice conditions. Historic along track CTD data collected by a Royal Navy submarine during summer 1996 allow a unique examination of horizontal variability and associated wavenumber spectra within the Arctic Ocean halocline. Spectral analysis indicates that potential energy variance under perennial sea-ice in the Amundsen Basin is O(100) less than within the marginal ice zone (MIZ) of Fram Strait. Spectra from all regions show a transition in scaling at wavelengths of approximately 5-7 km. At scales greater than the transition wavelength to 50 km, energy spectra are consistent with a k 23 scaling (where k is a wavenumber) and interior quasigeostrophic dynamics. The scaling of spectra at these scales is extremely similar between regions suggesting similar dynamics and energy exchange pathways. The k 23 scaling is steeper than typically found in regions of midlatitude open ocean. At scales below the transition wavelength to 300 m, spectra are close to a k 25/3 scaling or flatter, indicating a change in dynamics, which is potentially due to internal waves dominating variability at small scales.

Response of Total and Eddy Kinetic Energy to the Recent Spinup of the Beaufort Gyre

“…Hence, our EKE fields are not only reflecting the presence of coherent eddies. Keeping these caveats in mind, we find that EKE is generally low in the ice-covered Arctic and of the same order of magnitude as the MKE, consistent with previous observations (e.g., Timmermans et al 2012;Marcinko et al 2015). This is because some of the key ingredients generating turbulence in the open ocean are missing: low net surface stress, due to a dampening of wind stress by sea ice, and high stratification lead to low levels of energy (Rainville and Woodgate 2009).…”

A Tale of Two Spicy Seas

“…In the lowest wavenumber range (largest horizontal scales), there are markedly different slopes between near-surface temperature spectra from the BoB (warm colors) and the Arctic (cool colors). The Arctic data have a -3 spectral slope, which is theoretically associated with potential energy of quasi-geostrophic motions and has been observed previously in the Arctic (Charney, 1971;Timmermans et al, 2012;Timmermans and Winsor, 2013;Marcinko et al, 2015). In contrast, the BoB data in this example exhibit a -2 spectral slope; the same slope persists in almost all long BoB sections acquired during this experiment (not shown).…”

“…Timmermans et al (2012) notice the -3 slope in Ice-Tethered Profiler data from the Arctic and speculate that the difference between that slope and the -2 slope commonly observed in lower latitudes is related to ice cover. Marcinko et al (2015) compare these data with a wider variety of Arctic measurements, and find a -3 slope regardless of ice cover, and instead suggest that the Arctic behavior is dynamically related to strong upper-ocean salinity stratification, which in turn is related to ice but persists even in ice-free conditions. Our observations complicate that interpretation, as salinity stratification in both oceans as profiled here is very similar, both qualitatively and in the magnitude of the N 2 values shown in Figure 6, yet there are distinctly different low wave number spectral slopes.…”

Mesoscale observations of temperature and salinity in the Arctic Transpolar Drift: a high-resolution dataset from the MOSAiC Distributed Network

“…This allows to study spatiotemporal variability of temperature and salinity. In particular, horizontal wavenumber spectra can give insights into the length scales of variability induced by sub-meso-scale restratification and internal waves (Marcinko et al, 2015;Timmermans et al, 2012). Furthermore, submesoscale eddies and filaments between mesoscale features can be studied with this piecewise quasi-synoptic dataset.…”