Oceanic Internal Wave Field: Theory of Scale-invariant Spectra
Yuri V. Lvov, Kurt L. Polzin, Esteban G. Tabak, Naoto Yokoyama

TL;DR
This paper investigates scale-invariant solutions of a kinetic equation for oceanic internal waves, revealing divergence issues, identifying convergent solutions near the Garrett--Munk spectrum, and exploring effects of Earth's rotation.
Contribution
It introduces a detailed analysis of divergence in scale-invariant wave turbulence spectra and finds convergent solutions close to observed oceanic internal wave spectra, including effects of rotation.
Findings
Divergence in collision integral for most spectral exponents.
Existence of a convergent power-law solution near Garrett--Munk spectrum.
Rotation introduces an induced diffusion regime.
Abstract
Steady scale-invariant solutions of a kinetic equation describing the statistics of oceanic internal gravity waves based on wave turbulence theory are investigated. It is shown in the non-rotating scale-invariant limit that the collision integral in the kinetic equation diverges for almost all spectral power-law exponents. These divergences come from resonant interactions with the smallest horizontal wavenumbers and/or the largest horizontal wavenumbers with extreme scale-separations. We identify a small domain in which the scale-invariant collision integral converges and numerically find a convergent power-law solution. This numerical solution is close to the Garrett--Munk spectrum. Power-law exponents which potentially permit a balance between the infra-red and ultra-violet divergences are investigated. The balanced exponents are generalizations of an exact solution of the…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsOceanographic and Atmospheric Processes · Ocean Waves and Remote Sensing · Climate variability and models
