Spectral Description of Low Frequency Oceanic Variability

Abstract

A dynamic model is used with observations to provide an approximate spectral description of low frequency oceanic variability. Such a spectrum can be used to aid design of observational strategy. Analytic formulas for model spectra are derived. It is found that all model spectra are related to each other through phi (k,l,w,n,phi,lambda). Estimated spectra are derived from various measurements: (1) The vertical structure of kinetic energy and potential energy is inferred from current meter and temperature mooring measurements, respectively. (2) Satellite altimetry measurements produce the geographic distributions of surface kinetic energy magnitude and the frequency and wavenumber spectra of sea surface height. (3) XBT measurements yield the temperature wavenumber spectra. (4) Current meter and temperature mooring measurements provide the frequency spectra of horizontal velocity and temperature. A simple form for a geographically varying phi (k,l,w,n,phi,lambda) is found. This study shows that large-scale low-frequency motions are primarily governed by quasi-geostrophic dynamics. In the North Pacific, real oceanic motions with energy levels varying from about 10-40% of the total in each frequency band are indistinguishable from the simplest theoretical Rossby wave description. Non-equatorial latitudes display some energy with frequencies too high for consistency with linear theory.

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 2000
Accession Number
ADA384828

Entities

People

  • Xiaoyun Zang

Organizations

  • Massachusetts Institute of Technology

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Climate Change
  • Computational Fluid Dynamics
  • Computational Science
  • Data Science
  • Doppler Effect
  • Frequency Bands
  • Geography
  • Grids
  • Information Science
  • Jet Propulsion
  • Oceanography
  • Ridges
  • Standing Waves
  • Stratified Fluids
  • Surveys
  • Topography
  • Two Dimensional

Fields of Study

  • Environmental science

Readers

  • Atmospheric Science / Meteorology, specifically Wind Wave Turbulence.
  • Oceanography.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • Space