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Gravity Wave Diagnosis Using Empirical Normal Modes

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  • 1 Department of Atmospheric and Oceanic Sciences, McGill University, Montreal, Quebec, Canada
  • | 2 Recherche en Prévision Numérique, Service de l’Environnement Atmosphérique, Dorval, Quebec, Canada
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Abstract

The theory of empirical normal modes (ENMs) is adapted to diagnose gravity waves generated by a relatively high-resolution numerical model solving the primitive equations. The ENM approach is based on the principal component analysis (which consists of finding the most efficient basis explaining the variance of a time series), except that it takes advantage of wave-activity conservation laws. In the present work, the small-amplitude version of the pseudoenergy is used to extract from data quasi-monochromatic three-dimensional empirical modes that describe atmospheric wave activity. The spatial distributions of these quasi-monochromatic modes are identical to the normal modes of the linearized primitive equations when the underlying dynamics can be described with a stochastic linear and forced model, thus establishing a bridge between statistics and dynamics. This diagnostic method is used to study inertia–gravity wave generation, propagation, transience, and breaking over the Rockies, the North Pacific, and Central America in the troposphere–stratosphere–mesosphere Geophysical Fluid Dynamics Laboratory SKYHI general circulation model at a resolution of 1° of latitude by 1.2° of longitude. Besides the action of mountains in exciting orographic waves, inertia–gravity wave activity has been found to be generated at the jet stream level as a possible consequence of a sustained nonlinear and ageostrophic flow. In the tropical region of the model (Central America), the inertia–gravity wave source mechanism produced mainly waves with a westward vertical tilt. A significant proportion of these inertia–gravity waves was able to reach the model mesosphere without much dissipation and absorption.

* Current affiliation: Centre National de Recherches Météorologiques, Météo-France, CNRM/GMAP, Toulouse, France.

Corresponding author address: Dr. Gilbert Brunet, Recherche en Prévision Numérique, 2121 route Trans-canadienne, Dorval, PQ H9P 1J3, Canada. E-mail: gilbert.brunet@ec.gc.ca

Abstract

The theory of empirical normal modes (ENMs) is adapted to diagnose gravity waves generated by a relatively high-resolution numerical model solving the primitive equations. The ENM approach is based on the principal component analysis (which consists of finding the most efficient basis explaining the variance of a time series), except that it takes advantage of wave-activity conservation laws. In the present work, the small-amplitude version of the pseudoenergy is used to extract from data quasi-monochromatic three-dimensional empirical modes that describe atmospheric wave activity. The spatial distributions of these quasi-monochromatic modes are identical to the normal modes of the linearized primitive equations when the underlying dynamics can be described with a stochastic linear and forced model, thus establishing a bridge between statistics and dynamics. This diagnostic method is used to study inertia–gravity wave generation, propagation, transience, and breaking over the Rockies, the North Pacific, and Central America in the troposphere–stratosphere–mesosphere Geophysical Fluid Dynamics Laboratory SKYHI general circulation model at a resolution of 1° of latitude by 1.2° of longitude. Besides the action of mountains in exciting orographic waves, inertia–gravity wave activity has been found to be generated at the jet stream level as a possible consequence of a sustained nonlinear and ageostrophic flow. In the tropical region of the model (Central America), the inertia–gravity wave source mechanism produced mainly waves with a westward vertical tilt. A significant proportion of these inertia–gravity waves was able to reach the model mesosphere without much dissipation and absorption.

* Current affiliation: Centre National de Recherches Météorologiques, Météo-France, CNRM/GMAP, Toulouse, France.

Corresponding author address: Dr. Gilbert Brunet, Recherche en Prévision Numérique, 2121 route Trans-canadienne, Dorval, PQ H9P 1J3, Canada. E-mail: gilbert.brunet@ec.gc.ca

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