Abstract

Abstract. Gravity waves are important drivers of dynamic processes in particular in the middle atmosphere. To analyse atmospheric data for gravity wave signals, it is essential to separate gravity wave perturbations from atmospheric variability due to other dynamic processes. Common methods to separate small-scale gravity wave signals from a large-scale background are separation methods depending on filters in either the horizontal or vertical wavelength domain. However, gravity waves are not the only process that could lead to small-scale perturbations in the atmosphere. Recently, concerns have been raised that vertical wavelength filtering can lead to misinterpretation of other wave-like perturbations, such as inertial instability effects, as gravity wave perturbations. In this paper we assess the ability of different spectral background removal approaches to separate gravity waves and inertial instabilities using artificial inertial instability perturbations, global model data and satellite observations. We investigate a horizontal background removal (which applies a zonal wavenumber filter with additional smoothing of the spectral components in meridional and vertical direction), a sophisticated filter based on 2D time–longitude spectral analysis (see Ern et al., 2011) and a vertical wavelength Butterworth filter. Critical thresholds for the vertical wavelength and zonal wavenumber are analysed. Vertical filtering has to cut deep into the gravity wave spectrum in order to remove inertial instability remnants from the perturbations (down to 6 km cutoff wavelength). Horizontal filtering, however, removes inertial instability remnants in global model data at wavenumbers far lower than the typical gravity wave scales for the case we investigated. Specifically, a cutoff zonal wavenumber of 6 in the stratosphere is sufficient to eliminate inertial instability structures. Furthermore, we show that for infrared limb-sounding satellite profiles it is possible as well to effectively separate perturbations of inertial instabilities from those of gravity waves using a cutoff zonal wavenumber of 6. We generalize the findings of our case study by examining a 1-year time series of SABER (Sounding of the Atmosphere using Broadband Emission Radiometry) data.

Highlights

  • In the middle atmosphere, various wave and wave-like processes shape the global structures of temperature and winds

  • The Sounding of the Atmosphere using Broadband Emission Radiometry (SABER) instrument is operated on board the Thermosphere Ionosphere Mesosphere Energetics and Dynamics (TIMED) satellite, which was launched in December 2001 and is still operational to this date

  • The lower row gives the corresponding plots from ERA5 gravity wave temperature perturbations, calculated using vertical filtering with a fifth-order Butterworth filter with a cutoff vertical wavelength of 15 km. (The definition of gravity wave temperature perturbations and the Butterworth filter will be explained in more detail in Sect. 3.4 and 3.1, respectively.) The lower row shows the modelled inertial instability signal as white contours for comparison with the ERA5 structures

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Summary

Introduction

Various wave and wave-like processes shape the global structures of temperature and winds. Schmidt et al (2016) confirmed that different techniques (in particular vertical spectral filtering versus horizontal S-transform analysis) have a large impact on the global gravity wave activity results, especially comparing gravity wave potential energy densities and gravity wave momentum flux estimates. The methodic limitations of vertical- and horizontal-scale separation approaches can be tested by showcasing the challenge of removing a background in a more complex atmospheric data set, where inertial instability signals, gravity wave perturbations and other fluctuations are simultaneously present.

SABER temperatures
ERA5 temperatures
Artificial inertial instability perturbations
Background removal methods and diagnostic quantities
Vertical filtering
Time–horizontal filtering for limb-sounder data
Diagnostic quantities
A remnant signal threshold for a “successful” background removal
Situation in a climatology
Case study and 1-year time series of SABER
Summary and conclusion
Full Text
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