Influences of source conditions on mountain wave penetration into the stratosphere and mesosphere

Kaifler, Bernd; Kaifler, Natalie; Ehard, Benedikt; Dörnbrack, Andreas; Rapp, Markus; Fritts, David C.

doi:10.1002/2015gl066465

Cited by 62 publications

(86 citation statements)

References 29 publications

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“…Orographic gravity wave variances are also strongly correlated with the zonal background wind at 40 km altitude (with ρ s varying between 0.6 and 0.8), which we attributed to the AIRS observational filter. However, in a recent study of gravity wave measurements by a Raleigh/Raman lidar at Lauder, New Zealand, Kaifler et al (2015) also found enhanced correlation of the observed stratospheric gravity wave activity with the stratospheric winds. Weaker correlations are found with respect to low-level winds at 2 km altitude (with ρ s varying between 0.2 and 0.4), but this may be mostly due to the fact that the low-level winds at the hotspots were rarely below the threshold required for launching waves.…”

Section: Discussionmentioning

confidence: 99%

Stratospheric gravity waves at Southern Hemisphere orographic hotspots: 2003–2014 AIRS/Aqua observations

2016

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Abstract. Stratospheric gravity waves from small-scale orographic sources are currently not well-represented in general circulation models. This may be a reason why many simulations have difficulty reproducing the dynamical behavior of the Southern Hemisphere polar vortex in a realistic manner. Here we discuss a 12-year record (2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014) of stratospheric gravity wave activity at Southern Hemisphere orographic hotspots as observed by the Atmospheric InfraRed Sounder (AIRS) aboard the National Aeronautics and Space Administration's (NASA) Aqua satellite. We introduce a simple and effective approach, referred to as the "two-box method", to detect gravity wave activity from infrared nadir sounder measurements and to discriminate between gravity waves from orographic and other sources. From austral midfall to mid-spring (April-October) the contributions of orographic sources to the observed gravity wave occurrence frequencies were found to be largest for the Andes (90 %), followed by the Antarctic Peninsula (76 %), Kerguelen Islands (73 %), Tasmania (70 %), New Zealand (67 %), Heard Island (60 %), and other hotspots (24-54 %). Mountain wave activity was found to be closely correlated with peak terrain altitudes, and with zonal winds in the lower troposphere and mid-stratosphere. We propose a simple model to predict the occurrence of mountain wave events in the AIRS observations using zonal wind thresholds at 3 and 750 hPa. The model has significant predictive skill for hotspots where gravity wave activity is primarily due to orographic sources. It typically reproduces seasonal variations of the mountain wave occurrence frequencies at the Antarctic Peninsula and Kerguelen Islands from near zero to over 60 % with mean absolute errors of 4-5 percentage points. The prediction model can be used to disentangle upper level wind effects on observed occurrence frequencies from low-level source and other influences. The data and methods presented here can help to identify interesting case studies in the vast amount of AIRS data, which could then be further explored to study the specific characteristics of stratospheric gravity waves from orographic sources and to support model validation.

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Section: Discussionmentioning

confidence: 99%

Stratospheric gravity waves at Southern Hemisphere orographic hotspots: 2003–2014 AIRS/Aqua observations

2016

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“…The vertical time series for the hydrostatic "nonrotating" regime are shown directly above the top of the mountain (Figure 10d). Due to the smaller vertical group velocity c gz ≈ α U = 1 m·s −1 using Equation (22), the waves need about 11 h to propagate to an altitude of 40 km and to achieve a stationary state characterized by horizontal phase lines; see Figure 10d.…”

Section: Resultsmentioning

confidence: 99%

“…As α is very small, the ground-based horizontal group velocity is close to U. According to Equation (22), the wave energy is propagated upward and gains an increasing downwind component with decreasing α. The phase angle varies between 0 • for k −1 = U/ f and 90 • for k → ∞.…”

Section: Archetypal Regimes Of Vertically-propagating Internal Gravitmentioning

confidence: 99%

“…Most of these studies aimed at determining the gravity wave potential energy as a measure of wave activity and as an indication where waves become dissipated by various processes. With the recent advancement of the lidar technology (higher power, higher resolution, autonomous deployment allowing one to record long time series), middle atmosphere lidar measurements were applied to separate individual wave packets and to estimate if they propagate upward or downward, e.g., [22].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

On the Interpretation of Gravity Wave Measurements by Ground-Based Lidars

Dörnbrack¹,

Gisinger²,

Kaifler³

2017

Atmosphere

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This paper asks the simple question: How can we interpret vertical time series of middle atmosphere gravity wave measurements by ground-based temperature lidars? Linear wave theory is used to show that the association of identified phase lines with quasi-monochromatic waves should be considered with great care. The ambient mean wind has a substantial effect on the inclination of the detected phase lines. The lack of knowledge about the wind might lead to a misinterpretation of the vertical propagation direction of the observed gravity waves. In particular, numerical simulations of three archetypal atmospheric mountain wave regimes show a sensitivity of virtual lidar observations on the position relative to the mountain and on the scale of the mountain.

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“…The highest value zero means that the wave packet in question was detected in the selected interval only. Recently, this technique was developed and applied successfully to time series of ground-based Rayleigh lidar profiles and is described and discussed in detail by Kaifler et al (2015Kaifler et al ( , 2017.…”

Section: Two-dimensional Wavelet Analysismentioning

confidence: 99%