Numerical Simulations of High‐Frequency Gravity Wave Propagation Through Fine Structures in the Mesosphere. Issue 16 (28th August 2019)

• Record Type: Journal Article
• Authors: Mixa, Tyler; Fritts, David; Lund, Thomas; Laughman, Brian; Wang, Ling; Kantha, Lakshmi
• Is Part Of: Journal of geophysical research. Volume 124:Issue 16(2019)
• Page Start: 9372
• Page End: 9390
• Publication Date: 2019-08-28
• Subjects: Atmospheric physics -- Periodicals; Geophysics -- Periodicals; 551.5
• DOI: 10.1029/2018JD029746
• Languages: English
• ISSNs: 2169-897X

Abstract

Abstract: An anelastic numerical model is used to study the influences of fine structure (FS) in the wind and stability profiles on gravity wave (GW) propagation in the Mesosphere and Lower Thermosphere (MLT). Large amplitude GWs interacting with FS, that is, thin regions of enhanced wind and stability, evolve very differently depending on the precise vorticity source and sink terms for small‐scale motions induced by the FS gradients. The resulting small‐scale dynamics are deterministic, promoting local instabilities, dissipation, and momentum deposition at locations and orientations determined by the initial FS. The resulting momentum depositions yield significant changes to the background wind structure, having scales and amplitudes comparable to the effects of large‐scale features in the ambient atmosphere. The deterministic nature of the large‐scale impacts further suggests that they can be estimated without fully resolving the underlying instability dynamics. Given the significant amplitudes and ubiquitous occurrence of FS throughout the atmosphere, the influences of these important and diverse flow evolutions merit inclusion in broader modeling efforts. Key Points: Fine structures produce a diversity of impacts on gravity wave propagation and instability evolution having large‐scale implications Stability and shear effects produce distinctive vortical evolutions that determine the locations and time scales of wave energy dissipation Large‐scale consequences can be predicted and merit inclusion in broader modeling efforts