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Abstract Fritts, Wang, Lund, and Thorpe (2022,https://doi.org/10.1017/jfm.2021.1085) and Fritts, Wang, Thorpe, and Lund (2022,https://doi.org/10.1017/jfm.2021.1086) described a 3‐dimensional direct numerical simulation of interacting Kelvin‐Helmholtz instability (KHI) billows and resulting tube and knot (T&K) dynamics that arise at a stratified shear layer defined by an idealized, large‐amplitude inertia‐gravity wave. Using similar initial conditions, we performed a high‐resolution compressible simulation to explore the emission of GWs by these dynamics. The simulation confirms that such shear can induce strong KHI with large horizontal scales and billow depths that readily emit GWs having high frequencies, small horizontal wavelengths, and large vertical group velocities. The density‐weighted amplitudes of GWs reveal “fishbone” structures in vertical cross sections above and below the KHI source. Our results reveal that KHI, and their associated T&K dynamics, may be an important additional source of high‐frequency, small‐scale GWs at higher altitudes. 

Abstract Gravity waves (GWs) and their associated multi‐scale dynamics are known to play fundamental roles in energy and momentum transport and deposition processes throughout the atmosphere. We describe an initial machine learning model—the Compressible Atmosphere Model Network (CAM‐Net). CAM‐Net is trained on high‐resolution simulations by the state‐of‐the‐art model Complex Geometry Compressible Atmosphere Model (CGCAM). Two initial applications to a Kelvin‐Helmholtz instability source and mountain wave generation, propagation, breaking, and Secondary GW (SGW) generation in two wind environments are described here. Results show that CAM‐Net can capture the key 2‐D dynamics modeled by CGCAM with high precision. Spectral characteristics of primary and SGWs estimated by CAM‐Net agree well with those from CGCAM. Our results show that CAM‐Net can achieve a several order‐of‐magnitude acceleration relative to CGCAM without sacrificing accuracy and suggests a potential for machine learning to enable efficient and accurate descriptions of primary and secondary GWs in global atmospheric models. 

Abstract Kjellstrand et al. (2022),https://10.1029/2021JD036232describes the evolution and dynamics of a strong, large‐scale Kelvin‐Helmholtz instability (KHI) event observed in polar mesospheric clouds (PMCs) on 12 July 2018 by high‐resolution imagers aboard the PMC Turbulence (PMC Turbo) stratospheric long‐duration balloon experiment. The imaging provides evidence of KH billow interactions and instabilities that are strongly influenced by gravity waves at larger scales. Specific features include initially separated regions of KHI, secondary convective and KH instabilities of individual billows, and “tubes” and “knots” that arise where billow cores are mis‐aligned or discontinuous along their axes. This study describes a direct numerical simulation of KH billow interactions in a periodic domain seeded with random initial noise that enables excitation of multiple KH billows exhibiting variable phase structures that capture multiple features of the observed KHI dynamics. Variable KH billow phases along their axes yield initial vortex tubes having diagonal alignments that link adjacent, but mis‐aligned, billow cores. Weak initial vortex tubes and billow cores having nearly orthogonal alignments amplify, interact strongly, and drive intense vortex knots at these sites. These vortex tube and knot (T&K) dynamics excite “twist waves” that unravel the initial vortex tubes, and drive increasingly strong vortex interactions and a cascade of energy and enstrophy to successively smaller scales in the turbulence inertial range. The implications of T&K dynamics are much more rapid and intense breakdown and decay of the KH billows, and significantly enhanced energy dissipation rates, where these interactions occur.