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Title: Impacts of Limited Model Resolution on the Representation of Mountain Wave and Secondary Wave Dynamics in Local and Global Models: 2. Mountain Wave and Secondary Wave Evolutions in the Thermosphere
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Journal of Geophysical Research: Atmospheres
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National Science Foundation
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  1. Abstract This paper addresses the compressible nonlinear dynamics accompanying increasing mountain wave (MW) forcing over the southern Andes and propagation into the mesosphere and lower thermosphere (MLT) under winter conditions. A stretched grid provides very high resolution of the MW dynamics in a large computational domain. A slow increase of cross-mountain winds enables MWs to initially break in the mesosphere and extend to lower and higher altitudes thereafter. MW structure and breaking is strongly modulated by static mean and semidiurnal tide fields exhibiting a critical level at ~114 km for zonal MW propagation. Varying vertical group velocities for different zonal wavelengths λ x yield initial breaking in the lee of the major Andes peaks for λ x ~ 50 km, and extending significantly upstream for larger λ x approaching the critical level at later times. The localized extent of the Andes terrain in latitude leads to “ship wave” responses above the individual peaks at earlier times, and a much larger ship-wave response at 100 km and above as the larger-scale MWs achieve large amplitudes. Other responses above regions of MW breaking include large-scale secondary gravity waves and acoustic waves that achieve very large amplitudes extending well into the thermosphere. MWmore »breaking also causes momentum deposition that yields local decelerations initially, which merge and extend horizontally thereafter and persist throughout the event. Companion papers examine the associated momentum fluxes, mean-flow evolution, gravity wave–tidal interactions, and the MW instability dynamics and sources of secondary gravity waves and acoustic waves.« less
  2. Abstract

    Long‐term efforts have sought to extend global model resolution to smaller scales enabling more accurate descriptions of gravity wave (GW) sources and responses, given their major roles in coupling and variability throughout the atmosphere. Such studies reveal significant improvements accompanying increasing resolution, but no guidance on what is sufficient to approximate reality. We take the opposite approach, using a finite‐volume model solving the Navier‐Stokes equations exactly. The reference simulation addresses mountain wave (MW) generation and responses over the Southern Andes described using isotropic 500 m, central resolution by Fritts et al. (2021), Lund et al. (2020), Reductions of horizontal resolution to 1 and 2 km result in (a) systematic increases in initial MW breaking altitudes, (b) weaker, larger‐scale generation of secondary GWs and acoustic waves accompanying these dynamics, and (c) significantly weaker and less extended responses in the mesosphere in latitude and longitude. Horizontal resolution of 4 km largely suppresses instabilities, but allows weak, sustained mean‐flow interactions. Responses for 8 km resolution are very weak and fail to capture any aspects of the high‐resolution responses. The chosen mean winds allow efficient MW penetration into the mesosphere and lower thermosphere, hence only exhibit strong pseudo‐momentum deposition and mean wind decelerations at highermore »altitudes. A companion paper by Fritts et al. (2022), the impacts of decreasing resolution on responses in the thermosphere.

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    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.

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