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  1. Abstract

    The gravity wave drag parametrization of the Whole Atmosphere Community Climate Model (WACCM) has been modified to include the wave‐driven atmospheric vertical mixing caused by propagating, non‐breaking, gravity waves. The strength of this atmospheric mixing is represented in the model via the “effective wave diffusivity” coefficient (Kwave). UsingKwave, a new total dynamical diffusivity (KDyn) is defined.KDynrepresents the vertical mixing of the atmosphere by both breaking (dissipating) and vertically propagating (non‐dissipating) gravity waves. Here we show that, when the new diffusivity is used, the downward fluxes of Fe and Na between 80 and 100 km largely increase. Larger meteoric ablation injection rates of these metals (within a factor 2 of measurements) can now be used in WACCM, which produce Na and Fe layers in good agreement with lidar observations. Mesospheric CO2is also significantly impacted, with the largest CO2concentration increase occurring between 80 and 90 km, where model‐observations agreement improves. However, in regions where the model overestimates CO2concentration, the new parametrization exacerbates the model bias. The mesospheric cooling simulated by the new parametrization, while needed, is currently too strong almost everywhere. The summer mesopause in both hemispheres becomes too cold by about 30 K compared to observations, but it shifts upward, partially correcting the WACCM low summer mesopause. Our results highlight the far‐reaching implications and the necessity of representing vertically propagating non‐breaking gravity waves in climate models. This novel method of modeling gravity waves contributes to growing evidence that it is time to move away from dissipative‐only gravity wave parametrizations.

     
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  2. The precision of lidar measurements is limited by noise associated with the optical detection process. Photon noise also introduces biases in the second-order statistics of the data, such as the variances and fluxes of the measured temperature, wind, and species variations, and establishes noise floors in the computed fluctuation spectra. When the signal-to-noise ratio is low, these biases and noise floors can completely obscure the atmospheric processes being observed. We describe a novel data processing technique for eliminating the biases and noise floors. The technique involves acquiring two statistically independent datasets, covering the same altitude range and time period, from which the various second-order statistics are computed. The efficacy of the technique is demonstrated using Na Doppler lidar observations of temperature in the upper mesosphere and lower thermosphere acquired recently at McMurdo Station, Antarctica. The results show that this new technique enables observations of key atmospheric parameters in regions where the signal-to-noise ratio is far too low to apply conventional processing approaches.

     
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