Local and Upscale Impacts of Airflow over Mountains: Finite-Amplitude Dynamics and Downslope Windstorm Predictability

Abstract

Mountain waves are gravity waves generated by flow over terrain. The generation of these waves is significant for both their local impacts in mountainous areas, such as strong downslope winds, and their broader impacts on the general circulation through effects such as gravity wave drag and the Brewer-Dobson circulation. In this dissertation, we analyze two facets of mountain wave-induced dynamics: impacts of nonlinearity on mountain wave amplitude and predictability of downslope windstorms. Linear assumptions have long been used to formulate parameterizations of gravity wave drag. However, previous results have indicated significant discrepancies between linear theory and the nonlinear solution, particularly in the presence of the tropopause. In our work, we conduct a systematic evaluation of nonlinear effects in a 2D framework, beginning with the simplest case of two layers of constant static stability and no shear. We then include cases of more complexity, including low-level inversions and forward shear in the troposphere. Our results verify the results from previous studies. In addition, our results show that in the presence of a tropopause and strong forward shear, there can exist a much larger amplification or deamplification of the surface pressure drag due to finite-amplitude nonlinear effects than was previously recognized. This has significant implications for the parameterization of gravity wave drag, as the accurate estimation of the surface pressure drag is important for representing the true drag aloft. More direct impacts to communities in the immediate lee of mountains are caused by downslope windstorms. Such windstorms can cause large amounts of property damage and extreme fire spread, and their accurate prediction is therefore important. Although some early studies suggested high predictability for downslope windstorms, more recent analyses have found limited predictability of these events. However, there is a theoretical reason to suspect higher predictability in cases with a mean-state critical level, and this is supported by at least one previous study. To investigate downslope windstorm predictability, we compare ensemble simulations from the NCAR ensemble to observed winds at stations determined to be susceptible to strong downslope flows. Although our results indicate some improvement in predictive skill for the mean-state critical level regime in certain contexts, the trade-offs are such that there is no significant practical difference in either ensemble spread or forecast skill for the mean-state critical-level regime.