The impact of transience in the interaction between orographic gravity waves and mean flow
Authors:
Felix Jochum, Ray Chew, François Lott, Georg S. Voelker, Jan Weinkaemmerer, and Ulrich Achatz
Abstract:
A Lagrangian gravity-wave parameterization (MS-GWaM, Multi-Scale Gravity-Wave Model) that allows for fully transient wave-mean-flow interaction and horizontal propagation is applied to orographic gravity waves for the first time. Both linear and nonlinear mountain waves are modeled in idealized simulations within the pseudo-incompressible flow solver PincFlow. Two-dimensional flows over monochromatic orographies are considered, using MS-GWaM either in its fully transient implementation or in a steady-state implementation that represents classic mountain-wave parameterizations. Comparisons of wave-resolving simulations (not using MS-GWaM) and coarse-resolution simulations (using MS-GWaM) show that allowing for transience leads to a significantly more accurate forcing of the resolved mean flow.
The model is able to reproduce the transient forcing of linearly generated mountain waves that slowly propagate upwards, in contrast to the instantaneous distribution of wave energy in classic parameterizations. At high altitudes, wave breaking induces a wind reversal that is captured by the transient model but inhibited in steady-state simulations, due to the assumption of critical level formation. This shows that transience can have a substantial impact in the interaction between mountain waves and mean flow.
Plain Language Summary:
This study examines how mountain-generated atmospheric gravity waves interact with the surrounding air flow, focusing on the importance of time-dependent (transient) effects. Researchers used a model called MS-GWaM to simulate these interactions, comparing its fully transient version to a traditional steady-state approach. Findings indicate that accounting for transience leads to more accurate predictions of how these waves influence overall wind patterns. Notably, the transient model successfully captures wind reversals at high altitudes caused by wave breaking, a phenomenon that steady-state models fail to represent. This highlights the significant role of time-dependent processes in understanding and predicting atmospheric dynamics.