Nonlinear time series analysis of atmospheric gravity waves

Abstract

Internal gravity waves in the Earth's atmosphere are a very interesting and complex research subject in modern atmospheric physics. When the stable layering of the atmosphere is disturbed by for example wind flow over mountain ranges or strong convection through thunderstorms the gravity tries to restore the layers' equilibrium state, and oscillations then set in which propagate as vertical waves. These gravity waves, with wavelength ranges of tens to thousands of kilometers and periods from a few minutes to days, can propagate upwards from the tropopause to the stratosphere, mesosphere and thermosphere where they eventually break. They transfer energy, momentum and chemical species between the different atmospheric layers and play an important role on atmosphere winds, temperatures, chemistry and especially turbulence. A better understanding of the excitation and propagation of these waves would contribute to climate models. We apply techniques from nonlinear time series analysis and the analysis of complex networks to highly nonlinear data sets of experimental and simulated time series of wind velocities and other atmosphere parameters above Scandinavian mountain ranges with different initial conditions. A comparison of linear cross correlations and the mutual information of varying altitude levels reveals dependences between certain altitude levels. Comparing measurements of the upwind side, the downwind side and from above the mountains shows that the mountains cause correlations between different altitude levels. A correlation network analysis allows to compare the different network topologies or to study the network modules and intramodular hubs. This gives further insight into the nature of gravity waves.