Looking for Nonlinearities in the Large Scale Dynamics of the Atmosphere

Linear models can accurately account for the fundamental circulation patterns observed in the large scale atmospheric flow. It is as if single realizations of a multivariate random variable were observed – what we call the weather maps – whose deviations from a mean equilibrium state have no preferred direction or sign. A multivariate Gaussian distribution would be a good model for these observations. Traditionally, the mean state is believed to be unique. By now, though, the atmospheric sciences literature has well documented the existence of important low-frequency anomalies, their persistence in time and space, and their recurrence. These phenomena cannot be explained by simple linear models, hinting to more complex, nonlinear generator mechanisms. Methods of addressing the issue – mainly work of the last two decades – range from strictly mathematical developments to data analysis of observations from nature. Scientists have written down the equations and analysed the behavior of simple and complex nonlinear dynamical systems, in order to confirm that the large scale dynamics are thus better represented – and the concepts of multiple