The Relationship between Statistically Linear and Nonlinear Feedbacks and Zonal-Mean Flow Variability in an Idealized Climate Model

Abstract

Abstract Simulations using an idealized, atmospheric general circulation model (GCM) subjected to various thermal forcings are analyzed via a combination of probability density function (PDF) estimation and spectral analysis techniques. Seven different GCM runs are examined, each model run being characterized by different values in the strength of the tropical heating and high-latitude cooling. For each model run, it is shown that a linear stochastic model constructed in the phase space of the ten leading empirical orthogonal functions (EOFs) of the zonal-mean zonal flow provides an excellent statistical approximation to the simulated zonal flow variability, which includes zonal index fluctuations, and quasi-oscillatory, poleward, zonal-mean flow anomaly propagation. Statistically significant deviations from the above linear stochastic null hypothesis arise in the form of a few anomalously persistent, or statistically nonlinear, flow patterns, which occupy particular regions of the model’s phase space. Some of these nonlinear regimes occur during certain phases of the poleward propagation; however, such an association is, in general, weak. This indicates that the regimes and oscillations in the model may be governed by distinct dynamical mechanisms.