Probing the linearity and nonlinearity in the transitions of the atmospheric circulation

Abstract. In this paper, we apply the principles of information theory that relate to the definition of nonlinear predictability, which is a measure that describes both the linear and nonlinear components of a system. By comparing this measure to a measure of linear predictability, one can assess whether a given system has a strong nonlinear or a strong linear component. This provides insights as to whether the system should be modelled by a nonlinear model or by a linear model. We apply these ideas to a known dynamical system and to a time series that describe the transitions in atmospheric circulation.