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We expose a phenomenon that can occur in the process of joint state and parameter estimation. Such estimation is crucial in tuning parameters for climate models and offline parameterizations used in the models. We show how a bimodal distribution can temporarily appear during this process and that a scheme relying on linear and Gaussian approximations may cause it to get trapped in the wrong mode and hence lead to faulty estimation. We propose a practical and effective resolution using a two-stage filtering process.

We study the transport properties of nonautonomous chaotic dynamical systems over a finite-time duration. We are particularly interested in those regions that remain coherent and relatively nondispersive over finite periods of time, despite the chaotic nature of the system. We develop a novel probabilistic methodology based upon transfer operators that automatically detect maximally coherent sets. The approach is very simple to implement, requiring only singular vector computations of a matrix of transitions induced by the dynamics. We illustrate our new methodology on an idealized stratospheric flow and in two and three-dimensional analyses of European Centre for Medium Range Weather Forecasting (ECMWF) reanalysis data., (© 2010 American Institute of Physics.)

The "edge" of the Antarctic polar vortex is known to behave as a barrier to the meridional (poleward) transport of ozone during the austral winter. This chemical isolation of the polar vortex from the middle and low latitudes produces an ozone minimum in the vortex region, intensifying the ozone hole relative to that which would be produced by photochemical processes alone. Observational determination of the vortex edge remains an active field of research. In this paper, we obtain objective estimates of the structure of the polar vortex by introducing a technique based on transfer operators that aims to find regions with minimal external transport. Applying this technique to European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-40 three-dimensional velocity data, we produce an improved three-dimensional estimate of the vortex location in the upper stratosphere where the vortex is most pronounced. This computational approach has wide potential application in detecting and analyzing mixing structures in a variety of atmospheric, oceanographic, and general fluid dynamical settings.