On Robust Estimation of Low-Frequency Variability Trends in Discrete Markovian Sequences of Atmospheric Circulation Patterns

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Abstract
Identification and analysis of temporal trends and low-frequency variability in discrete time series is an important practical topic in the understanding and prediction of many atmospheric processes, for example, in analysis of climate change. Widely used numerical techniques of trend identification (like local Gaussian kernel smoothing) impose some strong mathematical assumptions on the analyzed data and are not robust to model sensitivity. The latter issue becomes crucial when analyzing historical observation data with a short record. Two global robust numerical methods for the trend estimation in discrete nonstationary Markovian data based on different sets of implicit mathematical assumptions are introduced and compared here. The methods are first compared on a simple model example; then the importance of mathematical assumptions on the data is explained and numerical problems of local Gaussian kernel smoothing are demonstrated. Presented methods are applied to analysis of the historical sequ... Abstract Identification and analysis of temporal trends and low-frequency variability in discrete time series is an important practical topic in the understanding and prediction of many atmospheric processes, for example, in analysis of climate change. Widely used numerical techniques of trend identification (like local Gaussian kernel smoothing) impose some strong mathematical assumptions on the analyzed data and are not robust to model sensitivity. The latter issue becomes crucial when analyzing historical observation data with a short record. Two global robust numerical methods for the trend estimation in discrete nonstationary Markovian data based on different sets of implicit mathematical assumptions are introduced and compared here. The methods are first compared on a simple model example; then the importance of mathematical assumptions on the data is explained and numerical problems of local Gaussian kernel smoothing are demonstrated. Presented methods are applied to analysis of the historical sequ...

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