Abstract
The low-frequency variability of the extratropical atmosphere involves hemispheric-scale recurring, often persistent, states known as teleconnection patterns or regimes, which can have a profound impact on predictability on intra-seasonal and longer timescales. However, reliable data-driven identification and dynamical representation of such states are still challenging problems in modeling the dynamics of the atmosphere. We present a new method, which allows us both to detect recurring regimes of atmospheric variability and to obtain dynamical variables serving as an embedding for these regimes. The method combines two approaches from nonlinear data analysis: partitioning a network of recurrent states with studying its properties by the recurrence quantification analysis and the kernel principal component analysis. We apply the method to study teleconnection patterns in a quasi-geostrophical model of atmospheric circulation over the extratropical hemisphere as well as to reanalysis data of geopotential height anomalies in the mid-latitudes of the Northern Hemisphere atmosphere in the winter seasons from 1981 to the present. It is shown that the detected regimes as well as the obtained set of dynamical variables explain large-scale weather patterns, which are associated, in particular, with severe winters over Eurasia and North America. The method presented opens prospects for improving empirical modeling and long-term forecasting of large-scale atmospheric circulation regimes.
The behavior of weather systems over the mid-latitudes is well known as strongly chaotic and having a very limited horizon of reliable forecasting. While movements of synoptic-scale structures like cyclones and anticyclones are predicted well within 1–2 weeks, larger structures of atmospheric circulation with longer time scales are still poorly investigated. As it is shown by models and data analysis, dynamics on these time scales, also called the low-frequency variability, is characterized by recurrent global patterns, or regimes, which can strongly impact long-term weather conditions in different regions. However, both identification and dynamical representation of such regimes based on data is a controversial problem due to the lack of robust and reliable methods of data analysis/data analysis methods. Here, we suggest a method that allows us to detect the regimes and, simultaneously, obtain dynamical variables representing their dynamics. The method involves and joins together/combines several approaches from nonlinear data analysis: partitioning a network of recurrent states, recurrence quantification analysis, and nonlinear principal component analysis. Studying winter low-frequency variability (LFV) in the Northern Hemisphere (NH) mid-latitudes by the suggested method allows us to reveal and investigate dynamical properties of large-scale weather patterns, which are associated, in particular, with severe winters over Eurasia and North America. The results presented open prospects for improving data-driven modeling and long-term forecasting of large-scale atmospheric circulation regimes.
