  The low frequency variability of the extratropical atmosphere involves
hemispheric-scale recurring, often persistent, states known as teleconnection
patterns or regimes, which can have 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 dynamics of the atmosphere. We present a new
method, which allows 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.
