  Detecting recurrent weather patterns and understanding the transitions
between such regimes are key to advancing our knowledge on the low-frequency
variability of the atmosphere and have important implications in terms of
weather and climate-related risks. We adapt an analysis pipeline inspired by
Markov State Modelling and detect in an unsupervised manner the dominant winter
mid-latitude Northern Hemisphere weather patterns in the Atlantic and Pacific
sectors. The daily 500 hPa geopotential height fields are first classified in
$\sim 200$ microstates. The weather dynamics is then represented in the basis
of these microstates and the slowest decaying modes are identified from the
spectral properties of the transition probability matrix. These modes are
defined on the basis of the nonlinear dynamical processes of the system and not
as tentative metastable states as often done in Markov state analysis. In the
Atlantic and Pacific sectors slow relaxation processes are mainly related to
transitions between blocked regimes and zonal flow. We also find strong
evidence of a dynamical regime associated with the simultaneous
Atlantic-Pacific blocking. When the analysis is performed in a broader
geographical region of the Atlantic sector, we discover that the slowest
relaxation modes of the system are associated with transitions between
dynamical regimes that resemble teleconnection patterns like the North Atlantic
Oscillation and weather regimes like the Scandinavian and Greenland blocking,
yet have a much stronger dynamical foundation than classical methods based e.g.
on EOF analysis. Our method clarifies that, as a result of the lack of a
time-scale separation in the atmospheric variability of the mid-latitudes,
there is no clear-cut way to represent the atmospheric dynamics in terms of
few, well-defined modes of variability.
