Metastability, atmospheric midlatitude circulation regimes and large-scale teleconnection: a data-driven approach

TL;DR

This paper introduces a novel data-driven method using hidden Markov models and graph theory to identify and analyze atmospheric circulation regimes and their teleconnections, enhancing understanding of midlatitude variability and predictability.

Contribution

It develops a new probabilistic approach to detect circulation regimes as communities of states, linking metastability with large-scale teleconnection patterns.

Findings

Identified four persistent circulation regimes in the Northern Hemisphere winter.

Found high correlations between regimes and ENSO and PDO with lead times up to one year.

Demonstrated the method's effectiveness in capturing regime dynamics and surface impacts.

Abstract

The low-frequency variability of the mid-latitude atmosphere involves complex nonlinear and chaotic dynamical processes posing predictability challenges. It is characterized by sporadically recurring, often long-lived patterns of atmospheric circulation of hemispheric scale known as weather regimes. The evolution of these circulation regimes in addition to their link to large-scale teleconnections can help extend the limits of atmospheric predictability. They also play a key role in sub- and inter-seasonal weather forecasting. Their identification and modeling remains an issue, however, due to their intricacy, including a clear conceptual picture. In recent years, the concept of metastability has been developed to explain regimes formation. This suggests an interpretation of circulation regimes as communities of states in which the atmospheric system remains in their neighborhood for abnormally longer than typical baroclinic timescales. Here we develop a new and effective method to identify such communities by constructing and analyzing an operator of the system's evolution via hidden Markov model (HMM). The method makes use of graph theory and is based on probabilistic approach to partition the HMM transition matrix into weakly interacting blocks -- communities of hidden states -- associated with regimes. The approach involves nonlinear kernel principal component mapping to consistently embed the system state space for HMM building. Application to northern winter hemisphere using geopotential heights from reanalysis yields four persistent and recurrent circulation regimes. Statistical and dynamical characteristics of these circulation regimes and surface impacts are discussed. In particular, unexpected high correlations are obtained with EL-Nino Southern Oscillation and Pacific decadal oscillation with lead times of up to one year.