Trends and seasonal signals in Atlantic feature-based jet stream characteristics and in weather types

Abstract. Recent studies have highlighted the link between upper-level jet dynamics, especially the persistence of certain jet configurations, and extreme summer weather in Europe. The weaker and more variable nature of the jets in summer makes it difficult to apply the tools developed to study them in winter, at least not without modifications. Here, to further investigate the link between jets and persistent summer weather, we present two complementary approaches to characterize the jet dynamics in the North Atlantic sector and use them primarily on the summer circulation.

First, we apply the self-organizing map (SOM) clustering algorithm to create a 2D distance-preserving discrete feature space to the tropopause-level wind field over the North Atlantic. The dynamics of the tropopause-level wind can then be described by the time series of visited SOM clusters, in which a long stay in a given cluster relates to a persistent state and a rapid transition between clusters that are far apart relates to a sudden considerable shift in the configuration of upper-level flow.

Second, we adapt and apply a jet axis detection and tracking algorithm to extract individual jets and classify them in the canonical categories of eddy-driven and subtropical jets (EDJ and STJ, respectively). Then, we compute a wide range of jet indices on each jet to provide easily interpretable scalar time series representing upper-tropospheric dynamics.

This work will exclusively focus on the characterization of historical trends, seasonal cycles, and other statistical properties of the jet stream dynamics, while ongoing and future work will use the tools presented here and apply them to the study of connections between jet dynamics and extreme weather. The SOM allows the identification of specific jet configurations, each one representative of a large number of days in historical time series, whose frequency or persistence had increased or decreased in the last decades. Detecting and categorizing jets adds a layer of interpretability and precision to previously and newly defined jet properties, allowing for a finer characterization of their trends and seasonal signals.

Detecting jets on flattened pressure levels instead the 2PVU surface is more robust in summer, and finding wind-direction-aligned subsets of 0-contours in a wind shear field is a fast and robust way to extract jet features. Using the SOM, we highlight a trend towards more negative NAO, and isolate predictable and/or persistent circulation patterns. Using properties of the jet features, we confirm that jets get faster and narrower in winter, but not so clearly in summer, and find no significant trend in jet latitude. Finally, both methods agree on a sudden flow transition in June.