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We develop a novel technique for characterising the latitude (ϕ‾), tilt (α) and intensity (Umean) of the North Atlantic eddy-driven jet using a feature identification method and two-dimensional moment analysis. Applying this technique to the ERA5 reanalysis, the distribution of the daily winter ϕ‾ is unimodal, which is in contrast to the trimodal distribution of the daily jet latitude index (JLI) described by Woollings et al. (2010). We show that our method exhibits a higher persistence than the JLI, casting doubt on the previous interpretations of the trimodal distribution as evidence for regime behaviour of the North Atlantic jet. It also explicitly and straightforwardly handles days where the jet is split. Although climatologically α is positive, indicating a tilt from south to east, around a fifth of winter days show negative α. When plotted as a function of the North Atlantic Oscillation and East Atlantic pattern indices, there is a higher fraction of explained variance in the daily ϕ‾ within each quadrant of the phase space than is found for JLI, supporting the conclusion that ϕ‾ has smoother variations than JLI and has a closer relationship with these indices. Our method is simple, requiring only the daily 850 hPa zonal wind as input data, and diagnoses the jet in a more informative and robust way than other methods using low-level wind fields.

Sudden-stratospheric-warming (SSW) events are often followed by significant weather and climate impacts at the surface. By affecting the North Atlantic Oscillation (NAO), SSWs can lead to periods of extreme cold in parts of Europe and North America. Previous studies have used observations and free-running climate models to try to identify features of the atmosphere prior to an SSW that can determine the subsequent impact at the surface. However, the limited observational record makes it difficult to accurately quantify these relationships. Here, we instead use a large ensemble of seasonal hindcasts. We first test whether the hindcasts reproduce the observed characteristics of SSWs and their surface signature. We find that the simulations are statistically indistinguishable from the observations, in terms of the overall risk of an SSW per winter (56 %), the frequency of SSWs with negative NAO responses (65 %), the magnitude of the NAO responses, and the frequency of wavenumber-2-dominated SSWs (26 %). We also assess the relationships between prior conditions and the NAO response in the 30 d following an SSW. We find that there is little information in the precursor state to guide differences in the subsequent NAO behaviour between one SSW and another, reflecting the substantial natural variability between SSW events. The strongest relationships with the NAO response are from pre-SSW sea level pressure anomalies over the polar cap and from zonal-wind anomalies in the lower stratosphere, both exhibiting correlations of around 0.3. The pre-SSW NAO has little bearing on its post-SSW state. The strength of the pre-SSW zonal-wind anomalies at 10 hPa is also not significantly correlated with the NAO response. Finally, we find that the mean NAO response in the first 10 d following wave-2-dominated SSWs is much more strongly negative than in wave-1 cases. However, the subsequent response in days 11–30 is very similar regardless of the dominant wavenumber. In all cases, the composite mean responses are the result of very broad distributions from individual SSW events, necessitating a probabilistic analysis using large ensembles.