Severe winter windstorms are amongst the most damaging weather events for Europe and show significant interannual variability. While surface variables (temperature, precipitation) have been successfully predicted for some time now, predictability of severe windstorms caused by extra-tropical cyclones remains less well explored. This study investigates windstorm prediction skill of the UK Met Office Global Seasonal Forecast System Version 5 (GloSea5) for the Northeast-Atlantic and European region. Based on an objective Lagrangian tracking of severe, damage relevant windstorms, three storm parameters are analysed: windstorm frequency and two intensity measures. Firstly, skill based on direct tracking of simulated windstorms is diagnosed. Significant positive skill for storm frequency and intensity is found over an extended area at the downstream end of the storm track, i.e., from the UK to southern Scandinavia. The skill for frequency agrees well with previous studies for older model versions, while the results of event-based intensity are novel. Receiver Operating Characteristic Curves for three smaller regions reveal significant skill for high and low storm activity seasons. Second, skill of windstorm characteristics based on their multi-linear regressions to three dominant large-scale circulation patterns [i.e., the North Atlantic Oscillation (NAO), the Scandinavian Pattern (SCA), and the East-Atlantic Pattern (EA)] are analysed. Although these large-scale patterns explain up to 80% of the interannual variance of windstorm frequency and up to 60% for intensity, the forecast skill for the respectively linear-regressed windstorms do not show systematically higher skill than the direct tracking approach. The signal-to-noise ratio of windstorm characteristics (frequency, intensity) is also quantified, confirming that the signal-to-noise paradox extends to windstorm predictions.

A large data set from 40 weather stations in Iceland is explored for persistence in monthly mean temperatures. There are great seasonal and regional variations in the persistence. Extremely high values of correlation (r > 0.8) of temperatures with subsequent months are found. These values are higher than reported elsewhere in the scientific literature. The highest values are found in coastal regions in the summer, while in the early winter there is overall little correlation. In general, there are two distinct maxima in the temperature correlations, one in late winter/early spring and one in the summer. In most seasons, there is greater correlation at the coast than inland. The high correlations are linked to snow melt, persistence in sea surface temperatures, weak winds and strong static stability. Remarkably low correlations reveal a negative feedback process: A warm May leads to less snow in inland regions, which favours a cold sea breeze in coastal regions in June. In regions of high correlation, the persistence is indeed useful for subseasonal forecasting of mean temperatures in late winter/spring and summer.

<p>Severe Atlantic winter storms are affecting densely populated regions of Europe (e.g. UK, France, Germany, etc.). Consequently, different parts of the society, financial industry (e.g., insurance) and last but not least the general public are interested in skilful forecasts for the upcoming storm season (usually December to March). To allow for a best possible use of steadily improved seasonal forecasts, the understanding which factors contribute to realise forecast skill is essential and will allow for an assessment whether to expect a forecast to be skilful or not.</p><p>This study analyses the predictability of the seasonal forecast model of the UK MetOffice, the GloSea5. Windstorm events are identified and tracked following Leckebusch et al. (2008) via the exceedance of the 98<sup>th</sup> percentile of the near surface wind speed.</p><p>Seasonal predictability of windstorm frequency in comparison to observations (based e.g., on ERA5 reanalysis) are calculated and different statistical methods (skill scores) are compared.</p><p>Large scale patterns (e.g., NAO, AO, EAWR, etc.) and dynamical factors (e.g., Eady Growth Rate) are analysed and their predictability is assessed in comparison to storm frequency forecast skill. This will lead to an idea how the forecast skill of windstorms is depending on the forecast skill of forcing factors conditional to the phase of large-scale variability modes. Thus, we deduce information, which factors are most important to generate seasonal forecast skill for severe extra-tropical windstorms.</p><p>The results can be used to get a better understanding of the resulting skill for the upcoming windstorm season.</p>

Abstract. Winter windstorms are one of the most damaging meteorological events in the extra-tropics. Their impact on society makes it essential to understand and improve the seasonal forecast of these extreme events. Skilful predictions on a seasonal time scale have been shown in previous studies by investigating hindcasts from various forecast centres. This study aims to connect forecast skill to relevant dynamical factors. Therefore, 10 factors have been selected which are known to influence either windstorms directly or their synoptic systems, cyclones. These factors are tested with ERA5 and GloSea5 seasonal hindcasts for their relation to windstorm forecast performance. Following GloSea5 factors’ validation contributing to windstorms, the seasonal forecast skill of the factors themselves and the relevance and influence of their forecast quality to windstorm forecast quality is assessed. Factors like mean-sea-level pressure, sea surface temperature, equivalent potential temperature and Eady Growth Rate show coherent results within these three steps, meaning these factors are skilfully predicted in relevant regions leading to increased forecast skill of winter windstorms. Nevertheless, not all factors show this clear signal of forecast skill improvement for winter windstorms, and this might indicate potential for further model improvements or further understanding to improve seasonal winter windstorm predictions.

<p>It is known from previous studies that the winter windstorm season is significantly predictable on a seasonal timescale, especially over the British Isles and southern Scandinavia. Winter windstorms are one of the most damaging extreme events for the European continent. Hence, it is important to know that this skill exists as well as to understand how the forecast model reaches this performance to increase the usability of such forecasts.</p><p>Here, we link these extreme events to the three most dominant large-scale weather patterns over Europe. A combination of the three leading patterns explains up to 80% of the variability in windstorm frequency and ~60% of storm intensity. A statistical multi-linear model based on these patterns shows similar areas of skill but with lower skill over Europe.</p><p>This new investigation uses multiple dynamical atmospheric factors known to be related to windstorms, cyclones, their intensification and genesis. Among the factors examined are jet stream strength and location, Rossby wave source, Eady growth rate and potential vorticity. To understand the influence of these factors on windstorm forecast skill, we apply a three step conceptual approach: first to understand the link between windstorms in observations and hindcasts. Second, we analyse the forecast skill of the factors themselves. In the last step we diagnose significant changes in forecast skill of the dynamical factors between well and poorly predicted windstorm years.</p><p>Factors like MSLP, tropical Atlantic rainfall, jet location, PV in 350K, or Eady Growth Rate all show significant results in individual steps but none of the dynamical factors show significant results in all 3 steps. This could mean that an improved representation of factors and their link to windstorms could improve windstorm seasonal forecast skill.</p>