NOAA 20th century and ERA-20C reanalysis datasets are evaluated regarding the representation of extra-tropical cyclones and windstorms over the Northern and Southern Hemisphere during the respective 6-month winter seasons. The results indicate substantial differences in low-frequency variability between the two datasets -especially in the first half of the 20th century -expressed in different signs and/or magnitudes of long-term trends. This is hampering a reliable analysis of real long-term trends of cyclone and windstorm activity. However, higher-frequency variability is in good agreement between both datasets especially for the Northern Hemisphere.

Political decisions, adaptation planning, and impact assessments need reliable estimates of future climate change and related uncertainties. In order to provide these estimates, different approaches to constrain, filter, or weight climate model projections into probabilistic distributions have been proposed. However, an assessment of multiple such methods to, for example, expose cases of agreement or disagreement, is often hindered by a lack of coordination, with methods focusing on a variety of variables, time periods, regions, or model pools. Here, a consistent framework is developed to allow a quantitative comparison of eight different methods; focus is given to summer temperature and precipitation change in three spatial regimes in Europe in 2041-2060 relative to 1995-2014. The analysis draws on projections from several large ensembles, the CMIP5 multi-model ensemble, and perturbed physics ensembles, all using the high-emission scenario RCP8.5. The methods’ key features are summarized, assumptions are discussed and resulting constrained distributions are presented. Method agreement is found to be dependent on the investigated region but is generally higher for median changes than for the uncertainty ranges. This study, therefore, highlights the importance of providing clear context about how different methods affect the assessed uncertainty, particularly the upper and lower percentiles that are of interest to risk-averse stakeholders. The comparison also exposes cases where diverse lines of evidence lead to diverging constraints; additional work is needed to understand how the underlying differences between methods lead to such disagreements and to provide clear guidance to users.

Winter windstorms are known to be among the most dangerous and loss intensive natural hazards in Europe. In order to gain a better understanding of their variability and driving mechanisms, this study analyses the temporal variability which is often referred to as serial or seasonal clustering. This is realized by developing a statistical model relating the winter storm counts to known teleconnection patterns affecting European weather and climate conditions (e.g., North Atlantic Oscillation [NAO], Scandinavian pattern [SCA], etc.). The statistical model is developed via a stepwise Poisson regression approach that is applied to windstorm counts and large-scale indices retrieved from the ERA-20C reanalysis. Significant largescale drivers accountable for the inter-annual variability of storms for several European regions are identified and compared. In addition to the SCA and the NAO which are found to be the essential drivers for most areas within the European domain, other teleconnections (e.g., East Atlantic pattern) are found to be more significant for the inter-annual variability in certain regions. Furthermore, the statistical model allows an estimation of the expected number of storms per winter season and also whether a season has the characteristic of being what we define an active or inactive season. The statistical model reveals high skill particularly over British Isles and central Europe; however, even for regions with less frequent storm events (e.g., southern and eastern Europe) the model shows adequate positive skill. This feature could be of specific interest for the actuarial sector.

Extratropical cyclones and their associated extreme wind speeds are a major cause of vast damage and large insured losses in several European countries. Reliable seasonal predictions of severe extratropical winter cyclones and associated windstorms would thus have great social and economic benefits, especially in the insurance sector. We analyse the climatological representation and assess the seasonal prediction skill of wintertime extratropical cyclones and windstorms in three multi-member seasonal prediction systems: ECMWF-System3, ECMWF-System4 and Met Office-GloSea5, based on hindcasts over a 20-year period (1992)(1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011). Small to moderate positive skill in forecasting the winter frequency of extratropical cyclones and windstorms is found over most of the Northern Hemisphere. The skill is highest for extratropical cyclones at the downstream end of the Pacific storm track and for windstorms at the downstream end of the Atlantic storm track. We also assess the forecast skill of windstorm frequency by using the North Atlantic Oscillation (NAO) as the predictor. Prediction skill improves when using this technique over parts of the British Isles and North Sea in GloSea5 and ECMWF-System4, but reduces over central western Europe. This suggests that using the NAO is a simple and effective method for predicting windstorm frequency, but that increased forecast skill can be achieved in some regions by identifying windstorms directly using an objective tracking algorithm. Consequently, in addition to the large-scale influence of the NAO, other factors may contribute to the predictability of windstorm frequency seen in existing forecast suites, across impact-relevant regions of Europe.Overall, this study reveals for the first time significant skill in forecasting the winter frequency of high-impact windstorms ahead of the season in regions that are vulnerable to such events.