Nat. Hazards Earth Syst. Sci., 17, 1795–1810, 2017 https://doi.org/10.5194/nhess-17-1795-2017 © Author(s) 2017. This work is distributed under the Creative Commons Attribution 3.0 License. Revisiting the synoptic-scale predictability of severe European winter storms using ECMWF ensemble reforecasts Florian Pantillon, Peter Knippertz, and Ulrich Corsmeier Institute of Meteorology and Climate Research, Karlsruhe Institute of Technology, Karlsruhe, Germany Correspondence to: Florian Pantillon (fl[email protected]) Received: 30 March 2017 – Discussion started: 31 March 2017 Revised: 15 September 2017 – Accepted: 18 September 2017 – Published: 19 October 2017 Abstract. New insights into the synoptic-scale predictability tion lies in the trends in frequency and intensity of winter of 25 severe European winter storms of the 1995–2015 pe- storms in the current and future climate. To date, there is lit- riod are obtained using the homogeneous ensemble refore- tle agreement between climate models and between identifi- cast dataset from the European Centre for Medium-Range cation methods (see Feser et al., 2015, for a review). The in- Weather Forecasts. The predictability of the storms is as- tensity of storms is not necessarily related to their impact and sessed with different metrics including (a) the track and in- storm losses are better estimated from the strength of winds tensity to investigate the storms’ dynamics and (b) the Storm or wind gusts exceeding a certain threshold (Klawa and Ul- Severity Index to estimate the impact of the associated wind brich, 2003). Numerous studies are therefore dedicated to the gusts. The storms are well predicted by the whole ensemble estimation of the footprint of strong winds and gusts associ- up to 2–4 days ahead. At longer lead times, the number of ated with winter storms as well as their return periods (Della- members predicting the observed storms decreases and the Marta et al., 2009; Hofherr and Kunz, 2010; Donat et al., ensemble average is not clearly defined for the track and in- 2011; Haas and Pinto, 2012; Seregina et al., 2014). These tensity. The Extreme Forecast Index and Shift of Tails are studies often require a combination of dynamical and statis- therefore computed from the deviation of the ensemble from tical models to adequately represent the footprints. the model climate. Based on these indices, the model has At shorter timescales, most studies concentrate on the de- some skill in forecasting the area covered by extreme wind tailed investigation of case studies of severe storms and on gusts up to 10 days, which indicates a clear potential for early the ability of numerical weather prediction systems to fore- warnings. However, large variability is found between the in- cast them. Although the general life cycle of extratropical dividual storms. The poor predictability of outliers appears cyclones has been known for almost one century, the intensi- related to their physical characteristics such as explosive in- fication of storms and the generation of strong winds involve tensification or small size. Longer datasets with more cases physical processes of different scales that are still not fully would be needed to further substantiate these points. understood (Hewson and Neu, 2015). Recent advances have resulted from the attention drawn by devastating storms. The damaging winds over southeast England during the “Great Storm” of October 1987, which were observed at the tip of 1 Introduction the cloud head bounding the bent-back front, now form the archetypal example of a phenomenon known as the sting jet One of the most important natural hazards over Europe (Browning, 2004). The destructions caused by storm Lothar arises from winter storms associated with low-pressure sys- over central Europe in December 1999 revealed the impor- tems from the North Atlantic, also referred to as cyclonic tance of diabatic processes in a way similar to a diabatic windstorms (Lamb and Frydendahl, 1991). These storms are Rossby wave for the rapid intensification of the storm over therefore the focus of various fields of research involving the the North Atlantic (Wernli et al., 2002). The severe wind weather and climate communities as well the wind power and gusts observed during the passage of storm Kyrill in Jan- reinsurance industries. At longer timescales, a crucial ques- Published by Copernicus Publications on behalf of the European Geosciences Union. 1796 F. Pantillon et al.: Revisiting the predictability of winter storms uary 2007 over central Europe finally emphasized the role of dataset that is ideal for comparing historical events (Hamill convection embedded in the cold front including the forma- et al., 2006, 2013). The predictability of severe storms is thus tion of cold-season derechos (Fink et al., 2009; Gatzen et al., not restricted to single case studies here but encompasses 2011). a number of events that allow a statistical analysis. Situa- These historical storms were poorly forecast when they tions with less severe or no storms are also included to check occurred and thus captured even greater attention in the whether the results are biased by the focus on extreme events. weather research community, which resulted in prolific sci- Furthermore, three methods are combined here to assess the entific literature on specific storms. In particular, Buizza predictability of the storms. In addition to the track and in- and Hollingsworth(2002) recognised early the potential of tensity and to the unusually strong winds, a third, novel ap- the ensemble prediction system of the European Centre for proach is added for the impact of the storms measured by Medium-Range Weather Forecasts (ECMWF) to predict the their gust footprint. To the best of the authors’ knowledge, storms Anatol, Lothar and Martin in December 1999. They the impact of severe winter storms has been extensively stud- showed that ensemble forecasts offer a more consistent pic- ied in the climatological community but its predictability has ture between different initialisation times than deterministic not been documented in the peer-reviewed literature. forecasts and additionally provide early indications of the The paper is organised as follows. Section2 describes the chance of an intense storm. Lalaurette(2003) further showed reforecast and reanalysis model data and the selection of se- that the extremeness of the ensemble as a whole, measured by vere storms, as well as the three different methods used to its deviation from the model climate, allows identifying areas assess the predictability of the storms in these data. Section3 of unusually strong winds up to 120 h lead time in the case of presents the results obtained for general storm characteris- Lothar, although failing in the case of Martin. Petroliagis and tics and using either the ensemble average and spread or in- Pinson(2014) and Boisserie et al.(2016) recently extended dividual ensemble members. Section4 discusses the skill for this method to longer periods with, respectively, operational early warning on the 5–10 day timescale using either selected and retrospective forecasts. While they found contrasting re- storms or the whole dataset. Section5 finally gives the con- sults from case to case, the authors confirmed the potential of clusions of the study. ensemble forecasts for the early warning of severe European storms. A more statistical approach was proposed by Froude et al. 2 Data and methods (2007a,b), who identified storms as objects with a tracking algorithm and systematically compared their position and in- 2.1 Model data tensity between forecasts and analyses. They investigated the predictability of a large number of extratropical cyclones in This study makes extensive use of the ensemble reforecast both deterministic and ensemble forecasts and found a slow from the ECMWF (Hagedorn et al., 2008, 2012). The en- bias in the track forecasts. For this large sample ranging from semble reforecast is based on the current version of the op- shallow to deep cyclones, they further found large errors in erational model but with a lighter configuration to reduce the intensity forecasts but contrasting biases that depend on computing time. It is initialised from the ECMWF Retro- the region and on the model. Pirret et al.(2017) recently ap- spective Analysis (ERA) Interim (Dee et al., 2011) and en- plied this tracking approach to severe European storms in op- semble members are obtained from initial perturbations com- erational ECMWF ensemble forecasts and found a negative puted with singular vectors. In contrast to the operational bias in intensity in addition to the slow bias in track. They model, stochastic perturbations of physical processes are not further investigated the relative contribution of diabatic and applied to the ensemble members. Since mid-May 2015, the baroclinic processes to the intensification of the storms. Al- ensemble reforecast contains 10 perturbed members in ad- though they succeeded in showing a significant correlation dition to a control member and it is run twice a week – ev- between the track and the dynamics of the storms, they strug- ery Monday and Thursday at 00:00 UTC – for the current gled to find an impact on predictability. Their results were, date in the past 20 years. Until mid-March 2016, when the however, limited by the use of operational forecasts, whose model resolution was upgraded, the horizontal grid spacing skill improves with updates in the model version and with was approximately 30 km for the first 10 days and was then increases in the horizontal resolution in particular. coarser at longer lead times until 46 days. All 10-day en- Building on these previous studies, the predictability of semble reforecasts computed between mid-October 2015 and severe European winter storms is systematically investigated mid-March 2016 are used here, which represents a homoge- here for a 20-year period in an ensemble prediction system neous dataset of nearly 10 000 individual reforecasts for the by taking advantage of the recently available ECMWF retro- winter seasons 1995/1996 to 2014/2015.