A Catalog of High-Impact Windstorms in Switzerland Since 1859

Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | , 2 , M. Imhof 1 , C. Welker 1 3822 3821 1 , O. Martius 1 , and N. Philipp 1 , S. Brönnimann 1 For this study, we collected quantitative (e.g., volumes of windfall timber, losses The final catalog encompasses approximately 240 high-impact windstorms in This discussion paper is/has beenSystem under Sciences review (NHESS). for Please the refer journal to Natural the Hazards corresponding and final Earth paper in NHESS if available. Oeschger Centre for Climate Change Research and Institute of Geography, Interkantonaler Rückversicherungsverband, Bern, Switzerland and forests fromLothar recent, (December extreme 1999) windstorms have such been perceived as as Vivian unprecedented (February and 1990) unanticipated and century compared to thea co-variability mid-20th of century hazard and in related both damage on damage decadal and time scales. wind data1 indicates Introduction Windstorms have accounted for approximatelynatural 1/3 hazards of the in total Switzerlanddamage losses to since factor buildings 1950 from to (Imhof, Swiss 2011). forests Storms (Usbeck are et also al., the main 2010b). The damages to buildings windstorms are detected. Strong stormsthus in added the to wind the measurements catalog. and reanalysis are Switzerland since 1859. Itextreme windstorms. features Evidence three of robust high severity winter classes storm and activity in contains the eight early and late 20th only. to buildings) andimpacts from descriptive historical (e.g.,declustered quantitative windstorms. forestry data To were orinformation define processed by was insurance windstorm extreme classified value reports) severity, usingdamage statistics. normalized information, a Descriptive information as conceptual and well as guideline. on indicates comparison Validation that with with the wind most independent measurements hazardous and a winter reanalysis storms are captured, while too few moderate In recent decades, extremely hazardous windstormsto have caused buildings, unanticipated losses infrastructureand and scientific forests interest in inHowever, high-resolution the wind Switzerland. intensity data and This and damage frequency statistics has mostly of span increased historical recent high-impact decades societal storms. Abstract N. von Wattenwyl 1 University of Bern,2 Bern, Switzerland Received: 17 April 2014 – Accepted: 5Correspondence May to: 2014 P. Stucki – ([email protected]) Published: 28 May 2014 Published by Copernicus Publications on behalf of the European Geosciences Union. A catalog of high-impact windstormsSwitzerland in since 1859 P. Stucki Nat. Hazards Earth Syst. Sci.www.nat-hazards-earth-syst-sci-discuss.net/2/3821/2014/ Discuss., 2, 3821–3862, 2014 doi:10.5194/nhessd-2-3821-2014 © Author(s) 2014. CC Attribution 3.0 License. 5 25 20 10 15 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | cult ffi Beaufort 7– ≥ 3824 3823 er from possible inhomogeneities such as relocation and change of ff cult and must correct for a number of dynamic socio-economic factors in the first ffi cult because adequate wind records in Switzerland are very sparse before 1980. In A direct assessment of potential losses using historical wind measurements is For summer, Wang et al. (2011) found a decrease in geostrophic wind maxima over Alternatively, storminess and the potential losses from windstorms are often Variations and trends in reported windstorm losses can arise from (i) changes in As a consequence, several compilations of high-impact windstorms in Switzerland Such data show that economic losses from windstorms significantly increased in Public perception of a potentially increasing windstorm hazard (Schmith et al., 1998) ffi et al., 2008; Brönnimann et al., 2012). Alternatively, the global 20CR dataset reaches European windstorms and theirto impacts capture in maximum the winds summerlosses. Wind in half damage summer year. in In accurately summerwith fact, is and it primarily severe to due is assess to thunderstorms di enhanced high resulting by wind on economic concurrent gust hail, speeds smaller torrential associated rain spatial or lightning scales (Kron etdi (Imhof, al., 2012). 2011),addition, and they is su often instruments, changes in observed variables and times (Schiesser et al., 1997a; Matulla and independent wind measurements2010b). from the Zurich climate station (Usbeck etthe al., Alpine regionAtlantic from region). 1878 To our to knowledge, there 2007 are (c.f. no other Cornes climatological studies and on Jones, Central 2011, for a northeast over Western Europe.from For the winter, central theySchiesser Alps et detected to al. a (1997a)9, Paris notable found respectively) from increase negative between the trends overfrom 1864 in mid an Zurich. and winter to area storm 1995 However,Martius the days (2013) they using detected late ( pronounced historical did decadal-scale 20thnorthern variations wind not of Switzerland century. hazardous for speed the winds address In winter over observations halfensemble contrast, multi-decadal year members and of variability. since around the Welker 1900. Twentieth They Century analyzed and Reanalysis the (20CR; Compo et al., 2011) assessed via proxiesstorminess (overview for a in region north Cusack,derived of 2013). the geostrophic eastern Matulla Alps winds.between from et the They the 1920s 99th al. and found the percentileobservations (2008) 1990s. of Wang a pressure- et assessed to al. gradual (2011) investigate used increase the sub-daily surface in pressure occurrence frequency annual of storminess strong geostrophic winds place. Usbeck et al. (2010a) found evidence ofstorms increasing between damage 1858 to Swiss and forests 2007. from winter the frequency anddevelopments intensity such of as the changesand hazard not in itself least population, as from1999; values well Bengtsson (iii) at and as enhanced Nilsson, risk, from 2007; awarenessKron Usbeck damage (ii) and et et al., susceptibility al., socio-economic reporting 2012). 2010a; Hence, Bouwer, ofis 2011; attributing loss Imhof, di windstorm 2011; losses events to (Crichton, changes in the actual hazard monitor losses from windstorms (Kron et al., 2012). Switzerland between 1950 and 2010to-year (Imhof, variability 2011). Furthermore, in a windstorm considerableand losses year- becomes Lothar apparent, in and the the 1990s losses clearly from Vivian dominate the statistics. On a centennial time scale, wind loss potential (MunichRe, Re, 2006; Kron 1973, et 2002; al., Bresch 2012). et al.,have 2000; WSL, been 2001; produced Swiss Pfister, (Grünenfelder, 1999; 1990; Bründl and Brändli,due Rickli, to 1996; 2002). the sparse However, Schiesser these informationevents, available et compilations or in restrictions have earlier al., in limitations times, 1997b; insurance seasonality the small and companies number dating of have (Brändli, described increasingly 1996; Pfister, set 1999). up In turn, electronic databases to assess and 2002). motivated several studies onstorms the (e.g., intensity Pfister, and 1999).and occurrence Such preventive frequency measures, knowledge of e.g., is in high-impact required the for adequate field risk of analyses forest management or assessment of (Holenstein, 1994; Schüepp et al., 1994; Brändli, 1996; WSL, 2001; Bründl and Rickli, 5 5 25 20 10 15 25 20 10 15 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ected buildings are ff ected buildings, total losses) since ff 3826 3825 cient information is available (Bütikofer, 1987). ffi The article is structured as follows. Section 2 specifies the sources used to compile It has to be taken into account that loss data are not perfectly accurate: e.g., dating All these approaches to assess hazard from past windstorms (i.e., based on damage, of Switzerland. Fromadditionally 1968, available. The monthly data were and digitized annual and analyzed numbers to find of a a suitable selection insurances for natural lossstatistics of and daily damage storm to damage1991. data buildings. (number For of these a cantons, it provided 2.1.2 Association of Public Insurance CompaniesThe for Association Buildings of (VKF) Public Insurance Companiescenter for Buildings of (VKF) is the the1984. competence cantonal They fire contain insurers. monthly Damage statistics sums are of available storm from and 1941 total to damage for most cantons 2.1 Datasets from the insurance industry 2.1.1 Intercantonal Reinsurance (IRV) The Intercantonal Reinsurance (IRV) is the association of the 19 state-owned cantonal The damage data usedhave in been this gathered study from have documentary beenmostly sources. continuous, provided Data electronic by datasets from the containing the insurancesome quantitative insurance industry information industry descriptive and or are partially metadata.own Data electronic from stormquantitative collection documentary and database sources descriptive (STC, and areindependent mostly hereafter). historic sporadic. compiled These Wind wind in records data speeda are are our summary measurements based of both from the on specific 20CR Zurich. properties and Refer of each also source. Table 1 for conclusions are given in Sect. 6. 2 Data in the winter half year. A summary, recommendations for use of the catalog and basic long-term quantitative and descriptive informationand about historical losses. windstorm damages Ini.e., Sect. loss 3, we normalization,the describe declustering same the section, and techniquesinformation. we subsequent applied Section introduce 4 to extreme a focuseswindstorms classify value on (denoted conceptual serial analyses. the CAT-DAM). guideline data, In finalserial In Sect. for and compilation sporadic 5, indexing impact of information CAT-DAM of the is asindependent well measurements damage-based descriptive compared as taken to set to at wind of datathe Zurich independent retrieved generation climate from of station. 20CR and a We furthermore wind-based set describe of high-wind days (denoted CAT-WIND) occurring tends to spread around extrememay events, may have be uncertain missing meteorological for2012). small reasons It events, (Bouwer, and is 2011; damage therefore Imhof, importantfor 2011; comparisons to Kron and use et validation. multiple, i.e., al., damage data as well as wind data wind or proxydeficiencies. information) In are thisbefore recognized study, using methods wind we withWe observations focus primarily specific and use on damage advantages reanalysis informationother documentary from data and documentary the impact for insurance sources. industry, The comparison forestry dataof aim reports and high-impact and of in windstorms validation. the a in studysu Switzerland is first since to 1859. establish step an This extended is catalog the time span for which consistent in spaceis and relatively coarse. time.represented, Thus, However, which the has the considerable complex ramifications 2-degreesystems orography for such the spatial as of representation foehn Switzerland winds resolution of (Stucki local is et of wind al., not 2012; 20CR realistically Welker and Martius, 2013). back to 1871 and provides instantaneous wind speed information which is physically 5 5 20 15 10 25 20 10 15 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Elementarschäden- ). www.fxtop.com 3828 3827 er and Rommel (1936), among other sources. ff er and Rommel (1936) ff er and Rommel (1936) is a comprehensive study of historical losses from ff ; ESF) is a private foundation since 1901. It makes subsidiary contributions to 2.2.3 Brändli (1996) Brändli (1996) provides a chronicleIt of uses severe Bütikofer winter (1987) storms and inThe Lanz-Stau Switzerland condensed since information 1515. wasconcept for published storm in severity assessment. Pfister (1999). We also consider their Lanz-Stau natural perils in Switzerland since aroundwide 1860 compulsory with the insurance objective against of natural promotingto a hazards. Bütikofer nation- Gathered (1987). data rubrics are similar Bütikofer (1987) collected historical forestcaused damages by in windstorms Switzerland and from othersnow 1800 hazardous pressure. weather to It events 1960 is such primarilyto as completely based drought, cover on frost, the administrative larger or and1860. damages The forestry in digital the reports version main and was forested unavailable,such claims areas so as of we date Switzerland re-digitized and the after place, windstorm storm information type, spatial extent2.2.2 and damage descriptions. Lanz-Stau These three sources are used for validation. 2.2 Data from documentary sources 2.2.1 Bütikofer (1987) AON Benfield, two companies involved in international catastrophe loss estimations. analyses, US dollars were converted towhich Swiss are francs based by using on historical databanks exchange from rates for the rates European before Central the Bank year and 2000 several (available European at state Data provided byto Munich 2011. Re Winter cover stormslocal 148 (October–March) windstorms. windstorm are Overall and distinguished events insured(original from in losses values). tempest the are We storms given excluded period and per the from event minor, in 1980 i.e., million non-monetized US loss dollars events. For further largest provider of insurancenon-state-regulated Swiss for cantons. natural The lossMovables windstorm-related and and data building damage losses reach tolost are back in buildings distinguished. to the early in Some 1984. periods of the asof large the month. seven proportions Therefore, temporal of the information the data lossesthe is are were daily assigned split series to into the starts one first inthe monthly day damage-based January and set 1994. one of The daily windstorms; movables resolved the series series; building are losses used2.1.4 for for validation the only. setup of Munich Re‘s NatCatSERVICE database (MRE) were assigned by literature review (see Sect. 2.2.6). 2.1.3 Swiss Mobiliar (MOB) Swiss Mobiliar (MOB) is the largest private insurer against property loss, and the of months with high storm activity in that period, and the most probable storm days fonds repair costs from damages thatbased are electronic dataset caused was by provided non-insurable spanninglists natural 1982–2013. Further events. of An insurance-related high-impact event- windstorms in Central Europe were available from Perils AG and 2.1.5 Further insurance-related sources The Swiss fund for non-insurable losses from natural hazards ( 5 5 20 15 10 20 25 15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | E, ◦ ce for ffi ered a range of ff er and Rommel (1936), ff N; see Brönnimann et al., ◦ E, 48 ◦ ce of Meteorology and Climatology ffi 3830 3829 ecting Graubünden in 1922. In eight cases, Burger ff cult due to the heterogeneity of the gathered information. A statistical ffi N; see Welker and Martius, 2013) for the winter half year. We extracted the ◦ After the windstorm Vivian in 1990, damage publications by the Swiss Agency for the Defining severe and extreme windstormsthem and finding is appropriate criteria di to distinguish 46–48 ensemble median of thesetime 56 series field (denoted field-means. 20CRseries Then, we CH) for produced the with a 20CR the2012) daily was grid largest compiled, resolved point value hereinafter closest referred per to to day. as Analogously, Zurich 20CR a Zurich. (8 time 3 Methods We use version 2 of2 the degree 20CR spatial ensemble dataset, and aet 6-hourly global al., atmospheric temporal reanalysis 2011, resolution with for spanningwind details). speeds 1871–2010 For averaged (see all over Compo all 56 20CR ensemble Switzerland members, grid we cells computed (i.e., instantaneous the domain 6–10 We analyze homogenizedZurich daily climate maxima station (operated of byMeteoSwiss), hourly the which Federal wind are O available speed forto the present measurements (Usbeck winter from et half al., yearlong-term 2010b; time (October–March) called series from OBS of 1891 Zurich maximumwith hereafter). wind the OBS data damage-based in Zurich set Switzerland. is We of the use windstorms. only it for comparisons 2.3.2 Twentieth Century Reanalysis (20CR) and related them to all dates within that year2.3 when substantial windstorms Wind occurred. information 2.3.1 Historic wind speed measurements from Zurich (OBS Zurich) to forests since 1879. Schiesser et al. (1997b) adopted these annual damage values Holenstein (1994) listed the years of occurrence of 17 storm events with large damage dates, meteorological features andVKF loss loss descriptions of data.quantitative the windfall The information storms on a from Neue windstorma the series Zürcher number in monthly of 1967 other Zeitung in events particular, since (NZZ) but 1902. also on newspaper o Environment, Forests and Landscape (today mostlythe Environment) integrated featured in lists the of Federal historicala O storm activity. Grünenfelder figure (1990) contains featuring 14 individual years with the largest forest damages since 1900. forest damages related to a windstorm in 1962. 2.2.6 Further documentary sources The archives of several Swiss newspapers were examined in order to detect exact (1932) is in agreementsix with windfall estimates Bütikofer are (1987) lower and and five Lanz-Stau have been2.2.5 unused so far. Swiss Forestry Journal The Swiss Forestrysociety. Journal Its archive is reachesused the back by current the to publication aforementioned 1850and chroniclers. organ (details additional Comparisons information of in revealed could good Bütikofer, the be 1987) documentation found Swiss and in forestry a has range been of articles, among them a list of Burger (1932) compiled a listindicates of storm 29 type, historical wind storms direction,the between location, storms 1879 and have and amount only 1930. of monthlyexcept The windfall time for list timber. stamps, an Although they unknown could be storm attributed a to specific events 2.2.4 Burger (1932) 5 5 20 15 10 20 15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | windstorms ected area and ff Moderate windstorms can either ciently large quantitative ffi Severe 3832 3831 25 to 30 extreme values) of damage and wind data ≥ ect”, “numerous chimneys destroyed” or “very well-known storm” are ff er and Rommel (1936) as “well-known storms” in 3–5 cantons were ff storms have regional to (supra-)national extent and hence leave a very large er and Rommel, 1936; Bütikofer, 1987; or newspaper articles) are accompanied ff As an example of such inter-subjective assignment, windstorms with descriptions The conceptual guideline given in Fig. 1a is the visual translation of Table 2. Minor For this study, we opted for a three-level classification based on two dimensions. Such information can be transformed into indices, which describe the deviation from 1 ‰ of the total actuarial values; and nation-wide windfall typically exceeds an annual 10 cantonal entries were given index 3 (extreme). This list was used for validation of maximum losses (e.g., Brodin and Rootzén, 2009). and refine thethree documentary examples is classification. given The in the classified Supplement. descriptive3.2 information for Quantitative data After adjustments for socio-economic changestime-series over time, (i.e., su containing allow the useimpact of windstorms extreme (Bengtsson and value Nilsson, 2007; statistics Della-Marta et in al., 2009) order or probable to assess return periods of high- in Lanz-Stau assigned index 1 (moderate),≥ 6–9 cantonal entriesthe were other given documentary index information. In 2series addition, (severe), between the and classification 1875 of and 20 1995 storms or by storm Brändli (1996) was reviewed and used to validate be locally veryforce intense winds (e.g., whichcomprise tornadoes) destroy “devastating”, buildings or “terrible”, “massive”, and haveExtreme “enormous”, numerous, a and entire “huge”, largedocumentary among forest footprint others. footprint plots. from of Key> hurricane the words windstorm “catastrophe”.timber harvest. Insured losses are typically windstorms with isolated damagecan or be extent of are larger not spatialwindstorms considered. damage extent or comprises larger localwindows, intensity. numerous Damage cultivated land from and moderate windfall structures. Typical key“considerable”, words timber, “significant”, of “extraordinary” this substantial class and are similar. “numerous”, damages to roofs, Scale (Table 2). duration of damaging winds. More than threediscriminate classes from were descriptions,potential not and of fewer feasible quantitative classes data. becausemagnitude, We would as storm use neglect duration two severities thea is dimensions, classification are discrimination implicitly scheme which by hard included) are Brändli (1996;Switzerland) storm and to cf. with Pfister, intensity information spatial 1999; from for (or BAFU extent. historical (2008), winter For Usbeck storms et in this, al. (2010a), we or refine the Beaufort added. a normal state. Indexinget is al., a state-of-the-art 2005). method Often,2005) in based a historical on three-level climatology one classificationby (Brázdil to is Lamb several used and dimensions. (Pfister, Frydendahl For 1999; (1991) instance, Brázdil include the et maximum storm al., wind severity indices speeds, a 3.1 Descriptive information Generally, information on the severityback of in windstorms time. Sporadic becomes quantitative moreStau values descriptive in going the windstorm databasewith (e.g., from further Lanz- descriptions“devastating e of an event. For instance, qualitative attributes such as from the continuous insurance datasets. Furthermore,found sporadic quantitative in information the forestryIndices or insurance were reports applied werereports. also to assembled Here, the to descriptive quantitative (ii)for information series. extreme descriptive is events), but information mostly can contained also qualitative contain (e.g., in valuable, isolated use such quantitative of information. historical superlatives approach was used for (i) quantitative time series (e.g., in currency or timber volumes) 5 5 25 20 10 15 25 20 10 15 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | erently ff ). Starting from , BKI) provided by the Zurich www.fsw.uzh.ch ect on the distribution of the most ). The two indices are almost identical ff Baukostenindex 3834 3833 www.stadt-zuerich.ch ce (available at ffi In order to avoid systematic, very large over-correction, only the conservative BKI The second method is a combination of two historical construction cost indices for We compared the original with adjusted annual storm and total losses between In this study, the original loss data were normalized to year-of-2010 levels of The first method involves dividing current windstorm losses by the total current However, there is also considerable potential for introducing additional bias. 10 % larger. conditions. inflation correction (Imhof, 2011). Forindex the (for period prior construction toEconomic 1919, material) History we used provided of the the by wholesale University the of Research Zurich Center (histat, for Social and actuarial values. The results are probable amounts of loss under present-day portfolio actuarial value, which gives loss ratios. These can be multiplied by year-of-2010 total exposure. We restricted theserial data adjustments manipulations to (e.g., currency well-recognizedoriginal followed values and by in construction not USD). cost more adjustments for than two 3.2.2 Construction costs vs. actuarial values There are severalmethods, namely approaches construction costs vs. for total actuarial inflation-related values. normalization. We tested two in vulnerability in mostbuildings cases, (Imhof, such 2011). On as the increasingimproper other demands or hand, in statistical over-adjustments, bias facilities which canresulting and can values. easily quality in be of introduced turn by hamper correct interpretation of the quality of buildings,systems. Such increasing dynamics actuarial must beof values accounted past for and losses by normalization, to changingBrodin i.e., and near-present the forest Rootzén, levels adjustment 2009; management of Barredo,2011; 2010; exposed Kron Usbeck wealth et et al., al., (Pielke 2010a; 2012). and Bouwer, 2011; Landsea, Imhof, 1998; Normalization attempts mostly assumevalues a at spatially risk, homogeneous which development is of normally the not the case, and they cannot account for changes Our catalog shall reach backchanges to the in 19th century. the Since then,changes value there distribution have are been in dramatic mostChanges urbanized, obvious agricultural furthermore in and manifest inflation, forested in areas. but The the also in growing population number, density volumes and and wealth. construction 3.2.1 Normalization of economic loss applied model until 1960,are and not that captured. additional cost-driving dynamics in recent periods statistics o adjustment was applied in theavailable for present all study. series. Moreover, the Aslosses. total a In actuarial result, value we fact, is have theapproximately to not corrected accept constant systematic losses under-estimations until in of costs years around gradually with increase 1960, despite low the while to adjustment. average This in indicates storminess good the remain correction more by the recent decades, the 1920, we use the construction price index ( hazardous years than thefrom actuarial then values destroyed method.probably buildings underestimates The to the BKI present-day damage methodbuilding stock. potential reconstruction adjusts The of actuarial losses costs. method pastof Hence, increased magnitude, storms normalized because it it losses to is up most the sensitiveday to to present, several increases orders damage in larger building stock. to Itsusceptible simulates to a present- storms. building Therefore,past it stock storms. may that largely misestimate was the much damage potential smaller of in reality and di in the overlapping period≤ from 1920 to the 1960s, and1941 then and BKI gradually 1984. becomes The BKI method has less e 5 5 25 20 10 15 25 20 10 15 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | or of the Klafter in 2011 (Kurz et al., 1998; 250 % in parts of southern erences are found, ranging 1 ff − > erences are probably large, but ha ff 3 3836 3835 cient number of independent episodes from the ffi 50 % in a 12 year period; Rootzén and Tajvidi, 1997). > of windfall timber immediately after the Vivian storm in 3 ects a couple of years later. For instance, Grünenfelder (1990) ff on behalf of the same federal authority. ect is known as loss history (Kron et al., 2012), which can stem from ff 3 35 % in some areas of the Jura mountains to − < ected buildings by VKF and MOB. However, we applied a minimum separation filter of At this stage of the study, the magnitude of economic impacts from windstorms Here, we use the peak over threshold (POT) method, where GPD is applied to Another e In a first step, windfall timber was therefore attributed to a forestry region and then ff instances, no declustering wasa applied to the monthlytwo sums days for of declustering losses of daily anddays, resolved number data only (IRV, of MOB). the Within a largest sequenceattenuated daily of over-representation storm damage of is very chosen persistentclaims around storms as a and an peak the indicator day related (Imhof, of spreading 2011). storm of severity. This was assessed withoutquantitative discrimination of series meteorological contain processes. a Nevertheless, all su one data point persuitable time for window event-based as data. For withbut instance, the pre-filtered MRE block losses. can maxima Thirdly, be wemajority method. thought can Secondly, of of POT assume the as is independence events amostly of occurred set neglected samples of several in as serial, days event-based the apart. datasets. vast In Assuming temporal fact, independence shorter in time most intervals are shown that therange of General extreme windstorm-related Pareto variables such DistributionDella-Marta as wind et (GPD) speeds al., (Ceppi is etRootzén, 2009), al., a 2009), windstorm 2008; or suitable windfall losses timber modelapplications (Bengtsson (Rootzén (e.g., and and for Nilsson, Coles, 2007), 2001; Tajvidi, a besides Sanders, 1997; many 2005). wide Brodin other and threshold exceedances. ThisPOT has method several uses advantages the for information this of all study. available First exceeding of samples, all, i.e., more the than Time series of normalizedinstance, losses from the windstorms claims showproportion statistical from of particularities. the all For claims mostSuch (e.g., extreme extreme values storms form may the account heavy for tail a in very the large statistical distribution. It has been 3.2.5 GPD/POT 4.9 million m mean Swiss midland andi.e. prealpine the approximate tree formula sizes afterProdan Denzin using (see for forestry tree Kleinn, trunks 2013). concepts and and the formulas, tree height curve after misestimating, new observationsecondary storm of e dispersedestimated damage 3.5 million m orFebruary 1990, from while the a few integration years of later, Holenstein (1994) published an estimate of 3.2.4 Uncertainties of estimation There are furtherinstance, uncertainty causes comes from ofnumber the of uncertainty conversion of trees in into old cubic units the meters such of as estimates wood. We of converted numbers accrued of trees losses. assuming For Switzerland. specifically normalized following Ginzlervolume estimates per et event were al. corrected forprovided (2011). stock in density In Usbeck using et the a adjustment al. curve (2010a).mid-19th second Indeed, century mean step, increased forest the density from inUsbeck about overall Switzerland et 120 since al., to the 2010a; 369 BAFU, m no 2012). information Again, on regional historical di stock densities per forestry region was available. An analogue procedurecubic was meters. In applied fact, to the1880 total the forested and area collected 2000 in Switzerland amounts (Ginzlerfrom increased of by et 22 windfall % al., between timber 2011), but in large local di 3.2.3 Total forest area and growing stock 5 5 25 20 10 15 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ciently large ffi . Finally, a return period of three windstorms over the studied period, severe windstorms. The class associated with extreme 3838 3837 er and Rommel (1936), was compared to available ected buildings, and volume of windfall timber, moderate ff ff set. ff windstorms in the documentaryproportions sources. and within-series The variations are quantitative small.one series There case, are deliver the two over-representation quite exceptions, of though. similar extremeloss In windstorms series in is the a relatively result short(the of windfall the two subjective VKF classification (see series),be Sect. the 3.2.8). linked In classified to the monthly other aextreme case data windstorm windstorms were event in by only 44 literature consideredo (17) research. if years Only they were 15 could selected, (5) which mostly explains severe the and classification Figure 2 depictsemphasis the of proportion descriptivemoderate of windstorms. information classes This isinformation systematic for on (STC bias each series, severe ishistorical series. see windstorms reflected perception Sect. It threshold, in at 2) i.e., is all an and the under-representation series salient can of expense from be that smaller of and sporadic explained the moderate by the concept of the and February 1879.losses to Eventually, be one we (two) order(s) subjectively of defined magnitude lower severe than extreme (moderate) losses. 4 storm The damage-based windstorm data set 4.1 Partial series The applied procedures yielded 12 partial time series of classified windstorms. A deviation from thislosses to procedure forested was areas over necessaryfrom the Brändli for period (1996) and a 1860–1927. Lanz-Stau The smallcost normalized sample estimations series, for of collected the 21 extreme quantified windstorms in February 1967 (NZZ), January 1919, 3.2.8 Subjective classification of accrued losses,respectively (Fig. 1b). number Values with a ofwindstorms, return which period a were of not less considered than forbetween one the year catalog. 1 represent Values related minor and to returnreturn 3 periods years periods between represent 3 anddecades 30 years should is lead called to approximately six which is a numberstorms. that may also reflect the societal notion of the most memorable (2001). Similar thresholds to comparable variablesreasonable. in other series were selected where 3.2.7 Return periods Three classes of windstorm severity were defined according to the return periods to ensure meaningfulreasonable behavior number of of samples.several the The exceeding selected underlying threshold values,considerations asymptotic should but are approximately leaving assumptions reflect that abe (i) (e.g., statistically the independent i.i.d.; scale of(ii) and the Coles, the modified chosen behavior shape 2001). ofapproximately threshold, of the linear. Particular i.e., the To mean meet fitting stable excessconducted these curve of above requirements, by values should threshold the use with selection chosen increasing with of threshold; thresholds POT diagnostic was should plots be according to Ceppi et al. (2008) and Coles in Fig. 1b); this assures identical distribution (Coles, 2001). 3.2.6 Threshold selection Thresholds used with extreme value statistics are required to be su season with the largest damage values (winter half year in this case; shown exemplarily 5 5 20 15 10 20 15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ering ff er and Rommel, 1936) ff 3840 3839 ering parameters. ff Moreover, the conceptual guideline is well compatible with the return period Specifically, we perform intra-composite verifications and comparisons to modern- Periods of high windstorm frequencies and intensities are found both in the early CAT-DAM is displayed in the online Supplement and summarized in Fig. 3. It approach. For instance, the tornado in August 1890 is considered a local, but Considering the ratherclasses ordinal is nature excellent ofonly (Fig. the 4). winter All weighted stormreturn extreme means, levels winter Vivian the are storms well is groupingObviously, are reproduced of the on (at correctly 1.4 the approach classified; the for(e.g., of and severe threshold severe expressing and windstorms 2.5 of severity foroverall having levels index extreme consistency a depending windstorms). in 2 return on theperiods (severe). classification, period return of even The time between periods if and 3 applied threshold di to and partial 30 years), series ensures over di 5.1 CAT-DAM compared to damage information 5.1.1 Intra-composite verification The accuracy ofextreme value the analysis to proposed the weighted windstorm means of severity the composite classes series (see is Sect. 4.2). verified by applying representation. However, such sources are valuable to validate the windstormperiod catalog. data tocompleteness assess and accuracy (i) of the theinformation windstorm (for accuracy catalog. the Comparisons of winter to half thewind-based long-term year) windstorm wind three-level address selections, (iii) and classification, (iv) thealso and the concurrence infer variability of a (ii) of wind-based the the windstorm data damage- occurrence. set and We of winter storms (CAT-WIND). A range of sources hasinhomogeneities been (Table 1). unused so For far instance,limited because to the of the building large seven loss spatialwind Swiss data datasets or cantons are provided temporal continuous with by over data free-market a MOB centennial building time are insurance. scale, but The are available limited in their spatial 5 Validation Also the latter stormand flash was floods a (e.g., Muriset, thunderstorm 2003). with concurrent hail,and intense late decades precipitation, of1930s–1950s the and 20th the century. 1970s, less Decadesgaps distinct with in also high-impact low around windstorm windstorm 1870. activity features CAT-DAM a severity during actual thorough were these validation the periods. with Such independent peculiarities data sets, require which is provided in the next section. the prevailing weather typesummer windstorms during occurred all on 8 classified Juneformer 1861 storm winter and is 1 storms based July on 1987. (not oneof The series shown). classification only, building which of Extreme is the losses. the construction-costrevealed Literature adjusted that series research the losses (primarily probably Lanz-Stau included an unknown share from hail and flooding. comprises 202 annualmoderate. In windstorms; winter 8 (summer),moderate there of windstorms. are Hence, these 6destructive winter than are (2) summer storms storms extreme, extreme, as tend 43 astorms 59 general to (16) (of rule. a severe be severe Based total on of and extreme more CAT-DAM, 119 and23 winter 70 winter numerous storms) February 135 (65) occurred and 1935, on 20All more 23 February 1879, storms February 4 January except 1967, 1919, for 26 1919 February (foehn) 1990, were and storms 26 December with 1999. westerly winds, which is also The composite series generatedwindstorm data from set the The (CAT-DAM). final partialeight class series weights. indices are These is set weights called byindex are the the averages the rounded within damage-based mean indices the of of four continuous the sources four (IRV, MRE, sporadic MOB STC and series VKF). and the 4.2 Composite series 5 5 20 25 15 10 25 20 10 15 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | of timber. Even assuming large 3 50 %) in Germany, but small in Switzerland > 300000 m 3842 3841 > er strongly even between neighboring countries. For ff ering methodologies and variables analyzed to produce the windstorm sets. ff The relative fraction of losses from each storm with regard to theOn total losses the other hand, supra-national windstorms are only partially captured, as uncertainties. Typically, summer stormsimpact. are This convective means storms that some with of regional the to smaller-scale local summer storms were presumably For instance, the wind-fieldWuerttemberg, based a storm similar-sized catalog areaonly by co-list adjacent Heneka 5 to et out northern of al. the Switzerland, (2006) strongest for and 20 Baden- windstorms. 5.1.5 CAT-DAM Comparison to further impact information Most existing compilations considerthe windstorms detection, during classification the and winter validation half of year summer only, storms as involves considerable represent windstorm losses in Switzerland well. impacts from winter storms can di instance, losses from Kyrill were highestand ( France. This isdue due to to di topographical and meteorological particularities, and also with loss data fromXynthia five (February recent 2010), winter Kyrill(December storms, 1999). (January Loss which 2007), data are by JeanettPerils IRV Joachim and and (October MOB AON (December are 2002), Benfield 2012), compared (see and to Sect. independent Lothar 2 data and from Table 1). from all fivespecific storms fractions of is losses shown areSwitzerland. within in This seven percent Fig. indicates in that 5b. all data MOB On sets, and the and IRV even one lower data, for used hand, for the constructing estimated CAT-DAM, country- in while CAT-DAM, a few high-impact summer storms may be5.1.4 missed. Comparison to international catalogs The comparability of CAT-DAM to international windstorm classifications is evaluated In summary, important severe and extreme winter storms since around 1980 are listed exception is a summerfrom storm hail on and heavy 29 rain July with 2005, an which unknown contribution resulted of in wind substantial gusts damage (IRV, MRE, STC). 5.1.3 Comparison to ESF/MOB datasets The completeness of CAT-DAM backwith to two the independent early dataloss data 1980s sets, from is which MOB. Both checked are(Sect. were by damage 3.2). treated comparisons In in data 11 the from of samewith 12 way ESF at as instances least and all when one other a the severe quantitative windstorm classification, series building is it listed is in also both recorded validation in datasets (not CAT-DAM shown). The that Calvann was moremoderate in destructive four to outwas forests of classified five than partial extreme to in series.Anyhow, buildings, Similarly, the a the higher as STC rating windstorm windfall it for in windfall timberThis was November timber series, and would 1962 classified but not the have not fact ledclassification in to that for a two no winter higher other storms overrating overall since rating. series. is the evident 1960s. strongly confirm the robustness of the 5.1.2 Comparison to independent windfall timberThe data accuracy of the classification backof to damage 1962 is to furthermore forests validatedForest for with indications 11 Institute destructive (details storms inweighted since means Gardiner 1962, in provided et (see CAT-DAM by Sect. al.,in the 4.2). 2010). European The 9 We datasets out agree comparedNovember on 1962 of these the have a classification values 11 lower to class windstorms in the than CAT-DAM (Fig. in Gardiner 5a). et al. Calvann (2010). It (January seems 2003) and a windstorm in range of severeuncertainty windfall in losses the with windfallthat estimate, the it was three-leveldiscrimination clearly of classification windstorm a severities. severe is summer a storm. We robust infer method that provides a meaningful catastrophic event following the conceptual guideline and, in fact, lies in the upper 5 5 25 20 10 15 20 25 10 15 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | windstorm days is x 3844 3843 for extreme windstorms) is rather large. Obviously, not all 1 − Figure 7 shows concurrence of winter storms in the wind series (OBS Zurich, 20CR Distributions of high-wind days within the two 20CR time series are closely related. However, there are some prominent misses in 20CR, such as the well-documented westerly storm in February 1879 or the extreme foehn storm in January 1919 DAM). The 119 winter stormsto since 1859 the in series are CAT-DAM length. themore smallest severe In sample to addition, relative extreme windstorms CAT-DAM features than fewer the moderate wind series. windstormsMoreover, and they are similar tofor the distributions two of high-wind short daysThis periods in indicates OBS around good Zurich, except quality 1900comparisons of to and CAT-DAM. all 1980 three wind (see series also and Brönnimann confirms et their usefulness al., for 2012). For the following comparisons, we applydata, the classify GPD/POT the methodology data to according the toin wind their the speed return same periods way (see as Sect. the 3), damage and data present for them theZurich composite and series. 20CR CH) with regard to the damage-based data set of windstorms (CAT- and CAT-DAM. The four windiestDAM. days Half in of each the ofwith the top three windstorms 30 wind high-wind in series days CAT-DAM, andthe are in then storm all OBS the days. in Zurich We CAT- ratio andlarge-scale infer drops in windstorms from 20CR to in this CH approximately (northern) that20CR still one CH. except coincide Switzerland third for are of foehn reflected storms, in the OBS most5.2.3 Zurich hazardous and in CAT-DAM compared to classified wind speeds in OBS Zurich and 20CR Concurrence of high-wind days into OBS Zurich, CAT-DAM is 20CR examined CH next. and Thewind 20CR speed storm Zurich and days with the in regard ratio of thecalculated co-occurrences (Fig. three in 6b). wind for CAT-DAM From the series this top perspective, are concurrence ranked is by largest between OBS Zurich 5.2.2 Concurrence of CAT-DAM with ranked wind speeds To assess concurrence, theZurich. In windstorm general, classes aHowever, of high index the CAT-DAM are concurs spread compared withapproximately high to 11–18 of wind m OBS s speed mean observationssevere (Fig. wind winter 6a). storms speeds in Switzerlandvice at result versa. in This OBS extraordinary is windfoehn Zurich particularly speeds storms true at within in for Zurich CAT-DAM since a foehn and also 1900 storms: Brönnimann resulted class et none in al., of (e.g., 2012). extreme the values eleven at documented OBS Zurich (see Historical windstorms occurringcontinuous, during long-term the wind winterZurich datasets. (see half These Sect. 2.3 year are for are details). OBS compared Zurich,5.2.1 20CR to CH three and Windstorm classes 20CR in CAT-DAM compared to OBS Zurich contain large losses from haildescribing the and hazard other is coincident rather hazards. vague,in and At summer the remain times, meteorological open. the causes It terminology of followsincorporate storm that damage the damage from convective summer multiple stormswind weather in gusts may phenomena. CAT-DAM must The be uncertain considered contribution when analyzing of the summer5.2 samples of the CAT-DAM compared catalog. to wind information gusts, concurrent heavy precipitationA comparison or of hail major hail maystorms days contribute in in Switzerland to CAT-DAM revealed between that 2003 the andco-occurred 13 overall 2010 out with to damages. of the a summer 15losses major summer windstorms hail attributed during event to this (L. windstorms period Nisi, by personal chroniclers communication, or 2014). insurance Thus, companies presumably not detected by chroniclers or the coarse measurement network. Besides wind 5 5 25 15 20 10 20 10 15 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ff ≥ er and Rommel ff subsequent winter x 3846 3845 ) and the 30 days with the largest wind speeds in 20CR (based on averaged 1 − erences become larger going further back in time. For instance, the frequency ff 1 %) for each of the four windstorm series. However, the longest period without Moreover, the decadal-scale windstorm variability largely exceeds trends. This is Di We infer from these comparisons (refer also Sect. 5.1) thatOn decadal CAT-DAM most time scales, likely concurrence of the wind series with is CAT-DAM good back On the other hand, OBS Zurich and 20CR over-estimate a number of winter storms < the low storminess in either. CAT-DAM Hence, theredriven is variation a in strong indication windstorms that over aof Switzerland climate- windstorm manifests in losses. a decadal-scale variability found by countingweighting the the number storm counts of withtriple their storms for severities (e.g., index per double 3; weight wintersmall not for and shown). half index non-significant 2 Subsequent year trends. storms, logit in regression for each count series data reveals or only by ( windstorms in (OBS CAT-DAM Zurich) lasts(6) 15 winter (6) winter half half years years betweenperiod around 1967 1940 without and and 1984. a 9 In high-windperiods 20CR day with CH low between (20CR storminess Zurich), Octoberexplained in the and by longest both March randomness, wind- lasts and and as damage-based 6 datasets shown (4) can above, years. hardly under-sampling Long be cannot fully explain between a meaningful extension (i.e., avoidingand redundancy a with see CAT-DAM; representative Fig. selection 6b) of the largest high-wind days5.4 only. Decadal-scale variability in storminess The windstorm occurrences can be consideredWe as a calculated Poisson the point process probabilities (nothalf shown). that no years. windstorm In occurs theory, during storm-free periods of more than five years are very unlikely 14.7 m s ranks of wind speedsthese in are 20CR additional series). to CAT-WIND containsare CAT-DAM. Windstorms 54 not occurring windstorms, included during and the 31 duesummer summer of storms to half over low year Switzerland. representativity Considering of the top the 30 available high-wind long-term days wind is a data tradeo for It features the 30 days with the largest mean hourly wind speeds in OBS Zurich ( uses of the windstorm catalog. Forof such winter purposes, storms we produced from a OBS wind-based Zurich data and set 20CR called CAT-WIND (see the Supplement). and intensity of winterDAM storms due in to the the 1920s(1936). over-representation to Lower of 1930s might these concurrence bereflects decades among increasing over-estimated in overall in the Lanz-Stau uncertainties. CAT- time series5.3 prior to The around wind-based data 1895 set probably ofIt winter might storms be important to minimize possible misses of the strongest storms for specific is reflected in OBS Zurichto and 20CR, severe although winter the windin storms. series CAT-DAM during This feature some these indicates moderate periods,and possible which the misses could inclusion of beSwitzerland of due moderate during the to these winter decades conservative VKF storms in normalization loss both the data. wind Nevertheless, and damage we data see sets. low storminess over over the northernmost parts(defined of in Switzerland. this This explains case20CR) why as are 5 concurrent not of severe found 24 or in high-wind extreme CAT-DAM. days windstorms in OBScontains Zurich all and hazardous winternot be storms captured. since 1859, while some moderateto storms 1935. The may lack of severe windstorms in during CAT-DAM the 1970s and around 1940 and increasing uncertainties going back in time. with rather isolatedKirsten impacts on 12 around MarchHowever, and it 2008 is north was considered a severe of in minor 20CR Zurich. storm (moderate in according For OBS to Zurich) instance, our due windstorm to damage high information. winds (Brönnimann et al., 2012). This may be due to the coarse horizontal resolution of 20CR 5 5 25 20 10 15 25 20 10 15 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ce of Meteorology ffi ering time periods. ff . 3848 3847 We are grateful for the support from the Alfred Bretscher-Fonds für The present article sets the historical context for recent natural hazard events and The set of summer storms includesWe find convective concurrent storms periods with with enhanced concurrent, or reduced winter storm activity in The catalog most likely contains all hazardous winter storms since 1859, while some The catalog provides a basis for a range of practical and scientific applications The emphasis on decadal-scale variations of windstorms over Switzerland is in line From a historical perspective, Pfister (2009) defined the disaster gap as a period it extends traditional compilations.socio-economic factors Hence, (e.g., monetized it material may values,and societal impacts) contribute perceptions vs. processes of to in losses nature theor (e.g., climate an understanding variability) extreme of that windstorm add event. up to a moderate incoherencies prior to 1890.high Storminess until in around Switzerlandwere 1920, during characterized then by the low a 20th gradualin to century increase the medium from was 1990s, the until calm20th and around 1970s century a 1970. to could the The quieter have extremevariability latest situation storms been is 40 since. equally years present We stormy indamage presume as information. both the that the last particularly wind few data the decades. (i.e., early The the decadal hazard) as well as the loss and superimposed and notparticularly necessarily moderate summer wind-induced stormscatalog damage. are cannot probably be Moreover, missed. considered a Therefore, as the comprehensive number for windstorm the of summer season. all damage as well as wind datasets considered here, although there are some windstorms (CAT-WIND) additionally provides thefrom windiest observations at winter Zurich storm and days 20CR. derived from GPD/POT cana be used range as ofUncertainties a semi-objective may physical proxy arise andprocedures, for from among windstorm others, economic data and severity require availability parameters over error-tolerant and classifications. and quality, normalization overmoderate storms and may di not fitting beis captured, particularly possible oversampling during from the 1940s aroundbecomes and 1920 more 1970s. to There uncertain 1935, and for completeness the of 19th the catalog century. To compensate, a wind-based set of such as analysesin of potential the wind novelwindfall hazard. timber combination A volumes) methodological of withto benefit extreme descriptive traditional may damage historian be value information) for indexing found statistics impact procedures assessment. (e.g., (e.g., Particularly, return applied applied periods to normalized in occurrence of high-impacthowever, they windstorms may fit reflect(windstorm a into Vivian) decreased the to societal disaster document interest the gap. rather in More moderate the impacts importantly, decades from prior windstorms. to 1990 6 Summary and conclusions We present a catalog of approximately 240in high-impact Switzerland windstorms since (120 1859, winter storms) featuring three robust severity classes. with earlier studiesstorminess that (e.g., Matulla found et pronounced al., 2008; decadal Welker and variations Martius, inof 2013, 2014). rare Central loss-intense European natural1970s, hazards and in he Switzerland inferred from a the societal late loss 18th of century disaster to memory. the In this context, our gaps institutes for opening theirRückversicherungsverband/Vereinigung archives and Kantonaler deliveringMobiliar indispensable Versicherungen, Feuerversicherungen, information: Munich Re, Interkantonaler Schweizerischer Schweizerische Tilo Elementarschädenfonds and Usbeck Perils AG. is acknowledged for providing historic instrumental wind speed data taken at Klima- undand Luftverschmutzungsforschung Climatology and MeteoSwiss. We from are the thankful Federal to the O following insurance companies and Acknowledgements. The Supplement related to thisdoi:10.5194/nhessd-2-3821-2014-supplement article is available online at 5 5 25 20 10 15 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ce of ffi entlichte ff , 2007. , last access: 16 May , 2010. ce of Science Innovative and ffi , 2011. 10.5194/nhess-7-515-2007 10.5194/nhess-10-97-2010 3850 3849 ce – EFIATLANTIC, Crestas, France, 2010. ffi www.fsw.uzh.ch/histstat/main.php ce. ffi 10.1029/2011JD016007 , last access: 16 May 2014. http://fxtop.com/ , S. D., and Worley, S. J.: The Twentieth Century Reanalysis Project, Q. J. Roy. ff Schweiz, Swiss Forestry Journal (Schweizerische2011 Zeitschrift (in für German). Forstwesen), 162, 337–343, Marzano, M., Nicoll, B.,Destructive Orazio, Storms C., in Peyron, J., EuropeanInstitute Schelhaas, Forests: Atlantic M., Past European Schuck, and Regional A., O Forthcoming and Impacts, Usbeck, European T.: Forest storms over Europe, Int. J. Climatol., 459, 437–459, 2009. 2014. Marshall, G. J., Maugeri,Woodru M., Mok, H.Meteor. Y., Soc., Nordli, 137, O., 1–28, Ross, 2011. T. F., Trigo, R.over M., the Wang, X. 1851–2003 L., Res.-Atmos., period 116, using D16110, doi: the EMULATE gridded MSLP data series, J. Geophys. 704, 2013. Snow and Landscape Research, 77, 207–216, 2002. Lizentiatsarbeit im Fach Schweizergeschichte, Univ. Bern, Bern, 1987 (in German). Observations over Switzerland, Arbeitsberichte der MeteoSchweiz, Zurich, 219, 43, 2008. Gleason, B. E.,Crouthamel, Vose, R. R. I., S., Grant, Rutledge, A. G., N., Bessemoulin, Groisman, P., Brönnimann, P. Y., S., Jones, Brunet, P. D., M., Kruk, M. C., Kruger, A. C., Rose, London, 102–103, 1999. Sardeshmukh, P. D., and Usbeck,Meteorol. Z., T.: 21, Extreme 13–27, winds 2012. at northern mid-latitudes since 1871, Versuchswes., Eidgenössische Forschungsanstalt für Wald,Zurich, Schnee 17„ und 341–376, 1932 Landschaftt (in WSL, German). in Europe – the state of the art, ClimaticReinsurance Change, Company, 70, Zurich, 363–430, 2000. 2005. storm losses, Insur. Math. Econ., 44, 345–356, 2009. Zustand, Bern, 1224, 174, 2012 (in German and French). Hazards Earth Syst. Sci., 10, 97–104, doi: Meteorol. Soc., 92, 39–46, 2011. pp., 1996 (in German). sen von nationaler Bedeutung im Wald,0801, Umwelt-Vollzug, 132, Bundesamt 2008, für Umwelt, 2008 Bern, (in Bern, German). Nat. Hazards Earth Syst. Sci., 7, 515–521, doi: BAFU (Bundesamt für Umwelt): Jahrbuch Wald und Holz – Annuaire La forêtBarredo, et J. le bois, I.: Umwelt- No upward trend in normalised windstorm losses in Europe: 1970–2008, Nat. 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3854 3853

Point Damage

•••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••• Wind ••••••••••••••• Origin of data Spatial extent Time intervals Series length Usage Impact measure ce (Stadt Zürich Statistik): ffi Properties of used sources. access: 16 May 2014 (in German). Switzerland since end of the 19th century, Atmos. Sci. Lett., 15, 86–91, doi: temperatures associated with2014. hazardous winds in Switzerland, Clim.Landschaft, Dynam., Birmensdorf, 2001 in (in German). review, Jourdain, S., and Yin,Europe, X.: 1878–2007, Clim. Trends Dynam., and 37, low-frequency 2355–2371, variability 2011. of storminess over western 2013. Intercantonal Reinsurance (IRV) Munich Re NatCatSERVICE (MRE) Swiss Mobiliar Movables Losses (MOB) Vgg. Kantonaler Feuerversicherungen (VKF) Storm Colection Database (STC) Elementarschädenfonds (ESF) Swiss Mobiliar Building Losses Perils AG AON Benfield European Forest Institute OBS Zurich (Usbeck et al.,20CR 2010b) Zurich 20CR CH Table 1. Zurich Statistics O WSL: Lothar der Orkan 1999 Ereignisanalyse, Eidg. Forschungsanstalt für Wald, Schnee und Welker, C. and Martius, O.: Decadal-scale variability in hazardous winds in northern Wang, X. L., Wan, H., Zwiers, F. W., Swail, V. R., Compo, G. P., Allan, R. J., Vose, R.Welker, S., C. and Martius, O.: Large-scale atmospheric flow conditions and sea surface 5 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | here), 3 3 100 10000 m wi, su ≥ 10'000m 11–12 1 ‰ 633000 9 Annual harvest > > At numerous places > > > axis) of 1, 3 and 30 years x 3 . ∗ 3 3 Surface area) ∼ 41000m 633'000m 10 41000 1 3 10 30 (Usbeck: 70 000) (Usbeck:500 000) > Hundreds or thousands thousands of trees Entire forest plots Large uprooting and splintering Many thousands m > 3 b) ) events. The color bar visualizes the analogy 3 3856 3855 Surface area) 4–9 ( regional ∼ extreme moderate 9 10000 Return level plot of the normalized, declustered windfall A small number of houses Capsized boats Several thousand m Some larger damage tosolid structures Over larger areas> Over very large SignificantSerious Very significant areas/nation-wide Very large Superlatives In living memory > axis) for moderate (all 64 exceedances (b) y Extent severe severe extreme Extent moderate 9 to light structuresor trees damage to light structures or trees damage to solid< structures of damage to solid structures not ; black dots for winter, blue circles for summer storms) from the storm severe 3 3 moderate considered ) and extreme (633 000 m

3 local

substantial catastrophic

Conceptual guideline for the three-step classiﬁcation of windstorm severity, based Damage a) Conceptual guideline for indexing of windstorm damage Tiles,windows,chimneys buildings NumerousWindfall timber m Vast area of numerous damage IndexSummary Isolated Not damage consideredSpatial Numerous extent smaller Moderate# CantonsBeaufort scale Isolated Numerous largerLinguistic terms Isolated Severe damage Enormous Local amount Substantial damage Devastating damage 3–5 ( Stables solid Extreme Catastrophic damage (Sub-)RegionalQuays massive wallsTrees forests Regional to national A number of Some stables damage(eq. 2010) Numerous houses Numerous trees Heavy damage to quays Breakdowns Strong trees, Entire forests . Dates of the three most extreme windstorm events are indicated. Adapted from Brändli (1996); Pﬁster (1999); Usbeck et al. (2010a); Lamb and Fyrendahl (1991); Beaufort Scale. ∗ (a) Table 2. collection database (STC) between 1959 and 1967. Return periods ( timber volumes (m set the threshold returnsevere levels (41 000 ( m to on the two dimensions intensity andfor spatial the extent. Minor windstorm storms catalog. areextreme not Classes considered (red). are (green See box) called also moderate Table (yellow 1. boxes), severe (orange), and Figure 1. (a)

IRV number of build. l. build. of number IRV

IRV insured building losses building insured IRV

MRE overall losses overall MRE

MRE insured losses insured MRE

MOB insured movables daily movables insured MOB

MOB insured movables m. movables insured MOB VKF number of build. l. build. of number VKF

3858 3857 VKF ins. build. l. l. build. ins. VKF

STC windfall timber windfall STC

STC windfall losses windfall STC

STC building costs building STC STC descriptive STC 0.2 0.4 0.0 0.6 0.8 1.0 Proportion of moderate (yellow), severe (orange), and extreme (red) windstorm Compilation of the damage-based data set of windstorms (CAT-DAM): temporal Figure 3. distribution and severity classesand of windstorm in damage thesymbolized in by the black 12 vertical composite bars series contributingand (yellow extreme (upper in events Figs. partial are panel) 1 red. Blue and serieshorizontal negative 2), tips distance severe indicate (lower events bars concurrences are indicate in marked panel). theare: the in partial IRV analyzed orange series. Moderate Intercantonal period Black Reinsurance, per windstormVKF MRE series. Association Abbreviations Munich events of of Re Public the are Insurance NatCatSERVICE,from sources Companies MOB literature for review. Swiss See Buildings, Mobiliar, text STC for storm details. collection database severity classes per partial series (refer Sect. 2 or Fig. 3 for abbreviations). Figure 2. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Lothar Jeanett 300100 weighted means fit .95 confidence interval windstorm Vivian Joachim Xynthia Kyrill b) 3860 3859 Return period (years) 3 10 30 7 Lothar m3] 2.51 1.37 x VivianPolly 1962-11-07 Feb1967

2 3 1 Return level Return SturmC Relative losses (%) from five hazardous windstorms (see Sect. 5.1.4) Calvann Lore (b) erent countries of Central Europe and over the continent with regard to the ff Quinten 5 6 Weighted means of the CAT-DAM classification with regard to adjusted damage 1962-04-17 Kyrill Return level plot using the weighted means of the CAT-DAM classification (dots), Jennifer EFI adjusted damage to forests [10

2 3 a) 1 Classification Figure 5. (a) to forests for 11 windstormsForest since Institute 1962 EFI (circles, (Gardiner see et Sect.an al., 5.1.2 ideal 2010). for Filled details), grouping. boxes from as the in European Fig. 1. Grey dotted lines visualize since 1999 in di total losses from the ﬁvesources windstorms. are The as bars in of Fig. each 3, color plus add PRL up Perils to AG, 100 %. AON Abbreviations Benﬁeld of (see text for details). Figure 4. e.g., the weighted mean forre-calculated windstorm return Vivian levels (orange as dot) a isvertical function 2.5. lines). Grey of Filled horizontal return boxes lines are periods indicate as of in three Fig. and 1. thirty years (grey dotted Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Ratio of (b) 1.5 interquartile ± OBS Zurich 20CR CH 20CR Zurich axis). 1960 1980 2000 x Top x winter storms by rank of wind x speed Top axis) as a function of the days with highest wind y 3862 3861

0.4 1.0 0.6 0.8 b) Coincident storms / total storms total / storms Coincident 3 observations 1860 1880 1900 1920 1940 Windstorm classes 1 2 Boxplots (box gives median and interquartile range, whiskers 0 5 a) 20CR CH 1871 - 2008 20CR Zurich 1871 - 2008

CAT-DAM Mar Oct - 1859 - 2011 Zurich OBS 1891 - 2008 10 15

Top to bottom: classiﬁed winter (October to March) storms from the damage-based Wind speed (m/s) speed Wind Figure 7. Figure 6. (a) data set classiﬁed (CAT-DAM), windstorm daysZurich), from from wind 20CR observations atnear CH Zurich Zurich). (six station Horizontal grid (OBS andadditionally points vertical indicate over bars scaled Switzerland) wind are speeds. as and in from Fig. 20CR 3.; Zurich the (grid bar lengths point of the wind series coincidental windstorms to total windstorms ( speeds at OBS Zurich and in 20CR from October to March ( range) of theobservations largest are mean jittered hourly grey dots) wind as speed a at function OBS of windstorm Zurich class (1891–2011, in October–March; CAT-DAM.