Extreme wind storms over Europe in present and future climate: a cluster analysis approach

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Meteorologische Zeitschrift, Vol.17, No. 1, 067-082 (February 2008)c© by Gebrüder Borntraeger 2008 Article

Extreme wind storms over Europe in present and futureclimate: a cluster analysis approach

GREGORC. LECKEBUSCH∗1, ANDREAS WEIMER2, JOAQUIM G. PINTO3, MARK REYERS3 andPETER SPETH3

1Institute for Meteorology, Free University Berlin, Berlin, Germany2Hannover Re, Bermuda, United Kingdom3Institute for Geophysics and Meteorology, University of Cologne, Cologne, Germany

(Manuscript received May 16, 2007; in revised form December 20,2007; accepted January 2, 2008)

AbstractBoreal winter wind storm situations over Central Europe areinvestigated by means of an objective clusteranalysis. Surface data from the NCEP-Reanalysis and ECHAM4/OPYC3-climate change GHG simulation(IS92a) are considered. To achieve an optimum separation ofclusters of extreme storm conditions, 55 clus-ters of weather patterns are differentiated. To reduce the computational effort, a PCA is initially performed,leading to a data reduction of about 98 %. The clustering itself was computed on 3-day periods constructedwith the first six PCs using “k-means” clustering algorithm.The applied method enables an evaluation ofthe time evolution of the synoptic developments. The climate change signal is constructed by a projectionof the GCM simulation on the EOFs attained from the NCEP-Reanalysis. Consequently, the same clustersare obtained and frequency distributions can be compared. For Central Europe, four primary storm clustersare identified. These clusters feature almost 72 % of the historical extreme storms events and add only to5 % of the total relative frequency. Moreover, they show a statistically significant signature in the associ-ated wind fields over Europe. An increased frequency of Central European storm clusters is detected withenhanced GHG conditions, associated with an enhancement ofthe pressure gradient over Central Europe.Consequently, more intense wind events over Central Europeare expected. The presented algorithm will behighly valuable for the analysis of huge data amounts as is required for e.g. multi-model ensemble analysis,particularly because of the enormous data reduction.

ZusammenfassungEuropäische Winterstürme werden anhand von NCEP-Reanalysen und Modelldaten (ECHAM4/OPYC3) fürrezente und potentiell geänderte zukünftige klimatische Verhältnisse (Szenario IPCC IS92a) untersucht. MitHilfe eines objektiven Algorithmus’ werden 3-tägige Episoden des Bodendrucks klassifiziert und Sturm-situationen identifiziert. Somit wird der wichtigen zeitlichen Entwicklung eines Sturms Rechnung getra-gen. Als optimal erweist sich eine Einteilung in 55 Klassen,wobei eine Hauptkomponenten-Analyse (PCA)vorweg durchgeführt wurde (Datenreduktion um 98 %). Zur Identifikation des Klimaänderungssignals wer-den die Modelldaten auf Empirischen Orthogonal-Funktionen (EOFs), erhalten aus NCEP-Reanalysen, pro-jiziert und somit die Änderung der relativen Auftrittshäufigkeit verschiedener Sturmklassen identifiziert. Esergeben sich vier Haupt- und neun Nebensturmklassen, wobei72 % der historischen Stürme den Haupt-sturmklassen zugeordnet werden, die restlichen den Nebenklassen. Insgesamt stellen die Hauptsturmklasseneinen Anteil von ca. 5 % aller klassifizierter Episoden dar. Unter erhöhten Treibhausgas-Konzentrationenergibt sich ein Anstieg der Auftrittshäufigkeit in den vier Hauptsturmklassen verbunden mit einer Verstärkungdes mittleren Druckgradienten über Zentral-Europa, bei gleichzeitigem häufigerem Auftreten von extremenWindgeschwindigkeiten in Bodennähe. Die hier vorgestellte Methodik erweist sich als äußerst wertvoll inzweifacher Hinsicht: erstens wird die zeitliche Entwicklung eines Sturms berücksichtigt und zweitens wirddurch die große Datenreduktion die Anwendung auf Ensemble von Modelldaten möglich, was eine bessereAbschätzung von Unsicherheitsmaßen im Klimaänderungssignal ermöglicht.

1 Introduction

One of the most dominant features of the mid-latitudeclimate variability is the existence of cyclones, typicallydeveloping at the polar frontal zone. These cyclones af-fect the living conditions e.g. over Europe and thereforeinfluence the socio-economic structures of our societies.

∗Corresponding author: Gregor C. Leckebusch, Institut für Meteo-rologie, Freie Universität Berlin Carl-Heinrich-Becker-Weg 6-10,12165 Berlin, Germany, e-mail: [email protected]

The damage related wind storms associated with cy-clones (e.g. the Christmas 1999 French storm “Lothar”,26 December 1999, or two days later “Martin”, 28 De-cember 1999, c.f. ULBRICH et al., 2001) are of extremerelevance in the context of climate change due to theirheavy loss potential (LECKEBUSCHet al., 2007; PINTO

et al., 2007a).Mid-latitude cyclones represent one of the mecha-

nisms in which available potential energy can be con-verted to turbulent kinetic energy and thus leading to a

DOI: 10.1127/0941-2948/2008/0266

0941-2948/2008/0266 $ 7.20

c© Gebrüder Borntraeger, Berlin, Stuttgart 2008

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68 G.C. Leckebusch et al.: Extreme wind storms Meteorol. Z., 17, 2008

reduction of the available potential energy. Therefore,the cyclone systems act as a mechanism to reduce themeridional temperature gradient via the transport of en-ergy to higher latitudes. As far as climate change con-ditions are investigated, it seems reasonable to expectan increase of the meridional temperature gradient inthe upper troposphere (e.g. CUBASCH et al., 1999; YIN,2005). These would imply a more vigorous energy trans-port to the polar regions than in the present climate.One of these mechanisms could be enhanced cycloneactivity in association with the polar front, and there-fore increased storm activity e.g. above Europe. Ad-ditionally, an increase of the water vapour content ofthe atmosphere (due to higher temperatures) can oc-cur (IPCC, 1996, 2001, 2007). The conversion of watervapour to liquid water and the related latent heat releaseacts as an additional energy source with increased liftingpotential in a baroclinic disturbance. Thus, a warmer cli-mate may also lead to an intensification of mid-latitudecyclone systems.

In consideration of these mechanisms, the detectionof climate change based on observational data of thelast decades also considered cyclone activity. An in-creased frequency and intensification of extreme wintercyclones over the northern hemisphere was detected byseveral authors (LAMBERT, 1996; GRAHAM and DIAZ ,2001; GENG and SUGI, 2001; MCCABE et al., 2001;PACIOREK et al., 2002). However, some of these resultswere met with criticism based on the inhomogeneity ofthe observational data that was analysed (NICHOLLS etal., 1996, BENGTSSONet al., 2004). Furthermore, theoccurrence of extreme winter storms over Europe duringthe last decades, i.e. 1958 to 1998, seems to be stronglyrelated to the increasing trend in the North AtlanticOscillation (WASA, 2000; ALEXANDERSSON et al.,2000; BÄRRING and VON STORCH, 2004; ALEXAN -DER et al., 2005). Thus, from observational data orrelated products, no clear trend of historical stormi-ness over Europe and the Northeast Atlantic can beidentified. Additionally, results from Netherlands sta-tion data reveal different trends than re-analysis prod-ucts (SMITS et al., 2005). On the other hand, severalglobal climate models (GCMs) show indication of anincrease in storm activity over the North Atlantic andEurope for climate change conditions (e.g. ULBRICH

and CHRISTOPH, 1999; KHARIN and ZWIERS, 2000;LECKEBUSCH and ULBRICH, 2004; LECKEBUSCH etal., 2006; PINTO et al., 2006, 2007b), in particular forcentral Europe and the North and Baltic Sea Coast.

The scope of this study is to identify whether thefrequency and intensity of episodes including extremewind events are modulated under climate change con-ditions. One simple, more statistically based approachwould be the application of extreme value theory (EVT),in order to assess changes in the extreme high part of

the wind distribution. However, the approach used hereincludes additionally the underlying atmospheric condi-tions leading to extreme events, and seems thus moresuitable to the scope of this study. As the damage relatedwind gusts cannot be resolved at the horizontal and timescale resolution of global or even regional climate mod-els, it seems reasonable to investigate parameters whichare both strongly related to extreme wind events andwell simulated by GCMs. In boreal winter, strong windevents are connected to deep cyclones, which are bestrepresented by the time development of surface data.Therefore, the attempt is made to analyse the climatesignal of changing wind storms over Europe by meansof an objective cluster analysis (cf. e.g. TRYON, 1939;MAC QUEEN, 1967) of the mean sea level pressure(MSLP). The performance of climate models with re-spect to the MSLP is well documented and observationaldata are available for validation (e.g. NCEP-NCAR re-analysis).

In order to omit the abovementioned shortcomingswith NCEP data a consistent dataset of historical meansea level pressure was developed recently (EMULATE:European and North Atlantic Daily to Multidecadal Cli-mate Variability Project, ANSELL et al., 2006). Themain objective of the EMULATE project was to developdata sets suitable for detecting changes in climatolog-ical extreme events like heat waves. For this purpose,it might be sufficient to analyse daily averaged MSLPvalues on a relative coarse 5◦ latitude by longitude gridresolution. Here, we propose to analyse highly time de-pendant systems like winter storms. Therefore, it is cru-cial to base our study on instantaneous data at higherspatial and temporal resolution (e.g. 12 hourly instanta-neous values). It is not within the scope of this study toinvestigate historical trends in the near surface observa-tion data set.

Applying a cluster analysis, it will be possible toseparate different classes of weather regimes and deter-mine their frequency distributions with respect to dif-ferent climate change scenario simulations. In particu-lar, the classes often connected with wind storms pro-vide relevant information about the dimension of climatechange. The term weather regime is commonly used inthe context of large-scale circulation pattern investiga-tions and typically describes objectivly identified, distin-guishable situations. Several studies made use of differ-ent identification techniques including cluster analysis(e.g. VAUTARD and LEGRAS, 1988; CHENG and WAL -LACE, 1993, VAUTARD, 1990; MICHELANGELI et al.,1995; PLAUT and SIMONNET, 2001; YIOU and NOGAJ,2004; BECK et al., 2007).

The paper is structured as follows: chapter 2 gives anoverview of the data used and the applied investigationmethods, including a brief outline of the cluster analy-sis procedure. The results of the cluster analysis for the

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Meteorol. Z., 17, 2008 G.C. Leckebusch et al.: Extreme wind storms 69

present day climate based on observation-near reanaly-sis data are presented in chapter 3, as well as the climatechange signal with respect to anthropogenic enhancedgreenhouse gas (GHG) concentrations. Chapter 4 con-tains a short summary and concluding remarks.

2 Data and investigation method2.1 DataInvestigations for the present day climate are carried outbased on the NCEP-NCAR reanalysis (NCEPR) data.Details about this data set are widely published and canbe found e.g. in KALNAY et al. (1996). Due to inho-mogeneities before the year 1958 (cf. KISTLER et al.,2001), only the winter (October to March) of 40 yearsfrom 1958 to 1998 were considered. The area of studyis 35◦ W to 35◦ E, and 35◦ N to 70◦ N with a spa-tial resolution of 2.5◦ x 2.5◦. Twice daily data are used.The cluster analysis is performed for the geopotentialheight of the 1000 hPa level. Additionally, the MSLP,the zonal and meridional wind with 6 hourly resolutionare considered for the identification of cyclone tracksand for the identification of extreme wind events. In thisstudy wind events are regarded as extreme, if the local98th percentile of the daily maximum wind speed is ex-ceeded (cf. chapter 3.1). The wind data is taken from thefirst model level (σ = 0.995), corresponding to a heightof about 50 m above the ground. As near-surface windspeeds (e.g. 10 m) are significantly underestimated at alllatitudes by the NCEPR (SMITH et al., 2001), we se-lected this higher model level. KISTLER et al. (2001)also highlight the inhomogeneity in 1979 through theintroduction of satellite data. Applying our investigationmethod to the periods before and after this date, no sig-nificant change of the results could be identified com-pared to the complete time span (not shown).

To address the question of climate change impact,model simulations for the present day climate and theIPCC IS92a scenario were compared. In this study, theECHAM4/OPYC3 global climate model was used (Eu-ropean Centre Model Hamburg Version 4 / Ocean Py-cnocline Model 3). The atmospheric model has a spec-tral resolution of T42, with 19 irregularly spaced ver-tical levels (details can be found e.g. in ROECKNER

et al., 1996, 1998; IPCC, 2001). The ocean is repre-sented by a full dynamical 11-layer model in isopycnalcoordinates (OBERHUBER 1993). The components arecoupled quasi-synchronously and the exchange is per-formed daily via surface fluxes of e.g. momentum andheat, sea surface temperature or sea ice (BACHER et al.,1998).

A transient scenario run (No. 22670) was used. Asa consequence of the changing greenhouse gas con-centrations in the atmosphere, the radiative forcing in-creases gradually with time, using observed data be-tween 1860 and 1990, and thereafter the IPCC emission

scenario IS92a (IPCC, 1992). A discussion of the ini-tialisation procedure, the warm bias and further detailscan be found in ROECKNER et al. (1998). The modelyears 120-170 were selected for the present day climate,while for the climate change period the years 279–329were chosen. This corresponds to anthropogenic climateconditions with a 2 to 3 fold increase in greenhouse gasconcentrations (KNIPPERTZ et al., 2000). The changesin cyclone activity, baroclinicity and MSLP between thetwo periods were analysed in PINTO et al. (2006). More-over, impacts on extreme cyclones (defined as the top5 % based on the strength of their circulation) and se-vere wind events were further explored in LECKEBUSCH

et al. (2006).

2.2 Investigation method

The cluster analysis is an objective identification methodwhich can be applied to different atmospheric datasets (e.g. GERSTENGARBE et al., 1999; FOVELL andFOVELL, 1993; MARTINEU et al., 1999; UNAL et al.,2003). The application of this method ensures the com-parability of the results for different climate scenarios.Additionally, the specific performance of the method al-lows the processing of huge data amounts, which is anecessary condition for this study. The method is basedon the assumption that a) extreme storm events aboveEurope are part of the atmospheric circulation and thatb) the circulation itself can be separated into differentclasses of weather regimes. The cluster analysis permitsthe separation of elements without having to prescribea cluster partition in advance. Thus, the cluster struc-ture is only dominated be the analysed data itself. Forweather features, this represents a separation similar tothe Grosswetterlagen (HESS and BREZOWSKY, 1952)or the Lamb-Weather-Types (LAMB , 1972) but with anobjective method. Both general overviews and detailedinformation about the multiple different techniques ofcluster analysis can be found in the literature (e.g.TRYON and BAILEY , 1970; JAIN and DUBES, 1988;SPÄTH, 1985) and will not be illustrated here. Ratherthe particularities of the used “K-means”-algorithm (acommonly used clustering method based on Euclideandistance, also named “square-error-clustering”) will bediscussed.

The time evolution of the synoptic situation is highlyrelevant for the occurrence of extreme wind events andshould be assessed by the applied investigation algo-rithm. The typical synoptic time scale is often definedfrom 2.5 up to 6 days (BLACKMON , 1976). In the caseof rapid developments of (storm) cyclones a timescale of3 days seems reasonable. So, the elements of the clusteranalysis are 3-day episodes of the 1000 hPa geopoten-tial height or corresponding parameters (e.g. the MSLP).Thus, rather than the circulation pattern at a fixed date, it

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Table 1: Absolute and relative frequency of occurrence for 46 most

important European winter storms (cf. KLAWA and ULBRICH, 2003)

with respect to identified storm cluster.Storm Cluster Absolute frequency Relative frequency [%]

PSC 1 11 23.9

PSC 2 9 19.6

PSC 3 8 17.4

PSC 4 5 10.9

SSC 1 3 6.5

SSC 2 2 4.3

SSC 3 2 4.3

SSC 4 1 2.2

SSC 5 1 2.2

SSC 6 1 2.2

SSC 7 1 2.2

SSC 8 1 2.2

SSC 9 1 2.2

is the fundamental aspect of time development of a syn-optic situation that will be accounted for in this study.In the case of twice daily data (NCEPR or GCM), 6 dis-junctive possibilities exist to construct the 3-day peri-ods. To avoid information losses due to the overlap of asynoptic system relative to the 3-day periods, the analy-sis was performed with the “K-means”-algorithm forall 6 possibilities. The “K-means”-algorithm minimizesan objective function calculating the sum of the vari-ances for each cluster depending on the achieved parti-tion (so called “minimum-variance constraint method”,WISHART, 1969). The variance within one cluster is cal-culated via the differences between each element andthe cluster centroid, which is constructed as the sumover all elements within the cluster. The best fitted par-tition (clustering) is achieved if the objective functionwas minimized. This is performed with the appropriatenumerical algorithms: a combination of a minimal dis-tance and a exchange method (HARTIGAN and WONG,1979). In order to achieve the mathematically best solu-tion of the cluster classification, it is necessary to calcu-late the cluster analysis several times with different start-ing partitions in order to determine the most appropriatenumber of clusters. Here, 20 different starting partitionswere considered: partitions by chance as well as parti-tion packages. Thus, 120 cluster analyses were calcu-lated and the analysis with the best minimized objectivefunction was selected. The sensitivity tests showed thatthe most suitable number of classes is 55. This numberof distinguishable situations is thus much higher thanthose derived from studies of so called weather regimes(e.g. VAUTARD, 1990), mostly separating between up to10 stationary large-scale patterns, not allowing for theidentification of weather situations responsible for theoccurrence of extremes.

In most cluster algorithms the computing time hasa quadratic dependence on the amount M of the ob-jects (e.g. the hierarchical scheme from WARD, 1963),and therefore also from their dimension d. For 3-dayepisodes of the 1000 hPa geopotential height fields overa North-Atlantic/European window this would add up

1 13 19 25 31

Component of episode vector

-1000

0

1000

2000

PC

7

Figure 1: Element of the cluster analysis: 36 dimensional vector of

the first 6 PC’s of the episode from the 16–18 February 1962, the

“Hamburg” storm surge (NCEP-Reanalysis). The time development

of the relevant second PC is highlighted by triangles.

to an enormous computational effort, which can be re-duced by pre-applying principal component analysis(PCA) to the data sets. The dimension d of an ob-ject of the cluster analysis (3-day episodes of the 1000hPa geopotential height fields) depends then on thenumber of EOF’s (PC’s) used to represent the origi-nal field. FUENTES and HEIMANN (2000) suggest the99 % level of explained accumulated variance as limitfor the number of recognized PC’s. For the purposeof this study we found more accuracy in selecting thePC’s via the “Rule N”-algorithm from PREISENDOR-FER (1988). The time series of higher PC’s are similarto random processes i.e. not significant, and their con-sideration could add unwanted uncertainties to our re-sults. This can be avoided if only PC’s are chosen whichexceed the 95 % significance level of the “Rule N” testparameter (PREISENDORFER, 1988; WILKS, 1995). Asthe “Rule N”-algorithm tends to deliver “conservative”results, the 6th EOF and corresponding PC will also beincluded, additionally to the first 5 EOF’s which ex-ceeded the 95 % significance level. For 3-day episodes,with twice daily data and the first 6 recognized PC’s,this leads to 36 dimensional episode vectors for the clus-ter analysis (Fig. 1). Compared to the original horizon-tal area (including 465 grid points) over the NortheastAtlantic and Europe, this procedure will lead to a datareduction of 98 %.

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1 . EO

F

1. EOF 2. EOF

26.1 % 21.0 % 18.7 %

9.3 % 5.7 % 3.4 %

3. EOF

4. EOF 5. EOF 6. EOF

Figure 2: The first 6 EOF’s of the 1000 hPa geopotential height. NCEP-Reanalysisfor the 40 winters 1958/59 to 1997/98, including the

explained variance of each PC.

3 Results

3.1 Present day climate conditions

After eliminating the average diurnal variation (in borealwinter up to 5 gpm above Central Europe) the PCA wasconducted for the NCEPR 1000 hPa geopotential heightfields (Fig. 2). As a complete discussion of the PCA it-self exceeds the scope of this study, only crucial pointsare highlighted here. The first 6 EOF’s explain 84.2 % ofthe total variance and the primary mode is characterizedby a low pressure (negative PC) centre above the south-ern part of the Norwegian Sea and the Faeroes. The sec-ond and third EOF’s are dipoles with axes from north-west to southeast and from southwest to northeast, re-spectively. Although the 6th EOF explains only 3.4 % ofthe total variance, it is of special relevance for this study:For positive PC’s it acts in direction of an enhance-ment of the pressure gradient within the recombinedfield. The clustering algorithm was performed based onthe episode vectors with the corresponding PC’s. Fromthe total of 55 clusters, four could be identified as pri-mary storm cluster (PSC) for Central Europe. In addi-tion, nine less extreme storm classes could be revealed(secondary storm cluster, SSC). PSC’s are character-ized by a higher relative frequency of historical extremewind events in comparison to the SSC’s. Additionally,the PSC’s include typically more extreme cyclones con-nected with more intense wind events. Note that some ofthe SSC’s are frequently direct precursors or successorsof the PSC’s. Based on the 50 most relevant Europeanstorms given in KLAWA and ULBRICH (2003) the sep-aration between primary and secondary storm clusters

were justified by the frequency distributions of storms(cf. Table 1). For the 46 events which occurred betweenOctober and March, and are thus analysed in this study,appr. 72 % occur in the PSCs.

Originally, every cluster is represented by its cen-troid. Taking the way of construction into account, thispattern might be artificial and not physically meaning-ful. Therefore, it is important to compare each cluster’scentroid with a sample of episodes from NCEPR chosenrandomly to secure the homogeneity within each cluster.

In order to perform a meteorological interpretationof the clusters, 3-day episodes starting each day of theNCEPR (in addition to the 3-day disjunctive episodes)were assigned to the clusters by minimizing the Euclid-ian distance between the episode and the centroid. Theadvantage of this choice is that (extreme) wind episodeswill not be divided into two different episodes. Thus,the cluster partition was achieved recognizing only “dis-junctive episodes”, but for the meteorological interpre-tation all “daily starting episodes” were allocated tothe appropriate cluster. For further interpretation for allepisodes dedicated to each cluster, cyclone tracks com-puted with an automatic algorithm based on MURRAY

and SIMMONDS (1991) adapted by PINTO et al. (2005)to the Northern Hemisphere cyclone characteristics wereused. Only tracks which remained at least 12 h withinthe investigation area and had a total lifetime of morethan two days are regarded. In order to achieve compa-rable results for each cluster, the number of cyclones pergrid box (2.5◦ x 2.5◦) were normalized by the numberof episodes within each cluster. Thus, for clusters featur-ing a fast succession of systems or with various steering

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Figure 3a: Examples of the development of geopotential 1000 hPa for the primary storm cluster in NCEPR PSC1 (5–7 January 1991). The

episodes represented are the nearest to the centroid of the clusters. Negative values are represented in dashed lines. For more details see

text.

systems with more than one secondary disturbance, thisvalue may exceed 1.

The PSC1 is characterized by a deep cyclone systembetween Iceland and Great Britain, which weakens to-wards the end of the episode, while the steering systemremains stationary. Results from the homogeneity testand track analysis show that Central Europe is mainlyaffected by one or more secondary systems on its south-ern flanks (Fig. 4a, 91 % of all episodes in this cluster).Additionally, deep cyclones may shift from the North-east Atlantic south of Iceland to the Norwegian Sea (9%). This pattern is associated with enhanced risk ofstorm events for Central Europe, as documented e.g. by“Daria” (25–26 January 1990), “Nana” (11–13 February1990), “Urania” (9–12 January 1993), “Gisela” (24–26February 1997). Nearly a quarter of all European stormsituations are allocated to this cluster (cf. Table 1), whileits relative frequency is only 1.22 %. The typical circu-lation pattern is presented by the nearest element to thecentroid of this cluster (cf. Fig. 3a).

The PSC2 is characterized by cyclones travellingfrom the Northeast Atlantic (west or northwest of theBritish Islands) with increasing core pressure into theBaltic area. Most of the cyclones cross over the Skager-rak, leading to a track density of nearly 1 in this area(cf. Fig. 4b). Only a few systems shift directly fromthe Northeast Atlantic to the Norwegian Sea. Nearly21 % of historical events are classified in this PSC (e.g.“Coranna” (11–12 November 1992) or “Quena” (8–9December 1993), and the elevated track density identi-fied (e.g. over the Skagerrak) is highly relevant for Cen-tral Europe. The relative frequency of this cluster is 1.91%. A typical representative for this cluster is given bythe centroid nearest episode including the storm from 9December 1993. (Fig. 3b).

The PSC3 is characterized by a deepening cyclonesystem above the Norwegian Sea, which weakens andslowly moves southeastward in the second half of theperiod. This cluster is characterised by the highest meanof the Euclidian distance between the elements and the

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Figure 3b: Examples of the development of geopotential 1000 hPa for the primary storm cluster in NCEPR PSC2 (9–11 December 1993).

The episodes represented are the nearest to the centroid of the clusters. Negative values are represented in dashed lines. For more details

see text.

centroid compared to the other PSCs, representing ahigher degree of diversity of elements in this cluster. Infact, the homogeneity test shows that particularly fast,zonal travelling cyclone systems with secondary distur-bances on more southerly tracks, belong to this cluster(Fig.4c). A typical element is again given for the situ-ation nearest to the centroid (Fig. 3c). For this clusterit should be noted that these disturbances have only asmall horizontal extension and cause strong gradients inthe pressure fields (which are not completely resolvedby the recombination of the original fields via the first 6PC’s). The meteorological interpretation clearly revealsthe dominant role of this cluster for the storm climateover Europe. The combination of fast eastward prop-agating systems with the frontal systems of the asso-ciated secondary lows affect large areas of the BritishIslands, Central and Eastern Europe. Episodes of manyhistorical storm events are classified in this cluster: e.g.the European storms “Vivian” (25–27 February 1990),“Wiebke” (28 February–1 March 1990), “Victoria” (19–

21 December 1993). This PSC is the rarest of the stormclusters, with a relative frequency of only 0.66 %.

The PSC4 is characterized by the transition froma zonal to a north-westerly flow regime above Cen-tral Europe, as revealed by the centroid nearest situa-tion starting from 15.03.1994 (Fig. 3d). The analysis ofthe associated cyclone tracks documents the relevanceof this PSC for the storm climate above Central Europe(Fig.4d). In nearly 70 % of the episodes the tracked cy-clone systems migrate from an area above Iceland acrossthe North Sea or South Scandinavia into the area of theBaltic Sea. This implies the possibility of extreme windspeeds in the vicinity of the respective frontal systemse.g. above Central Europe. Most of the synoptic situa-tions leading to storm surge conditions over the GermanBight are classified in this PSC (e.g. the Hamburg stormsurge, 16–18 February 1962). The relative frequency ofthis PSC is 1.37 %.

In total, the four PSC’s add only to 5.17 % of the to-tal relative frequency (of 55 clusters) but add to almost

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Figure 3c: Examples of the development of geopotential 1000 hPa for the primary storm cluster in NCEPR PSC3 (18–20 December 1991).


see text.

72 % of the historical storms. The remaining historicalstorms are associated with the nine SSC’s. An overviewof the four PSC’s and the nine SSC’s for Central Eu-rope (including the associated frequency of occurrenceof historical storms) is given in Table1.

Statistical Significance of the PSC associated windfields

To document the relevance of the identified PSC’s interms of their associated extreme wind speed potential,we tested the relative frequency of occurrence of ex-treme wind speeds during episodes of each PSC withcomparable thresholds extracted from NCEPR windspeed data. This leads to the identification of regionswith wind speeds significantly higher than the corre-sponding thresholds from NCEPR climatology. KLAWA

and ULBRICH (2003) have shown that the 98th per-centile of the local wind climate typically has to beexceeded for wind related damages to occur. For thewinter (ONDJF) months the 95th and 98th percentile

value of the NCEPR wind speed climatology (fromthe first σ -level) are given in Fig. 5a and 5b, respec-tively. These thresholds show the effects of wind speedreduction through the surface roughness. The maximareached over the Northeast Atlantic are about 22–24 m/s,whereas over continental Europe the wind speed reachesabout 10–12 m/s. As this study aims at the identificationof regions which are affected by extreme wind eventsin connection with the occurrence of wind storms, therelative frequency of exceedance of the 98th percentilewithin each PSC is determined (Fig. 6). Thus, damageprone areas in Europe with respect to different circula-tion patterns can be detected.

The statistical significance is tested by applying abootstrap-method. For each PSC, we consider its fre-quency in the NCEPR (e.g. 48 episodes for PSC3). Fur-ther, 1000 synthetic resamples of 48 episodes were gen-erated randomly based on NCEPR for PSC3 (analogousfor the other PSC’s). Next, it was tested for each grid-point how many of the 1000 resamples reached or sur-passed the percentage of exceedance of the 98th per-

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Figure 3d: Examples of the development of geopotential 1000 hPa for the primary storm cluster in NCEPR PSC4 (15–17 March 1994).


see text.

centile which had been obtained for PSC3 (cf. Fig. 6c).A significance at the 95 % confidence level indicates thatthis value was reached less than 50 times in the 1000artificial resamples, while a significance at the 99.99 %confidence level means that the value was never reached.

Particularly important for the European storm cli-mate are circulation patterns with a steering cyclonesouth of Iceland, accompanied by deep secondary lows,crossing vast parts of West and Central Europe (PSC1).These situations feature a significant exceedance of thelocal climatological 98th percentile of wind speed oversouthern parts of the United Kingdom, northern France,the Bay of Biscay including northern Spain, Germany,and Poland (Fig. 6a). Maximum values of the relativefrequency of exceedance of this threshold within thiscluster occur over northern France, the Benelux and Ger-many with values up to 25 %.

To a lesser (more regional) extent, the wind fields re-lated to the PSC2 lead to damage prone situations. Thus,cyclones travelling from west of the British Islands to

the Baltic Sea, cause extreme wind speeds mostly overGermany (with values of appr. 15 % rel. frequency of ex-ceedance) as they weaken on their path (Fig. 6b). Highlyrelevant for the European storm climate are also the pe-riods of the PSC3: fast zonally travelling cyclone sys-tems with secondary disturbances tracking over Europe.A band of highly significant exceedance of NCEPR’s98th percentile values for this cluster wind climatology isrevealed from the Northeast Atlantic to the Baltic States(Fig. 6c). The values over northern Germany exceed35 %, meaning that roughly each third episode of thiscluster may potentially be a storm for this area. This isconsistent with the fact that some of the most severe Eu-ropean storms are classified in PSC3. The historical win-ter storms classified in this PSC have frequently led sig-nificant Europe-wide losses, confirming the results fromthe significance test.

For the PSC4, the 98th percentile of the local windspeed of the NCEPR climatology is exceeded over theNorth Sea, Denmark, northern Germany, and vast parts

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d) c)

<0.2 <0.3 <0.4 <0.5 <0.6 <0.7 <0.8 <0.9 <1.0 <1.1

b)

<0.2 <0.3 <0.4 <0.5 <0.6 <0.7 <0.8 <0.9 <1.0 <1.1

a)

Figure 4: Standardised cyclone track density per 2.5x2.5 grid box area of a) PSC 1; b) PSC 2; c) PSC 3; d) PSC 4, explanations are given

in the text. Included are only tracks with a minimum lifetime of 48 h. Contour interval: 0.1 systems per grid box.

Figure 5: NCEPR wind speed climatology for the firstσ -level (0.995) in m/s for winter (October–March) a) 95th and b) 98th percentile

value.

of eastern Central Europe, where values over 25 %are reached (Fig. 6d). The occurrence of extreme windspeeds over the aforementioned areas, possibly leadingto storm surges over the German Bight, is well docu-mented by the statistically significant frequency of ex-

ceedance over North Sea area and Germany.Summarising, the findings from the statistical signif-

icance tests with respect to wind speeds confirm the rel-evance and the accuracy of the storm clusters for CentralEurope. This gains further importance in the context of

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a) b)

c) d)Figure 6: Relative frequency of exceedance of the local 98th percentile of the local wind for the primary storm cluster (a) PSC1 (b) PSC2

(c) PSC3 (d) PSC4. Light shaded areas indicate significant results at the 95 % confidence level. Dark shaded areas indicate significant

values above the 99.99 % confidence level. Significance was tested usingthe bootstrap method. For more details see text.

the pre-applied data reduction of nearly 98 %. We canconclude that the identified clusters are meteorologicallymeaningful and represent typical circulation conditionsfor the present day climate. Thus, it is justified to ap-ply this approach to model simulations in order to detectsignals of climate change for winter storms over Europe.

3.2 Climate change signalThe impact of climate change can now be determinedbased on the frequencies of occurrence for the stormclusters for the present day (control) period and the sce-nario period for the GCM. A stringent condition for thisapproach is an identical data base. In other words, theelements of the cluster analysis from the GCM and theNCEPR data must be comparable. Thus, the model dataare projected onto the empirical orthogonal functionsachieved from the NCEPR data. The field(Φ(x, t)) ofany model data period can be selected in terms of theNCEPR data:

Φ(x, t) =

Φ̄NCEPR(x)+M∑

i=1ai(t) · eo f NCEPR

i (x)(3.1)

The new coefficientsai(t) (principle components) arecalculated via:

ai(t) =M∑j=1

(Φ( j, t)− Φ̄NCEPR( j)) · eo f NCEPR( j) (3.2)

Analogous to the NCEPR data, only the first 6 coeffi-cients are used to construct the episode vectors, resultingin a formal identical and thus comparable element of thecluster analysis. This episode vector can now be classi-fied into the cluster derived from the NCEPR data. Testshave revealed that due to the large number of clusters(55) and not changing basic physics very similar parti-tions will be identified if the data are projected onto theempirical orthogonal functions achieved from the GCMcontrol and scenario period data (not shown). Thus, theclimate change signal will more project onto existingpatterns. This finding is similar to previous ones (e.g.CORTI et al., 1999), investigating 500 hPa anomaly pat-terns.

For Central Europe the relative frequencies of the pri-mary storm cluster (PSC) are given in Table 2. Withrespect to the NCEPR data, the PSC3 and PSC1 showvery low relative frequencies (0.66 % and 1.22 %, re-spectively). The storm clusters are well represented bythe GCMs (except for the PSC1, see below), althougha systematic underestimation for the 4 PSC’s and the 9SSC’s (not shown) is documented. The climate signalfor all PSC’s is positive, indicating a trend to a higherfrequency of occurrence for storm-prone situations overCentral Europe. In order to enhance the compatibilityof the frequencies of occurrence and to ensure a bet-ter interpretation of the climate signal, we calculatedcorrected relative frequencies (cf. Table 2) by normaliz-

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Table 2: Frequency distribution of the Primary Storm Clusters for

Central Europe. AF: the absolute frequency is given; RF marks the

relative frequency in % of totally 7200 3-day episodes for NCEPR

(1958–1998) and 8900 3-day episodes for ECHAM4/OPYC3

simulations (50 years each), respectively. CRF: corrected relative

frequency. The last line gives significance level of the climate

change signal.

Cluster PSC1 PSC2 PSC3 PSC4

AF NCEP 89 139 48 101 AF CON 36 130 41 130

AF SCEN 85 232 68 197

RF NCEP 1.22 1.91 0.66 1.37

RF CON 0.40 1.46 0.46 1.46

RF SCEN 0.96 2.61 0.76 2.21

CRF SCEN 2.93 3.41 1.09 2.07

SIG 99.9% 99.9% 99% 99.9%

ing the absolute frequencies with respect to the NCEPRdata. These corrected relative frequencies were testedfor significance with a two-sidedχ2-test of the 2x2 con-tingency tables. For all PSC’s of Central Europe, the cli-mate change signal reveals significance at the 99.9 %level, except for the PSC3, which is significant on the99 % level (Table 2). Despite the significance of thesefindings, an additional comment has to be made for thePSC1: Based on the insufficient reproduction of deep cy-clones in the model (due to lower horizontal resolution),these situations might not be completely resolved. Thismay bring uncertainties in the magnitude of the climatechange signal.

For further meteorological interpretation weanalysed the long-term winter average of the 1000 hPageopotential height. A systematic overestimation ofabout 16 gpm (thus a tendency to higher pressure) canbe identified all over the Northeast Atlantic and WesternEurope by the climate model (Fig. 7a) compared toNCEPR (cf. also MSLP signal in PINTO et al., 2006).Additionally, the climate change signal amplifies thepositive bias over the Iberian Peninsula (Fig. 7b),whereas a decrease of the geopotential height over theIsland area is simulated. This leads to an enhancementof the winter mean north-south pressure gradient (con-sistent with the positive changes of e.g. PSC3) includingfast, zonal travelling cyclone systems with secondarydisturbances affecting Central Europe. Indeed, PINTO

et al. (2006) identified an increase of cyclone depthtendencies near the British Isles, which goes well withthe above result. In summary, the ECHAM4/OPYC3global climate model simulates under enhanced green-house gas concentrations a significant increase of thefrequency of occurrence of winter storm situations forCentral Europe.

4 Summary and conclusions

An objective method has been used to classify meteoro-logical circulation regimes responsible for winter stormsover Europe. Based on the 40 years NCEPR (October toMarch), a cluster analysis of twice daily data has beenperformed to determine the frequency of occurrence of(3-day) weather developments associated with extremestorm events. In order to reduce computational costs(which have a quadratic dependency from the number ofobjects), we reduced the data amount via principle com-ponent analysis by about 98 %. Based on the first 6 PC’s,we constructed a 3-day episode vector as an object ofthe applied “k-means” cluster algorithm (cf. HARTIGAN

and WONG, 1979), separating the 1000 hPa geopotentialheight field into 55 meteorological meaningful clusters.In a second step we evaluated the climate change signalof the ECHAM4/OPYC3 coupled global climate modelwith respect to the IS92a scenario (IPCC, 1992), with-out the effects from sulphate aerosols and troposphericozone.

For Central Europe, four principle storm clusters(PSC) could be identified. They are significantly respon-sible for the occurrence of extreme wind speeds underpresent day climate conditions. This is confirmed by thefact that many historical storm events are classified tothese storm clusters.

PSC1 consists of flow pattern characterized by adeep cyclone system between Iceland and Great Britain,which remains stationary and weakens to the end of theepisode (e.g. “Daria”: 25th–26th January 1990). PSC2is characterized by cyclone systems travelling from theNortheast Atlantic (west or northwest of the British Is-lands) with increasing core pressure into the Baltic area.(e.g. “Coranna”: 11th–12th November 1992). PSC3 in-cludes situations with fast, zonal travelling cyclone sys-tems with secondary disturbances (e.g. “Vivian” (25th–27th February 1990). PSC4 is characterized by the tran-sition from a zonal to a north-westerly flow regimeabove Central Europe, including e.g. the historical Ham-burg storm surge (16th–18th February 1962). In total,the four PSC’s, which include almost 72 % of the his-torical storms, comprise only 5.17 % of the total rela-tive frequency. In spite of deficits of the GCM’s capa-bility to reproduce the exact frequencies of occurrencefor the identified storm-prone clusters (a feature whichis strongly related to the MSLP representation in themodel, cf. PINTO et al., 2006), the ECHAM4/OPYC3model simulation can be used to evaluate the climatechange influence on the frequency of different storm sit-uations. Results show a significant increase of the stormsituations over Central Europe.

These results document the usefulness of a clusteralgorithm as an objective tool for the detection of cli-mate change signals. Moreover, they give evidence that

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Table 3: Frequency distribution of the Secondary Storm Cluster for Central Europe. AF: the absolute frequency is given; RF marks the

relative frequency in % of totally 7200 3-day episodes for NCEPR (1958–1998) and 8900 3-day episodes for ECHAM4/OPYC3 simulations

(50 years each), respectively. CRF: corrected relative frequency. The last line gives significance level of the climate change signal, ns means

non significant (< 90 %). Cluster SSC1 SSC2 SSC3 SSC4 SSC5 SSC6 SSC7 SSC8 SSC9

AF NCEP 110 114 156 173 154 60 108 142 139 AF CON 129 75 135 151 212 40 76 241 227

AF SCEN 216 63 174 201 263 34 89 351 247

RF NCEP 1.51 1.56 2.14 2.37 2.11 0.82 1.48 1.95 1.91

RF CON 1.45 0.84 1.52 1.70 2.38 0.45 0.85 2.71 2.55

RF SCEN 2.43 0.71 1.96 2.26 2.96 0.38 1.00 3.95 2.78

CRF SCEN 2.53 1.32 2.76 3.15 2.62 0.69 1.74 2.84 2.08

SIG 99.9% ns 95% 99% 90% ns ns 99.9% 99.9%

Figure 7: Difference of the winter (ONDJFM) average 1000 hPa geopotential height in gpm. a) ECHAM4/OPYC3 control climate (1880–

1930) minus NCEPR b) ECHAM4/OPYC3 scenario (2039–2089) minus control climate. Shaded are areas above the 95 % significance

level.

there is an advantage in using a weather typing approachwhich considers the time evolution of the fields, and notonly the patterns at single dates. Considering the pre-viously mentioned data reduction of about 98 %, thefindings are even the more remarkable. Nevertheless, thedata reduction may also lead to negative effects, e.g. forepisodes with less pronounced pressure gradients or lessextreme cyclone systems. In fact, for the classificationof such “weaker” episodes a change to more than 6 PC’sshould be considered. This is, however, not the objec-tive of this study. Another factor to be considered forextreme storm events are situations with local sharp gra-dients of the pressure field, which are not well resolvedby the GCM and therefore not included in the first 6PC’s. This may lead to a possibly wrong classificationof such an episode.

Additionally, a newly developed clustering schemeSANDRA (Simulated ANnealing and Diversified Ran-domisation), recently published by PHILIPP et al. (in

press), is able to reduce the influence of chance in thecluster assignment, which is especially important fortrend analyses. Nevertheless, it is not in the scope of thisstudy to perform any trend analysis for the recent cli-mate. Instead of that it is focused on the implementationof the meteorological time development of a storm situ-ation and the data reduction achieved here, making thistechnique applicable for large data sets. In this sense thechoice of a specific clustering technique may be variableand could be replaced by others, and is thus of secondorder interest in the context of this study.

The presented results go well with findings fromprevious studies concerning the European storm cli-mate from the perspective of the middle troposphereor the considered ECHAM4 simulation. ULBRICH andCHRISTOPH(1999) identified a northward shift and in-tensification of the 500 hPa stormtrack for the samescenario simulation, which is concordant with our find-ings. For the surface climate KNIPPERTZ et al. (2000)

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revealed a northeast-ward shift of the cyclone tracksand an increasing number of deep cyclones under 970hPa. More recently, PINTO et al. (2006) detected a sig-nificant decrease of overall cyclone track density be-tween 35 and 55 degrees North, and a small increasenorthwards. Furthermore, their results show that cy-clones become not only deeper at higher latitudes, butalso more intense (quantified in terms of the inten-sity of circulation). On the other hand, an overall de-crease for the weaker cyclones is detected, in partic-ular at higher latitudes. Thus, the simulated changescan not solely be attributed to alterations in MSLP. In-stead, enhanced upper-tropospheric baroclinicity sug-gest more favourable conditions for the development ofstronger systems at higher latitudes, especially at thedelta regions of the North Atlantic. Indeed, LECKE-BUSCH et al. (2006) detected an enhanced number ofextreme cyclones (5 % strongest in terms of circula-tion intensity) near the British Isles for this scenariorun. This is reflected in the higher frequency of oc-currence of corresponding clusters in this study (e.g.PSC4 for Central Europe). For timeslice experimentswith ECHAM4 (at higher resolution, T106), similar re-sults are published (e.g. KAAS et al. 2001). A slightlydifferent result has been achieved by CARNELL andSENIOR (1998), analysing ensembles of experimentsof the Hadley Centre coupled ocean-atmosphere modelHadCM2. Although the total number of Northern Hemi-sphere storms decrease (due to reduced baroclinicity inthe North Atlantic source regions) a tendency towardsdeeper low centres is identified. Additionally, the cy-clone tracks are shorter with decreases at the northeast-ern ends. LECKEBUSCH et al. (2006) detected increas-ing cyclone activity for western parts of Central Eu-rope for extreme cyclones for HadCM3, HadAM3P andECHAM5 GCMs, although the climate change signalwas not always spatially coherent with the ECHAM4GCM. However, for Southern Europe, all GCMs showconcordant results.

Obviously, different GCMs increase the uncertaintyof the results obtained, even though for a certain modelthe findings are statistical significant. These problemsmay only be solved if a variety of different modelsare used to produce ensemble simulations. The analy-sis of multi-model ensembles (cf. e.g. LAMBERT andFYFE, 2006) would be a great step beyond the presentsingle model analysis in the discussion of the robust-ness of climate change detection. Thus, the objectivemethod of the applied cluster algorithm, in conjunc-tion with the pre-applied data reduction of about 98 %,as presented in this study, may be an efficient tool foranalysing multi-model ensembles with their enormousdata amounts. Such an application is currently being per-formed for three SRES ensemble experiments with theECHAM5/MPI-OM1 GCM (nine simulations), whose

cyclone activity has recently been analysed in PINTO etal. (2007b).

Acknowledgements

This work was funded by the German Research Foun-dation (DFG) within the Collaborative Research Cen-tre 419: Environmental Problems of an IndustrialConurbation – Environmental problems of industrialpopulation centres; Scientific solution strategies andsocio-economic implications. We whish to thank Prof.SCHRADER, Dr. KNAB, and Dr. WICHERN of ZAIK(Zentrum für Angewandte Informatik Köln) for theirvery useful help and allocation of the basic cluster al-gorithm.

The very contructive comments of the two anony-mous reviewers were helpful for the improvements ofthe manuscript.

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Extreme wind storms over Europe in present and future climate: a cluster analysis approach

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