Winter Storms in Switzerland - Analysis of Weather Classes

Winter storms in Switzerland - Analysis of weather classes

Master’s Thesis

Faculty of Science University of Bern

presented by Mikhael¨ Schwander 2013

Supervisor: Prof. Dr. Stefan Bronnimann¨ Institute of Geography of the University of Bern and Oeschger Centre for Climate Change Research, University of Bern

Co-Supervisor: Prof. Dr. Olivia Romppainen-Martius Institute of Geography of the University of Bern and Oeschger Centre for Climate Change Research, University of Bern

Advisors: Dr. Christoph Welker Peter Stucki Institute of Geography of the University of Bern and Oeschger Centre for Climate Change Research, University of Bern

Abstract

This study investigates windstorms in Switzerland in the 1981-2012 period. The aim was to identify weather patterns that are conducive to high-wind days. For this, two automatic weather type classifications are used (GWT and CAP) to classify days with high-winds. These days have previously been identify using wind gust measurements from ground weather stations. Based on one of the classifications (CAP27), wind regions of Switzerland are determined. The outcome of the CAP27 classification is analyzed using composite plots of several weather parameters. The analysis focuses on winter storms and foehn storms. The results show a pre- dominance of westerly flows over Switzerland during high-wind days. A low pressure system over Northern Europe and a high pressure system over the Azores generate a tight pressure gradient and strong winds over Switzerland. A shift of the anticyclone further north on the Atlantic generates a northwesterly flow. A small shift in the flow’s direction can significantly change the regions affected due to the particular topography of the Alps. The main differences are observed between the Plateau, the Alps and the Alpine South side. South-north oriented valleys have the particularity to be very sensitive to foehn winds in case of a southerly flow over the Alps. A more detailed analysis of composites (using the ERA-Interim reanalysis data set) shows the difficulty to model the influence of the Alps on the large-scale flow. A higher- resolution reanalysis could probably a better estimation of the flow in Switzerland. Also extreme windstorms result from a combination of several factors that need to be analyzed more precisely because they cannot be identified on composites. Contents

Abstract i

Table of Contents i

1 Introduction 1 1.1 Motivation ...... 1 1.2 State of research and aim of study ...... 2

2 Data 5 2.1 Weather stations ...... 5 2.2 Weather types classiﬁcations ...... 5 2.3 ERA-Interim ...... 8

3 Methods 9 3.1 Definition of high-wind events ...... 9 3.2 Definition of wind regions ...... 10 3.3 Definition of foehn events ...... 12 3.4 Analysis of high-wind events ...... 12

4 Results 13 4.1 High-wind days classiﬁcation ...... 13 4.1.1 GWT ...... 13 4.1.2 CAP ...... 15 4.2 Switzerland’s wind regions ...... 18 4.2.1 Clustering outcome ...... 18 4.2.2 Application of weather types classiﬁcation CAP27 on regions . . . . . 20

i CONTENTS ii

4.3 Foehn stations high-wind days classiﬁcation ...... 22 4.4 Weather parameters for Switzerland [CAP27] ...... 23 4.4.1 Sea Level Pressure et 500 hPa Geopotential Height ...... 23 4.4.2 850 hPa Wind ...... 25 4.4.3 300 hPa Wind (jet stream) and Potential Vorticity ...... 26 4.4.4 North Atlantic Oscillation - East Atlantic Pattern ...... 27 4.4.5 Sea Level Pressure and 500 hPa Geopotential Height Differences . . . 28 4.4.6 850 hPa Wind Differences ...... 30 4.4.7 Sea Level Pressure Anomaly ...... 31 4.4.8 Sea Level Pressure and 500 hPa Geopotential Height Standard Deviation 31 4.4.9 Sea Surface Temperature Anomalies ...... 32 4.5 Weather parameters for foehn stations [CAP27] ...... 32 4.5.1 Sea Level Pressure et 500 hPa Geopotential Height ...... 32 4.5.2 850 hPa Wind ...... 34 4.5.3 300 hPa Wind (jet stream) and Potential Vorticity ...... 34 4.5.4 North Atlantic Oscillation - East Atlantic Pattern ...... 35 4.5.5 Sea Level Pressure et 500 hPa Geopotential Height Differences . . . . 35 4.5.6 Sea Level Pressure Anomaly ...... 37 4.6 Speciﬁc events ...... 38 4.6.1 26 December 1999 ...... 38 4.6.2 26 January 1995 ...... 42 4.6.3 29 January 1986 ...... 45

5 Discussion 49 5.1 Evaluation of the Weather Type Classiﬁcations and Clustering ...... 49 5.1.1 GWT ...... 49 5.1.2 CAP and evaluation of the weather stations clustering ...... 50 5.2 Analysis of the Weather Parameters for the CAP27 WTC ...... 52 5.2.1 Winter storms ...... 52 5.2.2 Foehn storms ...... 54 5.3 Analysis of Three Windstorms ...... 55 5.3.1 26 December 1999 ...... 55 CONTENTS iii

5.3.2 26 January 1995 ...... 56 5.3.3 29 January 1986 ...... 57

6 Conclusion 58

Appendix 60

Bibliography 67 List of Tables

4.1 HWDs values corresponding to ﬁgures 4.3, 4.5 and 4.11 ...... 17

iv List of Figures

2.1 Distribution of the 137 SwissMetNet stations ...... 6 2.2 GWT prototypes patterns ...... 8

3.1 Daily median winter wind gust values for Switzerland ...... 10 3.2 Winter high-wind days for Switzerland ...... 10 3.3 Clustering of stations based on the CAP27 classiﬁcation ...... 11

4.1 Classification of the HWDs according to the GWT10-z500 and GWT10-msl WTCs ...... 13 4.2 Classification of the HWDs according to the GWT18-z500 and GWT18-msl WTCs ...... 14 4.3 Classification of the HWDs according to the GWT26-z500 and GWT26-msl WTCs ...... 15 4.4 Classification of the HWDs according to the CAP9 and CAP18 WTCs . . . . . 16 4.5 Classification of the HWDs according to the CAP27 WTC ...... 16 4.6 Clustering of Swiss weather stations into 3 groups based on the CAP27 WTC . 18 4.7 Clustering of Swiss weather stations into 6 groups based on the CAP27 WTC . 19 4.8 CAP27 classification corresponding to figure 4.7 clustering for the regions North 1 and North 2 ...... 20 4.9 CAP27 classification corresponding to figure 4.7 clustering for regions Alps 1 and Alps 2 ...... 21 4.10 CAP27 classification corresponding to figure 4.7 clustering for regions West and South ...... 22 4.11 Classification of the HWDs according to the CAP27 for the foehn stations . . . 22 4.12 z500 and SLP composites of all HWDs for CAP27 types 10, 15, 20, 21, 24 and 27...... 24 4.13 850 hPa wind composites of all HWD for CAP27 types 10, 15, 20, 21, 24 and 27 25

v LIST OF FIGURES vi

4.14 300 hPa wind and PV composites of all HWD for CAP27 types 10, 15, 20, 21, 24 and 27 ...... 27 4.15 NAO and EA phases for the HWDMs ...... 28 4.16 Differences between the composites of all days and the composites of all HWDs for CAP27 types 10, 15, 20, 21, 24 and 27 ...... 29 4.17 Differences between the composites of all days and the composites of all HWD for CAP27 types 10, 15, 20, 21, 24 and 27 ...... 30 4.18 Sea level pressure anomalies for the CAP27 types 10, 15, 20, 21, 24 and 27 . . 31 4.19 Standard deviation of the SLP and z500 HWDs composites for the CAP27 types 10, 15, 20, 21, 24 and 27 ...... 32 4.20 z500 and SLP composites of all foehn stations HWDs for CAP27 types 4, 17, 19, 21, 24 and 27 ...... 33 4.21 850 hPa wind composites of all foehn stations HWDs for CAP27 types 4, 17, 19, 21, 24, 27 ...... 34 4.22 300 hPa wind and PV composites of all foehn stations HWDs for CAP27 types 4, 17, 19, 21, 24 and 27 ...... 35 4.23 NAO and EA phases for the foehn stations HWDMs ...... 36 4.24 Differences between the composites of all days and the composites of all HWDs (for the foehn stations) for CAP27 types 4, 17, 19, 21, 24 and 27 ...... 37 4.25 Sea level pressure anomalies for the foehn stations HWDs in CAP27 types 10, 15, 20, 21, 24 and 27 ...... 38 4.26 z500 and SLP on the 26.12.1999 ...... 39 4.27 850 hPa wind on the 26.12.1999 ...... 40 4.28 300 hPa on the 26.12.1999 ...... 40 4.29 PV on the 26.12.1999 ...... 41 4.30 SLP and t850 on the 26.12.1999 ...... 41 4.31 Sea surface temperature anomalies on the 26.12.1999 and 26.01.1995 . . . . . 41 4.32 Sea level pressure anomalies on the 26.12.1999, 26.01.1995 and 29.01.1986 . . 42 4.33 z500 and SLP on the 26.01.1995 ...... 42 4.34 850 hPa winds on the 26.01.1995 ...... 43 4.35 300 hPa wind on the 26.01.1995 ...... 44 4.36 PV on the 26.01.1995 ...... 44 4.37 SLP and t850 on the 26.01.1995 ...... 45 4.38 z500 and SLP on the 29.01.1989 ...... 46 LIST OF FIGURES vii

4.39 850 hPa wind on the 29.01.1989 ...... 46 4.40 300 hPa wind on the 29.01.1989 ...... 47 4.41 PV on the 29.01.1989 ...... 47 4.42 SLP and t850 on the 29.01.1989 ...... 48 Chapter 1

Introduction

1.1 Motivation

Windstorms in Switzerland are among the most extreme weather events in terms of natural and socio-economics impacts. Infrastructures and forests are affected almost every winter by storms. Most of the time storm related damage are not significant, but the impacts of the most intense storms can be disastrous, e.g. “Vivian” (February 1990) or “Lothar” (December 1999). Forests are quite vulnerable to to high winds. In fact, the most severe and most frequent damage to forests in Central Europe are caused by windstorms (Usbeck et al., 2010). Also the extension of forests increases the vulnerability. Usbeck et al. (2010) showed that storm damage to forests in Switzerland was 17 times greater during the period from 1958 to 2008 than from 1908 to 1957, which mainly can be explained by the increase in growing stock. More than two-thirds of windstorms observed since 1500 occurred during winter (October- March) (Pfister, 1999). Extratropical cyclones are generally more frequent and intense during winter due to an increased meridional temperature gradient (baroclinicity). In Central Europe, the frequency and intensity of extratropical cyclones increase from south to north (Hofherr and Kunz, 2010). On a regional scale, the intensity is in general difficult to predict especially over Switzerland’s complex topography (Jungo et al., 2002).This particular terrain can lead to a non-negligible reinforcement of wind speeds. This is for example the case for easterly winds that are channeled on the Swiss Plateau between the Jura mountains and the Alps (Jungo et al., 2002; Weber and Furger, 2001). Topography also has a major influence in case of foehn events where north-south oriented valley are favorable to an increase in wind gusts (e.g. OcCC, 2003; Richner and Hachler,¨ 2013). Regarding the importance of storms’ impacts, it is essential to understand the consequences of climate change on these events. The modification in extreme weather due to climate change has probably a stronger impact than the change in the mean state of the climate (Bronnimann et al., 2012). According to the Swiss Climate Change Scenarios report (CH2011, 2011), climate change projections suggest a decrease in the number of windstorms but an increase in intensity over Europe. However, at the present the level of scientific understanding is still relatively

1 Chapter 1. Introduction 2

low. The understanding of synoptic-scale associated with past windstorms over Europe and Switzerland is therefore necessary to better understand the changes in storm tracks and intensity according to climate change projection. To project future changes in winter storms, it is first essential to have a good knowledge on the weather conditions responsible for past storms. To analyze these weather condition, it is convenient to work with a classification of atmospheric circulation states in different types. These type classifications (WTCs) describe a certain state of the atmosphere over a defined time frame and area (Rohrer, 2012). They aim is to assign weather and atmospheric circulation states into different types (Weusthoff, 2011).

1.2 State of research and aim of study

In the North Atlantic sector, extratropical cyclones generally form along the polar front, a hyperbaroclinic zone where the cold polar air and the warm subtropical air meet (Pinto et al., 2009). Many of the perturbations move eastward, intensify, and are often responsible for extreme wind and precipitation events (Pinto et al., 2009). The occurrence of winter storms is associated with several favorable conditions such as a strong meridional temperature gradient, a strong jet stream, perturbations in the upper-troposphere and the condensation of water vapour (OcCC, 2003). Each of these factors plays a specific role in storm development and their relative importance varies as a function of the general weather situation. Some of the most intense storms that have affected Switzerland over the past 30 years have been the object of several studies. This is the case, for example, for “Vivian” in 1990 (e.g. Goyette et al., 2001; Schuepp¨ et al., 1994), “Lothar” in 1999 (e.g. Braun et al., 2003; Brundl¨ and Rickli, 2002; Riviere` et al., 2010; Wernli et al., 2002) or “Kyrill” in 2007 (e.g. Fink et al., 2009). Different meteorological conditions were conducive to these storms. Schuepp¨ et al. (1994) have identified five meteorological factors favorable to storm Vivian on the 27th February 1990 (strong gradient in temperature, unusual southern track of secondary lows, warm air blocked in Switzerland by the incoming of cold air, the blocked air escaped into the Alps and got channeled, long duration of the event). Concerning Lothar on the 26th December 1999, Wernli et al. (2002) have studied the factors that led to a rapid intensification of the storm (see Some earlier studies identified trends in windstorm frequency and intensify associated with climate change (e.g. Bengtsson et al., 2006, 2009). Bengtsson et al. (2009) showed that between the 20th and the 21st century the number of cyclones is modeled to slightly decrease. For the strongest storms, there is no significant changes. Geng and Sugi (2003) also found a decrease in the number of cyclones in the Northern Hemisphere. Goyette (2011) analyzed potential future changes over Switzerland. He found no significant changes in mean wind speed but an increase in the frequency of days with northwesterly flow for the period 2071 to 2100. Chang and Fu (2002) studied the interdecadal variability in winter storm intensity in the North- ern Hemisphere. They showed that storm track intensity has been 30% weaker during the Chapter 1. Introduction 3

1960’s and early 1970’s than during the 1990’s. Trigo (2006) found an increase in the number of storms in the region from the Labrador Sea to Scandinavia and at the same time a decrease from the Azores to Central Europe during the second part of the 20th century (using two different reanalysis data sets). Similar results were found by (Weisse et al., 2005). This is also consistent with trends observed for Switzerland (Schiesser et al., 1997). Schiesser et al. (1997) found a negative trend in number and also in duration of winter storms over Switzerland during the 20th century. To explain these variations, studies focus on the understanding of extratropical cyclones development by looking at the correlation with mesoscale climate variability Donat et al. (e.g. 2010); Pinto et al. (2009); Rogers (1997). These lower-frequency variations in European windstorms are often linked with large-scale circulation patterns, like the North Atlantic Oscillation (NAO) or the El Nino˜ Southern Oscillation (ENSO). Donat et al. (2010) found that about 80% of Cen- tral European storm days were associated with westerly flow regimes. In addition, they sowed that only 5% to 7% of the storm days occurred in slightly negative NAO phases and none in extremely negative NAO phases. Consequently, most of the storms occurred in positive NAO phases (Donat et al., 2010). These results are similar to those found by Pinto et al. (2009). Moron and Plaut (2003) studied the impact of ENSO on winter weather regimes over the North Atlantic and Europe. An increase in positive NAO conditions in November and December is observed during El Nino˜ conditions. An increase in the prevailingness of anticyclones over Greenland and negative NAO conditions was observed for January-March (Moron and Plaut, 2003). One purpose of research on storm genesis and evolution is also to investigate and project storm- related losses in current and in future climate Schwierz et al. (e.g. 2010). According to Barredo (2010) no significant trend in windstorm losses has been observed in Europe over the past 40 years. Cusack (2012) analyzed a 101 years storm loss record for the Netherlands. They found no positive trend in storm losses but even a decrease over the pas 20 years. Concerning future changes in storm intensity and losses, Schwierz et al. (2010) have coupled a regional multi- model ensemble to a probabilistic operational insurance loss model. They found an increase in the intensity of windstorms in Central Europe. An even stronger increase might even be observed for the most extreme events. Linked to an increase in storm number, related annual losses are also expected to increase significantly (Schwierz et al., 2010). One method that can be applied to study winter storms is through automatic weather and atmospheric type classifications. These are often used to describe and analyze weather and climate condition (Philipp et al., 2010). Huth et al. (2008) describe that classifications are long- established, they were first done manually (subjective) and were mostly used in weather pre- diction. With increasing computer efficiency also a change in the application of classifications was observed. They are now more used in climate research and less in weather forecasting (Huth et al., 2008). Notably because of the manual classifications’ subjectivity, automated (objective) classifications have been developed (Rohrer, 2012). The evaluation of past, present and future climate condition is one of the possible applications of classification analysis (Huth et al., 2008). Chapter 1. Introduction 4

By now, not many studies on windstorms in Switzerland using weather classifications exist. Donat et al. (2010) analyzed winter storms over Central Europe using circulation weather types. Leckebusch et al. (2008) used a cluster analysis combination with a principal component analysis to study windstorms over Europe in present and future climate. Concerning Switzerland, Jungo et al. (2002) used principal component and cluster analysis to study the probability of exceeding a gust speed based on the mean wind in relation to weather types for station observations. Goyette (2011) studied the synoptic conditions of 30 windstorms over Switzerland. Weber and Furger (2001) used wind measurement data of one year to define 16 distinct near-surface flow patterns using an automated classification scheme from Weber and Kaufmann (1995). They also compared the 16 obtained patterns with the synoptic weather types of Schuepp¨ (1979) to linked the low-level winds with the large-scale flow. However, they focuses more on the wind direction and not on the intensity. No research using automatic weather types to classify past windstorms over Switzerland have yet been done. Therefore the aim of this study is to identify large-scale circulations responsible for high-wind events over Switzerland. The classification of high-wind days is done by the use of weather stations measurements and automatic WTCs (GWT and CAP) to answer the following questions:

• Based on predeﬁned weather type classiﬁcations, which atmospheric conditions are conducive to high-wind event in Switzerland in the period 1981-2012.

• Which atmospheric conditions are responsible for the most extreme high-wind events such as windstorm Lothar?

• Do all regions of Switzerland show a similar pattern in the storm-weather types relation?

The thesis is structured as follow: Chapter 2 and 3 present the data and method used to classify and analyze high-wind events. Chapter 4 shows the results of the classiﬁcations. It also shows the outcome of a the stations’ clustering and composite plots of several weather parameters. All the results are then discussed in chapter 5. The thesis ends with a conclusion summarizing the main outcomes of the research (6). Chapter 2

Data

2.1 Weather stations

High-wind days (HWDs) are selected by using wind measurements from 137 weather stations in Switzerland that are part of the network of ground-based automatic weather stations (Swiss- MetNet)1. All the recorded measurements are retrieved from the MeteoSwiss IDAweb page (data portal for research and teaching)2. Among other parameters, these stations record wind speed and provide measurements - depending on the stations - every 10 minutes or every hour. For this study, the daily maximum wind gust measurements (1 second sustained wind) are selected. The use of maximum wind gusts (and not daily mean values) is justified by the fact that gusts are a good indicator of a storm intensity and they have a strong destructive potential (Jungo et al., 2002). Most of these automatic measurements are not controlled manually by Me- teoSwiss, the dataset may therefore contain some erroneous values. Wherever possible these values are being identified and removed. Figure 2.1 shows the location of the SwissMetNet stations. Even if sometimes weather stations networks are too coarse to derive a storm climatology and give a valuable resolution of the topography (Hofherr and Kunz, 2010), the MeteoSwiss’ network (SwissMetNet) has the advantage to be dense, also in the Alps and offers a coverage of all regions and altitudes. It gives a good estimation of the topography and its influence on the winds.

2.2 Weather types classiﬁcations

As described in Weusthoff (2011), to analyze and classify the HWDs, weather types classiﬁca- tions (WTCs) are an appropriate tool. WTCs assign weather and atmospheric circulation states into different types. These types give a summary of weather and atmospheric parameters (e.g.

1http://www.meteosuisse.admin.ch/web/en/climate/observation systems/surface/swissmetnet.html 2http://www.meteosuisse.admin.ch/web/en/services/data portal/idaweb.html

5 Chapter 2. Data 6

Figure 2.1: Distribution of the 137 SwissMetNet stations used in this study. sea level pressure) in order to identify recurrent dynamical patterns for a specific region. Two main types of classification can be distinguished: manual (subjective) and automatic (objective) WTCs. MeteoSwiss is using automatic classifications since 2011. They have the advantage to be subjective and more homogeneous over time than manual WTCs. This condensed set of parameters facilitate the analysis of weather situations but has the disadvantage that it also re- duces significantly the information available (e.g. in composites) and WTCs might not be well suited to represent extremes. This is why some HWDs have to be analyzed more in details to fully understand the specific mechanisms and parameters involved. Many WTCs have been collected within the “Harmonisation and Applications of Weather Type Classifications for Eu- ropean regions” (COST Action 733) (Philipp et al., 2010). Schiemann and Frei (2010) have analyzed the COST 733 catalogue for the Alpine region, they looked at the capability of WTCs to predict surface climate variations. Based on this study, two classifications have been chosen by MeteoSwiss: GrossWetterTypes (GWT) and Cluster Analysis of Principal Components (CAP). Both WTCs have been re-calculated back to 1957 using the ERA-40 and ERA-Interim reanalyses (Weusthoff, 2011). The first WTC is based on fixed thresholds and the second one on an optimization algorithm. These two WTCs are therefore used for this study.

GrossWetterTypes (GWT) GWT (Large-scale weather types) is an automatic classification method developed by Beck (2000) based on Hess and Brezowsky (1952) European Grosswetterlagen/-typen. The weather classes are computed following three prototypes patterns (zonal (Z), meridional (M) and cy- Chapter 2. Data 7 clonic (vorticity), see figure 2.2). The correlation coefficients are calculated between each field in the input dataset (e.g. daily sea level pressure) and the three prototypes. The first prototype is characterized by a zonal pattern with increasing mean sea level pressure (msl) values from north to south. The second is a meridional pattern with increasing msl values from west to east. The last one is a cyclonic pattern (minimum msl in the center). This means that for each day the correlation of the msl field is computed for the zonality, meridionality and cyclonic prototypes. Thus three correlation values are computed and each day can then be assigned to a weather type. The basic GWT classification consists of 10 types; the 8 main wind directions (N, NE, E, SE, S, SW, W, NW), and high and low pressure system. Rohrer (2012) explains that these two high and low pressure system classes are defined by a maximum in the vorticity correlation coefficient. Positive (negative) vorticity values determine the assignment to low (high) pressure GWT. The other eight circulation types are defined by their zonality and meridionality correlation coefficients. Thus, pure patterns (Z = 1, M = 0) stand for a west-east type and pure meridional patterns (Z = 0, M = 1) for a north-south type. A southwest-northeast type is therefore defined by Z = 0.7 and M = 0.7 and so on (Rohrer, 2012). Based on these 10 situations, two more classifications are calculated, respectively containing 18 and 26 types. Furthermore, each of these three classifications are calculated based on sea level pressure and also on 500 hPa geopotential height as input data. The used GWT classifications are listed below.

• GWT10-z500, 8 wind directions and low/high pressure based on geopotential height at 500hPa.

• GWT10-msl, 8 wind directions and low/high pressure based on mean sea level pressure.

• GWT18-z500, 2x8 wind directions (cyclonic and anticyclonic) and low/high pressure based on geopotential height at 500hPa.

• GWT18-msl, 2x8 wind directions (cyclonic and anticyclonic) and low/high pressure based on mean sea level pressure.

• GWT26-z500, 3x8 win directions (cyclonic, anticyclonic and indifferent) and low/high pressure based on geopotential height at 500hPa.

• GWT26-msl, 3x8 win directions (cyclonic, anticyclonic and indifferent) and low/high pressure based on mean sea level pressure.

Cluster Analysis of Principal Components (CAP) The CAP is the second classification selected by MeteoSwiss and that is used in this work. This WTC is the result of the combination of two different methods. It consists of a k-means cluster analysis of principal component scores and a hierarchical cluster analysis. The principal component analysis (PCA) is first used to reduce the input data to fewer variables. The first few obtained principal components (PCs) explain most of the variability of the input data. Then a two-step cluster analysis is used to classify the PCs’ time series. The first one is a hierarchical Chapter 2. Data 8

Figure 2.2: Prototypes for the pure zonal pattern (left), the pure meridional pattern (middle) and the pure cyclonic pattern (right) of the GWT classifications. (Rohrer, 2012) cluster, then a k-means cluster analysis is performed on the results of the first cluster. The CAP is based on the mean sea level pressure and three classifications are generated: CAP9, CAP18 and CAP27.

2.3 ERA-Interim

To analyze the general atmospheric circulation and more speciﬁc parameters in detail, the ERA- Interim reanalysis data set is used. ERA-Interim is the latest global reanalysis dataset from the European Centre for Medium-Range Weather Forecasts (ECMWF). It initially covered the period from 1 January 1989 onwards and has since been extended to 1979. The ERA-Interim project was created in 2006 to complement the ERA-40 project (1952-2002) and prepare the next envisaged project that will convert the entire 20th century (Berrisford et al., 2009) & (Dee et al., 2011). The reanalysis output consists of 3-dimensional data on 60 “full” levels and 37 pressure levels (Berrisford et al., 2009). It has a 6-hourly time resolution and a spatial resolution of about 0.75◦ x 0.75◦ longitude and latitude. Several variables are available for each level; for this work, variables such as wind, sea level pressure and potential vorticity are used. Chapter 3

Methods

3.1 Deﬁnition of high-wind events

The wind measurement dataset may contain some erroneous values. A first step is to identify and remove these values. For example, some values such as “99.9 m/s” are found in the dataset. They are obviously outliers and are replaced by missing values (NA). The second step consists of computing the median across all stations of all wind gust values for every day from January 1981 to March 2012. As almost all windstorms occur in winter, only data from October to March are analyzed. The median (and not the mean) is computed to reduce the weight of some extreme values. Even if outliers have been previously removed, the dataset might still contain some mismeasurements (extreme values) that could influence the mean. The median allows to reduce the weight of these extreme values. Note that even if some erroneous values could still appear in the dataset, they will not influence significantly the final results as measurement of many stations are always computed together. Therefore, the weight of the potential erroneous values will have a very small impact on the median. This steps performed, the outcome comprises one value per day for the whole Switzerland. The third step consists in selecting the most windy days that will be called “high-wind days” (HWDs). We define these HWDs based on the 98th percentile. This threshold is chosen because it gives a statistically sufficiently large number of events. Also the 98th percentile threshold has already been used in several studies on windstorms (e.g.; Bronnimann et al., 2012; Klawa, Ulbrich et al., 2003; Schwierz et al., 2010). Each point on figure 3.1 represents the median value for each day, the red points are the days above the threshold. These HWDs are shown on figure 3.2, the date is indicated for HWDs with a median value above 25 m/s. In the last step, these HWDs are attributed to weather types and the results are presented in the form of histograms. One histogram is made for each weather classification (6 GWT and 3 CAP).

9 Chapter 3. Methods 10

●

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●●●● ● ● ● ● ● ● ● ●● ● ●● ● ●● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ●● ● ● ●● ● ● ● ●●● ● ● ● ●● ● ● ● ● ●●● ●●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ●●● ● ●●●● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ●● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ●● ● ●● ● ● ●● ●● ● ● ●● ● ● ● ● ●● ● ● ●● ● ● ●● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ●●● ● ● ●● ● ● ●●●● ● ● ● ● ●●● ● ●● ●● ● ● ● ●● ●● ●●● ● ●● ● ● ● ● ●● ●● ● ● ● ●● ● ● ● ● ●● ● ● ●● ● ●●● ● ● ● ●●●● ●● ●● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ●●● ●● ● ●● ● ●● ●● ●● ●● ● ●● ● ● ● ● Wind Gust [m/s] ● ● ● ● ●● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ●●●● ● ●● ● ● ● ● ●● ● ●● ● ● ●●● ● ● ● ● ● ●● ● ● ●●● ●●● ● ● ● ● ● ● ● ●●●● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ●● ●●● ●● ● ● ● ●● ● ●● ● ● ● ●● ● ● ● ● ● ●●● ●● ● ● ● ●● ● ● ● ● ●● ●● ● ●● ● ●● ● ● ● ●●● ●● ● ● ● ●●● ● ● ● ● ● ● ●●● ●●● ● ●●● ●●● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ●●●● ●●●● ● ● ● ● ●● ● ● ● ●● ● ● ●●●●● ● ● ●● ●●● ● ●● ● ●● ●●● ●● ●● ● ●● ● ● ● ●●● ● ● ● ●●● ●● ● ● ● ●● ●●● ●● ● ● ●●● ●●●●●● ● ● ●● ● ● ●● ●●● ●●● ● ●● ● ● ● ● ●●● ● ● ● ● ● ●●● ●●● ●●● ● ●● ● ●● ● ● ● ● ● ●●●●● ● ● ●● ● ● ●●●● ●●● ●●●● ●●● ● ● ● ● ●● ●●● ● ● ● ●●● ● ● ●● ● ● ● ●● ● ● ●● ● ● ●●●● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ●● ● ● ● ●● ●●● ●●● ● ● ● ●● ● ● ● ●● ● ● ●●●● ●● ● ●● ●● ●●● ●● ●● ● ● ●● ● ●● ● ● ●● ●●● ●● ●●● ● ● ● ●● ●● ● ●●● ● ●● ● ● ●● ●● ● ●● ●●● ●● ● ● ● ●● ● ● ● ●● ●●● ● ●●●● ●●● ●● ●● ● ● ●●● ● ● ●● ● ● ●● ● ●● ● ● ●● ● ●● ● ●● ● ● ●● ● ● ● ● ● ● ● ●●● ● ● ●● ●● ● ●● ● ●● ●●●●● ● ● ●●●●● ● ● ● ● ● ● ●● ●● ●● ● ●● ● ● ●● ● ●● ●●● ● ● ●●● ●● ●● ● ● ● ●●●●● ● ● ●● ● ● ●● ● ●●● ●● ●●●● ● ● ● ●●●● ●● ● ●●● ● ●● ● ●● ● ●● ●● ● ●● ● ● ● ● ● ●●● ● ●● ●●● ● ● ● ●● ●● ●● ●● ●● ●●● ● ● ● ● ● ●●● ● ● ● ● ●●● ● ● ●●● ●●●●● ● ● ● ● ● ●● ●●●●●● ● ● ● ● ●● ●●● ● ●● ● ● ●●●● ● ● ●● ● ●● ●● ●● ●● ●● ●●●● ●●● ● ● ● ● ●●● ●● ● ●●● ●● ●● ●● ● ● ●●● ●● ● ● ● ● ● ● ●● ● ● ● ● ●●●● ● ● ● ●● ● ●● ●●● ● ● ●● ●●● ● ●●●● ● ● ● ● ● ● ● ● ●● ● ●● ●●●● ●● ● ● ● ●● ● ● ● ● ● ●● ● ●● ● ●● ●● ● ● ●● ● ● ● ● ● ●●● ●● ●● ● ●●●●●● ● ●●●●● ●●●●●●● ●●● ●● ●●●●●● ● ●●● ●● ● ●● ● ●● ● ●● ●●●●● ●●● ●●●● ● ●● ●● ● ● ● ● ●● ●● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ●● ●● ● ●● ● ●● ● ●●● ● ●●●●● ●●●●● ●●●● ● ● ●●●●● ● ●● ●● ● ● ● ●●● ●● ● ●●●●● ● ● ● ● ● ●● ●●● ● ●● ●●● ● ●● ●● ●● ●● ● ●●●● ● ●● ●●●● ●● ●●● ● ● ●●●● ●●●● ●● ●●●● ● ●● ● ●● ● ●● ● ● ●● ● ●●●● ● ● ●●● ●●●●●● ●●●●● ●●● ● ● ●● ● ● ● ● ●●● ●● ● ● ● ●● ●●●● ● ● ●● ●●● ● ●● ●●● ● ●● ● ●● ●●● ● ●● ● ● ● ● ●●●●● ●● ● ●● ●● ●● ● ●● ● ●● ● ● ●● ● ● ● ●●● ●●● ●● ●● ● ● ●● ● ● ●● ●●●● ● ●● ● ● ●●● ● ● ●● ● ● ● ● ● ●● ●● ●● ●● ●● ●●●● ● ●● ●●●●● ● ● ●●●●● ● ●●● ●● ● ●●● ●●●●● ●●● ●● ●●●● ●● ●●●● ●●● ●● ● ●● ● ● ●●●●● ●● ●●● ● ● ● ●● ●●●● ● ● ●● ●●●● ● ●● ●● ●● ●● ●●●● ●●● ●●● ● ●●●● ●● ● ● ●● ●●●●●● ●● ● ●●● ●●● ● ● ●● ●●● ●● ●● ● ● ●●●● ●●●●●● ● ● ● ● ● ● ●●● ● ● ● ●● ● ●● ● ● ● ● ●● ●●●● ●●● ●● ●● ● ● ● ●● ●● ● ●● ● ●●● ●●●● ●● ●●●●● ●●● ● ●● ● ●● ● ● ● ●●●●● ●● ● ●●●● ●● ● ● ●●● ●● ●● ● ●●●● ● ● ●● ● ●●●●● ●●● ●●● ●● ●●● ●●● ●● ●● ● ●●●● ●● ●●● ● ●● ●●● ●● ●● ●●●●● ● ● ●●●●●● ●●●● ●●● ●●●●●● ●●●● ●●● ● ●●●● ● ● ● ●●●●●● ●●●● ● ● ●●●● ● ● ●● ● ● ●● ● ●● ●●● ●● ● ● ●●● ● ● ●● ● ● ●● ●●●●● ● ●●● ●● ● ●●● ●● ● ●● ● ●● ● ● ●● ●●●●● ● ●● ●●●● ● ● ●●●● ● ●● ● ●● ● ●●●● ●●●●●● ●●● ●● ●● ●● ● ● ● ●● ●● ●●● ●●●● ●●●● ●●● ● ● ●● ● ●●● ●●●●● ●●●● ● ●●● ●●●● ● ●● ●●●●● ●● ●● ●●●●●● ●●● ●● ●●●● ●●●● ●●●●● ● ●● ● ●●●● ●● ●●●● ●●●●● ● ● ●● ●●● ● ●●● ●●●●● ● ● ●●●●●● ●●●●● ● ●● ●●●●● ● ●●● ●● ● ●● ●●●●●● ● ● ●●●● ●●●● ●● ● ● ●●●● ● ●●●●●●● ●●●● ● ● ●● ●●●●●● ● ●●●● ● ●● ●●● ● ●● ●●●● ●●● ●●●● ●●●●●● ●● ●● ●● ● ●●●● ●●●●● ●● ● ● ●●● ●● ● ● ●● ● ● ●● ● ● ● ●● ● ●●● ●●● ●●●●● ●●●● ●● ●● ●● ●● ●●●● ●●●●● ●●●● ●●●●● ●●●●●● ●●●● ● ● ● ● ●● ●●●●●● ●● ●●●● ● ● ●●● ●●●●● ●●●●● ●●●●● ●● ● ●●● ●● ●●● ●●●●●●● ●●● ●● ●●●●● ●●●●●●● ●● ●● ●● ●●●●● ●●● ● ●●●●●● ● ● ●●● ● ● ● ● ●●●●● ● ●●● ●● ●●● ●● ●● ●● ● ●● ●● ● ●● ● ●●● ●●●●● ●● ● ● ●●●●● ● ●● ● ●● ● ●●●●● ●●●●● ●●●● ●●● ● ●● ●● ● ● ● ● ●● ●●●● ● ● ●●●● ●●●●●● ● ● ●●● ●●● ●●●●● ●● ● ●● ●●● ● ●● ●●● ● ●● ●● ●●●●● ●● ●●●●● ●● ●● ● ● ●●● ● ● ●● ●●● ●● ●●●●●●● ●●●● ●●●●●● ● ● ●●●●●● ● ● ●●●●●● ● ● ●●● ●●●● ●●● ● ● ●● ●●● ●●●● ● ● ● ●● ●● ● ● ●● ●● ●●●● ●●●●●● ●●●● ●●●●● ●●● ●● ● ●● ● ●● ● ● ●●●●● ●●●●● ● ● ●●●● ●● ●●●● ●● ●●●● ● ● ● ● ● ● ● ●●● ●●● ● ●●●●●● ●●● ● ●● ●●●● ●●●●●● ● ● ●●●●● ●●● ●● ● ● ● ●●●● ●●● ●●●●● ●●●● ●● ● ●●●● ● ● ●●● ● ● ●●●● ●● ●●●● ●●● ●●●● ●●● ●●● ●● ● ● ● ●●● ●●●●●●● ● ● ● ● ● ●● ● ●● ● ●● ●●●●●● ● ●● ●● ●● ● ●●● ●● ● ●●●● ●●●●● ●●●● ● ●● ●●●●● ●●●●●● ●●●●●● ●●●●●● ●●●●● ●●●●● ● ●● ●●●● ● ●● ●●●●●● ●●●●● ●●●●● ●● ●● ● ●● ●● ●●●● ●● ●●●●● ●● ● ● ● ●●●●● ●●● ●●●● ●● ●●●●● ●●● ●●● ●● ●●● ●●●● ●●●● ●● ● ●●● ●● ● ● ● ● ●●●● ●●●● ●●●● ● ● ●● ●●● ●●● ●●● ●●●● ● ●●●●●● ●● ● ●● ●● ● ●●●● ●●●● ●● ● ●●●● ●●●● ● ●● ●●● ● ●●●● ●● ●● ● ●●●●●●● ●● ●●● ●● ●●● ●●●●●●● ●●●●● ●●● ● ●●●●●● ●●●●●●● ●● ●●● ●●●●●●●● ●●●●● ●●●●● ●●● ●●●● ●●●● ●●●●●●●● ●●● ● ● ●● ● ●● ●●●●● ●●●●●● ●●●●●● ●●●●●●● ●● ●●●● ●●●● ●●●● ●●●● ●●●●● ●●●● ●● ●●●● ●●●●● ●●●●● ●●● ● ● ●●● ●● ●●●●● ●●●● ●●●● ●●●● ● ● ●●●● ● ● ●●● ●● ●● ● ●●● ●●● ●●●● ●●●● ●●●●● ●●● ● ●●●●●● ● ●●● ●●●● ●●●● ●●● ● ● ●●●● ●●● ●●●● ●●●●●● ●●● ●● ● ●●● ●● ● ●●●● ● ● ●●● ●● ●●● ●●● ● ● ●● ●●●● ●●● ●● ●●● ● ● ●● ●● ●● ●●● ●●●●● ●●●● ●●●● ●● ●●●● ●●●●●● ●●● ● ●● ●●● ●● ●● ●●●●●● ● ●● ●●●● ●●●●●● ● ●● ●●●●● ●●●●●● ●●●● ●●●●●● ●●●●●●●● ●●●●●● ●●●●● ●●●●● ●●●●● ●●●● ●●●●●● ● ●●●●●● ●●●● ● ● ●● ●●● ●●● ●●●● ●●●● ●●●● ●● ●● ●●●● ●● ● ●●●●● ●●●●● ●●● ● ● ●● ●●● ●●●● ●● ●● ●● ●● ● ● ● ● ●●● ●● ●●●● ● ● ●● ●●●● ●● ● ●●● ●●● ●● ● ● ●● ●●●● ● ●● ●● ●●● ● ●●●● ●●●● ●●●● ●●●●● ●●●●● ●● ●● ●●●●● ●●● ●●● ●● ●●● ●●● ●●● ●●●● ● ● ●● ●● ●●●●● ●●●●● ● ● ●●●● ●●●●● ●●●●● ●●●●●● ●● ● ● ●● ●● ●● ●●● ●●● ●●● ●●● ●●●●●● ●● ●●●●● ●●● ●●● ●●● ●●● ● ●●●●● ●●●●●●● ●●●● ●●●●● ●●●● ●●●●●● ● ●●●●●●● ●●● ●●●●● ●●●● ● ●●●●●● ●●●●●● ●● ● ●● ●●●●● ● ● ● ●●●● ● ●●●●● ● ● ●●● ●●● ●●●● ●●●●●● ●●●● ●● ●●● ●●●● ●●●● ●●●●● ●●●● ●● ● ● ●● ● ● ●●● ● ●● ●●● ●●●● ●● ●●● ●●● ●●● ● ●●● 5 10● ● ● ●●●● 15 20● ●● 25●●●● ●●● ●● 30 ● ●● ●●● ● ●●●● ●●●● ●●●● ●● ●● ●● ●●● ● ●●●●● ●●● ●●●●● ● ● ● ●●●●● ●●● ●●●●● ● ●●●● ● ●●● ●● ● ● ●● ●●●● ●● ●●● ●● ● ●●●● ●●● ● ●● ●●● ● ● ●● ● ● ● ● ●●● ●● ●●● ● ●●● ● ● ● ● ● ●● ●●● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●●● ● ● ●● ● ● ●●●● ●●● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ●● ●● ●●●● ●● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

1980 1985 1990 1995 2000 2005 2010 Date Figure 3.1: Daily median winter wind gust values for Switzerland. Shown is the 98th percentile threshold (blue line) and days above threshold (high-wind days) (red points). 35

1999−12−26 ●

1990−02−27 ● 1995−01−26 ● 30

1994−01−28 ●

1997−02−13 ● 1990−02−15 1999−12−12 1986−12−19 ● ● 2011−12−16 ● 1990−02−26 2003−01−02 2009−01−23 ● ● ● ● ● 25 Wind Gust [m/s] ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

20 ● ● ● ● ●● ● ● ● ●

1980 1985 1990 1995 2000 2005 2010 Date Figure 3.2: Winter high-wind days for Switzerland. Shown are all the days above the 98th percentile threshold and the date for the days above 25 m/s. 3.2 Deﬁnition of wind regions

Based on the different weather classiﬁcations, a subdivision of Switzerland into several “wind- regions” is performed. The K-means clustering method is used to classify each station into different regions (clusters). As deﬁned in Piot (2011), the aim of the K-means algorithm is to assign each element (station) to the closest centroid. (x1, x2, ...., xn) is a set of observation where each observation is a vector. The vectors are partitioned into k sets S={S1, S2, ..., Sk} in Chapter 3. Methods 11

order to minimize the within-cluster sum of squares. µi is the mean of points in Si.

Concretely, the first step consists of computing a HWDs repartition into the WTC (e.g. CAP27) for each station. Thus, there is 137 repartitions of the same WTC (137 histograms). The second step is to define an arbitrary number of clusters having each a centroid. It means for example six arbitrary WTC repartitions (six histograms of the same WTC, each having a different repartition). The third step is to assign each station to the closest centroid. This means to the arbitrary histogram showing the most resemblance. For this, the Euclidean distance is used. Each centroid is recalculated for the cluster receiving or losing an item. The forth step is to recalculate the positions of the centroids when all items have been assigned. Then steps three and four are repeated until the centroids no longer move. A different repartition of the stations into regions can be made based on each of the nine weather classifications. Also, the number of centroids (regions) can vary and the adequate number of regions is arbitrary selected. Finally, an histogram of the WTC for each region can be made. Figure 3.3 shows the outcome for the CAP27 classification, it will be discussed in chapters 4.2.1 and 5.1.2.

● BAR

HLL ● ● SHA STK ●● HAI LEI GUT MOE ● ● BAS ● RHF● ● BEZ ● BIZ ● LAE● KLO ● TAE ● ● ARH FAH ● RUE ● REH ● STG ● BUS SMA ● GOE● ● ● HOE

EBK ●WYN ● MOA ● ● SAE ● WAE ● SCM ● GRE ● EGO ● CHZ CHA AEG EIN QUI VAD ● ● BIL ● KOP ● ● ● ● ● CDF ●CHM●CRM LUZ GLA NEU ● ● RAG BRL ● BER ● NAP PIL ● ● ● MUB ● SPF ● ● LAG ● ELM ALT ● PAA GIH ● CMA ● CHU ● FRE ● PAY ● ENG ● ● WFJDAV NAS ● ● ARO ● ●● SCU MAH ● GRA PLF ● TIT VAB● ● ● ● AMS ● BRZ● MER ● ● DIS ● INT GUE BUF BOL MAE ●●ANT AND ● ● CHE ● ● ● PMA ●SMM ● ORO MLS JUN ● GRH ● BIE PUY ● ● PIO HIR ● SAM ● PRE ● CHD ● ABO ● ULR ● ● ● CLA ● ROE ●COM ● SBE DOL ● ● EGH ● MTR ● SIACOV BEH ● ● CGI ● BOU ● ● ● ROB ● AIG ● DIA CEV ● ● MVE ● VIS ● ● LOR GVE ● SIO EVI ●FEY ● GRC ● CIM ● ● ● OTL● MAG EVO ● ATT ● ● ZER GOR ● LUG ● MRP ● ● GEN GSB ● ● SBO

Figure 3.3: Clustering of stations based on the CAP27 classiﬁcation with six prediﬁned clusters. Chapter 3. Methods 12

3.3 Deﬁnition of foehn events

All regions being not affected the same way by windstorms, it makes sense to focus on more regional events. Foehn storms are a good example of events occurring on a more regional scale. Despite having a regional characteristic, foehn wind can be very strong and damaging. Due to there specific topography, some alpine valleys are known to be very exposed to these events (Richner and Hachler,¨ 2013). To be able to produce a list of foehn storms, several stations that get regularly affected by foehn wind need to be selected. The foehn intensity is measured by 17 stations of the SwissMetNet (based on the north-south pressure and potential temperature differences) (Durr,¨ 2008). Some of these stations are only affected by the most extreme foehn storms (e.g. Zurich).¨ Therefore, not all these stations are selected and another list of stations exposed to foehn wind is needed to make a comparison. Following the clustering, one group of stations was identified as “foehn stations” (green points in figure 3.3). The idea is then to select the stations that are part of this group and also present in SwissMetNet foehn stations list. Thus, eight stations (Aigle, Altdorf, Adelboden, Chur, Engelberg, Glaris, Sion and Vaduz) appear in both lists and are used to produce a dataset of foehn events. Again the 98th percentile threshold is chosen but this time data for all months are utilized. Although foehn storms mostly occur in spring and fall, they can take place all year long. In a last step, histograms are also made for each weather classification. It is important to note that even if many foehn events appear in the list, some other “regular” windstorms are also part of it. Thus, it can sometimes be difficult to differentiate events that result from a foehn situation from winter storms.

3.4 Analysis of high-wind events

Although the weather classiﬁcations histograms give a good summary of the weather/atmospheric situations conducive to HWDs, they do not tell much about the weather parameters responsible for these strong winds. To have a better understanding of these situations, it is therefore needed to analyze several weather parameters separately with the ERA-Interim reanalysis. These weather parameters are shown and analyzed in chapter 5 and 6. Each event having its own particularities, some are being analyzed more in details in chapter 4.6. Chapter 4

Results

4.1 High-wind days classiﬁcation

The outcome of the classiﬁcation of the 118 HWDs into the different WTCs (GWT and CAP) is presented in this section. The WTCs GWT26-z500, GWT26-msl and CAP27 are analyzed more into detail. The histograms represent the absolute and relative number of HWDs for each weather type (bar). Each type summarizes the general ﬂow (and wind direction) over Switzerland. In the description, the full name of the type is given in italic and the number in brackets [].

4.1.1 GWT

In this section, histograms for the GWT classiﬁcations are shown (GWT10-z500, GWT10-msl, GWT18-z500, GWT18-msl, GWT26-z500 and GWT26-msl).

GWT10−z500 GWT10−msl

75% 90 55 45% 85 80 50 75 45 70 65 40 60 31% 35 55 50 30 45 40 25 35 20 30 12% 12% Number of High−Wind Days 25 18% Number of High−Wind Days 15 20 15 10 7% 10 5 5 1% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0 0 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 Weather Classes Weather Classes

Figure 4.1: Classiﬁcation of the HWDs according to the GWT10-z500 (left) and GWT10-msl (right) WTCs. The absolute number of HWDs are shown in left label bar and the relative number of HWDs labeled on top of the bars.

13 Chapter 4. Results 14

GWT10-z500 Figure 4.1 (left) shows that 75% of the HWDs happen during a west wind [1] situation, 18% with northwesterly [3] winds and 7% with southwesterly [2] winds.

GWT10-msl On (ﬁgure 4.1, right), types 1 to 4 explain all the HWDs. West [1] type contains again most of the HWDs (45%), then northwest [3] type with 31%. Southwest [2] and north [4] winds represent each 12% of the HWDs. Finally, note that the northeast [5] type has only 1% of all HWDs.

GWT18-z500 Figure 4.2 (left) represents a more detailed classiﬁcation with 18 classes. 89 HWDs (about 75%) are linked to westerly winds. About 60% of these west situations are anticyclonic [9] and 30% cyclonic [1]. Northwesterly cyclonic [3] winds represent 12% of HWDs (6% northwest anticyclonic [11]). Southwesterly cyclonic [2] winds represent only 2% of HWDs (5% southwest anticyclonic [10]).

GWT18−z500 GWT18−msl

55 45% 35 29% 50

45 30

40 31% 25 20% 35

30 20 16%

25 15 20 10% 8% 12% Number of High−Wind Days 15 Number of High−Wind Days 10 8%

10 6% 4% 5% 5 3% 5 2% 1% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 Weather Classes Weather Classes

Figure 4.2: Classiﬁcation of the HWDs according to the GWT18-z500 (left) and GWT18-msl (right) WTCs. The absolute number of HWDs is shown in left label bar and the relative number of HWDs labeled on top of the bars.

GWT18-msl On ﬁgure 4.2 (right), 9 types explain all the HWDs. Westerly anticyclonic [9] winds (29%) and northwesterly cyclonic [3] (20%) represent half of all HWDs. 16% of HWDs happen with westerly cyclonic [1] winds, 10% with northwesterly anticyclonic [11] winds, 8% (2%) with southwesterly anticyclonic [10] (cyclonic [2]) winds, 4% (8%) with northerly anticyclonic [12] (cyclonic [4]) winds. Again, northeasterly [5] winds represent only 1% of all HWDs.

GWT26-z500 Figure 4.3 (left, green bars) shows that westerly indifferent [17] winds represent 31% of all HWDs. Westerly anticyclonic [9] and cyclonic [1] winds represent respectively 25% and 19% of all HWDs. 16% of HWDs result from northwest indifferent [19] (8%) and cyclonic [3] (8%) Chapter 4. Results 15

types. Each of the southwest indifferent [18], southwest anticyclonic [10] and northwest anticyclonic [11] types explain 3% of HWDs. Southwesterly cyclonic [2] winds are only responsible for 1% of all HWDs. Concerning the probability of HWDs for each class (blue bars), the values are quite low. Types 17 and 1 show the highest value with 9% and 7%. Types 9 and 3 have values of only 4% and 3%. The others types do not show number higher than 2%.

GWT26-msl West indifferent [17] (19%), west anticyclonic [9] (19%) and northwest indifferent [19] (16%) types represent most of the HWDs (ﬁgure 4.3, right).West cyclonic [1] and southwest cyclonic [2] types each represent 7% of all events. Classes 2, 4, 5, 11, 12, 18 and 20 all have values below 5%. The probability of HWDs values (blues bars) are higher in comparison to GWT26-z500. Types 3, 17 and 19 show values of 16%. Types 1, 4 and 20 have respectively values of 10%, 8% and 5%. The others classes have values between 1% and 3%.

GWT26−z500 GWT26−msl 38 24 36 22 34 32 20 30 28 18 26 16 24 22 14 20 12 18 16 10 14 12 8 10 6 8 6 4 4 2 2 0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 Weather Classes Weather Classes

Figure 4.3: Classiﬁcation of the HWDs according to the GWT26-z500 (left) and GWT26-msl (right) WTCs. The absolute number of HWDs is shown in green bars and the ratio between the number of HWDs and the total of days in each class (HWDs probability for each type) shown in blue bars. The label has the same scale fot the absolute values (green bars) and the relative values [%] (blue bars). Exact values are shown in table 4.1.

4.1.2 CAP

In this section, histograms for the three CAP classiﬁcations are shown (CAP9, CAP18, CAP27).

CAP9 Figure 4.4 (left) shows that 66% of all HWDs result from west-southwest cyclonic [7] (33%) and westerly flow over Southern Europe cyclonic [9] (33%) types. The west-southwest cyclonic flat pressure [2] type is responsible for 19% of all HWDs. Then come, north cyclonic [6] (8%), westerly flow over Northern Europe [3] (6%) and northeast indifferent [1] (1%) types. Chapter 4. Results 16

CAP9 CAP18

33% 33% 40 25% 30 35 23% 25 30 18% 25 20 16% 19% 20 15

15 10

Number of High−Wind Days 8% Number of High−Wind Days 10 6% 5% 4% 4% 5 5 2% 1% 1% 1% 1% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0 0 1 2 3 4 5 6 7 8 9 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 Weather Classes Weather Classes

Figure 4.4: Classiﬁcation of the HWDs according to the CAP9 (left) and CAP18 (right) WTCs. The absolute number of HWDs is shown in left label bar and the relative number of HWDs labeled on top of the bars.

CAP18 Figure 4.4 (right) shows that one quarter of all HWDs result from westerly cyclonic [11] winds. Westerly flow over Southern Europe cyclonic [18] type has a value of 23%. Northwesterly cyclonic [8] winds are responsible for 18% of all HWDs and west-southwest cyclonic [14] winds for 16%. Others types have values below 5%. CAP27 It can be seen on figure 4.5 that one quarter (26%) of all HWDs are occuring with westerly cyclonic [21] winds. West-northwesterly [15] winds explain 19% of all HWDs. West indifferent [20], west-southwest cyclonic [24] and westerly flow over Southern Europe cyclonic [27] types have values around 14%. 8% of HWDs are happening with northwesterly cyclonic [10] winds.

CAP27 32 30 28 26 24 22 20 18 16 14 12 10 8 6 4 2 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 Weather Classes

Figure 4.5: Classiﬁcation of the HWDs according to the CAP27 WTC. The absolute number of HWDs is shown in green bars and the ratio between the number of HWDs and the total of days in each class (HWD probability for each type) shown in blue bars. The label has the same scale fot the absolute values (green bars) and the relative values [%] (blue bars) Exact values are shown in table 4.1. Chapter 4. Results 17

GWT26-z500 GWT26-msl CAP27 CAP27-Foehn a b c a b c a b c a b c 1 22 19% 7% 8 7% 10% 2 1% 1% 2 1 1% 0% 3 3% 4% 1 0% 0% 3 9 8% 3% 12 10% 16% 4 6 5% 8% 1 1% 1% 21 9% 11% 5 1 1% 1% 1 1% 0% 6 3 1% 2% 7 8 2 1% 1% 9 30 25% 4% 22 19% 3% 10 3 3% 1% 8 7% 1% 9 8% 5% 7 3% 4% 11 3 3% 0% 5 4% 2% 12 2 2% 1% 13 2 2% 1% 1 0% 0% 14 4 2% 3% 15 22 19% 13% 12 5% 7% 16 17 37 31% 9% 23 19% 16% 31 13% 13% 18 4 3% 2% 3 3% 3% 19 9 8% 2% 19 16% 16% 2 2% 1% 58 25% 36% 20 6 5% 5% 15 13% 6% 5 2% 2% 21 31 26% 14% 25 11% 11% 22 1 0% 1% 23 1 0% 1% 24 17 14% 10% 37 16% 22% 25 2 2% 2% 4 2% 3% 26 27 ------16 14% 18% 19 8% 22%

Table 4.1: HWDs values corresponding to ﬁgures 4.3, 4.5 and 4.11. a = Number of HWDs; b = Relative number of HWDs; c = Number of HWDs/Total of days (for each class) [%].

Concerning which weather circulation (type) is more likely to generate strong winds over Switzerland (blue bars). 18% of all days with a westerly ﬂow over Southern Europe cyclonic [27] are HWDs. Even if most HWDs result from westerly cyclonic [21] winds, only 14% of the days in this type are HWDs. West-northwest cyclonic [15] type has a value of 13% and west-southwest cyclonic [24] type a value of 10%. West indifferent [20] and northwest cyclonic [10] types have values of 6% and 5%. Chapter 4. Results 18

4.2 Switzerland’s wind regions

Wind regions have been determined by using the K-means clustering method. The possible results are multiple, because the method can be applied using each of the nine WTCs. However, the more detailed the WTC is, the more relevant and precise the outcome will be. For this reason only the results for the CAP27 (the most detailed WTC) are shown.

4.2.1 Clustering outcome

First, it is important to note that the outcome of the K-means clustering is function of the pre- deﬁned number of cluster. This is why the method was repeated several times with different number of groups. Moreover, it is not relevant to show all the possible results so only two results (3 and 6 clusters) are analyzed.

● BAR

HLL ● ● SHA STK ●● HAI LEI GUT MOE ● ● BAS ● RHF● ● BEZ ● BIZ ● LAE● KLO ● TAE ● ● ARH FAH ● RUE ● REH ● STG ● BUS SMA ● GOE● ● ● HOE

Figure 4.6: Clustering of Swiss weather stations into 3 groups based on the CAP27 WTC.

Three clusters With a number of clusters set to only 3, the main so-called wind regions of Switzerland can already be identified with the country basically divided in three parts from north to south (figure 4.6). A first region (blue) contains most of the stations of the Swiss Plateau and Jura. Addition- ally, some weather stations in the Prealps and stations on top of mountains (e.g. Les Diablerets, Santis,¨ Moleson,´ Pilatus) are part of this first cluster. A second region (green) includes a majority of stations in the Prealps/Alps, mainly situated in valleys (e.g. Aigle, Sion, Altdorf, Chur). Also four stations of the western part of Switzerland (between Geneva and Lausanne) are part of this group. The last region (red) includes the stations from the southern part of Switzerland. This means all the Ticino stations and also some in the Valais and South of the Graubunden¨ Chapter 4. Results 19

(Engadin). Furthermore a few exceptions can be observed, some stations part of a cluster are not at all situated in the same regions that the other stations of the same cluster (e.g. Beznau). Although this result shows some interesting features, it still remains a bit too coarse. Therefore a division with more predeﬁned clusters has been done so the regions particularities due to the relief are more taken into account.

Six clusters Figure 4.7 shows the outcome with a number of clusters set to 6. First of all, the same main structure can be found as in figure 4.6 with different clusters for the North, the South and the Alps. Almost all stations of the Swiss Plateau, Jura and some of the Prealps fall into two clusters (blue and light blue). It is difficult to distinguish any geographical differences between these two clusters. On the Plateau, a few stations of the extreme west are part of another cluster (violet). In the Prealps/Alps two clusters are found (green and red). Even though they are some exceptions, many green stations are located in south-north oriented valleys as red stations are located in east-west oriented valleys. One cluster (yellow) includes almost all the stations of the Ticino and also four situated in two lateral valleys of Engadin. A specific name is given to each of the six regions.

Blue = North 1 Red = Alps 2 Light blue = North 2 Violet = West Green = Alps 1 (foehn) Yellow = South

● BAR

HLL ● ● SHA STK ●● HAI LEI GUT MOE ● ● BAS ● RHF● ● BEZ ● BIZ ● LAE● KLO ● TAE ● ● ARH FAH ● RUE ● REH ● STG ● BUS SMA ● GOE● ● ● HOE

Figure 4.7: Clustering of Swiss weather stations into 6 groups based on the CAP27 WTC. Chapter 4. Results 20

4.2.2 Application of weather types classiﬁcation CAP27 on regions

Using the CAP27 classiﬁcation, a histogram for each of the six clusters is produced, showing the WTC distribution for each cluster. The histograms’ colors correspond to the ones used on ﬁgure 4.7.

North 1 The first cluster includes mostly stations located on the Plateau, Jura and Prealps from Le Bou- veret to Sankt-Gallen. Except the Jungfraujoch and the Titlis stations, they are all situated at low or middle altitudes. For these stations it can be seen on figure 4.8 (left) that around 50% of the HWDs are linked to three types (west-northwest anticyclonic [15], west cyclonic [21] and west-southwest cyclonic [24]). Both types, west indifferent [20] and westerly flow over Southern Europe cyclonic [27], represent 10% (each) of HWDs. Northwesterly indifferent [5] winds represent around 8% of HWD. The others types have values of 3% and less.

32 30.7 17.9 30 18 17.3 17.2 28 16 26 24 14 22 12 20 10.7 10.3 18 17.0 10 16 14.1 7.7 14 8 11.3 12 10.4 6 10 8 3.5 4 3.0 6 4.3

Relative Number of High−Wind Days [%] Number of High−Wind Days Relative [%] Number of High−Wind Days Relative 3.9 1.8 2.0 4 2 1.3 1.2 2.1 0.8 0.8 0.6 0.8 1.2 1.2 1.0 0.4 0.3 0.3 0.4 0.3 0.4 0.4 0.3 0.2 2 0.5 0.0 0.1 0.2 0.1 0.1 0.3 0.2 0.2 0.1 0.1 0.2 0.2 0.1 0.3 0.1 0.0 0.1 0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 Weather Classes Weather Classes

Figure 4.8: CAP27 classiﬁcation corresponding to ﬁgure 4.7 clustering for the regions North 1 (left) and North 2 (right). The relative number of HWDs is shown in left label bar and labeled on top of the bars.

North 2 The second cluster covers the same regions as the ﬁrst one. All stations are again situated between the Jura and the Prealps. It is interesting to note that most of the stations situated on top of a mountain (in the Prealps) are found in this group, e.g. Moleson,´ Diablerets, Napf, Pilatus and Santis.¨ Figure 4.8 (right) shows that for these stations around one third of all HWDs are resulting from westerly cyclonic [21] winds. As for the North 1 cluster, the west-southwest cyclonic [24] type includes 17% of HWDs. Westerly ﬂow over Southern Europe cyclonic [27], west-northwest cyclonic [15] and west indifferent [20] types have values between 10% and 14%. Alps 1 This cluster regroups stations that are situated in the Prealps and Alps with no east-west dis- Chapter 4. Results 21

tinction. Almost all of the them are located in a valley, south-north oriented mostly. They are also some stations at high altitudes (e.g. Gutsch).¨ For this cluster, it is shown on ﬁgure 4.9 (left) that around 17% of HWDs are found in the west- southwest cyclonic [24] type. Southwest cyclonic [19], south-southwest cyclonic [17] and west cyclonic [21] types have values between 16% and 13%. West-northwest cyclonic [15] (6.9%) and westerly ﬂow over Southern Europe cyclonic [27] (9.5%) types have lower values.

17.8 20 19.1 18 18.8

16.0 18 16 16

14 13.3 13.1 14 12 12 10 9.5 10 8.9 8 6.9 7.3 8 7.2 7.0 6 6 5.0 4.4 4.5 4 3.3 3.0 2.9 4 2.9 2.7

Relative Number of High−Wind Days [%] Number of High−Wind Days Relative [%] Number of High−Wind Days Relative 2.5 1.7 1.6 1.7 1.8 2 1.2 1.2 1.3 1.1 1.3 0.8 2 1.1 1.1 0.6 0.6 0.7 0.6 0.8 0.3 0.2 0.3 0.2 0.4 0.4 0.3 0.4 0.3 0.3 0.4 0.4 0.2 0.2 0.0 0.1 0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 Weather Classes Weather Classes

Figure 4.9: CAP27 classiﬁcation corresponding to ﬁgure 4.7 clustering for regions Alps 1 (left) and Alps 2 (right). The relative number of HWDs is shown in left label bar and labeled on top of the bars.

Alps 2 A second cluster encompasses alpine stations. Here the stations are mostly located in west-east (or southwest-northeast) oriented valleys (e.g. Central Valais, Engadin). It can be seen on ﬁgure 4.9 (right) that both types northwest cyclonic [10] and west-northwest [15] have values of around 19%. West indifferent [20], west cyclonic [21], west-southwest cyclonic [24] and westerly ﬂow over Southern Europe cyclonic [27] types represent each between 9% and 7% of HWDs. Others types have values below 5%.

West One cluster regroups all stations of the western part of Switzerland. There are only eight stations, all west from a line Pully-Mathod. These stations are found along the Lake of Geneva or in the Jura mountains. Figure 4.10 (left) shows that west cyclonic [21] and west-southwest cyclonic [24] types have again values around 16%. However, easterly anticyclonic/indifferent [16] winds represent 13% of HWDs. 10% of HWDs result from the westerly ﬂow over Southern Europe cyclonic [27] type. The others types have values of 7% and below. South The last cluster encompasses stations from the Ticino (except Monte-Generoso), four stations of the South Engadin and also one in the Oberwallis (Ulrichen). Weather type northwest anticyclonic [10] represents more than 15% of all HWDs (ﬁgure 4.10, right). The values of west-northwest anticyclonic [11], northeast cyclonic [22] and northwest indifferent [5] types lie between 13% and 11%. Then there are types with values between 8% Chapter 4. Results 22

16.0 15.6 16 15.5 16

14 14 13.0 12.7

12 11.3 12 11.1

10.2 10 10

7.9 7.6 8 7.4 8

6.3 6.0 5.9 6.0 6 5.3 6

4.1 3.5 4 4 3.1 2.9 2.6 2.6

Relative Number of High−Wind Days [%] Number of High−Wind Days Relative 2.1 [%] Number of High−Wind Days Relative 1.8 1.9 1.9 1.9 1.6 1.5 2 2 1.2 1.1 1.0 0.8 0.9 0.9 0.8 0.5 0.5 0.5 0.5 0.3 0.2 0.4 0.0 0.0 0.0 0.1 0.2 0.1 0.1 0.1 0.0 0.1 0.2 0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 Weather Classes Weather Classes

Figure 4.10: CAP27 classiﬁcation corresponding to ﬁgure 4.7 clustering for regions West (left) and South (right). The relative number of HWDs is shown in left label bar and labeled on top of the bars. and 6% (east anticyclonic/indifferent [16], west-northwest cyclonic [15], east cyclonic [23] and east indifferent [9]).

4.3 Foehn stations high-wind days classiﬁcation

The weather stations of Aigle, Altdorf, Adelboden, Chur, Engelberg, Glaris, Sion and Vaduz are the ones considered as foehn stations. There are regularly affected by foehn events. See chapter 3.3. The following histogram 4.11 shows the classiﬁcation outcome for these stations.

CAP27−foehn

60 58 56 54 52 50 48 46 44 42 40 38 36 34 32 30 28 26 24 22 20 18 16 14 12 10 8 6 4 2 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 Weather Classes

Figure 4.11: Classiﬁcation of the HWDs according to the CAP27 for the foehn stations. The absolute number of HWDs is shown in green bars and the ratio between the number of HWDs and the total of days in each class (HWD probability for each type) is shown in blue bars. The label has the same scale fot the absolute values (green bars) and the relative values [%] (blue bars). Exact values are shown in table 4.1. Chapter 4. Results 23

Figure 4.11 shows some significant differences in comparison with figure 4.5. Values for classes 15 (5%), 20 (2%), 21 (11%) and 27 (8%) are much lower in the foehn histogram (green bars). On the other hand, three classes show higher values, west-southwest cyclonic flat pressure [4] (9%), south-southwest cyclonic [17] (13%) and southwest cyclonic [19] (25%). Even if the values are not exactly the same, figure 4.11 and 4.9 (left) have similar results. Concerning the probability of HWD for each class, the southwest cyclonic [19] type have the highest value (36%). West-southwest cyclonic [24] and westerly flow over Southern Europe cyclonic [27] types have each a value of 22%. 13% of south-southwest cyclonic [17] days are HWDs. Then come west cyclonic [21] (11%), west-southwest cyclonic flat pressure [4] (11%) and west-northwest cyclonic [15] (7%) types. The others types have values below 4%.

4.4 Weather parameters for Switzerland [CAP27]

Several parameters showing HWDs mean weather situation are presented in this part of the results. Composite plots of these weather parameters are made for the six CAP27 weather types that contains most of the HWDs. This means six plots for each parameter, one for each type (10, 15, 20, 21, 24, 27). In addition, some composite plots for the same types but including all days from 1981 to 2010 are made (see Appendix ﬁgure 6), in order to produce difference plots between HWDs composites and all days composites. In the description, if the types’ names are given the number is written in brackets []. Or if the type number is given, then the corresponding letter identifying the plots is given in brackets.

4.4.1 Sea Level Pressure et 500 hPa Geopotential Height

In this section, composite plots of the sea level pressure (SLP) and the 500 hPa geopotential height (z500) of the 6 CAP27 types are shown (figure 4.12). These composites allows to have an overview of the mean general situation for each type at low (SLP) and high (z500) levels. The 500 hPa level is not disturbed by the Alps (Weber and Furger, 2001) which is not the case for the low-level. All composites have a similar general atmospheric pattern with a high pressure system centered near the Azores islands and a low pressure system over Scandinavia or over the North Sea. These cyclones and anticyclones are visible at low-levels through the SLP and high levels through the z500. NorthWest, cyclonic [10] Figure 4.12 a) shows the high pressure system (SLP = 1030 hPa / z500 = 580 gpdm) located off the coasts of Portugal and the depression (990 hPa / 508 gpdm) located over the Baltic Sea. This situation produces a northwesterly flow with a tight pressure gradient between Great Britain and the Alps. West-NorthWest, cyclonic [15] The situation on figure 4.12 b) is quite similar to a). Again the high pressure system (1025 hPa / 580 gpdm) is located near Portugal and the low pressure system (985 hPa / 502 gpdm) over Chapter 4. Results 24 the Baltic Sea. The flow over Switzerland is a little bit more west-northwest oriented. West, indifferent [20] On figure 4.12 c), the flow is more zonal with the anticyclone (1030 hPa / 580 gpdm) centered over Spain and extending over South Europe and North Africa. The low pressure system (985 hPa / 502 gpdm) is situated off the coasts of Norway. A westerly flow prevails over most parts of Europe and Switzerland.

Figure 4.12: Composites of all HWDs for CAP27 types 10 [a)], 15 [b)], 20 [c)], 21 [d)], 24 [e)] and 27 [f)]. Shown in color is the geopotential height at 500 hPa (in gpdm) and in contour the sea level pressure (in hPa).

West, cyclonic [21] The flow is again almost zonal on figure 4.12 d). The anticyclone (1015 hPa / 580 gpdm) is centered between the Azores, Spain and Algeria. The low pressure system (985 hPa / 508 gpdm) is found between Scotland and Norway. Therefore the dominant flow over Western and Central Europe is zonally oriented. West-southWest, cyclonic [24] Again on figure 4.12 e), the high pressure system (1020 hPa / 580 gpdm) is situated between the Azores and North Africa. But the center of the depression (985 hPa / 508 gpdm) is found more south and is therefore located over the North Sea. A westerly flow is predominant over Western Europe. Westerly Flow over Southern Europe, cyclonic [27] Figure 4.12 f) shows the anticyclone (1025 hPa / 580 gpdm) located off the coast of Morocco and the cyclone (985 hPa / 508 gpdm) centered between Denmark, Norway and Scotland. The flow is west-northwest oriented over Western Europe and west oriented over Centrale Europe. It is also shifted south in comparison to the other composites. Chapter 4. Results 25

4.4.2 850 hPa Wind

In this section, composites of the wind field at 850 hPa (∼ 1500 a.s.l.) are shown. The wind speed and direction are represented. Although the relief in Switzerland cannot be seen, these composites are a good way to analyze the general flow over Europe and also give a valuable estimation of the winds’ intensity. The 850 hPa winds has the advantage to be connected to the surface winds (Weber and Furger, 2001) but on an European scale are not too much disturb by small reliefs. It means that the impact of the Alps on a large-scale is visible. NorthWest, cyclonic [10] Figure 4.13 a) shows northwesterly winds over Western and Central Europe with two regions having stronger velocities. Values above 22 m/s can be seen along the French Mediterranean coast. Also over Switzerland and more generally north of the Alps, wind speed values are close to or exceed 20 m/s. West-NorthWest, cyclonic [15] The wind field on figure 4.13 b) is quite similar to a). High wind speeds are found over the same regions. However, high wind zones are a bit larger and the maximum speeds higher, especially north of the Alps with values above 22 m/s. West, indifferent [20] On figure 4.13 c) the flow comes straight from west with stong winds over France, Germany and Switzerland. The highest speed values (>25 m/s) are exactly found over Switzerland on the Alpine north side.

Figure 4.13: Composites of all HWD for CAP27 types 10 [a)], 15 [b)], 20 [c)], 21 [d)], 24 [e)] and 27 [f)]. Shown in color is the wind at 850 hPa (in m/s) and the direction (arrows).

West, cyclonic [21] On figure 4.13 d), large parts of Western and Central Europe are affected by westerly winds. The strongest winds are found over Northern France, South Germany and Northern Switzerland Chapter 4. Results 26 with values around 25 m/s. Close to the Alps the flow is a bit more west-southwest oriented. West-southWest, cyclonic [24] Southwesterly winds are dominant on figure 4.13 e). Between the French Atlantic coast and Southern Germany the winds are stronger than 20 m/s. The strongest winds are located around Switzerland with values of 24 m/s. Westerly Flow over Southern Europe, cyclonic [27] Figure 4.13 f) shows westerly winds over Western Europe, especially on the west and central part of France and also on the Mediterranean coast. The strongest winds velocities (>24 m/s) are found off the coasts of France. Values over Switzerland are lower (∼ 19 m/s)

4.4.3 300 hPa Wind (jet stream) and Potential Vorticity

Winds maps at 300 hPa (∼ 9000 a.s.l.) give a representation of the jet stream and intensity. It has been shown that in some storm cases the jet stream played a role in the cyclone reinforcement (Fink et al., 2009; Riviere` et al., 2010; Wernli et al., 2002). The six composites plots show the same general pattern with the highest intensity located between Western Europe and the Atlantic. It also makes a more or less big meander over the Atlantic. The flow vary then from west to northwest over Europe. The 3 pvu line is also shown on the plots. It follows the same trajectory than the jet. NorthWest, cyclonic [10] On figure 4.14 a) the jet stream makes a meander over the North Atlantic and so has a northwesterly orientation between Ireland and Switzerland. It is also over this region that the jet has its maximum velocity with winds speeds above 52 m/s over the Manche channel. Over Cen- tral Europe it diverges and makes another meander to then have a westerly flow over Eastern Europe. West-NorthWest, cyclonic [15] The jet shows strong similarities with a) on figure b). However, the jet is a little bit less strong (maximum 50 m/s over France) and has a more west-northwesterly orientation over Western Europe. West, indifferent [20] On figure c) the meander is almost not visible and shifted eastward, therefore the jet’s flow is almost zonal and thus westerly oriented over Europe. Strongest winds are again found over the Manche with a maximum value of 50 m/s. West, cyclonic [21] A similar pattern than c) is found on figure d). The jet stream is almost west-east oriented but is a bit narrower. This means that the stream is stronger with winds up to 54 m/s and has strong divergence over Eastern Europe. West-southWest, cyclonic [24] On figure e) there is almost no more meander, just a convergence zone on the East Atlantic and Chapter 4. Results 27

Figure 4.14: Composites of all HWD for CAP27 types 10 [a)], 15 [b)], 20 [c)], 21 [d)], 24 [e)] and 27 [f)]. Shown in color is the wind at 300 hPa (in m/s) and the direction (arrows). Shown by the blue line is the 3 pvu on the 320 K isentrope. a maximum speed over the French west coast (46 m/s). Westerly Flow over Southern Europe, cyclonic [27] On ﬁgure f) a meander is again found over the Atlantic. Thus the ﬂow is west-northwesterly oriented over Western Europe. Of all composites, this one has the strongest wind velocities (>60 m/s off the coasts of France). The 3 pvu (blue) line shows a similar pattern than the jet stream trajectory with a more or less pronounced ridge of low PV values over the Atlantic and a trough of high values over Western Europe. The PV gradient is the tightest where the jet the strongest is (not shown). As for the jet, there is also a divergence zone over Eastern Europe and Russia.

4.4.4 North Atlantic Oscillation - East Atlantic Pattern

The North Atlantic Oscillation (NAO) and the East Atlantic Pattern (EA) indexes are a good way to summarize and simplify the general atmospheric situation in the North Atlantic. Figure 4.15 can help to see whether the HWDs are link to a certain phase of the well-known monthly circulation modes. It also closely related to the position of the jet over the Atlantic (Woollings et al., 2010). Each point represents a month between 1981 and 2012 in which, one or more HWD occured (HWDM). Only monthly values are available for the EA, that is why no daily values are used. Figure 4.15 (left) includes all HWDMs, apparently the number of HWDMs with a positive and a negative EA index is quite similar. However, there is almost no HWDMs with a EA value above 1.5 as several months have a value below -1.5. Concerning the NAO index, there is a majority HWDMs with a positive value. Eighteen HWDMs have a NAO value below 0 and Chapter 4. Results 28 only 11 HWDM have a value lower than -0.5.

● ● ●

●

● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●

● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

● 0.5 1 1.5 2 2.5 0.5 1 1.5 2 2.5 ● ● ● −2.5 −2 −1.5 −1 −0.5 ● ● −2.5 −2 −1.5 −1 −0.5 ● ● ● ● ● ● ● ● ● ● ●● ● ● ● NAO NAO ● ● 0.5● ● 1 1.5 2 2.5 0.5 1 1.5 2 2.5 ●

● ● ● ● ●

● ●

●

●● −2.5 −2 −1.5 −1 −0.5 −2.5 −2 −1.5 −1 −0.5

EA EA

Figure 4.15: North Atlantic Oscillation (NAO) and East Atlantic Pattern (EA) for all the months containing at least one HWD (left) and at least one HWD with a median value >24 m/s (right).

Figure 4.15 (right) shows only the months with HWDs having a median value above 24 m/s. This represents 12 HWDMs. Again most of the HWDMs have a positive NAO value, one has a value equal to 0 and another one slightly negative value. 9 of them have a NAO value above 0.5 (8 above 1.0). They also all (except one) have a positive EA value. 7 of them have an EA value above 0.5 (6 above 1.0).

4.4.5 Sea Level Pressure and 500 hPa Geopotential Height Differences

Figure 4.16 represents the geopotential height at 500 hPa (z500 [gpdm] - colors) and the sea level pressure (slp [hPa] - contour lines) absolute difference between two composites of the six types. More precisely, it consists of the difference between the composites of all days of one type (from 1981 to 2012) and the composite of all the HWDs (for the same period) of the same type (figure 4.12). The six difference composites have a similar configuration with a positive difference at low and high levels around the Iberian Peninsula and a negative difference (at low and high levels) at high latitudes between Greenland and Russia. NorthWest, cyclonic [10] On figure 4.16 a), negative geopotential height values are found from Greenland to the Caspian Sea with minimum values on Western Russia (-12 gpdm). Positive geopotential values are found between the Atlantic (north of the Azores) and Greece with maximum values higher than 6 gpdm. The sea level pressure differences are located on the same regions. The lowest value is located over Southern Sweden (-9 hPa) and the highest values of the coasts of Spain (Galicia) (6 hPa). Chapter 4. Results 29

West-NorthWest, cyclonic [15] Figure 4.16 b) shows again negative z500 values between Greenland and Russia with lowest values of -11 gpdm (-12 between Iceland and Norway) and positive values between the Atlantic and Italy (maximum of 10 gpdm over Northern Spain). The negative SLP values are also located between Greenland and Russia with the lowest value over Scandinavia (-14 hPa). The positive SLP values are found above the same region than the positive z500 values, they are just a little bit shifted to the west. Thus the highest value is found of the coasts of Galicia (7 hPa). West, indifferent [20] Figure 4.16 c) appears to be a little bit different even if again the same pattern is found. The z500 negative values are situated between Newfoundland and Scandinavia. Near Newfound- land, values are lower than -12 gpdm and around -10 gpdm northeast of Iceland. Positive values are located between Turkey and off the coasts of Portugal with maximum values higher than 4 gpdm. Negative SLP values are also located near Newfoundland (-7 hPa) and northeast of Iceland (-8 hPa). Over Spain positive values of 4 hPa are found and also over Scotland.

Figure 4.16: Differences between the composites of all days and the composites of all HWDs for CAP27 types 10 [a)], 15 [b)], 20 [c)], 21 [d)], 24 [e)] and 27 [f)]. Shown in color is the geopotential height at 500 hPa absolute difference (in gpdm) and in contour the sea level pressure absolute difference (in hPa).

West, cyclonic [21] Figure 4.16 d) has negative z500 values between Greenland and the Black Sea with the lowest values between Iceland and Greenland (-8 gpdm) and South Sweden (-6 gpdm). Positive values stretch from the Atlantic to Italy with the highest values situated off the coasts of Galicia (9 gpdm). It is exactly at the same location that are found the maximum SLP difference (6 hPa). A negative value of 9 hPa is found over Iceland and -6 hPa over South Sweden. West-southWest, cyclonic [24] Chapter 4. Results 30

Figure 4.16 e) shows again negative z500 values over the same regions. The lowest z500 differences are found between Norway and Spitzberg (-8 gpdm). The maximum z500 values are located over the Iberian Peninsula with values around 10 gpdm. The minimum SLP differences values are found over North Finland (-9 hPa) and over Denmark (-8 hPa). The maximum SLP value is located over Galicia (8 hPa). Westerly Flow over Southern Europe, cyclonic [27] On ﬁgure 4.16 f), two sectors of strong z500 differences can be distinguished: South Greenland (<-12 gpdm) and all over the Iberian Peninsula (>12 gpdm), on the others regions (e.g. Scan- dinavia, Eastern Europe) the values are contained between -4 and 4 hPa. The SLP differences are strong between Iceland and Scotland (-12 hPa). The maximum positive value is found over Spain (7 hPa).

4.4.6 850 hPa Wind Differences

In complement to z500/SLP differences, 850 hPa wind composites differences have been made on the same principle (Figure 4.17). All six plots have a similar pattern with usually the strongest wind velocities differences over Western and Central Europe. Type 24 [e)] shows the highest difference north of the Alps with values around 8 m/s. Type 15 [b)] has values around 7 m/s over the same region. Type 27 [e)] shows also high differences but more over Western France (8.5 m/s). Type 10, 20 [a), c)] and 21 [d)] all have lower values, between 5 and 7 m/s.

Figure 4.17: Differences between the composites of all days and the composites of all HWDs for CAP27 types 10 [a)], 15 [b)], 20 [c)], 21 [d)], 24 [e)] and 27 [f)]. Shown in color is the 850 hPa wind (in m/s). Chapter 4. Results 31

4.4.7 Sea Level Pressure Anomaly

The sea level pressure anomalies for each type are shown on ﬁgure 4.18. There is a strong negative anomaly on each plot, corresponding to the depression’s location. Type 27 [d)] has the strongest negative anomaly covering most of Europe (<-27 hPa) and type 20 [c)] shows the smallest negative anomaly (around -18 hPa). All the types do not show the same pattern concerning positive anomalies. Types 10 [a)] and 15 [b)] have a positive anomaly of 9 hPa over the Eastern Atlantic, extending up to Greenland. Types 20 and 21 [d)] both have an anomaly of 6 hPa between the Azores, Southern Europe and Northern Africa. Types 24 [e)] and 27 just have small or no positive anomalies.

Figure 4.18: Sea level pressure anomalies (in hPa) based on the 1981-2010 daily running mean with a 7 days window for the HWDs in CAP27 types 10 [a)], 15 [b)], 20 [c)], 21 [d)], 24 [e)] and 27 [f)].

4.4.8 Sea Level Pressure and 500 hPa Geopotential Height Standard De- viation

Figure 4.19 shows the z500 (colors) and the SLP (contours) standard deviation (std) for the 6 HWD composite plots. The general pattern is rather similar between the 6 plots. For the z500 and the SLP std, there is always a zone of low values over Western/Central Europe. Over North Africa and Southern Europe the values are also small. The high std values are divided into two sectors. One is situated between Greenland and the North Atlantic and the other over Russia (close to Finland). Figure a) [10] shows the highest values (24 (z500) and 19 (SLP)) between Greenland and Iceland. c) [15] has the lowest maximum for the z500 std (around 17). The other plots have maximum z500 std values around 20 and maximum SLP std values between 14 and 20. Chapter 4. Results 32

Figure 4.19: Standard deviation of the SLP and z500 HWDs composites for the CAP27 types 10 [a)], 15 [b)], 20 [c)], 21 [d)], 24 [e)] and 27 [f)]. Shown in color is the geopotential height at 500 hPa standard deviation and in contour the sea level pressure standard deviation.

4.4.9 Sea Surface Temperature Anomalies

The sea surface temperature (SST) in the North Atlantic is a factor that can influence the development of low pressure system and therefore enhance winds over Europe (e.g.; Wernli et al., 2002). However, composites of SST anomalies for the HWD do not show any significant temperature differences. For all the 6 types composites, the anomalies values are contained between -0.5◦ and -0.5◦ . This is why no SST anomalies plots are shown here. The potential triggering role of the SST anomalies will be investigated more into details for some specific events in chapter 4.6.

4.5 Weather parameters for foehn stations [CAP27]

Similar to section 4.4, composites plots for the foehn stations HWD are shown in this section. The 6 CAP27 types that contains most of the foehn HWD are number 4, 17, 19, 21, 24 and 27.

4.5.1 Sea Level Pressure et 500 hPa Geopotential Height

The 6 SLP-z500 composite plots for the weather types conducive to HWD for foehn stations show the same general pattern with a high pressure system around the Azores and a low pressure system around the British Islands. However, there is strong difference in the SLP and z500 values from a class to another. Chapter 4. Results 33

West-SouthWest, cyclonic, flat pressure [4] On figure 4.20 a), the anticyclone (1025 hPa / 580 gpdm) can be seen on the Atlantic but no strong low pressure system is visible. A weak through is located between the British Islands and Iceland with a 1010 hPa SLP value and 538 gpdm z500. Therefore a weak southwesterly flow circulates over Europe and Switzerland. South-SouthWest, cyclonic [17] Figure 4.20 b) shows the high pressure system over the Antlantic (off the coasts of Marocco). The low pressure system is centered over Irland. The flow over Switzerland is south-southwest oriented. An “foehn nose” formed by the SLP can be seen over the Alps. SouthWest, cyclonic [19] Again, on figure 4.20 c) the anticyclone (1020 hPa / 580 gpdm) is located over the Atlantic and the cyclonic system (1000 hPa / 526 gpdm) is centered between the British Islands and Northwest France. The flow over Switzerland is south-southwest oriented with an SLP “foehn nose” over the Alps.

Figure 4.20: Composites of all foehn stations HWDs for CAP27 types 4 [a)], 17 [b)], 19 [c)], 21 [d)], 24 [e)] and 27 [f)]. Shown in color is the geopotential height at 500 hPa (in gpdm) and in contour the sea level pressure (in hPa)

West, cyclonic [21] Figure 4.20 d) has the high pressure system (1025 hPa / 580 gpdm) located off the coasts of Marocco. North of Scotland, the low pressure system (985 hPa / 514 gpdm) is found. the ﬂow over Central Europe and Switzerland is west oriented. West-southWest, cyclonic [24] Figure 4.20 e) shows again the anticyclone (1020 hPa - 580 gpdm) over the Atlantic (between the Azores and Marocco) and the low pressure system (990 hPa - 520 gpdm) over the British Islands. A west-southwesterly ﬂow is visible over Switzerland. Chapter 4. Results 34

Westerly Flow over Southern Europe, cyclonic [27] Also on ﬁgure 4.20 f) the anticyclone (1025 hPa / 580 gpdm) is located off the coasts of Marocco. The low pressure system (985 hPa / 508 gpdm) is located between England and Norway. The ﬂow is west oriented over Switzerland and the Alps.

4.5.2 850 hPa Wind

The 850 hPa wind composites plots (figure 4.21) show two different patterns: an almost zonal flow for types 21, 24 and 27, and a meridional flow for types 4, 17 and 19. Figure a) [4], b) [17] and c) [19] show a general southwesterly flow over Europe with weak to moderate wind velocities (maximum 10-13 m/s). One exception is in the Rhone Valley where modelled winds are south oriented and a bit stronger (13-18 m/s). On figure d) [20], e) [21] and f) [24], the flow is west to southwest oriented. The mean speed over Western Europe is around 15-18 m/s. Figure d) shows a maximum speed of 19 m/s north of the Alps. On figure f), the strongest winds (24 m/s) are found on the Atlantic very close to France and Spain.

Figure 4.21: Composites of all foehn stations HWDs for CAP27 types 4 [a)], 17 [b)], 19 [c)], 21 [d)], 24 [e)], 27 [f)]. Shown in color is the wind at 850 hPa (in m/s) and the direction (arrows).

4.5.3 300 hPa Wind (jet stream) and Potential Vorticity

Figure 4.22 show the jet stream trajectory and intensity for the six weather types and also the 3 pvu line. a), b) and c) have a similar pattern with the jet making a meander over Western Europe. This means there is a ridge over the Atlantic and Eastern Europe and a trough over Western Europe. The flow is then southwest oriented over Switzerland. The maximum velocities are between 26 m/s (a)) and 34 m/s (b) and c)). The 3 pvu line follows the same trajectory than the jet. Composites d), e) and f) also have strong similarities between each other. The flow Chapter 4. Results 35 is more zonal with less pronounced meanders. f) has the maximum jet speed (46 m/s), d) and e) have values between 34 and 40 m/s. Here too the 3 pvu line follows the jet’s trajectory. In comparison with figure 4.14, the composite plots show much lower jet velocities.

Figure 4.22: Composites of all foehn stations HWDs for CAP27 types 4 [a)], 17 [b)], 19 [c)], 21 [d)], 24 [e)] and 27 [f)]. Shown in color is the wind at 300 hPa (in m/s) and the direction (arrows). Shown by the blue line is the 3 pvu on the 320 K isentrope.

4.5.4 North Atlantic Oscillation - East Atlantic Pattern

Similarly to ﬁgure 4.15, ﬁgure 4.23 shows the repartition of HWDM for the NAO and EA indexes. The left plot shows a random repartition of the HWDM. The points are almost uniformly distributed. There are just a little bit more HWDM with positive NAO and EA phases, but there no clear tendencies can be deducted. If only the months with HWD having a median value higher than 28 m/s are taken, then a tendency can be distinguished, at least for the EA index. Most of the HWDM have a positive EA value. Ten HWDM have a value equal or higher than 0.5 and 4 have a value equal or lower than 0. Concerning the NOA, even if there more HWDM with a positive value, the signal is less clear. There are only 7 HWDM with a value higher than 0.5, the other 7 HWDM are close to 0 or have a negative value.

4.5.5 Sea Level Pressure et 500 hPa Geopotential Height Differences

Similarly to figure 4.16, figure 4.24 shows the difference between the composite plot of all days of one type (from 1981 to 2012) and the composite plot for the foehn stations HWD. West-SouthWest, cyclonic, flat pressure [4] Figure 4.24 a) shows negative difference values over Western Europe and Eastern Atlantic. The Chapter 4. Results 36

●

● ● ●

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 0.5 1● 1.5● 2 2.5 ● 0.5 1 1.5 2 2.5 ● ● −2.5 −2● −1.5 −1 −0.5 ● ● ● −2.5 −2 −1.5 −1 −0.5 ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●

NAO ● NAO ● ● ● ● ● 0.5● 1● 1.5 2 2.5 0.5 1 1.5 2 2.5 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●

●

● ● ● ● −2.5 −2 −1.5 −1 −0.5 −2.5 −2 −1.5 −1 −0.5

EA EA

Figure 4.23: North Atlantic Oscillation (NAO) and East Atlantic Pattern (EA) for all the months containing at least one HWD for the foehn stations (left) and at least one HWD with a median value >28 m/s also for the foehn stations (right). lowest z500 value (-10 gpdm) is found over Spain (Galicia) and over the lowest SLP value (-4 hPa) is bit more north close to France (Bretagne). Two centers of positive difference values are found, one over Scandinavia (7 gpdm / 5 hPa) and over the Atlantic (south of Greenland) (4 gpdm / 5 hPa). South-SouthWest, cyclonic [17] On figure 4.24 b), strong negative difference values can be seen off the coasts of France and Spain (<-12 gpdm / -10 hPa). A center of positive SLP values is found over Eastern Europe (6 hPa). Small positive z500 values are located south of Finland (2 gpdm). SouthWest, cyclonic [19] Figure 4.24 c) shows again negative difference values over Eastern Atlantic (-12 gpdm / -8 hPa). South of Greenland positive values are found (7 gpdm / 5 hPa). A center of positive values is found over the Eastern Alps (6 gpdm / 3 hPa). West, cyclonic [21] On figure 4.24 d) the strongest SLP negative values are found north of Scotland (-11 hPa) and west of Scotland negative z500 values (-8 gpdm). The strongest z500 positive difference values are located over Eastern Europe (8 hPa) and SLP positive values are centered between Italy and the Black Sea (4 hPa). Weaker positive values are also found over the Atlantic (close to the Azores). West-southWest, cyclonic [24] On figure 4.24 e), strong negative difference values are located on the Eastern Atlantic (<-12 gpdm / -14 hPa). Values are also negative over Russia (-8 gpdm / -3 hPa). Positive values are located over the Alps (4 gpdm / 4 hPa). Chapter 4. Results 37

Figure 4.24: Differences between the composites of all days and the composites of all HWDs (for the foehn stations) for CAP27 types 4 [a)], 17 [b)], 19 [c)], 21 [d)], 24 [e)] and 27 [f)]. Shown in color is the wind at 850 hPa absolute difference (in m/s) and the direction (arrows).

Westerly Flow over Southern Europe, cyclonic [27] Figure 4.24 f) shows strong z500 negative values between Ireland and Iceland (<-12 gpdm) and also negative SLP values extending a bit more south (-14 hPa). Positive difference values are located between the Azores and Eastern Europe with a maximum of 11 gpdm over Italy and a maximum of 5 hPa over Greece.

4.5.6 Sea Level Pressure Anomaly

The sea level pressure anomalies vary signiﬁcantly from a type to another (ﬁgure 4.25). Types 4 [a)], 17 [b)] and 19 [c)] have a negative anomaly centered southwest of England. The values are somewhere between -9 hPa for type 4 and -18 hPa for the two others. Types 21 [a)], 24 [b)] and 27 [c)] all have a strong negative anomaly (-24 to <-27 hPa) located between the North Sea and Northern France. Positive anomalies can mostly be seen on type 17 (12 hPa over Eastern Europe) and on type 24 (over the Arctic). Chapter 4. Results 38

Figure 4.25: Sea level pressure anomalies (in hPa) based on the 1981-2010 daily running mean with a 7 days window for the foehn stations HWDs in the CAP27 types 4 [a)], 17 [b)], 19 [c)], 21 [d)], 24 [e)] and 27 [f)]. 4.6 Speciﬁc events

To have a better understanding of the causes of windstorms, three events (HWD) are presented and will be analyzed in the discussion part. It is also a way to compare speciﬁc HWDs weather situation with the WTCs composites plots. Different parameters such as the sea level pressure (SLP), 500 hPa geopotential height (z500), 850 and 300 hPa winds, potential vorticity and temperature at 850 hPa are shown and will be analyzed in chapter 5.3. The three HWDs are the following: two windstorms, 26.12.1999 (Lothar) and 26.01.1995 (Wilma) and a foehnstorm on the 29.01.1986.

4.6.1 26 December 1999

The storm Lothar on the 26th December 1999 is probably the best known winter storm of the past decades in Switzerland. It is in fact the strongest storm of the last 30 years according to the median value of all Swiss weather stations (33.65 m/s). Due to its extreme intensity, it has been the subject of many studies (e.g.; Brundl¨ and Rickli, 2002; Riviere` et al., 2010; Wernli et al., 2002). Therefore this work will not go into too much details concerning the causes of the storm, but it will be analyzed in a way to compare the the weather situation of that day with the corresponding WTC composites. Several plots of four time steps (00, 06, 12 and 18 UTC) of the 26.12.1999 are shown to have an overview of the storm’s evolution. It is categorized as “westerly ﬂow over Southern Europe, cyclonic” type [27] in CAP27. The general situation shown on ﬁgure 4.26 is characterized by a strong cyclone located between Iceland and the Norwegian Sea with a SLP minimum around 960 hPa and a z500 of 496 gpdm. An anticyclone is located between the Azores and North Africa with a pressure maximum Chapter 4. Results 39

Figure 4.26: Synoptic situation on the 26.12.1999 for: a) 00 UTC, b) 06 UTC, c) 12 UTC and d) 18 UTC. Shown are the Sea level pressure (in hPa) in contour and 500 hPa geopotential height (in gpdm) in color. around 1030 hPa. A small depression (995 hPa) can be seen south of the low pressure system center (situated south of Ireland at 00 UTC [a)]). It quickly gained strength between 00 UTC and 12 UTC [c)], the pressure dropped below 980 hPa. It can be seen more in detail on figure 4.30 that the center low pressure is around 977 hPa. Associated to it, a strong pressure gradient can be seen over France, Germany and Switzerland with the moving depression. It moved quickly eastward and was already over Poland at 18 UTC [d)] with a rising SLP. The associated winds at 850 hPa to this small but strong depression are shown on figure 4.27. At 00 UTC [a)] the strongest winds are found off the coasts of France with a speed above 46 m/s. This wind maxima moved over France, Germany and Switzerland between 6 [b)] and 12 UTC [c)] with winds still above 40 m/s. More precisely, the strongest winds are affecting Switzerland at 12 UTC with a maximum of 38 m/s. However, the resolution is not good enough to see exactly which regions mostly get affected. The wind speed decreased rather quickly whilst the depression was moving eastward. The jet stream on the 26th December 1999 was especially strong. It is on figure 4.28 almost zonally oriented over the North Atlantic where the strongest winds can be seen with values above 100 m/s. A divergence zone corresponding to the jet exit can be seen over Central Europe with a meander over the East Mediterranean Sea. The potential vorticity evolution is shown on figure 4.29. The storm can easily be identified with a small wave developing over France between 00 [a)] and 6 UTC [b)]. It formed a small through of high PV values located north of France and a ridge of lower PV values over Germany. This wave moved eastward and the disappeared at 18 UTC [d)] with the storm weakening. Figure 4.30 shows a more precise situation of the SLP over Europe and also the temperature at Chapter 4. Results 40

Figure 4.27: Wind ﬁeld on the 26.12.1999 for: a) 00 UTC, b) 06 UTC, c) 12 UTC and d) 18 UTC. Shown is the wind at 850 hPa (in m/s) in color and the direction (arrows).

Figure 4.28: Jet stream on the 26.12.1999 for: a) 06 UTC, b) 12 UTC. Shown is the wind at 300 hPa (in m/s) in color and the direction (arrows).

850 hPa at 6 [a)] and 12 UTC [b)]. The depression Lothar with the associated tight pressure gradient is visible over North France and then moving eastward over Germany. Also a strong temperature gradient can be seen southeastward of the center low, it corresponds to the cold front position. The front was over the Bretagne in France at 6 UTC and 6 hours later already over the Alps in Switzerland. The difference between the warm and the cold sector at 850 hPa is around 7◦C. Figure 4.31 a) shows the Atlantic SST anomaly for the 26.12.1999. Positive anomalies dom- inate the North Atlantic with positive value around 1-1.5◦C in the center of the ocean. The strongest anomaly if found off the coast of Newfoundland, a maximum of 2.75◦C is found there. There is also a zone of positive values south of Spitzberg. A small sector of negative (around -1◦C) anomalies is located south of Greenland. The Mediterranean Sea has positive anomalies on the east and negative on the west. The SLP anomaly plot on ﬁgure 4.32 a) shows a strong negative anomaly (<-36 hPa) between Scotland, Norway and Greenland. It extends down to Italy and east to Russia. A second center Chapter 4. Results 41

Figure 4.29: PV on the 26.12.1999 for: a) 00 UTC, b) 06 UTC, c) 12 UTC and d) 18 UTC. Shown is the potential vorticity distribution (in pvu) on the 320 K isentrope and the wind direction and intensity (arrows).

Figure 4.30: SLP and t850 on the 26.12.1999 for: a) 06 UTC, b) 12 UTC. Shown is the temperature at 850 hPa (in ◦C) in colors and the sea level pressure (in hPa) in contours. of strong negative anomalies is found over Canada near the Hudson Bay. Others smaller nega- tives anomalies are situated over the Paciﬁc and Siberia. Close to Europe, only over Northern Africa a zone of positive anomaly can be seen (8 hPa). A area of strong positive anomalies extends from Mexico to Northern Canada. Over the Middle Paciﬁc the SLP shows also a positive anomaly.

Figure 4.31: Sea surface temperature anomalies (in ◦C) based on the 1981-2010 daily running mean with a 7 days window on the 26.12.1999 [a)] and 26.01.1995 [b)]. Chapter 4. Results 42

Figure 4.32: Sea level pressure anomalies (in hPa) based on the 1981-2010 daily running mean with a 7 days window on the 26.12.1999 [a)], 26.01.1995 [b)] and 29.01.1986 [c)].

4.6.2 26 January 1995

After Lothar and Vivian (27th February 1990) the storm of the 26th January 1995 (sometimes called Wilma) is the third strongest winter storm that occurred in Switzerland since 1981 according to the median wind gust value of all stations (29.9 m/s). It is categorized as “west- southwest, cyclonic” type [24] in CAP27.

Figure 4.33: Synoptic situation on the 26.01.1995 for: a) 00 UTC, b) 06 UTC, c) 12 UTC and d) 18 UTC. Shown are: Sea level pressure (in hPa) in contour and 500 hPa geopotential height (in gpdm) in color.

On the 26.01.1995, the weather over Switzerland was influenced by a low pressure system centered over Scandinavia with a central pressure of 980 hPa and a geopotential height value of 590 gpdm (figure 4.33). A 1025 hpa anticyclone was located between the Azores and North Africa. Between these two weather systems, a secondary depression was located over England Chapter 4. Results 43 at 00 UTC [a)]. This 985 hPa center was responsible of the strong winds that affected parts of Europe and Switzerland. This smaller depression moved eastward during the day to be situated over North Germany and Poland at 18 UTC [d)]. Also visible on figure 4.37, the flow was first southwest oriented and then turned northwest at 18 UTC. It is also at that moment that the strongest pressure gradient was located over Switzerland. This southwesterly flow appears clearly in the 850 hPa wind field (figure 4.34). The winds were first the strongest over France, the Benelux and Germany with a maximum speed around 34 m/s. The Alps and Switzerland were at this time on the edge of the storm. They were affected later (18 UTC [d)]) by first strong southwest (then northwest) winds, the maximum value is around 30 m/s. It is essential to note that these maps give only a general overview of the winds field over Europe. The complex topography of Switzerland and the consequences on the winds speed and direction do not appear here.

Figure 4.34: Wind ﬁeld on the 26.01.1995 for: a) 00 UTC, b) 06 UTC, c) 12 UTC and d) 18 UTC. Shown is the wind at 850 hPa (in m/s) in color and the direction (arrows).

The 300 hPa winds (jet stream) at 12 [a)] and 18 UTC [b)] form several meanders over the Atlantic and Europe (4.35). The jet forms a ridge over the Atlantic and a small trough over Western Europe. The situation is almost similar at 12 and 18 UTC, there is just a small shift of the meanders to the east. At 18 UTC, the strongest winds are found over the British Islands and North France (76 m/s). The small trough is over Eastern France, this corresponds also to the jet exit. This trough is also visible on the PV plot (ﬁgure 4.36). A PV streamer moving eastward stretches from Central Europa to North Africa (at 18 UTC [d)]). Close to the secondary cyclone center, a zone of lower PV values is visible, also moving to the east. At 18 UTC, a small zone of lower PV values appears in the through over Switzerland, its location corresponds to the jet exit zone. Chapter 4. Results 44

Figure 4.35: Jet stream on the 26.01.1995 for: a) 12 UTC, b) 18 UTC. Shown is the wind at 300 hPa (in m/s) in color and the direction (arrows).

Figure 4.36: PV on the 26.01.1995 for: a) 00 UTC, b) 06 UTC, c) 12 UTC and d) 18 UTC. Shown is the potential vorticity distribution (in pvu) on the 320 K isentrope and the wind direction and intensity (arrows).

As for the storm Lothar, strong winds are associated to a cold front. Figure 4.37 gives a more detailed view of the the small low pressure system at 12 [a)] and 18 UTC [b)]. In addition to the tight pressure gradient, a strong temperature gradient is clearly visible. The cold front can therefore be identiﬁed over Northwest France at 12 UTC. Six hours later it was already situated on a Pyrenees-Alps line. The difference is around 10◦C between the warm and the cold sector at 850 hPa. Figure 4.31 b) shows the SST anomalies for the 26.01.1995. The situation is similar to the 26.12.1999 with strong positive anomalies close to Newfoundland (more than 2.75◦C at some places). There are also positive anomalies (around 1◦C) between the Azores and Portugal. Around Scandinavia and north of Iceland the anomalies are also positive. Just southeast of Greenland and south of Iceland the water shows negative values (around -1◦C). The SLP anomalies are shown on ﬁgure 4.32 b). Almost the whole European continent is covered with negative SLP anomalies. There are two centers of minimum values (around -28 Chapter 4. Results 45

Figure 4.37: SLP and t850 on the 26.12.1999 for: a) 12 UTC, b) 18 UTC. Shown is the temperature at 850 hPa (in ◦C) in colors and the sea level pressure (in hPa) in contours. hPa), over Northern Germany and over Scandinavia. Negative anomalies also extend on a big part of the North Atlantic with the lowest values off the coasts of Newfoundland (-36 hPa). The Paciﬁc is also mostly covered with negative values, except north (close to Russia and Alaska) where strong positive values (32 hPa) are found. These positive anomalies also extend all over the Arctic region.

4.6.3 29 January 1986

The windstorm of the 29th January 1986 differs from the two previous ones concerning the general situation and the regions affected. This storm resulted from a southerly flow leading to a foehn situation over the Alps. It is therefore the type of windstorm that mostly affects alpine valleys and not so much the Swiss Plateau. For the 8 selected foehn stations it is one of the strongest storm of the past 30 years with a median wind gust value of 33 m/s. It does not appear in the list of HWD of all the Switzerland’s stations. On the 29.01.1986 the general circulation was meridional with a strong anticyclone (1040 hPa) over the Atlantic and a depression (985 hPa) first centered southeast of Iceland (figure 4.38) [a)]. There was also a surface anticyclone with pressure higher than 1040 hPa over Russia. In the following hours, a second center low over England deepened in pressure and moved southward to then form a cut-off also visible in altitude (low geopotential over the British Islands). Also, the formation of another low pressure center over the Mediterranean off the coast of France during the afternoon can be seen on figure 4.42. The flow over the Alps was first southwest oriented and then turned to south and increase with the deepening of the low over the Mediterranean. An “foehn nose” formed by the isobars is visible over the Alps. On figure 4.39, the strongest winds (30 m/s) are founds over the Atlantic southwest of the depression. Southerly winds dominates over Central Europe, the strongest speeds (around 20 m/s) appear to be in the French Rhone Valley. According to these plots, the winds seem to be quite weak over Switzerland. But again it is not precise enough to identify the wind speed in the Alps and in the small valleys where the station are located. The jet stream also shows a meridional structure with strong northwesterly winds over the Atlantic (80 m/s) (figure 4.40). It makes a meander over South of Europe and the Mediterranean Chapter 4. Results 46

Figure 4.38: Synoptic situation on the 29.01.1989 for: a) 00 UTC, b) 06 UTC, c) 12 UTC and d) 18 UTC. Shown are: Sea level pressure (in hPa) in contour and 500 hPa geopotential height (in gpdm) in color.

Figure 4.39: Wind ﬁeld on the 29.01.1989 for: a) 00 UTC, b) 06 UTC, c) 12 UTC and d) 18 UTC. Shown is the wind at 850 hPa (in m/s) in color and the direction (arrows).

Sea to then have a southwest direction over the Alps. The jet left exit corresponds to the location of the small depression developing over the Mediterranean Sea. Over Switzerland and Central Europe the jet is much weaker than over the Atlantic with speeds from 30 to 40 m/s. The same structure appears on ﬁgure 4.41. A big ridge of low PV values appears on the Atlantic up to South Greenland. A trough of high PV values covers most of Europe. The PV forms a streamer from Spain to the Atlantic. At 06 UTC [a)], this streamer then forms a cut-off off the coasts of Morocco. Finally, a long incursion of lower PV values elongates from Italy toward Chapter 4. Results 47

Figure 4.40: Jet stream on the 29.01.1989 for: a) 12 UTC, b) 18 UTC. Shown is the wind at 300 hPa (in m/s) in color and the direction (arrows).

Scandinavia.

Figure 4.41: PV on the 29.01.1989 for: a) 00 UTC, b) 06 UTC, c) 12 UTC and d) 18 UTC. Shown is the potential vorticity distribution (in pvu) on the 320 K isentrope and the wind direction and intensity (arrows).

A more precise situation of the SLP at 12 [a)] and 18 UTC [b)] can be seen on ﬁgure 4.42. The small depression (997 hPa at 18 UTC) that formed south of France is clearly visible. Concerning the temperature, this southerly ﬂow brought warm air at 850 hPa (+3◦C) north of the Alps. Just south of the mountains, the temperatures were colder with values around -7◦C at 850 hPa. Again, the resolution is not good enough to fully see the situation in the Alps, however there is still a strong temperature gradient between the south and the north side of the Alps. Chapter 4. Results 48

Figure 4.42: SLP and t850 on the 29.01.1989 for: a) 12 UTC, b) 18 UTC. Shown is the temperature at 850 hPa (in ◦C) in colors and the sea level pressure (in hPa) in contours.

Figure 4.32 c) shows the SLP anomaly. A negative anomaly is centered over France (Bretagne) and surrounded with positive anomalies that extend from the Atlantic up to Scandinavia. Also most of the Arctic region and Russia have positive anomalies. Strong negative anomalies are found over Northern Quebec´ and all over the North Paciﬁc. Chapter 5

Discussion

The results are discussed and analyzed in this chapter, divided in three parts. The ﬁrst part focuses on the outcome of the WTCs (GWT and CAP) and also the clustering of the weather stations. In the second part, the different weather parameters for winter storms and foehn storms are analyzed. The third part aims to explain three speciﬁc storms and also compare these results with composites.

5.1 Evaluation of the Weather Type Classiﬁcations and Clus- tering

5.1.1 GWT

The main outcome of the GWT classifications is that winter storms affecting most regions of Switzerland only result from westerly situations (from southwesterly to northerly flows). However, there is a difference between classifications based on the mean sea level pressure (msl) and the ones based on the geopotential height at 500 hPa (z500). Based on the z500, only westerly, southwesterly and northwesterly flows are conducive to HWDs, whereas based on the msl, also the north type appears in the HWDs classification. This can be explain by the fact that surface winds are more directly linked to the flow at low-level (msl) than to the flow at high altitude (z500). A north oriented flow at 500 hPa does not necessarily mean that the low-level flow will also have the similar orientation. This can be seen on the MeteoSwiss composites plots for type 4 for example (see Appendix figure 4). Based on this affirmation, the GWTs-msl classifications seem to be more appropriate to class HWDs. Another dissimilarity between z500 and msl GWTs is found in the probabilities of HWDs (blue bars). The values for most types are higher in the msl classification. This difference lies in the fact that the classification of all days (from 1981 to 2012) is more homogeneous for the msl classification. In the z500 classification most of the days are found in the 9 types that are conducive to HWDs. For example, type 17 contains more than 400 days in the z500 and only

49 Chapter 5. Discussion 50 around 150 in the msl (see Appendix ﬁgures 1 and 2). There is therefore an non-negligible difference in the probability values. Westerly types regroup most of the HWDs, but it is interesting to note that on the GWT26-msl plot the west cyclonic type contains only 7% of the HWDs as the west anticyclonic type has a value of 19%. The opposite could be expected as windstorms are mostly caused by cyclone. But based on the probabilities, it can be seen that the cyclonic type is more often conducive to HWDs. In total (taking all days into account), the anticyclonic type is more than six times more frequent than the cyclonic type. It can thus be afﬁrmed that there are less west cyclonic than west anticyclonic days, but the cyclonic circulation is more likely to engender a HWD.

5.1.2 CAP and evaluation of the weather stations clustering

The CAP27 classification confirms that westerly situations (southwesterly to northwesterly flows) are conducive to HWDs in Switzerland. The six types (10, 15, 20, 21, 24 and 27) contains most of the HWDs and are all cyclonic (except number 20 [indifferent]), which is pertinent in regard to the fact that the CAP classification is based on the msl. Unlike the GWT26 classification, the total number of days in the CAP27 is better distributed between the types and the six HWDs types almost all have around 150 days. Only the type 27 shows a lower value with around 90 days (see Appendix figure 3). Knowing this, it is difficult to find an explanation to the HWDs repartition between the six types. The high number of HWDs in type 21 could be explain by the fact that it has the tightest pressure gradient and the strongest winds (based on the composite plots) in comparison to the other types, it is therefore probably the most favorable situation to the occurrence of HWDs. Donat et al. (2010) used circulation weather types to analyze the occurrence of windstorms in Central Europe. It has also be found that westerly flow situations are conducive to around 80% of all windstorms and the remaining 20% occur with nothwesterly flows. It is further explained that storm days with northwesterly situations are on average stronger. The reason given is that in a northwesterly situation, cyclones follow a path from the British Isles to the North Sea and thus the intensity remains high because the low stays longer over water (on the North Sea). Cyclones linked to westerly flows make landfall earlier and then lose in intensity. Another explanation could come from the larger number of events linked to westerly situations and therefore the set contains more “average” events that influence the mean. This is not confirm with the CAP27 classification of HWDs in Switzerland. Almost all types contain some strong events as well as many “average” HWDs. One reason could come from the region of study that is much larger and closer to the North Sea in Donat et al. (2010). Nevertheless, it also explained that most intensive and destructive storms are associated with westerly flows which is also the case in this work. Even if on a large-scale westerly winds are responsible for almost all HWDs, on a local scale the situation is often different but this does not appear in figure 4.5. This is why the clustering of the stations into several groups is important. The clustering in three groups already shows that Chapter 5. Discussion 51 the wind direction and intensity in Switzerland is strongly linked to the topography. The three clusters roughly correspond to three Swiss main regions (Jura-Plateau, Alps, South Alps). This shows that all the stations do not respond the same way to various weather situations because of the alpine relief. The same structure is found when the number of clusters is set to six. This more detailed repartition allows to distinguish some smaller regions and confirms that each regions has its own particularities often related to the topography. It also helps to understand how the wind intensity and direction is influenced by the mountain relief. This clustering outcome is consistent with Ceppi et al. (2008) results. Even if their study focuses on return periods, they identified some similar “wind regions” (e.g. Foehn, Plateau/Jura) based on five specific storms. In the following, each cluster is analyzed to identify how and why stations respond to different weather situations. For both clusters “North 1” and “North 2” the same six westerly types contain most of the HWDs, but the main difference between both regions is that for “North 2” type 21 regroups most of the HWDs and that northwesterly types (10 and 15) contain less HWDs. It is difficult to find a reason why a slight difference is found between both clusters. The “North 2” stations are mostly situated on the Plateau but a few are also on top of mountains. Stations on mountains are usually less influenced by the relief and are thus exposed to all wind directions, as on the Plateau the wind gets canalized in a west-southwesterly direction. These stations are therefore more sensitive to southwesterly and westerly flows than to northwesterly flows (the Jura acting as a barrier). Several stations in group “Alps 1” are situated in south-north oriented valleys. Richner and Hachler¨ (2013) describe that the channeling effect of valleys is strong and that foehn areas are primarily defined by topography. It is also said that major valleys perpendicular to the ridge are the areas with the highest foehn frequency. So it is expected that these stations are sensitive to south foehn as they are situated in valleys such as the Rhone (Aigle, Evionnaz), Rhine (Vaduz, Bad Ragaz, Chur), Reuss (Altdorf) and Hasli (Meiringen). It is confirmed with figure 4.9 left, types 17 and 19 (southerly flows) represent around 30% of all HWDs. Figure 4.11 (that shows the HWDs classification for the eight selected foehn stations) also has a similar pattern with types 17, 19 and 4 containing most of the HWDs. Stations of the “Alps 2” cluster are mostly affected by northwesterly situations. Most of them are in the inner Alps and in southwest-northeast oriented valleys. The topography plays a less important role in this case. The explanation probably lies in the fact that northwesterly flows penetrate better into the Alps as the composite plots suggest. Weber and Furger (2001) have analyzed the near-surface wind patterns in Switzerland and compared the 16 identified patterns with the synoptic weather types of Schuepp¨ (1979). It is found that the strongest winds in the inner Alps are most of the time linked to northerly flows. Upvalley winds can also result from these types of synoptic flows. The few stations part of the “West” cluster have the particularity to be situated in the narrowest part of the Swiss Plateau between the Jura and the Alps. Like in the Alpine valleys, the channeling effect of the topography causes an acceleration of the wind. In this region, the mountain Chapter 5. Discussion 52 ridges form a funnel from east to west and easterly winds (bise) are therefore enhanced. Even if the bise can affect most parts of the Switzerland, it is always the strongest in the western part of the country. The “South” cluster shows the most differences in the HWDs repartition in comparison to the other ones. Most of the time when the North of the Alps is affected by a strong winds, the South is only marginally concerned. This can be seen in the return period plots presented in Ceppi et al. (2008). However, stations in Ticino are also sensitive to northwesterly flows, probably due to the presence of north foehn winds. Easterly flows also generate HWDs over Ticino. A low pressure system over Italy or the Mediterranean Sea induces a strong easterly flow over the Southern Alps and Northern Italy.

5.2 Analysis of the Weather Parameters for the CAP27 WTC

5.2.1 Winter storms

Although the general weather situation on the six composites (10, 15, 20, 21, 24, 27 - figure 4.12) is very similar, the small differences in the flow direction due to the high and low systems position can significantly change the regions affected in Switzerland. On an European scale the differences are found in the flow’s direction (southwesterly to northwesterly) and in the wind intensity. The composites show the low pressure system centered - according to the different weather types - between the Norwegian Sea, the North Sea and Scandinavia, it is consistent with the results found by Goyette (2011) and Schiesser et al. (1997). The orientation of the flow is mostly driven by the position of the anticyclone. If the high pressure system is located over the Azores and Northern Africa/Southern Europe, the cyclone will be forced to move at high latitudes. If the anticyclone extend less to the east and higher in latitude over the North Atlantic, the low pressure system will then extend more to the south. It is confirmed by the SLP anomalies on figure 4.18 with positive values over the Atlantic for types 10 and 15 that lead to a northwesterly flow over Europe. The flow’s intensity is directly linked to the pressure gradient. A comparison between the winds speed on figure 4.13 and the mean values of the wind gusts measurements shows some dissimilarities. For example, type 27 has the highest gusts mean value (23.65 m/s), but the mean plot shows strong winds over France, and Switzerland is only marginally affected. On the opposite, type 21 shows the strongest winds of all the plots, but has a mean value of 21.72 m/s. This does not mean that the wind composite plots are wrong, but it confirms that the resolution is somewhat too coarse to have a valuable representation of the wind field and intensity over Switzerland. However, the general flow and intensity seem to be rather well represented on a larger scale. Concerning the flow over Europe, it can be said that although there are some variations in its direction, all types have a (almost) zonally flow. This is confirmed by figure 4.15 with the Chapter 5. Discussion 53 majority of HWDMs having a positive NAO index. A positive NAO has for characteristics to increase the zonal flow (Nesterov, 2009). Furthermore, Nesterov (2009) explains that a positive NAO combined with a positive EA will also enhanced the zonal flow, while a positive NAO combined with a negative EA will weaken it. Similar results have also be found by Woollings et al. (2010) who analyzed the jet stream trajectory and intensity. Franzke and Feldstein (2005) found that the NAO mostly shows the latitude of a cyclone and the EA its intensity. These results are consistent with 4.15 (right) where most of the strongest HWDs have both a NAO and EA positive phase. The jet stream (figure 4.14) is also quite strong for most of the types and the low center is on all types close to the jet streak left exit. Thus, it can be assumed that the jet often plays a role in the development of the depression. Even if the weather situation on the composite plots of HWDs is close to the “all days” composites, there is still a noticeable difference in the general pressure field. Two things can be assumed based on the difference plots (figure 4.16). The first one is that the systems centers have almost the same position (between both composites) and the second one is a significant increase of the pressure gradient over Western Europe. One exception is type 20 where the depression is shifted to the north on the HWDs composite. The position remains almost the same but the pressure difference between the high and low pressure systems is increased. There is in consequences no modification of the flow’s orientation over Switzerland but an augmentation of the wind speed due to the tightest pressure gradient. The 850 hPa wind difference plots (figure 4.17) confirm that types having the biggest pressure differences (15, 24 and 27) also have the strongest wind differences. Another element that can be picked out is that HWDs are also linked to a decrease of SLP an z500 on a large region between Greenland and Russia and that the difference is not only concentrated on one point. The standard deviation plots (figure 4.19) show the lowest values around the Alps. This is due to the fact that it corresponds to the domain where the classification is calculated. HWDs are already classified in function of the situation over this domain, it seems therefore logical that the standard deviation stays low over this region. Concretely, it means that for a same type, there is no strong variation of the SLP and z500 over Switzerland between the HWDs. Then, it is consistent that strong variations are found on the edge of the high and low pressure system. These variations do probably not influence much the winds over Switzerland. So based on these plots, it appears that the weather types give a valuable summary of the SLP and z500 fields over the Alps and define quite well the position of the high and low pressure systems. The SLP anomalies (figure 4.18) confirm that the cyclone (and anticyclone) position and intensity determine the HWDs in Switzerland and that on average the situation further away as almost no influence. Over the all Northern Hemisphere, composite anomalies are only found in Europe. Therefore, the weather pattern over Europe cannot directly be linked to the situation somewhere else on the Northern Hemisphere. Chapter 5. Discussion 54

5.2.2 Foehn storms

The six foehn types can be divided into two groups. The first one contains types 4, 17 and 19 where the flow is southwesterly to southerly oriented (meridional). The second one regroups types 21, 24 and 27 where the flow is more southwesterly to westerly oriented (zonal). The weather situations of the second groups have already been identify as conducive to HWDs for the whole Switzerland. Now that only the foehn stations are taken into account, some differences can be observed between figures 4.20 [d)], [e)], [f)] and 4.12 [d)], [e)], [f)]. The weather systems are a little bit less pronounced and the flow over Europe is therefore weaker. The high pressure system being slightly weaker, it allows the low pressure system to extend more southward. This little shift of the depression to the south has for consequence to produce a flow more southwesterly oriented over the Alps which can generate south foehn situations. However, these three types do not only generate foehn storms, they also produce generalized strong westerly winds over the Alps. A storm can also in the same day generate both a foehn situation and then high-winds affecting most of the regions. This is for example the case for the storm Lothar in 1999 (type 27) as it is shown in Brundl¨ and Rickli (2002). Types of the other group (figure 4.20 [a)], [b)], [c)]) all have a meridional circulation. The anticyclone is shifted to the west on the Atlantic, thus the depression can extend more easily to the south. The south flow over the Alps resulting from this situation - even weak - is known to generate south foehn in the Alps (Richner and Hachler,¨ 2013). According to Richner and Hachler¨ (2013) the forecasting of foehn is a difficult task, however one of the best tool for identifying a foehn situation over the Alps is the surface pressure field. The formation of a SLP foehn nose (or knee) over the Alps is a good indicator. However, it does not always confirm the presence of foehn wind. The foehn wind intensity can be sometimes correlated with the difference of pressure (due to the foehn nose) between the south (high) and the north (low) of the alpine ridge, or even between the inner Alps and the Plateau (Giroud Gaillard, 2011). For the nowcasting of foehn, MeteoSwiss uses the method developed by Durr¨ (2008). It is based on several parameters, but the principal one is the difference in potential temperature between the station Gutsch¨ and each station for which the foehn is nowcasted. The wind speeds for types 4, 17 and 19 on figure 4.21 are much weaker in comparison to the other types. The particular and regional characteristics of foehn wind makes it difficult for the reanalysis to model it correctly. The acceleration seen in the French Rhone Valley might suggest that with a higher resolution it could give a better estimation of the situation in the alpine valleys. But the resolution would have to be precise enough to model the valleys knowing that strong foehn winds are strongly related to the topography. The more homogeneous repartition of the HWDMs in figure 4.23 is related to the presence of a meridional circulation during foehn days. The upper level jet is also weaker. It is consistent with the fact that the meridional circulation increases with a negative NAO (Nesterov, 2009; Woollings et al., 2010). It is in this type of situation with a meander over Western Europe that cyclone circulate lower in latitude and affect Southern Europe. The repartition of the strongest HWDs is also consistent with (Nesterov, 2009). A positive NAO and EA is synonymous with Chapter 5. Discussion 55 an enhanced zonal circulation (winter storms). A negative NAO and positive EA is likely to increase the meridional circulation intensity (foehn storms). The differences plots (figures 4.24) confirm that the foehn stations are more sensitive to southerly flows. Negative values on the East Atlantic are synonymous with a shift of the cyclone southward due to a less present anticyclone on the Azores. This is the case for types 4, 17, 19 and 24. The increased meridional circulation is also reflected in the positive SLP and z500 differences values over the Atlantic on one side and over Eastern Europe and Scandinavia on the other side. This confirmed with the positive SLP anomalies on figure 4.25. An enhanced foehn effect can also be observed with a diminution of the SLP values north of the Alps and an increase south of them. Even if it is not the case on the composite plots, the negative values on the differences plots suggest that the southward shifted cyclone can sometimes form a cut-off west of Switzerland that engender a foehn situation in the Alps (see chapter 5.3.3). Types 21 and 27 have a less pronounced southward shift of the low pressure system, it is somewhat moved to the east toward Greenland. However a small foehn nose can also be identified in the difference values with an increased pressure on the windward side of the Alps and a decreased pressure on the lee side. To summarize, according to the CAP27 classifications (chapters 4.3 and chapter 4.1.2) and to the composite plots (4.5), HWDs affecting the foehn stations results from a strong low pressure system (winter storm) that affects most of Switzerland (types 21, 24, 27) or from a southerly flow over the Alps which produces foehn winds (types 4, 17, 19). However, in some situations a winter storm can be preceded by a foehn flow in the alpine valleys. This is for example the case on the day of Lothar (see chapter 5.3.1).

5.3 Analysis of Three Windstorms

5.3.1 26 December 1999

As it has benn described in Brundl¨ and Rickli (2002), Riviere` et al. (2010) and Wernli et al. (2002), a particular and explosive situation on the 26.12.1999 caused the strongest and most damaging windstorm in Switzerland of the past decades. Wernli et al. (2002) have identified three key ingredients responsible for the explosive development of storm Lothar. The first one is a straight and very intense jet stream that extended zonally over the North Atlantic. This strong upper-level jet is well visible on figure 4.28. This jet was linked to an exceptionally strong baroclinicity over the North Atlantic. A strong PV gradient is also visible on figure 4.29. The second point is the particular “diabatic Rossby waves character” during the genesis of the small depression in the Western Atlantic south of the jet that was characterized by a strong positive low-level PV anomaly in a strong baroclinic environment. The third element is a rapid “bottom-up” intensification when the low-level depression moved to the northern side of the jet axis. This had for consequence the formation of a tropopause fold which then almost merged with the low-level PV anomaly. It resulted a narrow and intense vertical vortex. The low-level Chapter 5. Discussion 56

PV anomaly lead to a northward advection of tropospheric air to the east of the cyclone and a downward advection of stratospheric air on the western side. This is visible on figure 4.29 b) over France (advection of stratosperic air) and Germany (advection of tropospheric air). Also the strong positive SST anomaly on the Western Atlantic (figure 4.31 [a)]) might have played a role in the storm development. Wernli et al. (2002) explains that the water-vapor ”source regions“ correspond to the location of the positive SST anomaly. The general situation on the 26.12.1999 in comparison with the type 27 composites plots shows the anticyclone centered at the same position and the main cyclone located a bit more north. The composite’s flow over Switzerland is west-northwesterly oriented. On the day of Lothar it turned from west-southwest to west-northwest during the day. However the situation over Souther Europe correspond to the type 27 ”westerly flow over Southern Europe“. As it can be expected, the wind speed values are lower on the composite, but a similar pattern in the intensity is visible. On the 26th December the strongest winds (according to the reanalysis) where found off the coasts of France and decreased whilst moving eastward. The particularly strong jet stream on that day is confirmed with speeds almost twice faster as it can be seen on the composite. The main difference comes from the storm Lothar itself that is not visible on the composite. Each event is different and it is this kind of small but important anomaly that cannot be seen on the composite. Without this small cyclone, the 26.12.1999 would probably have been a regular HWD. Weather types can therefore give a valuable overview of the atmospheric situation during HWDs, but the small perturbation that can happen on a specific day leading to an extreme storm can not be anticipated using weather types composites.

5.3.2 26 January 1995

The storm Wilma of the 26.01.1995 was a typical winter storm in the genesis and development of the cyclone. The low pressure finds its origin just south of Newfoundland on the 22th January. It deepened in SLP during the next days to reach the value of 990 hPa. While moving over the North Sea it deepened to 978 hPa. Consecutively to the dissipation of a small incursion of low PV over the Atlantic, the PV gradient over Ireland increased at 12 UTC (figure 4.36). It lead to the reinforcement of the jet over the same region and on its left exit a diminution of the geopotential height at 500 hPa. On the 25th, the depression was only visible at the surface. The diminution of the z500 following the quick jet’s strengthening increased the trough over the North Sea having probably for consequence to reinforce the surface low. Also the cyclone being located at the right entrance of the second jet streak (over Southern Scandinavia) is another reason that can explain its reinforcement. As it is explained in Baehr et al. (1999), a cyclone located between two jet streaks (left exit and right entrance) can deepened due to the flow’s divergence. The depression moved faster than the main one centered over Scandinavia, thus the flow between 12 and 18 UTC took a straight northerly orientation bringing polar air over Western Europe having for consequence to increase the temperature gradient. The associated cold front as well as the winds were therefore enhanced. The similar pattern (in comparison Chapter 5. Discussion 57 with Lothar) of the SST anomalies over the North Atlantic could be have favoured the initial genesis of the low pressure system close to Newfoundland. In comparison to Lothar, Wilma was a less explosive storm in its development, but they are still some particularities that makes it different from a regular HWD. It is therefore not so well summarized by the composites (type 24) even if the secondary low was centered on the North Sea like in the composite. It is interesting to see that the flow’s orientation over Switzerland changed during the day. It went from a west-southwesterly to a northwesterly situation. So the categorization of a day into a weather type will sometimes depend at which time of the day the weather situation is taken. Maybe a two times daily classification of the weather situation into WTCs would increase their reliability.

5.3.3 29 January 1986

The situation that lead to a foehn storm in the Alps on the 29th January 1986 is linked to the combination of several factors. A low pressure system circulated for several days between Greenland and Iceland, it then moved toward Scotland between the 28th and the 29th. On the 28th, a secondary depression started to developed south of the first one, on the left exit of the intense jet streak. Thus, a shift of the cyclone center occured. The pressure started to rise between Iceland and Scotland following the displacement of the jet streak. This increase of pressure on the left entrance of the jet is mostly visible on the z500 field. Also the emplacement of the low pressure system between two strong anticyclones fostered it southward displacement. The strong positive SLP anomalies are visible west, east and north of the cyclone on figure 4.32 c). The low that started to developed at 12 UTC close to the coast of Southern France reinforced the southerly flow over the Alps. It coincides with the cold air flowing over the relatively warm sea temperature. An upper trough that moves on the Mediterranean Sea can bring favorable conditions to a cyclogenesis especially in winter with an important sea-land temperature gradient (Kouroutzoglou et al., 2011; Trigo et al., 2002). However, even with the development of a new low, the pressure gradient over the Alps (and the associated winds) were not particularly strong. As explained in 5.2.2, the strong winds resulted from a south foehn situation. A high reanalysis resolution would be needed to have a better understanding of the situation. Like in every foehn storm the topography did surely have a significant influence in the wind speeds as all stations are located in valleys. The difference of temperature at 850 hPa that is visible between Northern Italy and Germany on figure 4.42 could be due to the presence of an inversion layer in the Po Valley. As it is explained in Lentink (2012), the pressure and temperature gradient across the Alps is related to a blocking. It mostly occurs because of the Alps and is also enhanced by the form of the Po Valley. Close to the surface, winds take a easterly direction (figure 4.39 c) and d)). The air in the valley is therefore decoupled from the large-scale southwesterly flow above the Alps. Another explanation could be the presence of precipitations on the south of the Alps on that day. These precipitations limited the diurnal warming. A clearer sky associated to the warm southerly flow had for consequence a increase in temperature over Germany. Chapter 6

Conclusion

This study focused on the large-scale atmospheric patterns that are conducive to high-wind days in Switzerland. Wind gust measurements from Swiss weather stations were used for the 1981- 2012 period. The HWDs were then classify into two automated weather type classifications (GWT and CAP), with a focus on CAP27. Reanalysis data (ERA-Interim) were used to analyze the weather patterns conducive to HWDs. As a result, the 118 HWDs selected are in a grand majority resulting from westerly flows over Switzerland (southwesterly to northwesterly) with some small variations within the different WTCs. Classifications based on the mean sea level pressure offer a better overview of the wind fields affecting Switzerland than the ones based on the 500 hPa geopotential height. Most of the average HWDs and also most of the strongest ones are linked to a westerly or west- southwesterly flow, but a few strong HWDs are also resulting from a northwesterly flow. It is therefore not possible to identify one specific weather situation responsible for the strongest storms. It was found in the clustering of the stations based on CAP27 that depending on their location, stations are not affected the same way by large-scale flows. Six groups of stations were identify. Stations on the Swiss Plateau are mainly sensitive to westerly or southwesterly flows and to a lesser extent to easterly flows. In the Alps, the location of the stations strongly influences their sensitivity to the large-scale flow. Inner Alps stations are mostly sensitive to northwesterly situations, but it is also linked to the valleys’ orientation. In fact, the stations in south-north oriented valleys show a strong sensitivity to southerly flows known to produce south foehn wind. Finally, stations located on the south side of the Alps are mostly affected by easterly and northwesterly flows. Northwesterlies usually generate north foehn winds in this region. The analysis of composites plots of the CAP27 weather types that are conducive to HWDs explains in part the causes of HWDs (and windstorms). For all HWDs (except foehn days) Switzerland is located between two systems; an anticyclone located in the Azores region and a cyclone situated close to Scandinavia. The orientation of the flow is closely related to the anticyclone’s position over the Atlantic. A stronger anticyclone and a deeper cyclone increase the pressure gradient over Switzerland and therefore the wind speeds. Although the reanalysis’

58 Chapter 6. Conclusion 59 resolution is a bit coarse to clearly see the situation over Switzerland. However, the influence of the Alps on the large-scale flow can still be observed. Foehn storms results from southwesterly to southerly flow over the Alps. The strong winds are generated by the flow crossing the Alps. Therefore these HWDs can mostly be explain through the topography. In fact, the large-scale flow (and the winds) over the Alps are not especially strong. The analysis of three windstorms shows that the composites give a good summary of the situation during HWDs but fail to offer a valuable representation of the strongest windstorms. This is explained by the fact that strong windstorms result from a combination of small weather parameters in addition to the general circulation. Also foehn storms are not visible in the composites plots probably due to the resolution that is not high enough for this kind of events. A reanalysis with a higher resolution could increase the understanding of the Alps’ influence on the wind field. Appendix

List of weather stations TAE Aadorf / Tanikon¨ 539 COM Acquarossa / Comprovasco 575 ABO Adelboden 1320 AIG Aigle 381 ALT Altdorf 438 ARH Altenrhein 398 AMS Amsoldingen 638 AND Andeer 987 ANT Andermatt 1442 ARO Arosa 1840 RAG Bad Ragaz 496 BAR Bargen 740 BAS Basel / Binningen 316 BER Bern / Zollikofen 552 BEZ Beznau 325 BIL Biel/Bienne 433 BIZ Bischofszell 470 BIE Biere` 683 BOL Boltigen 820 BOU Bouveret 374 BRZ Brienz 567 BUS Buchs / Aarau 386 BUF Buffalora 1968 FRE Bullet / La Frtaz 1205 CEV Cevio 416 CHZ Cham 440 CHA Chasseral 1599 CHM Chaumont 1073 CHU Chur 556 CHD Chateau-d’Oexˆ 1029 CIM Cimetta 1661

60 Appendix 61

GSB Col du Grand St-Bernard 2472 CMA Crap Masegn 2480 CRM Cressier 431 DAV Davos 1594 DEM Delemont´ 439 DIS Disentis / Sedrun 1197 EBK Ebnat-Kappel 623 EGH Eggishorn 2893 EGO Egolzwil 521 EIN Einsiedeln 910 ELM Elm 958 ENG Engelberg 1035 EVI Evionnaz 480 EVO Evolne / Villa 1825 FAH Fahy 596 FEY Fey 737 GRA Fribourg / Posieux 646 GVE Geneve-Cointrin´ 420 GIH Giswil 475 GLA Glarus 516 GOR Gornergrat 3129 GRE Grenchen 430 GRH Grimsel Hospiz 1980 GRC Grachen¨ 1550 GOE Gosgen¨ 380 GUE Gutsch¨ ob Andermatt 2287 GUT Guttingen¨ 440 HLL Hallau 419 HIR Hinterrhein 1611 HOE Hrnli 1132 INT Interlaken 577 JUN Jungfraujoch 3580 KOP Koppigen 484 BRL La Brevine´ 1048 CDF La Chaux-de-Fonds 1018 DOL La Doleˆ 1669 LAG Langnau i.E. 745 CHE Le Chenit 1015 MLS Le Moleson´ 1974 LEI Leibstadt 341 ATT Les Attelas 2730 DIA Les Diablerets 2966 OTL Locarno / Monti 366 Appendix 62

LOR Lodrino 261 LUG Lugano 273 LUZ Luzern 454 LAE Lagern¨ 845 MAG Magadino / Cadenazzo 203 MAH Mathod 435 MTR Matro 2171 MER Meiringen 588 MVE Montana 1427 GEN Monte Generoso 1608 MRP Monte Rosa-Plattje 2885 CLA Montreux-Clarens 405 MOA Mosen 452 MAE Mannlichen¨ 2230 MOE Mohlin¨ 344 MUB Muhleberg¨ 479 NAS Naluns / Schlivera 2400 NAP Napf 1403 NEU Neuchatelˆ 485 CGI Nyon / Changins 455 AEG Oberageri¨ 724 ORO Oron 827 BEH Passo del Bernina 2307 PAY Payerne 490 PAA Payerne 445 PIL Pilatus 2106 PIO Piotta 990 COV Piz Corvatsch 3305 PMA Piz Martegnas 2670 PLF Plaffeien 1042 ROB Poschiavo / Robbia 1078 PUY Pully 455 QUI Quinten 419 RHF Rheinfelden 300 ROE Robii 1894 RUE Runenberg¨ 611 SBE S. Bernardino 1638 HAI Salen-Reutenen 718 SAM Samedan 1708 SHA Schaffhausen 438 SCM Schmerikon 408 SPF Schupfheim¨ 742 SCU Scuol 1303 Appendix 63

SIA Segl-Maria 1798 SIO Sion 482 PRE St-Prex 425 STG St. Gallen 775 SMM Sta. Maria, Val Mstair 1383 SBO Stabio 353 STK Steckborn 398 SAE Sntis 2502 TIT Titlis 3040 ULR Ulrichen 1345 VAD Vaduz 457 VAB Valbella 1569 VIS Visp 639 WFJ Weissﬂuhjoch 2690 WYN Wynau 422 WAE Wadenswil¨ 485 ZER Zermatt 1638 REH Zrich / Affoltern 443 SMA Zrich / Fluntern 555 KLO Zrich / Kloten 426

Table 1: List of the SwissMetNet stations. Shown in the ﬁrst column is the abbreviation, in the second column the full name and in the third column the altitude (a.s.l).

GWT26−z500

819 800

715 700

600

500 430 394 400 366 365

318 Number of Days 275 300 262 245 209 200 192

128 121 113 103 87 94 93 92 79 76 100 61 40 40 33 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26

Weather Classes

Figure 1: Classiﬁcation of all days for the 1981-2012 period according to the GWT26-z500 WTC. The absolute number of days is labeled on top of the bars. Appendix 64

GWT26−msl

635 610 600

500

400 393 358 334 330 332 312 300 251

Number of Days 231 229 208

200 175 159 144 146 115 122 121 115 100 80 81 73 73 66 57

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26

Weather Classes

Figure 2: Classiﬁcation of all days for the 1981-2012 period according to the GWT26-msl WTC. The absolute number of days is labeled on top of the bars.

CAP27

448

400 393

330

301 302 300 289

258 237 228 223 218 222 205 196 200 182 175

Number of Days 169 169 160 159 160 147 138 141 127

101 100 87

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27

Weather Classes

Figure 3: Classiﬁcation of all days for the 1981-2012 period according to the CAP27 WTC. The absolute number of days is labeled on top of the bars. Appendix 65

Figure 4: Composites for the GTW26-z500 type 4 (winter). Shown is the geopotential height (left) and the sea level pressure (right). Precipiations are indicated in colors and the temperature anomaly is given. Retrieved from http://www.meteosuisse.admin.ch/web/en/services/dataportal/standardproducts/Weathert ypeclass.html

Figure 5: Composites of all winter (Oct-Mar) days from 1981 to 2010 for CAP27 types 10 [a)], 15 [b)], 20 [c)], 21 [d)], 24 [e)] and 27 [f)]. Shown in color is the geopotential height at 500 hPa (in gpdm) and in contour the sea level pressure (in hPa) Appendix 66

Figure 6: Composites of all days from 1981 to 2010 for CAP27 types 4 [a)], 17 [b)], 19 [c)], 21 [d)], 24 [e)] and 27 [f)]. Shown in color is the geopotential height at 500 hPa (in gpdm) and in contour the sea level pressure (in hPa) Bibliography

Baehr, C. et al. (1999). “Dynamical characterization of the FASTEX cyclogenesis cases”. In: Quarterly Journal of the Royal Meteorological Society 125.561, pp. 3469–3494. Barredo, J. (2010). “No upward trend in normalised windstorm losses in Europe”. In: Nat. Hazards Earth Syst. Sci 10, pp. 97–104. Beck, C. (2000). Zirkulationsdynamische Variabilitt im Bereich Nordatlantik-Europa seit 1780. Ed. by W. G. Arbeiten, p. 350. Bengtsson, L., Hodges, K. I. and Roeckner, E. (2006). “Storm tracks and climate change”. In: Journal of Climate 19.15, pp. 3518–3543. Bengtsson, L., Hodges, K. I. and Keenlyside, N. (2009). “Will extratropical storms intensify in a warmer climate?” In: Journal of Climate 22.9, pp. 2276–2301. Berrisford, P. et al. (2009). “The ERA-Interim Archive.” In: ERA report series 1, pp. 1–16. Braun, S et al. (2003). “Forest damages by the storm Lotharin permanent observation plots in Switzerland: The significance of soil acidification and nitrogen deposition”. In: Water, Air and Soil Pollution 142, pp. 327–340. Bronnimann, S et al. (2012). “Extreme winds at northern mid-latitudes since 1871”. In: Mete- orologische Zeitschrift 21.1, pp. 13–27. Brundl,¨ M. and Rickli, C. (2002). “The storm Lothar 1999 in Switzerland - an incident analysis”. In: For. Snow Landsc. Res 77.1, p. 2. Ceppi, P., Della-Marta, P. M. and Appenzeller, C. (2008). “Extreme value analysis of wind speed observations over Switzerland”. In: CH2011 (2011). Swiss Climate Change Scenarios CH2011. Tech. rep., p. 88. Chang, E. K. and Fu, Y. (2002). “Interdecadal variations in Northern Hemisphere winter storm track intensity”. In: Journal of Climate 15.6, pp. 642–658. Cusack, S. (2012). “A 101 year record of windstorms in the Netherlands”. In: Climatic Change 116.3-4, pp. 693–704. Dee, D. et al. (2011). “The ERA-Interim reanalysis: Configuration and performance of the data assimilation system”. In: Quarterly Journal of the Royal Meteorological Society 137.656, pp. 553–597. Donat, M. G. et al. (2010). “Examination of wind storms over Central Europe with respect to circulation weather types and NAO phases”. In: International Journal of Climatology 30.9, pp. 1289–1300.

67 Bibliography 68

Durr,¨ B. (2008). “Automatisiertes Verfahren zur Bestimmung von Fhn in Alpentlern”. In: Ar- beitsberichte der MeteoSchweiz 223, p. 22. Fink, A. et al. (2009). “The European storm Kyrill in January 2007: synoptic evolution, meteorological impacts and some considerations with respect to climate change”. In: Nat. Hazards Earth Syst. Sci 9.2, pp. 405–423. Franzke, C. and Feldstein, S. B. (2005). “The continuum and dynamics of Northern Hemisphere teleconnection patterns”. In: Journal of the atmospheric sciences 62.9, pp. 3250–3267. Geng, Q. and Sugi, M. (2003). “Possible change of extratropical cyclone activity due to enhanced greenhouse gases and sulfate aerosols-study with a high-resolution AGCM”. In: Journal of Climate 16.13, pp. 2262–2274. Giroud Gaillard, M. (2011). Frequence´ de foehn temptueux dans le Chablais. URL: http : //www.meteoschweiz.admin.ch/web/fr/meteo/actualite_meteo/frequences_de_ fort.html. Goyette, S. (2011). “Synoptic conditions of extreme windstorms over Switzerland in a changing climate”. In: Climate Dynamics 36.5-6, pp. 845–866. Goyette, S. et al. (2001). “Numerical investigation of an extreme storm with the Canadian Regional Climate Model: The case study of windstorm Vivian, Switzerland, February 27, 1990”. In: Climate Dynamics 18.1-2, pp. 145–168. Hess, P. and Brezowsky, H. (1952). Katalog der Grosswetterlagen Europas. Ed. by D. Wetter- dienst, p. 39. Hofherr, T. and Kunz, M. (2010). “Extreme wind climatology of winter storms in Germany”. In: Climate research (Open Access for articles 4 years old and older) 41.2, p. 105. Huth, R. et al. (2008). “Classifications of atmospheric circulation patterns”. In: Annals of the New York Academy of Sciences 1146.1, pp. 105–152. Jungo, P., Goyette, S. and Beniston, M. (2002). “Daily wind gust speed probabilities over Switzerland according to three types of synoptic circulation”. In: International journal of climatology 22.4, pp. 485–499. Klawa, M, Ulbrich, U et al. (2003). “A model for the estimation of storm losses and the identifi- cation of severe winter storms in Germany”. In: Natural Hazards and Earth System Science 3.6, pp. 725–732. Kouroutzoglou, J et al. (2011). “Climatological aspects of explosive cyclones in the Mediter- ranean”. In: International Journal of Climatology 31.12, pp. 1785–1802. Leckebusch, G. C. et al. (2008). “Extreme wind storms over Europe in present and future climate: a cluster analysis approach”. In: Meteorologische Zeitschrift 17.1, pp. 67–82. Lentink, H. (2012). “Extreme foehn in Switzerland”. MA thesis. Moron, V. and Plaut, G. (2003). “The impact of El Nino-southern oscillation upon weather regimes over Europe and the North Atlantic during boreal winter”. In: International Journal of Climatology 23.4, pp. 363–379. Nesterov, E. (2009). “East atlantic oscillation of the atmospheric circulation”. In: Russian Me- teorology and Hydrology 34.12, pp. 794–800. OcCC (2003). Extreme Events and Climate Change. Tech. rep. Bern. Pfister, C (1999). Wetternachhersage: 500 Jahre Klimavariationen und Naturkatastrophen. Bibliography 69

Philipp, A. et al. (2010). “Cost733cat-A database of weather and circulation type classifications”. In: Physics and Chemistry of the Earth, Parts A/B/C 35.9, pp. 360–373. Pinto, J. G. et al. (2009). “Factors contributing to the development of extreme North Atlantic cyclones and their relationship with the NAO”. In: Climate dynamics 32.5, pp. 711–737. Piot, M. (2011). Statistics in Climate Sciences I + II. Richner, H. and Hachler,¨ P. (2013). “Understanding and Forecasting Alpine Foehn”. In: Moun- tain Weather Research and Forecasting. Springer, pp. 219–260. Riviere,` G et al. (2010). “The essential ingredients leading to the explosive growth stage of the European wind storm Lothar of Christmas 1999”. In: Quarterly Journal of the Royal Meteorological Society 136.648, pp. 638–652. Rogers, J. C. (1997). “North Atlantic storm track variability and its association to the North Atlantic Oscillation and climate variability of northern Europe”. In: Journal of Climate 10.7, pp. 1635–1647. Rohrer, M. (2012). “Climate change and circulation types in the Alpine region”. MA thesis. ETHZ. Schiemann, R. and Frei, C. (2010). “How to quantify the resolution of surface climate by circulation types: An example for Alpine precipitation”. In: Physics and Chemistry of the Earth, Parts A/B/C 35.9, pp. 403–410. Schiesser, H., Pfister, C and Bader, J. (1997). “Winter storms in Switzerland north of the Alps 1864/1865–1993/1994”. In: Theoretical and applied climatology 58.1-2, pp. 1–19. Schuepp,¨ M (1979). “Witterungsklimatologie, Klimatologie der Schweiz”. In: Band III, Schweiz- erische Meteorologische Anstalt. Schuepp,¨ M et al. (1994). “The winterstorm Vivian of 27 February 1990: about the meteorological development, wind forces and damage situation in the forests of Switzerland”. In: Theoretical and applied climatology 49.3, pp. 183–200. Schwierz, C. et al. (2010). “Modelling European winter wind storm losses in current and future climate”. In: Climatic change 101.3-4, pp. 485–514. Trigo, I. F., Bigg, G. R. and Davies, T. D. (2002). “Climatology of cyclogenesis mechanisms in the Mediterranean”. In: Monthly Weather Review 130.3, pp. 549–569. Trigo, I. F. (2006). “Climatology and interannual variability of storm-tracks in the Euro-Atlantic sector: a comparison between ERA-40 and NCEP/NCAR reanalyses”. In: Climate Dynam- ics 26.2-3, pp. 127–143. Usbeck, T. et al. (2010). “Wind speed measurements and forest damage in Canton Zurich (Cen- tral Europe) from 1891 to winter 2007”. In: International Journal of Climatology 30.3, pp. 347–358. Weber, R. O. and Furger, M. (2001). “Climatology of near-surface wind patterns over Switzer- land”. In: International journal of climatology 21.7, pp. 809–827. Weber, R. O. and Kaufmann, P. (1995). “Automated classification scheme for wind fields”. In: Journal of Applied Meteorology 34.5, pp. 1133–1141. Weisse, R., Storch, H. von and Feser, F. (2005). “Northeast Atlantic and North Sea storminess as simulated by a regional climate model during 1958-2001 and comparison with observations”. In: Journal of Climate 18.3, pp. 465–479. Bibliography 70

Wernli, H. et al. (2002). “Dynamical aspects of the life cycle of the winter storm Lothar(24– 26 December 1999)”. In: Quarterly Journal of the Royal Meteorological Society 128.580, pp. 405–429. Weusthoff, T. (2011). “Weather Type Classiﬁcation at MeteoSwiss - Introduction of new automatic classiﬁcation schemes”. In: Arbeitsberichte der MeteoSchweiz 235, p. 46. Woollings, T., Hannachi, A. and Hoskins, B. (2010). “Variability of the North Atlantic eddy- driven jet stream”. In: Quarterly Journal of the Royal Meteorological Society 136.649, pp. 856–868. Acknowledgement

I first would like to thank my supervisors Olivia Romppainen-Martius and Stefan Bronnimann¨ for all their help, advices and also for their availability. And especially Olivia who always found time for a discussion in spite of her new responsibilities outside of work. I also thank my advisors Christoph Welker and Peter Stucki for their advices and explanations. I also thank the people form GIUB fifth floor and from the computer pool for making my work even more exciting. Thank you to my roommates in the “Dennikofe-WG” and to the IGFB members who made my stay in Bern even greater. Finally thank you to my parents, especially for their monetary support during all my studies.